diff --git a/.gitattributes b/.gitattributes index 805cd2af4b595f5c6f9faa9460edd8deee807ba2..0e969599f9b14d4d2305db9c47009ef82bdb4e97 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,3 +1,5 @@ *.gif filter=lfs diff=lfs merge=lfs -text *.png filter=lfs diff=lfs merge=lfs -text *.jpg filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +. filter=lfs diff=lfs merge=lfs -text diff --git a/PTI/.gitignore b/PTI/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..de1f96da726780d0a060ad50d8a2d87eef0fd37f --- /dev/null +++ b/PTI/.gitignore @@ -0,0 +1,4 @@ +checkpoints +__pycache__ +embeddings +test diff --git a/PTI/LICENSE b/PTI/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..490821a63031f8f8d115b2edee69ae26ac5ea0b1 --- /dev/null +++ b/PTI/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 Daniel Roich + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/PTI/README.md b/PTI/README.md new file mode 100644 index 0000000000000000000000000000000000000000..19baf6bdb2e87aeeb87527be49969a579cc3f0e1 --- /dev/null +++ b/PTI/README.md @@ -0,0 +1,230 @@ +# PTI: Pivotal Tuning for Latent-based editing of Real Images (ACM TOG 2022) + + + + + +Inference Notebook: + +

+ +
+Pivotal Tuning Inversion (PTI) enables employing off-the-shelf latent based +semantic editing techniques on real images using StyleGAN. +PTI excels in identity preserving edits, portrayed through recognizable figures — +Serena Williams and Robert Downey Jr. (top), and in handling faces which +are clearly out-of-domain, e.g., due to heavy makeup (bottom). +
+

+ +## Description +Official Implementation of our PTI paper + code for evaluation metrics. PTI introduces an optimization mechanizem for solving the StyleGAN inversion task. +Providing near-perfect reconstruction results while maintaining the high editing abilitis of the native StyleGAN latent space W. For more details, see + +## Recent Updates +**2021.07.01**: Fixed files download phase in the inference notebook. Which might caused the notebook not to run smoothly. + +**2021.06.29**: Added support for CPU. In order to run PTI on CPU please change `device` parameter under `configs/global_config.py` to "cpu" instead of "cuda". + +**2021.06.25** : Adding mohawk edit using StyleCLIP+PTI in inference notebook. + Updating documentation in inference notebook due to Google Drive rate limit reached. + Currently, Google Drive does not allow to download the pretrined models using Colab automatically. Manual intervention might be needed. + +## Getting Started +### Prerequisites +- Linux or macOS +- NVIDIA GPU + CUDA CuDNN (Not mandatory bur recommended) +- Python 3 + +### Installation +- Dependencies: + 1. lpips + 2. wandb + 3. pytorch + 4. torchvision + 5. matplotlib + 6. dlib +- All dependencies can be installed using *pip install* and the package name + +## Pretrained Models +Please download the pretrained models from the following links. + +### Auxiliary Models +We provide various auxiliary models needed for PTI inversion task. +This includes the StyleGAN generator and pre-trained models used for loss computation. +| Path | Description +| :--- | :---------- +|[FFHQ StyleGAN](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl) | StyleGAN2-ada model trained on FFHQ with 1024x1024 output resolution. +|[Dlib alignment](https://drive.google.com/file/d/1HKmjg6iXsWr4aFPuU0gBXPGR83wqMzq7/view?usp=sharing) | Dlib alignment used for images preproccessing. +|[FFHQ e4e encoder](https://drive.google.com/file/d/1ALC5CLA89Ouw40TwvxcwebhzWXM5YSCm/view?usp=sharing) | Pretrained e4e encoder. Used for StyleCLIP editing. + +Note: The StyleGAN model is used directly from the official [stylegan2-ada-pytorch implementation](https://github.com/NVlabs/stylegan2-ada-pytorch). +For StyleCLIP pretrained mappers, please see [StyleCLIP's official routes](https://github.com/orpatashnik/StyleCLIP/blob/main/utils.py) + + +By default, we assume that all auxiliary models are downloaded and saved to the directory `pretrained_models`. +However, you may use your own paths by changing the necessary values in `configs/path_configs.py`. + + +## Inversion +### Preparing your Data +In order to invert a real image and edit it you should first align and crop it to the correct size. To do so you should perform *One* of the following steps: +1. Run `notebooks/align_data.ipynb` and change the "images_path" variable to the raw images path +2. Run `utils/align_data.py` and change the "images_path" variable to the raw images path + + +### Weights And Biases +The project supports [Weights And Biases](https://wandb.ai/home) framework for experiment tracking. For the inversion task it enables visualization of the losses progression and the generator intermediate results during the initial inversion and the *Pivotal Tuning*(PT) procedure. + +The log frequency can be adjusted using the parameters defined at `configs/global_config.py` under the "Logs" subsection. + +There is no no need to have an account. However, in order to use the features provided by Weights and Biases you first have to register on their site. + + +### Running PTI +The main training script is `scripts/run_pti.py`. The script receives aligned and cropped images from paths configured in the "Input info" subscetion in + `configs/paths_config.py`. +Results are saved to directories found at "Dirs for output files" under `configs/paths_config.py`. This includes inversion latent codes and tuned generators. +The hyperparametrs for the inversion task can be found at `configs/hyperparameters.py`. They are intilized to the default values used in the paper. + +## Editing +By default, we assume that all auxiliary edit directions are downloaded and saved to the directory `editings`. +However, you may use your own paths by changing the necessary values in `configs/path_configs.py` under "Edit directions" subsection. + +Example of editing code can be found at `scripts/latent_editor_wrapper.py` + +## Inference Notebooks +To help visualize the results of PTI we provide a Jupyter notebook found in `notebooks/inference_playground.ipynb`. +The notebook will download the pretrained models and run inference on a sample image found online or +on images of your choosing. It is recommended to run this in [Google Colab](https://colab.research.google.com/github/danielroich/PTI/blob/main/notebooks/inference_playground.ipynb). + +The notebook demonstrates how to: +- Invert an image using PTI +- Visualise the inversion and use the PTI output +- Edit the image after PTI using InterfaceGAN and StyleCLIP +- Compare to other inversion methods + +## Evaluation +Currently the repository supports qualitative evaluation for reconstruction of: PTI, SG2 (*W Space*), e4e, SG2Plus (*W+ Space*). +As well as editing using InterfaceGAN and GANSpace for the same inversion methods. +To run the evaluation please see `evaluation/qualitative_edit_comparison.py`. Examples of the evaluation scripts are: + +

+ +
+Reconsturction comparison between different methods. The images order is: Original image, W+ inversion, e4e inversion, W inversion, PTI inversion +
+

+ +

+ +
+InterfaceGAN pose edit comparison between different methods. The images order is: Original, W+, e4e, W, PTI +
+

+ +

+ + +
+Image per edit or several edits without comparison +
+

+ +### Coming Soon - Quantitative evaluation and StyleCLIP qualitative evaluation + +## Repository structure +| Path | Description +| :--- | :--- +| ├  configs | Folder containing configs defining Hyperparameters, paths and logging +| ├  criteria | Folder containing various loss and regularization criterias for the optimization +| ├  dnnlib | Folder containing internal utils for StyleGAN2-ada +| ├  docs | Folder containing the latent space edit directions +| ├  editings | Folder containing images displayed in the README +| ├  environment | Folder containing Anaconda environment used in our experiments +| ├  licenses | Folder containing licenses of the open source projects used in this repository +| ├  models | Folder containing models used in different editing techniques and first phase inversion +| ├  notebooks | Folder with jupyter notebooks to demonstrate the usage of PTI end-to-end +| ├  scripts | Folder with running scripts for inversion, editing and metric computations +| ├  torch_utils | Folder containing internal utils for StyleGAN2-ada +| ├  training | Folder containing the core training logic of PTI +| ├  utils | Folder with various utility functions + + +## Credits +**StyleGAN2-ada model and implementation:** +https://github.com/NVlabs/stylegan2-ada-pytorch +Copyright © 2021, NVIDIA Corporation. +Nvidia Source Code License https://nvlabs.github.io/stylegan2-ada-pytorch/license.html + +**LPIPS model and implementation:** +https://github.com/richzhang/PerceptualSimilarity +Copyright (c) 2020, Sou Uchida +License (BSD 2-Clause) https://github.com/richzhang/PerceptualSimilarity/blob/master/LICENSE + +**e4e model and implementation:** +https://github.com/omertov/encoder4editing +Copyright (c) 2021 omertov +License (MIT) https://github.com/omertov/encoder4editing/blob/main/LICENSE + +**StyleCLIP model and implementation:** +https://github.com/orpatashnik/StyleCLIP +Copyright (c) 2021 orpatashnik +License (MIT) https://github.com/orpatashnik/StyleCLIP/blob/main/LICENSE + +**InterfaceGAN implementation:** +https://github.com/genforce/interfacegan +Copyright (c) 2020 genforce +License (MIT) https://github.com/genforce/interfacegan/blob/master/LICENSE + +**GANSpace implementation:** +https://github.com/harskish/ganspace +Copyright (c) 2020 harkish +License (Apache License 2.0) https://github.com/harskish/ganspace/blob/master/LICENSE + + +## Acknowledgments +This repository structure is based on [encoder4editing](https://github.com/omertov/encoder4editing) and [ReStyle](https://github.com/yuval-alaluf/restyle-encoder) repositories + +## Contact +For any inquiry please contact us at our email addresses: danielroich@gmail.com or ron.mokady@gmail.com + + +## Citation +If you use this code for your research, please cite: +``` +@article{roich2021pivotal, + title={Pivotal Tuning for Latent-based Editing of Real Images}, + author={Roich, Daniel and Mokady, Ron and Bermano, Amit H and Cohen-Or, Daniel}, + publisher = {Association for Computing Machinery}, + journal={ACM Trans. Graph.}, + year={2021} +} +``` diff --git a/PTI/__init__.py b/PTI/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/configs/__init__.py b/PTI/configs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/configs/evaluation_config.py b/PTI/configs/evaluation_config.py new file mode 100644 index 0000000000000000000000000000000000000000..16b621d4a47df9e25828c4235cf1692899d14d50 --- /dev/null +++ b/PTI/configs/evaluation_config.py @@ -0,0 +1 @@ +evaluated_methods = ['e4e', 'SG2', 'SG2Plus'] \ No newline at end of file diff --git a/PTI/configs/global_config.py b/PTI/configs/global_config.py new file mode 100644 index 0000000000000000000000000000000000000000..bf3a20e61b0baf5e85377570cdf0f235bade21bd --- /dev/null +++ b/PTI/configs/global_config.py @@ -0,0 +1,12 @@ +# Device +cuda_visible_devices = '0' +device = 'cuda:0' + +# Logs +training_step = 1 +image_rec_result_log_snapshot = 100 +pivotal_training_steps = 0 +model_snapshot_interval = 400 + +# Run name to be updated during PTI +run_name = '' diff --git a/PTI/configs/hyperparameters.py b/PTI/configs/hyperparameters.py new file mode 100644 index 0000000000000000000000000000000000000000..1a4c89323561c3fe0d1f1b0962926ff89b49221e --- /dev/null +++ b/PTI/configs/hyperparameters.py @@ -0,0 +1,28 @@ +## Architechture +lpips_type = "alex" +first_inv_type = "w" +optim_type = "adam" + +## Locality regularization +latent_ball_num_of_samples = 1 +locality_regularization_interval = 1 +use_locality_regularization = False +regulizer_l2_lambda = 0.1 +regulizer_lpips_lambda = 0.1 +regulizer_alpha = 30 + +## Loss +pt_l2_lambda = 1 +pt_lpips_lambda = 1 + +## Steps +LPIPS_value_threshold = 0.06 +max_pti_steps = 350 +first_inv_steps = 450 +max_images_to_invert = 30 + +## Optimization +pti_learning_rate = 3e-4 +first_inv_lr = 5e-3 +train_batch_size = 1 +use_last_w_pivots = False diff --git a/PTI/configs/paths_config.py b/PTI/configs/paths_config.py new file mode 100644 index 0000000000000000000000000000000000000000..2e1e65ddb127eec0dc0007ddc8472f4e3c51932a --- /dev/null +++ b/PTI/configs/paths_config.py @@ -0,0 +1,31 @@ +## Pretrained models paths +e4e = 'PTI/pretrained_models/e4e_ffhq_encode.pt' +stylegan2_ada_ffhq = '../PTI/pretrained_models/ffhq.pkl' +style_clip_pretrained_mappers = '' +ir_se50 = 'PTI/pretrained_models/model_ir_se50.pth' +dlib = 'PTI/pretrained_models/align.dat' + +## Dirs for output files +checkpoints_dir = 'PTI/checkpoints' +embedding_base_dir = 'PTI/embeddings' +styleclip_output_dir = 'PTI/StyleCLIP_results' +experiments_output_dir = 'PTI/output' + +## Input info +### Input dir, where the images reside +input_data_path = '' +### Inversion identifier, used to keeping track of the inversion results. Both the latent code and the generator +input_data_id = 'barcelona' + +## Keywords +pti_results_keyword = 'PTI' +e4e_results_keyword = 'e4e' +sg2_results_keyword = 'SG2' +sg2_plus_results_keyword = 'SG2_plus' +multi_id_model_type = 'multi_id' + +## Edit directions +interfacegan_age = 'PTI/editings/interfacegan_directions/age.pt' +interfacegan_smile = 'PTI/editings/interfacegan_directions/smile.pt' +interfacegan_rotation = 'PTI/editings/interfacegan_directions/rotation.pt' +ffhq_pca = 'PTI/editings/ganspace_pca/ffhq_pca.pt' diff --git a/PTI/criteria/__init__.py b/PTI/criteria/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/criteria/l2_loss.py b/PTI/criteria/l2_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..c7ac2753b02dfa9d21ccf03fa3b87b9d6fc3f01d --- /dev/null +++ b/PTI/criteria/l2_loss.py @@ -0,0 +1,8 @@ +import torch + +l2_criterion = torch.nn.MSELoss(reduction='mean') + + +def l2_loss(real_images, generated_images): + loss = l2_criterion(real_images, generated_images) + return loss diff --git a/PTI/criteria/localitly_regulizer.py b/PTI/criteria/localitly_regulizer.py new file mode 100644 index 0000000000000000000000000000000000000000..09a5a40d44153bd0110d22b2d9a4d50970cf7515 --- /dev/null +++ b/PTI/criteria/localitly_regulizer.py @@ -0,0 +1,65 @@ +import torch +import numpy as np +from PTI.criteria import l2_loss +from PTI.configs import hyperparameters +from PTI.configs import global_config + + +class Space_Regulizer: + def __init__(self, original_G, lpips_net): + self.original_G = original_G + self.morphing_regulizer_alpha = hyperparameters.regulizer_alpha + self.lpips_loss = lpips_net + + def get_morphed_w_code(self, new_w_code, fixed_w): + interpolation_direction = new_w_code - fixed_w + interpolation_direction_norm = torch.norm(interpolation_direction, p=2) + direction_to_move = hyperparameters.regulizer_alpha * \ + interpolation_direction / interpolation_direction_norm + result_w = fixed_w + direction_to_move + self.morphing_regulizer_alpha * fixed_w + \ + (1 - self.morphing_regulizer_alpha) * new_w_code + + return result_w + + def get_image_from_ws(self, w_codes, G): + return torch.cat([G.synthesis(w_code, noise_mode='none', force_fp32=True) for w_code in w_codes]) + + def ball_holder_loss_lazy(self, new_G, num_of_sampled_latents, w_batch, use_wandb=False): + loss = 0.0 + + z_samples = np.random.randn( + num_of_sampled_latents, self.original_G.z_dim) + w_samples = self.original_G.mapping(torch.from_numpy(z_samples).to(global_config.device), None, + truncation_psi=0.5) + territory_indicator_ws = [self.get_morphed_w_code( + w_code.unsqueeze(0), w_batch) for w_code in w_samples] + + for w_code in territory_indicator_ws: + new_img = new_G.synthesis( + w_code, noise_mode='none', force_fp32=True) + with torch.no_grad(): + old_img = self.original_G.synthesis( + w_code, noise_mode='none', force_fp32=True) + + if hyperparameters.regulizer_l2_lambda > 0: + l2_loss_val = l2_loss.l2_loss(old_img, new_img) + if use_wandb: + wandb.log({f'space_regulizer_l2_loss_val': l2_loss_val.detach().cpu()}, + step=global_config.training_step) + loss += l2_loss_val * hyperparameters.regulizer_l2_lambda + + if hyperparameters.regulizer_lpips_lambda > 0: + loss_lpips = self.lpips_loss(old_img, new_img) + loss_lpips = torch.mean(torch.squeeze(loss_lpips)) + if use_wandb: + wandb.log({f'space_regulizer_lpips_loss_val': loss_lpips.detach().cpu()}, + step=global_config.training_step) + loss += loss_lpips * hyperparameters.regulizer_lpips_lambda + + return loss / len(territory_indicator_ws) + + def space_regulizer_loss(self, new_G, w_batch, use_wandb): + ret_val = self.ball_holder_loss_lazy( + new_G, hyperparameters.latent_ball_num_of_samples, w_batch, use_wandb) + return ret_val diff --git a/PTI/dnnlib/__init__.py b/PTI/dnnlib/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2f08cf36f11f9b0fd94c1b7caeadf69b98375b04 --- /dev/null +++ b/PTI/dnnlib/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +from .util import EasyDict, make_cache_dir_path diff --git a/PTI/dnnlib/util.py b/PTI/dnnlib/util.py new file mode 100644 index 0000000000000000000000000000000000000000..76725336d01e75e1c68daa88be47f4fde0bbc63b --- /dev/null +++ b/PTI/dnnlib/util.py @@ -0,0 +1,477 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Miscellaneous utility classes and functions.""" + +import ctypes +import fnmatch +import importlib +import inspect +import numpy as np +import os +import shutil +import sys +import types +import io +import pickle +import re +import requests +import html +import hashlib +import glob +import tempfile +import urllib +import urllib.request +import uuid + +from distutils.util import strtobool +from typing import Any, List, Tuple, Union + + +# Util classes +# ------------------------------------------------------------------------------------------ + + +class EasyDict(dict): + """Convenience class that behaves like a dict but allows access with the attribute syntax.""" + + def __getattr__(self, name: str) -> Any: + try: + return self[name] + except KeyError: + raise AttributeError(name) + + def __setattr__(self, name: str, value: Any) -> None: + self[name] = value + + def __delattr__(self, name: str) -> None: + del self[name] + + +class Logger(object): + """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file.""" + + def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True): + self.file = None + + if file_name is not None: + self.file = open(file_name, file_mode) + + self.should_flush = should_flush + self.stdout = sys.stdout + self.stderr = sys.stderr + + sys.stdout = self + sys.stderr = self + + def __enter__(self) -> "Logger": + return self + + def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: + self.close() + + def write(self, text: Union[str, bytes]) -> None: + """Write text to stdout (and a file) and optionally flush.""" + if isinstance(text, bytes): + text = text.decode() + if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash + return + + if self.file is not None: + self.file.write(text) + + self.stdout.write(text) + + if self.should_flush: + self.flush() + + def flush(self) -> None: + """Flush written text to both stdout and a file, if open.""" + if self.file is not None: + self.file.flush() + + self.stdout.flush() + + def close(self) -> None: + """Flush, close possible files, and remove stdout/stderr mirroring.""" + self.flush() + + # if using multiple loggers, prevent closing in wrong order + if sys.stdout is self: + sys.stdout = self.stdout + if sys.stderr is self: + sys.stderr = self.stderr + + if self.file is not None: + self.file.close() + self.file = None + + +# Cache directories +# ------------------------------------------------------------------------------------------ + +_dnnlib_cache_dir = None + +def set_cache_dir(path: str) -> None: + global _dnnlib_cache_dir + _dnnlib_cache_dir = path + +def make_cache_dir_path(*paths: str) -> str: + if _dnnlib_cache_dir is not None: + return os.path.join(_dnnlib_cache_dir, *paths) + if 'DNNLIB_CACHE_DIR' in os.environ: + return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths) + if 'HOME' in os.environ: + return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths) + if 'USERPROFILE' in os.environ: + return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib', *paths) + return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths) + +# Small util functions +# ------------------------------------------------------------------------------------------ + + +def format_time(seconds: Union[int, float]) -> str: + """Convert the seconds to human readable string with days, hours, minutes and seconds.""" + s = int(np.rint(seconds)) + + if s < 60: + return "{0}s".format(s) + elif s < 60 * 60: + return "{0}m {1:02}s".format(s // 60, s % 60) + elif s < 24 * 60 * 60: + return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60) + else: + return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60) + + +def ask_yes_no(question: str) -> bool: + """Ask the user the question until the user inputs a valid answer.""" + while True: + try: + print("{0} [y/n]".format(question)) + return strtobool(input().lower()) + except ValueError: + pass + + +def tuple_product(t: Tuple) -> Any: + """Calculate the product of the tuple elements.""" + result = 1 + + for v in t: + result *= v + + return result + + +_str_to_ctype = { + "uint8": ctypes.c_ubyte, + "uint16": ctypes.c_uint16, + "uint32": ctypes.c_uint32, + "uint64": ctypes.c_uint64, + "int8": ctypes.c_byte, + "int16": ctypes.c_int16, + "int32": ctypes.c_int32, + "int64": ctypes.c_int64, + "float32": ctypes.c_float, + "float64": ctypes.c_double +} + + +def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]: + """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes.""" + type_str = None + + if isinstance(type_obj, str): + type_str = type_obj + elif hasattr(type_obj, "__name__"): + type_str = type_obj.__name__ + elif hasattr(type_obj, "name"): + type_str = type_obj.name + else: + raise RuntimeError("Cannot infer type name from input") + + assert type_str in _str_to_ctype.keys() + + my_dtype = np.dtype(type_str) + my_ctype = _str_to_ctype[type_str] + + assert my_dtype.itemsize == ctypes.sizeof(my_ctype) + + return my_dtype, my_ctype + + +def is_pickleable(obj: Any) -> bool: + try: + with io.BytesIO() as stream: + pickle.dump(obj, stream) + return True + except: + return False + + +# Functionality to import modules/objects by name, and call functions by name +# ------------------------------------------------------------------------------------------ + +def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]: + """Searches for the underlying module behind the name to some python object. + Returns the module and the object name (original name with module part removed).""" + + # allow convenience shorthands, substitute them by full names + obj_name = re.sub("^np.", "numpy.", obj_name) + obj_name = re.sub("^tf.", "tensorflow.", obj_name) + + # list alternatives for (module_name, local_obj_name) + parts = obj_name.split(".") + name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)] + + # try each alternative in turn + for module_name, local_obj_name in name_pairs: + try: + module = importlib.import_module(module_name) # may raise ImportError + get_obj_from_module(module, local_obj_name) # may raise AttributeError + return module, local_obj_name + except: + pass + + # maybe some of the modules themselves contain errors? + for module_name, _local_obj_name in name_pairs: + try: + importlib.import_module(module_name) # may raise ImportError + except ImportError: + if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"): + raise + + # maybe the requested attribute is missing? + for module_name, local_obj_name in name_pairs: + try: + module = importlib.import_module(module_name) # may raise ImportError + get_obj_from_module(module, local_obj_name) # may raise AttributeError + except ImportError: + pass + + # we are out of luck, but we have no idea why + raise ImportError(obj_name) + + +def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any: + """Traverses the object name and returns the last (rightmost) python object.""" + if obj_name == '': + return module + obj = module + for part in obj_name.split("."): + obj = getattr(obj, part) + return obj + + +def get_obj_by_name(name: str) -> Any: + """Finds the python object with the given name.""" + module, obj_name = get_module_from_obj_name(name) + return get_obj_from_module(module, obj_name) + + +def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any: + """Finds the python object with the given name and calls it as a function.""" + assert func_name is not None + func_obj = get_obj_by_name(func_name) + assert callable(func_obj) + return func_obj(*args, **kwargs) + + +def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any: + """Finds the python class with the given name and constructs it with the given arguments.""" + return call_func_by_name(*args, func_name=class_name, **kwargs) + + +def get_module_dir_by_obj_name(obj_name: str) -> str: + """Get the directory path of the module containing the given object name.""" + module, _ = get_module_from_obj_name(obj_name) + return os.path.dirname(inspect.getfile(module)) + + +def is_top_level_function(obj: Any) -> bool: + """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'.""" + return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__ + + +def get_top_level_function_name(obj: Any) -> str: + """Return the fully-qualified name of a top-level function.""" + assert is_top_level_function(obj) + module = obj.__module__ + if module == '__main__': + module = os.path.splitext(os.path.basename(sys.modules[module].__file__))[0] + return module + "." + obj.__name__ + + +# File system helpers +# ------------------------------------------------------------------------------------------ + +def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]: + """List all files recursively in a given directory while ignoring given file and directory names. + Returns list of tuples containing both absolute and relative paths.""" + assert os.path.isdir(dir_path) + base_name = os.path.basename(os.path.normpath(dir_path)) + + if ignores is None: + ignores = [] + + result = [] + + for root, dirs, files in os.walk(dir_path, topdown=True): + for ignore_ in ignores: + dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)] + + # dirs need to be edited in-place + for d in dirs_to_remove: + dirs.remove(d) + + files = [f for f in files if not fnmatch.fnmatch(f, ignore_)] + + absolute_paths = [os.path.join(root, f) for f in files] + relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths] + + if add_base_to_relative: + relative_paths = [os.path.join(base_name, p) for p in relative_paths] + + assert len(absolute_paths) == len(relative_paths) + result += zip(absolute_paths, relative_paths) + + return result + + +def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None: + """Takes in a list of tuples of (src, dst) paths and copies files. + Will create all necessary directories.""" + for file in files: + target_dir_name = os.path.dirname(file[1]) + + # will create all intermediate-level directories + if not os.path.exists(target_dir_name): + os.makedirs(target_dir_name) + + shutil.copyfile(file[0], file[1]) + + +# URL helpers +# ------------------------------------------------------------------------------------------ + +def is_url(obj: Any, allow_file_urls: bool = False) -> bool: + """Determine whether the given object is a valid URL string.""" + if not isinstance(obj, str) or not "://" in obj: + return False + if allow_file_urls and obj.startswith('file://'): + return True + try: + res = requests.compat.urlparse(obj) + if not res.scheme or not res.netloc or not "." in res.netloc: + return False + res = requests.compat.urlparse(requests.compat.urljoin(obj, "/")) + if not res.scheme or not res.netloc or not "." in res.netloc: + return False + except: + return False + return True + + +def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False, cache: bool = True) -> Any: + """Download the given URL and return a binary-mode file object to access the data.""" + assert num_attempts >= 1 + assert not (return_filename and (not cache)) + + # Doesn't look like an URL scheme so interpret it as a local filename. + if not re.match('^[a-z]+://', url): + return url if return_filename else open(url, "rb") + + # Handle file URLs. This code handles unusual file:// patterns that + # arise on Windows: + # + # file:///c:/foo.txt + # + # which would translate to a local '/c:/foo.txt' filename that's + # invalid. Drop the forward slash for such pathnames. + # + # If you touch this code path, you should test it on both Linux and + # Windows. + # + # Some internet resources suggest using urllib.request.url2pathname() but + # but that converts forward slashes to backslashes and this causes + # its own set of problems. + if url.startswith('file://'): + filename = urllib.parse.urlparse(url).path + if re.match(r'^/[a-zA-Z]:', filename): + filename = filename[1:] + return filename if return_filename else open(filename, "rb") + + assert is_url(url) + + # Lookup from cache. + if cache_dir is None: + cache_dir = make_cache_dir_path('downloads') + + url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest() + if cache: + cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*")) + if len(cache_files) == 1: + filename = cache_files[0] + return filename if return_filename else open(filename, "rb") + + # Download. + url_name = None + url_data = None + with requests.Session() as session: + if verbose: + print("Downloading %s ..." % url, end="", flush=True) + for attempts_left in reversed(range(num_attempts)): + try: + with session.get(url) as res: + res.raise_for_status() + if len(res.content) == 0: + raise IOError("No data received") + + if len(res.content) < 8192: + content_str = res.content.decode("utf-8") + if "download_warning" in res.headers.get("Set-Cookie", ""): + links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link] + if len(links) == 1: + url = requests.compat.urljoin(url, links[0]) + raise IOError("Google Drive virus checker nag") + if "Google Drive - Quota exceeded" in content_str: + raise IOError("Google Drive download quota exceeded -- please try again later") + + match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", "")) + url_name = match[1] if match else url + url_data = res.content + if verbose: + print(" done") + break + except KeyboardInterrupt: + raise + except: + if not attempts_left: + if verbose: + print(" failed") + raise + if verbose: + print(".", end="", flush=True) + + # Save to cache. + if cache: + safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name) + cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name) + temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name) + os.makedirs(cache_dir, exist_ok=True) + with open(temp_file, "wb") as f: + f.write(url_data) + os.replace(temp_file, cache_file) # atomic + if return_filename: + return cache_file + + # Return data as file object. + assert not return_filename + return io.BytesIO(url_data) diff --git a/PTI/docs/joker_original.jpg b/PTI/docs/joker_original.jpg new file mode 100644 index 0000000000000000000000000000000000000000..efce3b70648574eb0b5e97019f8e96b588f235bc --- /dev/null +++ b/PTI/docs/joker_original.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0d95d9ee133077c3f49b62fe57d287a5602cc397fe7013f2ac7102ecb7d1b9ee +size 90764 diff --git a/PTI/docs/joker_rotation.jpg b/PTI/docs/joker_rotation.jpg new file mode 100644 index 0000000000000000000000000000000000000000..0d1e884f1e365b74339f9f0b214b3f437d4827ae --- /dev/null +++ b/PTI/docs/joker_rotation.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:955c8de232ebf68138e24f8ce597b59532366f26531858e53b24f47aefdbae77 +size 84629 diff --git a/PTI/docs/model_rec.jpg b/PTI/docs/model_rec.jpg new file mode 100644 index 0000000000000000000000000000000000000000..4e93d741c2bd9816f0636433929e0bd4b4b22f4a --- /dev/null +++ b/PTI/docs/model_rec.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb1aa0b71f7cbf188d11080a874d906f83075884b40df681086c64ce56f44129 +size 483363 diff --git a/PTI/docs/stern_rotation.jpg b/PTI/docs/stern_rotation.jpg new file mode 100644 index 0000000000000000000000000000000000000000..74d0d23bcd347e72a3efd03b20b3c10a8e699438 --- /dev/null +++ b/PTI/docs/stern_rotation.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8dfd82db9de045f86f6b8504f33cf92f7b75523fca3ed2d40a8087fff250d21f +size 728617 diff --git a/PTI/docs/teaser.jpg b/PTI/docs/teaser.jpg new file mode 100644 index 0000000000000000000000000000000000000000..847cd0f45ac030ab618e74f83ec3320888d76f9b --- /dev/null +++ b/PTI/docs/teaser.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d38d072a0344c41c0c7c11d153def6f30b2870cd4c958185d560c0e76f7bccbb +size 177450 diff --git a/PTI/docs/tyron_edit.jpg b/PTI/docs/tyron_edit.jpg new file mode 100644 index 0000000000000000000000000000000000000000..a0e717d7d90addfaf52481f8b5f059e96841e7d0 --- /dev/null +++ b/PTI/docs/tyron_edit.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d725e6aeb21d5a0d3cee2293eed0dff667fd03fd4224116361bd449ddfdf6dfd +size 116990 diff --git a/PTI/docs/tyron_original.jpg b/PTI/docs/tyron_original.jpg new file mode 100644 index 0000000000000000000000000000000000000000..d0df1e6ea3671d3dcdeee4b85940a64c79027b38 --- /dev/null +++ b/PTI/docs/tyron_original.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ad7b57b16850c1b1a4eb9dd8b65a94175816b1834514249bb685935c012c0f09 +size 137293 diff --git a/PTI/editings/ganspace.py b/PTI/editings/ganspace.py new file mode 100644 index 0000000000000000000000000000000000000000..ee1e28c76de89f690e563902def42e3738dc677f --- /dev/null +++ b/PTI/editings/ganspace.py @@ -0,0 +1,21 @@ +import torch + + +def edit(latents, pca, edit_directions): + edit_latents = [] + for latent in latents: + for pca_idx, start, end, strength in edit_directions: + delta = get_delta(pca, latent, pca_idx, strength) + delta_padded = torch.zeros(latent.shape).to('cuda') + delta_padded[start:end] += delta.repeat(end - start, 1) + edit_latents.append(latent + delta_padded) + return torch.stack(edit_latents) + + +def get_delta(pca, latent, idx, strength): + w_centered = latent - pca['mean'].to('cuda') + lat_comp = pca['comp'].to('cuda') + lat_std = pca['std'].to('cuda') + w_coord = torch.sum(w_centered[0].reshape(-1)*lat_comp[idx].reshape(-1)) / lat_std[idx] + delta = (strength - w_coord)*lat_comp[idx]*lat_std[idx] + return delta diff --git a/PTI/editings/ganspace_pca/ffhq_pca.pt b/PTI/editings/ganspace_pca/ffhq_pca.pt new file mode 100644 index 0000000000000000000000000000000000000000..c07f44fc070557d62ad2c8b105486ebf78a1f82d Binary files /dev/null and b/PTI/editings/ganspace_pca/ffhq_pca.pt differ diff --git a/PTI/editings/interfacegan_directions/age.pt b/PTI/editings/interfacegan_directions/age.pt new file mode 100644 index 0000000000000000000000000000000000000000..73b4e6c9848e68d4d033146c20921cd0594f5943 Binary files /dev/null and b/PTI/editings/interfacegan_directions/age.pt differ diff --git a/PTI/editings/interfacegan_directions/rotation.pt b/PTI/editings/interfacegan_directions/rotation.pt new file mode 100644 index 0000000000000000000000000000000000000000..919dfda31918ecc39ab44bf2131cca5712c6c37c Binary files /dev/null and b/PTI/editings/interfacegan_directions/rotation.pt differ diff --git a/PTI/editings/interfacegan_directions/smile.pt b/PTI/editings/interfacegan_directions/smile.pt new file mode 100644 index 0000000000000000000000000000000000000000..3c44456cefdeecf940ab21e0c2d3024a3a6d6432 Binary files /dev/null and b/PTI/editings/interfacegan_directions/smile.pt differ diff --git a/PTI/editings/latent_editor.py b/PTI/editings/latent_editor.py new file mode 100644 index 0000000000000000000000000000000000000000..32554e8010c4da27aaded1b0ce938bd37d5e242b --- /dev/null +++ b/PTI/editings/latent_editor.py @@ -0,0 +1,23 @@ +import torch + +from configs import paths_config +from editings import ganspace +from utils.data_utils import tensor2im + + +class LatentEditor(object): + + def apply_ganspace(self, latent, ganspace_pca, edit_directions): + edit_latents = ganspace.edit(latent, ganspace_pca, edit_directions) + return edit_latents + + def apply_interfacegan(self, latent, direction, factor=1, factor_range=None): + edit_latents = [] + if factor_range is not None: # Apply a range of editing factors. for example, (-5, 5) + for f in range(*factor_range): + edit_latent = latent + f * direction + edit_latents.append(edit_latent) + edit_latents = torch.cat(edit_latents) + else: + edit_latents = latent + factor * direction + return edit_latents diff --git a/PTI/evaluation/experiment_setting_creator.py b/PTI/evaluation/experiment_setting_creator.py new file mode 100644 index 0000000000000000000000000000000000000000..c8ad234ba845d84ddd435424a7fe9ed238af3ff6 --- /dev/null +++ b/PTI/evaluation/experiment_setting_creator.py @@ -0,0 +1,43 @@ +import glob +import os +from configs import global_config, paths_config, hyperparameters +from scripts.latent_creators.sg2_plus_latent_creator import SG2PlusLatentCreator +from scripts.latent_creators.e4e_latent_creator import E4ELatentCreator +from scripts.run_pti import run_PTI +import pickle +import torch +from utils.models_utils import toogle_grad, load_old_G + + +class ExperimentRunner: + + def __init__(self, run_id=''): + self.images_paths = glob.glob(f'{paths_config.input_data_path}/*') + self.target_paths = glob.glob(f'{paths_config.input_data_path}/*') + self.run_id = run_id + self.sampled_ws = None + + self.old_G = load_old_G() + + toogle_grad(self.old_G, False) + + def run_experiment(self, run_pt, create_other_latents, use_multi_id_training, use_wandb=False): + if run_pt: + self.run_id = run_PTI(self.run_id, use_wandb=use_wandb, use_multi_id_training=use_multi_id_training) + if create_other_latents: + sg2_plus_latent_creator = SG2PlusLatentCreator(use_wandb=use_wandb) + sg2_plus_latent_creator.create_latents() + e4e_latent_creator = E4ELatentCreator(use_wandb=use_wandb) + e4e_latent_creator.create_latents() + + torch.cuda.empty_cache() + + return self.run_id + + +if __name__ == '__main__': + os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' + os.environ['CUDA_VISIBLE_DEVICES'] = global_config.cuda_visible_devices + + runner = ExperimentRunner() + runner.run_experiment(True, False, False) diff --git a/PTI/evaluation/qualitative_edit_comparison.py b/PTI/evaluation/qualitative_edit_comparison.py new file mode 100644 index 0000000000000000000000000000000000000000..39ed13264a9df5a257746f02b070c54934eb3117 --- /dev/null +++ b/PTI/evaluation/qualitative_edit_comparison.py @@ -0,0 +1,156 @@ +import os +from random import choice +from string import ascii_uppercase +from PIL import Image +from tqdm import tqdm +from scripts.latent_editor_wrapper import LatentEditorWrapper +from evaluation.experiment_setting_creator import ExperimentRunner +import torch +from configs import paths_config, hyperparameters, evaluation_config +from utils.log_utils import save_concat_image, save_single_image +from utils.models_utils import load_tuned_G + + +class EditComparison: + + def __init__(self, save_single_images, save_concatenated_images, run_id): + + self.run_id = run_id + self.experiment_creator = ExperimentRunner(run_id) + self.save_single_images = save_single_images + self.save_concatenated_images = save_concatenated_images + self.latent_editor = LatentEditorWrapper() + + def save_reconstruction_images(self, image_latents, new_inv_image_latent, new_G, target_image): + if self.save_concatenated_images: + save_concat_image(self.concat_base_dir, image_latents, new_inv_image_latent, new_G, + self.experiment_creator.old_G, + 'rec', + target_image) + + if self.save_single_images: + save_single_image(self.single_base_dir, new_inv_image_latent, new_G, 'rec') + target_image.save(f'{self.single_base_dir}/Original.jpg') + + def create_output_dirs(self, full_image_name): + output_base_dir_path = f'{paths_config.experiments_output_dir}/{paths_config.input_data_id}/{self.run_id}/{full_image_name}' + os.makedirs(output_base_dir_path, exist_ok=True) + + self.concat_base_dir = f'{output_base_dir_path}/concat_images' + self.single_base_dir = f'{output_base_dir_path}/single_images' + + os.makedirs(self.concat_base_dir, exist_ok=True) + os.makedirs(self.single_base_dir, exist_ok=True) + + def get_image_latent_codes(self, image_name): + image_latents = [] + for method in evaluation_config.evaluated_methods: + if method == 'SG2': + image_latents.append(torch.load( + f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}/' + f'{paths_config.pti_results_keyword}/{image_name}/0.pt')) + else: + image_latents.append(torch.load( + f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}/{method}/{image_name}/0.pt')) + new_inv_image_latent = torch.load( + f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}/{paths_config.pti_results_keyword}/{image_name}/0.pt') + + return image_latents, new_inv_image_latent + + def save_interfacegan_edits(self, image_latents, new_inv_image_latent, interfacegan_factors, new_G, target_image): + new_w_inv_edits = self.latent_editor.get_single_interface_gan_edits(new_inv_image_latent, + interfacegan_factors) + + inv_edits = [] + for latent in image_latents: + inv_edits.append(self.latent_editor.get_single_interface_gan_edits(latent, interfacegan_factors)) + + for direction, edits in new_w_inv_edits.items(): + for factor, edit_tensor in edits.items(): + if self.save_concatenated_images: + save_concat_image(self.concat_base_dir, [edits[direction][factor] for edits in inv_edits], + new_w_inv_edits[direction][factor], + new_G, + self.experiment_creator.old_G, + f'{direction}_{factor}', target_image) + if self.save_single_images: + save_single_image(self.single_base_dir, new_w_inv_edits[direction][factor], new_G, + f'{direction}_{factor}') + + def save_ganspace_edits(self, image_latents, new_inv_image_latent, factors, new_G, target_image): + new_w_inv_edits = self.latent_editor.get_single_ganspace_edits(new_inv_image_latent, factors) + inv_edits = [] + for latent in image_latents: + inv_edits.append(self.latent_editor.get_single_ganspace_edits(latent, factors)) + + for idx in range(len(new_w_inv_edits)): + if self.save_concatenated_images: + save_concat_image(self.concat_base_dir, [edit[idx] for edit in inv_edits], new_w_inv_edits[idx], + new_G, + self.experiment_creator.old_G, + f'ganspace_{idx}', target_image) + if self.save_single_images: + save_single_image(self.single_base_dir, new_w_inv_edits[idx], new_G, + f'ganspace_{idx}') + + def run_experiment(self, run_pt, create_other_latents, use_multi_id_training, use_wandb=False): + images_counter = 0 + new_G = None + interfacegan_factors = [val / 2 for val in range(-6, 7) if val != 0] + ganspace_factors = range(-20, 25, 5) + self.experiment_creator.run_experiment(run_pt, create_other_latents, use_multi_id_training, use_wandb) + + if use_multi_id_training: + new_G = load_tuned_G(self.run_id, paths_config.multi_id_model_type) + + for idx, image_path in tqdm(enumerate(self.experiment_creator.images_paths), + total=len(self.experiment_creator.images_paths)): + + if images_counter >= hyperparameters.max_images_to_invert: + break + + image_name = image_path.split('.')[0].split('/')[-1] + target_image = Image.open(self.experiment_creator.target_paths[idx]) + + if not use_multi_id_training: + new_G = load_tuned_G(self.run_id, image_name) + + image_latents, new_inv_image_latent = self.get_image_latent_codes(image_name) + + self.create_output_dirs(image_name) + + self.save_reconstruction_images(image_latents, new_inv_image_latent, new_G, target_image) + + self.save_interfacegan_edits(image_latents, new_inv_image_latent, interfacegan_factors, new_G, target_image) + + self.save_ganspace_edits(image_latents, new_inv_image_latent, ganspace_factors, new_G, target_image) + + target_image.close() + torch.cuda.empty_cache() + images_counter += 1 + + +def run_pti_and_full_edit(iid): + evaluation_config.evaluated_methods = ['SG2Plus', 'e4e', 'SG2'] + edit_figure_creator = EditComparison(save_single_images=True, save_concatenated_images=True, + run_id=f'{paths_config.input_data_id}_pti_full_edit_{iid}') + edit_figure_creator.run_experiment(True, True, use_multi_id_training=False, use_wandb=False) + + +def pti_no_comparison(iid): + evaluation_config.evaluated_methods = [] + edit_figure_creator = EditComparison(save_single_images=True, save_concatenated_images=True, + run_id=f'{paths_config.input_data_id}_pti_no_comparison_{iid}') + edit_figure_creator.run_experiment(True, False, use_multi_id_training=False, use_wandb=False) + + +def edits_for_existed_experiment(run_id): + evaluation_config.evaluated_methods = ['SG2Plus', 'e4e', 'SG2'] + edit_figure_creator = EditComparison(save_single_images=True, save_concatenated_images=True, + run_id=run_id) + edit_figure_creator.run_experiment(False, True, use_multi_id_training=False, use_wandb=False) + + +if __name__ == '__main__': + iid = ''.join(choice(ascii_uppercase) for i in range(7)) + pti_no_comparison(iid) diff --git a/PTI/models/StyleCLIP/__init__.py b/PTI/models/StyleCLIP/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/models/StyleCLIP/criteria/__init__.py b/PTI/models/StyleCLIP/criteria/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/models/StyleCLIP/criteria/clip_loss.py b/PTI/models/StyleCLIP/criteria/clip_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..18176ee8eb0d992d69d5b951d7f36e2efa92a37b --- /dev/null +++ b/PTI/models/StyleCLIP/criteria/clip_loss.py @@ -0,0 +1,17 @@ + +import torch +import clip + + +class CLIPLoss(torch.nn.Module): + + def __init__(self, opts): + super(CLIPLoss, self).__init__() + self.model, self.preprocess = clip.load("ViT-B/32", device="cuda") + self.upsample = torch.nn.Upsample(scale_factor=7) + self.avg_pool = torch.nn.AvgPool2d(kernel_size=opts.stylegan_size // 32) + + def forward(self, image, text): + image = self.avg_pool(self.upsample(image)) + similarity = 1 - self.model(image, text)[0] / 100 + return similarity \ No newline at end of file diff --git a/PTI/models/StyleCLIP/criteria/id_loss.py b/PTI/models/StyleCLIP/criteria/id_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..a828023e115243e48918538d31b91d662cd12d0f --- /dev/null +++ b/PTI/models/StyleCLIP/criteria/id_loss.py @@ -0,0 +1,39 @@ +import torch +from torch import nn + +from models.facial_recognition.model_irse import Backbone + + +class IDLoss(nn.Module): + def __init__(self, opts): + super(IDLoss, self).__init__() + print('Loading ResNet ArcFace') + self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se') + self.facenet.load_state_dict(torch.load(opts.ir_se50_weights)) + self.pool = torch.nn.AdaptiveAvgPool2d((256, 256)) + self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112)) + self.facenet.eval() + self.opts = opts + + def extract_feats(self, x): + if x.shape[2] != 256: + x = self.pool(x) + x = x[:, :, 35:223, 32:220] # Crop interesting region + x = self.face_pool(x) + x_feats = self.facenet(x) + return x_feats + + def forward(self, y_hat, y): + n_samples = y.shape[0] + y_feats = self.extract_feats(y) # Otherwise use the feature from there + y_hat_feats = self.extract_feats(y_hat) + y_feats = y_feats.detach() + loss = 0 + sim_improvement = 0 + count = 0 + for i in range(n_samples): + diff_target = y_hat_feats[i].dot(y_feats[i]) + loss += 1 - diff_target + count += 1 + + return loss / count, sim_improvement / count diff --git a/PTI/models/StyleCLIP/global_directions/GUI.py b/PTI/models/StyleCLIP/global_directions/GUI.py new file mode 100644 index 0000000000000000000000000000000000000000..19f7f8cce9305819b22664642799200d9e1cfff0 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/GUI.py @@ -0,0 +1,103 @@ + + +from tkinter import Tk,Frame ,Label,Button,messagebox,Canvas,Text,Scale +from tkinter import HORIZONTAL + +class View(): + def __init__(self,master): + + self.width=600 + self.height=600 + + + self.root=master + self.root.geometry("600x600") + + self.left_frame=Frame(self.root,width=600) + self.left_frame.pack_propagate(0) + self.left_frame.pack(fill='both', side='left', expand='True') + + self.retrieval_frame=Frame(self.root,bg='snow3') + self.retrieval_frame.pack_propagate(0) + self.retrieval_frame.pack(fill='both', side='right', expand='True') + + self.bg_frame=Frame(self.left_frame,bg='snow3',height=600,width=600) + self.bg_frame.pack_propagate(0) + self.bg_frame.pack(fill='both', side='top', expand='True') + + self.command_frame=Frame(self.left_frame,bg='snow3') + self.command_frame.pack_propagate(0) + self.command_frame.pack(fill='both', side='bottom', expand='True') +# self.command_frame.grid(row=1, column=0,padx=0, pady=0) + + self.bg=Canvas(self.bg_frame,width=self.width,height=self.height, bg='gray') + self.bg.place(relx=0.5, rely=0.5, anchor='center') + + self.mani=Canvas(self.retrieval_frame,width=1024,height=1024, bg='gray') + self.mani.grid(row=0, column=0,padx=0, pady=42) + + self.SetCommand() + + + + + def run(self): + self.root.mainloop() + + def helloCallBack(self): + category=self.set_category.get() + messagebox.showinfo( "Hello Python",category) + + def SetCommand(self): + + tmp = Label(self.command_frame, text="neutral", width=10 ,bg='snow3') + tmp.grid(row=1, column=0,padx=10, pady=10) + + tmp = Label(self.command_frame, text="a photo of a", width=10 ,bg='snow3') + tmp.grid(row=1, column=1,padx=10, pady=10) + + self.neutral = Text ( self.command_frame, height=2, width=30) + self.neutral.grid(row=1, column=2,padx=10, pady=10) + + + tmp = Label(self.command_frame, text="target", width=10 ,bg='snow3') + tmp.grid(row=2, column=0,padx=10, pady=10) + + tmp = Label(self.command_frame, text="a photo of a", width=10 ,bg='snow3') + tmp.grid(row=2, column=1,padx=10, pady=10) + + self.target = Text ( self.command_frame, height=2, width=30) + self.target.grid(row=2, column=2,padx=10, pady=10) + + tmp = Label(self.command_frame, text="strength", width=10 ,bg='snow3') + tmp.grid(row=3, column=0,padx=10, pady=10) + + self.alpha = Scale(self.command_frame, from_=-15, to=25, orient=HORIZONTAL,bg='snow3', length=250,resolution=0.01) + self.alpha.grid(row=3, column=2,padx=10, pady=10) + + + tmp = Label(self.command_frame, text="disentangle", width=10 ,bg='snow3') + tmp.grid(row=4, column=0,padx=10, pady=10) + + self.beta = Scale(self.command_frame, from_=0.08, to=0.4, orient=HORIZONTAL,bg='snow3', length=250,resolution=0.001) + self.beta.grid(row=4, column=2,padx=10, pady=10) + + self.reset = Button(self.command_frame, text='Reset') + self.reset.grid(row=5, column=1,padx=10, pady=10) + + + self.set_init = Button(self.command_frame, text='Accept') + self.set_init.grid(row=5, column=2,padx=10, pady=10) + +#%% +if __name__ == "__main__": + master=Tk() + self=View(master) + self.run() + + + + + + + \ No newline at end of file diff --git a/PTI/models/StyleCLIP/global_directions/GenerateImg.py b/PTI/models/StyleCLIP/global_directions/GenerateImg.py new file mode 100644 index 0000000000000000000000000000000000000000..0c6dee48f2d6d9ac37c00ee77c7a46c2cc6b25e1 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/GenerateImg.py @@ -0,0 +1,50 @@ + +import os +import numpy as np +import argparse +from manipulate import Manipulator + +from PIL import Image +#%% + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='Process some integers.') + + parser.add_argument('--dataset_name',type=str,default='ffhq', + help='name of dataset, for example, ffhq') + + args = parser.parse_args() + dataset_name=args.dataset_name + + if not os.path.isdir('./data/'+dataset_name): + os.system('mkdir ./data/'+dataset_name) + #%% + M=Manipulator(dataset_name=dataset_name) + np.set_printoptions(suppress=True) + print(M.dataset_name) + #%% + + M.img_index=0 + M.num_images=50 + M.alpha=[0] + M.step=1 + lindex,bname=0,0 + + M.manipulate_layers=[lindex] + codes,out=M.EditOneC(bname) + #%% + + for i in range(len(out)): + img=out[i,0] + img=Image.fromarray(img) + img.save('./data/'+dataset_name+'/'+str(i)+'.jpg') + #%% + w=np.load('./npy/'+dataset_name+'/W.npy') + + tmp=w[:M.num_images] + tmp=tmp[:,None,:] + tmp=np.tile(tmp,(1,M.Gs.components.synthesis.input_shape[1],1)) + + np.save('./data/'+dataset_name+'/w_plus.npy',tmp) + + \ No newline at end of file diff --git a/PTI/models/StyleCLIP/global_directions/GetCode.py b/PTI/models/StyleCLIP/global_directions/GetCode.py new file mode 100644 index 0000000000000000000000000000000000000000..62e64dc8cbc5ad2bb16aef5da8f6d41c26b24170 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/GetCode.py @@ -0,0 +1,232 @@ + + + +import os +import pickle +import numpy as np +from dnnlib import tflib +import tensorflow as tf + +import argparse + +def LoadModel(dataset_name): + # Initialize TensorFlow. + tflib.init_tf() + model_path='./model/' + model_name=dataset_name+'.pkl' + + tmp=os.path.join(model_path,model_name) + with open(tmp, 'rb') as f: + _, _, Gs = pickle.load(f) + return Gs + +def lerp(a,b,t): + return a + (b - a) * t + +#stylegan-ada +def SelectName(layer_name,suffix): + if suffix==None: + tmp1='add:0' in layer_name + tmp2='shape=(?,' in layer_name + tmp4='G_synthesis_1' in layer_name + tmp= tmp1 and tmp2 and tmp4 + else: + tmp1=('/Conv0_up'+suffix) in layer_name + tmp2=('/Conv1'+suffix) in layer_name + tmp3=('4x4/Conv'+suffix) in layer_name + tmp4='G_synthesis_1' in layer_name + tmp5=('/ToRGB'+suffix) in layer_name + tmp= (tmp1 or tmp2 or tmp3 or tmp5) and tmp4 + return tmp + + +def GetSNames(suffix): + #get style tensor name + with tf.Session() as sess: + op = sess.graph.get_operations() + layers=[m.values() for m in op] + + + select_layers=[] + for layer in layers: + layer_name=str(layer) + if SelectName(layer_name,suffix): + select_layers.append(layer[0]) + return select_layers + +def SelectName2(layer_name): + tmp1='mod_bias' in layer_name + tmp2='mod_weight' in layer_name + tmp3='ToRGB' in layer_name + + tmp= (tmp1 or tmp2) and (not tmp3) + return tmp + +def GetKName(Gs): + + layers=[var for name, var in Gs.components.synthesis.vars.items()] + + select_layers=[] + for layer in layers: + layer_name=str(layer) + if SelectName2(layer_name): + select_layers.append(layer) + return select_layers + +def GetCode(Gs,random_state,num_img,num_once,dataset_name): + rnd = np.random.RandomState(random_state) #5 + + truncation_psi=0.7 + truncation_cutoff=8 + + dlatent_avg=Gs.get_var('dlatent_avg') + + dlatents=np.zeros((num_img,512),dtype='float32') + for i in range(int(num_img/num_once)): + src_latents = rnd.randn(num_once, Gs.input_shape[1]) + src_dlatents = Gs.components.mapping.run(src_latents, None) # [seed, layer, component] + + # Apply truncation trick. + if truncation_psi is not None and truncation_cutoff is not None: + layer_idx = np.arange(src_dlatents.shape[1])[np.newaxis, :, np.newaxis] + ones = np.ones(layer_idx.shape, dtype=np.float32) + coefs = np.where(layer_idx < truncation_cutoff, truncation_psi * ones, ones) + src_dlatents_np=lerp(dlatent_avg, src_dlatents, coefs) + src_dlatents=src_dlatents_np[:,0,:].astype('float32') + dlatents[(i*num_once):((i+1)*num_once),:]=src_dlatents + print('get all z and w') + + tmp='./npy/'+dataset_name+'/W' + np.save(tmp,dlatents) + + +def GetImg(Gs,num_img,num_once,dataset_name,save_name='images'): + print('Generate Image') + tmp='./npy/'+dataset_name+'/W.npy' + dlatents=np.load(tmp) + fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True) + + all_images=[] + for i in range(int(num_img/num_once)): + print(i) + images=[] + for k in range(num_once): + tmp=dlatents[i*num_once+k] + tmp=tmp[None,None,:] + tmp=np.tile(tmp,(1,Gs.components.synthesis.input_shape[1],1)) + image2= Gs.components.synthesis.run(tmp, randomize_noise=False, output_transform=fmt) + images.append(image2) + + images=np.concatenate(images) + + all_images.append(images) + + all_images=np.concatenate(all_images) + + tmp='./npy/'+dataset_name+'/'+save_name + np.save(tmp,all_images) + +def GetS(dataset_name,num_img): + print('Generate S') + tmp='./npy/'+dataset_name+'/W.npy' + dlatents=np.load(tmp)[:num_img] + + with tf.Session() as sess: + init = tf.global_variables_initializer() + sess.run(init) + + Gs=LoadModel(dataset_name) + Gs.print_layers() #for ada + select_layers1=GetSNames(suffix=None) #None,'/mul_1:0','/mod_weight/read:0','/MatMul:0' + dlatents=dlatents[:,None,:] + dlatents=np.tile(dlatents,(1,Gs.components.synthesis.input_shape[1],1)) + + all_s = sess.run( + select_layers1, + feed_dict={'G_synthesis_1/dlatents_in:0': dlatents}) + + layer_names=[layer.name for layer in select_layers1] + save_tmp=[layer_names,all_s] + return save_tmp + + + + +def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False): + """Convert a minibatch of images from float32 to uint8 with configurable dynamic range. + Can be used as an output transformation for Network.run(). + """ + if nchw_to_nhwc: + images = np.transpose(images, [0, 2, 3, 1]) + + scale = 255 / (drange[1] - drange[0]) + images = images * scale + (0.5 - drange[0] * scale) + + np.clip(images, 0, 255, out=images) + images=images.astype('uint8') + return images + + +def GetCodeMS(dlatents): + m=[] + std=[] + for i in range(len(dlatents)): + tmp= dlatents[i] + tmp_mean=tmp.mean(axis=0) + tmp_std=tmp.std(axis=0) + m.append(tmp_mean) + std.append(tmp_std) + return m,std + + + +#%% +if __name__ == "__main__": + + + parser = argparse.ArgumentParser(description='Process some integers.') + + parser.add_argument('--dataset_name',type=str,default='ffhq', + help='name of dataset, for example, ffhq') + parser.add_argument('--code_type',choices=['w','s','s_mean_std'],default='w') + + args = parser.parse_args() + random_state=5 + num_img=100_000 + num_once=1_000 + dataset_name=args.dataset_name + + if not os.path.isfile('./model/'+dataset_name+'.pkl'): + url='https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/' + name='stylegan2-'+dataset_name+'-config-f.pkl' + os.system('wget ' +url+name + ' -P ./model/') + os.system('mv ./model/'+name+' ./model/'+dataset_name+'.pkl') + + if not os.path.isdir('./npy/'+dataset_name): + os.system('mkdir ./npy/'+dataset_name) + + if args.code_type=='w': + Gs=LoadModel(dataset_name=dataset_name) + GetCode(Gs,random_state,num_img,num_once,dataset_name) +# GetImg(Gs,num_img=num_img,num_once=num_once,dataset_name=dataset_name,save_name='images_100K') #no need + elif args.code_type=='s': + save_name='S' + save_tmp=GetS(dataset_name,num_img=2_000) + tmp='./npy/'+dataset_name+'/'+save_name + with open(tmp, "wb") as fp: + pickle.dump(save_tmp, fp) + + elif args.code_type=='s_mean_std': + save_tmp=GetS(dataset_name,num_img=num_img) + dlatents=save_tmp[1] + m,std=GetCodeMS(dlatents) + save_tmp=[m,std] + save_name='S_mean_std' + tmp='./npy/'+dataset_name+'/'+save_name + with open(tmp, "wb") as fp: + pickle.dump(save_tmp, fp) + + + + + diff --git a/PTI/models/StyleCLIP/global_directions/GetGUIData.py b/PTI/models/StyleCLIP/global_directions/GetGUIData.py new file mode 100644 index 0000000000000000000000000000000000000000..52f77213ab88edf8b33eff166b89b9e56ac4ff01 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/GetGUIData.py @@ -0,0 +1,67 @@ + +import os +import numpy as np +import argparse +from manipulate import Manipulator +import torch +from PIL import Image +#%% + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='Process some integers.') + + parser.add_argument('--dataset_name',type=str,default='ffhq', + help='name of dataset, for example, ffhq') + + parser.add_argument('--real', action='store_true') + + args = parser.parse_args() + dataset_name=args.dataset_name + + if not os.path.isdir('./data/'+dataset_name): + os.system('mkdir ./data/'+dataset_name) + #%% + M=Manipulator(dataset_name=dataset_name) + np.set_printoptions(suppress=True) + print(M.dataset_name) + #%% + #remove all .jpg + names=os.listdir('./data/'+dataset_name+'/') + for name in names: + if '.jpg' in name: + os.system('rm ./data/'+dataset_name+'/'+name) + + + #%% + if args.real: + latents=torch.load('./data/'+dataset_name+'/latents.pt') + w_plus=latents.cpu().detach().numpy() + else: + w=np.load('./npy/'+dataset_name+'/W.npy') + tmp=w[:50] #only use 50 images + tmp=tmp[:,None,:] + w_plus=np.tile(tmp,(1,M.Gs.components.synthesis.input_shape[1],1)) + np.save('./data/'+dataset_name+'/w_plus.npy',w_plus) + + #%% + tmp=M.W2S(w_plus) + M.dlatents=tmp + + M.img_index=0 + M.num_images=len(w_plus) + M.alpha=[0] + M.step=1 + lindex,bname=0,0 + + M.manipulate_layers=[lindex] + codes,out=M.EditOneC(bname) + #%% + + for i in range(len(out)): + img=out[i,0] + img=Image.fromarray(img) + img.save('./data/'+dataset_name+'/'+str(i)+'.jpg') + #%% + + + \ No newline at end of file diff --git a/PTI/models/StyleCLIP/global_directions/Inference.py b/PTI/models/StyleCLIP/global_directions/Inference.py new file mode 100644 index 0000000000000000000000000000000000000000..a292787c88a370b15b4f0d633ac27bb5bed2b510 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/Inference.py @@ -0,0 +1,106 @@ + + +from manipulate import Manipulator +import tensorflow as tf +import numpy as np +import torch +import clip +from MapTS import GetBoundary,GetDt + +class StyleCLIP(): + + def __init__(self,dataset_name='ffhq'): + print('load clip') + device = "cuda" if torch.cuda.is_available() else "cpu" + self.model, preprocess = clip.load("ViT-B/32", device=device) + self.LoadData(dataset_name) + + def LoadData(self, dataset_name): + tf.keras.backend.clear_session() + M=Manipulator(dataset_name=dataset_name) + np.set_printoptions(suppress=True) + fs3=np.load('./npy/'+dataset_name+'/fs3.npy') + + self.M=M + self.fs3=fs3 + + w_plus=np.load('./data/'+dataset_name+'/w_plus.npy') + self.M.dlatents=M.W2S(w_plus) + + if dataset_name=='ffhq': + self.c_threshold=20 + else: + self.c_threshold=100 + self.SetInitP() + + def SetInitP(self): + self.M.alpha=[3] + self.M.num_images=1 + + self.target='' + self.neutral='' + self.GetDt2() + img_index=0 + self.M.dlatent_tmp=[tmp[img_index:(img_index+1)] for tmp in self.M.dlatents] + + + def GetDt2(self): + classnames=[self.target,self.neutral] + dt=GetDt(classnames,self.model) + + self.dt=dt + num_cs=[] + betas=np.arange(0.1,0.3,0.01) + for i in range(len(betas)): + boundary_tmp2,num_c=GetBoundary(self.fs3,self.dt,self.M,threshold=betas[i]) + print(betas[i]) + num_cs.append(num_c) + + num_cs=np.array(num_cs) + select=num_cs>self.c_threshold + + if sum(select)==0: + self.beta=0.1 + else: + self.beta=betas[select][-1] + + + def GetCode(self): + boundary_tmp2,num_c=GetBoundary(self.fs3,self.dt,self.M,threshold=self.beta) + codes=self.M.MSCode(self.M.dlatent_tmp,boundary_tmp2) + return codes + + def GetImg(self): + + codes=self.GetCode() + out=self.M.GenerateImg(codes) + img=out[0,0] + return img + + + + +#%% +if __name__ == "__main__": + style_clip=StyleCLIP() + self=style_clip + + + + + + + + + + + + + + + + + + + + diff --git a/PTI/models/StyleCLIP/global_directions/MapTS.py b/PTI/models/StyleCLIP/global_directions/MapTS.py new file mode 100644 index 0000000000000000000000000000000000000000..2160a62cdbb0278d213076637f79b1e6f66db906 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/MapTS.py @@ -0,0 +1,394 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Feb 4 17:36:31 2021 + +@author: wuzongze +""" + +import os +#os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" +#os.environ["CUDA_VISIBLE_DEVICES"] = "1" #(or "1" or "2") + +import sys + +#sys.path=['', '/usr/local/tensorflow/avx-avx2-gpu/1.14.0/python3.7/site-packages', '/usr/local/matlab/2018b/lib/python3.7/site-packages', '/cs/labs/danix/wuzongze/pythonV/venv3.7/lib/python37.zip', '/cs/labs/danix/wuzongze/pythonV/venv3.7/lib/python3.7', '/cs/labs/danix/wuzongze/pythonV/venv3.7/lib/python3.7/lib-dynload', '/usr/lib/python3.7', '/cs/labs/danix/wuzongze/pythonV/venv3.7/lib/python3.7/site-packages', '/cs/labs/danix/wuzongze/pythonV/venv3.7/lib/python3.7/site-packages/copkmeans-1.5-py3.7.egg', '/cs/labs/danix/wuzongze/pythonV/venv3.7/lib/python3.7/site-packages/spherecluster-0.1.7-py3.7.egg', '/usr/lib/python3/dist-packages', '/usr/local/lib/python3.7/dist-packages', '/usr/lib/python3/dist-packages/IPython/extensions'] + +import tensorflow as tf + +import numpy as np +import torch +import clip +from PIL import Image +import pickle +import copy +import matplotlib.pyplot as plt + +def GetAlign(out,dt,model,preprocess): + imgs=out + imgs1=imgs.reshape([-1]+list(imgs.shape[2:])) + + tmp=[] + for i in range(len(imgs1)): + + img=Image.fromarray(imgs1[i]) + image = preprocess(img).unsqueeze(0).to(device) + tmp.append(image) + + image=torch.cat(tmp) + + with torch.no_grad(): + image_features = model.encode_image(image) + image_features = image_features / image_features.norm(dim=-1, keepdim=True) + + image_features1=image_features.cpu().numpy() + + image_features1=image_features1.reshape(list(imgs.shape[:2])+[512]) + + fd=image_features1[:,1:,:]-image_features1[:,:-1,:] + + fd1=fd.reshape([-1,512]) + fd2=fd1/np.linalg.norm(fd1,axis=1)[:,None] + + tmp=np.dot(fd2,dt) + m=tmp.mean() + acc=np.sum(tmp>0)/len(tmp) + print(m,acc) + return m,acc + + +def SplitS(ds_p,M,if_std): + all_ds=[] + start=0 + for i in M.mindexs: + tmp=M.dlatents[i].shape[1] + end=start+tmp + tmp=ds_p[start:end] +# tmp=tmp*M.code_std[i] + + all_ds.append(tmp) + start=end + + all_ds2=[] + tmp_index=0 + for i in range(len(M.s_names)): + if (not 'RGB' in M.s_names[i]) and (not len(all_ds[tmp_index])==0): + +# tmp=np.abs(all_ds[tmp_index]/M.code_std[i]) +# print(i,tmp.mean()) +# tmp=np.dot(M.latent_codes[i],all_ds[tmp_index]) +# print(tmp) + if if_std: + tmp=all_ds[tmp_index]*M.code_std[i] + else: + tmp=all_ds[tmp_index] + + all_ds2.append(tmp) + tmp_index+=1 + else: + tmp=np.zeros(len(M.dlatents[i][0])) + all_ds2.append(tmp) + return all_ds2 + + +imagenet_templates = [ + 'a bad photo of a {}.', +# 'a photo of many {}.', + 'a sculpture of a {}.', + 'a photo of the hard to see {}.', + 'a low resolution photo of the {}.', + 'a rendering of a {}.', + 'graffiti of a {}.', + 'a bad photo of the {}.', + 'a cropped photo of the {}.', + 'a tattoo of a {}.', + 'the embroidered {}.', + 'a photo of a hard to see {}.', + 'a bright photo of a {}.', + 'a photo of a clean {}.', + 'a photo of a dirty {}.', + 'a dark photo of the {}.', + 'a drawing of a {}.', + 'a photo of my {}.', + 'the plastic {}.', + 'a photo of the cool {}.', + 'a close-up photo of a {}.', + 'a black and white photo of the {}.', + 'a painting of the {}.', + 'a painting of a {}.', + 'a pixelated photo of the {}.', + 'a sculpture of the {}.', + 'a bright photo of the {}.', + 'a cropped photo of a {}.', + 'a plastic {}.', + 'a photo of the dirty {}.', + 'a jpeg corrupted photo of a {}.', + 'a blurry photo of the {}.', + 'a photo of the {}.', + 'a good photo of the {}.', + 'a rendering of the {}.', + 'a {} in a video game.', + 'a photo of one {}.', + 'a doodle of a {}.', + 'a close-up photo of the {}.', + 'a photo of a {}.', + 'the origami {}.', + 'the {} in a video game.', + 'a sketch of a {}.', + 'a doodle of the {}.', + 'a origami {}.', + 'a low resolution photo of a {}.', + 'the toy {}.', + 'a rendition of the {}.', + 'a photo of the clean {}.', + 'a photo of a large {}.', + 'a rendition of a {}.', + 'a photo of a nice {}.', + 'a photo of a weird {}.', + 'a blurry photo of a {}.', + 'a cartoon {}.', + 'art of a {}.', + 'a sketch of the {}.', + 'a embroidered {}.', + 'a pixelated photo of a {}.', + 'itap of the {}.', + 'a jpeg corrupted photo of the {}.', + 'a good photo of a {}.', + 'a plushie {}.', + 'a photo of the nice {}.', + 'a photo of the small {}.', + 'a photo of the weird {}.', + 'the cartoon {}.', + 'art of the {}.', + 'a drawing of the {}.', + 'a photo of the large {}.', + 'a black and white photo of a {}.', + 'the plushie {}.', + 'a dark photo of a {}.', + 'itap of a {}.', + 'graffiti of the {}.', + 'a toy {}.', + 'itap of my {}.', + 'a photo of a cool {}.', + 'a photo of a small {}.', + 'a tattoo of the {}.', +] + + +def zeroshot_classifier(classnames, templates,model): + with torch.no_grad(): + zeroshot_weights = [] + for classname in classnames: + texts = [template.format(classname) for template in templates] #format with class + texts = clip.tokenize(texts).cuda() #tokenize + class_embeddings = model.encode_text(texts) #embed with text encoder + class_embeddings /= class_embeddings.norm(dim=-1, keepdim=True) + class_embedding = class_embeddings.mean(dim=0) + class_embedding /= class_embedding.norm() + zeroshot_weights.append(class_embedding) + zeroshot_weights = torch.stack(zeroshot_weights, dim=1).cuda() + return zeroshot_weights + + +def GetDt(classnames,model): + text_features=zeroshot_classifier(classnames, imagenet_templates,model).t() + + dt=text_features[0]-text_features[1] + dt=dt.cpu().numpy() + +# t_m1=t_m/np.linalg.norm(t_m) +# dt=text_features.cpu().numpy()[0]-t_m1 + print(np.linalg.norm(dt)) + dt=dt/np.linalg.norm(dt) + return dt + + +def GetBoundary(fs3,dt,M,threshold): + tmp=np.dot(fs3,dt) + + ds_imp=copy.copy(tmp) + select=np.abs(tmp)", self.text_n) + self.view.target.bind("", self.text_t) + self.view.alpha.bind('', self.ChangeAlpha) + self.view.beta.bind('', self.ChangeBeta) + self.view.set_init.bind('', self.SetInit) + self.view.reset.bind('', self.Reset) + self.view.bg.bind('', self.open_img) + + + self.drawn = None + + self.view.target.delete(1.0, "end") + self.view.target.insert("end", self.style_clip.target) +# + self.view.neutral.delete(1.0, "end") + self.view.neutral.insert("end", self.style_clip.neutral) + + + def Reset(self,event): + self.style_clip.GetDt2() + self.style_clip.M.alpha=[0] + + self.view.beta.set(self.style_clip.beta) + self.view.alpha.set(0) + + img=self.style_clip.GetImg() + img=Image.fromarray(img) + img = ImageTk.PhotoImage(img) + self.addImage_m(img) + + + def SetInit(self,event): + codes=self.style_clip.GetCode() + self.style_clip.M.dlatent_tmp=[tmp[:,0] for tmp in codes] + print('set init') + + def ChangeAlpha(self,event): + tmp=self.view.alpha.get() + self.style_clip.M.alpha=[float(tmp)] + + img=self.style_clip.GetImg() + print('manipulate one') + img=Image.fromarray(img) + img = ImageTk.PhotoImage(img) + self.addImage_m(img) + + def ChangeBeta(self,event): + tmp=self.view.beta.get() + self.style_clip.beta=float(tmp) + + img=self.style_clip.GetImg() + print('manipulate one') + img=Image.fromarray(img) + img = ImageTk.PhotoImage(img) + self.addImage_m(img) + + def ChangeDataset(self,event): + + dataset_name=self.view.set_category.get() + + self.style_clip.LoadData(dataset_name) + + self.view.target.delete(1.0, "end") + self.view.target.insert("end", self.style_clip.target) + + self.view.neutral.delete(1.0, "end") + self.view.neutral.insert("end", self.style_clip.neutral) + + def text_t(self,event): + tmp=self.view.target.get("1.0",'end') + tmp=tmp.replace('\n','') + + self.view.target.delete(1.0, "end") + self.view.target.insert("end", tmp) + + print('target',tmp,'###') + self.style_clip.target=tmp + self.style_clip.GetDt2() + self.view.beta.set(self.style_clip.beta) + self.view.alpha.set(3) + self.style_clip.M.alpha=[3] + + img=self.style_clip.GetImg() + print('manipulate one') + img=Image.fromarray(img) + img = ImageTk.PhotoImage(img) + self.addImage_m(img) + + + def text_n(self,event): + tmp=self.view.neutral.get("1.0",'end') + tmp=tmp.replace('\n','') + + self.view.neutral.delete(1.0, "end") + self.view.neutral.insert("end", tmp) + + print('neutral',tmp,'###') + self.style_clip.neutral=tmp + self.view.target.delete(1.0, "end") + self.view.target.insert("end", tmp) + + + def run(self): + self.root.mainloop() + + def addImage(self,img): + self.view.bg.create_image(self.view.width/2, self.view.height/2, image=img, anchor='center') + self.image=img #save a copy of image. if not the image will disappear + + def addImage_m(self,img): + self.view.mani.create_image(512, 512, image=img, anchor='center') + self.image2=img + + + def openfn(self): + filename = askopenfilename(title='open',initialdir='./data/'+self.style_clip.M.dataset_name+'/',filetypes=[("all image format", ".jpg"),("all image format", ".png")]) + return filename + + def open_img(self,event): + x = self.openfn() + print(x) + + + img = Image.open(x) + img2 = img.resize(( 512,512), Image.ANTIALIAS) + img2 = ImageTk.PhotoImage(img2) + self.addImage(img2) + + img = ImageTk.PhotoImage(img) + self.addImage_m(img) + + img_index=x.split('/')[-1].split('.')[0] + img_index=int(img_index) + print(img_index) + self.style_clip.M.img_index=img_index + self.style_clip.M.dlatent_tmp=[tmp[img_index:(img_index+1)] for tmp in self.style_clip.M.dlatents] + + + self.style_clip.GetDt2() + self.view.beta.set(self.style_clip.beta) + self.view.alpha.set(3) + + #%% +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='Process some integers.') + + parser.add_argument('--dataset_name',type=str,default='ffhq', + help='name of dataset, for example, ffhq') + + args = parser.parse_args() + dataset_name=args.dataset_name + + self=PlayInteractively(dataset_name) + self.run() + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/PTI/models/StyleCLIP/global_directions/SingleChannel.py b/PTI/models/StyleCLIP/global_directions/SingleChannel.py new file mode 100644 index 0000000000000000000000000000000000000000..ecaa7ec7898d37f8f5db171f9141a5253af3fa73 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/SingleChannel.py @@ -0,0 +1,109 @@ + + + +import numpy as np +import torch +import clip +from PIL import Image +import copy +from manipulate import Manipulator +import argparse + +def GetImgF(out,model,preprocess): + imgs=out + imgs1=imgs.reshape([-1]+list(imgs.shape[2:])) + + tmp=[] + for i in range(len(imgs1)): + + img=Image.fromarray(imgs1[i]) + image = preprocess(img).unsqueeze(0).to(device) + tmp.append(image) + + image=torch.cat(tmp) + with torch.no_grad(): + image_features = model.encode_image(image) + + image_features1=image_features.cpu().numpy() + image_features1=image_features1.reshape(list(imgs.shape[:2])+[512]) + + return image_features1 + +def GetFs(fs): + tmp=np.linalg.norm(fs,axis=-1) + fs1=fs/tmp[:,:,:,None] + fs2=fs1[:,:,1,:]-fs1[:,:,0,:] # 5*sigma - (-5)* sigma + fs3=fs2/np.linalg.norm(fs2,axis=-1)[:,:,None] + fs3=fs3.mean(axis=1) + fs3=fs3/np.linalg.norm(fs3,axis=-1)[:,None] + return fs3 + +#%% +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='Process some integers.') + + parser.add_argument('--dataset_name',type=str,default='cat', + help='name of dataset, for example, ffhq') + args = parser.parse_args() + dataset_name=args.dataset_name + + #%% + device = "cuda" if torch.cuda.is_available() else "cpu" + model, preprocess = clip.load("ViT-B/32", device=device) + #%% + M=Manipulator(dataset_name=dataset_name) + np.set_printoptions(suppress=True) + print(M.dataset_name) + #%% + img_sindex=0 + num_images=100 + dlatents_o=[] + tmp=img_sindex*num_images + for i in range(len(M.dlatents)): + tmp1=M.dlatents[i][tmp:(tmp+num_images)] + dlatents_o.append(tmp1) + #%% + + all_f=[] + M.alpha=[-5,5] #ffhq 5 + M.step=2 + M.num_images=num_images + select=np.array(M.mindexs)<=16 #below or equal to 128 resolution + mindexs2=np.array(M.mindexs)[select] + for lindex in mindexs2: #ignore ToRGB layers + print(lindex) + num_c=M.dlatents[lindex].shape[1] + for cindex in range(num_c): + + M.dlatents=copy.copy(dlatents_o) + M.dlatents[lindex][:,cindex]=M.code_mean[lindex][cindex] + + M.manipulate_layers=[lindex] + codes,out=M.EditOneC(cindex) + image_features1=GetImgF(out,model,preprocess) + all_f.append(image_features1) + + all_f=np.array(all_f) + + fs3=GetFs(all_f) + + #%% + file_path='./npy/'+M.dataset_name+'/' + np.save(file_path+'fs3',fs3) + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/PTI/models/StyleCLIP/global_directions/__init__.py b/PTI/models/StyleCLIP/global_directions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/models/StyleCLIP/global_directions/data/ffhq/w_plus.npy b/PTI/models/StyleCLIP/global_directions/data/ffhq/w_plus.npy new file mode 100644 index 0000000000000000000000000000000000000000..db524aae88e16239679a8f72ccb3403fd16c95a9 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/data/ffhq/w_plus.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:394f0f166305654f49cd1b0cd3d4f2b7a51e740a449a1ebfa1c69f79d01399fa +size 2506880 diff --git a/PTI/models/StyleCLIP/global_directions/dnnlib/__init__.py b/PTI/models/StyleCLIP/global_directions/dnnlib/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c73940d81233142ae3dcd9a37b7ec2185c5d5fc5 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/dnnlib/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +from .util import EasyDict, make_cache_dir_path diff --git a/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/__init__.py b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ca852844ec488c0134bffa647e25a40646ff4718 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/__init__.py @@ -0,0 +1,20 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +from . import autosummary +from . import network +from . import optimizer +from . import tfutil +from . import custom_ops + +from .tfutil import * +from .network import Network + +from .optimizer import Optimizer + +from .custom_ops import get_plugin diff --git a/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/autosummary.py b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/autosummary.py new file mode 100644 index 0000000000000000000000000000000000000000..56dfb96093bb5b1129a99585b4ce655b98d80009 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/autosummary.py @@ -0,0 +1,193 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Helper for adding automatically tracked values to Tensorboard. + +Autosummary creates an identity op that internally keeps track of the input +values and automatically shows up in TensorBoard. The reported value +represents an average over input components. The average is accumulated +constantly over time and flushed when save_summaries() is called. + +Notes: +- The output tensor must be used as an input for something else in the + graph. Otherwise, the autosummary op will not get executed, and the average + value will not get accumulated. +- It is perfectly fine to include autosummaries with the same name in + several places throughout the graph, even if they are executed concurrently. +- It is ok to also pass in a python scalar or numpy array. In this case, it + is added to the average immediately. +""" + +from collections import OrderedDict +import numpy as np +import tensorflow as tf +from tensorboard import summary as summary_lib +from tensorboard.plugins.custom_scalar import layout_pb2 + +from . import tfutil +from .tfutil import TfExpression +from .tfutil import TfExpressionEx + +# Enable "Custom scalars" tab in TensorBoard for advanced formatting. +# Disabled by default to reduce tfevents file size. +enable_custom_scalars = False + +_dtype = tf.float64 +_vars = OrderedDict() # name => [var, ...] +_immediate = OrderedDict() # name => update_op, update_value +_finalized = False +_merge_op = None + + +def _create_var(name: str, value_expr: TfExpression) -> TfExpression: + """Internal helper for creating autosummary accumulators.""" + assert not _finalized + name_id = name.replace("/", "_") + v = tf.cast(value_expr, _dtype) + + if v.shape.is_fully_defined(): + size = np.prod(v.shape.as_list()) + size_expr = tf.constant(size, dtype=_dtype) + else: + size = None + size_expr = tf.reduce_prod(tf.cast(tf.shape(v), _dtype)) + + if size == 1: + if v.shape.ndims != 0: + v = tf.reshape(v, []) + v = [size_expr, v, tf.square(v)] + else: + v = [size_expr, tf.reduce_sum(v), tf.reduce_sum(tf.square(v))] + v = tf.cond(tf.is_finite(v[1]), lambda: tf.stack(v), lambda: tf.zeros(3, dtype=_dtype)) + + with tfutil.absolute_name_scope("Autosummary/" + name_id), tf.control_dependencies(None): + var = tf.Variable(tf.zeros(3, dtype=_dtype), trainable=False) # [sum(1), sum(x), sum(x**2)] + update_op = tf.cond(tf.is_variable_initialized(var), lambda: tf.assign_add(var, v), lambda: tf.assign(var, v)) + + if name in _vars: + _vars[name].append(var) + else: + _vars[name] = [var] + return update_op + + +def autosummary(name: str, value: TfExpressionEx, passthru: TfExpressionEx = None, condition: TfExpressionEx = True) -> TfExpressionEx: + """Create a new autosummary. + + Args: + name: Name to use in TensorBoard + value: TensorFlow expression or python value to track + passthru: Optionally return this TF node without modifications but tack an autosummary update side-effect to this node. + + Example use of the passthru mechanism: + + n = autosummary('l2loss', loss, passthru=n) + + This is a shorthand for the following code: + + with tf.control_dependencies([autosummary('l2loss', loss)]): + n = tf.identity(n) + """ + tfutil.assert_tf_initialized() + name_id = name.replace("/", "_") + + if tfutil.is_tf_expression(value): + with tf.name_scope("summary_" + name_id), tf.device(value.device): + condition = tf.convert_to_tensor(condition, name='condition') + update_op = tf.cond(condition, lambda: tf.group(_create_var(name, value)), tf.no_op) + with tf.control_dependencies([update_op]): + return tf.identity(value if passthru is None else passthru) + + else: # python scalar or numpy array + assert not tfutil.is_tf_expression(passthru) + assert not tfutil.is_tf_expression(condition) + if condition: + if name not in _immediate: + with tfutil.absolute_name_scope("Autosummary/" + name_id), tf.device(None), tf.control_dependencies(None): + update_value = tf.placeholder(_dtype) + update_op = _create_var(name, update_value) + _immediate[name] = update_op, update_value + update_op, update_value = _immediate[name] + tfutil.run(update_op, {update_value: value}) + return value if passthru is None else passthru + + +def finalize_autosummaries() -> None: + """Create the necessary ops to include autosummaries in TensorBoard report. + Note: This should be done only once per graph. + """ + global _finalized + tfutil.assert_tf_initialized() + + if _finalized: + return None + + _finalized = True + tfutil.init_uninitialized_vars([var for vars_list in _vars.values() for var in vars_list]) + + # Create summary ops. + with tf.device(None), tf.control_dependencies(None): + for name, vars_list in _vars.items(): + name_id = name.replace("/", "_") + with tfutil.absolute_name_scope("Autosummary/" + name_id): + moments = tf.add_n(vars_list) + moments /= moments[0] + with tf.control_dependencies([moments]): # read before resetting + reset_ops = [tf.assign(var, tf.zeros(3, dtype=_dtype)) for var in vars_list] + with tf.name_scope(None), tf.control_dependencies(reset_ops): # reset before reporting + mean = moments[1] + std = tf.sqrt(moments[2] - tf.square(moments[1])) + tf.summary.scalar(name, mean) + if enable_custom_scalars: + tf.summary.scalar("xCustomScalars/" + name + "/margin_lo", mean - std) + tf.summary.scalar("xCustomScalars/" + name + "/margin_hi", mean + std) + + # Setup layout for custom scalars. + layout = None + if enable_custom_scalars: + cat_dict = OrderedDict() + for series_name in sorted(_vars.keys()): + p = series_name.split("/") + cat = p[0] if len(p) >= 2 else "" + chart = "/".join(p[1:-1]) if len(p) >= 3 else p[-1] + if cat not in cat_dict: + cat_dict[cat] = OrderedDict() + if chart not in cat_dict[cat]: + cat_dict[cat][chart] = [] + cat_dict[cat][chart].append(series_name) + categories = [] + for cat_name, chart_dict in cat_dict.items(): + charts = [] + for chart_name, series_names in chart_dict.items(): + series = [] + for series_name in series_names: + series.append(layout_pb2.MarginChartContent.Series( + value=series_name, + lower="xCustomScalars/" + series_name + "/margin_lo", + upper="xCustomScalars/" + series_name + "/margin_hi")) + margin = layout_pb2.MarginChartContent(series=series) + charts.append(layout_pb2.Chart(title=chart_name, margin=margin)) + categories.append(layout_pb2.Category(title=cat_name, chart=charts)) + layout = summary_lib.custom_scalar_pb(layout_pb2.Layout(category=categories)) + return layout + +def save_summaries(file_writer, global_step=None): + """Call FileWriter.add_summary() with all summaries in the default graph, + automatically finalizing and merging them on the first call. + """ + global _merge_op + tfutil.assert_tf_initialized() + + if _merge_op is None: + layout = finalize_autosummaries() + if layout is not None: + file_writer.add_summary(layout) + with tf.device(None), tf.control_dependencies(None): + _merge_op = tf.summary.merge_all() + + file_writer.add_summary(_merge_op.eval(), global_step) diff --git a/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/custom_ops.py b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/custom_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..702471e2006af6858345c1225c1e55b0acd17d32 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/custom_ops.py @@ -0,0 +1,181 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""TensorFlow custom ops builder. +""" + +import glob +import os +import re +import uuid +import hashlib +import tempfile +import shutil +import tensorflow as tf +from tensorflow.python.client import device_lib # pylint: disable=no-name-in-module + +from .. import util + +#---------------------------------------------------------------------------- +# Global configs. + +cuda_cache_path = None +cuda_cache_version_tag = 'v1' +do_not_hash_included_headers = True # Speed up compilation by assuming that headers included by the CUDA code never change. +verbose = True # Print status messages to stdout. + +#---------------------------------------------------------------------------- +# Internal helper funcs. + +def _find_compiler_bindir(): + hostx64_paths = sorted(glob.glob('C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64'), reverse=True) + if hostx64_paths != []: + return hostx64_paths[0] + hostx64_paths = sorted(glob.glob('C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64'), reverse=True) + if hostx64_paths != []: + return hostx64_paths[0] + hostx64_paths = sorted(glob.glob('C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64'), reverse=True) + if hostx64_paths != []: + return hostx64_paths[0] + vc_bin_dir = 'C:/Program Files (x86)/Microsoft Visual Studio 14.0/vc/bin' + if os.path.isdir(vc_bin_dir): + return vc_bin_dir + return None + +def _get_compute_cap(device): + caps_str = device.physical_device_desc + m = re.search('compute capability: (\\d+).(\\d+)', caps_str) + major = m.group(1) + minor = m.group(2) + return (major, minor) + +def _get_cuda_gpu_arch_string(): + gpus = [x for x in device_lib.list_local_devices() if x.device_type == 'GPU'] + if len(gpus) == 0: + raise RuntimeError('No GPU devices found') + (major, minor) = _get_compute_cap(gpus[0]) + return 'sm_%s%s' % (major, minor) + +def _run_cmd(cmd): + with os.popen(cmd) as pipe: + output = pipe.read() + status = pipe.close() + if status is not None: + raise RuntimeError('NVCC returned an error. See below for full command line and output log:\n\n%s\n\n%s' % (cmd, output)) + +def _prepare_nvcc_cli(opts): + cmd = 'nvcc ' + opts.strip() + cmd += ' --disable-warnings' + cmd += ' --include-path "%s"' % tf.sysconfig.get_include() + cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'protobuf_archive', 'src') + cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'com_google_absl') + cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'eigen_archive') + + compiler_bindir = _find_compiler_bindir() + if compiler_bindir is None: + # Require that _find_compiler_bindir succeeds on Windows. Allow + # nvcc to use whatever is the default on Linux. + if os.name == 'nt': + raise RuntimeError('Could not find MSVC/GCC/CLANG installation on this computer. Check compiler_bindir_search_path list in "%s".' % __file__) + else: + cmd += ' --compiler-bindir "%s"' % compiler_bindir + cmd += ' 2>&1' + return cmd + +#---------------------------------------------------------------------------- +# Main entry point. + +_plugin_cache = dict() + +def get_plugin(cuda_file, extra_nvcc_options=[]): + cuda_file_base = os.path.basename(cuda_file) + cuda_file_name, cuda_file_ext = os.path.splitext(cuda_file_base) + + # Already in cache? + if cuda_file in _plugin_cache: + return _plugin_cache[cuda_file] + + # Setup plugin. + if verbose: + print('Setting up TensorFlow plugin "%s": ' % cuda_file_base, end='', flush=True) + try: + # Hash CUDA source. + md5 = hashlib.md5() + with open(cuda_file, 'rb') as f: + md5.update(f.read()) + md5.update(b'\n') + + # Hash headers included by the CUDA code by running it through the preprocessor. + if not do_not_hash_included_headers: + if verbose: + print('Preprocessing... ', end='', flush=True) + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + cuda_file_ext) + _run_cmd(_prepare_nvcc_cli('"%s" --preprocess -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir))) + with open(tmp_file, 'rb') as f: + bad_file_str = ('"' + cuda_file.replace('\\', '/') + '"').encode('utf-8') # __FILE__ in error check macros + good_file_str = ('"' + cuda_file_base + '"').encode('utf-8') + for ln in f: + if not ln.startswith(b'# ') and not ln.startswith(b'#line '): # ignore line number pragmas + ln = ln.replace(bad_file_str, good_file_str) + md5.update(ln) + md5.update(b'\n') + + # Select compiler configs. + compile_opts = '' + if os.name == 'nt': + compile_opts += '"%s"' % os.path.join(tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.lib') + elif os.name == 'posix': + compile_opts += f' --compiler-options \'-fPIC\'' + compile_opts += f' --compiler-options \'{" ".join(tf.sysconfig.get_compile_flags())}\'' + compile_opts += f' --linker-options \'{" ".join(tf.sysconfig.get_link_flags())}\'' + else: + assert False # not Windows or Linux, w00t? + compile_opts += f' --gpu-architecture={_get_cuda_gpu_arch_string()}' + compile_opts += ' --use_fast_math' + for opt in extra_nvcc_options: + compile_opts += ' ' + opt + nvcc_cmd = _prepare_nvcc_cli(compile_opts) + + # Hash build configuration. + md5.update(('nvcc_cmd: ' + nvcc_cmd).encode('utf-8') + b'\n') + md5.update(('tf.VERSION: ' + tf.VERSION).encode('utf-8') + b'\n') + md5.update(('cuda_cache_version_tag: ' + cuda_cache_version_tag).encode('utf-8') + b'\n') + + # Compile if not already compiled. + cache_dir = util.make_cache_dir_path('tflib-cudacache') if cuda_cache_path is None else cuda_cache_path + bin_file_ext = '.dll' if os.name == 'nt' else '.so' + bin_file = os.path.join(cache_dir, cuda_file_name + '_' + md5.hexdigest() + bin_file_ext) + if not os.path.isfile(bin_file): + if verbose: + print('Compiling... ', end='', flush=True) + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + bin_file_ext) + _run_cmd(nvcc_cmd + ' "%s" --shared -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir)) + os.makedirs(cache_dir, exist_ok=True) + intermediate_file = os.path.join(cache_dir, cuda_file_name + '_' + uuid.uuid4().hex + '_tmp' + bin_file_ext) + shutil.copyfile(tmp_file, intermediate_file) + os.rename(intermediate_file, bin_file) # atomic + + # Load. + if verbose: + print('Loading... ', end='', flush=True) + plugin = tf.load_op_library(bin_file) + + # Add to cache. + _plugin_cache[cuda_file] = plugin + if verbose: + print('Done.', flush=True) + return plugin + + except: + if verbose: + print('Failed!', flush=True) + raise + +#---------------------------------------------------------------------------- diff --git a/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/network.py b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/network.py new file mode 100644 index 0000000000000000000000000000000000000000..ff0c169eabdc579041dac0650fbc6da956646594 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/network.py @@ -0,0 +1,781 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Helper for managing networks.""" + +import types +import inspect +import re +import uuid +import sys +import copy +import numpy as np +import tensorflow as tf + +from collections import OrderedDict +from typing import Any, List, Tuple, Union, Callable + +from . import tfutil +from .. import util + +from .tfutil import TfExpression, TfExpressionEx + +# pylint: disable=protected-access +# pylint: disable=attribute-defined-outside-init +# pylint: disable=too-many-public-methods + +_import_handlers = [] # Custom import handlers for dealing with legacy data in pickle import. +_import_module_src = dict() # Source code for temporary modules created during pickle import. + + +def import_handler(handler_func): + """Function decorator for declaring custom import handlers.""" + _import_handlers.append(handler_func) + return handler_func + + +class Network: + """Generic network abstraction. + + Acts as a convenience wrapper for a parameterized network construction + function, providing several utility methods and convenient access to + the inputs/outputs/weights. + + Network objects can be safely pickled and unpickled for long-term + archival purposes. The pickling works reliably as long as the underlying + network construction function is defined in a standalone Python module + that has no side effects or application-specific imports. + + Args: + name: Network name. Used to select TensorFlow name and variable scopes. Defaults to build func name if None. + func_name: Fully qualified name of the underlying network construction function, or a top-level function object. + static_kwargs: Keyword arguments to be passed in to the network construction function. + """ + + def __init__(self, name: str = None, func_name: Any = None, **static_kwargs): + # Locate the user-specified build function. + assert isinstance(func_name, str) or util.is_top_level_function(func_name) + if util.is_top_level_function(func_name): + func_name = util.get_top_level_function_name(func_name) + module, func_name = util.get_module_from_obj_name(func_name) + func = util.get_obj_from_module(module, func_name) + + # Dig up source code for the module containing the build function. + module_src = _import_module_src.get(module, None) + if module_src is None: + module_src = inspect.getsource(module) + + # Initialize fields. + self._init_fields(name=(name or func_name), static_kwargs=static_kwargs, build_func=func, build_func_name=func_name, build_module_src=module_src) + + def _init_fields(self, name: str, static_kwargs: dict, build_func: Callable, build_func_name: str, build_module_src: str) -> None: + tfutil.assert_tf_initialized() + assert isinstance(name, str) + assert len(name) >= 1 + assert re.fullmatch(r"[A-Za-z0-9_.\\-]*", name) + assert isinstance(static_kwargs, dict) + assert util.is_pickleable(static_kwargs) + assert callable(build_func) + assert isinstance(build_func_name, str) + assert isinstance(build_module_src, str) + + # Choose TensorFlow name scope. + with tf.name_scope(None): + scope = tf.get_default_graph().unique_name(name, mark_as_used=True) + + # Query current TensorFlow device. + with tfutil.absolute_name_scope(scope), tf.control_dependencies(None): + device = tf.no_op(name="_QueryDevice").device + + # Immutable state. + self._name = name + self._scope = scope + self._device = device + self._static_kwargs = util.EasyDict(copy.deepcopy(static_kwargs)) + self._build_func = build_func + self._build_func_name = build_func_name + self._build_module_src = build_module_src + + # State before _init_graph(). + self._var_inits = dict() # var_name => initial_value, set to None by _init_graph() + self._all_inits_known = False # Do we know for sure that _var_inits covers all the variables? + self._components = None # subnet_name => Network, None if the components are not known yet + + # Initialized by _init_graph(). + self._input_templates = None + self._output_templates = None + self._own_vars = None + + # Cached values initialized the respective methods. + self._input_shapes = None + self._output_shapes = None + self._input_names = None + self._output_names = None + self._vars = None + self._trainables = None + self._var_global_to_local = None + self._run_cache = dict() + + def _init_graph(self) -> None: + assert self._var_inits is not None + assert self._input_templates is None + assert self._output_templates is None + assert self._own_vars is None + + # Initialize components. + if self._components is None: + self._components = util.EasyDict() + + # Choose build func kwargs. + build_kwargs = dict(self.static_kwargs) + build_kwargs["is_template_graph"] = True + build_kwargs["components"] = self._components + + # Override scope and device, and ignore surrounding control dependencies. + with tfutil.absolute_variable_scope(self.scope, reuse=False), tfutil.absolute_name_scope(self.scope), tf.device(self.device), tf.control_dependencies(None): + assert tf.get_variable_scope().name == self.scope + assert tf.get_default_graph().get_name_scope() == self.scope + + # Create input templates. + self._input_templates = [] + for param in inspect.signature(self._build_func).parameters.values(): + if param.kind == param.POSITIONAL_OR_KEYWORD and param.default is param.empty: + self._input_templates.append(tf.placeholder(tf.float32, name=param.name)) + + # Call build func. + out_expr = self._build_func(*self._input_templates, **build_kwargs) + + # Collect output templates and variables. + assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple) + self._output_templates = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr) + self._own_vars = OrderedDict((var.name[len(self.scope) + 1:].split(":")[0], var) for var in tf.global_variables(self.scope + "/")) + + # Check for errors. + if len(self._input_templates) == 0: + raise ValueError("Network build func did not list any inputs.") + if len(self._output_templates) == 0: + raise ValueError("Network build func did not return any outputs.") + if any(not tfutil.is_tf_expression(t) for t in self._output_templates): + raise ValueError("Network outputs must be TensorFlow expressions.") + if any(t.shape.ndims is None for t in self._input_templates): + raise ValueError("Network input shapes not defined. Please call x.set_shape() for each input.") + if any(t.shape.ndims is None for t in self._output_templates): + raise ValueError("Network output shapes not defined. Please call x.set_shape() where applicable.") + if any(not isinstance(comp, Network) for comp in self._components.values()): + raise ValueError("Components of a Network must be Networks themselves.") + if len(self._components) != len(set(comp.name for comp in self._components.values())): + raise ValueError("Components of a Network must have unique names.") + + # Initialize variables. + if len(self._var_inits): + tfutil.set_vars({self._get_vars()[name]: value for name, value in self._var_inits.items() if name in self._get_vars()}) + remaining_inits = [var.initializer for name, var in self._own_vars.items() if name not in self._var_inits] + if self._all_inits_known: + assert len(remaining_inits) == 0 + else: + tfutil.run(remaining_inits) + self._var_inits = None + + @property + def name(self): + """User-specified name string.""" + return self._name + + @property + def scope(self): + """Unique TensorFlow scope containing template graph and variables, derived from the user-specified name.""" + return self._scope + + @property + def device(self): + """Name of the TensorFlow device that the weights of this network reside on. Determined by the current device at construction time.""" + return self._device + + @property + def static_kwargs(self): + """EasyDict of arguments passed to the user-supplied build func.""" + return copy.deepcopy(self._static_kwargs) + + @property + def components(self): + """EasyDict of sub-networks created by the build func.""" + return copy.copy(self._get_components()) + + def _get_components(self): + if self._components is None: + self._init_graph() + assert self._components is not None + return self._components + + @property + def input_shapes(self): + """List of input tensor shapes, including minibatch dimension.""" + if self._input_shapes is None: + self._input_shapes = [t.shape.as_list() for t in self.input_templates] + return copy.deepcopy(self._input_shapes) + + @property + def output_shapes(self): + """List of output tensor shapes, including minibatch dimension.""" + if self._output_shapes is None: + self._output_shapes = [t.shape.as_list() for t in self.output_templates] + return copy.deepcopy(self._output_shapes) + + @property + def input_shape(self): + """Short-hand for input_shapes[0].""" + return self.input_shapes[0] + + @property + def output_shape(self): + """Short-hand for output_shapes[0].""" + return self.output_shapes[0] + + @property + def num_inputs(self): + """Number of input tensors.""" + return len(self.input_shapes) + + @property + def num_outputs(self): + """Number of output tensors.""" + return len(self.output_shapes) + + @property + def input_names(self): + """Name string for each input.""" + if self._input_names is None: + self._input_names = [t.name.split("/")[-1].split(":")[0] for t in self.input_templates] + return copy.copy(self._input_names) + + @property + def output_names(self): + """Name string for each output.""" + if self._output_names is None: + self._output_names = [t.name.split("/")[-1].split(":")[0] for t in self.output_templates] + return copy.copy(self._output_names) + + @property + def input_templates(self): + """Input placeholders in the template graph.""" + if self._input_templates is None: + self._init_graph() + assert self._input_templates is not None + return copy.copy(self._input_templates) + + @property + def output_templates(self): + """Output tensors in the template graph.""" + if self._output_templates is None: + self._init_graph() + assert self._output_templates is not None + return copy.copy(self._output_templates) + + @property + def own_vars(self): + """Variables defined by this network (local_name => var), excluding sub-networks.""" + return copy.copy(self._get_own_vars()) + + def _get_own_vars(self): + if self._own_vars is None: + self._init_graph() + assert self._own_vars is not None + return self._own_vars + + @property + def vars(self): + """All variables (local_name => var).""" + return copy.copy(self._get_vars()) + + def _get_vars(self): + if self._vars is None: + self._vars = OrderedDict(self._get_own_vars()) + for comp in self._get_components().values(): + self._vars.update((comp.name + "/" + name, var) for name, var in comp._get_vars().items()) + return self._vars + + @property + def trainables(self): + """All trainable variables (local_name => var).""" + return copy.copy(self._get_trainables()) + + def _get_trainables(self): + if self._trainables is None: + self._trainables = OrderedDict((name, var) for name, var in self.vars.items() if var.trainable) + return self._trainables + + @property + def var_global_to_local(self): + """Mapping from variable global names to local names.""" + return copy.copy(self._get_var_global_to_local()) + + def _get_var_global_to_local(self): + if self._var_global_to_local is None: + self._var_global_to_local = OrderedDict((var.name.split(":")[0], name) for name, var in self.vars.items()) + return self._var_global_to_local + + def reset_own_vars(self) -> None: + """Re-initialize all variables of this network, excluding sub-networks.""" + if self._var_inits is None or self._components is None: + tfutil.run([var.initializer for var in self._get_own_vars().values()]) + else: + self._var_inits.clear() + self._all_inits_known = False + + def reset_vars(self) -> None: + """Re-initialize all variables of this network, including sub-networks.""" + if self._var_inits is None: + tfutil.run([var.initializer for var in self._get_vars().values()]) + else: + self._var_inits.clear() + self._all_inits_known = False + if self._components is not None: + for comp in self._components.values(): + comp.reset_vars() + + def reset_trainables(self) -> None: + """Re-initialize all trainable variables of this network, including sub-networks.""" + tfutil.run([var.initializer for var in self._get_trainables().values()]) + + def get_output_for(self, *in_expr: TfExpression, return_as_list: bool = False, **dynamic_kwargs) -> Union[TfExpression, List[TfExpression]]: + """Construct TensorFlow expression(s) for the output(s) of this network, given the input expression(s). + The graph is placed on the current TensorFlow device.""" + assert len(in_expr) == self.num_inputs + assert not all(expr is None for expr in in_expr) + self._get_vars() # ensure that all variables have been created + + # Choose build func kwargs. + build_kwargs = dict(self.static_kwargs) + build_kwargs.update(dynamic_kwargs) + build_kwargs["is_template_graph"] = False + build_kwargs["components"] = self._components + + # Build TensorFlow graph to evaluate the network. + with tfutil.absolute_variable_scope(self.scope, reuse=True), tf.name_scope(self.name): + assert tf.get_variable_scope().name == self.scope + valid_inputs = [expr for expr in in_expr if expr is not None] + final_inputs = [] + for expr, name, shape in zip(in_expr, self.input_names, self.input_shapes): + if expr is not None: + expr = tf.identity(expr, name=name) + else: + expr = tf.zeros([tf.shape(valid_inputs[0])[0]] + shape[1:], name=name) + final_inputs.append(expr) + out_expr = self._build_func(*final_inputs, **build_kwargs) + + # Propagate input shapes back to the user-specified expressions. + for expr, final in zip(in_expr, final_inputs): + if isinstance(expr, tf.Tensor): + expr.set_shape(final.shape) + + # Express outputs in the desired format. + assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple) + if return_as_list: + out_expr = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr) + return out_expr + + def get_var_local_name(self, var_or_global_name: Union[TfExpression, str]) -> str: + """Get the local name of a given variable, without any surrounding name scopes.""" + assert tfutil.is_tf_expression(var_or_global_name) or isinstance(var_or_global_name, str) + global_name = var_or_global_name if isinstance(var_or_global_name, str) else var_or_global_name.name + return self._get_var_global_to_local()[global_name] + + def find_var(self, var_or_local_name: Union[TfExpression, str]) -> TfExpression: + """Find variable by local or global name.""" + assert tfutil.is_tf_expression(var_or_local_name) or isinstance(var_or_local_name, str) + return self._get_vars()[var_or_local_name] if isinstance(var_or_local_name, str) else var_or_local_name + + def get_var(self, var_or_local_name: Union[TfExpression, str]) -> np.ndarray: + """Get the value of a given variable as NumPy array. + Note: This method is very inefficient -- prefer to use tflib.run(list_of_vars) whenever possible.""" + return self.find_var(var_or_local_name).eval() + + def set_var(self, var_or_local_name: Union[TfExpression, str], new_value: Union[int, float, np.ndarray]) -> None: + """Set the value of a given variable based on the given NumPy array. + Note: This method is very inefficient -- prefer to use tflib.set_vars() whenever possible.""" + tfutil.set_vars({self.find_var(var_or_local_name): new_value}) + + def __getstate__(self) -> dict: + """Pickle export.""" + state = dict() + state["version"] = 5 + state["name"] = self.name + state["static_kwargs"] = dict(self.static_kwargs) + state["components"] = dict(self.components) + state["build_module_src"] = self._build_module_src + state["build_func_name"] = self._build_func_name + state["variables"] = list(zip(self._get_own_vars().keys(), tfutil.run(list(self._get_own_vars().values())))) + state["input_shapes"] = self.input_shapes + state["output_shapes"] = self.output_shapes + state["input_names"] = self.input_names + state["output_names"] = self.output_names + return state + + def __setstate__(self, state: dict) -> None: + """Pickle import.""" + + # Execute custom import handlers. + for handler in _import_handlers: + state = handler(state) + + # Get basic fields. + assert state["version"] in [2, 3, 4, 5] + name = state["name"] + static_kwargs = state["static_kwargs"] + build_module_src = state["build_module_src"] + build_func_name = state["build_func_name"] + + # Create temporary module from the imported source code. + module_name = "_tflib_network_import_" + uuid.uuid4().hex + module = types.ModuleType(module_name) + sys.modules[module_name] = module + _import_module_src[module] = build_module_src + exec(build_module_src, module.__dict__) # pylint: disable=exec-used + build_func = util.get_obj_from_module(module, build_func_name) + + # Initialize fields. + self._init_fields(name=name, static_kwargs=static_kwargs, build_func=build_func, build_func_name=build_func_name, build_module_src=build_module_src) + self._var_inits.update(copy.deepcopy(state["variables"])) + self._all_inits_known = True + self._components = util.EasyDict(state.get("components", {})) + self._input_shapes = copy.deepcopy(state.get("input_shapes", None)) + self._output_shapes = copy.deepcopy(state.get("output_shapes", None)) + self._input_names = copy.deepcopy(state.get("input_names", None)) + self._output_names = copy.deepcopy(state.get("output_names", None)) + + def clone(self, name: str = None, **new_static_kwargs) -> "Network": + """Create a clone of this network with its own copy of the variables.""" + static_kwargs = dict(self.static_kwargs) + static_kwargs.update(new_static_kwargs) + net = object.__new__(Network) + net._init_fields(name=(name or self.name), static_kwargs=static_kwargs, build_func=self._build_func, build_func_name=self._build_func_name, build_module_src=self._build_module_src) + net.copy_vars_from(self) + return net + + def copy_own_vars_from(self, src_net: "Network") -> None: + """Copy the values of all variables from the given network, excluding sub-networks.""" + + # Source has unknown variables or unknown components => init now. + if (src_net._var_inits is not None and not src_net._all_inits_known) or src_net._components is None: + src_net._get_vars() + + # Both networks are inited => copy directly. + if src_net._var_inits is None and self._var_inits is None: + names = [name for name in self._get_own_vars().keys() if name in src_net._get_own_vars()] + tfutil.set_vars(tfutil.run({self._get_vars()[name]: src_net._get_vars()[name] for name in names})) + return + + # Read from source. + if src_net._var_inits is None: + value_dict = tfutil.run(src_net._get_own_vars()) + else: + value_dict = src_net._var_inits + + # Write to destination. + if self._var_inits is None: + tfutil.set_vars({self._get_vars()[name]: value for name, value in value_dict.items() if name in self._get_vars()}) + else: + self._var_inits.update(value_dict) + + def copy_vars_from(self, src_net: "Network") -> None: + """Copy the values of all variables from the given network, including sub-networks.""" + + # Source has unknown variables or unknown components => init now. + if (src_net._var_inits is not None and not src_net._all_inits_known) or src_net._components is None: + src_net._get_vars() + + # Source is inited, but destination components have not been created yet => set as initial values. + if src_net._var_inits is None and self._components is None: + self._var_inits.update(tfutil.run(src_net._get_vars())) + return + + # Destination has unknown components => init now. + if self._components is None: + self._get_vars() + + # Both networks are inited => copy directly. + if src_net._var_inits is None and self._var_inits is None: + names = [name for name in self._get_vars().keys() if name in src_net._get_vars()] + tfutil.set_vars(tfutil.run({self._get_vars()[name]: src_net._get_vars()[name] for name in names})) + return + + # Copy recursively, component by component. + self.copy_own_vars_from(src_net) + for name, src_comp in src_net._components.items(): + if name in self._components: + self._components[name].copy_vars_from(src_comp) + + def copy_trainables_from(self, src_net: "Network") -> None: + """Copy the values of all trainable variables from the given network, including sub-networks.""" + names = [name for name in self._get_trainables().keys() if name in src_net._get_trainables()] + tfutil.set_vars(tfutil.run({self._get_vars()[name]: src_net._get_vars()[name] for name in names})) + + def convert(self, new_func_name: str, new_name: str = None, **new_static_kwargs) -> "Network": + """Create new network with the given parameters, and copy all variables from this network.""" + if new_name is None: + new_name = self.name + static_kwargs = dict(self.static_kwargs) + static_kwargs.update(new_static_kwargs) + net = Network(name=new_name, func_name=new_func_name, **static_kwargs) + net.copy_vars_from(self) + return net + + def setup_as_moving_average_of(self, src_net: "Network", beta: TfExpressionEx = 0.99, beta_nontrainable: TfExpressionEx = 0.0) -> tf.Operation: + """Construct a TensorFlow op that updates the variables of this network + to be slightly closer to those of the given network.""" + with tfutil.absolute_name_scope(self.scope + "/_MovingAvg"): + ops = [] + for name, var in self._get_vars().items(): + if name in src_net._get_vars(): + cur_beta = beta if var.trainable else beta_nontrainable + new_value = tfutil.lerp(src_net._get_vars()[name], var, cur_beta) + ops.append(var.assign(new_value)) + return tf.group(*ops) + + def run(self, + *in_arrays: Tuple[Union[np.ndarray, None], ...], + input_transform: dict = None, + output_transform: dict = None, + return_as_list: bool = False, + print_progress: bool = False, + minibatch_size: int = None, + num_gpus: int = 1, + assume_frozen: bool = False, + **dynamic_kwargs) -> Union[np.ndarray, Tuple[np.ndarray, ...], List[np.ndarray]]: + """Run this network for the given NumPy array(s), and return the output(s) as NumPy array(s). + + Args: + input_transform: A dict specifying a custom transformation to be applied to the input tensor(s) before evaluating the network. + The dict must contain a 'func' field that points to a top-level function. The function is called with the input + TensorFlow expression(s) as positional arguments. Any remaining fields of the dict will be passed in as kwargs. + output_transform: A dict specifying a custom transformation to be applied to the output tensor(s) after evaluating the network. + The dict must contain a 'func' field that points to a top-level function. The function is called with the output + TensorFlow expression(s) as positional arguments. Any remaining fields of the dict will be passed in as kwargs. + return_as_list: True = return a list of NumPy arrays, False = return a single NumPy array, or a tuple if there are multiple outputs. + print_progress: Print progress to the console? Useful for very large input arrays. + minibatch_size: Maximum minibatch size to use, None = disable batching. + num_gpus: Number of GPUs to use. + assume_frozen: Improve multi-GPU performance by assuming that the trainable parameters will remain changed between calls. + dynamic_kwargs: Additional keyword arguments to be passed into the network build function. + """ + assert len(in_arrays) == self.num_inputs + assert not all(arr is None for arr in in_arrays) + assert input_transform is None or util.is_top_level_function(input_transform["func"]) + assert output_transform is None or util.is_top_level_function(output_transform["func"]) + output_transform, dynamic_kwargs = _handle_legacy_output_transforms(output_transform, dynamic_kwargs) + num_items = in_arrays[0].shape[0] + if minibatch_size is None: + minibatch_size = num_items + + # Construct unique hash key from all arguments that affect the TensorFlow graph. + key = dict(input_transform=input_transform, output_transform=output_transform, num_gpus=num_gpus, assume_frozen=assume_frozen, dynamic_kwargs=dynamic_kwargs) + def unwind_key(obj): + if isinstance(obj, dict): + return [(key, unwind_key(value)) for key, value in sorted(obj.items())] + if callable(obj): + return util.get_top_level_function_name(obj) + return obj + key = repr(unwind_key(key)) + + # Build graph. + if key not in self._run_cache: + with tfutil.absolute_name_scope(self.scope + "/_Run"), tf.control_dependencies(None): + with tf.device("/cpu:0"): + in_expr = [tf.placeholder(tf.float32, name=name) for name in self.input_names] + in_split = list(zip(*[tf.split(x, num_gpus) for x in in_expr])) + + out_split = [] + for gpu in range(num_gpus): + with tf.device(self.device if num_gpus == 1 else "/gpu:%d" % gpu): + net_gpu = self.clone() if assume_frozen else self + in_gpu = in_split[gpu] + + if input_transform is not None: + in_kwargs = dict(input_transform) + in_gpu = in_kwargs.pop("func")(*in_gpu, **in_kwargs) + in_gpu = [in_gpu] if tfutil.is_tf_expression(in_gpu) else list(in_gpu) + + assert len(in_gpu) == self.num_inputs + out_gpu = net_gpu.get_output_for(*in_gpu, return_as_list=True, **dynamic_kwargs) + + if output_transform is not None: + out_kwargs = dict(output_transform) + out_gpu = out_kwargs.pop("func")(*out_gpu, **out_kwargs) + out_gpu = [out_gpu] if tfutil.is_tf_expression(out_gpu) else list(out_gpu) + + assert len(out_gpu) == self.num_outputs + out_split.append(out_gpu) + + with tf.device("/cpu:0"): + out_expr = [tf.concat(outputs, axis=0) for outputs in zip(*out_split)] + self._run_cache[key] = in_expr, out_expr + + # Run minibatches. + in_expr, out_expr = self._run_cache[key] + out_arrays = [np.empty([num_items] + expr.shape.as_list()[1:], expr.dtype.name) for expr in out_expr] + + for mb_begin in range(0, num_items, minibatch_size): + if print_progress: + print("\r%d / %d" % (mb_begin, num_items), end="") + + mb_end = min(mb_begin + minibatch_size, num_items) + mb_num = mb_end - mb_begin + mb_in = [src[mb_begin : mb_end] if src is not None else np.zeros([mb_num] + shape[1:]) for src, shape in zip(in_arrays, self.input_shapes)] + mb_out = tf.get_default_session().run(out_expr, dict(zip(in_expr, mb_in))) + + for dst, src in zip(out_arrays, mb_out): + dst[mb_begin: mb_end] = src + + # Done. + if print_progress: + print("\r%d / %d" % (num_items, num_items)) + + if not return_as_list: + out_arrays = out_arrays[0] if len(out_arrays) == 1 else tuple(out_arrays) + return out_arrays + + def list_ops(self) -> List[TfExpression]: + _ = self.output_templates # ensure that the template graph has been created + include_prefix = self.scope + "/" + exclude_prefix = include_prefix + "_" + ops = tf.get_default_graph().get_operations() + ops = [op for op in ops if op.name.startswith(include_prefix)] + ops = [op for op in ops if not op.name.startswith(exclude_prefix)] + return ops + + def list_layers(self) -> List[Tuple[str, TfExpression, List[TfExpression]]]: + """Returns a list of (layer_name, output_expr, trainable_vars) tuples corresponding to + individual layers of the network. Mainly intended to be used for reporting.""" + layers = [] + + def recurse(scope, parent_ops, parent_vars, level): + if len(parent_ops) == 0 and len(parent_vars) == 0: + return + + # Ignore specific patterns. + if any(p in scope for p in ["/Shape", "/strided_slice", "/Cast", "/concat", "/Assign"]): + return + + # Filter ops and vars by scope. + global_prefix = scope + "/" + local_prefix = global_prefix[len(self.scope) + 1:] + cur_ops = [op for op in parent_ops if op.name.startswith(global_prefix) or op.name == global_prefix[:-1]] + cur_vars = [(name, var) for name, var in parent_vars if name.startswith(local_prefix) or name == local_prefix[:-1]] + if not cur_ops and not cur_vars: + return + + # Filter out all ops related to variables. + for var in [op for op in cur_ops if op.type.startswith("Variable")]: + var_prefix = var.name + "/" + cur_ops = [op for op in cur_ops if not op.name.startswith(var_prefix)] + + # Scope does not contain ops as immediate children => recurse deeper. + contains_direct_ops = any("/" not in op.name[len(global_prefix):] and op.type not in ["Identity", "Cast", "Transpose"] for op in cur_ops) + if (level == 0 or not contains_direct_ops) and (len(cur_ops) != 0 or len(cur_vars) != 0): + visited = set() + for rel_name in [op.name[len(global_prefix):] for op in cur_ops] + [name[len(local_prefix):] for name, _var in cur_vars]: + token = rel_name.split("/")[0] + if token not in visited: + recurse(global_prefix + token, cur_ops, cur_vars, level + 1) + visited.add(token) + return + + # Report layer. + layer_name = scope[len(self.scope) + 1:] + layer_output = cur_ops[-1].outputs[0] if cur_ops else cur_vars[-1][1] + layer_trainables = [var for _name, var in cur_vars if var.trainable] + layers.append((layer_name, layer_output, layer_trainables)) + + recurse(self.scope, self.list_ops(), list(self._get_vars().items()), 0) + return layers + + def print_layers(self, title: str = None, hide_layers_with_no_params: bool = False) -> None: + """Print a summary table of the network structure.""" + rows = [[title if title is not None else self.name, "Params", "OutputShape", "WeightShape"]] + rows += [["---"] * 4] + total_params = 0 + + for layer_name, layer_output, layer_trainables in self.list_layers(): + num_params = sum(int(np.prod(var.shape.as_list())) for var in layer_trainables) + weights = [var for var in layer_trainables if var.name.endswith("/weight:0")] + weights.sort(key=lambda x: len(x.name)) + if len(weights) == 0 and len(layer_trainables) == 1: + weights = layer_trainables + total_params += num_params + + if not hide_layers_with_no_params or num_params != 0: + num_params_str = str(num_params) if num_params > 0 else "-" + output_shape_str = str(layer_output.shape) + weight_shape_str = str(weights[0].shape) if len(weights) >= 1 else "-" + rows += [[layer_name, num_params_str, output_shape_str, weight_shape_str]] + + rows += [["---"] * 4] + rows += [["Total", str(total_params), "", ""]] + + widths = [max(len(cell) for cell in column) for column in zip(*rows)] + print() + for row in rows: + print(" ".join(cell + " " * (width - len(cell)) for cell, width in zip(row, widths))) + print() + + def setup_weight_histograms(self, title: str = None) -> None: + """Construct summary ops to include histograms of all trainable parameters in TensorBoard.""" + if title is None: + title = self.name + + with tf.name_scope(None), tf.device(None), tf.control_dependencies(None): + for local_name, var in self._get_trainables().items(): + if "/" in local_name: + p = local_name.split("/") + name = title + "_" + p[-1] + "/" + "_".join(p[:-1]) + else: + name = title + "_toplevel/" + local_name + + tf.summary.histogram(name, var) + +#---------------------------------------------------------------------------- +# Backwards-compatible emulation of legacy output transformation in Network.run(). + +_print_legacy_warning = True + +def _handle_legacy_output_transforms(output_transform, dynamic_kwargs): + global _print_legacy_warning + legacy_kwargs = ["out_mul", "out_add", "out_shrink", "out_dtype"] + if not any(kwarg in dynamic_kwargs for kwarg in legacy_kwargs): + return output_transform, dynamic_kwargs + + if _print_legacy_warning: + _print_legacy_warning = False + print() + print("WARNING: Old-style output transformations in Network.run() are deprecated.") + print("Consider using 'output_transform=dict(func=tflib.convert_images_to_uint8)'") + print("instead of 'out_mul=127.5, out_add=127.5, out_dtype=np.uint8'.") + print() + assert output_transform is None + + new_kwargs = dict(dynamic_kwargs) + new_transform = {kwarg: new_kwargs.pop(kwarg) for kwarg in legacy_kwargs if kwarg in dynamic_kwargs} + new_transform["func"] = _legacy_output_transform_func + return new_transform, new_kwargs + +def _legacy_output_transform_func(*expr, out_mul=1.0, out_add=0.0, out_shrink=1, out_dtype=None): + if out_mul != 1.0: + expr = [x * out_mul for x in expr] + + if out_add != 0.0: + expr = [x + out_add for x in expr] + + if out_shrink > 1: + ksize = [1, 1, out_shrink, out_shrink] + expr = [tf.nn.avg_pool(x, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW") for x in expr] + + if out_dtype is not None: + if tf.as_dtype(out_dtype).is_integer: + expr = [tf.round(x) for x in expr] + expr = [tf.saturate_cast(x, out_dtype) for x in expr] + return expr diff --git a/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/__init__.py b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..43cce37364064146fd30e18612b1d9e3a84f513a --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +# empty diff --git a/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/fused_bias_act.cu b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/fused_bias_act.cu new file mode 100644 index 0000000000000000000000000000000000000000..0268f14395319003240b4a5a59141d703e9a4257 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/fused_bias_act.cu @@ -0,0 +1,220 @@ +// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#define EIGEN_USE_GPU +#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__ +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/shape_inference.h" +#include + +using namespace tensorflow; +using namespace tensorflow::shape_inference; + +#define OP_CHECK_CUDA_ERROR(CTX, CUDA_CALL) do { cudaError_t err = CUDA_CALL; OP_REQUIRES(CTX, err == cudaSuccess, errors::Internal(cudaGetErrorName(err))); } while (false) + +//------------------------------------------------------------------------ +// CUDA kernel. + +template +struct FusedBiasActKernelParams +{ + const T* x; // [sizeX] + const T* b; // [sizeB] or NULL + const T* xref; // [sizeX] or NULL + const T* yref; // [sizeX] or NULL + T* y; // [sizeX] + + int grad; + int axis; + int act; + float alpha; + float gain; + float clamp; + + int sizeX; + int sizeB; + int stepB; + int loopX; +}; + +template +static __global__ void FusedBiasActKernel(const FusedBiasActKernelParams p) +{ + const float expRange = 80.0f; + const float halfExpRange = 40.0f; + const float seluScale = 1.0507009873554804934193349852946f; + const float seluAlpha = 1.6732632423543772848170429916717f; + + // Loop over elements. + int xi = blockIdx.x * p.loopX * blockDim.x + threadIdx.x; + for (int loopIdx = 0; loopIdx < p.loopX && xi < p.sizeX; loopIdx++, xi += blockDim.x) + { + // Load and apply bias. + float x = (float)p.x[xi]; + if (p.b) + x += (float)p.b[(xi / p.stepB) % p.sizeB]; + float xref = (p.xref) ? (float)p.xref[xi] : 0.0f; + float yref = (p.yref) ? (float)p.yref[xi] : 0.0f; + float yy = (p.gain != 0.0f) ? yref / p.gain : 0.0f; + + // Evaluate activation func. + float y; + switch (p.act * 10 + p.grad) + { + // linear + default: + case 10: y = x; break; + case 11: y = x; break; + case 12: y = 0.0f; break; + + // relu + case 20: y = (x > 0.0f) ? x : 0.0f; break; + case 21: y = (yy > 0.0f) ? x : 0.0f; break; + case 22: y = 0.0f; break; + + // lrelu + case 30: y = (x > 0.0f) ? x : x * p.alpha; break; + case 31: y = (yy > 0.0f) ? x : x * p.alpha; break; + case 32: y = 0.0f; break; + + // tanh + case 40: { float c = expf(x); float d = 1.0f / c; y = (x < -expRange) ? -1.0f : (x > expRange) ? 1.0f : (c - d) / (c + d); } break; + case 41: y = x * (1.0f - yy * yy); break; + case 42: y = x * (1.0f - yy * yy) * (-2.0f * yy); break; + + // sigmoid + case 50: y = (x < -expRange) ? 0.0f : 1.0f / (expf(-x) + 1.0f); break; + case 51: y = x * yy * (1.0f - yy); break; + case 52: y = x * yy * (1.0f - yy) * (1.0f - 2.0f * yy); break; + + // elu + case 60: y = (x >= 0.0f) ? x : expf(x) - 1.0f; break; + case 61: y = (yy >= 0.0f) ? x : x * (yy + 1.0f); break; + case 62: y = (yy >= 0.0f) ? 0.0f : x * (yy + 1.0f); break; + + // selu + case 70: y = (x >= 0.0f) ? seluScale * x : (seluScale * seluAlpha) * (expf(x) - 1.0f); break; + case 71: y = (yy >= 0.0f) ? x * seluScale : x * (yy + seluScale * seluAlpha); break; + case 72: y = (yy >= 0.0f) ? 0.0f : x * (yy + seluScale * seluAlpha); break; + + // softplus + case 80: y = (x > expRange) ? x : logf(expf(x) + 1.0f); break; + case 81: y = x * (1.0f - expf(-yy)); break; + case 82: { float c = expf(-yy); y = x * c * (1.0f - c); } break; + + // swish + case 90: y = (x < -expRange) ? 0.0f : x / (expf(-x) + 1.0f); break; + case 91: + case 92: + { + float c = expf(xref); + float d = c + 1.0f; + if (p.grad == 1) + y = (xref > halfExpRange) ? x : x * c * (xref + d) / (d * d); + else + y = (xref > halfExpRange) ? 0.0f : x * c * (xref * (2.0f - d) + 2.0f * d) / (d * d * d); + yref = (xref < -expRange) ? 0.0f : xref / (expf(-xref) + 1.0f) * p.gain; + } + break; + } + + // Apply gain. + y *= p.gain; + + // Clamp. + if (p.clamp >= 0.0f) + { + if (p.grad == 0) + y = (fabsf(y) < p.clamp) ? y : (y >= 0.0f) ? p.clamp : -p.clamp; + else + y = (fabsf(yref) < p.clamp) ? y : 0.0f; + } + + // Store. + p.y[xi] = (T)y; + } +} + +//------------------------------------------------------------------------ +// TensorFlow op. + +template +struct FusedBiasActOp : public OpKernel +{ + FusedBiasActKernelParams m_attribs; + + FusedBiasActOp(OpKernelConstruction* ctx) : OpKernel(ctx) + { + memset(&m_attribs, 0, sizeof(m_attribs)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("grad", &m_attribs.grad)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("axis", &m_attribs.axis)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("act", &m_attribs.act)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("alpha", &m_attribs.alpha)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("gain", &m_attribs.gain)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("clamp", &m_attribs.clamp)); + OP_REQUIRES(ctx, m_attribs.grad >= 0, errors::InvalidArgument("grad must be non-negative")); + OP_REQUIRES(ctx, m_attribs.axis >= 0, errors::InvalidArgument("axis must be non-negative")); + OP_REQUIRES(ctx, m_attribs.act >= 0, errors::InvalidArgument("act must be non-negative")); + } + + void Compute(OpKernelContext* ctx) + { + FusedBiasActKernelParams p = m_attribs; + cudaStream_t stream = ctx->eigen_device().stream(); + + const Tensor& x = ctx->input(0); // [...] + const Tensor& b = ctx->input(1); // [sizeB] or [0] + const Tensor& xref = ctx->input(2); // x.shape or [0] + const Tensor& yref = ctx->input(3); // x.shape or [0] + p.x = x.flat().data(); + p.b = (b.NumElements()) ? b.flat().data() : NULL; + p.xref = (xref.NumElements()) ? xref.flat().data() : NULL; + p.yref = (yref.NumElements()) ? yref.flat().data() : NULL; + OP_REQUIRES(ctx, b.NumElements() == 0 || m_attribs.axis < x.dims(), errors::InvalidArgument("axis out of bounds")); + OP_REQUIRES(ctx, b.dims() == 1, errors::InvalidArgument("b must have rank 1")); + OP_REQUIRES(ctx, b.NumElements() == 0 || b.NumElements() == x.dim_size(m_attribs.axis), errors::InvalidArgument("b has wrong number of elements")); + OP_REQUIRES(ctx, xref.NumElements() == 0 || xref.NumElements() == x.NumElements(), errors::InvalidArgument("xref has wrong number of elements")); + OP_REQUIRES(ctx, yref.NumElements() == 0 || yref.NumElements() == x.NumElements(), errors::InvalidArgument("yref has wrong number of elements")); + OP_REQUIRES(ctx, x.NumElements() <= kint32max, errors::InvalidArgument("x is too large")); + + p.sizeX = (int)x.NumElements(); + p.sizeB = (int)b.NumElements(); + p.stepB = 1; + for (int i = m_attribs.axis + 1; i < x.dims(); i++) + p.stepB *= (int)x.dim_size(i); + + Tensor* y = NULL; // x.shape + OP_REQUIRES_OK(ctx, ctx->allocate_output(0, x.shape(), &y)); + p.y = y->flat().data(); + + p.loopX = 4; + int blockSize = 4 * 32; + int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1; + void* args[] = {&p}; + OP_CHECK_CUDA_ERROR(ctx, cudaLaunchKernel((void*)FusedBiasActKernel, gridSize, blockSize, args, 0, stream)); + } +}; + +REGISTER_OP("FusedBiasAct") + .Input ("x: T") + .Input ("b: T") + .Input ("xref: T") + .Input ("yref: T") + .Output ("y: T") + .Attr ("T: {float, half}") + .Attr ("grad: int = 0") + .Attr ("axis: int = 1") + .Attr ("act: int = 0") + .Attr ("alpha: float = 0.0") + .Attr ("gain: float = 1.0") + .Attr ("clamp: float = -1.0"); +REGISTER_KERNEL_BUILDER(Name("FusedBiasAct").Device(DEVICE_GPU).TypeConstraint("T"), FusedBiasActOp); +REGISTER_KERNEL_BUILDER(Name("FusedBiasAct").Device(DEVICE_GPU).TypeConstraint("T"), FusedBiasActOp); + +//------------------------------------------------------------------------ diff --git a/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/fused_bias_act.py b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/fused_bias_act.py new file mode 100644 index 0000000000000000000000000000000000000000..79991b0497d3d92f25194a31668b9568048163f8 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/fused_bias_act.py @@ -0,0 +1,211 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom TensorFlow ops for efficient bias and activation.""" + +import os +import numpy as np +import tensorflow as tf +from .. import custom_ops +from ...util import EasyDict + +def _get_plugin(): + return custom_ops.get_plugin(os.path.splitext(__file__)[0] + '.cu') + +#---------------------------------------------------------------------------- + +activation_funcs = { + 'linear': EasyDict(func=lambda x, **_: x, def_alpha=None, def_gain=1.0, cuda_idx=1, ref='y', zero_2nd_grad=True), + 'relu': EasyDict(func=lambda x, **_: tf.nn.relu(x), def_alpha=None, def_gain=np.sqrt(2), cuda_idx=2, ref='y', zero_2nd_grad=True), + 'lrelu': EasyDict(func=lambda x, alpha, **_: tf.nn.leaky_relu(x, alpha), def_alpha=0.2, def_gain=np.sqrt(2), cuda_idx=3, ref='y', zero_2nd_grad=True), + 'tanh': EasyDict(func=lambda x, **_: tf.nn.tanh(x), def_alpha=None, def_gain=1.0, cuda_idx=4, ref='y', zero_2nd_grad=False), + 'sigmoid': EasyDict(func=lambda x, **_: tf.nn.sigmoid(x), def_alpha=None, def_gain=1.0, cuda_idx=5, ref='y', zero_2nd_grad=False), + 'elu': EasyDict(func=lambda x, **_: tf.nn.elu(x), def_alpha=None, def_gain=1.0, cuda_idx=6, ref='y', zero_2nd_grad=False), + 'selu': EasyDict(func=lambda x, **_: tf.nn.selu(x), def_alpha=None, def_gain=1.0, cuda_idx=7, ref='y', zero_2nd_grad=False), + 'softplus': EasyDict(func=lambda x, **_: tf.nn.softplus(x), def_alpha=None, def_gain=1.0, cuda_idx=8, ref='y', zero_2nd_grad=False), + 'swish': EasyDict(func=lambda x, **_: tf.nn.sigmoid(x) * x, def_alpha=None, def_gain=np.sqrt(2), cuda_idx=9, ref='x', zero_2nd_grad=False), +} + +#---------------------------------------------------------------------------- + +def fused_bias_act(x, b=None, axis=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'): + r"""Fused bias and activation function. + + Adds bias `b` to activation tensor `x`, evaluates activation function `act`, + and scales the result by `gain`. Each of the steps is optional. In most cases, + the fused op is considerably more efficient than performing the same calculation + using standard TensorFlow ops. It supports first and second order gradients, + but not third order gradients. + + Args: + x: Input activation tensor. Can have any shape, but if `b` is defined, the + dimension corresponding to `axis`, as well as the rank, must be known. + b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type + as `x`. The shape must be known, and it must match the dimension of `x` + corresponding to `axis`. + axis: The dimension in `x` corresponding to the elements of `b`. + The value of `axis` is ignored if `b` is not specified. + act: Name of the activation function to evaluate, or `"linear"` to disable. + Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc. + See `activation_funcs` for a full list. `None` is not allowed. + alpha: Shape parameter for the activation function, or `None` to use the default. + gain: Scaling factor for the output tensor, or `None` to use default. + See `activation_funcs` for the default scaling of each activation function. + If unsure, consider specifying `1.0`. + clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable + the clamping (default). + impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default). + + Returns: + Tensor of the same shape and datatype as `x`. + """ + + impl_dict = { + 'ref': _fused_bias_act_ref, + 'cuda': _fused_bias_act_cuda, + } + return impl_dict[impl](x=x, b=b, axis=axis, act=act, alpha=alpha, gain=gain, clamp=clamp) + +#---------------------------------------------------------------------------- + +def _fused_bias_act_ref(x, b, axis, act, alpha, gain, clamp): + """Slow reference implementation of `fused_bias_act()` using standard TensorFlow ops.""" + + # Validate arguments. + x = tf.convert_to_tensor(x) + b = tf.convert_to_tensor(b) if b is not None else tf.constant([], dtype=x.dtype) + act_spec = activation_funcs[act] + assert b.shape.rank == 1 and (b.shape[0] == 0 or b.shape[0] == x.shape[axis]) + assert b.shape[0] == 0 or 0 <= axis < x.shape.rank + if alpha is None: + alpha = act_spec.def_alpha + if gain is None: + gain = act_spec.def_gain + + # Add bias. + if b.shape[0] != 0: + x += tf.reshape(b, [-1 if i == axis else 1 for i in range(x.shape.rank)]) + + # Evaluate activation function. + x = act_spec.func(x, alpha=alpha) + + # Scale by gain. + if gain != 1: + x *= gain + + # Clamp. + if clamp is not None: + clamp = np.asarray(clamp, dtype=x.dtype.name) + assert clamp.shape == () and clamp >= 0 + x = tf.clip_by_value(x, -clamp, clamp) + return x + +#---------------------------------------------------------------------------- + +def _fused_bias_act_cuda(x, b, axis, act, alpha, gain, clamp): + """Fast CUDA implementation of `fused_bias_act()` using custom ops.""" + + # Validate arguments. + x = tf.convert_to_tensor(x) + empty_tensor = tf.constant([], dtype=x.dtype) + b = tf.convert_to_tensor(b) if b is not None else empty_tensor + act_spec = activation_funcs[act] + assert b.shape.rank == 1 and (b.shape[0] == 0 or b.shape[0] == x.shape[axis]) + assert b.shape[0] == 0 or 0 <= axis < x.shape.rank + if alpha is None: + alpha = act_spec.def_alpha + if gain is None: + gain = act_spec.def_gain + + # Special cases. + if act == 'linear' and b is None and gain == 1.0: + return x + if act_spec.cuda_idx is None: + return _fused_bias_act_ref(x=x, b=b, axis=axis, act=act, alpha=alpha, gain=gain, clamp=clamp) + + # CUDA op. + cuda_op = _get_plugin().fused_bias_act + cuda_kwargs = dict(axis=int(axis), act=int(act_spec.cuda_idx), gain=float(gain)) + if alpha is not None: + cuda_kwargs['alpha'] = float(alpha) + if clamp is not None: + clamp = np.asarray(clamp, dtype=x.dtype.name) + assert clamp.shape == () and clamp >= 0 + cuda_kwargs['clamp'] = float(clamp.astype(np.float32)) + def ref(tensor, name): + return tensor if act_spec.ref == name else empty_tensor + + # Forward pass: y = func(x, b). + def func_y(x, b): + y = cuda_op(x=x, b=b, xref=empty_tensor, yref=empty_tensor, grad=0, **cuda_kwargs) + y.set_shape(x.shape) + return y + + # Backward pass: dx, db = grad(dy, x, y) + def grad_dx(dy, x, y): + dx = cuda_op(x=dy, b=empty_tensor, xref=ref(x,'x'), yref=ref(y,'y'), grad=1, **cuda_kwargs) + dx.set_shape(x.shape) + return dx + def grad_db(dx): + if b.shape[0] == 0: + return empty_tensor + db = dx + if axis < x.shape.rank - 1: + db = tf.reduce_sum(db, list(range(axis + 1, x.shape.rank))) + if axis > 0: + db = tf.reduce_sum(db, list(range(axis))) + db.set_shape(b.shape) + return db + + # Second order gradients: d_dy, d_x = grad2(d_dx, d_db, x, y) + def grad2_d_dy(d_dx, d_db, x, y): + d_dy = cuda_op(x=d_dx, b=d_db, xref=ref(x,'x'), yref=ref(y,'y'), grad=1, **cuda_kwargs) + d_dy.set_shape(x.shape) + return d_dy + def grad2_d_x(d_dx, d_db, x, y): + d_x = cuda_op(x=d_dx, b=d_db, xref=ref(x,'x'), yref=ref(y,'y'), grad=2, **cuda_kwargs) + d_x.set_shape(x.shape) + return d_x + + # Fast version for piecewise-linear activation funcs. + @tf.custom_gradient + def func_zero_2nd_grad(x, b): + y = func_y(x, b) + @tf.custom_gradient + def grad(dy): + dx = grad_dx(dy, x, y) + db = grad_db(dx) + def grad2(d_dx, d_db): + d_dy = grad2_d_dy(d_dx, d_db, x, y) + return d_dy + return (dx, db), grad2 + return y, grad + + # Slow version for general activation funcs. + @tf.custom_gradient + def func_nonzero_2nd_grad(x, b): + y = func_y(x, b) + def grad_wrap(dy): + @tf.custom_gradient + def grad_impl(dy, x): + dx = grad_dx(dy, x, y) + db = grad_db(dx) + def grad2(d_dx, d_db): + d_dy = grad2_d_dy(d_dx, d_db, x, y) + d_x = grad2_d_x(d_dx, d_db, x, y) + return d_dy, d_x + return (dx, db), grad2 + return grad_impl(dy, x) + return y, grad_wrap + + # Which version to use? + if act_spec.zero_2nd_grad: + return func_zero_2nd_grad(x, b) + return func_nonzero_2nd_grad(x, b) + +#---------------------------------------------------------------------------- diff --git a/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/upfirdn_2d.cu b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/upfirdn_2d.cu new file mode 100644 index 0000000000000000000000000000000000000000..7aad60d53e57d4f3e60f36a24df80a6278f1bb63 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/upfirdn_2d.cu @@ -0,0 +1,359 @@ +// Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#define EIGEN_USE_GPU +#define __CUDA_INCLUDE_COMPILER_INTERNAL_HEADERS__ +#include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/framework/shape_inference.h" +#include + +using namespace tensorflow; +using namespace tensorflow::shape_inference; + +//------------------------------------------------------------------------ +// Helpers. + +#define OP_CHECK_CUDA_ERROR(CTX, CUDA_CALL) do { cudaError_t err = CUDA_CALL; OP_REQUIRES(CTX, err == cudaSuccess, errors::Internal(cudaGetErrorName(err))); } while (false) + +static __host__ __device__ __forceinline__ int floorDiv(int a, int b) +{ + int t = 1 - a / b; + return (a + t * b) / b - t; +} + +//------------------------------------------------------------------------ +// CUDA kernel params. + +template +struct UpFirDn2DKernelParams +{ + const T* x; // [majorDim, inH, inW, minorDim] + const T* k; // [kernelH, kernelW] + T* y; // [majorDim, outH, outW, minorDim] + + int upx; + int upy; + int downx; + int downy; + int padx0; + int padx1; + int pady0; + int pady1; + + int majorDim; + int inH; + int inW; + int minorDim; + int kernelH; + int kernelW; + int outH; + int outW; + int loopMajor; + int loopX; +}; + +//------------------------------------------------------------------------ +// General CUDA implementation for large filter kernels. + +template +static __global__ void UpFirDn2DKernel_large(const UpFirDn2DKernelParams p) +{ + // Calculate thread index. + int minorIdx = blockIdx.x * blockDim.x + threadIdx.x; + int outY = minorIdx / p.minorDim; + minorIdx -= outY * p.minorDim; + int outXBase = blockIdx.y * p.loopX * blockDim.y + threadIdx.y; + int majorIdxBase = blockIdx.z * p.loopMajor; + if (outXBase >= p.outW || outY >= p.outH || majorIdxBase >= p.majorDim) + return; + + // Setup Y receptive field. + int midY = outY * p.downy + p.upy - 1 - p.pady0; + int inY = min(max(floorDiv(midY, p.upy), 0), p.inH); + int h = min(max(floorDiv(midY + p.kernelH, p.upy), 0), p.inH) - inY; + int kernelY = midY + p.kernelH - (inY + 1) * p.upy; + + // Loop over majorDim and outX. + for (int loopMajor = 0, majorIdx = majorIdxBase; loopMajor < p.loopMajor && majorIdx < p.majorDim; loopMajor++, majorIdx++) + for (int loopX = 0, outX = outXBase; loopX < p.loopX && outX < p.outW; loopX++, outX += blockDim.y) + { + // Setup X receptive field. + int midX = outX * p.downx + p.upx - 1 - p.padx0; + int inX = min(max(floorDiv(midX, p.upx), 0), p.inW); + int w = min(max(floorDiv(midX + p.kernelW, p.upx), 0), p.inW) - inX; + int kernelX = midX + p.kernelW - (inX + 1) * p.upx; + + // Initialize pointers. + const T* xp = &p.x[((majorIdx * p.inH + inY) * p.inW + inX) * p.minorDim + minorIdx]; + const T* kp = &p.k[kernelY * p.kernelW + kernelX]; + int xpx = p.minorDim; + int kpx = -p.upx; + int xpy = p.inW * p.minorDim; + int kpy = -p.upy * p.kernelW; + + // Inner loop. + float v = 0.0f; + for (int y = 0; y < h; y++) + { + for (int x = 0; x < w; x++) + { + v += (float)(*xp) * (float)(*kp); + xp += xpx; + kp += kpx; + } + xp += xpy - w * xpx; + kp += kpy - w * kpx; + } + + // Store result. + p.y[((majorIdx * p.outH + outY) * p.outW + outX) * p.minorDim + minorIdx] = (T)v; + } +} + +//------------------------------------------------------------------------ +// Specialized CUDA implementation for small filter kernels. + +template +static __global__ void UpFirDn2DKernel_small(const UpFirDn2DKernelParams p) +{ + //assert(kernelW % upx == 0); + //assert(kernelH % upy == 0); + const int tileInW = ((tileOutW - 1) * downx + kernelW - 1) / upx + 1; + const int tileInH = ((tileOutH - 1) * downy + kernelH - 1) / upy + 1; + __shared__ volatile float sk[kernelH][kernelW]; + __shared__ volatile float sx[tileInH][tileInW]; + + // Calculate tile index. + int minorIdx = blockIdx.x; + int tileOutY = minorIdx / p.minorDim; + minorIdx -= tileOutY * p.minorDim; + tileOutY *= tileOutH; + int tileOutXBase = blockIdx.y * p.loopX * tileOutW; + int majorIdxBase = blockIdx.z * p.loopMajor; + if (tileOutXBase >= p.outW | tileOutY >= p.outH | majorIdxBase >= p.majorDim) + return; + + // Load filter kernel (flipped). + for (int tapIdx = threadIdx.x; tapIdx < kernelH * kernelW; tapIdx += blockDim.x) + { + int ky = tapIdx / kernelW; + int kx = tapIdx - ky * kernelW; + float v = 0.0f; + if (kx < p.kernelW & ky < p.kernelH) + v = (float)p.k[(p.kernelH - 1 - ky) * p.kernelW + (p.kernelW - 1 - kx)]; + sk[ky][kx] = v; + } + + // Loop over majorDim and outX. + for (int loopMajor = 0, majorIdx = majorIdxBase; loopMajor < p.loopMajor & majorIdx < p.majorDim; loopMajor++, majorIdx++) + for (int loopX = 0, tileOutX = tileOutXBase; loopX < p.loopX & tileOutX < p.outW; loopX++, tileOutX += tileOutW) + { + // Load input pixels. + int tileMidX = tileOutX * downx + upx - 1 - p.padx0; + int tileMidY = tileOutY * downy + upy - 1 - p.pady0; + int tileInX = floorDiv(tileMidX, upx); + int tileInY = floorDiv(tileMidY, upy); + __syncthreads(); + for (int inIdx = threadIdx.x; inIdx < tileInH * tileInW; inIdx += blockDim.x) + { + int relInY = inIdx / tileInW; + int relInX = inIdx - relInY * tileInW; + int inX = relInX + tileInX; + int inY = relInY + tileInY; + float v = 0.0f; + if (inX >= 0 & inY >= 0 & inX < p.inW & inY < p.inH) + v = (float)p.x[((majorIdx * p.inH + inY) * p.inW + inX) * p.minorDim + minorIdx]; + sx[relInY][relInX] = v; + } + + // Loop over output pixels. + __syncthreads(); + for (int outIdx = threadIdx.x; outIdx < tileOutH * tileOutW; outIdx += blockDim.x) + { + int relOutY = outIdx / tileOutW; + int relOutX = outIdx - relOutY * tileOutW; + int outX = relOutX + tileOutX; + int outY = relOutY + tileOutY; + + // Setup receptive field. + int midX = tileMidX + relOutX * downx; + int midY = tileMidY + relOutY * downy; + int inX = floorDiv(midX, upx); + int inY = floorDiv(midY, upy); + int relInX = inX - tileInX; + int relInY = inY - tileInY; + int kernelX = (inX + 1) * upx - midX - 1; // flipped + int kernelY = (inY + 1) * upy - midY - 1; // flipped + + // Inner loop. + float v = 0.0f; + #pragma unroll + for (int y = 0; y < kernelH / upy; y++) + #pragma unroll + for (int x = 0; x < kernelW / upx; x++) + v += sx[relInY + y][relInX + x] * sk[kernelY + y * upy][kernelX + x * upx]; + + // Store result. + if (outX < p.outW & outY < p.outH) + p.y[((majorIdx * p.outH + outY) * p.outW + outX) * p.minorDim + minorIdx] = (T)v; + } + } +} + +//------------------------------------------------------------------------ +// TensorFlow op. + +template +struct UpFirDn2DOp : public OpKernel +{ + UpFirDn2DKernelParams m_attribs; + + UpFirDn2DOp(OpKernelConstruction* ctx) : OpKernel(ctx) + { + memset(&m_attribs, 0, sizeof(m_attribs)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("upx", &m_attribs.upx)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("upy", &m_attribs.upy)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("downx", &m_attribs.downx)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("downy", &m_attribs.downy)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("padx0", &m_attribs.padx0)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("padx1", &m_attribs.padx1)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("pady0", &m_attribs.pady0)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("pady1", &m_attribs.pady1)); + OP_REQUIRES(ctx, m_attribs.upx >= 1 && m_attribs.upy >= 1, errors::InvalidArgument("upx and upy must be at least 1x1")); + OP_REQUIRES(ctx, m_attribs.downx >= 1 && m_attribs.downy >= 1, errors::InvalidArgument("downx and downy must be at least 1x1")); + } + + void Compute(OpKernelContext* ctx) + { + UpFirDn2DKernelParams p = m_attribs; + cudaStream_t stream = ctx->eigen_device().stream(); + + const Tensor& x = ctx->input(0); // [majorDim, inH, inW, minorDim] + const Tensor& k = ctx->input(1); // [kernelH, kernelW] + p.x = x.flat().data(); + p.k = k.flat().data(); + OP_REQUIRES(ctx, x.dims() == 4, errors::InvalidArgument("input must have rank 4")); + OP_REQUIRES(ctx, k.dims() == 2, errors::InvalidArgument("kernel must have rank 2")); + OP_REQUIRES(ctx, x.NumElements() <= kint32max, errors::InvalidArgument("input too large")); + OP_REQUIRES(ctx, k.NumElements() <= kint32max, errors::InvalidArgument("kernel too large")); + + p.majorDim = (int)x.dim_size(0); + p.inH = (int)x.dim_size(1); + p.inW = (int)x.dim_size(2); + p.minorDim = (int)x.dim_size(3); + p.kernelH = (int)k.dim_size(0); + p.kernelW = (int)k.dim_size(1); + OP_REQUIRES(ctx, p.kernelW >= 1 && p.kernelH >= 1, errors::InvalidArgument("kernel must be at least 1x1")); + + p.outW = (p.inW * p.upx + p.padx0 + p.padx1 - p.kernelW + p.downx) / p.downx; + p.outH = (p.inH * p.upy + p.pady0 + p.pady1 - p.kernelH + p.downy) / p.downy; + OP_REQUIRES(ctx, p.outW >= 1 && p.outH >= 1, errors::InvalidArgument("output must be at least 1x1")); + + Tensor* y = NULL; // [majorDim, outH, outW, minorDim] + TensorShape ys; + ys.AddDim(p.majorDim); + ys.AddDim(p.outH); + ys.AddDim(p.outW); + ys.AddDim(p.minorDim); + OP_REQUIRES_OK(ctx, ctx->allocate_output(0, ys, &y)); + p.y = y->flat().data(); + OP_REQUIRES(ctx, y->NumElements() <= kint32max, errors::InvalidArgument("output too large")); + + // Choose CUDA kernel to use. + void* cudaKernel = (void*)UpFirDn2DKernel_large; + int tileOutW = -1; + int tileOutH = -1; + + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 7 && p.kernelH <= 7 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 6 && p.kernelH <= 6 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 5 && p.kernelH <= 5 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 4 && p.kernelH <= 4 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 3 && p.kernelH <= 3 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 24 && p.kernelH <= 1 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 128; tileOutH = 8; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 20 && p.kernelH <= 1 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 128; tileOutH = 8; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 16 && p.kernelH <= 1 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 128; tileOutH = 8; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 12 && p.kernelH <= 1 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 128; tileOutH = 8; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 8 && p.kernelH <= 1 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 128; tileOutH = 8; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 1 && p.kernelH <= 24) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 32; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 1 && p.kernelH <= 20) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 32; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 1 && p.kernelH <= 16) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 32; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 1 && p.kernelH <= 12) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 32; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 1 && p.kernelH <= 8 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 32; } + + if (p.upx == 2 && p.upy == 2 && p.downx == 1 && p.downy == 1 && p.kernelW <= 8 && p.kernelH <= 8 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; } + if (p.upx == 2 && p.upy == 2 && p.downx == 1 && p.downy == 1 && p.kernelW <= 6 && p.kernelH <= 6 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; } + if (p.upx == 2 && p.upy == 2 && p.downx == 1 && p.downy == 1 && p.kernelW <= 4 && p.kernelH <= 4 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; } + if (p.upx == 2 && p.upy == 2 && p.downx == 1 && p.downy == 1 && p.kernelW <= 2 && p.kernelH <= 2 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 16; } + if (p.upx == 2 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 24 && p.kernelH <= 1 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 128; tileOutH = 8; } + if (p.upx == 2 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 20 && p.kernelH <= 1 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 128; tileOutH = 8; } + if (p.upx == 2 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 16 && p.kernelH <= 1 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 128; tileOutH = 8; } + if (p.upx == 2 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 12 && p.kernelH <= 1 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 128; tileOutH = 8; } + if (p.upx == 2 && p.upy == 1 && p.downx == 1 && p.downy == 1 && p.kernelW <= 8 && p.kernelH <= 1 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 128; tileOutH = 8; } + if (p.upx == 1 && p.upy == 2 && p.downx == 1 && p.downy == 1 && p.kernelW <= 1 && p.kernelH <= 24) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 32; } + if (p.upx == 1 && p.upy == 2 && p.downx == 1 && p.downy == 1 && p.kernelW <= 1 && p.kernelH <= 20) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 32; } + if (p.upx == 1 && p.upy == 2 && p.downx == 1 && p.downy == 1 && p.kernelW <= 1 && p.kernelH <= 16) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 32; } + if (p.upx == 1 && p.upy == 2 && p.downx == 1 && p.downy == 1 && p.kernelW <= 1 && p.kernelH <= 12) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 32; } + if (p.upx == 1 && p.upy == 2 && p.downx == 1 && p.downy == 1 && p.kernelW <= 1 && p.kernelH <= 8 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 32; } + + if (p.upx == 1 && p.upy == 1 && p.downx == 2 && p.downy == 2 && p.kernelW <= 8 && p.kernelH <= 8 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 8; } + if (p.upx == 1 && p.upy == 1 && p.downx == 2 && p.downy == 2 && p.kernelW <= 6 && p.kernelH <= 6 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 8; } + if (p.upx == 1 && p.upy == 1 && p.downx == 2 && p.downy == 2 && p.kernelW <= 4 && p.kernelH <= 4 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 8; } + if (p.upx == 1 && p.upy == 1 && p.downx == 2 && p.downy == 2 && p.kernelW <= 2 && p.kernelH <= 2 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 8; } + if (p.upx == 1 && p.upy == 1 && p.downx == 2 && p.downy == 1 && p.kernelW <= 24 && p.kernelH <= 1 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 8; } + if (p.upx == 1 && p.upy == 1 && p.downx == 2 && p.downy == 1 && p.kernelW <= 20 && p.kernelH <= 1 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 8; } + if (p.upx == 1 && p.upy == 1 && p.downx == 2 && p.downy == 1 && p.kernelW <= 16 && p.kernelH <= 1 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 8; } + if (p.upx == 1 && p.upy == 1 && p.downx == 2 && p.downy == 1 && p.kernelW <= 12 && p.kernelH <= 1 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 8; } + if (p.upx == 1 && p.upy == 1 && p.downx == 2 && p.downy == 1 && p.kernelW <= 8 && p.kernelH <= 1 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 64; tileOutH = 8; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 2 && p.kernelW <= 1 && p.kernelH <= 24) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 16; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 2 && p.kernelW <= 1 && p.kernelH <= 20) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 16; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 2 && p.kernelW <= 1 && p.kernelH <= 16) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 16; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 2 && p.kernelW <= 1 && p.kernelH <= 12) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 16; } + if (p.upx == 1 && p.upy == 1 && p.downx == 1 && p.downy == 2 && p.kernelW <= 1 && p.kernelH <= 8 ) { cudaKernel = (void*)UpFirDn2DKernel_small; tileOutW = 32; tileOutH = 16; } + + // Choose launch params. + dim3 blockSize; + dim3 gridSize; + if (tileOutW > 0 && tileOutH > 0) // small + { + p.loopMajor = (p.majorDim - 1) / 16384 + 1; + p.loopX = 1; + blockSize = dim3(32 * 8, 1, 1); + gridSize = dim3(((p.outH - 1) / tileOutH + 1) * p.minorDim, (p.outW - 1) / (p.loopX * tileOutW) + 1, (p.majorDim - 1) / p.loopMajor + 1); + } + else // large + { + p.loopMajor = (p.majorDim - 1) / 16384 + 1; + p.loopX = 4; + blockSize = dim3(4, 32, 1); + gridSize = dim3((p.outH * p.minorDim - 1) / blockSize.x + 1, (p.outW - 1) / (p.loopX * blockSize.y) + 1, (p.majorDim - 1) / p.loopMajor + 1); + } + + // Launch CUDA kernel. + void* args[] = {&p}; + OP_CHECK_CUDA_ERROR(ctx, cudaLaunchKernel(cudaKernel, gridSize, blockSize, args, 0, stream)); + } +}; + +REGISTER_OP("UpFirDn2D") + .Input ("x: T") + .Input ("k: T") + .Output ("y: T") + .Attr ("T: {float, half}") + .Attr ("upx: int = 1") + .Attr ("upy: int = 1") + .Attr ("downx: int = 1") + .Attr ("downy: int = 1") + .Attr ("padx0: int = 0") + .Attr ("padx1: int = 0") + .Attr ("pady0: int = 0") + .Attr ("pady1: int = 0"); +REGISTER_KERNEL_BUILDER(Name("UpFirDn2D").Device(DEVICE_GPU).TypeConstraint("T"), UpFirDn2DOp); +REGISTER_KERNEL_BUILDER(Name("UpFirDn2D").Device(DEVICE_GPU).TypeConstraint("T"), UpFirDn2DOp); + +//------------------------------------------------------------------------ diff --git a/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/upfirdn_2d.py b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/upfirdn_2d.py new file mode 100644 index 0000000000000000000000000000000000000000..55a31af7e146da7afeb964db018f14aca3134920 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/ops/upfirdn_2d.py @@ -0,0 +1,418 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom TensorFlow ops for efficient resampling of 2D images.""" + +import os +import numpy as np +import tensorflow as tf +from .. import custom_ops + +def _get_plugin(): + return custom_ops.get_plugin(os.path.splitext(__file__)[0] + '.cu') + +#---------------------------------------------------------------------------- + +def upfirdn_2d(x, k, upx=1, upy=1, downx=1, downy=1, padx0=0, padx1=0, pady0=0, pady1=0, impl='cuda'): + r"""Pad, upsample, FIR filter, and downsample a batch of 2D images. + + Accepts a batch of 2D images of the shape `[majorDim, inH, inW, minorDim]` + and performs the following operations for each image, batched across + `majorDim` and `minorDim`: + + 1. Upsample the image by inserting the zeros after each pixel (`upx`, `upy`). + + 2. Pad the image with zeros by the specified number of pixels on each side + (`padx0`, `padx1`, `pady0`, `pady1`). Specifying a negative value + corresponds to cropping the image. + + 3. Convolve the image with the specified 2D FIR filter (`k`), shrinking the + image so that the footprint of all output pixels lies within the input image. + + 4. Downsample the image by throwing away pixels (`downx`, `downy`). + + This sequence of operations bears close resemblance to scipy.signal.upfirdn(). + The fused op is considerably more efficient than performing the same calculation + using standard TensorFlow ops. It supports gradients of arbitrary order. + + Args: + x: Input tensor of the shape `[majorDim, inH, inW, minorDim]`. + k: 2D FIR filter of the shape `[firH, firW]`. + upx: Integer upsampling factor along the X-axis (default: 1). + upy: Integer upsampling factor along the Y-axis (default: 1). + downx: Integer downsampling factor along the X-axis (default: 1). + downy: Integer downsampling factor along the Y-axis (default: 1). + padx0: Number of pixels to pad on the left side (default: 0). + padx1: Number of pixels to pad on the right side (default: 0). + pady0: Number of pixels to pad on the top side (default: 0). + pady1: Number of pixels to pad on the bottom side (default: 0). + impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default). + + Returns: + Tensor of the shape `[majorDim, outH, outW, minorDim]`, and same datatype as `x`. + """ + + impl_dict = { + 'ref': _upfirdn_2d_ref, + 'cuda': _upfirdn_2d_cuda, + } + return impl_dict[impl](x=x, k=k, upx=upx, upy=upy, downx=downx, downy=downy, padx0=padx0, padx1=padx1, pady0=pady0, pady1=pady1) + +#---------------------------------------------------------------------------- + +def _upfirdn_2d_ref(x, k, upx, upy, downx, downy, padx0, padx1, pady0, pady1): + """Slow reference implementation of `upfirdn_2d()` using standard TensorFlow ops.""" + + x = tf.convert_to_tensor(x) + k = np.asarray(k, dtype=np.float32) + assert x.shape.rank == 4 + inH = x.shape[1].value + inW = x.shape[2].value + minorDim = _shape(x, 3) + kernelH, kernelW = k.shape + assert inW >= 1 and inH >= 1 + assert kernelW >= 1 and kernelH >= 1 + assert isinstance(upx, int) and isinstance(upy, int) + assert isinstance(downx, int) and isinstance(downy, int) + assert isinstance(padx0, int) and isinstance(padx1, int) + assert isinstance(pady0, int) and isinstance(pady1, int) + + # Upsample (insert zeros). + x = tf.reshape(x, [-1, inH, 1, inW, 1, minorDim]) + x = tf.pad(x, [[0, 0], [0, 0], [0, upy - 1], [0, 0], [0, upx - 1], [0, 0]]) + x = tf.reshape(x, [-1, inH * upy, inW * upx, minorDim]) + + # Pad (crop if negative). + x = tf.pad(x, [[0, 0], [max(pady0, 0), max(pady1, 0)], [max(padx0, 0), max(padx1, 0)], [0, 0]]) + x = x[:, max(-pady0, 0) : x.shape[1].value - max(-pady1, 0), max(-padx0, 0) : x.shape[2].value - max(-padx1, 0), :] + + # Convolve with filter. + x = tf.transpose(x, [0, 3, 1, 2]) + x = tf.reshape(x, [-1, 1, inH * upy + pady0 + pady1, inW * upx + padx0 + padx1]) + w = tf.constant(k[::-1, ::-1, np.newaxis, np.newaxis], dtype=x.dtype) + x = tf.nn.conv2d(x, w, strides=[1,1,1,1], padding='VALID', data_format='NCHW') + x = tf.reshape(x, [-1, minorDim, inH * upy + pady0 + pady1 - kernelH + 1, inW * upx + padx0 + padx1 - kernelW + 1]) + x = tf.transpose(x, [0, 2, 3, 1]) + + # Downsample (throw away pixels). + return x[:, ::downy, ::downx, :] + +#---------------------------------------------------------------------------- + +def _upfirdn_2d_cuda(x, k, upx, upy, downx, downy, padx0, padx1, pady0, pady1): + """Fast CUDA implementation of `upfirdn_2d()` using custom ops.""" + + x = tf.convert_to_tensor(x) + k = np.asarray(k, dtype=np.float32) + majorDim, inH, inW, minorDim = x.shape.as_list() + kernelH, kernelW = k.shape + assert inW >= 1 and inH >= 1 + assert kernelW >= 1 and kernelH >= 1 + assert isinstance(upx, int) and isinstance(upy, int) + assert isinstance(downx, int) and isinstance(downy, int) + assert isinstance(padx0, int) and isinstance(padx1, int) + assert isinstance(pady0, int) and isinstance(pady1, int) + + outW = (inW * upx + padx0 + padx1 - kernelW) // downx + 1 + outH = (inH * upy + pady0 + pady1 - kernelH) // downy + 1 + assert outW >= 1 and outH >= 1 + + cuda_op = _get_plugin().up_fir_dn2d + kc = tf.constant(k, dtype=x.dtype) + gkc = tf.constant(k[::-1, ::-1], dtype=x.dtype) + gpadx0 = kernelW - padx0 - 1 + gpady0 = kernelH - pady0 - 1 + gpadx1 = inW * upx - outW * downx + padx0 - upx + 1 + gpady1 = inH * upy - outH * downy + pady0 - upy + 1 + + @tf.custom_gradient + def func(x): + y = cuda_op(x=x, k=kc, upx=int(upx), upy=int(upy), downx=int(downx), downy=int(downy), padx0=int(padx0), padx1=int(padx1), pady0=int(pady0), pady1=int(pady1)) + y.set_shape([majorDim, outH, outW, minorDim]) + @tf.custom_gradient + def grad(dy): + dx = cuda_op(x=dy, k=gkc, upx=int(downx), upy=int(downy), downx=int(upx), downy=int(upy), padx0=int(gpadx0), padx1=int(gpadx1), pady0=int(gpady0), pady1=int(gpady1)) + dx.set_shape([majorDim, inH, inW, minorDim]) + return dx, func + return y, grad + return func(x) + +#---------------------------------------------------------------------------- + +def filter_2d(x, k, gain=1, padding=0, data_format='NCHW', impl='cuda'): + r"""Filter a batch of 2D images with the given FIR filter. + + Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` + and filters each image with the given filter. The filter is normalized so that + if the input pixels are constant, they will be scaled by the specified `gain`. + Pixels outside the image are assumed to be zero. + + Args: + x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. + k: FIR filter of the shape `[firH, firW]` or `[firN]` (separable). + gain: Scaling factor for signal magnitude (default: 1.0). + padding: Number of pixels to pad or crop the output on each side (default: 0). + data_format: `'NCHW'` or `'NHWC'` (default: `'NCHW'`). + impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default). + + Returns: + Tensor of the same shape and datatype as `x`. + """ + + assert isinstance(padding, int) + k = _FilterKernel(k=k, gain=gain) + assert k.w == k.h + pad0 = k.w // 2 + padding + pad1 = (k.w - 1) // 2 + padding + return _simple_upfirdn_2d(x, k, pad0=pad0, pad1=pad1, data_format=data_format, impl=impl) + +#---------------------------------------------------------------------------- + +def upsample_2d(x, k=None, factor=2, gain=1, padding=0, data_format='NCHW', impl='cuda'): + r"""Upsample a batch of 2D images with the given filter. + + Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` + and upsamples each image with the given filter. The filter is normalized so that + if the input pixels are constant, they will be scaled by the specified `gain`. + Pixels outside the image are assumed to be zero, and the filter is padded with + zeros so that its shape is a multiple of the upsampling factor. + + Args: + x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. + k: FIR filter of the shape `[firH, firW]` or `[firN]` (separable). + The default is `[1] * factor`, which corresponds to nearest-neighbor + upsampling. + factor: Integer upsampling factor (default: 2). + gain: Scaling factor for signal magnitude (default: 1.0). + padding: Number of pixels to pad or crop the output on each side (default: 0). + data_format: `'NCHW'` or `'NHWC'` (default: `'NCHW'`). + impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default). + + Returns: + Tensor of the shape `[N, C, H * factor, W * factor]` or + `[N, H * factor, W * factor, C]`, and same datatype as `x`. + """ + + assert isinstance(factor, int) and factor >= 1 + assert isinstance(padding, int) + k = _FilterKernel(k if k is not None else [1] * factor, gain * (factor ** 2)) + assert k.w == k.h + pad0 = (k.w + factor - 1) // 2 + padding + pad1 = (k.w - factor) // 2 + padding + return _simple_upfirdn_2d(x, k, up=factor, pad0=pad0, pad1=pad1, data_format=data_format, impl=impl) + +#---------------------------------------------------------------------------- + +def downsample_2d(x, k=None, factor=2, gain=1, padding=0, data_format='NCHW', impl='cuda'): + r"""Downsample a batch of 2D images with the given filter. + + Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` + and downsamples each image with the given filter. The filter is normalized so that + if the input pixels are constant, they will be scaled by the specified `gain`. + Pixels outside the image are assumed to be zero, and the filter is padded with + zeros so that its shape is a multiple of the downsampling factor. + + Args: + x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. + k: FIR filter of the shape `[firH, firW]` or `[firN]` (separable). + The default is `[1] * factor`, which corresponds to average pooling. + factor: Integer downsampling factor (default: 2). + gain: Scaling factor for signal magnitude (default: 1.0). + padding: Number of pixels to pad or crop the output on each side (default: 0). + data_format: `'NCHW'` or `'NHWC'` (default: `'NCHW'`). + impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default). + + Returns: + Tensor of the shape `[N, C, H // factor, W // factor]` or + `[N, H // factor, W // factor, C]`, and same datatype as `x`. + """ + + assert isinstance(factor, int) and factor >= 1 + assert isinstance(padding, int) + k = _FilterKernel(k if k is not None else [1] * factor, gain) + assert k.w == k.h + pad0 = (k.w - factor + 1) // 2 + padding * factor + pad1 = (k.w - factor) // 2 + padding * factor + return _simple_upfirdn_2d(x, k, down=factor, pad0=pad0, pad1=pad1, data_format=data_format, impl=impl) + +#---------------------------------------------------------------------------- + +def upsample_conv_2d(x, w, k=None, factor=2, gain=1, padding=0, data_format='NCHW', impl='cuda'): + r"""Fused `upsample_2d()` followed by `tf.nn.conv2d()`. + + Padding is performed only once at the beginning, not between the operations. + The fused op is considerably more efficient than performing the same calculation + using standard TensorFlow ops. It supports gradients of arbitrary order. + + Args: + x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. + w: Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. + Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`. + k: FIR filter of the shape `[firH, firW]` or `[firN]` (separable). + The default is `[1] * factor`, which corresponds to nearest-neighbor + upsampling. + factor: Integer upsampling factor (default: 2). + gain: Scaling factor for signal magnitude (default: 1.0). + padding: Number of pixels to pad or crop the output on each side (default: 0). + data_format: `'NCHW'` or `'NHWC'` (default: `'NCHW'`). + impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default). + + Returns: + Tensor of the shape `[N, C, H * factor, W * factor]` or + `[N, H * factor, W * factor, C]`, and same datatype as `x`. + """ + + assert isinstance(factor, int) and factor >= 1 + assert isinstance(padding, int) + + # Check weight shape. + w = tf.convert_to_tensor(w) + ch, cw, _inC, _outC = w.shape.as_list() + inC = _shape(w, 2) + outC = _shape(w, 3) + assert cw == ch + + # Fast path for 1x1 convolution. + if cw == 1 and ch == 1: + x = tf.nn.conv2d(x, w, data_format=data_format, strides=[1,1,1,1], padding='VALID') + x = upsample_2d(x, k, factor=factor, gain=gain, padding=padding, data_format=data_format, impl=impl) + return x + + # Setup filter kernel. + k = _FilterKernel(k if k is not None else [1] * factor, gain * (factor ** 2)) + assert k.w == k.h + + # Determine data dimensions. + if data_format == 'NCHW': + stride = [1, 1, factor, factor] + output_shape = [_shape(x, 0), outC, (_shape(x, 2) - 1) * factor + ch, (_shape(x, 3) - 1) * factor + cw] + num_groups = _shape(x, 1) // inC + else: + stride = [1, factor, factor, 1] + output_shape = [_shape(x, 0), (_shape(x, 1) - 1) * factor + ch, (_shape(x, 2) - 1) * factor + cw, outC] + num_groups = _shape(x, 3) // inC + + # Transpose weights. + w = tf.reshape(w, [ch, cw, inC, num_groups, -1]) + w = tf.transpose(w[::-1, ::-1], [0, 1, 4, 3, 2]) + w = tf.reshape(w, [ch, cw, -1, num_groups * inC]) + + # Execute. + x = tf.nn.conv2d_transpose(x, w, output_shape=output_shape, strides=stride, padding='VALID', data_format=data_format) + pad0 = (k.w + factor - cw) // 2 + padding + pad1 = (k.w - factor - cw + 3) // 2 + padding + return _simple_upfirdn_2d(x, k, pad0=pad0, pad1=pad1, data_format=data_format, impl=impl) + +#---------------------------------------------------------------------------- + +def conv_downsample_2d(x, w, k=None, factor=2, gain=1, padding=0, data_format='NCHW', impl='cuda'): + r"""Fused `tf.nn.conv2d()` followed by `downsample_2d()`. + + Padding is performed only once at the beginning, not between the operations. + The fused op is considerably more efficient than performing the same calculation + using standard TensorFlow ops. It supports gradients of arbitrary order. + + Args: + x: Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`. + w: Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`. + Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`. + k: FIR filter of the shape `[firH, firW]` or `[firN]` (separable). + The default is `[1] * factor`, which corresponds to average pooling. + factor: Integer downsampling factor (default: 2). + gain: Scaling factor for signal magnitude (default: 1.0). + padding: Number of pixels to pad or crop the output on each side (default: 0). + data_format: `'NCHW'` or `'NHWC'` (default: `'NCHW'`). + impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default). + + Returns: + Tensor of the shape `[N, C, H // factor, W // factor]` or + `[N, H // factor, W // factor, C]`, and same datatype as `x`. + """ + + assert isinstance(factor, int) and factor >= 1 + assert isinstance(padding, int) + + # Check weight shape. + w = tf.convert_to_tensor(w) + ch, cw, _inC, _outC = w.shape.as_list() + assert cw == ch + + # Fast path for 1x1 convolution. + if cw == 1 and ch == 1: + x = downsample_2d(x, k, factor=factor, gain=gain, padding=padding, data_format=data_format, impl=impl) + x = tf.nn.conv2d(x, w, data_format=data_format, strides=[1,1,1,1], padding='VALID') + return x + + # Setup filter kernel. + k = _FilterKernel(k if k is not None else [1] * factor, gain) + assert k.w == k.h + + # Determine stride. + if data_format == 'NCHW': + s = [1, 1, factor, factor] + else: + s = [1, factor, factor, 1] + + # Execute. + pad0 = (k.w - factor + cw) // 2 + padding * factor + pad1 = (k.w - factor + cw - 1) // 2 + padding * factor + x = _simple_upfirdn_2d(x, k, pad0=pad0, pad1=pad1, data_format=data_format, impl=impl) + return tf.nn.conv2d(x, w, strides=s, padding='VALID', data_format=data_format) + +#---------------------------------------------------------------------------- +# Internal helpers. + +class _FilterKernel: + def __init__(self, k, gain=1): + k = np.asarray(k, dtype=np.float32) + k /= np.sum(k) + + # Separable. + if k.ndim == 1 and k.size >= 8: + self.w = k.size + self.h = k.size + self.kx = k[np.newaxis, :] + self.ky = k[:, np.newaxis] * gain + self.kxy = None + + # Non-separable. + else: + if k.ndim == 1: + k = np.outer(k, k) + assert k.ndim == 2 + self.w = k.shape[1] + self.h = k.shape[0] + self.kx = None + self.ky = None + self.kxy = k * gain + +def _simple_upfirdn_2d(x, k, up=1, down=1, pad0=0, pad1=0, data_format='NCHW', impl='cuda'): + assert isinstance(k, _FilterKernel) + assert data_format in ['NCHW', 'NHWC'] + assert x.shape.rank == 4 + y = x + if data_format == 'NCHW': + y = tf.reshape(y, [-1, _shape(y, 2), _shape(y, 3), 1]) + if k.kx is not None: + y = upfirdn_2d(y, k.kx, upx=up, downx=down, padx0=pad0, padx1=pad1, impl=impl) + if k.ky is not None: + y = upfirdn_2d(y, k.ky, upy=up, downy=down, pady0=pad0, pady1=pad1, impl=impl) + if k.kxy is not None: + y = upfirdn_2d(y, k.kxy, upx=up, upy=up, downx=down, downy=down, padx0=pad0, padx1=pad1, pady0=pad0, pady1=pad1, impl=impl) + if data_format == 'NCHW': + y = tf.reshape(y, [-1, _shape(x, 1), _shape(y, 1), _shape(y, 2)]) + return y + +def _shape(tf_expr, dim_idx): + if tf_expr.shape.rank is not None: + dim = tf_expr.shape[dim_idx].value + if dim is not None: + return dim + return tf.shape(tf_expr)[dim_idx] + +#---------------------------------------------------------------------------- diff --git a/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/optimizer.py b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/optimizer.py new file mode 100644 index 0000000000000000000000000000000000000000..cae5ffff3d11aaccd705d6936e080175ab97dd0e --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/optimizer.py @@ -0,0 +1,372 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Helper wrapper for a Tensorflow optimizer.""" + +import platform +import numpy as np +import tensorflow as tf + +from collections import OrderedDict +from typing import List, Union + +from . import autosummary +from . import tfutil +from .. import util + +from .tfutil import TfExpression, TfExpressionEx + +_collective_ops_warning_printed = False +_collective_ops_group_key = 831766147 +_collective_ops_instance_key = 436340067 + +class Optimizer: + """A Wrapper for tf.train.Optimizer. + + Automatically takes care of: + - Gradient averaging for multi-GPU training. + - Gradient accumulation for arbitrarily large minibatches. + - Dynamic loss scaling and typecasts for FP16 training. + - Ignoring corrupted gradients that contain NaNs/Infs. + - Reporting statistics. + - Well-chosen default settings. + """ + + def __init__(self, + name: str = "Train", # Name string that will appear in TensorFlow graph. + tf_optimizer: str = "tf.train.AdamOptimizer", # Underlying optimizer class. + learning_rate: TfExpressionEx = 0.001, # Learning rate. Can vary over time. + minibatch_multiplier: TfExpressionEx = None, # Treat N consecutive minibatches as one by accumulating gradients. + share: "Optimizer" = None, # Share internal state with a previously created optimizer? + use_loss_scaling: bool = False, # Enable dynamic loss scaling for robust mixed-precision training? + loss_scaling_init: float = 64.0, # Log2 of initial loss scaling factor. + loss_scaling_inc: float = 0.0005, # Log2 of per-minibatch loss scaling increment when there is no overflow. + loss_scaling_dec: float = 1.0, # Log2 of per-minibatch loss scaling decrement when there is an overflow. + report_mem_usage: bool = False, # Report fine-grained memory usage statistics in TensorBoard? + **kwargs): + + # Public fields. + self.name = name + self.learning_rate = learning_rate + self.minibatch_multiplier = minibatch_multiplier + self.id = self.name.replace("/", ".") + self.scope = tf.get_default_graph().unique_name(self.id) + self.optimizer_class = util.get_obj_by_name(tf_optimizer) + self.optimizer_kwargs = dict(kwargs) + self.use_loss_scaling = use_loss_scaling + self.loss_scaling_init = loss_scaling_init + self.loss_scaling_inc = loss_scaling_inc + self.loss_scaling_dec = loss_scaling_dec + + # Private fields. + self._updates_applied = False + self._devices = OrderedDict() # device_name => EasyDict() + self._shared_optimizers = OrderedDict() # device_name => optimizer_class + self._gradient_shapes = None # [shape, ...] + self._report_mem_usage = report_mem_usage + + # Validate arguments. + assert callable(self.optimizer_class) + + # Share internal state if requested. + if share is not None: + assert isinstance(share, Optimizer) + assert self.optimizer_class is share.optimizer_class + assert self.learning_rate is share.learning_rate + assert self.optimizer_kwargs == share.optimizer_kwargs + self._shared_optimizers = share._shared_optimizers # pylint: disable=protected-access + + def _get_device(self, device_name: str): + """Get internal state for the given TensorFlow device.""" + tfutil.assert_tf_initialized() + if device_name in self._devices: + return self._devices[device_name] + + # Initialize fields. + device = util.EasyDict() + device.name = device_name + device.optimizer = None # Underlying optimizer: optimizer_class + device.loss_scaling_var = None # Log2 of loss scaling: tf.Variable + device.grad_raw = OrderedDict() # Raw gradients: var => [grad, ...] + device.grad_clean = OrderedDict() # Clean gradients: var => grad + device.grad_acc_vars = OrderedDict() # Accumulation sums: var => tf.Variable + device.grad_acc_count = None # Accumulation counter: tf.Variable + device.grad_acc = OrderedDict() # Accumulated gradients: var => grad + + # Setup TensorFlow objects. + with tfutil.absolute_name_scope(self.scope + "/Devices"), tf.device(device_name), tf.control_dependencies(None): + if device_name not in self._shared_optimizers: + optimizer_name = self.scope.replace("/", "_") + "_opt%d" % len(self._shared_optimizers) + self._shared_optimizers[device_name] = self.optimizer_class(name=optimizer_name, learning_rate=self.learning_rate, **self.optimizer_kwargs) + device.optimizer = self._shared_optimizers[device_name] + if self.use_loss_scaling: + device.loss_scaling_var = tf.Variable(np.float32(self.loss_scaling_init), trainable=False, name="loss_scaling_var") + + # Register device. + self._devices[device_name] = device + return device + + def register_gradients(self, loss: TfExpression, trainable_vars: Union[List, dict]) -> None: + """Register the gradients of the given loss function with respect to the given variables. + Intended to be called once per GPU.""" + tfutil.assert_tf_initialized() + assert not self._updates_applied + device = self._get_device(loss.device) + + # Validate trainables. + if isinstance(trainable_vars, dict): + trainable_vars = list(trainable_vars.values()) # allow passing in Network.trainables as vars + assert isinstance(trainable_vars, list) and len(trainable_vars) >= 1 + assert all(tfutil.is_tf_expression(expr) for expr in trainable_vars + [loss]) + assert all(var.device == device.name for var in trainable_vars) + + # Validate shapes. + if self._gradient_shapes is None: + self._gradient_shapes = [var.shape.as_list() for var in trainable_vars] + assert len(trainable_vars) == len(self._gradient_shapes) + assert all(var.shape.as_list() == var_shape for var, var_shape in zip(trainable_vars, self._gradient_shapes)) + + # Report memory usage if requested. + deps = [loss] + if self._report_mem_usage: + self._report_mem_usage = False + try: + with tf.name_scope(self.id + '_mem'), tf.device(device.name), tf.control_dependencies([loss]): + deps.append(autosummary.autosummary(self.id + "/mem_usage_gb", tf.contrib.memory_stats.BytesInUse() / 2**30)) + except tf.errors.NotFoundError: + pass + + # Compute gradients. + with tf.name_scope(self.id + "_grad"), tf.device(device.name), tf.control_dependencies(deps): + loss = self.apply_loss_scaling(tf.cast(loss, tf.float32)) + gate = tf.train.Optimizer.GATE_NONE # disable gating to reduce memory usage + grad_list = device.optimizer.compute_gradients(loss=loss, var_list=trainable_vars, gate_gradients=gate) + + # Register gradients. + for grad, var in grad_list: + if var not in device.grad_raw: + device.grad_raw[var] = [] + device.grad_raw[var].append(grad) + + def apply_updates(self, allow_no_op: bool = False) -> tf.Operation: + """Construct training op to update the registered variables based on their gradients.""" + tfutil.assert_tf_initialized() + assert not self._updates_applied + self._updates_applied = True + all_ops = [] + + # Check for no-op. + if allow_no_op and len(self._devices) == 0: + with tfutil.absolute_name_scope(self.scope): + return tf.no_op(name='TrainingOp') + + # Clean up gradients. + for device_idx, device in enumerate(self._devices.values()): + with tfutil.absolute_name_scope(self.scope + "/Clean%d" % device_idx), tf.device(device.name): + for var, grad in device.grad_raw.items(): + + # Filter out disconnected gradients and convert to float32. + grad = [g for g in grad if g is not None] + grad = [tf.cast(g, tf.float32) for g in grad] + + # Sum within the device. + if len(grad) == 0: + grad = tf.zeros(var.shape) # No gradients => zero. + elif len(grad) == 1: + grad = grad[0] # Single gradient => use as is. + else: + grad = tf.add_n(grad) # Multiple gradients => sum. + + # Scale as needed. + scale = 1.0 / len(device.grad_raw[var]) / len(self._devices) + scale = tf.constant(scale, dtype=tf.float32, name="scale") + if self.minibatch_multiplier is not None: + scale /= tf.cast(self.minibatch_multiplier, tf.float32) + scale = self.undo_loss_scaling(scale) + device.grad_clean[var] = grad * scale + + # Sum gradients across devices. + if len(self._devices) > 1: + with tfutil.absolute_name_scope(self.scope + "/Broadcast"), tf.device(None): + if platform.system() == "Windows": # Windows => NCCL ops are not available. + self._broadcast_fallback() + elif tf.VERSION.startswith("1.15."): # TF 1.15 => NCCL ops are broken: https://github.com/tensorflow/tensorflow/issues/41539 + self._broadcast_fallback() + else: # Otherwise => NCCL ops are safe to use. + self._broadcast_nccl() + + # Apply updates separately on each device. + for device_idx, device in enumerate(self._devices.values()): + with tfutil.absolute_name_scope(self.scope + "/Apply%d" % device_idx), tf.device(device.name): + # pylint: disable=cell-var-from-loop + + # Accumulate gradients over time. + if self.minibatch_multiplier is None: + acc_ok = tf.constant(True, name='acc_ok') + device.grad_acc = OrderedDict(device.grad_clean) + else: + # Create variables. + with tf.control_dependencies(None): + for var in device.grad_clean.keys(): + device.grad_acc_vars[var] = tf.Variable(tf.zeros(var.shape), trainable=False, name="grad_acc_var") + device.grad_acc_count = tf.Variable(tf.zeros([]), trainable=False, name="grad_acc_count") + + # Track counter. + count_cur = device.grad_acc_count + 1.0 + count_inc_op = lambda: tf.assign(device.grad_acc_count, count_cur) + count_reset_op = lambda: tf.assign(device.grad_acc_count, tf.zeros([])) + acc_ok = (count_cur >= tf.cast(self.minibatch_multiplier, tf.float32)) + all_ops.append(tf.cond(acc_ok, count_reset_op, count_inc_op)) + + # Track gradients. + for var, grad in device.grad_clean.items(): + acc_var = device.grad_acc_vars[var] + acc_cur = acc_var + grad + device.grad_acc[var] = acc_cur + with tf.control_dependencies([acc_cur]): + acc_inc_op = lambda: tf.assign(acc_var, acc_cur) + acc_reset_op = lambda: tf.assign(acc_var, tf.zeros(var.shape)) + all_ops.append(tf.cond(acc_ok, acc_reset_op, acc_inc_op)) + + # No overflow => apply gradients. + all_ok = tf.reduce_all(tf.stack([acc_ok] + [tf.reduce_all(tf.is_finite(g)) for g in device.grad_acc.values()])) + apply_op = lambda: device.optimizer.apply_gradients([(tf.cast(grad, var.dtype), var) for var, grad in device.grad_acc.items()]) + all_ops.append(tf.cond(all_ok, apply_op, tf.no_op)) + + # Adjust loss scaling. + if self.use_loss_scaling: + ls_inc_op = lambda: tf.assign_add(device.loss_scaling_var, self.loss_scaling_inc) + ls_dec_op = lambda: tf.assign_sub(device.loss_scaling_var, self.loss_scaling_dec) + ls_update_op = lambda: tf.group(tf.cond(all_ok, ls_inc_op, ls_dec_op)) + all_ops.append(tf.cond(acc_ok, ls_update_op, tf.no_op)) + + # Last device => report statistics. + if device_idx == len(self._devices) - 1: + all_ops.append(autosummary.autosummary(self.id + "/learning_rate", tf.convert_to_tensor(self.learning_rate))) + all_ops.append(autosummary.autosummary(self.id + "/overflow_frequency", tf.where(all_ok, 0, 1), condition=acc_ok)) + if self.use_loss_scaling: + all_ops.append(autosummary.autosummary(self.id + "/loss_scaling_log2", device.loss_scaling_var)) + + # Initialize variables. + self.reset_optimizer_state() + if self.use_loss_scaling: + tfutil.init_uninitialized_vars([device.loss_scaling_var for device in self._devices.values()]) + if self.minibatch_multiplier is not None: + tfutil.run([var.initializer for device in self._devices.values() for var in list(device.grad_acc_vars.values()) + [device.grad_acc_count]]) + + # Group everything into a single op. + with tfutil.absolute_name_scope(self.scope): + return tf.group(*all_ops, name="TrainingOp") + + def reset_optimizer_state(self) -> None: + """Reset internal state of the underlying optimizer.""" + tfutil.assert_tf_initialized() + tfutil.run([var.initializer for device in self._devices.values() for var in device.optimizer.variables()]) + + def get_loss_scaling_var(self, device: str) -> Union[tf.Variable, None]: + """Get or create variable representing log2 of the current dynamic loss scaling factor.""" + return self._get_device(device).loss_scaling_var + + def apply_loss_scaling(self, value: TfExpression) -> TfExpression: + """Apply dynamic loss scaling for the given expression.""" + assert tfutil.is_tf_expression(value) + if not self.use_loss_scaling: + return value + return value * tfutil.exp2(self.get_loss_scaling_var(value.device)) + + def undo_loss_scaling(self, value: TfExpression) -> TfExpression: + """Undo the effect of dynamic loss scaling for the given expression.""" + assert tfutil.is_tf_expression(value) + if not self.use_loss_scaling: + return value + return value * tfutil.exp2(-self.get_loss_scaling_var(value.device)) # pylint: disable=invalid-unary-operand-type + + def _broadcast_nccl(self): + """Sum gradients across devices using NCCL ops (fast path).""" + from tensorflow.python.ops import nccl_ops # pylint: disable=no-name-in-module + for all_vars in zip(*[device.grad_clean.keys() for device in self._devices.values()]): + if any(x.shape.num_elements() > 0 for x in all_vars): + all_grads = [device.grad_clean[var] for device, var in zip(self._devices.values(), all_vars)] + all_grads = nccl_ops.all_sum(all_grads) + for device, var, grad in zip(self._devices.values(), all_vars, all_grads): + device.grad_clean[var] = grad + + def _broadcast_fallback(self): + """Sum gradients across devices using TensorFlow collective ops (slow fallback path).""" + from tensorflow.python.ops import collective_ops # pylint: disable=no-name-in-module + global _collective_ops_warning_printed, _collective_ops_group_key, _collective_ops_instance_key + if all(x.shape.num_elements() == 0 for device in self._devices.values() for x in device.grad_clean.values()): + return + if not _collective_ops_warning_printed: + print("------------------------------------------------------------------------") + print("WARNING: Using slow fallback implementation for inter-GPU communication.") + print("Please use TensorFlow 1.14 on Linux for optimal training performance.") + print("------------------------------------------------------------------------") + _collective_ops_warning_printed = True + for device in self._devices.values(): + with tf.device(device.name): + combo = [tf.reshape(x, [x.shape.num_elements()]) for x in device.grad_clean.values()] + combo = tf.concat(combo, axis=0) + combo = collective_ops.all_reduce(combo, merge_op='Add', final_op='Id', + group_size=len(self._devices), group_key=_collective_ops_group_key, + instance_key=_collective_ops_instance_key) + cur_ofs = 0 + for var, grad_old in device.grad_clean.items(): + grad_new = tf.reshape(combo[cur_ofs : cur_ofs + grad_old.shape.num_elements()], grad_old.shape) + cur_ofs += grad_old.shape.num_elements() + device.grad_clean[var] = grad_new + _collective_ops_instance_key += 1 + + +class SimpleAdam: + """Simplified version of tf.train.AdamOptimizer that behaves identically when used with dnnlib.tflib.Optimizer.""" + + def __init__(self, name="Adam", learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8): + self.name = name + self.learning_rate = learning_rate + self.beta1 = beta1 + self.beta2 = beta2 + self.epsilon = epsilon + self.all_state_vars = [] + + def variables(self): + return self.all_state_vars + + def compute_gradients(self, loss, var_list, gate_gradients=tf.train.Optimizer.GATE_NONE): + assert gate_gradients == tf.train.Optimizer.GATE_NONE + return list(zip(tf.gradients(loss, var_list), var_list)) + + def apply_gradients(self, grads_and_vars): + with tf.name_scope(self.name): + state_vars = [] + update_ops = [] + + # Adjust learning rate to deal with startup bias. + with tf.control_dependencies(None): + b1pow_var = tf.Variable(dtype=tf.float32, initial_value=1, trainable=False) + b2pow_var = tf.Variable(dtype=tf.float32, initial_value=1, trainable=False) + state_vars += [b1pow_var, b2pow_var] + b1pow_new = b1pow_var * self.beta1 + b2pow_new = b2pow_var * self.beta2 + update_ops += [tf.assign(b1pow_var, b1pow_new), tf.assign(b2pow_var, b2pow_new)] + lr_new = self.learning_rate * tf.sqrt(1 - b2pow_new) / (1 - b1pow_new) + + # Construct ops to update each variable. + for grad, var in grads_and_vars: + with tf.control_dependencies(None): + m_var = tf.Variable(dtype=tf.float32, initial_value=tf.zeros_like(var), trainable=False) + v_var = tf.Variable(dtype=tf.float32, initial_value=tf.zeros_like(var), trainable=False) + state_vars += [m_var, v_var] + m_new = self.beta1 * m_var + (1 - self.beta1) * grad + v_new = self.beta2 * v_var + (1 - self.beta2) * tf.square(grad) + var_delta = lr_new * m_new / (tf.sqrt(v_new) + self.epsilon) + update_ops += [tf.assign(m_var, m_new), tf.assign(v_var, v_new), tf.assign_sub(var, var_delta)] + + # Group everything together. + self.all_state_vars += state_vars + return tf.group(*update_ops) diff --git a/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/tfutil.py b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/tfutil.py new file mode 100644 index 0000000000000000000000000000000000000000..fe21100299251492ee6d49a7fab566ffb8283702 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/dnnlib/tflib/tfutil.py @@ -0,0 +1,262 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Miscellaneous helper utils for Tensorflow.""" + +import os +import numpy as np +import tensorflow as tf + +# Silence deprecation warnings from TensorFlow 1.13 onwards +import logging +logging.getLogger('tensorflow').setLevel(logging.ERROR) +import tensorflow.contrib # requires TensorFlow 1.x! +tf.contrib = tensorflow.contrib + +from typing import Any, Iterable, List, Union + +TfExpression = Union[tf.Tensor, tf.Variable, tf.Operation] +"""A type that represents a valid Tensorflow expression.""" + +TfExpressionEx = Union[TfExpression, int, float, np.ndarray] +"""A type that can be converted to a valid Tensorflow expression.""" + + +def run(*args, **kwargs) -> Any: + """Run the specified ops in the default session.""" + assert_tf_initialized() + return tf.get_default_session().run(*args, **kwargs) + + +def is_tf_expression(x: Any) -> bool: + """Check whether the input is a valid Tensorflow expression, i.e., Tensorflow Tensor, Variable, or Operation.""" + return isinstance(x, (tf.Tensor, tf.Variable, tf.Operation)) + + +def shape_to_list(shape: Iterable[tf.Dimension]) -> List[Union[int, None]]: + """Convert a Tensorflow shape to a list of ints. Retained for backwards compatibility -- use TensorShape.as_list() in new code.""" + return [dim.value for dim in shape] + + +def flatten(x: TfExpressionEx) -> TfExpression: + """Shortcut function for flattening a tensor.""" + with tf.name_scope("Flatten"): + return tf.reshape(x, [-1]) + + +def log2(x: TfExpressionEx) -> TfExpression: + """Logarithm in base 2.""" + with tf.name_scope("Log2"): + return tf.log(x) * np.float32(1.0 / np.log(2.0)) + + +def exp2(x: TfExpressionEx) -> TfExpression: + """Exponent in base 2.""" + with tf.name_scope("Exp2"): + return tf.exp(x * np.float32(np.log(2.0))) + + +def erfinv(y: TfExpressionEx) -> TfExpression: + """Inverse of the error function.""" + # pylint: disable=no-name-in-module + from tensorflow.python.ops.distributions import special_math + return special_math.erfinv(y) + + +def lerp(a: TfExpressionEx, b: TfExpressionEx, t: TfExpressionEx) -> TfExpressionEx: + """Linear interpolation.""" + with tf.name_scope("Lerp"): + return a + (b - a) * t + + +def lerp_clip(a: TfExpressionEx, b: TfExpressionEx, t: TfExpressionEx) -> TfExpression: + """Linear interpolation with clip.""" + with tf.name_scope("LerpClip"): + return a + (b - a) * tf.clip_by_value(t, 0.0, 1.0) + + +def absolute_name_scope(scope: str) -> tf.name_scope: + """Forcefully enter the specified name scope, ignoring any surrounding scopes.""" + return tf.name_scope(scope + "/") + + +def absolute_variable_scope(scope: str, **kwargs) -> tf.variable_scope: + """Forcefully enter the specified variable scope, ignoring any surrounding scopes.""" + return tf.variable_scope(tf.VariableScope(name=scope, **kwargs), auxiliary_name_scope=False) + + +def _sanitize_tf_config(config_dict: dict = None) -> dict: + # Defaults. + cfg = dict() + cfg["rnd.np_random_seed"] = None # Random seed for NumPy. None = keep as is. + cfg["rnd.tf_random_seed"] = "auto" # Random seed for TensorFlow. 'auto' = derive from NumPy random state. None = keep as is. + cfg["env.TF_CPP_MIN_LOG_LEVEL"] = "1" # 0 = Print all available debug info from TensorFlow. 1 = Print warnings and errors, but disable debug info. + cfg["env.HDF5_USE_FILE_LOCKING"] = "FALSE" # Disable HDF5 file locking to avoid concurrency issues with network shares. + cfg["graph_options.place_pruned_graph"] = True # False = Check that all ops are available on the designated device. True = Skip the check for ops that are not used. + cfg["gpu_options.allow_growth"] = True # False = Allocate all GPU memory at the beginning. True = Allocate only as much GPU memory as needed. + + # Remove defaults for environment variables that are already set. + for key in list(cfg): + fields = key.split(".") + if fields[0] == "env": + assert len(fields) == 2 + if fields[1] in os.environ: + del cfg[key] + + # User overrides. + if config_dict is not None: + cfg.update(config_dict) + return cfg + + +def init_tf(config_dict: dict = None) -> None: + """Initialize TensorFlow session using good default settings.""" + # Skip if already initialized. + if tf.get_default_session() is not None: + return + + # Setup config dict and random seeds. + cfg = _sanitize_tf_config(config_dict) + np_random_seed = cfg["rnd.np_random_seed"] + if np_random_seed is not None: + np.random.seed(np_random_seed) + tf_random_seed = cfg["rnd.tf_random_seed"] + if tf_random_seed == "auto": + tf_random_seed = np.random.randint(1 << 31) + if tf_random_seed is not None: + tf.set_random_seed(tf_random_seed) + + # Setup environment variables. + for key, value in cfg.items(): + fields = key.split(".") + if fields[0] == "env": + assert len(fields) == 2 + os.environ[fields[1]] = str(value) + + # Create default TensorFlow session. + create_session(cfg, force_as_default=True) + + +def assert_tf_initialized(): + """Check that TensorFlow session has been initialized.""" + if tf.get_default_session() is None: + raise RuntimeError("No default TensorFlow session found. Please call dnnlib.tflib.init_tf().") + + +def create_session(config_dict: dict = None, force_as_default: bool = False) -> tf.Session: + """Create tf.Session based on config dict.""" + # Setup TensorFlow config proto. + cfg = _sanitize_tf_config(config_dict) + config_proto = tf.ConfigProto() + for key, value in cfg.items(): + fields = key.split(".") + if fields[0] not in ["rnd", "env"]: + obj = config_proto + for field in fields[:-1]: + obj = getattr(obj, field) + setattr(obj, fields[-1], value) + + # Create session. + session = tf.Session(config=config_proto) + if force_as_default: + # pylint: disable=protected-access + session._default_session = session.as_default() + session._default_session.enforce_nesting = False + session._default_session.__enter__() + return session + + +def init_uninitialized_vars(target_vars: List[tf.Variable] = None) -> None: + """Initialize all tf.Variables that have not already been initialized. + + Equivalent to the following, but more efficient and does not bloat the tf graph: + tf.variables_initializer(tf.report_uninitialized_variables()).run() + """ + assert_tf_initialized() + if target_vars is None: + target_vars = tf.global_variables() + + test_vars = [] + test_ops = [] + + with tf.control_dependencies(None): # ignore surrounding control_dependencies + for var in target_vars: + assert is_tf_expression(var) + + try: + tf.get_default_graph().get_tensor_by_name(var.name.replace(":0", "/IsVariableInitialized:0")) + except KeyError: + # Op does not exist => variable may be uninitialized. + test_vars.append(var) + + with absolute_name_scope(var.name.split(":")[0]): + test_ops.append(tf.is_variable_initialized(var)) + + init_vars = [var for var, inited in zip(test_vars, run(test_ops)) if not inited] + run([var.initializer for var in init_vars]) + + +def set_vars(var_to_value_dict: dict) -> None: + """Set the values of given tf.Variables. + + Equivalent to the following, but more efficient and does not bloat the tf graph: + tflib.run([tf.assign(var, value) for var, value in var_to_value_dict.items()] + """ + assert_tf_initialized() + ops = [] + feed_dict = {} + + for var, value in var_to_value_dict.items(): + assert is_tf_expression(var) + + try: + setter = tf.get_default_graph().get_tensor_by_name(var.name.replace(":0", "/setter:0")) # look for existing op + except KeyError: + with absolute_name_scope(var.name.split(":")[0]): + with tf.control_dependencies(None): # ignore surrounding control_dependencies + setter = tf.assign(var, tf.placeholder(var.dtype, var.shape, "new_value"), name="setter") # create new setter + + ops.append(setter) + feed_dict[setter.op.inputs[1]] = value + + run(ops, feed_dict) + + +def create_var_with_large_initial_value(initial_value: np.ndarray, *args, **kwargs): + """Create tf.Variable with large initial value without bloating the tf graph.""" + assert_tf_initialized() + assert isinstance(initial_value, np.ndarray) + zeros = tf.zeros(initial_value.shape, initial_value.dtype) + var = tf.Variable(zeros, *args, **kwargs) + set_vars({var: initial_value}) + return var + + +def convert_images_from_uint8(images, drange=[-1,1], nhwc_to_nchw=False): + """Convert a minibatch of images from uint8 to float32 with configurable dynamic range. + Can be used as an input transformation for Network.run(). + """ + images = tf.cast(images, tf.float32) + if nhwc_to_nchw: + images = tf.transpose(images, [0, 3, 1, 2]) + return images * ((drange[1] - drange[0]) / 255) + drange[0] + + +def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False, shrink=1): + """Convert a minibatch of images from float32 to uint8 with configurable dynamic range. + Can be used as an output transformation for Network.run(). + """ + images = tf.cast(images, tf.float32) + if shrink > 1: + ksize = [1, 1, shrink, shrink] + images = tf.nn.avg_pool(images, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW") + if nchw_to_nhwc: + images = tf.transpose(images, [0, 2, 3, 1]) + scale = 255 / (drange[1] - drange[0]) + images = images * scale + (0.5 - drange[0] * scale) + return tf.saturate_cast(images, tf.uint8) diff --git a/PTI/models/StyleCLIP/global_directions/dnnlib/util.py b/PTI/models/StyleCLIP/global_directions/dnnlib/util.py new file mode 100644 index 0000000000000000000000000000000000000000..0c35b8923bb27bcd91fd0c14234480067138a3fc --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/dnnlib/util.py @@ -0,0 +1,472 @@ +# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Miscellaneous utility classes and functions.""" + +import ctypes +import fnmatch +import importlib +import inspect +import numpy as np +import os +import shutil +import sys +import types +import io +import pickle +import re +import requests +import html +import hashlib +import glob +import tempfile +import urllib +import urllib.request +import uuid + +from distutils.util import strtobool +from typing import Any, List, Tuple, Union + + +# Util classes +# ------------------------------------------------------------------------------------------ + + +class EasyDict(dict): + """Convenience class that behaves like a dict but allows access with the attribute syntax.""" + + def __getattr__(self, name: str) -> Any: + try: + return self[name] + except KeyError: + raise AttributeError(name) + + def __setattr__(self, name: str, value: Any) -> None: + self[name] = value + + def __delattr__(self, name: str) -> None: + del self[name] + + +class Logger(object): + """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file.""" + + def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True): + self.file = None + + if file_name is not None: + self.file = open(file_name, file_mode) + + self.should_flush = should_flush + self.stdout = sys.stdout + self.stderr = sys.stderr + + sys.stdout = self + sys.stderr = self + + def __enter__(self) -> "Logger": + return self + + def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: + self.close() + + def write(self, text: str) -> None: + """Write text to stdout (and a file) and optionally flush.""" + if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash + return + + if self.file is not None: + self.file.write(text) + + self.stdout.write(text) + + if self.should_flush: + self.flush() + + def flush(self) -> None: + """Flush written text to both stdout and a file, if open.""" + if self.file is not None: + self.file.flush() + + self.stdout.flush() + + def close(self) -> None: + """Flush, close possible files, and remove stdout/stderr mirroring.""" + self.flush() + + # if using multiple loggers, prevent closing in wrong order + if sys.stdout is self: + sys.stdout = self.stdout + if sys.stderr is self: + sys.stderr = self.stderr + + if self.file is not None: + self.file.close() + + +# Cache directories +# ------------------------------------------------------------------------------------------ + +_dnnlib_cache_dir = None + +def set_cache_dir(path: str) -> None: + global _dnnlib_cache_dir + _dnnlib_cache_dir = path + +def make_cache_dir_path(*paths: str) -> str: + if _dnnlib_cache_dir is not None: + return os.path.join(_dnnlib_cache_dir, *paths) + if 'DNNLIB_CACHE_DIR' in os.environ: + return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths) + if 'HOME' in os.environ: + return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths) + if 'USERPROFILE' in os.environ: + return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib', *paths) + return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths) + +# Small util functions +# ------------------------------------------------------------------------------------------ + + +def format_time(seconds: Union[int, float]) -> str: + """Convert the seconds to human readable string with days, hours, minutes and seconds.""" + s = int(np.rint(seconds)) + + if s < 60: + return "{0}s".format(s) + elif s < 60 * 60: + return "{0}m {1:02}s".format(s // 60, s % 60) + elif s < 24 * 60 * 60: + return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60) + else: + return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60) + + +def ask_yes_no(question: str) -> bool: + """Ask the user the question until the user inputs a valid answer.""" + while True: + try: + print("{0} [y/n]".format(question)) + return strtobool(input().lower()) + except ValueError: + pass + + +def tuple_product(t: Tuple) -> Any: + """Calculate the product of the tuple elements.""" + result = 1 + + for v in t: + result *= v + + return result + + +_str_to_ctype = { + "uint8": ctypes.c_ubyte, + "uint16": ctypes.c_uint16, + "uint32": ctypes.c_uint32, + "uint64": ctypes.c_uint64, + "int8": ctypes.c_byte, + "int16": ctypes.c_int16, + "int32": ctypes.c_int32, + "int64": ctypes.c_int64, + "float32": ctypes.c_float, + "float64": ctypes.c_double +} + + +def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]: + """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes.""" + type_str = None + + if isinstance(type_obj, str): + type_str = type_obj + elif hasattr(type_obj, "__name__"): + type_str = type_obj.__name__ + elif hasattr(type_obj, "name"): + type_str = type_obj.name + else: + raise RuntimeError("Cannot infer type name from input") + + assert type_str in _str_to_ctype.keys() + + my_dtype = np.dtype(type_str) + my_ctype = _str_to_ctype[type_str] + + assert my_dtype.itemsize == ctypes.sizeof(my_ctype) + + return my_dtype, my_ctype + + +def is_pickleable(obj: Any) -> bool: + try: + with io.BytesIO() as stream: + pickle.dump(obj, stream) + return True + except: + return False + + +# Functionality to import modules/objects by name, and call functions by name +# ------------------------------------------------------------------------------------------ + +def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]: + """Searches for the underlying module behind the name to some python object. + Returns the module and the object name (original name with module part removed).""" + + # allow convenience shorthands, substitute them by full names + obj_name = re.sub("^np.", "numpy.", obj_name) + obj_name = re.sub("^tf.", "tensorflow.", obj_name) + + # list alternatives for (module_name, local_obj_name) + parts = obj_name.split(".") + name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)] + + # try each alternative in turn + for module_name, local_obj_name in name_pairs: + try: + module = importlib.import_module(module_name) # may raise ImportError + get_obj_from_module(module, local_obj_name) # may raise AttributeError + return module, local_obj_name + except: + pass + + # maybe some of the modules themselves contain errors? + for module_name, _local_obj_name in name_pairs: + try: + importlib.import_module(module_name) # may raise ImportError + except ImportError: + if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"): + raise + + # maybe the requested attribute is missing? + for module_name, local_obj_name in name_pairs: + try: + module = importlib.import_module(module_name) # may raise ImportError + get_obj_from_module(module, local_obj_name) # may raise AttributeError + except ImportError: + pass + + # we are out of luck, but we have no idea why + raise ImportError(obj_name) + + +def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any: + """Traverses the object name and returns the last (rightmost) python object.""" + if obj_name == '': + return module + obj = module + for part in obj_name.split("."): + obj = getattr(obj, part) + return obj + + +def get_obj_by_name(name: str) -> Any: + """Finds the python object with the given name.""" + module, obj_name = get_module_from_obj_name(name) + return get_obj_from_module(module, obj_name) + + +def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any: + """Finds the python object with the given name and calls it as a function.""" + assert func_name is not None + func_obj = get_obj_by_name(func_name) + assert callable(func_obj) + return func_obj(*args, **kwargs) + + +def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any: + """Finds the python class with the given name and constructs it with the given arguments.""" + return call_func_by_name(*args, func_name=class_name, **kwargs) + + +def get_module_dir_by_obj_name(obj_name: str) -> str: + """Get the directory path of the module containing the given object name.""" + module, _ = get_module_from_obj_name(obj_name) + return os.path.dirname(inspect.getfile(module)) + + +def is_top_level_function(obj: Any) -> bool: + """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'.""" + return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__ + + +def get_top_level_function_name(obj: Any) -> str: + """Return the fully-qualified name of a top-level function.""" + assert is_top_level_function(obj) + module = obj.__module__ + if module == '__main__': + module = os.path.splitext(os.path.basename(sys.modules[module].__file__))[0] + return module + "." + obj.__name__ + + +# File system helpers +# ------------------------------------------------------------------------------------------ + +def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]: + """List all files recursively in a given directory while ignoring given file and directory names. + Returns list of tuples containing both absolute and relative paths.""" + assert os.path.isdir(dir_path) + base_name = os.path.basename(os.path.normpath(dir_path)) + + if ignores is None: + ignores = [] + + result = [] + + for root, dirs, files in os.walk(dir_path, topdown=True): + for ignore_ in ignores: + dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)] + + # dirs need to be edited in-place + for d in dirs_to_remove: + dirs.remove(d) + + files = [f for f in files if not fnmatch.fnmatch(f, ignore_)] + + absolute_paths = [os.path.join(root, f) for f in files] + relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths] + + if add_base_to_relative: + relative_paths = [os.path.join(base_name, p) for p in relative_paths] + + assert len(absolute_paths) == len(relative_paths) + result += zip(absolute_paths, relative_paths) + + return result + + +def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None: + """Takes in a list of tuples of (src, dst) paths and copies files. + Will create all necessary directories.""" + for file in files: + target_dir_name = os.path.dirname(file[1]) + + # will create all intermediate-level directories + if not os.path.exists(target_dir_name): + os.makedirs(target_dir_name) + + shutil.copyfile(file[0], file[1]) + + +# URL helpers +# ------------------------------------------------------------------------------------------ + +def is_url(obj: Any, allow_file_urls: bool = False) -> bool: + """Determine whether the given object is a valid URL string.""" + if not isinstance(obj, str) or not "://" in obj: + return False + if allow_file_urls and obj.startswith('file://'): + return True + try: + res = requests.compat.urlparse(obj) + if not res.scheme or not res.netloc or not "." in res.netloc: + return False + res = requests.compat.urlparse(requests.compat.urljoin(obj, "/")) + if not res.scheme or not res.netloc or not "." in res.netloc: + return False + except: + return False + return True + + +def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False, cache: bool = True) -> Any: + """Download the given URL and return a binary-mode file object to access the data.""" + assert num_attempts >= 1 + assert not (return_filename and (not cache)) + + # Doesn't look like an URL scheme so interpret it as a local filename. + if not re.match('^[a-z]+://', url): + return url if return_filename else open(url, "rb") + + # Handle file URLs. This code handles unusual file:// patterns that + # arise on Windows: + # + # file:///c:/foo.txt + # + # which would translate to a local '/c:/foo.txt' filename that's + # invalid. Drop the forward slash for such pathnames. + # + # If you touch this code path, you should test it on both Linux and + # Windows. + # + # Some internet resources suggest using urllib.request.url2pathname() but + # but that converts forward slashes to backslashes and this causes + # its own set of problems. + if url.startswith('file://'): + filename = urllib.parse.urlparse(url).path + if re.match(r'^/[a-zA-Z]:', filename): + filename = filename[1:] + return filename if return_filename else open(filename, "rb") + + assert is_url(url) + + # Lookup from cache. + if cache_dir is None: + cache_dir = make_cache_dir_path('downloads') + + url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest() + if cache: + cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*")) + if len(cache_files) == 1: + filename = cache_files[0] + return filename if return_filename else open(filename, "rb") + + # Download. + url_name = None + url_data = None + with requests.Session() as session: + if verbose: + print("Downloading %s ..." % url, end="", flush=True) + for attempts_left in reversed(range(num_attempts)): + try: + with session.get(url) as res: + res.raise_for_status() + if len(res.content) == 0: + raise IOError("No data received") + + if len(res.content) < 8192: + content_str = res.content.decode("utf-8") + if "download_warning" in res.headers.get("Set-Cookie", ""): + links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link] + if len(links) == 1: + url = requests.compat.urljoin(url, links[0]) + raise IOError("Google Drive virus checker nag") + if "Google Drive - Quota exceeded" in content_str: + raise IOError("Google Drive download quota exceeded -- please try again later") + + match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", "")) + url_name = match[1] if match else url + url_data = res.content + if verbose: + print(" done") + break + except: + if not attempts_left: + if verbose: + print(" failed") + raise + if verbose: + print(".", end="", flush=True) + + # Save to cache. + if cache: + safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name) + cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name) + temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name) + os.makedirs(cache_dir, exist_ok=True) + with open(temp_file, "wb") as f: + f.write(url_data) + os.replace(temp_file, cache_file) # atomic + if return_filename: + return cache_file + + # Return data as file object. + assert not return_filename + return io.BytesIO(url_data) diff --git a/PTI/models/StyleCLIP/global_directions/manipulate.py b/PTI/models/StyleCLIP/global_directions/manipulate.py new file mode 100644 index 0000000000000000000000000000000000000000..e1a2480caad8016fea0c06f0bfe521b25f084436 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/manipulate.py @@ -0,0 +1,278 @@ + + +import os +import os.path +import pickle +import numpy as np +import tensorflow as tf +from dnnlib import tflib +from global_directions.utils.visualizer import HtmlPageVisualizer + + +def Vis(bname,suffix,out,rownames=None,colnames=None): + num_images=out.shape[0] + step=out.shape[1] + + if colnames is None: + colnames=[f'Step {i:02d}' for i in range(1, step + 1)] + if rownames is None: + rownames=[str(i) for i in range(num_images)] + + + visualizer = HtmlPageVisualizer( + num_rows=num_images, num_cols=step + 1, viz_size=256) + visualizer.set_headers( + ['Name'] +colnames) + + for i in range(num_images): + visualizer.set_cell(i, 0, text=rownames[i]) + + for i in range(num_images): + for k in range(step): + image=out[i,k,:,:,:] + visualizer.set_cell(i, 1+k, image=image) + + # Save results. + visualizer.save(f'./html/'+bname+'_'+suffix+'.html') + + + + +def LoadData(img_path): + tmp=img_path+'S' + with open(tmp, "rb") as fp: #Pickling + s_names,all_s=pickle.load( fp) + dlatents=all_s + + pindexs=[] + mindexs=[] + for i in range(len(s_names)): + name=s_names[i] + if not('ToRGB' in name): + mindexs.append(i) + else: + pindexs.append(i) + + tmp=img_path+'S_mean_std' + with open(tmp, "rb") as fp: #Pickling + m,std=pickle.load( fp) + + return dlatents,s_names,mindexs,pindexs,m,std + + +def LoadModel(model_path,model_name): + # Initialize TensorFlow. + tflib.init_tf() + tmp=os.path.join(model_path,model_name) + with open(tmp, 'rb') as f: + _, _, Gs = pickle.load(f) + Gs.print_layers() + return Gs + +def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False): + """Convert a minibatch of images from float32 to uint8 with configurable dynamic range. + Can be used as an output transformation for Network.run(). + """ + if nchw_to_nhwc: + images = np.transpose(images, [0, 2, 3, 1]) + + scale = 255 / (drange[1] - drange[0]) + images = images * scale + (0.5 - drange[0] * scale) + + np.clip(images, 0, 255, out=images) + images=images.astype('uint8') + return images + + +def convert_images_from_uint8(images, drange=[-1,1], nhwc_to_nchw=False): + """Convert a minibatch of images from uint8 to float32 with configurable dynamic range. + Can be used as an input transformation for Network.run(). + """ + if nhwc_to_nchw: + images=np.rollaxis(images, 3, 1) + return images/ 255 *(drange[1] - drange[0])+ drange[0] + + +class Manipulator(): + def __init__(self,dataset_name='ffhq'): + self.file_path='./' + self.img_path=self.file_path+'npy/'+dataset_name+'/' + self.model_path=self.file_path+'model/' + self.dataset_name=dataset_name + self.model_name=dataset_name+'.pkl' + + self.alpha=[0] #manipulation strength + self.num_images=10 + self.img_index=0 #which image to start + self.viz_size=256 + self.manipulate_layers=None #which layer to manipulate, list + + self.dlatents,self.s_names,self.mindexs,self.pindexs,self.code_mean,self.code_std=LoadData(self.img_path) + + self.sess=tf.InteractiveSession() + init = tf.global_variables_initializer() + self.sess.run(init) + self.Gs=LoadModel(self.model_path,self.model_name) + self.num_layers=len(self.dlatents) + + self.Vis=Vis + self.noise_constant={} + + for i in range(len(self.s_names)): + tmp1=self.s_names[i].split('/') + if not 'ToRGB' in tmp1: + tmp1[-1]='random_normal:0' + size=int(tmp1[1].split('x')[0]) + tmp1='/'.join(tmp1) + tmp=(1,1,size,size) + self.noise_constant[tmp1]=np.random.random(tmp) + + tmp=self.Gs.components.synthesis.input_shape[1] + d={} + d['G_synthesis_1/dlatents_in:0']=np.zeros([1,tmp,512]) + names=list(self.noise_constant.keys()) + tmp=tflib.run(names,d) + for i in range(len(names)): + self.noise_constant[names[i]]=tmp[i] + + self.fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True) + self.img_size=self.Gs.output_shape[-1] + + def GenerateImg(self,codes): + + + num_images,step=codes[0].shape[:2] + + + out=np.zeros((num_images,step,self.img_size,self.img_size,3),dtype='uint8') + for i in range(num_images): + for k in range(step): + d={} + for m in range(len(self.s_names)): + d[self.s_names[m]]=codes[m][i,k][None,:] #need to change + d['G_synthesis_1/4x4/Const/Shape:0']=np.array([1,18, 512], dtype=np.int32) + d.update(self.noise_constant) + img=tflib.run('G_synthesis_1/images_out:0', d) + image=convert_images_to_uint8(img, nchw_to_nhwc=True) + out[i,k,:,:,:]=image[0] + return out + + + + def MSCode(self,dlatent_tmp,boundary_tmp): + + step=len(self.alpha) + dlatent_tmp1=[tmp.reshape((self.num_images,-1)) for tmp in dlatent_tmp] + dlatent_tmp2=[np.tile(tmp[:,None],(1,step,1)) for tmp in dlatent_tmp1] # (10, 7, 512) + + l=np.array(self.alpha) + l=l.reshape( + [step if axis == 1 else 1 for axis in range(dlatent_tmp2[0].ndim)]) + + if type(self.manipulate_layers)==int: + tmp=[self.manipulate_layers] + elif type(self.manipulate_layers)==list: + tmp=self.manipulate_layers + elif self.manipulate_layers is None: + tmp=np.arange(len(boundary_tmp)) + else: + raise ValueError('manipulate_layers is wrong') + + for i in tmp: + dlatent_tmp2[i]+=l*boundary_tmp[i] + + codes=[] + for i in range(len(dlatent_tmp2)): + tmp=list(dlatent_tmp[i].shape) + tmp.insert(1,step) + codes.append(dlatent_tmp2[i].reshape(tmp)) + return codes + + + def EditOne(self,bname,dlatent_tmp=None): + if dlatent_tmp==None: + dlatent_tmp=[tmp[self.img_index:(self.img_index+self.num_images)] for tmp in self.dlatents] + + boundary_tmp=[] + for i in range(len(self.boundary)): + tmp=self.boundary[i] + if len(tmp)<=bname: + boundary_tmp.append([]) + else: + boundary_tmp.append(tmp[bname]) + + codes=self.MSCode(dlatent_tmp,boundary_tmp) + + out=self.GenerateImg(codes) + return codes,out + + def EditOneC(self,cindex,dlatent_tmp=None): + if dlatent_tmp==None: + dlatent_tmp=[tmp[self.img_index:(self.img_index+self.num_images)] for tmp in self.dlatents] + + boundary_tmp=[[] for i in range(len(self.dlatents))] + + #'only manipulate 1 layer and one channel' + assert len(self.manipulate_layers)==1 + + ml=self.manipulate_layers[0] + tmp=dlatent_tmp[ml].shape[1] #ada + tmp1=np.zeros(tmp) + tmp1[cindex]=self.code_std[ml][cindex] #1 + boundary_tmp[ml]=tmp1 + + codes=self.MSCode(dlatent_tmp,boundary_tmp) + out=self.GenerateImg(codes) + return codes,out + + + def W2S(self,dlatent_tmp): + + all_s = self.sess.run( + self.s_names, + feed_dict={'G_synthesis_1/dlatents_in:0': dlatent_tmp}) + return all_s + + + + + + + + +#%% +if __name__ == "__main__": + + + M=Manipulator(dataset_name='ffhq') + + + #%% + M.alpha=[-5,0,5] + M.num_images=20 + lindex,cindex=6,501 + + M.manipulate_layers=[lindex] + codes,out=M.EditOneC(cindex) #dlatent_tmp + tmp=str(M.manipulate_layers)+'_'+str(cindex) + M.Vis(tmp,'c',out) + + + + + + + + + + + + + + + + + + + + diff --git a/PTI/models/StyleCLIP/global_directions/utils/__init__.py b/PTI/models/StyleCLIP/global_directions/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/models/StyleCLIP/global_directions/utils/editor.py b/PTI/models/StyleCLIP/global_directions/utils/editor.py new file mode 100644 index 0000000000000000000000000000000000000000..b1c2ac56fd7b4b127f948c6b8cf15874a8fe9d93 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/utils/editor.py @@ -0,0 +1,507 @@ +# python 3.7 +"""Utility functions for image editing from latent space.""" + +import os.path +import numpy as np + +__all__ = [ + 'parse_indices', 'interpolate', 'mix_style', + 'get_layerwise_manipulation_strength', 'manipulate', 'parse_boundary_list' +] + + +def parse_indices(obj, min_val=None, max_val=None): + """Parses indices. + + If the input is a list or tuple, this function has no effect. + + The input can also be a string, which is either a comma separated list of + numbers 'a, b, c', or a dash separated range 'a - c'. Space in the string will + be ignored. + + Args: + obj: The input object to parse indices from. + min_val: If not `None`, this function will check that all indices are equal + to or larger than this value. (default: None) + max_val: If not `None`, this function will check that all indices are equal + to or smaller than this field. (default: None) + + Returns: + A list of integers. + + Raises: + If the input is invalid, i.e., neither a list or tuple, nor a string. + """ + if obj is None or obj == '': + indices = [] + elif isinstance(obj, int): + indices = [obj] + elif isinstance(obj, (list, tuple, np.ndarray)): + indices = list(obj) + elif isinstance(obj, str): + indices = [] + splits = obj.replace(' ', '').split(',') + for split in splits: + numbers = list(map(int, split.split('-'))) + if len(numbers) == 1: + indices.append(numbers[0]) + elif len(numbers) == 2: + indices.extend(list(range(numbers[0], numbers[1] + 1))) + else: + raise ValueError(f'Invalid type of input: {type(obj)}!') + + assert isinstance(indices, list) + indices = sorted(list(set(indices))) + for idx in indices: + assert isinstance(idx, int) + if min_val is not None: + assert idx >= min_val, f'{idx} is smaller than min val `{min_val}`!' + if max_val is not None: + assert idx <= max_val, f'{idx} is larger than max val `{max_val}`!' + + return indices + + +def interpolate(src_codes, dst_codes, step=5): + """Interpolates two sets of latent codes linearly. + + Args: + src_codes: Source codes, with shape [num, *code_shape]. + dst_codes: Target codes, with shape [num, *code_shape]. + step: Number of interplolation steps, with source and target included. For + example, if `step = 5`, three more samples will be inserted. (default: 5) + + Returns: + Interpolated codes, with shape [num, step, *code_shape]. + + Raises: + ValueError: If the input two sets of latent codes are with different shapes. + """ + if not (src_codes.ndim >= 2 and src_codes.shape == dst_codes.shape): + raise ValueError(f'Shapes of source codes and target codes should both be ' + f'[num, *code_shape], but {src_codes.shape} and ' + f'{dst_codes.shape} are received!') + num = src_codes.shape[0] + code_shape = src_codes.shape[1:] + + a = src_codes[:, np.newaxis] + b = dst_codes[:, np.newaxis] + l = np.linspace(0.0, 1.0, step).reshape( + [step if axis == 1 else 1 for axis in range(a.ndim)]) + results = a + l * (b - a) + assert results.shape == (num, step, *code_shape) + + return results + + +def mix_style(style_codes, + content_codes, + num_layers=1, + mix_layers=None, + is_style_layerwise=True, + is_content_layerwise=True): + """Mixes styles from style codes to those of content codes. + + Each style code or content code consists of `num_layers` codes, each of which + is typically fed into a particular layer of the generator. This function mixes + styles by partially replacing the codes of `content_codes` from some certain + layers with those of `style_codes`. + + For example, if both style code and content code are with shape [10, 512], + meaning to have 10 layers and each employs a 512-dimensional latent code. And + the 1st, 2nd, and 3rd layers are the target layers to perform style mixing. + Then the top half of the content code (with shape [3, 512]) will be replaced + by the top half of the style code (also with shape [3, 512]). + + NOTE: This function also supports taking single-layer latent codes as inputs, + i.e., setting `is_style_layerwise` or `is_content_layerwise` as False. In this + case, the corresponding code will be first repeated for `num_layers` before + performing style mixing. + + Args: + style_codes: Style codes, with shape [num_styles, *code_shape] or + [num_styles, num_layers, *code_shape]. + content_codes: Content codes, with shape [num_contents, *code_shape] or + [num_contents, num_layers, *code_shape]. + num_layers: Total number of layers in the generative model. (default: 1) + mix_layers: Indices of the layers to perform style mixing. `None` means to + replace all layers, in which case the content code will be completely + replaced by style code. (default: None) + is_style_layerwise: Indicating whether the input `style_codes` are + layer-wise codes. (default: True) + is_content_layerwise: Indicating whether the input `content_codes` are + layer-wise codes. (default: True) + num_layers + + Returns: + Codes after style mixing, with shape [num_styles, num_contents, num_layers, + *code_shape]. + + Raises: + ValueError: If input `content_codes` or `style_codes` is with invalid shape. + """ + if not is_style_layerwise: + style_codes = style_codes[:, np.newaxis] + style_codes = np.tile( + style_codes, + [num_layers if axis == 1 else 1 for axis in range(style_codes.ndim)]) + if not is_content_layerwise: + content_codes = content_codes[:, np.newaxis] + content_codes = np.tile( + content_codes, + [num_layers if axis == 1 else 1 for axis in range(content_codes.ndim)]) + + if not (style_codes.ndim >= 3 and style_codes.shape[1] == num_layers and + style_codes.shape[1:] == content_codes.shape[1:]): + raise ValueError(f'Shapes of style codes and content codes should be ' + f'[num_styles, num_layers, *code_shape] and ' + f'[num_contents, num_layers, *code_shape] respectively, ' + f'but {style_codes.shape} and {content_codes.shape} are ' + f'received!') + + layer_indices = parse_indices(mix_layers, min_val=0, max_val=num_layers - 1) + if not layer_indices: + layer_indices = list(range(num_layers)) + + num_styles = style_codes.shape[0] + num_contents = content_codes.shape[0] + code_shape = content_codes.shape[2:] + + s = style_codes[:, np.newaxis] + s = np.tile(s, [num_contents if axis == 1 else 1 for axis in range(s.ndim)]) + c = content_codes[np.newaxis] + c = np.tile(c, [num_styles if axis == 0 else 1 for axis in range(c.ndim)]) + + from_style = np.zeros(s.shape, dtype=bool) + from_style[:, :, layer_indices] = True + results = np.where(from_style, s, c) + assert results.shape == (num_styles, num_contents, num_layers, *code_shape) + + return results + + +def get_layerwise_manipulation_strength(num_layers, + truncation_psi, + truncation_layers): + """Gets layer-wise strength for manipulation. + + Recall the truncation trick played on layer [0, truncation_layers): + + w = truncation_psi * w + (1 - truncation_psi) * w_avg + + So, when using the same boundary to manipulate different layers, layer + [0, truncation_layers) and layer [truncation_layers, num_layers) should use + different strength to eliminate the effect from the truncation trick. More + concretely, the strength for layer [0, truncation_layers) is set as + `truncation_psi`, while that for other layers are set as 1. + """ + strength = [1.0 for _ in range(num_layers)] + if truncation_layers > 0: + for layer_idx in range(0, truncation_layers): + strength[layer_idx] = truncation_psi + return strength + + +def manipulate(latent_codes, + boundary, + start_distance=-5.0, + end_distance=5.0, + step=21, + layerwise_manipulation=False, + num_layers=1, + manipulate_layers=None, + is_code_layerwise=False, + is_boundary_layerwise=False, + layerwise_manipulation_strength=1.0): + """Manipulates the given latent codes with respect to a particular boundary. + + Basically, this function takes a set of latent codes and a boundary as inputs, + and outputs a collection of manipulated latent codes. + + For example, let `step` to be 10, `latent_codes` to be with shape [num, + *code_shape], and `boundary` to be with shape [1, *code_shape] and unit norm. + Then the output will be with shape [num, 10, *code_shape]. For each 10-element + manipulated codes, the first code is `start_distance` away from the original + code (i.e., the input) along the `boundary` direction, while the last code is + `end_distance` away. Remaining codes are linearly interpolated. Here, + `distance` is sign sensitive. + + NOTE: This function also supports layer-wise manipulation, in which case the + generator should be able to take layer-wise latent codes as inputs. For + example, if the generator has 18 convolutional layers in total, and each of + which takes an independent latent code as input. It is possible, sometimes + with even better performance, to only partially manipulate these latent codes + corresponding to some certain layers yet keeping others untouched. + + NOTE: Boundary is assumed to be normalized to unit norm already. + + Args: + latent_codes: The input latent codes for manipulation, with shape + [num, *code_shape] or [num, num_layers, *code_shape]. + boundary: The semantic boundary as reference, with shape [1, *code_shape] or + [1, num_layers, *code_shape]. + start_distance: Start point for manipulation. (default: -5.0) + end_distance: End point for manipulation. (default: 5.0) + step: Number of manipulation steps. (default: 21) + layerwise_manipulation: Whether to perform layer-wise manipulation. + (default: False) + num_layers: Number of layers. Only active when `layerwise_manipulation` is + set as `True`. Should be a positive integer. (default: 1) + manipulate_layers: Indices of the layers to perform manipulation. `None` + means to manipulate latent codes from all layers. (default: None) + is_code_layerwise: Whether the input latent codes are layer-wise. If set as + `False`, the function will first repeat the input codes for `num_layers` + times before perform manipulation. (default: False) + is_boundary_layerwise: Whether the input boundary is layer-wise. If set as + `False`, the function will first repeat boundary for `num_layers` times + before perform manipulation. (default: False) + layerwise_manipulation_strength: Manipulation strength for each layer. Only + active when `layerwise_manipulation` is set as `True`. This field can be + used to resolve the strength discrepancy across layers when truncation + trick is on. See function `get_layerwise_manipulation_strength()` for + details. A tuple, list, or `numpy.ndarray` is expected. If set as a single + number, this strength will be used for all layers. (default: 1.0) + + Returns: + Manipulated codes, with shape [num, step, *code_shape] if + `layerwise_manipulation` is set as `False`, or shape [num, step, + num_layers, *code_shape] if `layerwise_manipulation` is set as `True`. + + Raises: + ValueError: If the input latent codes, boundary, or strength are with + invalid shape. + """ + if not (boundary.ndim >= 2 and boundary.shape[0] == 1): + raise ValueError(f'Boundary should be with shape [1, *code_shape] or ' + f'[1, num_layers, *code_shape], but ' + f'{boundary.shape} is received!') + + if not layerwise_manipulation: + assert not is_code_layerwise + assert not is_boundary_layerwise + num_layers = 1 + manipulate_layers = None + layerwise_manipulation_strength = 1.0 + + # Preprocessing for layer-wise manipulation. + # Parse indices of manipulation layers. + layer_indices = parse_indices( + manipulate_layers, min_val=0, max_val=num_layers - 1) + if not layer_indices: + layer_indices = list(range(num_layers)) + # Make latent codes layer-wise if needed. + assert num_layers > 0 + if not is_code_layerwise: + x = latent_codes[:, np.newaxis] + x = np.tile(x, [num_layers if axis == 1 else 1 for axis in range(x.ndim)]) + else: + x = latent_codes + if x.shape[1] != num_layers: + raise ValueError(f'Latent codes should be with shape [num, num_layers, ' + f'*code_shape], where `num_layers` equals to ' + f'{num_layers}, but {x.shape} is received!') + # Make boundary layer-wise if needed. + if not is_boundary_layerwise: + b = boundary + b = np.tile(b, [num_layers if axis == 0 else 1 for axis in range(b.ndim)]) + else: + b = boundary[0] + if b.shape[0] != num_layers: + raise ValueError(f'Boundary should be with shape [num_layers, ' + f'*code_shape], where `num_layers` equals to ' + f'{num_layers}, but {b.shape} is received!') + # Get layer-wise manipulation strength. + if isinstance(layerwise_manipulation_strength, (int, float)): + s = [float(layerwise_manipulation_strength) for _ in range(num_layers)] + elif isinstance(layerwise_manipulation_strength, (list, tuple)): + s = layerwise_manipulation_strength + if len(s) != num_layers: + raise ValueError(f'Shape of layer-wise manipulation strength `{len(s)}` ' + f'mismatches number of layers `{num_layers}`!') + elif isinstance(layerwise_manipulation_strength, np.ndarray): + s = layerwise_manipulation_strength + if s.size != num_layers: + raise ValueError(f'Shape of layer-wise manipulation strength `{s.size}` ' + f'mismatches number of layers `{num_layers}`!') + else: + raise ValueError(f'Unsupported type of `layerwise_manipulation_strength`!') + s = np.array(s).reshape( + [num_layers if axis == 0 else 1 for axis in range(b.ndim)]) + b = b * s + + if x.shape[1:] != b.shape: + raise ValueError(f'Latent code shape {x.shape} and boundary shape ' + f'{b.shape} mismatch!') + num = x.shape[0] + code_shape = x.shape[2:] + + x = x[:, np.newaxis] + b = b[np.newaxis, np.newaxis, :] + l = np.linspace(start_distance, end_distance, step).reshape( + [step if axis == 1 else 1 for axis in range(x.ndim)]) + results = np.tile(x, [step if axis == 1 else 1 for axis in range(x.ndim)]) + is_manipulatable = np.zeros(results.shape, dtype=bool) + is_manipulatable[:, :, layer_indices] = True + results = np.where(is_manipulatable, x + l * b, results) + assert results.shape == (num, step, num_layers, *code_shape) + + return results if layerwise_manipulation else results[:, :, 0] + + +def manipulate2(latent_codes, + proj, + mindex, + start_distance=-5.0, + end_distance=5.0, + step=21, + layerwise_manipulation=False, + num_layers=1, + manipulate_layers=None, + is_code_layerwise=False, + layerwise_manipulation_strength=1.0): + + + if not layerwise_manipulation: + assert not is_code_layerwise +# assert not is_boundary_layerwise + num_layers = 1 + manipulate_layers = None + layerwise_manipulation_strength = 1.0 + + # Preprocessing for layer-wise manipulation. + # Parse indices of manipulation layers. + layer_indices = parse_indices( + manipulate_layers, min_val=0, max_val=num_layers - 1) + if not layer_indices: + layer_indices = list(range(num_layers)) + # Make latent codes layer-wise if needed. + assert num_layers > 0 + if not is_code_layerwise: + x = latent_codes[:, np.newaxis] + x = np.tile(x, [num_layers if axis == 1 else 1 for axis in range(x.ndim)]) + else: + x = latent_codes + if x.shape[1] != num_layers: + raise ValueError(f'Latent codes should be with shape [num, num_layers, ' + f'*code_shape], where `num_layers` equals to ' + f'{num_layers}, but {x.shape} is received!') + # Make boundary layer-wise if needed. +# if not is_boundary_layerwise: +# b = boundary +# b = np.tile(b, [num_layers if axis == 0 else 1 for axis in range(b.ndim)]) +# else: +# b = boundary[0] +# if b.shape[0] != num_layers: +# raise ValueError(f'Boundary should be with shape [num_layers, ' +# f'*code_shape], where `num_layers` equals to ' +# f'{num_layers}, but {b.shape} is received!') + # Get layer-wise manipulation strength. + if isinstance(layerwise_manipulation_strength, (int, float)): + s = [float(layerwise_manipulation_strength) for _ in range(num_layers)] + elif isinstance(layerwise_manipulation_strength, (list, tuple)): + s = layerwise_manipulation_strength + if len(s) != num_layers: + raise ValueError(f'Shape of layer-wise manipulation strength `{len(s)}` ' + f'mismatches number of layers `{num_layers}`!') + elif isinstance(layerwise_manipulation_strength, np.ndarray): + s = layerwise_manipulation_strength + if s.size != num_layers: + raise ValueError(f'Shape of layer-wise manipulation strength `{s.size}` ' + f'mismatches number of layers `{num_layers}`!') + else: + raise ValueError(f'Unsupported type of `layerwise_manipulation_strength`!') +# s = np.array(s).reshape( +# [num_layers if axis == 0 else 1 for axis in range(b.ndim)]) +# b = b * s + +# if x.shape[1:] != b.shape: +# raise ValueError(f'Latent code shape {x.shape} and boundary shape ' +# f'{b.shape} mismatch!') + num = x.shape[0] + code_shape = x.shape[2:] + + x = x[:, np.newaxis] +# b = b[np.newaxis, np.newaxis, :] +# l = np.linspace(start_distance, end_distance, step).reshape( +# [step if axis == 1 else 1 for axis in range(x.ndim)]) + results = np.tile(x, [step if axis == 1 else 1 for axis in range(x.ndim)]) + is_manipulatable = np.zeros(results.shape, dtype=bool) + is_manipulatable[:, :, layer_indices] = True + + tmp=MPC(proj,x,mindex,start_distance,end_distance,step) + tmp = tmp[:, :,np.newaxis] + tmp1 = np.tile(tmp, [num_layers if axis == 2 else 1 for axis in range(tmp.ndim)]) + + + results = np.where(is_manipulatable, tmp1, results) +# print(results.shape) + assert results.shape == (num, step, num_layers, *code_shape) + return results if layerwise_manipulation else results[:, :, 0] + +def MPC(proj,x,mindex,start_distance,end_distance,step): + # x shape (batch_size,1,num_layers,feature) +# print(x.shape) + x1=proj.transform(x[:,0,0,:]) #/np.sqrt(proj.explained_variance_) # (batch_size,num_pc) + + x1 = x1[:, np.newaxis] + x1 = np.tile(x1, [step if axis == 1 else 1 for axis in range(x1.ndim)]) + + + l = np.linspace(start_distance, end_distance, step)[None,:] + x1[:,:,mindex]+=l + + tmp=x1.reshape((-1,x1.shape[-1])) #*np.sqrt(proj.explained_variance_) +# print('xxx') + x2=proj.inverse_transform(tmp) + x2=x2.reshape((x1.shape[0],x1.shape[1],-1)) + +# x1 = x1[:, np.newaxis] +# x1 = np.tile(x1, [step if axis == 1 else 1 for axis in range(x1.ndim)]) + + return x2 + + + + +def parse_boundary_list(boundary_list_path): + """Parses boundary list. + + Sometimes, a text file containing a list of boundaries will significantly + simplify image manipulation with a large amount of boundaries. This function + is used to parse boundary information from such list file. + + Basically, each item in the list should be with format + `($NAME, $SPACE_TYPE): $PATH`. `DISABLE` at the beginning of the line can + disable a particular boundary. + + Sample: + + (age, z): $AGE_BOUNDARY_PATH + (gender, w): $GENDER_BOUNDARY_PATH + DISABLE(pose, wp): $POSE_BOUNDARY_PATH + + Args: + boundary_list_path: Path to the boundary list. + + Returns: + A dictionary, whose key is a two-element tuple (boundary_name, space_type) + and value is the corresponding boundary path. + + Raise: + ValueError: If the given boundary list does not exist. + """ + if not os.path.isfile(boundary_list_path): + raise ValueError(f'Boundary list `boundary_list_path` does not exist!') + + boundaries = {} + with open(boundary_list_path, 'r') as f: + for line in f: + if line[:len('DISABLE')] == 'DISABLE': + continue + boundary_info, boundary_path = line.strip().split(':') + boundary_name, space_type = boundary_info.strip()[1:-1].split(',') + boundary_name = boundary_name.strip() + space_type = space_type.strip().lower() + boundary_path = boundary_path.strip() + boundaries[(boundary_name, space_type)] = boundary_path + return boundaries diff --git a/PTI/models/StyleCLIP/global_directions/utils/train_boundary.py b/PTI/models/StyleCLIP/global_directions/utils/train_boundary.py new file mode 100644 index 0000000000000000000000000000000000000000..710d062bc4b42913fcc5b12bd545e47af00c7123 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/utils/train_boundary.py @@ -0,0 +1,158 @@ + +import numpy as np +from sklearn import svm + + + + + +def train_boundary(latent_codes, + scores, + chosen_num_or_ratio=0.02, + split_ratio=0.7, + invalid_value=None, + logger=None, + logger_name='train_boundary'): + """Trains boundary in latent space with offline predicted attribute scores. + + Given a collection of latent codes and the attribute scores predicted from the + corresponding images, this function will train a linear SVM by treating it as + a bi-classification problem. Basically, the samples with highest attribute + scores are treated as positive samples, while those with lowest scores as + negative. For now, the latent code can ONLY be with 1 dimension. + + NOTE: The returned boundary is with shape (1, latent_space_dim), and also + normalized with unit norm. + + Args: + latent_codes: Input latent codes as training data. + scores: Input attribute scores used to generate training labels. + chosen_num_or_ratio: How many samples will be chosen as positive (negative) + samples. If this field lies in range (0, 0.5], `chosen_num_or_ratio * + latent_codes_num` will be used. Otherwise, `min(chosen_num_or_ratio, + 0.5 * latent_codes_num)` will be used. (default: 0.02) + split_ratio: Ratio to split training and validation sets. (default: 0.7) + invalid_value: This field is used to filter out data. (default: None) + logger: Logger for recording log messages. If set as `None`, a default + logger, which prints messages from all levels to screen, will be created. + (default: None) + + Returns: + A decision boundary with type `numpy.ndarray`. + + Raises: + ValueError: If the input `latent_codes` or `scores` are with invalid format. + """ +# if not logger: +# logger = setup_logger(work_dir='', logger_name=logger_name) + + if (not isinstance(latent_codes, np.ndarray) or + not len(latent_codes.shape) == 2): + raise ValueError(f'Input `latent_codes` should be with type' + f'`numpy.ndarray`, and shape [num_samples, ' + f'latent_space_dim]!') + num_samples = latent_codes.shape[0] + latent_space_dim = latent_codes.shape[1] + if (not isinstance(scores, np.ndarray) or not len(scores.shape) == 2 or + not scores.shape[0] == num_samples or not scores.shape[1] == 1): + raise ValueError(f'Input `scores` should be with type `numpy.ndarray`, and ' + f'shape [num_samples, 1], where `num_samples` should be ' + f'exactly same as that of input `latent_codes`!') + if chosen_num_or_ratio <= 0: + raise ValueError(f'Input `chosen_num_or_ratio` should be positive, ' + f'but {chosen_num_or_ratio} received!') + +# logger.info(f'Filtering training data.') + print('Filtering training data.') + if invalid_value is not None: + latent_codes = latent_codes[scores[:, 0] != invalid_value] + scores = scores[scores[:, 0] != invalid_value] + +# logger.info(f'Sorting scores to get positive and negative samples.') + print('Sorting scores to get positive and negative samples.') + + sorted_idx = np.argsort(scores, axis=0)[::-1, 0] + latent_codes = latent_codes[sorted_idx] + scores = scores[sorted_idx] + num_samples = latent_codes.shape[0] + if 0 < chosen_num_or_ratio <= 1: + chosen_num = int(num_samples * chosen_num_or_ratio) + else: + chosen_num = int(chosen_num_or_ratio) + chosen_num = min(chosen_num, num_samples // 2) + +# logger.info(f'Spliting training and validation sets:') + print('Filtering training data.') + + train_num = int(chosen_num * split_ratio) + val_num = chosen_num - train_num + # Positive samples. + positive_idx = np.arange(chosen_num) + np.random.shuffle(positive_idx) + positive_train = latent_codes[:chosen_num][positive_idx[:train_num]] + positive_val = latent_codes[:chosen_num][positive_idx[train_num:]] + # Negative samples. + negative_idx = np.arange(chosen_num) + np.random.shuffle(negative_idx) + negative_train = latent_codes[-chosen_num:][negative_idx[:train_num]] + negative_val = latent_codes[-chosen_num:][negative_idx[train_num:]] + # Training set. + train_data = np.concatenate([positive_train, negative_train], axis=0) + train_label = np.concatenate([np.ones(train_num, dtype=np.int), + np.zeros(train_num, dtype=np.int)], axis=0) +# logger.info(f' Training: {train_num} positive, {train_num} negative.') + print(f' Training: {train_num} positive, {train_num} negative.') + # Validation set. + val_data = np.concatenate([positive_val, negative_val], axis=0) + val_label = np.concatenate([np.ones(val_num, dtype=np.int), + np.zeros(val_num, dtype=np.int)], axis=0) +# logger.info(f' Validation: {val_num} positive, {val_num} negative.') + print(f' Validation: {val_num} positive, {val_num} negative.') + + # Remaining set. + remaining_num = num_samples - chosen_num * 2 + remaining_data = latent_codes[chosen_num:-chosen_num] + remaining_scores = scores[chosen_num:-chosen_num] + decision_value = (scores[0] + scores[-1]) / 2 + remaining_label = np.ones(remaining_num, dtype=np.int) + remaining_label[remaining_scores.ravel() < decision_value] = 0 + remaining_positive_num = np.sum(remaining_label == 1) + remaining_negative_num = np.sum(remaining_label == 0) +# logger.info(f' Remaining: {remaining_positive_num} positive, ' +# f'{remaining_negative_num} negative.') + print(f' Remaining: {remaining_positive_num} positive, ' + f'{remaining_negative_num} negative.') +# logger.info(f'Training boundary.') + print(f'Training boundary.') + + clf = svm.SVC(kernel='linear') + classifier = clf.fit(train_data, train_label) +# logger.info(f'Finish training.') + print(f'Finish training.') + + + if val_num: + val_prediction = classifier.predict(val_data) + correct_num = np.sum(val_label == val_prediction) +# logger.info(f'Accuracy for validation set: ' +# f'{correct_num} / {val_num * 2} = ' +# f'{correct_num / (val_num * 2):.6f}') + print(f'Accuracy for validation set: ' + f'{correct_num} / {val_num * 2} = ' + f'{correct_num / (val_num * 2):.6f}') + vacc=correct_num/len(val_label) + ''' + if remaining_num: + remaining_prediction = classifier.predict(remaining_data) + correct_num = np.sum(remaining_label == remaining_prediction) + logger.info(f'Accuracy for remaining set: ' + f'{correct_num} / {remaining_num} = ' + f'{correct_num / remaining_num:.6f}') + ''' + a = classifier.coef_.reshape(1, latent_space_dim).astype(np.float32) + return a / np.linalg.norm(a),vacc + + + + + diff --git a/PTI/models/StyleCLIP/global_directions/utils/visualizer.py b/PTI/models/StyleCLIP/global_directions/utils/visualizer.py new file mode 100644 index 0000000000000000000000000000000000000000..8c4a1fba06bf6bc680aa59bf645f796283f6f1c6 --- /dev/null +++ b/PTI/models/StyleCLIP/global_directions/utils/visualizer.py @@ -0,0 +1,605 @@ +# python 3.7 +"""Utility functions for visualizing results on html page.""" + +import base64 +import os.path +import cv2 +import numpy as np + +__all__ = [ + 'get_grid_shape', 'get_blank_image', 'load_image', 'save_image', + 'resize_image', 'add_text_to_image', 'fuse_images', 'HtmlPageVisualizer', + 'VideoReader', 'VideoWriter', 'adjust_pixel_range' +] + + +def adjust_pixel_range(images, min_val=-1.0, max_val=1.0, channel_order='NCHW'): + """Adjusts the pixel range of the input images. + + This function assumes the input array (image batch) is with shape [batch_size, + channel, height, width] if `channel_order = NCHW`, or with shape [batch_size, + height, width] if `channel_order = NHWC`. The returned images are with shape + [batch_size, height, width, channel] and pixel range [0, 255]. + + NOTE: The channel order of output images will remain the same as the input. + + Args: + images: Input images to adjust pixel range. + min_val: Min value of the input images. (default: -1.0) + max_val: Max value of the input images. (default: 1.0) + channel_order: Channel order of the input array. (default: NCHW) + + Returns: + The postprocessed images with dtype `numpy.uint8` and range [0, 255]. + + Raises: + ValueError: If the input `images` are not with type `numpy.ndarray` or the + shape is invalid according to `channel_order`. + """ + if not isinstance(images, np.ndarray): + raise ValueError(f'Images should be with type `numpy.ndarray`!') + + channel_order = channel_order.upper() + if channel_order not in ['NCHW', 'NHWC']: + raise ValueError(f'Invalid channel order `{channel_order}`!') + + if images.ndim != 4: + raise ValueError(f'Input images are expected to be with shape `NCHW` or ' + f'`NHWC`, but `{images.shape}` is received!') + if channel_order == 'NCHW' and images.shape[1] not in [1, 3]: + raise ValueError(f'Input images should have 1 or 3 channels under `NCHW` ' + f'channel order!') + if channel_order == 'NHWC' and images.shape[3] not in [1, 3]: + raise ValueError(f'Input images should have 1 or 3 channels under `NHWC` ' + f'channel order!') + + images = images.astype(np.float32) + images = (images - min_val) * 255 / (max_val - min_val) + images = np.clip(images + 0.5, 0, 255).astype(np.uint8) + if channel_order == 'NCHW': + images = images.transpose(0, 2, 3, 1) + + return images + + +def get_grid_shape(size, row=0, col=0, is_portrait=False): + """Gets the shape of a grid based on the size. + + This function makes greatest effort on making the output grid square if + neither `row` nor `col` is set. If `is_portrait` is set as `False`, the height + will always be equal to or smaller than the width. For example, if input + `size = 16`, output shape will be `(4, 4)`; if input `size = 15`, output shape + will be (3, 5). Otherwise, the height will always be equal to or larger than + the width. + + Args: + size: Size (height * width) of the target grid. + is_portrait: Whether to return a portrait size of a landscape size. + (default: False) + + Returns: + A two-element tuple, representing height and width respectively. + """ + assert isinstance(size, int) + assert isinstance(row, int) + assert isinstance(col, int) + if size == 0: + return (0, 0) + + if row > 0 and col > 0 and row * col != size: + row = 0 + col = 0 + + if row > 0 and size % row == 0: + return (row, size // row) + if col > 0 and size % col == 0: + return (size // col, col) + + row = int(np.sqrt(size)) + while row > 0: + if size % row == 0: + col = size // row + break + row = row - 1 + + return (col, row) if is_portrait else (row, col) + + +def get_blank_image(height, width, channels=3, is_black=True): + """Gets a blank image, either white of black. + + NOTE: This function will always return an image with `RGB` channel order for + color image and pixel range [0, 255]. + + Args: + height: Height of the returned image. + width: Width of the returned image. + channels: Number of channels. (default: 3) + is_black: Whether to return a black image or white image. (default: True) + """ + shape = (height, width, channels) + if is_black: + return np.zeros(shape, dtype=np.uint8) + return np.ones(shape, dtype=np.uint8) * 255 + + +def load_image(path): + """Loads an image from disk. + + NOTE: This function will always return an image with `RGB` channel order for + color image and pixel range [0, 255]. + + Args: + path: Path to load the image from. + + Returns: + An image with dtype `np.ndarray` or `None` if input `path` does not exist. + """ + if not os.path.isfile(path): + return None + + image = cv2.imread(path) + return image[:, :, ::-1] + + +def save_image(path, image): + """Saves an image to disk. + + NOTE: The input image (if colorful) is assumed to be with `RGB` channel order + and pixel range [0, 255]. + + Args: + path: Path to save the image to. + image: Image to save. + """ + if image is None: + return + + assert len(image.shape) == 3 and image.shape[2] in [1, 3] + cv2.imwrite(path, image[:, :, ::-1]) + + +def resize_image(image, *args, **kwargs): + """Resizes image. + + This is a wrap of `cv2.resize()`. + + NOTE: THe channel order of the input image will not be changed. + + Args: + image: Image to resize. + """ + if image is None: + return None + + assert image.ndim == 3 and image.shape[2] in [1, 3] + image = cv2.resize(image, *args, **kwargs) + if image.ndim == 2: + return image[:, :, np.newaxis] + return image + + +def add_text_to_image(image, + text='', + position=None, + font=cv2.FONT_HERSHEY_TRIPLEX, + font_size=1.0, + line_type=cv2.LINE_8, + line_width=1, + color=(255, 255, 255)): + """Overlays text on given image. + + NOTE: The input image is assumed to be with `RGB` channel order. + + Args: + image: The image to overlay text on. + text: Text content to overlay on the image. (default: '') + position: Target position (bottom-left corner) to add text. If not set, + center of the image will be used by default. (default: None) + font: Font of the text added. (default: cv2.FONT_HERSHEY_TRIPLEX) + font_size: Font size of the text added. (default: 1.0) + line_type: Line type used to depict the text. (default: cv2.LINE_8) + line_width: Line width used to depict the text. (default: 1) + color: Color of the text added in `RGB` channel order. (default: + (255, 255, 255)) + + Returns: + An image with target text overlayed on. + """ + if image is None or not text: + return image + + cv2.putText(img=image, + text=text, + org=position, + fontFace=font, + fontScale=font_size, + color=color, + thickness=line_width, + lineType=line_type, + bottomLeftOrigin=False) + + return image + + +def fuse_images(images, + image_size=None, + row=0, + col=0, + is_row_major=True, + is_portrait=False, + row_spacing=0, + col_spacing=0, + border_left=0, + border_right=0, + border_top=0, + border_bottom=0, + black_background=True): + """Fuses a collection of images into an entire image. + + Args: + images: A collection of images to fuse. Should be with shape [num, height, + width, channels]. + image_size: Int or two-element tuple. This field is used to resize the image + before fusing. `None` disables resizing. (default: None) + row: Number of rows used for image fusion. If not set, this field will be + automatically assigned based on `col` and total number of images. + (default: None) + col: Number of columns used for image fusion. If not set, this field will be + automatically assigned based on `row` and total number of images. + (default: None) + is_row_major: Whether the input images should be arranged row-major or + column-major. (default: True) + is_portrait: Only active when both `row` and `col` should be assigned + automatically. (default: False) + row_spacing: Space between rows. (default: 0) + col_spacing: Space between columns. (default: 0) + border_left: Width of left border. (default: 0) + border_right: Width of right border. (default: 0) + border_top: Width of top border. (default: 0) + border_bottom: Width of bottom border. (default: 0) + + Returns: + The fused image. + + Raises: + ValueError: If the input `images` is not with shape [num, height, width, + width]. + """ + if images is None: + return images + + if not images.ndim == 4: + raise ValueError(f'Input `images` should be with shape [num, height, ' + f'width, channels], but {images.shape} is received!') + + num, image_height, image_width, channels = images.shape + if image_size is not None: + if isinstance(image_size, int): + image_size = (image_size, image_size) + assert isinstance(image_size, (list, tuple)) and len(image_size) == 2 + width, height = image_size + else: + height, width = image_height, image_width + row, col = get_grid_shape(num, row=row, col=col, is_portrait=is_portrait) + fused_height = ( + height * row + row_spacing * (row - 1) + border_top + border_bottom) + fused_width = ( + width * col + col_spacing * (col - 1) + border_left + border_right) + fused_image = get_blank_image( + fused_height, fused_width, channels=channels, is_black=black_background) + images = images.reshape(row, col, image_height, image_width, channels) + if not is_row_major: + images = images.transpose(1, 0, 2, 3, 4) + + for i in range(row): + y = border_top + i * (height + row_spacing) + for j in range(col): + x = border_left + j * (width + col_spacing) + if image_size is not None: + image = cv2.resize(images[i, j], image_size) + else: + image = images[i, j] + fused_image[y:y + height, x:x + width] = image + + return fused_image + + +def get_sortable_html_header(column_name_list, sort_by_ascending=False): + """Gets header for sortable html page. + + Basically, the html page contains a sortable table, where user can sort the + rows by a particular column by clicking the column head. + + Example: + + column_name_list = [name_1, name_2, name_3] + header = get_sortable_html_header(column_name_list) + footer = get_sortable_html_footer() + sortable_table = ... + html_page = header + sortable_table + footer + + Args: + column_name_list: List of column header names. + sort_by_ascending: Default sorting order. If set as `True`, the html page + will be sorted by ascending order when the header is clicked for the first + time. + + Returns: + A string, which represents for the header for a sortable html page. + """ + header = '\n'.join([ + '', + '', + '', + '', + '', + '', + '', + '', + '', + '', + '', + '', + '', + '']) + for idx, column_name in enumerate(column_name_list): + header += f' \n' + header += '\n' + header += '\n' + header += '\n' + + return header + + +def get_sortable_html_footer(): + """Gets footer for sortable html page. + + Check function `get_sortable_html_header()` for more details. + """ + return '\n
{column_name}
\n\n\n\n' + + +def encode_image_to_html_str(image, image_size=None): + """Encodes an image to html language. + + Args: + image: The input image to encode. Should be with `RGB` channel order. + image_size: Int or two-element tuple. This field is used to resize the image + before encoding. `None` disables resizing. (default: None) + + Returns: + A string which represents the encoded image. + """ + if image is None: + return '' + + assert len(image.shape) == 3 and image.shape[2] in [1, 3] + + # Change channel order to `BGR`, which is opencv-friendly. + image = image[:, :, ::-1] + + # Resize the image if needed. + if image_size is not None: + if isinstance(image_size, int): + image_size = (image_size, image_size) + assert isinstance(image_size, (list, tuple)) and len(image_size) == 2 + image = cv2.resize(image, image_size) + + # Encode the image to html-format string. + encoded_image = cv2.imencode(".jpg", image)[1].tostring() + encoded_image_base64 = base64.b64encode(encoded_image).decode('utf-8') + html_str = f'' + + return html_str + + +class HtmlPageVisualizer(object): + """Defines the html page visualizer. + + This class can be used to visualize image results as html page. Basically, it + is based on an html-format sorted table with helper functions + `get_sortable_html_header()`, `get_sortable_html_footer()`, and + `encode_image_to_html_str()`. To simplify the usage, specifying the following + fields is enough to create a visualization page: + + (1) num_rows: Number of rows of the table (header-row exclusive). + (2) num_cols: Number of columns of the table. + (3) header contents (optional): Title of each column. + + NOTE: `grid_size` can be used to assign `num_rows` and `num_cols` + automatically. + + Example: + + html = HtmlPageVisualizer(num_rows, num_cols) + html.set_headers([...]) + for i in range(num_rows): + for j in range(num_cols): + html.set_cell(i, j, text=..., image=...) + html.save('visualize.html') + """ + + def __init__(self, + num_rows=0, + num_cols=0, + grid_size=0, + is_portrait=False, + viz_size=None): + if grid_size > 0: + num_rows, num_cols = get_grid_shape( + grid_size, row=num_rows, col=num_cols, is_portrait=is_portrait) + assert num_rows > 0 and num_cols > 0 + + self.num_rows = num_rows + self.num_cols = num_cols + self.viz_size = viz_size + self.headers = ['' for _ in range(self.num_cols)] + self.cells = [[{ + 'text': '', + 'image': '', + } for _ in range(self.num_cols)] for _ in range(self.num_rows)] + + def set_header(self, column_idx, content): + """Sets the content of a particular header by column index.""" + self.headers[column_idx] = content + + def set_headers(self, contents): + """Sets the contents of all headers.""" + if isinstance(contents, str): + contents = [contents] + assert isinstance(contents, (list, tuple)) + assert len(contents) == self.num_cols + for column_idx, content in enumerate(contents): + self.set_header(column_idx, content) + + def set_cell(self, row_idx, column_idx, text='', image=None): + """Sets the content of a particular cell. + + Basically, a cell contains some text as well as an image. Both text and + image can be empty. + + Args: + row_idx: Row index of the cell to edit. + column_idx: Column index of the cell to edit. + text: Text to add into the target cell. + image: Image to show in the target cell. Should be with `RGB` channel + order. + """ + self.cells[row_idx][column_idx]['text'] = text + self.cells[row_idx][column_idx]['image'] = encode_image_to_html_str( + image, self.viz_size) + + def save(self, save_path): + """Saves the html page.""" + html = '' + for i in range(self.num_rows): + html += f'\n' + for j in range(self.num_cols): + text = self.cells[i][j]['text'] + image = self.cells[i][j]['image'] + if text: + html += f' {text}

{image}\n' + else: + html += f' {image}\n' + html += f'\n' + + header = get_sortable_html_header(self.headers) + footer = get_sortable_html_footer() + + with open(save_path, 'w') as f: + f.write(header + html + footer) + + +class VideoReader(object): + """Defines the video reader. + + This class can be used to read frames from a given video. + """ + + def __init__(self, path): + """Initializes the video reader by loading the video from disk.""" + if not os.path.isfile(path): + raise ValueError(f'Video `{path}` does not exist!') + + self.path = path + self.video = cv2.VideoCapture(path) + assert self.video.isOpened() + self.position = 0 + + self.length = int(self.video.get(cv2.CAP_PROP_FRAME_COUNT)) + self.frame_height = int(self.video.get(cv2.CAP_PROP_FRAME_HEIGHT)) + self.frame_width = int(self.video.get(cv2.CAP_PROP_FRAME_WIDTH)) + self.fps = self.video.get(cv2.CAP_PROP_FPS) + + def __del__(self): + """Releases the opened video.""" + self.video.release() + + def read(self, position=None): + """Reads a certain frame. + + NOTE: The returned frame is assumed to be with `RGB` channel order. + + Args: + position: Optional. If set, the reader will read frames from the exact + position. Otherwise, the reader will read next frames. (default: None) + """ + if position is not None and position < self.length: + self.video.set(cv2.CAP_PROP_POS_FRAMES, position) + self.position = position + + success, frame = self.video.read() + self.position = self.position + 1 + + return frame[:, :, ::-1] if success else None + + +class VideoWriter(object): + """Defines the video writer. + + This class can be used to create a video. + + NOTE: `.avi` and `DIVX` is the most recommended codec format since it does not + rely on other dependencies. + """ + + def __init__(self, path, frame_height, frame_width, fps=24, codec='DIVX'): + """Creates the video writer.""" + self.path = path + self.frame_height = frame_height + self.frame_width = frame_width + self.fps = fps + self.codec = codec + + self.video = cv2.VideoWriter(filename=path, + fourcc=cv2.VideoWriter_fourcc(*codec), + fps=fps, + frameSize=(frame_width, frame_height)) + + def __del__(self): + """Releases the opened video.""" + self.video.release() + + def write(self, frame): + """Writes a target frame. + + NOTE: The input frame is assumed to be with `RGB` channel order. + """ + self.video.write(frame[:, :, ::-1]) diff --git a/PTI/models/StyleCLIP/mapper/__init__.py b/PTI/models/StyleCLIP/mapper/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/models/StyleCLIP/mapper/datasets/__init__.py b/PTI/models/StyleCLIP/mapper/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/models/StyleCLIP/mapper/datasets/latents_dataset.py b/PTI/models/StyleCLIP/mapper/datasets/latents_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..dde6ef52b7488e864ccd2fa2930b5100c1025c17 --- /dev/null +++ b/PTI/models/StyleCLIP/mapper/datasets/latents_dataset.py @@ -0,0 +1,15 @@ +from torch.utils.data import Dataset + + +class LatentsDataset(Dataset): + + def __init__(self, latents, opts): + self.latents = latents + self.opts = opts + + def __len__(self): + return self.latents.shape[0] + + def __getitem__(self, index): + + return self.latents[index] diff --git a/PTI/models/StyleCLIP/mapper/latent_mappers.py b/PTI/models/StyleCLIP/mapper/latent_mappers.py new file mode 100644 index 0000000000000000000000000000000000000000..63637adc9646986a3546edd19f4555a2f75a379f --- /dev/null +++ b/PTI/models/StyleCLIP/mapper/latent_mappers.py @@ -0,0 +1,81 @@ +import torch +from torch import nn +from torch.nn import Module + +from models.StyleCLIP.models.stylegan2.model import EqualLinear, PixelNorm + + +class Mapper(Module): + + def __init__(self, opts): + super(Mapper, self).__init__() + + self.opts = opts + layers = [PixelNorm()] + + for i in range(4): + layers.append( + EqualLinear( + 512, 512, lr_mul=0.01, activation='fused_lrelu' + ) + ) + + self.mapping = nn.Sequential(*layers) + + + def forward(self, x): + x = self.mapping(x) + return x + + +class SingleMapper(Module): + + def __init__(self, opts): + super(SingleMapper, self).__init__() + + self.opts = opts + + self.mapping = Mapper(opts) + + def forward(self, x): + out = self.mapping(x) + return out + + +class LevelsMapper(Module): + + def __init__(self, opts): + super(LevelsMapper, self).__init__() + + self.opts = opts + + if not opts.no_coarse_mapper: + self.course_mapping = Mapper(opts) + if not opts.no_medium_mapper: + self.medium_mapping = Mapper(opts) + if not opts.no_fine_mapper: + self.fine_mapping = Mapper(opts) + + def forward(self, x): + x_coarse = x[:, :4, :] + x_medium = x[:, 4:8, :] + x_fine = x[:, 8:, :] + + if not self.opts.no_coarse_mapper: + x_coarse = self.course_mapping(x_coarse) + else: + x_coarse = torch.zeros_like(x_coarse) + if not self.opts.no_medium_mapper: + x_medium = self.medium_mapping(x_medium) + else: + x_medium = torch.zeros_like(x_medium) + if not self.opts.no_fine_mapper: + x_fine = self.fine_mapping(x_fine) + else: + x_fine = torch.zeros_like(x_fine) + + + out = torch.cat([x_coarse, x_medium, x_fine], dim=1) + + return out + diff --git a/PTI/models/StyleCLIP/mapper/options/__init__.py b/PTI/models/StyleCLIP/mapper/options/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/models/StyleCLIP/mapper/options/test_options.py b/PTI/models/StyleCLIP/mapper/options/test_options.py new file mode 100644 index 0000000000000000000000000000000000000000..aab2e5a5bba1038b97110fa6c8e8bce14de7390c --- /dev/null +++ b/PTI/models/StyleCLIP/mapper/options/test_options.py @@ -0,0 +1,42 @@ +from argparse import ArgumentParser + + +class TestOptions: + + def __init__(self): + self.parser = ArgumentParser() + self.initialize() + + def initialize(self): + # arguments for inference script + self.parser.add_argument('--exp_dir', type=str, help='Path to experiment output directory') + self.parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to model checkpoint') + self.parser.add_argument('--couple_outputs', action='store_true', + help='Whether to also save inputs + outputs side-by-side') + + self.parser.add_argument('--mapper_type', default='LevelsMapper', type=str, help='Which mapper to use') + self.parser.add_argument('--no_coarse_mapper', default=False, action="store_true") + self.parser.add_argument('--no_medium_mapper', default=False, action="store_true") + self.parser.add_argument('--no_fine_mapper', default=False, action="store_true") + self.parser.add_argument('--stylegan_size', default=1024, type=int) + + self.parser.add_argument('--test_batch_size', default=2, type=int, help='Batch size for testing and inference') + self.parser.add_argument('--latents_test_path', default=None, type=str, help="The latents for the validation") + self.parser.add_argument('--test_workers', default=2, type=int, + help='Number of test/inference dataloader workers') + + self.parser.add_argument('--n_images', type=int, default=None, + help='Number of images to output. If None, run on all data') + + self.parser.add_argument('--run_id', type=str, default='PKNWUQRQRKXQ', + help='The generator id to use') + + self.parser.add_argument('--image_name', type=str, default='', + help='image to run on') + + self.parser.add_argument('--edit_name', type=str, default='', + help='edit type') + + def parse(self): + opts = self.parser.parse_args() + return opts diff --git a/PTI/models/StyleCLIP/mapper/options/train_options.py b/PTI/models/StyleCLIP/mapper/options/train_options.py new file mode 100644 index 0000000000000000000000000000000000000000..a365217f8b76d38aaef4a42b90152ec7a8e7bf1f --- /dev/null +++ b/PTI/models/StyleCLIP/mapper/options/train_options.py @@ -0,0 +1,49 @@ +from argparse import ArgumentParser + + +class TrainOptions: + + def __init__(self): + self.parser = ArgumentParser() + self.initialize() + + def initialize(self): + self.parser.add_argument('--exp_dir', type=str, help='Path to experiment output directory') + self.parser.add_argument('--mapper_type', default='LevelsMapper', type=str, help='Which mapper to use') + self.parser.add_argument('--no_coarse_mapper', default=False, action="store_true") + self.parser.add_argument('--no_medium_mapper', default=False, action="store_true") + self.parser.add_argument('--no_fine_mapper', default=False, action="store_true") + self.parser.add_argument('--latents_train_path', default="train_faces.pt", type=str, help="The latents for the training") + self.parser.add_argument('--latents_test_path', default="test_faces.pt", type=str, help="The latents for the validation") + self.parser.add_argument('--train_dataset_size', default=5000, type=int, help="Will be used only if no latents are given") + self.parser.add_argument('--test_dataset_size', default=1000, type=int, help="Will be used only if no latents are given") + + self.parser.add_argument('--batch_size', default=2, type=int, help='Batch size for training') + self.parser.add_argument('--test_batch_size', default=1, type=int, help='Batch size for testing and inference') + self.parser.add_argument('--workers', default=4, type=int, help='Number of train dataloader workers') + self.parser.add_argument('--test_workers', default=2, type=int, help='Number of test/inference dataloader workers') + + self.parser.add_argument('--learning_rate', default=0.5, type=float, help='Optimizer learning rate') + self.parser.add_argument('--optim_name', default='ranger', type=str, help='Which optimizer to use') + + self.parser.add_argument('--id_lambda', default=0.1, type=float, help='ID loss multiplier factor') + self.parser.add_argument('--clip_lambda', default=1.0, type=float, help='CLIP loss multiplier factor') + self.parser.add_argument('--latent_l2_lambda', default=0.8, type=float, help='Latent L2 loss multiplier factor') + + self.parser.add_argument('--stylegan_weights', default='../pretrained_models/stylegan2-ffhq-config-f.pt', type=str, help='Path to StyleGAN model weights') + self.parser.add_argument('--stylegan_size', default=1024, type=int) + self.parser.add_argument('--ir_se50_weights', default='../pretrained_models/model_ir_se50.pth', type=str, help="Path to facial recognition network used in ID loss") + self.parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to StyleCLIPModel model checkpoint') + + self.parser.add_argument('--max_steps', default=50000, type=int, help='Maximum number of training steps') + self.parser.add_argument('--image_interval', default=100, type=int, help='Interval for logging train images during training') + self.parser.add_argument('--board_interval', default=50, type=int, help='Interval for logging metrics to tensorboard') + self.parser.add_argument('--val_interval', default=2000, type=int, help='Validation interval') + self.parser.add_argument('--save_interval', default=2000, type=int, help='Model checkpoint interval') + + self.parser.add_argument('--description', required=True, type=str, help='Driving text prompt') + + + def parse(self): + opts = self.parser.parse_args() + return opts \ No newline at end of file diff --git a/PTI/models/StyleCLIP/mapper/styleclip_mapper.py b/PTI/models/StyleCLIP/mapper/styleclip_mapper.py new file mode 100644 index 0000000000000000000000000000000000000000..86c04bee5744a551f4c0d31359e0de1f5492ff7e --- /dev/null +++ b/PTI/models/StyleCLIP/mapper/styleclip_mapper.py @@ -0,0 +1,76 @@ +import torch +from torch import nn +from models.StyleCLIP.mapper import latent_mappers +from models.StyleCLIP.models.stylegan2.model import Generator + + +def get_keys(d, name): + if 'state_dict' in d: + d = d['state_dict'] + d_filt = {k[len(name) + 1:]: v for k, v in d.items() if k[:len(name)] == name} + return d_filt + + +class StyleCLIPMapper(nn.Module): + + def __init__(self, opts, run_id): + super(StyleCLIPMapper, self).__init__() + self.opts = opts + # Define architecture + self.mapper = self.set_mapper() + self.run_id = run_id + + self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256)) + # Load weights if needed + self.load_weights() + + def set_mapper(self): + if self.opts.mapper_type == 'SingleMapper': + mapper = latent_mappers.SingleMapper(self.opts) + elif self.opts.mapper_type == 'LevelsMapper': + mapper = latent_mappers.LevelsMapper(self.opts) + else: + raise Exception('{} is not a valid mapper'.format(self.opts.mapper_type)) + return mapper + + def load_weights(self): + if self.opts.checkpoint_path is not None: + print('Loading from checkpoint: {}'.format(self.opts.checkpoint_path)) + ckpt = torch.load(self.opts.checkpoint_path, map_location='cpu') + self.mapper.load_state_dict(get_keys(ckpt, 'mapper'), strict=True) + + def set_G(self, new_G): + self.decoder = new_G + + def forward(self, x, resize=True, latent_mask=None, input_code=False, randomize_noise=True, + inject_latent=None, return_latents=False, alpha=None): + if input_code: + codes = x + else: + codes = self.mapper(x) + + if latent_mask is not None: + for i in latent_mask: + if inject_latent is not None: + if alpha is not None: + codes[:, i] = alpha * inject_latent[:, i] + (1 - alpha) * codes[:, i] + else: + codes[:, i] = inject_latent[:, i] + else: + codes[:, i] = 0 + + input_is_latent = not input_code + images = self.decoder.synthesis(codes, noise_mode='const') + result_latent = None + # images, result_latent = self.decoder([codes], + # input_is_latent=input_is_latent, + # randomize_noise=randomize_noise, + # return_latents=return_latents) + + if resize: + images = self.face_pool(images) + + if return_latents: + return images, result_latent + else: + return images diff --git a/PTI/models/StyleCLIP/mapper/training/__init__.py b/PTI/models/StyleCLIP/mapper/training/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/models/StyleCLIP/mapper/training/coach.py b/PTI/models/StyleCLIP/mapper/training/coach.py new file mode 100644 index 0000000000000000000000000000000000000000..fd38eb226106a21e19beb306cd9b0de6a1e7db04 --- /dev/null +++ b/PTI/models/StyleCLIP/mapper/training/coach.py @@ -0,0 +1,242 @@ +import os + +import clip +import torch +import torchvision +from torch import nn +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter + +import criteria.clip_loss as clip_loss +from criteria import id_loss +from mapper.datasets.latents_dataset import LatentsDataset +from mapper.styleclip_mapper import StyleCLIPMapper +from mapper.training.ranger import Ranger +from mapper.training import train_utils + + +class Coach: + def __init__(self, opts): + self.opts = opts + + self.global_step = 0 + + self.device = 'cuda:0' + self.opts.device = self.device + + # Initialize network + self.net = StyleCLIPMapper(self.opts).to(self.device) + + # Initialize loss + if self.opts.id_lambda > 0: + self.id_loss = id_loss.IDLoss(self.opts).to(self.device).eval() + if self.opts.clip_lambda > 0: + self.clip_loss = clip_loss.CLIPLoss(opts) + if self.opts.latent_l2_lambda > 0: + self.latent_l2_loss = nn.MSELoss().to(self.device).eval() + + # Initialize optimizer + self.optimizer = self.configure_optimizers() + + # Initialize dataset + self.train_dataset, self.test_dataset = self.configure_datasets() + self.train_dataloader = DataLoader(self.train_dataset, + batch_size=self.opts.batch_size, + shuffle=True, + num_workers=int(self.opts.workers), + drop_last=True) + self.test_dataloader = DataLoader(self.test_dataset, + batch_size=self.opts.test_batch_size, + shuffle=False, + num_workers=int(self.opts.test_workers), + drop_last=True) + + self.text_inputs = torch.cat([clip.tokenize(self.opts.description)]).cuda() + + # Initialize logger + log_dir = os.path.join(opts.exp_dir, 'logs') + os.makedirs(log_dir, exist_ok=True) + self.log_dir = log_dir + self.logger = SummaryWriter(log_dir=log_dir) + + # Initialize checkpoint dir + self.checkpoint_dir = os.path.join(opts.exp_dir, 'checkpoints') + os.makedirs(self.checkpoint_dir, exist_ok=True) + self.best_val_loss = None + if self.opts.save_interval is None: + self.opts.save_interval = self.opts.max_steps + + def train(self): + self.net.train() + while self.global_step < self.opts.max_steps: + for batch_idx, batch in enumerate(self.train_dataloader): + self.optimizer.zero_grad() + w = batch + w = w.to(self.device) + with torch.no_grad(): + x, _ = self.net.decoder([w], input_is_latent=True, randomize_noise=False, truncation=1) + w_hat = w + 0.1 * self.net.mapper(w) + x_hat, w_hat = self.net.decoder([w_hat], input_is_latent=True, return_latents=True, randomize_noise=False, truncation=1) + loss, loss_dict = self.calc_loss(w, x, w_hat, x_hat) + loss.backward() + self.optimizer.step() + + # Logging related + if self.global_step % self.opts.image_interval == 0 or ( + self.global_step < 1000 and self.global_step % 1000 == 0): + self.parse_and_log_images(x, x_hat, title='images_train') + if self.global_step % self.opts.board_interval == 0: + self.print_metrics(loss_dict, prefix='train') + self.log_metrics(loss_dict, prefix='train') + + # Validation related + val_loss_dict = None + if self.global_step % self.opts.val_interval == 0 or self.global_step == self.opts.max_steps: + val_loss_dict = self.validate() + if val_loss_dict and (self.best_val_loss is None or val_loss_dict['loss'] < self.best_val_loss): + self.best_val_loss = val_loss_dict['loss'] + self.checkpoint_me(val_loss_dict, is_best=True) + + if self.global_step % self.opts.save_interval == 0 or self.global_step == self.opts.max_steps: + if val_loss_dict is not None: + self.checkpoint_me(val_loss_dict, is_best=False) + else: + self.checkpoint_me(loss_dict, is_best=False) + + if self.global_step == self.opts.max_steps: + print('OMG, finished training!') + break + + self.global_step += 1 + + def validate(self): + self.net.eval() + agg_loss_dict = [] + for batch_idx, batch in enumerate(self.test_dataloader): + if batch_idx > 200: + break + + w = batch + + with torch.no_grad(): + w = w.to(self.device).float() + x, _ = self.net.decoder([w], input_is_latent=True, randomize_noise=True, truncation=1) + w_hat = w + 0.1 * self.net.mapper(w) + x_hat, _ = self.net.decoder([w_hat], input_is_latent=True, randomize_noise=True, truncation=1) + loss, cur_loss_dict = self.calc_loss(w, x, w_hat, x_hat) + agg_loss_dict.append(cur_loss_dict) + + # Logging related + self.parse_and_log_images(x, x_hat, title='images_val', index=batch_idx) + + # For first step just do sanity test on small amount of data + if self.global_step == 0 and batch_idx >= 4: + self.net.train() + return None # Do not log, inaccurate in first batch + + loss_dict = train_utils.aggregate_loss_dict(agg_loss_dict) + self.log_metrics(loss_dict, prefix='test') + self.print_metrics(loss_dict, prefix='test') + + self.net.train() + return loss_dict + + def checkpoint_me(self, loss_dict, is_best): + save_name = 'best_model.pt' if is_best else 'iteration_{}.pt'.format(self.global_step) + save_dict = self.__get_save_dict() + checkpoint_path = os.path.join(self.checkpoint_dir, save_name) + torch.save(save_dict, checkpoint_path) + with open(os.path.join(self.checkpoint_dir, 'timestamp.txt'), 'a') as f: + if is_best: + f.write('**Best**: Step - {}, Loss - {:.3f} \n{}\n'.format(self.global_step, self.best_val_loss, loss_dict)) + else: + f.write('Step - {}, \n{}\n'.format(self.global_step, loss_dict)) + + def configure_optimizers(self): + params = list(self.net.mapper.parameters()) + if self.opts.optim_name == 'adam': + optimizer = torch.optim.Adam(params, lr=self.opts.learning_rate) + else: + optimizer = Ranger(params, lr=self.opts.learning_rate) + return optimizer + + def configure_datasets(self): + if self.opts.latents_train_path: + train_latents = torch.load(self.opts.latents_train_path) + else: + train_latents_z = torch.randn(self.opts.train_dataset_size, 512).cuda() + train_latents = [] + for b in range(self.opts.train_dataset_size // self.opts.batch_size): + with torch.no_grad(): + _, train_latents_b = self.net.decoder([train_latents_z[b: b + self.opts.batch_size]], + truncation=0.7, truncation_latent=self.net.latent_avg, return_latents=True) + train_latents.append(train_latents_b) + train_latents = torch.cat(train_latents) + + if self.opts.latents_test_path: + test_latents = torch.load(self.opts.latents_test_path) + else: + test_latents_z = torch.randn(self.opts.train_dataset_size, 512).cuda() + test_latents = [] + for b in range(self.opts.test_dataset_size // self.opts.test_batch_size): + with torch.no_grad(): + _, test_latents_b = self.net.decoder([test_latents_z[b: b + self.opts.test_batch_size]], + truncation=0.7, truncation_latent=self.net.latent_avg, return_latents=True) + test_latents.append(test_latents_b) + test_latents = torch.cat(test_latents) + + train_dataset_celeba = LatentsDataset(latents=train_latents.cpu(), + opts=self.opts) + test_dataset_celeba = LatentsDataset(latents=test_latents.cpu(), + opts=self.opts) + train_dataset = train_dataset_celeba + test_dataset = test_dataset_celeba + print("Number of training samples: {}".format(len(train_dataset))) + print("Number of test samples: {}".format(len(test_dataset))) + return train_dataset, test_dataset + + def calc_loss(self, w, x, w_hat, x_hat): + loss_dict = {} + loss = 0.0 + if self.opts.id_lambda > 0: + loss_id, sim_improvement = self.id_loss(x_hat, x) + loss_dict['loss_id'] = float(loss_id) + loss_dict['id_improve'] = float(sim_improvement) + loss = loss_id * self.opts.id_lambda + if self.opts.clip_lambda > 0: + loss_clip = self.clip_loss(x_hat, self.text_inputs).mean() + loss_dict['loss_clip'] = float(loss_clip) + loss += loss_clip * self.opts.clip_lambda + if self.opts.latent_l2_lambda > 0: + loss_l2_latent = self.latent_l2_loss(w_hat, w) + loss_dict['loss_l2_latent'] = float(loss_l2_latent) + loss += loss_l2_latent * self.opts.latent_l2_lambda + loss_dict['loss'] = float(loss) + return loss, loss_dict + + def log_metrics(self, metrics_dict, prefix): + for key, value in metrics_dict.items(): + #pass + print(f"step: {self.global_step} \t metric: {prefix}/{key} \t value: {value}") + self.logger.add_scalar('{}/{}'.format(prefix, key), value, self.global_step) + + def print_metrics(self, metrics_dict, prefix): + print('Metrics for {}, step {}'.format(prefix, self.global_step)) + for key, value in metrics_dict.items(): + print('\t{} = '.format(key), value) + + def parse_and_log_images(self, x, x_hat, title, index=None): + if index is None: + path = os.path.join(self.log_dir, title, f'{str(self.global_step).zfill(5)}.jpg') + else: + path = os.path.join(self.log_dir, title, f'{str(self.global_step).zfill(5)}_{str(index).zfill(5)}.jpg') + os.makedirs(os.path.dirname(path), exist_ok=True) + torchvision.utils.save_image(torch.cat([x.detach().cpu(), x_hat.detach().cpu()]), path, + normalize=True, scale_each=True, range=(-1, 1), nrow=self.opts.batch_size) + + def __get_save_dict(self): + save_dict = { + 'state_dict': self.net.state_dict(), + 'opts': vars(self.opts) + } + return save_dict \ No newline at end of file diff --git a/PTI/models/StyleCLIP/mapper/training/ranger.py b/PTI/models/StyleCLIP/mapper/training/ranger.py new file mode 100644 index 0000000000000000000000000000000000000000..9442fd10d42fcc19f4e0dd798d1573b31ed2c0a0 --- /dev/null +++ b/PTI/models/StyleCLIP/mapper/training/ranger.py @@ -0,0 +1,164 @@ +# Ranger deep learning optimizer - RAdam + Lookahead + Gradient Centralization, combined into one optimizer. + +# https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer +# and/or +# https://github.com/lessw2020/Best-Deep-Learning-Optimizers + +# Ranger has now been used to capture 12 records on the FastAI leaderboard. + +# This version = 20.4.11 + +# Credits: +# Gradient Centralization --> https://arxiv.org/abs/2004.01461v2 (a new optimization technique for DNNs), github: https://github.com/Yonghongwei/Gradient-Centralization +# RAdam --> https://github.com/LiyuanLucasLiu/RAdam +# Lookahead --> rewritten by lessw2020, but big thanks to Github @LonePatient and @RWightman for ideas from their code. +# Lookahead paper --> MZhang,G Hinton https://arxiv.org/abs/1907.08610 + +# summary of changes: +# 4/11/20 - add gradient centralization option. Set new testing benchmark for accuracy with it, toggle with use_gc flag at init. +# full code integration with all updates at param level instead of group, moves slow weights into state dict (from generic weights), +# supports group learning rates (thanks @SHolderbach), fixes sporadic load from saved model issues. +# changes 8/31/19 - fix references to *self*.N_sma_threshold; +# changed eps to 1e-5 as better default than 1e-8. + +import math +import torch +from torch.optim.optimizer import Optimizer + + +class Ranger(Optimizer): + + def __init__(self, params, lr=1e-3, # lr + alpha=0.5, k=6, N_sma_threshhold=5, # Ranger configs + betas=(.95, 0.999), eps=1e-5, weight_decay=0, # Adam configs + use_gc=True, gc_conv_only=False + # Gradient centralization on or off, applied to conv layers only or conv + fc layers + ): + + # parameter checks + if not 0.0 <= alpha <= 1.0: + raise ValueError(f'Invalid slow update rate: {alpha}') + if not 1 <= k: + raise ValueError(f'Invalid lookahead steps: {k}') + if not lr > 0: + raise ValueError(f'Invalid Learning Rate: {lr}') + if not eps > 0: + raise ValueError(f'Invalid eps: {eps}') + + # parameter comments: + # beta1 (momentum) of .95 seems to work better than .90... + # N_sma_threshold of 5 seems better in testing than 4. + # In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you. + + # prep defaults and init torch.optim base + defaults = dict(lr=lr, alpha=alpha, k=k, step_counter=0, betas=betas, N_sma_threshhold=N_sma_threshhold, + eps=eps, weight_decay=weight_decay) + super().__init__(params, defaults) + + # adjustable threshold + self.N_sma_threshhold = N_sma_threshhold + + # look ahead params + + self.alpha = alpha + self.k = k + + # radam buffer for state + self.radam_buffer = [[None, None, None] for ind in range(10)] + + # gc on or off + self.use_gc = use_gc + + # level of gradient centralization + self.gc_gradient_threshold = 3 if gc_conv_only else 1 + + def __setstate__(self, state): + super(Ranger, self).__setstate__(state) + + def step(self, closure=None): + loss = None + + # Evaluate averages and grad, update param tensors + for group in self.param_groups: + + for p in group['params']: + if p.grad is None: + continue + grad = p.grad.data.float() + + if grad.is_sparse: + raise RuntimeError('Ranger optimizer does not support sparse gradients') + + p_data_fp32 = p.data.float() + + state = self.state[p] # get state dict for this param + + if len(state) == 0: # if first time to run...init dictionary with our desired entries + # if self.first_run_check==0: + # self.first_run_check=1 + # print("Initializing slow buffer...should not see this at load from saved model!") + state['step'] = 0 + state['exp_avg'] = torch.zeros_like(p_data_fp32) + state['exp_avg_sq'] = torch.zeros_like(p_data_fp32) + + # look ahead weight storage now in state dict + state['slow_buffer'] = torch.empty_like(p.data) + state['slow_buffer'].copy_(p.data) + + else: + state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32) + state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32) + + # begin computations + exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] + beta1, beta2 = group['betas'] + + # GC operation for Conv layers and FC layers + if grad.dim() > self.gc_gradient_threshold: + grad.add_(-grad.mean(dim=tuple(range(1, grad.dim())), keepdim=True)) + + state['step'] += 1 + + # compute variance mov avg + exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) + # compute mean moving avg + exp_avg.mul_(beta1).add_(1 - beta1, grad) + + buffered = self.radam_buffer[int(state['step'] % 10)] + + if state['step'] == buffered[0]: + N_sma, step_size = buffered[1], buffered[2] + else: + buffered[0] = state['step'] + beta2_t = beta2 ** state['step'] + N_sma_max = 2 / (1 - beta2) - 1 + N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t) + buffered[1] = N_sma + if N_sma > self.N_sma_threshhold: + step_size = math.sqrt( + (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / ( + N_sma_max - 2)) / (1 - beta1 ** state['step']) + else: + step_size = 1.0 / (1 - beta1 ** state['step']) + buffered[2] = step_size + + if group['weight_decay'] != 0: + p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32) + + # apply lr + if N_sma > self.N_sma_threshhold: + denom = exp_avg_sq.sqrt().add_(group['eps']) + p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom) + else: + p_data_fp32.add_(-step_size * group['lr'], exp_avg) + + p.data.copy_(p_data_fp32) + + # integrated look ahead... + # we do it at the param level instead of group level + if state['step'] % group['k'] == 0: + slow_p = state['slow_buffer'] # get access to slow param tensor + slow_p.add_(self.alpha, p.data - slow_p) # (fast weights - slow weights) * alpha + p.data.copy_(slow_p) # copy interpolated weights to RAdam param tensor + + return loss \ No newline at end of file diff --git a/PTI/models/StyleCLIP/mapper/training/train_utils.py b/PTI/models/StyleCLIP/mapper/training/train_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0c55177f7442010bc1fcc64de3d142585c22adc0 --- /dev/null +++ b/PTI/models/StyleCLIP/mapper/training/train_utils.py @@ -0,0 +1,13 @@ + +def aggregate_loss_dict(agg_loss_dict): + mean_vals = {} + for output in agg_loss_dict: + for key in output: + mean_vals[key] = mean_vals.setdefault(key, []) + [output[key]] + for key in mean_vals: + if len(mean_vals[key]) > 0: + mean_vals[key] = sum(mean_vals[key]) / len(mean_vals[key]) + else: + print('{} has no value'.format(key)) + mean_vals[key] = 0 + return mean_vals diff --git a/PTI/models/StyleCLIP/models/__init__.py b/PTI/models/StyleCLIP/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/models/StyleCLIP/models/facial_recognition/__init__.py b/PTI/models/StyleCLIP/models/facial_recognition/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/models/StyleCLIP/models/facial_recognition/helpers.py b/PTI/models/StyleCLIP/models/facial_recognition/helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..b51fdf97141407fcc1c9d249a086ddbfd042469f --- /dev/null +++ b/PTI/models/StyleCLIP/models/facial_recognition/helpers.py @@ -0,0 +1,119 @@ +from collections import namedtuple +import torch +from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module + +""" +ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) +""" + + +class Flatten(Module): + def forward(self, input): + return input.view(input.size(0), -1) + + +def l2_norm(input, axis=1): + norm = torch.norm(input, 2, axis, True) + output = torch.div(input, norm) + return output + + +class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])): + """ A named tuple describing a ResNet block. """ + + +def get_block(in_channel, depth, num_units, stride=2): + return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)] + + +def get_blocks(num_layers): + if num_layers == 50: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=4), + get_block(in_channel=128, depth=256, num_units=14), + get_block(in_channel=256, depth=512, num_units=3) + ] + elif num_layers == 100: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=13), + get_block(in_channel=128, depth=256, num_units=30), + get_block(in_channel=256, depth=512, num_units=3) + ] + elif num_layers == 152: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=8), + get_block(in_channel=128, depth=256, num_units=36), + get_block(in_channel=256, depth=512, num_units=3) + ] + else: + raise ValueError("Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers)) + return blocks + + +class SEModule(Module): + def __init__(self, channels, reduction): + super(SEModule, self).__init__() + self.avg_pool = AdaptiveAvgPool2d(1) + self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False) + self.relu = ReLU(inplace=True) + self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False) + self.sigmoid = Sigmoid() + + def forward(self, x): + module_input = x + x = self.avg_pool(x) + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + x = self.sigmoid(x) + return module_input * x + + +class bottleneck_IR(Module): + def __init__(self, in_channel, depth, stride): + super(bottleneck_IR, self).__init__() + if in_channel == depth: + self.shortcut_layer = MaxPool2d(1, stride) + else: + self.shortcut_layer = Sequential( + Conv2d(in_channel, depth, (1, 1), stride, bias=False), + BatchNorm2d(depth) + ) + self.res_layer = Sequential( + BatchNorm2d(in_channel), + Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth), + Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth) + ) + + def forward(self, x): + shortcut = self.shortcut_layer(x) + res = self.res_layer(x) + return res + shortcut + + +class bottleneck_IR_SE(Module): + def __init__(self, in_channel, depth, stride): + super(bottleneck_IR_SE, self).__init__() + if in_channel == depth: + self.shortcut_layer = MaxPool2d(1, stride) + else: + self.shortcut_layer = Sequential( + Conv2d(in_channel, depth, (1, 1), stride, bias=False), + BatchNorm2d(depth) + ) + self.res_layer = Sequential( + BatchNorm2d(in_channel), + Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), + PReLU(depth), + Conv2d(depth, depth, (3, 3), stride, 1, bias=False), + BatchNorm2d(depth), + SEModule(depth, 16) + ) + + def forward(self, x): + shortcut = self.shortcut_layer(x) + res = self.res_layer(x) + return res + shortcut diff --git a/PTI/models/StyleCLIP/models/facial_recognition/model_irse.py b/PTI/models/StyleCLIP/models/facial_recognition/model_irse.py new file mode 100644 index 0000000000000000000000000000000000000000..b1c79e0366e4a6fd92011e86df80f8b31ec671ae --- /dev/null +++ b/PTI/models/StyleCLIP/models/facial_recognition/model_irse.py @@ -0,0 +1,84 @@ +from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module +from models.facial_recognition.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm + +""" +Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) +""" + + +class Backbone(Module): + def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True): + super(Backbone, self).__init__() + assert input_size in [112, 224], "input_size should be 112 or 224" + assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152" + assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se" + blocks = get_blocks(num_layers) + if mode == 'ir': + unit_module = bottleneck_IR + elif mode == 'ir_se': + unit_module = bottleneck_IR_SE + self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), + BatchNorm2d(64), + PReLU(64)) + if input_size == 112: + self.output_layer = Sequential(BatchNorm2d(512), + Dropout(drop_ratio), + Flatten(), + Linear(512 * 7 * 7, 512), + BatchNorm1d(512, affine=affine)) + else: + self.output_layer = Sequential(BatchNorm2d(512), + Dropout(drop_ratio), + Flatten(), + Linear(512 * 14 * 14, 512), + BatchNorm1d(512, affine=affine)) + + modules = [] + for block in blocks: + for bottleneck in block: + modules.append(unit_module(bottleneck.in_channel, + bottleneck.depth, + bottleneck.stride)) + self.body = Sequential(*modules) + + def forward(self, x): + x = self.input_layer(x) + x = self.body(x) + x = self.output_layer(x) + return l2_norm(x) + + +def IR_50(input_size): + """Constructs a ir-50 model.""" + model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False) + return model + + +def IR_101(input_size): + """Constructs a ir-101 model.""" + model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False) + return model + + +def IR_152(input_size): + """Constructs a ir-152 model.""" + model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False) + return model + + +def IR_SE_50(input_size): + """Constructs a ir_se-50 model.""" + model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False) + return model + + +def IR_SE_101(input_size): + """Constructs a ir_se-101 model.""" + model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False) + return model + + +def IR_SE_152(input_size): + """Constructs a ir_se-152 model.""" + model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False) + return model diff --git a/PTI/models/StyleCLIP/models/stylegan2/__init__.py b/PTI/models/StyleCLIP/models/stylegan2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/models/StyleCLIP/models/stylegan2/model.py b/PTI/models/StyleCLIP/models/stylegan2/model.py new file mode 100644 index 0000000000000000000000000000000000000000..9d5559203f4f3843fc814b090780ffa129a6fdf0 --- /dev/null +++ b/PTI/models/StyleCLIP/models/stylegan2/model.py @@ -0,0 +1,674 @@ +import math +import random + +import torch +from torch import nn +from torch.nn import functional as F + +from models.StyleCLIP.models.stylegan2.op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d + + +class PixelNorm(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, input): + return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8) + + +def make_kernel(k): + k = torch.tensor(k, dtype=torch.float32) + + if k.ndim == 1: + k = k[None, :] * k[:, None] + + k /= k.sum() + + return k + + +class Upsample(nn.Module): + def __init__(self, kernel, factor=2): + super().__init__() + + self.factor = factor + kernel = make_kernel(kernel) * (factor ** 2) + self.register_buffer('kernel', kernel) + + p = kernel.shape[0] - factor + + pad0 = (p + 1) // 2 + factor - 1 + pad1 = p // 2 + + self.pad = (pad0, pad1) + + def forward(self, input): + out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad) + + return out + + +class Downsample(nn.Module): + def __init__(self, kernel, factor=2): + super().__init__() + + self.factor = factor + kernel = make_kernel(kernel) + self.register_buffer('kernel', kernel) + + p = kernel.shape[0] - factor + + pad0 = (p + 1) // 2 + pad1 = p // 2 + + self.pad = (pad0, pad1) + + def forward(self, input): + out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad) + + return out + + +class Blur(nn.Module): + def __init__(self, kernel, pad, upsample_factor=1): + super().__init__() + + kernel = make_kernel(kernel) + + if upsample_factor > 1: + kernel = kernel * (upsample_factor ** 2) + + self.register_buffer('kernel', kernel) + + self.pad = pad + + def forward(self, input): + out = upfirdn2d(input, self.kernel, pad=self.pad) + + return out + + +class EqualConv2d(nn.Module): + def __init__( + self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True + ): + super().__init__() + + self.weight = nn.Parameter( + torch.randn(out_channel, in_channel, kernel_size, kernel_size) + ) + self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2) + + self.stride = stride + self.padding = padding + + if bias: + self.bias = nn.Parameter(torch.zeros(out_channel)) + + else: + self.bias = None + + def forward(self, input): + out = F.conv2d( + input, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.padding, + ) + + return out + + def __repr__(self): + return ( + f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},' + f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})' + ) + + +class EqualLinear(nn.Module): + def __init__( + self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None + ): + super().__init__() + + self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul)) + + if bias: + self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init)) + + else: + self.bias = None + + self.activation = activation + + self.scale = (1 / math.sqrt(in_dim)) * lr_mul + self.lr_mul = lr_mul + + def forward(self, input): + if self.activation: + out = F.linear(input, self.weight * self.scale) + out = fused_leaky_relu(out, self.bias * self.lr_mul) + + else: + out = F.linear( + input, self.weight * self.scale, bias=self.bias * self.lr_mul + ) + + return out + + def __repr__(self): + return ( + f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})' + ) + + +class ScaledLeakyReLU(nn.Module): + def __init__(self, negative_slope=0.2): + super().__init__() + + self.negative_slope = negative_slope + + def forward(self, input): + out = F.leaky_relu(input, negative_slope=self.negative_slope) + + return out * math.sqrt(2) + + +class ModulatedConv2d(nn.Module): + def __init__( + self, + in_channel, + out_channel, + kernel_size, + style_dim, + demodulate=True, + upsample=False, + downsample=False, + blur_kernel=[1, 3, 3, 1], + ): + super().__init__() + + self.eps = 1e-8 + self.kernel_size = kernel_size + self.in_channel = in_channel + self.out_channel = out_channel + self.upsample = upsample + self.downsample = downsample + + if upsample: + factor = 2 + p = (len(blur_kernel) - factor) - (kernel_size - 1) + pad0 = (p + 1) // 2 + factor - 1 + pad1 = p // 2 + 1 + + self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor) + + if downsample: + factor = 2 + p = (len(blur_kernel) - factor) + (kernel_size - 1) + pad0 = (p + 1) // 2 + pad1 = p // 2 + + self.blur = Blur(blur_kernel, pad=(pad0, pad1)) + + fan_in = in_channel * kernel_size ** 2 + self.scale = 1 / math.sqrt(fan_in) + self.padding = kernel_size // 2 + + self.weight = nn.Parameter( + torch.randn(1, out_channel, in_channel, kernel_size, kernel_size) + ) + + self.modulation = EqualLinear(style_dim, in_channel, bias_init=1) + + self.demodulate = demodulate + + def __repr__(self): + return ( + f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, ' + f'upsample={self.upsample}, downsample={self.downsample})' + ) + + def forward(self, input, style): + batch, in_channel, height, width = input.shape + + style = self.modulation(style).view(batch, 1, in_channel, 1, 1) + weight = self.scale * self.weight * style + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8) + weight = weight * demod.view(batch, self.out_channel, 1, 1, 1) + + weight = weight.view( + batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size + ) + + if self.upsample: + input = input.view(1, batch * in_channel, height, width) + weight = weight.view( + batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size + ) + weight = weight.transpose(1, 2).reshape( + batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size + ) + out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch) + _, _, height, width = out.shape + out = out.view(batch, self.out_channel, height, width) + out = self.blur(out) + + elif self.downsample: + input = self.blur(input) + _, _, height, width = input.shape + input = input.view(1, batch * in_channel, height, width) + out = F.conv2d(input, weight, padding=0, stride=2, groups=batch) + _, _, height, width = out.shape + out = out.view(batch, self.out_channel, height, width) + + else: + input = input.view(1, batch * in_channel, height, width) + out = F.conv2d(input, weight, padding=self.padding, groups=batch) + _, _, height, width = out.shape + out = out.view(batch, self.out_channel, height, width) + + return out + + +class NoiseInjection(nn.Module): + def __init__(self): + super().__init__() + + self.weight = nn.Parameter(torch.zeros(1)) + + def forward(self, image, noise=None): + if noise is None: + batch, _, height, width = image.shape + noise = image.new_empty(batch, 1, height, width).normal_() + + return image + self.weight * noise + + +class ConstantInput(nn.Module): + def __init__(self, channel, size=4): + super().__init__() + + self.input = nn.Parameter(torch.randn(1, channel, size, size)) + + def forward(self, input): + batch = input.shape[0] + out = self.input.repeat(batch, 1, 1, 1) + + return out + + +class StyledConv(nn.Module): + def __init__( + self, + in_channel, + out_channel, + kernel_size, + style_dim, + upsample=False, + blur_kernel=[1, 3, 3, 1], + demodulate=True, + ): + super().__init__() + + self.conv = ModulatedConv2d( + in_channel, + out_channel, + kernel_size, + style_dim, + upsample=upsample, + blur_kernel=blur_kernel, + demodulate=demodulate, + ) + + self.noise = NoiseInjection() + # self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1)) + # self.activate = ScaledLeakyReLU(0.2) + self.activate = FusedLeakyReLU(out_channel) + + def forward(self, input, style, noise=None): + out = self.conv(input, style) + out = self.noise(out, noise=noise) + # out = out + self.bias + out = self.activate(out) + + return out + + +class ToRGB(nn.Module): + def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]): + super().__init__() + + if upsample: + self.upsample = Upsample(blur_kernel) + + self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + def forward(self, input, style, skip=None): + out = self.conv(input, style) + out = out + self.bias + + if skip is not None: + skip = self.upsample(skip) + + out = out + skip + + return out + + +class Generator(nn.Module): + def __init__( + self, + size, + style_dim, + n_mlp, + channel_multiplier=2, + blur_kernel=[1, 3, 3, 1], + lr_mlp=0.01, + ): + super().__init__() + + self.size = size + + self.style_dim = style_dim + + layers = [PixelNorm()] + + for i in range(n_mlp): + layers.append( + EqualLinear( + style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu' + ) + ) + + self.style = nn.Sequential(*layers) + + self.channels = { + 4: 512, + 8: 512, + 16: 512, + 32: 512, + 64: 256 * channel_multiplier, + 128: 128 * channel_multiplier, + 256: 64 * channel_multiplier, + 512: 32 * channel_multiplier, + 1024: 16 * channel_multiplier, + } + + self.input = ConstantInput(self.channels[4]) + self.conv1 = StyledConv( + self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel + ) + self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False) + + self.log_size = int(math.log(size, 2)) + self.num_layers = (self.log_size - 2) * 2 + 1 + + self.convs = nn.ModuleList() + self.upsamples = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + self.noises = nn.Module() + + in_channel = self.channels[4] + + for layer_idx in range(self.num_layers): + res = (layer_idx + 5) // 2 + shape = [1, 1, 2 ** res, 2 ** res] + self.noises.register_buffer(f'noise_{layer_idx}', torch.randn(*shape)) + + for i in range(3, self.log_size + 1): + out_channel = self.channels[2 ** i] + + self.convs.append( + StyledConv( + in_channel, + out_channel, + 3, + style_dim, + upsample=True, + blur_kernel=blur_kernel, + ) + ) + + self.convs.append( + StyledConv( + out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel + ) + ) + + self.to_rgbs.append(ToRGB(out_channel, style_dim)) + + in_channel = out_channel + + self.n_latent = self.log_size * 2 - 2 + + def make_noise(self): + device = self.input.input.device + + noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)] + + for i in range(3, self.log_size + 1): + for _ in range(2): + noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device)) + + return noises + + def mean_latent(self, n_latent): + latent_in = torch.randn( + n_latent, self.style_dim, device=self.input.input.device + ) + latent = self.style(latent_in).mean(0, keepdim=True) + + return latent + + def get_latent(self, input): + return self.style(input) + + def forward( + self, + styles, + return_latents=False, + inject_index=None, + truncation=1, + truncation_latent=None, + input_is_latent=False, + noise=None, + randomize_noise=True, + ): + if not input_is_latent: + styles = [self.style(s) for s in styles] + + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers + else: + noise = [ + getattr(self.noises, f'noise_{i}') for i in range(self.num_layers) + ] + + if truncation < 1: + style_t = [] + + for style in styles: + style_t.append( + truncation_latent + truncation * (style - truncation_latent) + ) + + styles = style_t + + if len(styles) < 2: + inject_index = self.n_latent + + if styles[0].ndim < 3: + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + + else: + latent = styles[0] + + else: + if inject_index is None: + inject_index = random.randint(1, self.n_latent - 1) + + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1) + + latent = torch.cat([latent, latent2], 1) + + out = self.input(latent) + out = self.conv1(out, latent[:, 0], noise=noise[0]) + + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs + ): + out = conv1(out, latent[:, i], noise=noise1) + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) + + i += 2 + + image = skip + + if return_latents: + return image, latent + + else: + return image, None + + +class ConvLayer(nn.Sequential): + def __init__( + self, + in_channel, + out_channel, + kernel_size, + downsample=False, + blur_kernel=[1, 3, 3, 1], + bias=True, + activate=True, + ): + layers = [] + + if downsample: + factor = 2 + p = (len(blur_kernel) - factor) + (kernel_size - 1) + pad0 = (p + 1) // 2 + pad1 = p // 2 + + layers.append(Blur(blur_kernel, pad=(pad0, pad1))) + + stride = 2 + self.padding = 0 + + else: + stride = 1 + self.padding = kernel_size // 2 + + layers.append( + EqualConv2d( + in_channel, + out_channel, + kernel_size, + padding=self.padding, + stride=stride, + bias=bias and not activate, + ) + ) + + if activate: + if bias: + layers.append(FusedLeakyReLU(out_channel)) + + else: + layers.append(ScaledLeakyReLU(0.2)) + + super().__init__(*layers) + + +class ResBlock(nn.Module): + def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]): + super().__init__() + + self.conv1 = ConvLayer(in_channel, in_channel, 3) + self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True) + + self.skip = ConvLayer( + in_channel, out_channel, 1, downsample=True, activate=False, bias=False + ) + + def forward(self, input): + out = self.conv1(input) + out = self.conv2(out) + + skip = self.skip(input) + out = (out + skip) / math.sqrt(2) + + return out + + +class Discriminator(nn.Module): + def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]): + super().__init__() + + channels = { + 4: 512, + 8: 512, + 16: 512, + 32: 512, + 64: 256 * channel_multiplier, + 128: 128 * channel_multiplier, + 256: 64 * channel_multiplier, + 512: 32 * channel_multiplier, + 1024: 16 * channel_multiplier, + } + + convs = [ConvLayer(3, channels[size], 1)] + + log_size = int(math.log(size, 2)) + + in_channel = channels[size] + + for i in range(log_size, 2, -1): + out_channel = channels[2 ** (i - 1)] + + convs.append(ResBlock(in_channel, out_channel, blur_kernel)) + + in_channel = out_channel + + self.convs = nn.Sequential(*convs) + + self.stddev_group = 4 + self.stddev_feat = 1 + + self.final_conv = ConvLayer(in_channel + 1, channels[4], 3) + self.final_linear = nn.Sequential( + EqualLinear(channels[4] * 4 * 4, channels[4], activation='fused_lrelu'), + EqualLinear(channels[4], 1), + ) + + def forward(self, input): + out = self.convs(input) + + batch, channel, height, width = out.shape + group = min(batch, self.stddev_group) + stddev = out.view( + group, -1, self.stddev_feat, channel // self.stddev_feat, height, width + ) + stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8) + stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2) + stddev = stddev.repeat(group, 1, height, width) + out = torch.cat([out, stddev], 1) + + out = self.final_conv(out) + + out = out.view(batch, -1) + out = self.final_linear(out) + + return out + diff --git a/PTI/models/StyleCLIP/models/stylegan2/op/__init__.py b/PTI/models/StyleCLIP/models/stylegan2/op/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d0918d92285955855be89f00096b888ee5597ce3 --- /dev/null +++ b/PTI/models/StyleCLIP/models/stylegan2/op/__init__.py @@ -0,0 +1,2 @@ +from .fused_act import FusedLeakyReLU, fused_leaky_relu +from .upfirdn2d import upfirdn2d diff --git a/PTI/models/StyleCLIP/models/stylegan2/op/fused_act.py b/PTI/models/StyleCLIP/models/stylegan2/op/fused_act.py new file mode 100644 index 0000000000000000000000000000000000000000..2d575bc9198e6d46eee040eb374c6d8f64c3363c --- /dev/null +++ b/PTI/models/StyleCLIP/models/stylegan2/op/fused_act.py @@ -0,0 +1,40 @@ +import os + +import torch +from torch import nn +from torch.nn import functional as F + +module_path = os.path.dirname(__file__) + + + +class FusedLeakyReLU(nn.Module): + def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5): + super().__init__() + + self.bias = nn.Parameter(torch.zeros(channel)) + self.negative_slope = negative_slope + self.scale = scale + + def forward(self, input): + return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) + + +def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5): + rest_dim = [1] * (input.ndim - bias.ndim - 1) + input = input.cuda() + if input.ndim == 3: + return ( + F.leaky_relu( + input + bias.view(1, *rest_dim, bias.shape[0]), negative_slope=negative_slope + ) + * scale + ) + else: + return ( + F.leaky_relu( + input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope + ) + * scale + ) + diff --git a/PTI/models/StyleCLIP/models/stylegan2/op/upfirdn2d.py b/PTI/models/StyleCLIP/models/stylegan2/op/upfirdn2d.py new file mode 100644 index 0000000000000000000000000000000000000000..02fc25af780868d9b883631eb6b03a25c225d745 --- /dev/null +++ b/PTI/models/StyleCLIP/models/stylegan2/op/upfirdn2d.py @@ -0,0 +1,60 @@ +import os + +import torch +from torch.nn import functional as F + + +module_path = os.path.dirname(__file__) + + + +def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): + out = upfirdn2d_native( + input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1] + ) + + return out + + +def upfirdn2d_native( + input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 +): + _, channel, in_h, in_w = input.shape + input = input.reshape(-1, in_h, in_w, 1) + + _, in_h, in_w, minor = input.shape + kernel_h, kernel_w = kernel.shape + + out = input.view(-1, in_h, 1, in_w, 1, minor) + out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) + out = out.view(-1, in_h * up_y, in_w * up_x, minor) + + out = F.pad( + out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] + ) + out = out[ + :, + max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), + max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), + :, + ] + + out = out.permute(0, 3, 1, 2) + out = out.reshape( + [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] + ) + w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) + out = F.conv2d(out, w) + out = out.reshape( + -1, + minor, + in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, + in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, + ) + out = out.permute(0, 2, 3, 1) + out = out[:, ::down_y, ::down_x, :] + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 + + return out.view(-1, channel, out_h, out_w) \ No newline at end of file diff --git a/PTI/models/StyleCLIP/optimization/run_optimization.py b/PTI/models/StyleCLIP/optimization/run_optimization.py new file mode 100644 index 0000000000000000000000000000000000000000..766d0c81400951202bed51e3f1812e1260ccf071 --- /dev/null +++ b/PTI/models/StyleCLIP/optimization/run_optimization.py @@ -0,0 +1,128 @@ +import argparse +import math +import os +import pickle + +import torch +import torchvision +from torch import optim +from tqdm import tqdm + +from StyleCLIP.criteria.clip_loss import CLIPLoss +from StyleCLIP.models.stylegan2.model import Generator +import clip +from StyleCLIP.utils import ensure_checkpoint_exists + + +def get_lr(t, initial_lr, rampdown=0.25, rampup=0.05): + lr_ramp = min(1, (1 - t) / rampdown) + lr_ramp = 0.5 - 0.5 * math.cos(lr_ramp * math.pi) + lr_ramp = lr_ramp * min(1, t / rampup) + + return initial_lr * lr_ramp + + +def main(args, use_old_G): + ensure_checkpoint_exists(args.ckpt) + text_inputs = torch.cat([clip.tokenize(args.description)]).cuda() + os.makedirs(args.results_dir, exist_ok=True) + new_generator_path = f'/disk2/danielroich/Sandbox/stylegan2_ada_pytorch/checkpoints/model_{args.run_id}_{args.image_name}.pt' + old_generator_path = '/disk2/danielroich/Sandbox/pretrained_models/ffhq.pkl' + + if not use_old_G: + with open(new_generator_path, 'rb') as f: + G = torch.load(f).cuda().eval() + else: + with open(old_generator_path, 'rb') as f: + G = pickle.load(f)['G_ema'].cuda().eval() + + if args.latent_path: + latent_code_init = torch.load(args.latent_path).cuda() + elif args.mode == "edit": + latent_code_init_not_trunc = torch.randn(1, 512).cuda() + with torch.no_grad(): + latent_code_init = G.mapping(latent_code_init_not_trunc, None) + + latent = latent_code_init.detach().clone() + latent.requires_grad = True + + clip_loss = CLIPLoss(args) + + optimizer = optim.Adam([latent], lr=args.lr) + + pbar = tqdm(range(args.step)) + + for i in pbar: + t = i / args.step + lr = get_lr(t, args.lr) + optimizer.param_groups[0]["lr"] = lr + + img_gen = G.synthesis(latent, noise_mode='const') + + c_loss = clip_loss(img_gen, text_inputs) + + if args.mode == "edit": + l2_loss = ((latent_code_init - latent) ** 2).sum() + loss = c_loss + args.l2_lambda * l2_loss + else: + loss = c_loss + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + pbar.set_description( + ( + f"loss: {loss.item():.4f};" + ) + ) + if args.save_intermediate_image_every > 0 and i % args.save_intermediate_image_every == 0: + with torch.no_grad(): + img_gen = G.synthesis(latent, noise_mode='const') + + torchvision.utils.save_image(img_gen, + f"/disk2/danielroich/Sandbox/StyleCLIP/results/inference_results/{str(i).zfill(5)}.png", + normalize=True, range=(-1, 1)) + + if args.mode == "edit": + with torch.no_grad(): + img_orig = G.synthesis(latent_code_init, noise_mode='const') + + final_result = torch.cat([img_orig, img_gen]) + else: + final_result = img_gen + + return final_result + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--description", type=str, default="a person with purple hair", + help="the text that guides the editing/generation") + parser.add_argument("--ckpt", type=str, default="../pretrained_models/stylegan2-ffhq-config-f.pt", + help="pretrained StyleGAN2 weights") + parser.add_argument("--stylegan_size", type=int, default=1024, help="StyleGAN resolution") + parser.add_argument("--lr_rampup", type=float, default=0.05) + parser.add_argument("--lr", type=float, default=0.1) + parser.add_argument("--step", type=int, default=300, help="number of optimization steps") + parser.add_argument("--mode", type=str, default="edit", choices=["edit", "free_generation"], + help="choose between edit an image an generate a free one") + parser.add_argument("--l2_lambda", type=float, default=0.008, + help="weight of the latent distance (used for editing only)") + parser.add_argument("--latent_path", type=str, default=None, + help="starts the optimization from the given latent code if provided. Otherwose, starts from" + "the mean latent in a free generation, and from a random one in editing. " + "Expects a .pt format") + parser.add_argument("--truncation", type=float, default=0.7, + help="used only for the initial latent vector, and only when a latent code path is" + "not provided") + parser.add_argument("--save_intermediate_image_every", type=int, default=20, + help="if > 0 then saves intermidate results during the optimization") + parser.add_argument("--results_dir", type=str, default="results") + + args = parser.parse_args() + + result_image = main(args) + + torchvision.utils.save_image(result_image.detach().cpu(), os.path.join(args.results_dir, "final_result.jpg"), + normalize=True, scale_each=True, range=(-1, 1)) diff --git a/PTI/models/__init__.py b/PTI/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/models/e4e/__init__.py b/PTI/models/e4e/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/models/e4e/discriminator.py b/PTI/models/e4e/discriminator.py new file mode 100644 index 0000000000000000000000000000000000000000..16bf3722c7f2e35cdc9bd177a33ed0975e67200d --- /dev/null +++ b/PTI/models/e4e/discriminator.py @@ -0,0 +1,20 @@ +from torch import nn + + +class LatentCodesDiscriminator(nn.Module): + def __init__(self, style_dim, n_mlp): + super().__init__() + + self.style_dim = style_dim + + layers = [] + for i in range(n_mlp-1): + layers.append( + nn.Linear(style_dim, style_dim) + ) + layers.append(nn.LeakyReLU(0.2)) + layers.append(nn.Linear(512, 1)) + self.mlp = nn.Sequential(*layers) + + def forward(self, w): + return self.mlp(w) diff --git a/PTI/models/e4e/encoders/__init__.py b/PTI/models/e4e/encoders/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/models/e4e/encoders/helpers.py b/PTI/models/e4e/encoders/helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..c4a58b34ea5ca6912fe53c63dede0a8696f5c024 --- /dev/null +++ b/PTI/models/e4e/encoders/helpers.py @@ -0,0 +1,140 @@ +from collections import namedtuple +import torch +import torch.nn.functional as F +from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module + +""" +ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) +""" + + +class Flatten(Module): + def forward(self, input): + return input.view(input.size(0), -1) + + +def l2_norm(input, axis=1): + norm = torch.norm(input, 2, axis, True) + output = torch.div(input, norm) + return output + + +class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])): + """ A named tuple describing a ResNet block. """ + + +def get_block(in_channel, depth, num_units, stride=2): + return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)] + + +def get_blocks(num_layers): + if num_layers == 50: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=4), + get_block(in_channel=128, depth=256, num_units=14), + get_block(in_channel=256, depth=512, num_units=3) + ] + elif num_layers == 100: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=13), + get_block(in_channel=128, depth=256, num_units=30), + get_block(in_channel=256, depth=512, num_units=3) + ] + elif num_layers == 152: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=8), + get_block(in_channel=128, depth=256, num_units=36), + get_block(in_channel=256, depth=512, num_units=3) + ] + else: + raise ValueError("Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers)) + return blocks + + +class SEModule(Module): + def __init__(self, channels, reduction): + super(SEModule, self).__init__() + self.avg_pool = AdaptiveAvgPool2d(1) + self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False) + self.relu = ReLU(inplace=True) + self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False) + self.sigmoid = Sigmoid() + + def forward(self, x): + module_input = x + x = self.avg_pool(x) + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + x = self.sigmoid(x) + return module_input * x + + +class bottleneck_IR(Module): + def __init__(self, in_channel, depth, stride): + super(bottleneck_IR, self).__init__() + if in_channel == depth: + self.shortcut_layer = MaxPool2d(1, stride) + else: + self.shortcut_layer = Sequential( + Conv2d(in_channel, depth, (1, 1), stride, bias=False), + BatchNorm2d(depth) + ) + self.res_layer = Sequential( + BatchNorm2d(in_channel), + Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth), + Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth) + ) + + def forward(self, x): + shortcut = self.shortcut_layer(x) + res = self.res_layer(x) + return res + shortcut + + +class bottleneck_IR_SE(Module): + def __init__(self, in_channel, depth, stride): + super(bottleneck_IR_SE, self).__init__() + if in_channel == depth: + self.shortcut_layer = MaxPool2d(1, stride) + else: + self.shortcut_layer = Sequential( + Conv2d(in_channel, depth, (1, 1), stride, bias=False), + BatchNorm2d(depth) + ) + self.res_layer = Sequential( + BatchNorm2d(in_channel), + Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), + PReLU(depth), + Conv2d(depth, depth, (3, 3), stride, 1, bias=False), + BatchNorm2d(depth), + SEModule(depth, 16) + ) + + def forward(self, x): + shortcut = self.shortcut_layer(x) + res = self.res_layer(x) + return res + shortcut + + +def _upsample_add(x, y): + """Upsample and add two feature maps. + Args: + x: (Variable) top feature map to be upsampled. + y: (Variable) lateral feature map. + Returns: + (Variable) added feature map. + Note in PyTorch, when input size is odd, the upsampled feature map + with `F.upsample(..., scale_factor=2, mode='nearest')` + maybe not equal to the lateral feature map size. + e.g. + original input size: [N,_,15,15] -> + conv2d feature map size: [N,_,8,8] -> + upsampled feature map size: [N,_,16,16] + So we choose bilinear upsample which supports arbitrary output sizes. + """ + _, _, H, W = y.size() + return F.interpolate(x, size=(H, W), mode='bilinear', align_corners=True) + y diff --git a/PTI/models/e4e/encoders/model_irse.py b/PTI/models/e4e/encoders/model_irse.py new file mode 100644 index 0000000000000000000000000000000000000000..976ce2c61104efdc6b0015d895830346dd01bc10 --- /dev/null +++ b/PTI/models/e4e/encoders/model_irse.py @@ -0,0 +1,84 @@ +from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module +from encoder4editing.models.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm + +""" +Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) +""" + + +class Backbone(Module): + def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True): + super(Backbone, self).__init__() + assert input_size in [112, 224], "input_size should be 112 or 224" + assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152" + assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se" + blocks = get_blocks(num_layers) + if mode == 'ir': + unit_module = bottleneck_IR + elif mode == 'ir_se': + unit_module = bottleneck_IR_SE + self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), + BatchNorm2d(64), + PReLU(64)) + if input_size == 112: + self.output_layer = Sequential(BatchNorm2d(512), + Dropout(drop_ratio), + Flatten(), + Linear(512 * 7 * 7, 512), + BatchNorm1d(512, affine=affine)) + else: + self.output_layer = Sequential(BatchNorm2d(512), + Dropout(drop_ratio), + Flatten(), + Linear(512 * 14 * 14, 512), + BatchNorm1d(512, affine=affine)) + + modules = [] + for block in blocks: + for bottleneck in block: + modules.append(unit_module(bottleneck.in_channel, + bottleneck.depth, + bottleneck.stride)) + self.body = Sequential(*modules) + + def forward(self, x): + x = self.input_layer(x) + x = self.body(x) + x = self.output_layer(x) + return l2_norm(x) + + +def IR_50(input_size): + """Constructs a ir-50 model.""" + model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False) + return model + + +def IR_101(input_size): + """Constructs a ir-101 model.""" + model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False) + return model + + +def IR_152(input_size): + """Constructs a ir-152 model.""" + model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False) + return model + + +def IR_SE_50(input_size): + """Constructs a ir_se-50 model.""" + model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False) + return model + + +def IR_SE_101(input_size): + """Constructs a ir_se-101 model.""" + model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False) + return model + + +def IR_SE_152(input_size): + """Constructs a ir_se-152 model.""" + model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False) + return model diff --git a/PTI/models/e4e/encoders/psp_encoders.py b/PTI/models/e4e/encoders/psp_encoders.py new file mode 100644 index 0000000000000000000000000000000000000000..cbd9d849149ca5df3a5589015811dc17876a51d7 --- /dev/null +++ b/PTI/models/e4e/encoders/psp_encoders.py @@ -0,0 +1,200 @@ +from enum import Enum +import math +import numpy as np +import torch +from torch import nn +from torch.nn import Conv2d, BatchNorm2d, PReLU, Sequential, Module + +from PTI.models.e4e.encoders.helpers import get_blocks, bottleneck_IR, bottleneck_IR_SE, _upsample_add +from PTI.models.e4e.stylegan2.model import EqualLinear + + +class ProgressiveStage(Enum): + WTraining = 0 + Delta1Training = 1 + Delta2Training = 2 + Delta3Training = 3 + Delta4Training = 4 + Delta5Training = 5 + Delta6Training = 6 + Delta7Training = 7 + Delta8Training = 8 + Delta9Training = 9 + Delta10Training = 10 + Delta11Training = 11 + Delta12Training = 12 + Delta13Training = 13 + Delta14Training = 14 + Delta15Training = 15 + Delta16Training = 16 + Delta17Training = 17 + Inference = 18 + + +class GradualStyleBlock(Module): + def __init__(self, in_c, out_c, spatial): + super(GradualStyleBlock, self).__init__() + self.out_c = out_c + self.spatial = spatial + num_pools = int(np.log2(spatial)) + modules = [] + modules += [Conv2d(in_c, out_c, kernel_size=3, stride=2, padding=1), + nn.LeakyReLU()] + for i in range(num_pools - 1): + modules += [ + Conv2d(out_c, out_c, kernel_size=3, stride=2, padding=1), + nn.LeakyReLU() + ] + self.convs = nn.Sequential(*modules) + self.linear = EqualLinear(out_c, out_c, lr_mul=1) + + def forward(self, x): + x = self.convs(x) + x = x.view(-1, self.out_c) + x = self.linear(x) + return x + + +class GradualStyleEncoder(Module): + def __init__(self, num_layers, mode='ir', opts=None): + super(GradualStyleEncoder, self).__init__() + assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152' + assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se' + blocks = get_blocks(num_layers) + if mode == 'ir': + unit_module = bottleneck_IR + elif mode == 'ir_se': + unit_module = bottleneck_IR_SE + self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), + BatchNorm2d(64), + PReLU(64)) + modules = [] + for block in blocks: + for bottleneck in block: + modules.append(unit_module(bottleneck.in_channel, + bottleneck.depth, + bottleneck.stride)) + self.body = Sequential(*modules) + + self.styles = nn.ModuleList() + log_size = int(math.log(opts.stylegan_size, 2)) + self.style_count = 2 * log_size - 2 + self.coarse_ind = 3 + self.middle_ind = 7 + for i in range(self.style_count): + if i < self.coarse_ind: + style = GradualStyleBlock(512, 512, 16) + elif i < self.middle_ind: + style = GradualStyleBlock(512, 512, 32) + else: + style = GradualStyleBlock(512, 512, 64) + self.styles.append(style) + self.latlayer1 = nn.Conv2d(256, 512, kernel_size=1, stride=1, padding=0) + self.latlayer2 = nn.Conv2d(128, 512, kernel_size=1, stride=1, padding=0) + + def forward(self, x): + x = self.input_layer(x) + + latents = [] + modulelist = list(self.body._modules.values()) + for i, l in enumerate(modulelist): + x = l(x) + if i == 6: + c1 = x + elif i == 20: + c2 = x + elif i == 23: + c3 = x + + for j in range(self.coarse_ind): + latents.append(self.styles[j](c3)) + + p2 = _upsample_add(c3, self.latlayer1(c2)) + for j in range(self.coarse_ind, self.middle_ind): + latents.append(self.styles[j](p2)) + + p1 = _upsample_add(p2, self.latlayer2(c1)) + for j in range(self.middle_ind, self.style_count): + latents.append(self.styles[j](p1)) + + out = torch.stack(latents, dim=1) + return out + + +class Encoder4Editing(Module): + def __init__(self, num_layers, mode='ir', opts=None): + super(Encoder4Editing, self).__init__() + assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152' + assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se' + blocks = get_blocks(num_layers) + if mode == 'ir': + unit_module = bottleneck_IR + elif mode == 'ir_se': + unit_module = bottleneck_IR_SE + self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), + BatchNorm2d(64), + PReLU(64)) + modules = [] + for block in blocks: + for bottleneck in block: + modules.append(unit_module(bottleneck.in_channel, + bottleneck.depth, + bottleneck.stride)) + self.body = Sequential(*modules) + + self.styles = nn.ModuleList() + log_size = int(math.log(opts.stylegan_size, 2)) + self.style_count = 2 * log_size - 2 + self.coarse_ind = 3 + self.middle_ind = 7 + + for i in range(self.style_count): + if i < self.coarse_ind: + style = GradualStyleBlock(512, 512, 16) + elif i < self.middle_ind: + style = GradualStyleBlock(512, 512, 32) + else: + style = GradualStyleBlock(512, 512, 64) + self.styles.append(style) + + self.latlayer1 = nn.Conv2d(256, 512, kernel_size=1, stride=1, padding=0) + self.latlayer2 = nn.Conv2d(128, 512, kernel_size=1, stride=1, padding=0) + + self.progressive_stage = ProgressiveStage.Inference + + def get_deltas_starting_dimensions(self): + ''' Get a list of the initial dimension of every delta from which it is applied ''' + return list(range(self.style_count)) # Each dimension has a delta applied to it + + def set_progressive_stage(self, new_stage: ProgressiveStage): + self.progressive_stage = new_stage + print('Changed progressive stage to: ', new_stage) + + def forward(self, x): + x = self.input_layer(x) + + modulelist = list(self.body._modules.values()) + for i, l in enumerate(modulelist): + x = l(x) + if i == 6: + c1 = x + elif i == 20: + c2 = x + elif i == 23: + c3 = x + + # Infer main W and duplicate it + w0 = self.styles[0](c3) + w = w0.repeat(self.style_count, 1, 1).permute(1, 0, 2) + stage = self.progressive_stage.value + features = c3 + for i in range(1, min(stage + 1, self.style_count)): # Infer additional deltas + if i == self.coarse_ind: + p2 = _upsample_add(c3, self.latlayer1(c2)) # FPN's middle features + features = p2 + elif i == self.middle_ind: + p1 = _upsample_add(p2, self.latlayer2(c1)) # FPN's fine features + features = p1 + delta_i = self.styles[i](features) + w[:, i] += delta_i + return w diff --git a/PTI/models/e4e/latent_codes_pool.py b/PTI/models/e4e/latent_codes_pool.py new file mode 100644 index 0000000000000000000000000000000000000000..0281d4b5e80f8eb26e824fa35b4f908dcb6634e6 --- /dev/null +++ b/PTI/models/e4e/latent_codes_pool.py @@ -0,0 +1,55 @@ +import random +import torch + + +class LatentCodesPool: + """This class implements latent codes buffer that stores previously generated w latent codes. + This buffer enables us to update discriminators using a history of generated w's + rather than the ones produced by the latest encoder. + """ + + def __init__(self, pool_size): + """Initialize the ImagePool class + Parameters: + pool_size (int) -- the size of image buffer, if pool_size=0, no buffer will be created + """ + self.pool_size = pool_size + if self.pool_size > 0: # create an empty pool + self.num_ws = 0 + self.ws = [] + + def query(self, ws): + """Return w's from the pool. + Parameters: + ws: the latest generated w's from the generator + Returns w's from the buffer. + By 50/100, the buffer will return input w's. + By 50/100, the buffer will return w's previously stored in the buffer, + and insert the current w's to the buffer. + """ + if self.pool_size == 0: # if the buffer size is 0, do nothing + return ws + return_ws = [] + for w in ws: # ws.shape: (batch, 512) or (batch, n_latent, 512) + # w = torch.unsqueeze(image.data, 0) + if w.ndim == 2: + i = random.randint(0, len(w) - 1) # apply a random latent index as a candidate + w = w[i] + self.handle_w(w, return_ws) + return_ws = torch.stack(return_ws, 0) # collect all the images and return + return return_ws + + def handle_w(self, w, return_ws): + if self.num_ws < self.pool_size: # if the buffer is not full; keep inserting current codes to the buffer + self.num_ws = self.num_ws + 1 + self.ws.append(w) + return_ws.append(w) + else: + p = random.uniform(0, 1) + if p > 0.5: # by 50% chance, the buffer will return a previously stored latent code, and insert the current code into the buffer + random_id = random.randint(0, self.pool_size - 1) # randint is inclusive + tmp = self.ws[random_id].clone() + self.ws[random_id] = w + return_ws.append(tmp) + else: # by another 50% chance, the buffer will return the current image + return_ws.append(w) diff --git a/PTI/models/e4e/psp.py b/PTI/models/e4e/psp.py new file mode 100644 index 0000000000000000000000000000000000000000..032d8a37d6a7c0ad4635833f35eb75f279c203e9 --- /dev/null +++ b/PTI/models/e4e/psp.py @@ -0,0 +1,97 @@ +import matplotlib +from PTI.configs import paths_config +matplotlib.use('Agg') +import torch +from torch import nn +from PTI.models.e4e.encoders import psp_encoders +from PTI.models.e4e.stylegan2.model import Generator + + +def get_keys(d, name): + if 'state_dict' in d: + d = d['state_dict'] + d_filt = {k[len(name) + 1:]: v for k, v in d.items() if k[:len(name)] == name} + return d_filt + + +class pSp(nn.Module): + + def __init__(self, opts): + super(pSp, self).__init__() + self.opts = opts + # Define architecture + self.encoder = self.set_encoder() + self.decoder = Generator(opts.stylegan_size, 512, 8, channel_multiplier=2) + self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256)) + # Load weights if needed + self.load_weights() + + def set_encoder(self): + if self.opts.encoder_type == 'GradualStyleEncoder': + encoder = psp_encoders.GradualStyleEncoder(50, 'ir_se', self.opts) + elif self.opts.encoder_type == 'Encoder4Editing': + encoder = psp_encoders.Encoder4Editing(50, 'ir_se', self.opts) + else: + raise Exception('{} is not a valid encoders'.format(self.opts.encoder_type)) + return encoder + + def load_weights(self): + if self.opts.checkpoint_path is not None: + print('Loading e4e over the pSp framework from checkpoint: {}'.format(self.opts.checkpoint_path)) + ckpt = torch.load(self.opts.checkpoint_path, map_location='cpu') + self.encoder.load_state_dict(get_keys(ckpt, 'encoder'), strict=True) + self.decoder.load_state_dict(get_keys(ckpt, 'decoder'), strict=True) + self.__load_latent_avg(ckpt) + else: + print('Loading encoders weights from irse50!') + encoder_ckpt = torch.load(paths_config.ir_se50) + self.encoder.load_state_dict(encoder_ckpt, strict=False) + print('Loading decoder weights from pretrained!') + ckpt = torch.load(self.opts.stylegan_weights) + self.decoder.load_state_dict(ckpt['g_ema'], strict=False) + self.__load_latent_avg(ckpt, repeat=self.encoder.style_count) + + def forward(self, x, resize=True, latent_mask=None, input_code=False, randomize_noise=True, + inject_latent=None, return_latents=False, alpha=None): + if input_code: + codes = x + else: + codes = self.encoder(x) + # normalize with respect to the center of an average face + if self.opts.start_from_latent_avg: + if codes.ndim == 2: + codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1)[:, 0, :] + else: + codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1) + + if latent_mask is not None: + for i in latent_mask: + if inject_latent is not None: + if alpha is not None: + codes[:, i] = alpha * inject_latent[:, i] + (1 - alpha) * codes[:, i] + else: + codes[:, i] = inject_latent[:, i] + else: + codes[:, i] = 0 + + input_is_latent = not input_code + images, result_latent = self.decoder([codes], + input_is_latent=input_is_latent, + randomize_noise=randomize_noise, + return_latents=return_latents) + + if resize: + images = self.face_pool(images) + + if return_latents: + return images, result_latent + else: + return images + + def __load_latent_avg(self, ckpt, repeat=None): + if 'latent_avg' in ckpt: + self.latent_avg = ckpt['latent_avg'].to(self.opts.device) + if repeat is not None: + self.latent_avg = self.latent_avg.repeat(repeat, 1) + else: + self.latent_avg = None diff --git a/PTI/models/e4e/stylegan2/__init__.py b/PTI/models/e4e/stylegan2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/models/e4e/stylegan2/model.py b/PTI/models/e4e/stylegan2/model.py new file mode 100644 index 0000000000000000000000000000000000000000..ede4360148e260363887662bae7fe68c987ee60e --- /dev/null +++ b/PTI/models/e4e/stylegan2/model.py @@ -0,0 +1,674 @@ +import math +import random +import torch +from torch import nn +from torch.nn import functional as F + +from .op.fused_act import FusedLeakyReLU, fused_leaky_relu +from .op.upfirdn2d import upfirdn2d + + +class PixelNorm(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, input): + return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8) + + +def make_kernel(k): + k = torch.tensor(k, dtype=torch.float32) + + if k.ndim == 1: + k = k[None, :] * k[:, None] + + k /= k.sum() + + return k + + +class Upsample(nn.Module): + def __init__(self, kernel, factor=2): + super().__init__() + + self.factor = factor + kernel = make_kernel(kernel) * (factor ** 2) + self.register_buffer('kernel', kernel) + + p = kernel.shape[0] - factor + + pad0 = (p + 1) // 2 + factor - 1 + pad1 = p // 2 + + self.pad = (pad0, pad1) + + def forward(self, input): + out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad) + + return out + + +class Downsample(nn.Module): + def __init__(self, kernel, factor=2): + super().__init__() + + self.factor = factor + kernel = make_kernel(kernel) + self.register_buffer('kernel', kernel) + + p = kernel.shape[0] - factor + + pad0 = (p + 1) // 2 + pad1 = p // 2 + + self.pad = (pad0, pad1) + + def forward(self, input): + out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad) + + return out + + +class Blur(nn.Module): + def __init__(self, kernel, pad, upsample_factor=1): + super().__init__() + + kernel = make_kernel(kernel) + + if upsample_factor > 1: + kernel = kernel * (upsample_factor ** 2) + + self.register_buffer('kernel', kernel) + + self.pad = pad + + def forward(self, input): + out = upfirdn2d(input, self.kernel, pad=self.pad) + + return out + + +class EqualConv2d(nn.Module): + def __init__( + self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True + ): + super().__init__() + + self.weight = nn.Parameter( + torch.randn(out_channel, in_channel, kernel_size, kernel_size) + ) + self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2) + + self.stride = stride + self.padding = padding + + if bias: + self.bias = nn.Parameter(torch.zeros(out_channel)) + + else: + self.bias = None + + def forward(self, input): + out = F.conv2d( + input, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.padding, + ) + + return out + + def __repr__(self): + return ( + f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},' + f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})' + ) + + +class EqualLinear(nn.Module): + def __init__( + self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None + ): + super().__init__() + + self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul)) + + if bias: + self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init)) + + else: + self.bias = None + + self.activation = activation + + self.scale = (1 / math.sqrt(in_dim)) * lr_mul + self.lr_mul = lr_mul + + def forward(self, input): + if self.activation: + out = F.linear(input, self.weight * self.scale) + out = fused_leaky_relu(out, self.bias * self.lr_mul) + + else: + out = F.linear( + input, self.weight * self.scale, bias=self.bias * self.lr_mul + ) + + return out + + def __repr__(self): + return ( + f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})' + ) + + +class ScaledLeakyReLU(nn.Module): + def __init__(self, negative_slope=0.2): + super().__init__() + + self.negative_slope = negative_slope + + def forward(self, input): + out = F.leaky_relu(input, negative_slope=self.negative_slope) + + return out * math.sqrt(2) + + +class ModulatedConv2d(nn.Module): + def __init__( + self, + in_channel, + out_channel, + kernel_size, + style_dim, + demodulate=True, + upsample=False, + downsample=False, + blur_kernel=[1, 3, 3, 1], + ): + super().__init__() + + self.eps = 1e-8 + self.kernel_size = kernel_size + self.in_channel = in_channel + self.out_channel = out_channel + self.upsample = upsample + self.downsample = downsample + + if upsample: + factor = 2 + p = (len(blur_kernel) - factor) - (kernel_size - 1) + pad0 = (p + 1) // 2 + factor - 1 + pad1 = p // 2 + 1 + + self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor) + + if downsample: + factor = 2 + p = (len(blur_kernel) - factor) + (kernel_size - 1) + pad0 = (p + 1) // 2 + pad1 = p // 2 + + self.blur = Blur(blur_kernel, pad=(pad0, pad1)) + + fan_in = in_channel * kernel_size ** 2 + self.scale = 1 / math.sqrt(fan_in) + self.padding = kernel_size // 2 + + self.weight = nn.Parameter( + torch.randn(1, out_channel, in_channel, kernel_size, kernel_size) + ) + + self.modulation = EqualLinear(style_dim, in_channel, bias_init=1) + + self.demodulate = demodulate + + def __repr__(self): + return ( + f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, ' + f'upsample={self.upsample}, downsample={self.downsample})' + ) + + def forward(self, input, style): + batch, in_channel, height, width = input.shape + + style = self.modulation(style).view(batch, 1, in_channel, 1, 1) + weight = self.scale * self.weight * style + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8) + weight = weight * demod.view(batch, self.out_channel, 1, 1, 1) + + weight = weight.view( + batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size + ) + + if self.upsample: + input = input.view(1, batch * in_channel, height, width) + weight = weight.view( + batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size + ) + weight = weight.transpose(1, 2).reshape( + batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size + ) + out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch) + _, _, height, width = out.shape + out = out.view(batch, self.out_channel, height, width) + out = self.blur(out) + + elif self.downsample: + input = self.blur(input) + _, _, height, width = input.shape + input = input.view(1, batch * in_channel, height, width) + out = F.conv2d(input, weight, padding=0, stride=2, groups=batch) + _, _, height, width = out.shape + out = out.view(batch, self.out_channel, height, width) + + else: + input = input.view(1, batch * in_channel, height, width) + out = F.conv2d(input, weight, padding=self.padding, groups=batch) + _, _, height, width = out.shape + out = out.view(batch, self.out_channel, height, width) + + return out + + +class NoiseInjection(nn.Module): + def __init__(self): + super().__init__() + + self.weight = nn.Parameter(torch.zeros(1)) + + def forward(self, image, noise=None): + if noise is None: + batch, _, height, width = image.shape + noise = image.new_empty(batch, 1, height, width).normal_() + + return image + self.weight * noise + + +class ConstantInput(nn.Module): + def __init__(self, channel, size=4): + super().__init__() + + self.input = nn.Parameter(torch.randn(1, channel, size, size)) + + def forward(self, input): + batch = input.shape[0] + out = self.input.repeat(batch, 1, 1, 1) + + return out + + +class StyledConv(nn.Module): + def __init__( + self, + in_channel, + out_channel, + kernel_size, + style_dim, + upsample=False, + blur_kernel=[1, 3, 3, 1], + demodulate=True, + ): + super().__init__() + + self.conv = ModulatedConv2d( + in_channel, + out_channel, + kernel_size, + style_dim, + upsample=upsample, + blur_kernel=blur_kernel, + demodulate=demodulate, + ) + + self.noise = NoiseInjection() + # self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1)) + # self.activate = ScaledLeakyReLU(0.2) + self.activate = FusedLeakyReLU(out_channel) + + def forward(self, input, style, noise=None): + out = self.conv(input, style) + out = self.noise(out, noise=noise) + # out = out + self.bias + out = self.activate(out) + + return out + + +class ToRGB(nn.Module): + def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]): + super().__init__() + + if upsample: + self.upsample = Upsample(blur_kernel) + + self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + def forward(self, input, style, skip=None): + out = self.conv(input, style) + out = out + self.bias + + if skip is not None: + skip = self.upsample(skip) + + out = out + skip + + return out + + +class Generator(nn.Module): + def __init__( + self, + size, + style_dim, + n_mlp, + channel_multiplier=2, + blur_kernel=[1, 3, 3, 1], + lr_mlp=0.01, + ): + super().__init__() + + self.size = size + + self.style_dim = style_dim + + layers = [PixelNorm()] + + for i in range(n_mlp): + layers.append( + EqualLinear( + style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu' + ) + ) + + self.style = nn.Sequential(*layers) + + self.channels = { + 4: 512, + 8: 512, + 16: 512, + 32: 512, + 64: 256 * channel_multiplier, + 128: 128 * channel_multiplier, + 256: 64 * channel_multiplier, + 512: 32 * channel_multiplier, + 1024: 16 * channel_multiplier, + } + + self.input = ConstantInput(self.channels[4]) + self.conv1 = StyledConv( + self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel + ) + self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False) + + self.log_size = int(math.log(size, 2)) + self.num_layers = (self.log_size - 2) * 2 + 1 + + self.convs = nn.ModuleList() + self.upsamples = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + self.noises = nn.Module() + + in_channel = self.channels[4] + + for layer_idx in range(self.num_layers): + res = (layer_idx + 5) // 2 + shape = [1, 1, 2 ** res, 2 ** res] + self.noises.register_buffer(f'noise_{layer_idx}', torch.randn(*shape)) + + for i in range(3, self.log_size + 1): + out_channel = self.channels[2 ** i] + + self.convs.append( + StyledConv( + in_channel, + out_channel, + 3, + style_dim, + upsample=True, + blur_kernel=blur_kernel, + ) + ) + + self.convs.append( + StyledConv( + out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel + ) + ) + + self.to_rgbs.append(ToRGB(out_channel, style_dim)) + + in_channel = out_channel + + self.n_latent = self.log_size * 2 - 2 + + def make_noise(self): + device = self.input.input.device + + noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)] + + for i in range(3, self.log_size + 1): + for _ in range(2): + noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device)) + + return noises + + def mean_latent(self, n_latent): + latent_in = torch.randn( + n_latent, self.style_dim, device=self.input.input.device + ) + latent = self.style(latent_in).mean(0, keepdim=True) + + return latent + + def get_latent(self, input): + return self.style(input) + + def forward( + self, + styles, + return_latents=False, + return_features=False, + inject_index=None, + truncation=1, + truncation_latent=None, + input_is_latent=False, + noise=None, + randomize_noise=True, + ): + if not input_is_latent: + styles = [self.style(s) for s in styles] + + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers + else: + noise = [ + getattr(self.noises, f'noise_{i}') for i in range(self.num_layers) + ] + + if truncation < 1: + style_t = [] + + for style in styles: + style_t.append( + truncation_latent + truncation * (style - truncation_latent) + ) + + styles = style_t + + if len(styles) < 2: + inject_index = self.n_latent + + if styles[0].ndim < 3: + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: + latent = styles[0] + + else: + if inject_index is None: + inject_index = random.randint(1, self.n_latent - 1) + + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1) + + latent = torch.cat([latent, latent2], 1) + + out = self.input(latent) + out = self.conv1(out, latent[:, 0], noise=noise[0]) + + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs + ): + out = conv1(out, latent[:, i], noise=noise1) + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) + + i += 2 + + image = skip + + if return_latents: + return image, latent + elif return_features: + return image, out + else: + return image, None + + +class ConvLayer(nn.Sequential): + def __init__( + self, + in_channel, + out_channel, + kernel_size, + downsample=False, + blur_kernel=[1, 3, 3, 1], + bias=True, + activate=True, + ): + layers = [] + + if downsample: + factor = 2 + p = (len(blur_kernel) - factor) + (kernel_size - 1) + pad0 = (p + 1) // 2 + pad1 = p // 2 + + layers.append(Blur(blur_kernel, pad=(pad0, pad1))) + + stride = 2 + self.padding = 0 + + else: + stride = 1 + self.padding = kernel_size // 2 + + layers.append( + EqualConv2d( + in_channel, + out_channel, + kernel_size, + padding=self.padding, + stride=stride, + bias=bias and not activate, + ) + ) + + if activate: + if bias: + layers.append(FusedLeakyReLU(out_channel)) + + else: + layers.append(ScaledLeakyReLU(0.2)) + + super().__init__(*layers) + + +class ResBlock(nn.Module): + def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]): + super().__init__() + + self.conv1 = ConvLayer(in_channel, in_channel, 3) + self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True) + + self.skip = ConvLayer( + in_channel, out_channel, 1, downsample=True, activate=False, bias=False + ) + + def forward(self, input): + out = self.conv1(input) + out = self.conv2(out) + + skip = self.skip(input) + out = (out + skip) / math.sqrt(2) + + return out + + +class Discriminator(nn.Module): + def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]): + super().__init__() + + channels = { + 4: 512, + 8: 512, + 16: 512, + 32: 512, + 64: 256 * channel_multiplier, + 128: 128 * channel_multiplier, + 256: 64 * channel_multiplier, + 512: 32 * channel_multiplier, + 1024: 16 * channel_multiplier, + } + + convs = [ConvLayer(3, channels[size], 1)] + + log_size = int(math.log(size, 2)) + + in_channel = channels[size] + + for i in range(log_size, 2, -1): + out_channel = channels[2 ** (i - 1)] + + convs.append(ResBlock(in_channel, out_channel, blur_kernel)) + + in_channel = out_channel + + self.convs = nn.Sequential(*convs) + + self.stddev_group = 4 + self.stddev_feat = 1 + + self.final_conv = ConvLayer(in_channel + 1, channels[4], 3) + self.final_linear = nn.Sequential( + EqualLinear(channels[4] * 4 * 4, channels[4], activation='fused_lrelu'), + EqualLinear(channels[4], 1), + ) + + def forward(self, input): + out = self.convs(input) + + batch, channel, height, width = out.shape + group = min(batch, self.stddev_group) + stddev = out.view( + group, -1, self.stddev_feat, channel // self.stddev_feat, height, width + ) + stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8) + stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2) + stddev = stddev.repeat(group, 1, height, width) + out = torch.cat([out, stddev], 1) + + out = self.final_conv(out) + + out = out.view(batch, -1) + out = self.final_linear(out) + + return out diff --git a/PTI/models/e4e/stylegan2/op/__init__.py b/PTI/models/e4e/stylegan2/op/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d0918d92285955855be89f00096b888ee5597ce3 --- /dev/null +++ b/PTI/models/e4e/stylegan2/op/__init__.py @@ -0,0 +1,2 @@ +from .fused_act import FusedLeakyReLU, fused_leaky_relu +from .upfirdn2d import upfirdn2d diff --git a/PTI/models/e4e/stylegan2/op/fused_act.py b/PTI/models/e4e/stylegan2/op/fused_act.py new file mode 100644 index 0000000000000000000000000000000000000000..90949545ba955dabf2e17d8cf5e524d5cb190a63 --- /dev/null +++ b/PTI/models/e4e/stylegan2/op/fused_act.py @@ -0,0 +1,34 @@ +import os + +import torch +from torch import nn +from torch.nn import functional as F +from torch.autograd import Function + + +module_path = os.path.dirname(__file__) + + + +class FusedLeakyReLU(nn.Module): + def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5): + super().__init__() + + self.bias = nn.Parameter(torch.zeros(channel)) + self.negative_slope = negative_slope + self.scale = scale + + def forward(self, input): + return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) + + +def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5): + rest_dim = [1] * (input.ndim - bias.ndim - 1) + input = input.cuda() + return ( + F.leaky_relu( + input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope + ) + * scale + ) + diff --git a/PTI/models/e4e/stylegan2/op/fused_bias_act.cpp b/PTI/models/e4e/stylegan2/op/fused_bias_act.cpp new file mode 100644 index 0000000000000000000000000000000000000000..02be898f970bcc8ea297867fcaa4e71b24b3d949 --- /dev/null +++ b/PTI/models/e4e/stylegan2/op/fused_bias_act.cpp @@ -0,0 +1,21 @@ +#include + + +torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, + int act, int grad, float alpha, float scale); + +#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) + +torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, + int act, int grad, float alpha, float scale) { + CHECK_CUDA(input); + CHECK_CUDA(bias); + + return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)"); +} \ No newline at end of file diff --git a/PTI/models/e4e/stylegan2/op/fused_bias_act_kernel.cu b/PTI/models/e4e/stylegan2/op/fused_bias_act_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..c9fa56fea7ede7072dc8925cfb0148f136eb85b8 --- /dev/null +++ b/PTI/models/e4e/stylegan2/op/fused_bias_act_kernel.cu @@ -0,0 +1,99 @@ +// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. +// +// This work is made available under the Nvidia Source Code License-NC. +// To view a copy of this license, visit +// https://nvlabs.github.io/stylegan2/license.html + +#include + +#include +#include +#include +#include + +#include +#include + + +template +static __global__ void fused_bias_act_kernel(scalar_t* out, const scalar_t* p_x, const scalar_t* p_b, const scalar_t* p_ref, + int act, int grad, scalar_t alpha, scalar_t scale, int loop_x, int size_x, int step_b, int size_b, int use_bias, int use_ref) { + int xi = blockIdx.x * loop_x * blockDim.x + threadIdx.x; + + scalar_t zero = 0.0; + + for (int loop_idx = 0; loop_idx < loop_x && xi < size_x; loop_idx++, xi += blockDim.x) { + scalar_t x = p_x[xi]; + + if (use_bias) { + x += p_b[(xi / step_b) % size_b]; + } + + scalar_t ref = use_ref ? p_ref[xi] : zero; + + scalar_t y; + + switch (act * 10 + grad) { + default: + case 10: y = x; break; + case 11: y = x; break; + case 12: y = 0.0; break; + + case 30: y = (x > 0.0) ? x : x * alpha; break; + case 31: y = (ref > 0.0) ? x : x * alpha; break; + case 32: y = 0.0; break; + } + + out[xi] = y * scale; + } +} + + +torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, + int act, int grad, float alpha, float scale) { + int curDevice = -1; + cudaGetDevice(&curDevice); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); + + auto x = input.contiguous(); + auto b = bias.contiguous(); + auto ref = refer.contiguous(); + + int use_bias = b.numel() ? 1 : 0; + int use_ref = ref.numel() ? 1 : 0; + + int size_x = x.numel(); + int size_b = b.numel(); + int step_b = 1; + + for (int i = 1 + 1; i < x.dim(); i++) { + step_b *= x.size(i); + } + + int loop_x = 4; + int block_size = 4 * 32; + int grid_size = (size_x - 1) / (loop_x * block_size) + 1; + + auto y = torch::empty_like(x); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "fused_bias_act_kernel", [&] { + fused_bias_act_kernel<<>>( + y.data_ptr(), + x.data_ptr(), + b.data_ptr(), + ref.data_ptr(), + act, + grad, + alpha, + scale, + loop_x, + size_x, + step_b, + size_b, + use_bias, + use_ref + ); + }); + + return y; +} \ No newline at end of file diff --git a/PTI/models/e4e/stylegan2/op/upfirdn2d.cpp b/PTI/models/e4e/stylegan2/op/upfirdn2d.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d2e633dc896433c205e18bc3e455539192ff968e --- /dev/null +++ b/PTI/models/e4e/stylegan2/op/upfirdn2d.cpp @@ -0,0 +1,23 @@ +#include + + +torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel, + int up_x, int up_y, int down_x, int down_y, + int pad_x0, int pad_x1, int pad_y0, int pad_y1); + +#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) + +torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel, + int up_x, int up_y, int down_x, int down_y, + int pad_x0, int pad_x1, int pad_y0, int pad_y1) { + CHECK_CUDA(input); + CHECK_CUDA(kernel); + + return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)"); +} \ No newline at end of file diff --git a/PTI/models/e4e/stylegan2/op/upfirdn2d.py b/PTI/models/e4e/stylegan2/op/upfirdn2d.py new file mode 100644 index 0000000000000000000000000000000000000000..02fc25af780868d9b883631eb6b03a25c225d745 --- /dev/null +++ b/PTI/models/e4e/stylegan2/op/upfirdn2d.py @@ -0,0 +1,60 @@ +import os + +import torch +from torch.nn import functional as F + + +module_path = os.path.dirname(__file__) + + + +def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): + out = upfirdn2d_native( + input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1] + ) + + return out + + +def upfirdn2d_native( + input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 +): + _, channel, in_h, in_w = input.shape + input = input.reshape(-1, in_h, in_w, 1) + + _, in_h, in_w, minor = input.shape + kernel_h, kernel_w = kernel.shape + + out = input.view(-1, in_h, 1, in_w, 1, minor) + out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) + out = out.view(-1, in_h * up_y, in_w * up_x, minor) + + out = F.pad( + out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] + ) + out = out[ + :, + max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), + max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), + :, + ] + + out = out.permute(0, 3, 1, 2) + out = out.reshape( + [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] + ) + w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) + out = F.conv2d(out, w) + out = out.reshape( + -1, + minor, + in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, + in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, + ) + out = out.permute(0, 2, 3, 1) + out = out[:, ::down_y, ::down_x, :] + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 + + return out.view(-1, channel, out_h, out_w) \ No newline at end of file diff --git a/PTI/models/e4e/stylegan2/op/upfirdn2d_kernel.cu b/PTI/models/e4e/stylegan2/op/upfirdn2d_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..2a710aa6adc3d43ac93136a1814e3c39970e1c7e --- /dev/null +++ b/PTI/models/e4e/stylegan2/op/upfirdn2d_kernel.cu @@ -0,0 +1,272 @@ +// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. +// +// This work is made available under the Nvidia Source Code License-NC. +// To view a copy of this license, visit +// https://nvlabs.github.io/stylegan2/license.html + +#include + +#include +#include +#include +#include + +#include +#include + + +static __host__ __device__ __forceinline__ int floor_div(int a, int b) { + int c = a / b; + + if (c * b > a) { + c--; + } + + return c; +} + + +struct UpFirDn2DKernelParams { + int up_x; + int up_y; + int down_x; + int down_y; + int pad_x0; + int pad_x1; + int pad_y0; + int pad_y1; + + int major_dim; + int in_h; + int in_w; + int minor_dim; + int kernel_h; + int kernel_w; + int out_h; + int out_w; + int loop_major; + int loop_x; +}; + + +template +__global__ void upfirdn2d_kernel(scalar_t* out, const scalar_t* input, const scalar_t* kernel, const UpFirDn2DKernelParams p) { + const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1; + const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1; + + __shared__ volatile float sk[kernel_h][kernel_w]; + __shared__ volatile float sx[tile_in_h][tile_in_w]; + + int minor_idx = blockIdx.x; + int tile_out_y = minor_idx / p.minor_dim; + minor_idx -= tile_out_y * p.minor_dim; + tile_out_y *= tile_out_h; + int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w; + int major_idx_base = blockIdx.z * p.loop_major; + + if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h | major_idx_base >= p.major_dim) { + return; + } + + for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w; tap_idx += blockDim.x) { + int ky = tap_idx / kernel_w; + int kx = tap_idx - ky * kernel_w; + scalar_t v = 0.0; + + if (kx < p.kernel_w & ky < p.kernel_h) { + v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)]; + } + + sk[ky][kx] = v; + } + + for (int loop_major = 0, major_idx = major_idx_base; loop_major < p.loop_major & major_idx < p.major_dim; loop_major++, major_idx++) { + for (int loop_x = 0, tile_out_x = tile_out_x_base; loop_x < p.loop_x & tile_out_x < p.out_w; loop_x++, tile_out_x += tile_out_w) { + int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0; + int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0; + int tile_in_x = floor_div(tile_mid_x, up_x); + int tile_in_y = floor_div(tile_mid_y, up_y); + + __syncthreads(); + + for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w; in_idx += blockDim.x) { + int rel_in_y = in_idx / tile_in_w; + int rel_in_x = in_idx - rel_in_y * tile_in_w; + int in_x = rel_in_x + tile_in_x; + int in_y = rel_in_y + tile_in_y; + + scalar_t v = 0.0; + + if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) { + v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim + minor_idx]; + } + + sx[rel_in_y][rel_in_x] = v; + } + + __syncthreads(); + for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w; out_idx += blockDim.x) { + int rel_out_y = out_idx / tile_out_w; + int rel_out_x = out_idx - rel_out_y * tile_out_w; + int out_x = rel_out_x + tile_out_x; + int out_y = rel_out_y + tile_out_y; + + int mid_x = tile_mid_x + rel_out_x * down_x; + int mid_y = tile_mid_y + rel_out_y * down_y; + int in_x = floor_div(mid_x, up_x); + int in_y = floor_div(mid_y, up_y); + int rel_in_x = in_x - tile_in_x; + int rel_in_y = in_y - tile_in_y; + int kernel_x = (in_x + 1) * up_x - mid_x - 1; + int kernel_y = (in_y + 1) * up_y - mid_y - 1; + + scalar_t v = 0.0; + + #pragma unroll + for (int y = 0; y < kernel_h / up_y; y++) + #pragma unroll + for (int x = 0; x < kernel_w / up_x; x++) + v += sx[rel_in_y + y][rel_in_x + x] * sk[kernel_y + y * up_y][kernel_x + x * up_x]; + + if (out_x < p.out_w & out_y < p.out_h) { + out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + minor_idx] = v; + } + } + } + } +} + + +torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel, + int up_x, int up_y, int down_x, int down_y, + int pad_x0, int pad_x1, int pad_y0, int pad_y1) { + int curDevice = -1; + cudaGetDevice(&curDevice); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); + + UpFirDn2DKernelParams p; + + auto x = input.contiguous(); + auto k = kernel.contiguous(); + + p.major_dim = x.size(0); + p.in_h = x.size(1); + p.in_w = x.size(2); + p.minor_dim = x.size(3); + p.kernel_h = k.size(0); + p.kernel_w = k.size(1); + p.up_x = up_x; + p.up_y = up_y; + p.down_x = down_x; + p.down_y = down_y; + p.pad_x0 = pad_x0; + p.pad_x1 = pad_x1; + p.pad_y0 = pad_y0; + p.pad_y1 = pad_y1; + + p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) / p.down_y; + p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) / p.down_x; + + auto out = at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options()); + + int mode = -1; + + int tile_out_h; + int tile_out_w; + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 1; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 3 && p.kernel_w <= 3) { + mode = 2; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 3; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 2 && p.kernel_w <= 2) { + mode = 4; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 5; + tile_out_h = 8; + tile_out_w = 32; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && p.kernel_h <= 2 && p.kernel_w <= 2) { + mode = 6; + tile_out_h = 8; + tile_out_w = 32; + } + + dim3 block_size; + dim3 grid_size; + + if (tile_out_h > 0 && tile_out_w) { + p.loop_major = (p.major_dim - 1) / 16384 + 1; + p.loop_x = 1; + block_size = dim3(32 * 8, 1, 1); + grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim, + (p.out_w - 1) / (p.loop_x * tile_out_w) + 1, + (p.major_dim - 1) / p.loop_major + 1); + } + + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] { + switch (mode) { + case 1: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + + case 2: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + + case 3: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + + case 4: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + + case 5: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + + case 6: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + } + }); + + return out; +} \ No newline at end of file diff --git a/PTI/notebooks/align_data.ipynb b/PTI/notebooks/align_data.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..439b62e469c09c39585e72e67ddb190ec1d07a82 --- /dev/null +++ b/PTI/notebooks/align_data.ipynb @@ -0,0 +1,152 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "continuous-captain", + "metadata": {}, + "outputs": [], + "source": [ + "cd .." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "closing-bishop", + "metadata": {}, + "outputs": [], + "source": [ + "import dlib\n", + "import glob\n", + "import os\n", + "from tqdm import tqdm\n", + "from utils.alignment import align_face" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "false-healthcare", + "metadata": {}, + "outputs": [], + "source": [ + "images_path = '/disk2/danielroich/Sandbox/Data/Images/barcelona'\n", + "SHAPE_PREDICTOR_PATH = 'pretrained_models/shape_predictor_68_face_landmarks.dat'\n", + "IMAGE_SIZE = 1024" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "coordinate-australia", + "metadata": {}, + "outputs": [], + "source": [ + "predictor = dlib.shape_predictor(SHAPE_PREDICTOR_PATH)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "accurate-allowance", + "metadata": {}, + "outputs": [], + "source": [ + "os.chdir(images_path)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "australian-yellow", + "metadata": {}, + "outputs": [], + "source": [ + "images_names = glob.glob(f'*')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "secure-concentrate", + "metadata": {}, + "outputs": [], + "source": [ + "images_names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "basic-pickup", + "metadata": {}, + "outputs": [], + "source": [ + "aligned_images = []\n", + "for image_name in tqdm(images_names):\n", + " try:\n", + " aligned_image = align_face(filepath=f'{images_path}/{image_name}',\n", + " predictor=predictor, output_size=IMAGE_SIZE)\n", + " aligned_images.append(aligned_image)\n", + " except Exception as e:\n", + " print(e)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "textile-extraction", + "metadata": {}, + "outputs": [], + "source": [ + "os.makedirs(f'{images_path}/aligned', exist_ok=True)\n", + "os.makedirs(f'{images_path}/aligned/0', exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "civic-dictionary", + "metadata": {}, + "outputs": [], + "source": [ + "for image, name in zip(aligned_images,images_names):\n", + " real_name = name.split('.')[0]\n", + " try:\n", + " image.save(f'{images_path}/aligned/0/{real_name}.jpeg')\n", + " except Exception as e:\n", + " print(e)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "tough-celebrity", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/PTI/notebooks/inference_playground.ipynb b/PTI/notebooks/inference_playground.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b6616cc1a268067ea3f5a2663b43f3e4aea9b038 --- /dev/null +++ b/PTI/notebooks/inference_playground.ipynb @@ -0,0 +1,871 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "inference_playground.ipynb", + "provenance": [], + "collapsed_sections": [ + "FXXyiiTv-8IO", + "dF7BQqcepevP", + "GHBpF6YpvvAQ", + "J9OcEhQBeI6N", + "tVJKCZHp7lp1", + "YoEym2Z060l3", + "zl8wBNWanFbC", + "oVvfNAs9p08K", + "XdvGEIciqEBX", + "0bsEvunp6KVg", + "azSu-ZdZ3kmG", + "_uq1fvtCrbjW" + ] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "jKYXW52b34So" + }, + "source": [ + "PyDrive activation Used for bypassing Google Drive download rate limit" + ] + }, + { + "cell_type": "code", + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "cellView": "form", + "id": "3bkZFwv534Sp" + }, + "source": [ + "from pydrive.auth import GoogleAuth\n", + "from pydrive.drive import GoogleDrive\n", + "from google.colab import auth\n", + "from oauth2client.client import GoogleCredentials\n", + "\n", + "download_with_pydrive = True #@param {type:\"boolean\"} \n", + "\n", + "class Downloader(object):\n", + " def __init__(self, use_pydrive):\n", + " self.use_pydrive = use_pydrive\n", + "\n", + " if self.use_pydrive:\n", + " self.authenticate()\n", + " \n", + " def authenticate(self):\n", + " auth.authenticate_user()\n", + " gauth = GoogleAuth()\n", + " gauth.credentials = GoogleCredentials.get_application_default()\n", + " self.drive = GoogleDrive(gauth)\n", + " \n", + " def download_file(self, file_id, file_dst):\n", + " if self.use_pydrive:\n", + " downloaded = self.drive.CreateFile({'id':file_id})\n", + " downloaded.FetchMetadata(fetch_all=True)\n", + " downloaded.GetContentFile(file_dst)\n", + " else:\n", + " !gdown --id $file_id -O $file_dst\n", + "\n", + "downloader = Downloader(download_with_pydrive)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FXXyiiTv-8IO" + }, + "source": [ + "## Step 1 - Install Packages required by PTI" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "N2S-zNYvoIb_" + }, + "source": [ + "## Other packages are already builtin in the Colab interpreter\n", + "!pip install wandb\n", + "!pip install lpips\n", + "\n", + "## Used for faster inference of StyleGAN by enabling C++ code compilation\n", + "\n", + "!wget https://github.com/ninja-build/ninja/releases/download/v1.8.2/ninja-linux.zip\n", + "!sudo unzip ninja-linux.zip -d /usr/local/bin/\n", + "!sudo update-alternatives --install /usr/bin/ninja ninja /usr/local/bin/ninja 1 --force" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J9OcEhQBeI6N" + }, + "source": [ + "## Step 2 - Download Pretrained models" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ISvQXsspakIU" + }, + "source": [ + "import os\n", + "os.chdir('/content')\n", + "CODE_DIR = 'PTI'" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "HqYW5F1JapU2" + }, + "source": [ + "!git clone https://github.com/danielroich/PTI.git $CODE_DIR" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "PPM0ltx0cwim" + }, + "source": [ + "os.chdir(f'./{CODE_DIR}')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "iAL19fmlc1bH" + }, + "source": [ + "import os\n", + "import sys\n", + "import pickle\n", + "import numpy as np\n", + "from PIL import Image\n", + "import torch\n", + "from configs import paths_config, hyperparameters, global_config\n", + "from utils.align_data import pre_process_images\n", + "from scripts.run_pti import run_PTI\n", + "from IPython.display import display\n", + "import matplotlib.pyplot as plt\n", + "from scripts.latent_editor_wrapper import LatentEditorWrapper" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "kFDtNLwK6l4H" + }, + "source": [ + "current_directory = os.getcwd()\n", + "save_path = os.path.join(os.path.dirname(current_directory), CODE_DIR, \"pretrained_models\")\n", + "os.makedirs(save_path, exist_ok=True)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "uYd75JUw68sT" + }, + "source": [ + "## Download pretrained StyleGAN on FFHQ 1024x1024\n", + "downloader.download_file(\"125OG7SMkXI-Kf2aqiwLLHyCvSW-gZk3M\", os.path.join(save_path, 'ffhq.pkl'))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "IFQ6G14Y2fnj" + }, + "source": [ + "## Download Dlib tool for alingment, used for preprocessing images before PTI\n", + "downloader.download_file(\"1xPmn19T6Bdd-_RfCVlgNBbfYoh1muYxR\", os.path.join(save_path, 'align.dat'))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tVJKCZHp7lp1" + }, + "source": [ + "## Step 3 - Configuration Setup" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JDVVvzkW7sQH" + }, + "source": [ + "image_dir_name = 'image'\n", + "\n", + "## If set to true download desired image from given url. If set to False, assumes you have uploaded personal image to\n", + "## 'image_original' dir\n", + "use_image_online = True\n", + "image_name = 'personal_image'\n", + "use_multi_id_training = False\n", + "global_config.device = 'cuda'\n", + "paths_config.e4e = '/content/PTI/pretrained_models/e4e_ffhq_encode.pt'\n", + "paths_config.input_data_id = image_dir_name\n", + "paths_config.input_data_path = f'/content/PTI/{image_dir_name}_processed'\n", + "paths_config.stylegan2_ada_ffhq = '/content/PTI/pretrained_models/ffhq.pkl'\n", + "paths_config.checkpoints_dir = '/content/PTI/'\n", + "paths_config.style_clip_pretrained_mappers = '/content/PTI/pretrained_models'\n", + "hyperparameters.use_locality_regularization = False" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YoEym2Z060l3" + }, + "source": [ + "## Step 4 - Preproccess Data" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MhBRxOWLgDU_" + }, + "source": [ + "os.makedirs(f'./{image_dir_name}_original', exist_ok=True)\n", + "os.makedirs(f'./{image_dir_name}_processed', exist_ok=True)\n", + "os.chdir(f'./{image_dir_name}_original')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "c4qh03cFebvN" + }, + "source": [ + "## Download real face image\n", + "## If you want to use your own image skip this part and upload an image/images of your choosing to image_original dir\n", + "if use_image_online:\n", + " !wget -O personal_image.jpg https://static01.nyt.com/images/2019/09/09/opinion/09Hunter1/09Hunter1-superJumbo.jpg ## Photo of Sarena Wiliams" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 983 + }, + "id": "aGrZK2AFAYBR", + "outputId": "b73096b0-1f42-46d3-d6a8-c96ac3a6b42a" + }, + "source": [ + "original_image = Image.open(f'{image_name}.jpg')\n", + "original_image" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9Vv7NxY5klQd" + }, + "source": [ + "os.chdir('/content/PTI')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "mU0F_pfKg5Ki", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "167040ad-e3c3-4652-bc51-6b3187ad64bc" + }, + "source": [ + "pre_process_images(f'/content/PTI/{image_dir_name}_original')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "100%|██████████| 1/1 [00:08<00:00, 8.19s/it]\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Rqi4FVQSBLoE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 529 + }, + "outputId": "83f7bfaa-1494-4f60-e7d1-f99528b944a5" + }, + "source": [ + "aligned_image = Image.open(f'/content/PTI/{image_dir_name}_processed/{image_name}.jpeg')\n", + "aligned_image.resize((512,512))" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zl8wBNWanFbC" + }, + "source": [ + "## Step 5 - Invert images using PTI" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "cifqLGPT1QSh" + }, + "source": [ + "In order to run PTI and use StyleGAN2-ada, the cwd should the parent of 'torch_utils' and 'dnnlib'.\n", + "\n", + "In case use_multi_id_training is set to True and many images are inverted simultaneously\n", + "activating the regularization to keep the *W* Space intact is recommended.\n", + "\n", + "If indeed the regularization is activated then please increase the number of pti steps from 350 to 450 at least\n", + "using hyperparameters.max_pti_steps" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "uNY_3eNPkfH0" + }, + "source": [ + "os.chdir('/content/PTI')\n", + "model_id = run_PTI(use_wandb=False, use_multi_id_training=use_multi_id_training)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oVvfNAs9p08K" + }, + "source": [ + "## Visualize results" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Jm-oNVEI__fg" + }, + "source": [ + "def display_alongside_source_image(images): \n", + " res = np.concatenate([np.array(image) for image in images], axis=1) \n", + " return Image.fromarray(res) " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "vW5Smx-q4M0Q" + }, + "source": [ + "def load_generators(model_id, image_name):\n", + " with open(paths_config.stylegan2_ada_ffhq, 'rb') as f:\n", + " old_G = pickle.load(f)['G_ema'].cuda()\n", + " \n", + " with open(f'{paths_config.checkpoints_dir}/model_{model_id}_{image_name}.pt', 'rb') as f_new: \n", + " new_G = torch.load(f_new).cuda()\n", + "\n", + " return old_G, new_G" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "3qk0v9SOswW9" + }, + "source": [ + "generator_type = paths_config.multi_id_model_type if use_multi_id_training else image_name\n", + "old_G, new_G = load_generators(model_id, generator_type)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "6XNDq0nIFArI" + }, + "source": [ + "def plot_syn_images(syn_images): \n", + " for img in syn_images: \n", + " img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).detach().cpu().numpy()[0] \n", + " plt.axis('off') \n", + " resized_image = Image.fromarray(img,mode='RGB').resize((256,256)) \n", + " display(resized_image) \n", + " del img \n", + " del resized_image \n", + " torch.cuda.empty_cache()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "id": "yiJUiCGPvkuK" + }, + "source": [ + "If multi_id_training was used for several images.\n", + "You can alter the w_pivot index which is currently configured to 0, and then running\n", + "the visualization code again. Using the same generator on different latent codes." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "xpcsTHvB_nsG" + }, + "source": [ + "w_path_dir = f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}'\n", + "embedding_dir = f'{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}'\n", + "w_pivot = torch.load(f'{embedding_dir}/0.pt')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "6GnCz5UXRD_B" + }, + "source": [ + "old_image = old_G.synthesis(w_pivot, noise_mode='const', force_fp32 = True)\n", + "new_image = new_G.synthesis(w_pivot, noise_mode='const', force_fp32 = True)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "_HI4CDtdRWiM" + }, + "source": [ + "print('Upper image is the inversion before Pivotal Tuning and the lower image is the product of pivotal tuning')\n", + "plot_syn_images([old_image, new_image])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XdvGEIciqEBX" + }, + "source": [ + "## InterfaceGAN edits" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GIiLESK5RZRH" + }, + "source": [ + "latent_editor = LatentEditorWrapper()\n", + "latents_after_edit = latent_editor.get_single_interface_gan_edits(w_pivot, [-2, 2])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WZipWotFphHj" + }, + "source": [ + "In order to get different edits. Such as younger face or make the face smile more. Please change the factors passed to \"get_single_interface_gan_edits\".\n", + "Currently the factors are [-2,2]. You can pass for example: range(-3,3)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "W5BuOslS39jr" + }, + "source": [ + "for direction, factor_and_edit in latents_after_edit.items():\n", + " print(f'Showing {direction} change')\n", + " for latent in factor_and_edit.values():\n", + " old_image = old_G.synthesis(latent, noise_mode='const', force_fp32 = True)\n", + " new_image = new_G.synthesis(latent, noise_mode='const', force_fp32 = True)\n", + " plot_syn_images([old_image, new_image])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X9-rcE9RqG__" + }, + "source": [ + "## StyleCLIP editing" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0bsEvunp6KVg" + }, + "source": [ + "### Download pretrained models" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "qCUt4tI956ei" + }, + "source": [ + "mappers_base_dir = '/content/PTI/pretrained_models'" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "8SpnfqGc57Uz" + }, + "source": [ + "# More pretrained mappers can be found at: \"https://github.com/orpatashnik/StyleCLIP/blob/main/utils.py\"\n", + "# Download Afro mapper\n", + "downloader.download_file(\"1i5vAqo4z0I-Yon3FNft_YZOq7ClWayQJ\", os.path.join(mappers_base_dir, 'afro.pt'))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "edlFgsbOkdDO" + }, + "source": [ + "# Download Mohawk mapper\n", + "downloader.download_file(\"1oMMPc8iQZ7dhyWavZ7VNWLwzf9aX4C09\", os.path.join(mappers_base_dir, 'mohawk.pt'))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "7Mb43lOD8Rnb" + }, + "source": [ + "# Download e4e encoder, used for the first inversion step instead on the W inversion.\n", + "downloader.download_file(\"1cUv_reLE6k3604or78EranS7XzuVMWeO\", os.path.join(mappers_base_dir, 'e4e_ffhq_encode.pt'))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "azSu-ZdZ3kmG" + }, + "source": [ + "### Use PTI with e4e backbone for StyleCLIP" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hy1BHTZY3jNt" + }, + "source": [ + "# Changing first_inv_type to W+ makes the PTI use e4e encoder instead of W inversion in the first step\n", + "hyperparameters.first_inv_type = 'w+'\n", + "os.chdir('/content/PTI')\n", + "model_id = run_PTI(use_wandb=False, use_multi_id_training=use_multi_id_training)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Bf_076P7022B" + }, + "source": [ + "### Apply edit" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HARe-YH1086e" + }, + "source": [ + "from scripts.pti_styleclip import styleclip_edit" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "AbBtcOgLIyZi" + }, + "source": [ + "paths_config.checkpoints_dir = '/content/PTI'\n", + "os.chdir('/content/PTI')\n", + "styleclip_edit(use_multi_id_G=use_multi_id_training, run_id=model_id, edit_types = ['afro'], use_wandb=False)\n", + "styleclip_edit(use_multi_id_G=use_multi_id_training, run_id=model_id, edit_types = ['mohawk'], use_wandb=False)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "nylMZL0-ptWy" + }, + "source": [ + "original_styleCLIP_path = f'/content/PTI/StyleCLIP_results/{image_dir_name}/{image_name}/e4e/{image_name}_afro.jpg'\n", + "new_styleCLIP_path = f'/content/PTI/StyleCLIP_results/{image_dir_name}/{image_name}/PTI/{image_name}_afro.jpg'\n", + "original_styleCLIP = Image.open(original_styleCLIP_path).resize((256,256))\n", + "new_styleCLIP = Image.open(new_styleCLIP_path).resize((256,256))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "yk3HD_fsrSBU" + }, + "source": [ + "display_alongside_source_image([original_styleCLIP, new_styleCLIP])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ht9vedOWqNFB" + }, + "source": [ + "original_styleCLIP_path = f'/content/PTI/StyleCLIP_results/{image_dir_name}/{image_name}/e4e/{image_name}_mohawk.jpg'\n", + "new_styleCLIP_path = f'/content/PTI/StyleCLIP_results/{image_dir_name}/{image_name}/PTI/{image_name}_mohawk.jpg'\n", + "original_styleCLIP = Image.open(original_styleCLIP_path).resize((256,256))\n", + "new_styleCLIP = Image.open(new_styleCLIP_path).resize((256,256))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "qb4XxybIqO4w" + }, + "source": [ + "display_alongside_source_image([original_styleCLIP, new_styleCLIP])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o3drmxYQ5Naq" + }, + "source": [ + "## Other methods comparison" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_uq1fvtCrbjW" + }, + "source": [ + "### Invert image using other methods" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MuRqXSiTqMYT" + }, + "source": [ + "from scripts.latent_creators import e4e_latent_creator\n", + "from scripts.latent_creators import sg2_latent_creator\n", + "from scripts.latent_creators import sg2_plus_latent_creator" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "SCTROJAJrvI1" + }, + "source": [ + "e4e_latent_creator = e4e_latent_creator.E4ELatentCreator()\n", + "e4e_latent_creator.create_latents()\n", + "sg2_latent_creator = sg2_latent_creator.SG2LatentCreator(projection_steps = 600)\n", + "sg2_latent_creator.create_latents()\n", + "sg2_plus_latent_creator = sg2_plus_latent_creator.SG2PlusLatentCreator(projection_steps = 1200)\n", + "sg2_plus_latent_creator.create_latents()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "VwsX07ME_XwQ" + }, + "source": [ + "inversions = {}\n", + "sg2_embedding_dir = f'{w_path_dir}/{paths_config.sg2_results_keyword}/{image_name}'\n", + "inversions[paths_config.sg2_results_keyword] = torch.load(f'{sg2_embedding_dir}/0.pt')\n", + "e4e_embedding_dir = f'{w_path_dir}/{paths_config.e4e_results_keyword}/{image_name}'\n", + "inversions[paths_config.e4e_results_keyword] = torch.load(f'{e4e_embedding_dir}/0.pt')\n", + "sg2_plus_embedding_dir = f'{w_path_dir}/{paths_config.sg2_plus_results_keyword}/{image_name}'\n", + "inversions[paths_config.sg2_plus_results_keyword] = torch.load(f'{sg2_plus_embedding_dir}/0.pt')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Dnvs4ndY0fva" + }, + "source": [ + "def get_image_from_w(w, G):\n", + " if len(w.size()) <= 2:\n", + " w = w.unsqueeze(0) \n", + " img = G.synthesis(w, noise_mode='const', force_fp32=True) \n", + " img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).detach().cpu().numpy() \n", + " return img[0] " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "JUcxTVUT0wAd" + }, + "source": [ + "def plot_image_from_w(w, G): \n", + " img = get_image_from_w(w, G) \n", + " plt.axis('off') \n", + " resized_image = Image.fromarray(img,mode='RGB').resize((256,256)) \n", + " display(resized_image) " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "8DXy6lNxBGuG" + }, + "source": [ + "for inv_type, latent in inversions.items():\n", + " print(f'Displaying {inv_type} inversion')\n", + " plot_image_from_w(latent, old_G)\n", + "print(f'Displaying PTI inversion')\n", + "plot_image_from_w(w_pivot, new_G)" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/PTI/pretrained_models/.gitignore b/PTI/pretrained_models/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..d6b7ef32c8478a48c3994dcadc86837f4371184d --- /dev/null +++ b/PTI/pretrained_models/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore diff --git a/PTI/torch_utils/__init__.py b/PTI/torch_utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ece0ea08fe2e939cc260a1dafc0ab5b391b773d9 --- /dev/null +++ b/PTI/torch_utils/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +# empty diff --git a/PTI/torch_utils/custom_ops.py b/PTI/torch_utils/custom_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..4cc4e43fc6f6ce79f2bd68a44ba87990b9b8564e --- /dev/null +++ b/PTI/torch_utils/custom_ops.py @@ -0,0 +1,126 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import os +import glob +import torch +import torch.utils.cpp_extension +import importlib +import hashlib +import shutil +from pathlib import Path + +from torch.utils.file_baton import FileBaton + +#---------------------------------------------------------------------------- +# Global options. + +verbosity = 'brief' # Verbosity level: 'none', 'brief', 'full' + +#---------------------------------------------------------------------------- +# Internal helper funcs. + +def _find_compiler_bindir(): + patterns = [ + 'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64', + 'C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64', + 'C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64', + 'C:/Program Files (x86)/Microsoft Visual Studio */vc/bin', + ] + for pattern in patterns: + matches = sorted(glob.glob(pattern)) + if len(matches): + return matches[-1] + return None + +#---------------------------------------------------------------------------- +# Main entry point for compiling and loading C++/CUDA plugins. + +_cached_plugins = dict() + +def get_plugin(module_name, sources, **build_kwargs): + assert verbosity in ['none', 'brief', 'full'] + + # Already cached? + if module_name in _cached_plugins: + return _cached_plugins[module_name] + + # Print status. + if verbosity == 'full': + print(f'Setting up PyTorch plugin "{module_name}"...') + elif verbosity == 'brief': + print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True) + + try: # pylint: disable=too-many-nested-blocks + # Make sure we can find the necessary compiler binaries. + if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0: + compiler_bindir = _find_compiler_bindir() + if compiler_bindir is None: + raise RuntimeError(f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".') + os.environ['PATH'] += ';' + compiler_bindir + + # Compile and load. + verbose_build = (verbosity == 'full') + + # Incremental build md5sum trickery. Copies all the input source files + # into a cached build directory under a combined md5 digest of the input + # source files. Copying is done only if the combined digest has changed. + # This keeps input file timestamps and filenames the same as in previous + # extension builds, allowing for fast incremental rebuilds. + # + # This optimization is done only in case all the source files reside in + # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR + # environment variable is set (we take this as a signal that the user + # actually cares about this.) + source_dirs_set = set(os.path.dirname(source) for source in sources) + if len(source_dirs_set) == 1 and ('TORCH_EXTENSIONS_DIR' in os.environ): + all_source_files = sorted(list(x for x in Path(list(source_dirs_set)[0]).iterdir() if x.is_file())) + + # Compute a combined hash digest for all source files in the same + # custom op directory (usually .cu, .cpp, .py and .h files). + hash_md5 = hashlib.md5() + for src in all_source_files: + with open(src, 'rb') as f: + hash_md5.update(f.read()) + build_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) # pylint: disable=protected-access + digest_build_dir = os.path.join(build_dir, hash_md5.hexdigest()) + + if not os.path.isdir(digest_build_dir): + os.makedirs(digest_build_dir, exist_ok=True) + baton = FileBaton(os.path.join(digest_build_dir, 'lock')) + if baton.try_acquire(): + try: + for src in all_source_files: + shutil.copyfile(src, os.path.join(digest_build_dir, os.path.basename(src))) + finally: + baton.release() + else: + # Someone else is copying source files under the digest dir, + # wait until done and continue. + baton.wait() + digest_sources = [os.path.join(digest_build_dir, os.path.basename(x)) for x in sources] + torch.utils.cpp_extension.load(name=module_name, build_directory=build_dir, + verbose=verbose_build, sources=digest_sources, **build_kwargs) + else: + torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs) + module = importlib.import_module(module_name) + + except: + if verbosity == 'brief': + print('Failed!') + raise + + # Print status and add to cache. + if verbosity == 'full': + print(f'Done setting up PyTorch plugin "{module_name}".') + elif verbosity == 'brief': + print('Done.') + _cached_plugins[module_name] = module + return module + +#---------------------------------------------------------------------------- diff --git a/PTI/torch_utils/misc.py b/PTI/torch_utils/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..7829f4d9f168557ce8a9a6dec289aa964234cb8c --- /dev/null +++ b/PTI/torch_utils/misc.py @@ -0,0 +1,262 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +import re +import contextlib +import numpy as np +import torch +import warnings +import dnnlib + +#---------------------------------------------------------------------------- +# Cached construction of constant tensors. Avoids CPU=>GPU copy when the +# same constant is used multiple times. + +_constant_cache = dict() + +def constant(value, shape=None, dtype=None, device=None, memory_format=None): + value = np.asarray(value) + if shape is not None: + shape = tuple(shape) + if dtype is None: + dtype = torch.get_default_dtype() + if device is None: + device = torch.device('cpu') + if memory_format is None: + memory_format = torch.contiguous_format + + key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format) + tensor = _constant_cache.get(key, None) + if tensor is None: + tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device) + if shape is not None: + tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape)) + tensor = tensor.contiguous(memory_format=memory_format) + _constant_cache[key] = tensor + return tensor + +#---------------------------------------------------------------------------- +# Replace NaN/Inf with specified numerical values. + +try: + nan_to_num = torch.nan_to_num # 1.8.0a0 +except AttributeError: + def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin + assert isinstance(input, torch.Tensor) + if posinf is None: + posinf = torch.finfo(input.dtype).max + if neginf is None: + neginf = torch.finfo(input.dtype).min + assert nan == 0 + return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out) + +#---------------------------------------------------------------------------- +# Symbolic assert. + +try: + symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access +except AttributeError: + symbolic_assert = torch.Assert # 1.7.0 + +#---------------------------------------------------------------------------- +# Context manager to suppress known warnings in torch.jit.trace(). + +class suppress_tracer_warnings(warnings.catch_warnings): + def __enter__(self): + super().__enter__() + warnings.simplefilter('ignore', category=torch.jit.TracerWarning) + return self + +#---------------------------------------------------------------------------- +# Assert that the shape of a tensor matches the given list of integers. +# None indicates that the size of a dimension is allowed to vary. +# Performs symbolic assertion when used in torch.jit.trace(). + +def assert_shape(tensor, ref_shape): + if tensor.ndim != len(ref_shape): + raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}') + for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)): + if ref_size is None: + pass + elif isinstance(ref_size, torch.Tensor): + with suppress_tracer_warnings(): # as_tensor results are registered as constants + symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}') + elif isinstance(size, torch.Tensor): + with suppress_tracer_warnings(): # as_tensor results are registered as constants + symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}') + elif size != ref_size: + raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}') + +#---------------------------------------------------------------------------- +# Function decorator that calls torch.autograd.profiler.record_function(). + +def profiled_function(fn): + def decorator(*args, **kwargs): + with torch.autograd.profiler.record_function(fn.__name__): + return fn(*args, **kwargs) + decorator.__name__ = fn.__name__ + return decorator + +#---------------------------------------------------------------------------- +# Sampler for torch.utils.data.DataLoader that loops over the dataset +# indefinitely, shuffling items as it goes. + +class InfiniteSampler(torch.utils.data.Sampler): + def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5): + assert len(dataset) > 0 + assert num_replicas > 0 + assert 0 <= rank < num_replicas + assert 0 <= window_size <= 1 + super().__init__(dataset) + self.dataset = dataset + self.rank = rank + self.num_replicas = num_replicas + self.shuffle = shuffle + self.seed = seed + self.window_size = window_size + + def __iter__(self): + order = np.arange(len(self.dataset)) + rnd = None + window = 0 + if self.shuffle: + rnd = np.random.RandomState(self.seed) + rnd.shuffle(order) + window = int(np.rint(order.size * self.window_size)) + + idx = 0 + while True: + i = idx % order.size + if idx % self.num_replicas == self.rank: + yield order[i] + if window >= 2: + j = (i - rnd.randint(window)) % order.size + order[i], order[j] = order[j], order[i] + idx += 1 + +#---------------------------------------------------------------------------- +# Utilities for operating with torch.nn.Module parameters and buffers. + +def params_and_buffers(module): + assert isinstance(module, torch.nn.Module) + return list(module.parameters()) + list(module.buffers()) + +def named_params_and_buffers(module): + assert isinstance(module, torch.nn.Module) + return list(module.named_parameters()) + list(module.named_buffers()) + +def copy_params_and_buffers(src_module, dst_module, require_all=False): + assert isinstance(src_module, torch.nn.Module) + assert isinstance(dst_module, torch.nn.Module) + src_tensors = {name: tensor for name, tensor in named_params_and_buffers(src_module)} + for name, tensor in named_params_and_buffers(dst_module): + assert (name in src_tensors) or (not require_all) + if name in src_tensors: + tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad) + +#---------------------------------------------------------------------------- +# Context manager for easily enabling/disabling DistributedDataParallel +# synchronization. + +@contextlib.contextmanager +def ddp_sync(module, sync): + assert isinstance(module, torch.nn.Module) + if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel): + yield + else: + with module.no_sync(): + yield + +#---------------------------------------------------------------------------- +# Check DistributedDataParallel consistency across processes. + +def check_ddp_consistency(module, ignore_regex=None): + assert isinstance(module, torch.nn.Module) + for name, tensor in named_params_and_buffers(module): + fullname = type(module).__name__ + '.' + name + if ignore_regex is not None and re.fullmatch(ignore_regex, fullname): + continue + tensor = tensor.detach() + other = tensor.clone() + torch.distributed.broadcast(tensor=other, src=0) + assert (nan_to_num(tensor) == nan_to_num(other)).all(), fullname + +#---------------------------------------------------------------------------- +# Print summary table of module hierarchy. + +def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True): + assert isinstance(module, torch.nn.Module) + assert not isinstance(module, torch.jit.ScriptModule) + assert isinstance(inputs, (tuple, list)) + + # Register hooks. + entries = [] + nesting = [0] + def pre_hook(_mod, _inputs): + nesting[0] += 1 + def post_hook(mod, _inputs, outputs): + nesting[0] -= 1 + if nesting[0] <= max_nesting: + outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs] + outputs = [t for t in outputs if isinstance(t, torch.Tensor)] + entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs)) + hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()] + hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()] + + # Run module. + outputs = module(*inputs) + for hook in hooks: + hook.remove() + + # Identify unique outputs, parameters, and buffers. + tensors_seen = set() + for e in entries: + e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen] + e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen] + e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen] + tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs} + + # Filter out redundant entries. + if skip_redundant: + entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)] + + # Construct table. + rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']] + rows += [['---'] * len(rows[0])] + param_total = 0 + buffer_total = 0 + submodule_names = {mod: name for name, mod in module.named_modules()} + for e in entries: + name = '' if e.mod is module else submodule_names[e.mod] + param_size = sum(t.numel() for t in e.unique_params) + buffer_size = sum(t.numel() for t in e.unique_buffers) + output_shapes = [str(list(e.outputs[0].shape)) for t in e.outputs] + output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs] + rows += [[ + name + (':0' if len(e.outputs) >= 2 else ''), + str(param_size) if param_size else '-', + str(buffer_size) if buffer_size else '-', + (output_shapes + ['-'])[0], + (output_dtypes + ['-'])[0], + ]] + for idx in range(1, len(e.outputs)): + rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]] + param_total += param_size + buffer_total += buffer_size + rows += [['---'] * len(rows[0])] + rows += [['Total', str(param_total), str(buffer_total), '-', '-']] + + # Print table. + widths = [max(len(cell) for cell in column) for column in zip(*rows)] + print() + for row in rows: + print(' '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths))) + print() + return outputs + +#---------------------------------------------------------------------------- diff --git a/PTI/torch_utils/ops/__init__.py b/PTI/torch_utils/ops/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ece0ea08fe2e939cc260a1dafc0ab5b391b773d9 --- /dev/null +++ b/PTI/torch_utils/ops/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +# empty diff --git a/PTI/torch_utils/ops/bias_act.cpp b/PTI/torch_utils/ops/bias_act.cpp new file mode 100644 index 0000000000000000000000000000000000000000..5d2425d8054991a8e8b6f7a940fd0ff7fa0bb330 --- /dev/null +++ b/PTI/torch_utils/ops/bias_act.cpp @@ -0,0 +1,99 @@ +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include +#include +#include "bias_act.h" + +//------------------------------------------------------------------------ + +static bool has_same_layout(torch::Tensor x, torch::Tensor y) +{ + if (x.dim() != y.dim()) + return false; + for (int64_t i = 0; i < x.dim(); i++) + { + if (x.size(i) != y.size(i)) + return false; + if (x.size(i) >= 2 && x.stride(i) != y.stride(i)) + return false; + } + return true; +} + +//------------------------------------------------------------------------ + +static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::Tensor xref, torch::Tensor yref, torch::Tensor dy, int grad, int dim, int act, float alpha, float gain, float clamp) +{ + // Validate arguments. + TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); + TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x"); + TORCH_CHECK(xref.numel() == 0 || (xref.sizes() == x.sizes() && xref.dtype() == x.dtype() && xref.device() == x.device()), "xref must have the same shape, dtype, and device as x"); + TORCH_CHECK(yref.numel() == 0 || (yref.sizes() == x.sizes() && yref.dtype() == x.dtype() && yref.device() == x.device()), "yref must have the same shape, dtype, and device as x"); + TORCH_CHECK(dy.numel() == 0 || (dy.sizes() == x.sizes() && dy.dtype() == x.dtype() && dy.device() == x.device()), "dy must have the same dtype and device as x"); + TORCH_CHECK(x.numel() <= INT_MAX, "x is too large"); + TORCH_CHECK(b.dim() == 1, "b must have rank 1"); + TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds"); + TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements"); + TORCH_CHECK(grad >= 0, "grad must be non-negative"); + + // Validate layout. + TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense"); + TORCH_CHECK(b.is_contiguous(), "b must be contiguous"); + TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x"); + TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x"); + TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x"); + + // Create output tensor. + const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); + torch::Tensor y = torch::empty_like(x); + TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x"); + + // Initialize CUDA kernel parameters. + bias_act_kernel_params p; + p.x = x.data_ptr(); + p.b = (b.numel()) ? b.data_ptr() : NULL; + p.xref = (xref.numel()) ? xref.data_ptr() : NULL; + p.yref = (yref.numel()) ? yref.data_ptr() : NULL; + p.dy = (dy.numel()) ? dy.data_ptr() : NULL; + p.y = y.data_ptr(); + p.grad = grad; + p.act = act; + p.alpha = alpha; + p.gain = gain; + p.clamp = clamp; + p.sizeX = (int)x.numel(); + p.sizeB = (int)b.numel(); + p.stepB = (b.numel()) ? (int)x.stride(dim) : 1; + + // Choose CUDA kernel. + void* kernel; + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] + { + kernel = choose_bias_act_kernel(p); + }); + TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func"); + + // Launch CUDA kernel. + p.loopX = 4; + int blockSize = 4 * 32; + int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1; + void* args[] = {&p}; + AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream())); + return y; +} + +//------------------------------------------------------------------------ + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) +{ + m.def("bias_act", &bias_act); +} + +//------------------------------------------------------------------------ diff --git a/PTI/torch_utils/ops/bias_act.cu b/PTI/torch_utils/ops/bias_act.cu new file mode 100644 index 0000000000000000000000000000000000000000..dd8fc4756d7d94727f94af738665b68d9c518880 --- /dev/null +++ b/PTI/torch_utils/ops/bias_act.cu @@ -0,0 +1,173 @@ +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include "bias_act.h" + +//------------------------------------------------------------------------ +// Helpers. + +template struct InternalType; +template <> struct InternalType { typedef double scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; + +//------------------------------------------------------------------------ +// CUDA kernel. + +template +__global__ void bias_act_kernel(bias_act_kernel_params p) +{ + typedef typename InternalType::scalar_t scalar_t; + int G = p.grad; + scalar_t alpha = (scalar_t)p.alpha; + scalar_t gain = (scalar_t)p.gain; + scalar_t clamp = (scalar_t)p.clamp; + scalar_t one = (scalar_t)1; + scalar_t two = (scalar_t)2; + scalar_t expRange = (scalar_t)80; + scalar_t halfExpRange = (scalar_t)40; + scalar_t seluScale = (scalar_t)1.0507009873554804934193349852946; + scalar_t seluAlpha = (scalar_t)1.6732632423543772848170429916717; + + // Loop over elements. + int xi = blockIdx.x * p.loopX * blockDim.x + threadIdx.x; + for (int loopIdx = 0; loopIdx < p.loopX && xi < p.sizeX; loopIdx++, xi += blockDim.x) + { + // Load. + scalar_t x = (scalar_t)((const T*)p.x)[xi]; + scalar_t b = (p.b) ? (scalar_t)((const T*)p.b)[(xi / p.stepB) % p.sizeB] : 0; + scalar_t xref = (p.xref) ? (scalar_t)((const T*)p.xref)[xi] : 0; + scalar_t yref = (p.yref) ? (scalar_t)((const T*)p.yref)[xi] : 0; + scalar_t dy = (p.dy) ? (scalar_t)((const T*)p.dy)[xi] : one; + scalar_t yy = (gain != 0) ? yref / gain : 0; + scalar_t y = 0; + + // Apply bias. + ((G == 0) ? x : xref) += b; + + // linear + if (A == 1) + { + if (G == 0) y = x; + if (G == 1) y = x; + } + + // relu + if (A == 2) + { + if (G == 0) y = (x > 0) ? x : 0; + if (G == 1) y = (yy > 0) ? x : 0; + } + + // lrelu + if (A == 3) + { + if (G == 0) y = (x > 0) ? x : x * alpha; + if (G == 1) y = (yy > 0) ? x : x * alpha; + } + + // tanh + if (A == 4) + { + if (G == 0) { scalar_t c = exp(x); scalar_t d = one / c; y = (x < -expRange) ? -one : (x > expRange) ? one : (c - d) / (c + d); } + if (G == 1) y = x * (one - yy * yy); + if (G == 2) y = x * (one - yy * yy) * (-two * yy); + } + + // sigmoid + if (A == 5) + { + if (G == 0) y = (x < -expRange) ? 0 : one / (exp(-x) + one); + if (G == 1) y = x * yy * (one - yy); + if (G == 2) y = x * yy * (one - yy) * (one - two * yy); + } + + // elu + if (A == 6) + { + if (G == 0) y = (x >= 0) ? x : exp(x) - one; + if (G == 1) y = (yy >= 0) ? x : x * (yy + one); + if (G == 2) y = (yy >= 0) ? 0 : x * (yy + one); + } + + // selu + if (A == 7) + { + if (G == 0) y = (x >= 0) ? seluScale * x : (seluScale * seluAlpha) * (exp(x) - one); + if (G == 1) y = (yy >= 0) ? x * seluScale : x * (yy + seluScale * seluAlpha); + if (G == 2) y = (yy >= 0) ? 0 : x * (yy + seluScale * seluAlpha); + } + + // softplus + if (A == 8) + { + if (G == 0) y = (x > expRange) ? x : log(exp(x) + one); + if (G == 1) y = x * (one - exp(-yy)); + if (G == 2) { scalar_t c = exp(-yy); y = x * c * (one - c); } + } + + // swish + if (A == 9) + { + if (G == 0) + y = (x < -expRange) ? 0 : x / (exp(-x) + one); + else + { + scalar_t c = exp(xref); + scalar_t d = c + one; + if (G == 1) + y = (xref > halfExpRange) ? x : x * c * (xref + d) / (d * d); + else + y = (xref > halfExpRange) ? 0 : x * c * (xref * (two - d) + two * d) / (d * d * d); + yref = (xref < -expRange) ? 0 : xref / (exp(-xref) + one) * gain; + } + } + + // Apply gain. + y *= gain * dy; + + // Clamp. + if (clamp >= 0) + { + if (G == 0) + y = (y > -clamp & y < clamp) ? y : (y >= 0) ? clamp : -clamp; + else + y = (yref > -clamp & yref < clamp) ? y : 0; + } + + // Store. + ((T*)p.y)[xi] = (T)y; + } +} + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template void* choose_bias_act_kernel(const bias_act_kernel_params& p) +{ + if (p.act == 1) return (void*)bias_act_kernel; + if (p.act == 2) return (void*)bias_act_kernel; + if (p.act == 3) return (void*)bias_act_kernel; + if (p.act == 4) return (void*)bias_act_kernel; + if (p.act == 5) return (void*)bias_act_kernel; + if (p.act == 6) return (void*)bias_act_kernel; + if (p.act == 7) return (void*)bias_act_kernel; + if (p.act == 8) return (void*)bias_act_kernel; + if (p.act == 9) return (void*)bias_act_kernel; + return NULL; +} + +//------------------------------------------------------------------------ +// Template specializations. + +template void* choose_bias_act_kernel (const bias_act_kernel_params& p); +template void* choose_bias_act_kernel (const bias_act_kernel_params& p); +template void* choose_bias_act_kernel (const bias_act_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/PTI/torch_utils/ops/bias_act.h b/PTI/torch_utils/ops/bias_act.h new file mode 100644 index 0000000000000000000000000000000000000000..a32187e1fb7e3bae509d4eceaf900866866875a4 --- /dev/null +++ b/PTI/torch_utils/ops/bias_act.h @@ -0,0 +1,38 @@ +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +//------------------------------------------------------------------------ +// CUDA kernel parameters. + +struct bias_act_kernel_params +{ + const void* x; // [sizeX] + const void* b; // [sizeB] or NULL + const void* xref; // [sizeX] or NULL + const void* yref; // [sizeX] or NULL + const void* dy; // [sizeX] or NULL + void* y; // [sizeX] + + int grad; + int act; + float alpha; + float gain; + float clamp; + + int sizeX; + int sizeB; + int stepB; + int loopX; +}; + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template void* choose_bias_act_kernel(const bias_act_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/PTI/torch_utils/ops/bias_act.py b/PTI/torch_utils/ops/bias_act.py new file mode 100644 index 0000000000000000000000000000000000000000..4bcb409a89ccf6c6f6ecfca5962683df2d280b1f --- /dev/null +++ b/PTI/torch_utils/ops/bias_act.py @@ -0,0 +1,212 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom PyTorch ops for efficient bias and activation.""" + +import os +import warnings +import numpy as np +import torch +import dnnlib +import traceback + +from .. import custom_ops +from .. import misc + +#---------------------------------------------------------------------------- + +activation_funcs = { + 'linear': dnnlib.EasyDict(func=lambda x, **_: x, def_alpha=0, def_gain=1, cuda_idx=1, ref='', has_2nd_grad=False), + 'relu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.relu(x), def_alpha=0, def_gain=np.sqrt(2), cuda_idx=2, ref='y', has_2nd_grad=False), + 'lrelu': dnnlib.EasyDict(func=lambda x, alpha, **_: torch.nn.functional.leaky_relu(x, alpha), def_alpha=0.2, def_gain=np.sqrt(2), cuda_idx=3, ref='y', has_2nd_grad=False), + 'tanh': dnnlib.EasyDict(func=lambda x, **_: torch.tanh(x), def_alpha=0, def_gain=1, cuda_idx=4, ref='y', has_2nd_grad=True), + 'sigmoid': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x), def_alpha=0, def_gain=1, cuda_idx=5, ref='y', has_2nd_grad=True), + 'elu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.elu(x), def_alpha=0, def_gain=1, cuda_idx=6, ref='y', has_2nd_grad=True), + 'selu': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.selu(x), def_alpha=0, def_gain=1, cuda_idx=7, ref='y', has_2nd_grad=True), + 'softplus': dnnlib.EasyDict(func=lambda x, **_: torch.nn.functional.softplus(x), def_alpha=0, def_gain=1, cuda_idx=8, ref='y', has_2nd_grad=True), + 'swish': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x) * x, def_alpha=0, def_gain=np.sqrt(2), cuda_idx=9, ref='x', has_2nd_grad=True), +} + +#---------------------------------------------------------------------------- + +_inited = False +_plugin = None +_null_tensor = torch.empty([0]) + +def _init(): + global _inited, _plugin + if not _inited: + _inited = True + sources = ['bias_act.cpp', 'bias_act.cu'] + sources = [os.path.join(os.path.dirname(__file__), s) for s in sources] + try: + _plugin = custom_ops.get_plugin('bias_act_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math']) + except: + warnings.warn('Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc()) + return _plugin is not None + +#---------------------------------------------------------------------------- + +def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'): + r"""Fused bias and activation function. + + Adds bias `b` to activation tensor `x`, evaluates activation function `act`, + and scales the result by `gain`. Each of the steps is optional. In most cases, + the fused op is considerably more efficient than performing the same calculation + using standard PyTorch ops. It supports first and second order gradients, + but not third order gradients. + + Args: + x: Input activation tensor. Can be of any shape. + b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type + as `x`. The shape must be known, and it must match the dimension of `x` + corresponding to `dim`. + dim: The dimension in `x` corresponding to the elements of `b`. + The value of `dim` is ignored if `b` is not specified. + act: Name of the activation function to evaluate, or `"linear"` to disable. + Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc. + See `activation_funcs` for a full list. `None` is not allowed. + alpha: Shape parameter for the activation function, or `None` to use the default. + gain: Scaling factor for the output tensor, or `None` to use default. + See `activation_funcs` for the default scaling of each activation function. + If unsure, consider specifying 1. + clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable + the clamping (default). + impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default). + + Returns: + Tensor of the same shape and datatype as `x`. + """ + assert isinstance(x, torch.Tensor) + assert impl in ['ref', 'cuda'] + if impl == 'cuda' and x.device.type == 'cuda' and _init(): + return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b) + return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp) + +#---------------------------------------------------------------------------- + +@misc.profiled_function +def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None): + """Slow reference implementation of `bias_act()` using standard TensorFlow ops. + """ + assert isinstance(x, torch.Tensor) + assert clamp is None or clamp >= 0 + spec = activation_funcs[act] + alpha = float(alpha if alpha is not None else spec.def_alpha) + gain = float(gain if gain is not None else spec.def_gain) + clamp = float(clamp if clamp is not None else -1) + + # Add bias. + if b is not None: + assert isinstance(b, torch.Tensor) and b.ndim == 1 + assert 0 <= dim < x.ndim + assert b.shape[0] == x.shape[dim] + x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)]) + + # Evaluate activation function. + alpha = float(alpha) + x = spec.func(x, alpha=alpha) + + # Scale by gain. + gain = float(gain) + if gain != 1: + x = x * gain + + # Clamp. + if clamp >= 0: + x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type + return x + +#---------------------------------------------------------------------------- + +_bias_act_cuda_cache = dict() + +def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): + """Fast CUDA implementation of `bias_act()` using custom ops. + """ + # Parse arguments. + assert clamp is None or clamp >= 0 + spec = activation_funcs[act] + alpha = float(alpha if alpha is not None else spec.def_alpha) + gain = float(gain if gain is not None else spec.def_gain) + clamp = float(clamp if clamp is not None else -1) + + # Lookup from cache. + key = (dim, act, alpha, gain, clamp) + if key in _bias_act_cuda_cache: + return _bias_act_cuda_cache[key] + + # Forward op. + class BiasActCuda(torch.autograd.Function): + @staticmethod + def forward(ctx, x, b): # pylint: disable=arguments-differ + ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride()[1] == 1 else torch.contiguous_format + x = x.contiguous(memory_format=ctx.memory_format) + b = b.contiguous() if b is not None else _null_tensor + y = x + if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor: + y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp) + ctx.save_for_backward( + x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor, + b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor, + y if 'y' in spec.ref else _null_tensor) + return y + + @staticmethod + def backward(ctx, dy): # pylint: disable=arguments-differ + dy = dy.contiguous(memory_format=ctx.memory_format) + x, b, y = ctx.saved_tensors + dx = None + db = None + + if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: + dx = dy + if act != 'linear' or gain != 1 or clamp >= 0: + dx = BiasActCudaGrad.apply(dy, x, b, y) + + if ctx.needs_input_grad[1]: + db = dx.sum([i for i in range(dx.ndim) if i != dim]) + + return dx, db + + # Backward op. + class BiasActCudaGrad(torch.autograd.Function): + @staticmethod + def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ + ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride()[1] == 1 else torch.contiguous_format + dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp) + ctx.save_for_backward( + dy if spec.has_2nd_grad else _null_tensor, + x, b, y) + return dx + + @staticmethod + def backward(ctx, d_dx): # pylint: disable=arguments-differ + d_dx = d_dx.contiguous(memory_format=ctx.memory_format) + dy, x, b, y = ctx.saved_tensors + d_dy = None + d_x = None + d_b = None + d_y = None + + if ctx.needs_input_grad[0]: + d_dy = BiasActCudaGrad.apply(d_dx, x, b, y) + + if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]): + d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp) + + if spec.has_2nd_grad and ctx.needs_input_grad[2]: + d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim]) + + return d_dy, d_x, d_b, d_y + + # Add to cache. + _bias_act_cuda_cache[key] = BiasActCuda + return BiasActCuda + +#---------------------------------------------------------------------------- diff --git a/PTI/torch_utils/ops/conv2d_gradfix.py b/PTI/torch_utils/ops/conv2d_gradfix.py new file mode 100644 index 0000000000000000000000000000000000000000..e95e10d0b1d0315a63a76446fd4c5c293c8bbc6d --- /dev/null +++ b/PTI/torch_utils/ops/conv2d_gradfix.py @@ -0,0 +1,170 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom replacement for `torch.nn.functional.conv2d` that supports +arbitrarily high order gradients with zero performance penalty.""" + +import warnings +import contextlib +import torch + +# pylint: disable=redefined-builtin +# pylint: disable=arguments-differ +# pylint: disable=protected-access + +#---------------------------------------------------------------------------- + +enabled = False # Enable the custom op by setting this to true. +weight_gradients_disabled = False # Forcefully disable computation of gradients with respect to the weights. + +@contextlib.contextmanager +def no_weight_gradients(): + global weight_gradients_disabled + old = weight_gradients_disabled + weight_gradients_disabled = True + yield + weight_gradients_disabled = old + +#---------------------------------------------------------------------------- + +def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1): + if _should_use_custom_op(input): + return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias) + return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups) + +def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1): + if _should_use_custom_op(input): + return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias) + return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation) + +#---------------------------------------------------------------------------- + +def _should_use_custom_op(input): + assert isinstance(input, torch.Tensor) + if (not enabled) or (not torch.backends.cudnn.enabled): + return False + if input.device.type != 'cuda': + return False + if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']): + return True + warnings.warn(f'conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d().') + return False + +def _tuple_of_ints(xs, ndim): + xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim + assert len(xs) == ndim + assert all(isinstance(x, int) for x in xs) + return xs + +#---------------------------------------------------------------------------- + +_conv2d_gradfix_cache = dict() + +def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups): + # Parse arguments. + ndim = 2 + weight_shape = tuple(weight_shape) + stride = _tuple_of_ints(stride, ndim) + padding = _tuple_of_ints(padding, ndim) + output_padding = _tuple_of_ints(output_padding, ndim) + dilation = _tuple_of_ints(dilation, ndim) + + # Lookup from cache. + key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups) + if key in _conv2d_gradfix_cache: + return _conv2d_gradfix_cache[key] + + # Validate arguments. + assert groups >= 1 + assert len(weight_shape) == ndim + 2 + assert all(stride[i] >= 1 for i in range(ndim)) + assert all(padding[i] >= 0 for i in range(ndim)) + assert all(dilation[i] >= 0 for i in range(ndim)) + if not transpose: + assert all(output_padding[i] == 0 for i in range(ndim)) + else: # transpose + assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim)) + + # Helpers. + common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups) + def calc_output_padding(input_shape, output_shape): + if transpose: + return [0, 0] + return [ + input_shape[i + 2] + - (output_shape[i + 2] - 1) * stride[i] + - (1 - 2 * padding[i]) + - dilation[i] * (weight_shape[i + 2] - 1) + for i in range(ndim) + ] + + # Forward & backward. + class Conv2d(torch.autograd.Function): + @staticmethod + def forward(ctx, input, weight, bias): + assert weight.shape == weight_shape + if not transpose: + output = torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs) + else: # transpose + output = torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs) + ctx.save_for_backward(input, weight) + return output + + @staticmethod + def backward(ctx, grad_output): + input, weight = ctx.saved_tensors + grad_input = None + grad_weight = None + grad_bias = None + + if ctx.needs_input_grad[0]: + p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape) + grad_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, weight, None) + assert grad_input.shape == input.shape + + if ctx.needs_input_grad[1] and not weight_gradients_disabled: + grad_weight = Conv2dGradWeight.apply(grad_output, input) + assert grad_weight.shape == weight_shape + + if ctx.needs_input_grad[2]: + grad_bias = grad_output.sum([0, 2, 3]) + + return grad_input, grad_weight, grad_bias + + # Gradient with respect to the weights. + class Conv2dGradWeight(torch.autograd.Function): + @staticmethod + def forward(ctx, grad_output, input): + op = torch._C._jit_get_operation('aten::cudnn_convolution_backward_weight' if not transpose else 'aten::cudnn_convolution_transpose_backward_weight') + flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32] + grad_weight = op(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags) + assert grad_weight.shape == weight_shape + ctx.save_for_backward(grad_output, input) + return grad_weight + + @staticmethod + def backward(ctx, grad2_grad_weight): + grad_output, input = ctx.saved_tensors + grad2_grad_output = None + grad2_input = None + + if ctx.needs_input_grad[0]: + grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None) + assert grad2_grad_output.shape == grad_output.shape + + if ctx.needs_input_grad[1]: + p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape) + grad2_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, grad2_grad_weight, None) + assert grad2_input.shape == input.shape + + return grad2_grad_output, grad2_input + + _conv2d_gradfix_cache[key] = Conv2d + return Conv2d + +#---------------------------------------------------------------------------- diff --git a/PTI/torch_utils/ops/conv2d_resample.py b/PTI/torch_utils/ops/conv2d_resample.py new file mode 100644 index 0000000000000000000000000000000000000000..cd4750744c83354bab78704d4ef51ad1070fcc4a --- /dev/null +++ b/PTI/torch_utils/ops/conv2d_resample.py @@ -0,0 +1,156 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""2D convolution with optional up/downsampling.""" + +import torch + +from .. import misc +from . import conv2d_gradfix +from . import upfirdn2d +from .upfirdn2d import _parse_padding +from .upfirdn2d import _get_filter_size + +#---------------------------------------------------------------------------- + +def _get_weight_shape(w): + with misc.suppress_tracer_warnings(): # this value will be treated as a constant + shape = [int(sz) for sz in w.shape] + misc.assert_shape(w, shape) + return shape + +#---------------------------------------------------------------------------- + +def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True): + """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations. + """ + out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) + + # Flip weight if requested. + if not flip_weight: # conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False). + w = w.flip([2, 3]) + + # Workaround performance pitfall in cuDNN 8.0.5, triggered when using + # 1x1 kernel + memory_format=channels_last + less than 64 channels. + if kw == 1 and kh == 1 and stride == 1 and padding in [0, [0, 0], (0, 0)] and not transpose: + if x.stride()[1] == 1 and min(out_channels, in_channels_per_group) < 64: + if out_channels <= 4 and groups == 1: + in_shape = x.shape + x = w.squeeze(3).squeeze(2) @ x.reshape([in_shape[0], in_channels_per_group, -1]) + x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]]) + else: + x = x.to(memory_format=torch.contiguous_format) + w = w.to(memory_format=torch.contiguous_format) + x = conv2d_gradfix.conv2d(x, w, groups=groups) + return x.to(memory_format=torch.channels_last) + + # Otherwise => execute using conv2d_gradfix. + op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d + return op(x, w, stride=stride, padding=padding, groups=groups) + +#---------------------------------------------------------------------------- + +@misc.profiled_function +def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False): + r"""2D convolution with optional up/downsampling. + + Padding is performed only once at the beginning, not between the operations. + + Args: + x: Input tensor of shape + `[batch_size, in_channels, in_height, in_width]`. + w: Weight tensor of shape + `[out_channels, in_channels//groups, kernel_height, kernel_width]`. + f: Low-pass filter for up/downsampling. Must be prepared beforehand by + calling upfirdn2d.setup_filter(). None = identity (default). + up: Integer upsampling factor (default: 1). + down: Integer downsampling factor (default: 1). + padding: Padding with respect to the upsampled image. Can be a single number + or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + groups: Split input channels into N groups (default: 1). + flip_weight: False = convolution, True = correlation (default: True). + flip_filter: False = convolution, True = correlation (default: False). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + # Validate arguments. + assert isinstance(x, torch.Tensor) and (x.ndim == 4) + assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype) + assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32) + assert isinstance(up, int) and (up >= 1) + assert isinstance(down, int) and (down >= 1) + assert isinstance(groups, int) and (groups >= 1) + out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) + fw, fh = _get_filter_size(f) + px0, px1, py0, py1 = _parse_padding(padding) + + # Adjust padding to account for up/downsampling. + if up > 1: + px0 += (fw + up - 1) // 2 + px1 += (fw - up) // 2 + py0 += (fh + up - 1) // 2 + py1 += (fh - up) // 2 + if down > 1: + px0 += (fw - down + 1) // 2 + px1 += (fw - down) // 2 + py0 += (fh - down + 1) // 2 + py1 += (fh - down) // 2 + + # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve. + if kw == 1 and kh == 1 and (down > 1 and up == 1): + x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter) + x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) + return x + + # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample. + if kw == 1 and kh == 1 and (up > 1 and down == 1): + x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) + x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) + return x + + # Fast path: downsampling only => use strided convolution. + if down > 1 and up == 1: + x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter) + x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight) + return x + + # Fast path: upsampling with optional downsampling => use transpose strided convolution. + if up > 1: + if groups == 1: + w = w.transpose(0, 1) + else: + w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw) + w = w.transpose(1, 2) + w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw) + px0 -= kw - 1 + px1 -= kw - up + py0 -= kh - 1 + py1 -= kh - up + pxt = max(min(-px0, -px1), 0) + pyt = max(min(-py0, -py1), 0) + x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight)) + x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter) + if down > 1: + x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) + return x + + # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d. + if up == 1 and down == 1: + if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0: + return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight) + + # Fallback: Generic reference implementation. + x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) + x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) + if down > 1: + x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) + return x + +#---------------------------------------------------------------------------- diff --git a/PTI/torch_utils/ops/fma.py b/PTI/torch_utils/ops/fma.py new file mode 100644 index 0000000000000000000000000000000000000000..2eeac58a626c49231e04122b93e321ada954c5d3 --- /dev/null +++ b/PTI/torch_utils/ops/fma.py @@ -0,0 +1,60 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Fused multiply-add, with slightly faster gradients than `torch.addcmul()`.""" + +import torch + +#---------------------------------------------------------------------------- + +def fma(a, b, c): # => a * b + c + return _FusedMultiplyAdd.apply(a, b, c) + +#---------------------------------------------------------------------------- + +class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c + @staticmethod + def forward(ctx, a, b, c): # pylint: disable=arguments-differ + out = torch.addcmul(c, a, b) + ctx.save_for_backward(a, b) + ctx.c_shape = c.shape + return out + + @staticmethod + def backward(ctx, dout): # pylint: disable=arguments-differ + a, b = ctx.saved_tensors + c_shape = ctx.c_shape + da = None + db = None + dc = None + + if ctx.needs_input_grad[0]: + da = _unbroadcast(dout * b, a.shape) + + if ctx.needs_input_grad[1]: + db = _unbroadcast(dout * a, b.shape) + + if ctx.needs_input_grad[2]: + dc = _unbroadcast(dout, c_shape) + + return da, db, dc + +#---------------------------------------------------------------------------- + +def _unbroadcast(x, shape): + extra_dims = x.ndim - len(shape) + assert extra_dims >= 0 + dim = [i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)] + if len(dim): + x = x.sum(dim=dim, keepdim=True) + if extra_dims: + x = x.reshape(-1, *x.shape[extra_dims+1:]) + assert x.shape == shape + return x + +#---------------------------------------------------------------------------- diff --git a/PTI/torch_utils/ops/grid_sample_gradfix.py b/PTI/torch_utils/ops/grid_sample_gradfix.py new file mode 100644 index 0000000000000000000000000000000000000000..ca6b3413ea72a734703c34382c023b84523601fd --- /dev/null +++ b/PTI/torch_utils/ops/grid_sample_gradfix.py @@ -0,0 +1,83 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom replacement for `torch.nn.functional.grid_sample` that +supports arbitrarily high order gradients between the input and output. +Only works on 2D images and assumes +`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`.""" + +import warnings +import torch + +# pylint: disable=redefined-builtin +# pylint: disable=arguments-differ +# pylint: disable=protected-access + +#---------------------------------------------------------------------------- + +enabled = False # Enable the custom op by setting this to true. + +#---------------------------------------------------------------------------- + +def grid_sample(input, grid): + if _should_use_custom_op(): + return _GridSample2dForward.apply(input, grid) + return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False) + +#---------------------------------------------------------------------------- + +def _should_use_custom_op(): + if not enabled: + return False + if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']): + return True + warnings.warn(f'grid_sample_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.grid_sample().') + return False + +#---------------------------------------------------------------------------- + +class _GridSample2dForward(torch.autograd.Function): + @staticmethod + def forward(ctx, input, grid): + assert input.ndim == 4 + assert grid.ndim == 4 + output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False) + ctx.save_for_backward(input, grid) + return output + + @staticmethod + def backward(ctx, grad_output): + input, grid = ctx.saved_tensors + grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid) + return grad_input, grad_grid + +#---------------------------------------------------------------------------- + +class _GridSample2dBackward(torch.autograd.Function): + @staticmethod + def forward(ctx, grad_output, input, grid): + op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward') + grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False) + ctx.save_for_backward(grid) + return grad_input, grad_grid + + @staticmethod + def backward(ctx, grad2_grad_input, grad2_grad_grid): + _ = grad2_grad_grid # unused + grid, = ctx.saved_tensors + grad2_grad_output = None + grad2_input = None + grad2_grid = None + + if ctx.needs_input_grad[0]: + grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid) + + assert not ctx.needs_input_grad[2] + return grad2_grad_output, grad2_input, grad2_grid + +#---------------------------------------------------------------------------- diff --git a/PTI/torch_utils/ops/upfirdn2d.cpp b/PTI/torch_utils/ops/upfirdn2d.cpp new file mode 100644 index 0000000000000000000000000000000000000000..2d7177fc60040751d20e9a8da0301fa3ab64968a --- /dev/null +++ b/PTI/torch_utils/ops/upfirdn2d.cpp @@ -0,0 +1,103 @@ +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include +#include +#include "upfirdn2d.h" + +//------------------------------------------------------------------------ + +static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain) +{ + // Validate arguments. + TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); + TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x"); + TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32"); + TORCH_CHECK(x.numel() <= INT_MAX, "x is too large"); + TORCH_CHECK(f.numel() <= INT_MAX, "f is too large"); + TORCH_CHECK(x.dim() == 4, "x must be rank 4"); + TORCH_CHECK(f.dim() == 2, "f must be rank 2"); + TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1"); + TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1"); + TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1"); + + // Create output tensor. + const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); + int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx; + int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy; + TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1"); + torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format()); + TORCH_CHECK(y.numel() <= INT_MAX, "output is too large"); + + // Initialize CUDA kernel parameters. + upfirdn2d_kernel_params p; + p.x = x.data_ptr(); + p.f = f.data_ptr(); + p.y = y.data_ptr(); + p.up = make_int2(upx, upy); + p.down = make_int2(downx, downy); + p.pad0 = make_int2(padx0, pady0); + p.flip = (flip) ? 1 : 0; + p.gain = gain; + p.inSize = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0)); + p.inStride = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0)); + p.filterSize = make_int2((int)f.size(1), (int)f.size(0)); + p.filterStride = make_int2((int)f.stride(1), (int)f.stride(0)); + p.outSize = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0)); + p.outStride = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0)); + p.sizeMajor = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z; + p.sizeMinor = (p.inStride.z == 1) ? p.inSize.z : 1; + + // Choose CUDA kernel. + upfirdn2d_kernel_spec spec; + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] + { + spec = choose_upfirdn2d_kernel(p); + }); + + // Set looping options. + p.loopMajor = (p.sizeMajor - 1) / 16384 + 1; + p.loopMinor = spec.loopMinor; + p.loopX = spec.loopX; + p.launchMinor = (p.sizeMinor - 1) / p.loopMinor + 1; + p.launchMajor = (p.sizeMajor - 1) / p.loopMajor + 1; + + // Compute grid size. + dim3 blockSize, gridSize; + if (spec.tileOutW < 0) // large + { + blockSize = dim3(4, 32, 1); + gridSize = dim3( + ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor, + (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1, + p.launchMajor); + } + else // small + { + blockSize = dim3(256, 1, 1); + gridSize = dim3( + ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor, + (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1, + p.launchMajor); + } + + // Launch CUDA kernel. + void* args[] = {&p}; + AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream())); + return y; +} + +//------------------------------------------------------------------------ + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) +{ + m.def("upfirdn2d", &upfirdn2d); +} + +//------------------------------------------------------------------------ diff --git a/PTI/torch_utils/ops/upfirdn2d.cu b/PTI/torch_utils/ops/upfirdn2d.cu new file mode 100644 index 0000000000000000000000000000000000000000..ebdd9879f4bb16fc57a23cbc81f9de8ef54e4916 --- /dev/null +++ b/PTI/torch_utils/ops/upfirdn2d.cu @@ -0,0 +1,350 @@ +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include +#include "upfirdn2d.h" + +//------------------------------------------------------------------------ +// Helpers. + +template struct InternalType; +template <> struct InternalType { typedef double scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; +template <> struct InternalType { typedef float scalar_t; }; + +static __device__ __forceinline__ int floor_div(int a, int b) +{ + int t = 1 - a / b; + return (a + t * b) / b - t; +} + +//------------------------------------------------------------------------ +// Generic CUDA implementation for large filters. + +template static __global__ void upfirdn2d_kernel_large(upfirdn2d_kernel_params p) +{ + typedef typename InternalType::scalar_t scalar_t; + + // Calculate thread index. + int minorBase = blockIdx.x * blockDim.x + threadIdx.x; + int outY = minorBase / p.launchMinor; + minorBase -= outY * p.launchMinor; + int outXBase = blockIdx.y * p.loopX * blockDim.y + threadIdx.y; + int majorBase = blockIdx.z * p.loopMajor; + if (outXBase >= p.outSize.x | outY >= p.outSize.y | majorBase >= p.sizeMajor) + return; + + // Setup Y receptive field. + int midY = outY * p.down.y + p.up.y - 1 - p.pad0.y; + int inY = min(max(floor_div(midY, p.up.y), 0), p.inSize.y); + int h = min(max(floor_div(midY + p.filterSize.y, p.up.y), 0), p.inSize.y) - inY; + int filterY = midY + p.filterSize.y - (inY + 1) * p.up.y; + if (p.flip) + filterY = p.filterSize.y - 1 - filterY; + + // Loop over major, minor, and X. + for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++) + for (int minorIdx = 0, minor = minorBase; minorIdx < p.loopMinor & minor < p.sizeMinor; minorIdx++, minor += p.launchMinor) + { + int nc = major * p.sizeMinor + minor; + int n = nc / p.inSize.z; + int c = nc - n * p.inSize.z; + for (int loopX = 0, outX = outXBase; loopX < p.loopX & outX < p.outSize.x; loopX++, outX += blockDim.y) + { + // Setup X receptive field. + int midX = outX * p.down.x + p.up.x - 1 - p.pad0.x; + int inX = min(max(floor_div(midX, p.up.x), 0), p.inSize.x); + int w = min(max(floor_div(midX + p.filterSize.x, p.up.x), 0), p.inSize.x) - inX; + int filterX = midX + p.filterSize.x - (inX + 1) * p.up.x; + if (p.flip) + filterX = p.filterSize.x - 1 - filterX; + + // Initialize pointers. + const T* xp = &((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w]; + const float* fp = &p.f[filterX * p.filterStride.x + filterY * p.filterStride.y]; + int filterStepX = ((p.flip) ? p.up.x : -p.up.x) * p.filterStride.x; + int filterStepY = ((p.flip) ? p.up.y : -p.up.y) * p.filterStride.y; + + // Inner loop. + scalar_t v = 0; + for (int y = 0; y < h; y++) + { + for (int x = 0; x < w; x++) + { + v += (scalar_t)(*xp) * (scalar_t)(*fp); + xp += p.inStride.x; + fp += filterStepX; + } + xp += p.inStride.y - w * p.inStride.x; + fp += filterStepY - w * filterStepX; + } + + // Store result. + v *= p.gain; + ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v; + } + } +} + +//------------------------------------------------------------------------ +// Specialized CUDA implementation for small filters. + +template +static __global__ void upfirdn2d_kernel_small(upfirdn2d_kernel_params p) +{ + typedef typename InternalType::scalar_t scalar_t; + const int tileInW = ((tileOutW - 1) * downx + filterW - 1) / upx + 1; + const int tileInH = ((tileOutH - 1) * downy + filterH - 1) / upy + 1; + __shared__ volatile scalar_t sf[filterH][filterW]; + __shared__ volatile scalar_t sx[tileInH][tileInW][loopMinor]; + + // Calculate tile index. + int minorBase = blockIdx.x; + int tileOutY = minorBase / p.launchMinor; + minorBase -= tileOutY * p.launchMinor; + minorBase *= loopMinor; + tileOutY *= tileOutH; + int tileOutXBase = blockIdx.y * p.loopX * tileOutW; + int majorBase = blockIdx.z * p.loopMajor; + if (tileOutXBase >= p.outSize.x | tileOutY >= p.outSize.y | majorBase >= p.sizeMajor) + return; + + // Load filter (flipped). + for (int tapIdx = threadIdx.x; tapIdx < filterH * filterW; tapIdx += blockDim.x) + { + int fy = tapIdx / filterW; + int fx = tapIdx - fy * filterW; + scalar_t v = 0; + if (fx < p.filterSize.x & fy < p.filterSize.y) + { + int ffx = (p.flip) ? fx : p.filterSize.x - 1 - fx; + int ffy = (p.flip) ? fy : p.filterSize.y - 1 - fy; + v = (scalar_t)p.f[ffx * p.filterStride.x + ffy * p.filterStride.y]; + } + sf[fy][fx] = v; + } + + // Loop over major and X. + for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++) + { + int baseNC = major * p.sizeMinor + minorBase; + int n = baseNC / p.inSize.z; + int baseC = baseNC - n * p.inSize.z; + for (int loopX = 0, tileOutX = tileOutXBase; loopX < p.loopX & tileOutX < p.outSize.x; loopX++, tileOutX += tileOutW) + { + // Load input pixels. + int tileMidX = tileOutX * downx + upx - 1 - p.pad0.x; + int tileMidY = tileOutY * downy + upy - 1 - p.pad0.y; + int tileInX = floor_div(tileMidX, upx); + int tileInY = floor_div(tileMidY, upy); + __syncthreads(); + for (int inIdx = threadIdx.x; inIdx < tileInH * tileInW * loopMinor; inIdx += blockDim.x) + { + int relC = inIdx; + int relInX = relC / loopMinor; + int relInY = relInX / tileInW; + relC -= relInX * loopMinor; + relInX -= relInY * tileInW; + int c = baseC + relC; + int inX = tileInX + relInX; + int inY = tileInY + relInY; + scalar_t v = 0; + if (inX >= 0 & inY >= 0 & inX < p.inSize.x & inY < p.inSize.y & c < p.inSize.z) + v = (scalar_t)((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w]; + sx[relInY][relInX][relC] = v; + } + + // Loop over output pixels. + __syncthreads(); + for (int outIdx = threadIdx.x; outIdx < tileOutH * tileOutW * loopMinor; outIdx += blockDim.x) + { + int relC = outIdx; + int relOutX = relC / loopMinor; + int relOutY = relOutX / tileOutW; + relC -= relOutX * loopMinor; + relOutX -= relOutY * tileOutW; + int c = baseC + relC; + int outX = tileOutX + relOutX; + int outY = tileOutY + relOutY; + + // Setup receptive field. + int midX = tileMidX + relOutX * downx; + int midY = tileMidY + relOutY * downy; + int inX = floor_div(midX, upx); + int inY = floor_div(midY, upy); + int relInX = inX - tileInX; + int relInY = inY - tileInY; + int filterX = (inX + 1) * upx - midX - 1; // flipped + int filterY = (inY + 1) * upy - midY - 1; // flipped + + // Inner loop. + if (outX < p.outSize.x & outY < p.outSize.y & c < p.outSize.z) + { + scalar_t v = 0; + #pragma unroll + for (int y = 0; y < filterH / upy; y++) + #pragma unroll + for (int x = 0; x < filterW / upx; x++) + v += sx[relInY + y][relInX + x][relC] * sf[filterY + y * upy][filterX + x * upx]; + v *= p.gain; + ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v; + } + } + } + } +} + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p) +{ + int s = p.inStride.z, fx = p.filterSize.x, fy = p.filterSize.y; + + upfirdn2d_kernel_spec spec = {(void*)upfirdn2d_kernel_large, -1,-1,1, 4}; // contiguous + if (s == 1) spec = {(void*)upfirdn2d_kernel_large, -1,-1,4, 1}; // channels_last + + if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // contiguous + { + if (fx <= 7 && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 5 && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 3 && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + } + if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // channels_last + { + if (fx <= 7 && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 5 && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 3 && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + } + if (s != 1 && p.up.x == 2 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // contiguous + { + if (fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + if (fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1}; + } + if (s == 1 && p.up.x == 2 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // channels_last + { + if (fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + if (fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1}; + } + if (s != 1 && p.up.x == 2 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // contiguous + { + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1}; + } + if (s == 1 && p.up.x == 2 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // channels_last + { + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1}; + } + if (s != 1 && p.up.x == 1 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // contiguous + { + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1}; + } + if (s == 1 && p.up.x == 1 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // channels_last + { + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1}; + } + if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 2) // contiguous + { + if (fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + if (fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1}; + } + if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 2) // channels_last + { + if (fx <= 8 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + if (fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1}; + } + if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 1) // contiguous + { + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1}; + } + if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 1) // channels_last + { + if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1}; + } + if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 2) // contiguous + { + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1}; + } + if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 2) // channels_last + { + if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1}; + } + return spec; +} + +//------------------------------------------------------------------------ +// Template specializations. + +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel (const upfirdn2d_kernel_params& p); +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel (const upfirdn2d_kernel_params& p); +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/PTI/torch_utils/ops/upfirdn2d.h b/PTI/torch_utils/ops/upfirdn2d.h new file mode 100644 index 0000000000000000000000000000000000000000..c9e2032bcac9d2abde7a75eea4d812da348afadd --- /dev/null +++ b/PTI/torch_utils/ops/upfirdn2d.h @@ -0,0 +1,59 @@ +// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +// +// NVIDIA CORPORATION and its licensors retain all intellectual property +// and proprietary rights in and to this software, related documentation +// and any modifications thereto. Any use, reproduction, disclosure or +// distribution of this software and related documentation without an express +// license agreement from NVIDIA CORPORATION is strictly prohibited. + +#include + +//------------------------------------------------------------------------ +// CUDA kernel parameters. + +struct upfirdn2d_kernel_params +{ + const void* x; + const float* f; + void* y; + + int2 up; + int2 down; + int2 pad0; + int flip; + float gain; + + int4 inSize; // [width, height, channel, batch] + int4 inStride; + int2 filterSize; // [width, height] + int2 filterStride; + int4 outSize; // [width, height, channel, batch] + int4 outStride; + int sizeMinor; + int sizeMajor; + + int loopMinor; + int loopMajor; + int loopX; + int launchMinor; + int launchMajor; +}; + +//------------------------------------------------------------------------ +// CUDA kernel specialization. + +struct upfirdn2d_kernel_spec +{ + void* kernel; + int tileOutW; + int tileOutH; + int loopMinor; + int loopX; +}; + +//------------------------------------------------------------------------ +// CUDA kernel selection. + +template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p); + +//------------------------------------------------------------------------ diff --git a/PTI/torch_utils/ops/upfirdn2d.py b/PTI/torch_utils/ops/upfirdn2d.py new file mode 100644 index 0000000000000000000000000000000000000000..ceeac2b9834e33b7c601c28bf27f32aa91c69256 --- /dev/null +++ b/PTI/torch_utils/ops/upfirdn2d.py @@ -0,0 +1,384 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Custom PyTorch ops for efficient resampling of 2D images.""" + +import os +import warnings +import numpy as np +import torch +import traceback + +from .. import custom_ops +from .. import misc +from . import conv2d_gradfix + +#---------------------------------------------------------------------------- + +_inited = False +_plugin = None + +def _init(): + global _inited, _plugin + if not _inited: + sources = ['upfirdn2d.cpp', 'upfirdn2d.cu'] + sources = [os.path.join(os.path.dirname(__file__), s) for s in sources] + try: + _plugin = custom_ops.get_plugin('upfirdn2d_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math']) + except: + warnings.warn('Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc()) + return _plugin is not None + +def _parse_scaling(scaling): + if isinstance(scaling, int): + scaling = [scaling, scaling] + assert isinstance(scaling, (list, tuple)) + assert all(isinstance(x, int) for x in scaling) + sx, sy = scaling + assert sx >= 1 and sy >= 1 + return sx, sy + +def _parse_padding(padding): + if isinstance(padding, int): + padding = [padding, padding] + assert isinstance(padding, (list, tuple)) + assert all(isinstance(x, int) for x in padding) + if len(padding) == 2: + padx, pady = padding + padding = [padx, padx, pady, pady] + padx0, padx1, pady0, pady1 = padding + return padx0, padx1, pady0, pady1 + +def _get_filter_size(f): + if f is None: + return 1, 1 + assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] + fw = f.shape[-1] + fh = f.shape[0] + with misc.suppress_tracer_warnings(): + fw = int(fw) + fh = int(fh) + misc.assert_shape(f, [fh, fw][:f.ndim]) + assert fw >= 1 and fh >= 1 + return fw, fh + +#---------------------------------------------------------------------------- + +def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None): + r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`. + + Args: + f: Torch tensor, numpy array, or python list of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), + `[]` (impulse), or + `None` (identity). + device: Result device (default: cpu). + normalize: Normalize the filter so that it retains the magnitude + for constant input signal (DC)? (default: True). + flip_filter: Flip the filter? (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + separable: Return a separable filter? (default: select automatically). + + Returns: + Float32 tensor of the shape + `[filter_height, filter_width]` (non-separable) or + `[filter_taps]` (separable). + """ + # Validate. + if f is None: + f = 1 + f = torch.as_tensor(f, dtype=torch.float32) + assert f.ndim in [0, 1, 2] + assert f.numel() > 0 + if f.ndim == 0: + f = f[np.newaxis] + + # Separable? + if separable is None: + separable = (f.ndim == 1 and f.numel() >= 8) + if f.ndim == 1 and not separable: + f = f.ger(f) + assert f.ndim == (1 if separable else 2) + + # Apply normalize, flip, gain, and device. + if normalize: + f /= f.sum() + if flip_filter: + f = f.flip(list(range(f.ndim))) + f = f * (gain ** (f.ndim / 2)) + f = f.to(device=device) + return f + +#---------------------------------------------------------------------------- + +def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'): + r"""Pad, upsample, filter, and downsample a batch of 2D images. + + Performs the following sequence of operations for each channel: + + 1. Upsample the image by inserting N-1 zeros after each pixel (`up`). + + 2. Pad the image with the specified number of zeros on each side (`padding`). + Negative padding corresponds to cropping the image. + + 3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it + so that the footprint of all output pixels lies within the input image. + + 4. Downsample the image by keeping every Nth pixel (`down`). + + This sequence of operations bears close resemblance to scipy.signal.upfirdn(). + The fused op is considerably more efficient than performing the same calculation + using standard PyTorch ops. It supports gradients of arbitrary order. + + Args: + x: Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f: Float32 FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + up: Integer upsampling factor. Can be a single int or a list/tuple + `[x, y]` (default: 1). + down: Integer downsampling factor. Can be a single int or a list/tuple + `[x, y]` (default: 1). + padding: Padding with respect to the upsampled image. Can be a single number + or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + flip_filter: False = convolution, True = correlation (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + assert isinstance(x, torch.Tensor) + assert impl in ['ref', 'cuda'] + if impl == 'cuda' and x.device.type == 'cuda' and _init(): + return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f) + return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain) + +#---------------------------------------------------------------------------- + +@misc.profiled_function +def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1): + """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops. + """ + # Validate arguments. + assert isinstance(x, torch.Tensor) and x.ndim == 4 + if f is None: + f = torch.ones([1, 1], dtype=torch.float32, device=x.device) + assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] + assert f.dtype == torch.float32 and not f.requires_grad + batch_size, num_channels, in_height, in_width = x.shape + upx, upy = _parse_scaling(up) + downx, downy = _parse_scaling(down) + padx0, padx1, pady0, pady1 = _parse_padding(padding) + + # Upsample by inserting zeros. + x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1]) + x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1]) + x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx]) + + # Pad or crop. + x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)]) + x = x[:, :, max(-pady0, 0) : x.shape[2] - max(-pady1, 0), max(-padx0, 0) : x.shape[3] - max(-padx1, 0)] + + # Setup filter. + f = f * (gain ** (f.ndim / 2)) + f = f.to(x.dtype) + if not flip_filter: + f = f.flip(list(range(f.ndim))) + + # Convolve with the filter. + f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim) + if f.ndim == 4: + x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels) + else: + x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels) + x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels) + + # Downsample by throwing away pixels. + x = x[:, :, ::downy, ::downx] + return x + +#---------------------------------------------------------------------------- + +_upfirdn2d_cuda_cache = dict() + +def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1): + """Fast CUDA implementation of `upfirdn2d()` using custom ops. + """ + # Parse arguments. + upx, upy = _parse_scaling(up) + downx, downy = _parse_scaling(down) + padx0, padx1, pady0, pady1 = _parse_padding(padding) + + # Lookup from cache. + key = (upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain) + if key in _upfirdn2d_cuda_cache: + return _upfirdn2d_cuda_cache[key] + + # Forward op. + class Upfirdn2dCuda(torch.autograd.Function): + @staticmethod + def forward(ctx, x, f): # pylint: disable=arguments-differ + assert isinstance(x, torch.Tensor) and x.ndim == 4 + if f is None: + f = torch.ones([1, 1], dtype=torch.float32, device=x.device) + assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] + y = x + if f.ndim == 2: + y = _plugin.upfirdn2d(y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain) + else: + y = _plugin.upfirdn2d(y, f.unsqueeze(0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, np.sqrt(gain)) + y = _plugin.upfirdn2d(y, f.unsqueeze(1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, np.sqrt(gain)) + ctx.save_for_backward(f) + ctx.x_shape = x.shape + return y + + @staticmethod + def backward(ctx, dy): # pylint: disable=arguments-differ + f, = ctx.saved_tensors + _, _, ih, iw = ctx.x_shape + _, _, oh, ow = dy.shape + fw, fh = _get_filter_size(f) + p = [ + fw - padx0 - 1, + iw * upx - ow * downx + padx0 - upx + 1, + fh - pady0 - 1, + ih * upy - oh * downy + pady0 - upy + 1, + ] + dx = None + df = None + + if ctx.needs_input_grad[0]: + dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(not flip_filter), gain=gain).apply(dy, f) + + assert not ctx.needs_input_grad[1] + return dx, df + + # Add to cache. + _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda + return Upfirdn2dCuda + +#---------------------------------------------------------------------------- + +def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'): + r"""Filter a batch of 2D images using the given 2D FIR filter. + + By default, the result is padded so that its shape matches the input. + User-specified padding is applied on top of that, with negative values + indicating cropping. Pixels outside the image are assumed to be zero. + + Args: + x: Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f: Float32 FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + padding: Padding with respect to the output. Can be a single number or a + list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + flip_filter: False = convolution, True = correlation (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + padx0, padx1, pady0, pady1 = _parse_padding(padding) + fw, fh = _get_filter_size(f) + p = [ + padx0 + fw // 2, + padx1 + (fw - 1) // 2, + pady0 + fh // 2, + pady1 + (fh - 1) // 2, + ] + return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl) + +#---------------------------------------------------------------------------- + +def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'): + r"""Upsample a batch of 2D images using the given 2D FIR filter. + + By default, the result is padded so that its shape is a multiple of the input. + User-specified padding is applied on top of that, with negative values + indicating cropping. Pixels outside the image are assumed to be zero. + + Args: + x: Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f: Float32 FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + up: Integer upsampling factor. Can be a single int or a list/tuple + `[x, y]` (default: 1). + padding: Padding with respect to the output. Can be a single number or a + list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + flip_filter: False = convolution, True = correlation (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + upx, upy = _parse_scaling(up) + padx0, padx1, pady0, pady1 = _parse_padding(padding) + fw, fh = _get_filter_size(f) + p = [ + padx0 + (fw + upx - 1) // 2, + padx1 + (fw - upx) // 2, + pady0 + (fh + upy - 1) // 2, + pady1 + (fh - upy) // 2, + ] + return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl) + +#---------------------------------------------------------------------------- + +def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'): + r"""Downsample a batch of 2D images using the given 2D FIR filter. + + By default, the result is padded so that its shape is a fraction of the input. + User-specified padding is applied on top of that, with negative values + indicating cropping. Pixels outside the image are assumed to be zero. + + Args: + x: Float32/float64/float16 input tensor of the shape + `[batch_size, num_channels, in_height, in_width]`. + f: Float32 FIR filter of the shape + `[filter_height, filter_width]` (non-separable), + `[filter_taps]` (separable), or + `None` (identity). + down: Integer downsampling factor. Can be a single int or a list/tuple + `[x, y]` (default: 1). + padding: Padding with respect to the input. Can be a single number or a + list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` + (default: 0). + flip_filter: False = convolution, True = correlation (default: False). + gain: Overall scaling factor for signal magnitude (default: 1). + impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`). + + Returns: + Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. + """ + downx, downy = _parse_scaling(down) + padx0, padx1, pady0, pady1 = _parse_padding(padding) + fw, fh = _get_filter_size(f) + p = [ + padx0 + (fw - downx + 1) // 2, + padx1 + (fw - downx) // 2, + pady0 + (fh - downy + 1) // 2, + pady1 + (fh - downy) // 2, + ] + return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl) + +#---------------------------------------------------------------------------- diff --git a/PTI/torch_utils/persistence.py b/PTI/torch_utils/persistence.py new file mode 100644 index 0000000000000000000000000000000000000000..0186cfd97bca0fcb397a7b73643520c1d1105a02 --- /dev/null +++ b/PTI/torch_utils/persistence.py @@ -0,0 +1,251 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Facilities for pickling Python code alongside other data. + +The pickled code is automatically imported into a separate Python module +during unpickling. This way, any previously exported pickles will remain +usable even if the original code is no longer available, or if the current +version of the code is not consistent with what was originally pickled.""" + +import sys +import pickle +import io +import inspect +import copy +import uuid +import types +import dnnlib + +#---------------------------------------------------------------------------- + +_version = 6 # internal version number +_decorators = set() # {decorator_class, ...} +_import_hooks = [] # [hook_function, ...] +_module_to_src_dict = dict() # {module: src, ...} +_src_to_module_dict = dict() # {src: module, ...} + +#---------------------------------------------------------------------------- + +def persistent_class(orig_class): + r"""Class decorator that extends a given class to save its source code + when pickled. + + Example: + + from torch_utils import persistence + + @persistence.persistent_class + class MyNetwork(torch.nn.Module): + def __init__(self, num_inputs, num_outputs): + super().__init__() + self.fc = MyLayer(num_inputs, num_outputs) + ... + + @persistence.persistent_class + class MyLayer(torch.nn.Module): + ... + + When pickled, any instance of `MyNetwork` and `MyLayer` will save its + source code alongside other internal state (e.g., parameters, buffers, + and submodules). This way, any previously exported pickle will remain + usable even if the class definitions have been modified or are no + longer available. + + The decorator saves the source code of the entire Python module + containing the decorated class. It does *not* save the source code of + any imported modules. Thus, the imported modules must be available + during unpickling, also including `torch_utils.persistence` itself. + + It is ok to call functions defined in the same module from the + decorated class. However, if the decorated class depends on other + classes defined in the same module, they must be decorated as well. + This is illustrated in the above example in the case of `MyLayer`. + + It is also possible to employ the decorator just-in-time before + calling the constructor. For example: + + cls = MyLayer + if want_to_make_it_persistent: + cls = persistence.persistent_class(cls) + layer = cls(num_inputs, num_outputs) + + As an additional feature, the decorator also keeps track of the + arguments that were used to construct each instance of the decorated + class. The arguments can be queried via `obj.init_args` and + `obj.init_kwargs`, and they are automatically pickled alongside other + object state. A typical use case is to first unpickle a previous + instance of a persistent class, and then upgrade it to use the latest + version of the source code: + + with open('old_pickle.pkl', 'rb') as f: + old_net = pickle.load(f) + new_net = MyNetwork(*old_obj.init_args, **old_obj.init_kwargs) + misc.copy_params_and_buffers(old_net, new_net, require_all=True) + """ + assert isinstance(orig_class, type) + if is_persistent(orig_class): + return orig_class + + assert orig_class.__module__ in sys.modules + orig_module = sys.modules[orig_class.__module__] + orig_module_src = _module_to_src(orig_module) + + class Decorator(orig_class): + _orig_module_src = orig_module_src + _orig_class_name = orig_class.__name__ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._init_args = copy.deepcopy(args) + self._init_kwargs = copy.deepcopy(kwargs) + assert orig_class.__name__ in orig_module.__dict__ + _check_pickleable(self.__reduce__()) + + @property + def init_args(self): + return copy.deepcopy(self._init_args) + + @property + def init_kwargs(self): + return dnnlib.EasyDict(copy.deepcopy(self._init_kwargs)) + + def __reduce__(self): + fields = list(super().__reduce__()) + fields += [None] * max(3 - len(fields), 0) + if fields[0] is not _reconstruct_persistent_obj: + meta = dict(type='class', version=_version, module_src=self._orig_module_src, class_name=self._orig_class_name, state=fields[2]) + fields[0] = _reconstruct_persistent_obj # reconstruct func + fields[1] = (meta,) # reconstruct args + fields[2] = None # state dict + return tuple(fields) + + Decorator.__name__ = orig_class.__name__ + _decorators.add(Decorator) + return Decorator + +#---------------------------------------------------------------------------- + +def is_persistent(obj): + r"""Test whether the given object or class is persistent, i.e., + whether it will save its source code when pickled. + """ + try: + if obj in _decorators: + return True + except TypeError: + pass + return type(obj) in _decorators # pylint: disable=unidiomatic-typecheck + +#---------------------------------------------------------------------------- + +def import_hook(hook): + r"""Register an import hook that is called whenever a persistent object + is being unpickled. A typical use case is to patch the pickled source + code to avoid errors and inconsistencies when the API of some imported + module has changed. + + The hook should have the following signature: + + hook(meta) -> modified meta + + `meta` is an instance of `dnnlib.EasyDict` with the following fields: + + type: Type of the persistent object, e.g. `'class'`. + version: Internal version number of `torch_utils.persistence`. + module_src Original source code of the Python module. + class_name: Class name in the original Python module. + state: Internal state of the object. + + Example: + + @persistence.import_hook + def wreck_my_network(meta): + if meta.class_name == 'MyNetwork': + print('MyNetwork is being imported. I will wreck it!') + meta.module_src = meta.module_src.replace("True", "False") + return meta + """ + assert callable(hook) + _import_hooks.append(hook) + +#---------------------------------------------------------------------------- + +def _reconstruct_persistent_obj(meta): + r"""Hook that is called internally by the `pickle` module to unpickle + a persistent object. + """ + meta = dnnlib.EasyDict(meta) + meta.state = dnnlib.EasyDict(meta.state) + for hook in _import_hooks: + meta = hook(meta) + assert meta is not None + + assert meta.version == _version + module = _src_to_module(meta.module_src) + + assert meta.type == 'class' + orig_class = module.__dict__[meta.class_name] + decorator_class = persistent_class(orig_class) + obj = decorator_class.__new__(decorator_class) + + setstate = getattr(obj, '__setstate__', None) + if callable(setstate): + setstate(meta.state) # pylint: disable=not-callable + else: + obj.__dict__.update(meta.state) + return obj + +#---------------------------------------------------------------------------- + +def _module_to_src(module): + r"""Query the source code of a given Python module. + """ + src = _module_to_src_dict.get(module, None) + if src is None: + src = inspect.getsource(module) + _module_to_src_dict[module] = src + _src_to_module_dict[src] = module + return src + +def _src_to_module(src): + r"""Get or create a Python module for the given source code. + """ + module = _src_to_module_dict.get(src, None) + if module is None: + module_name = "_imported_module_" + uuid.uuid4().hex + module = types.ModuleType(module_name) + sys.modules[module_name] = module + _module_to_src_dict[module] = src + _src_to_module_dict[src] = module + exec(src, module.__dict__) # pylint: disable=exec-used + return module + +#---------------------------------------------------------------------------- + +def _check_pickleable(obj): + r"""Check that the given object is pickleable, raising an exception if + it is not. This function is expected to be considerably more efficient + than actually pickling the object. + """ + def recurse(obj): + if isinstance(obj, (list, tuple, set)): + return [recurse(x) for x in obj] + if isinstance(obj, dict): + return [[recurse(x), recurse(y)] for x, y in obj.items()] + if isinstance(obj, (str, int, float, bool, bytes, bytearray)): + return None # Python primitive types are pickleable. + if f'{type(obj).__module__}.{type(obj).__name__}' in ['numpy.ndarray', 'torch.Tensor']: + return None # NumPy arrays and PyTorch tensors are pickleable. + if is_persistent(obj): + return None # Persistent objects are pickleable, by virtue of the constructor check. + return obj + with io.BytesIO() as f: + pickle.dump(recurse(obj), f) + +#---------------------------------------------------------------------------- diff --git a/PTI/torch_utils/training_stats.py b/PTI/torch_utils/training_stats.py new file mode 100644 index 0000000000000000000000000000000000000000..26f467f9eaa074ee13de1cf2625cd7da44880847 --- /dev/null +++ b/PTI/torch_utils/training_stats.py @@ -0,0 +1,268 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Facilities for reporting and collecting training statistics across +multiple processes and devices. The interface is designed to minimize +synchronization overhead as well as the amount of boilerplate in user +code.""" + +import re +import numpy as np +import torch +import dnnlib + +from . import misc + +#---------------------------------------------------------------------------- + +_num_moments = 3 # [num_scalars, sum_of_scalars, sum_of_squares] +_reduce_dtype = torch.float32 # Data type to use for initial per-tensor reduction. +_counter_dtype = torch.float64 # Data type to use for the internal counters. +_rank = 0 # Rank of the current process. +_sync_device = None # Device to use for multiprocess communication. None = single-process. +_sync_called = False # Has _sync() been called yet? +_counters = dict() # Running counters on each device, updated by report(): name => device => torch.Tensor +_cumulative = dict() # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor + +#---------------------------------------------------------------------------- + +def init_multiprocessing(rank, sync_device): + r"""Initializes `torch_utils.training_stats` for collecting statistics + across multiple processes. + + This function must be called after + `torch.distributed.init_process_group()` and before `Collector.update()`. + The call is not necessary if multi-process collection is not needed. + + Args: + rank: Rank of the current process. + sync_device: PyTorch device to use for inter-process + communication, or None to disable multi-process + collection. Typically `torch.device('cuda', rank)`. + """ + global _rank, _sync_device + assert not _sync_called + _rank = rank + _sync_device = sync_device + +#---------------------------------------------------------------------------- + +@misc.profiled_function +def report(name, value): + r"""Broadcasts the given set of scalars to all interested instances of + `Collector`, across device and process boundaries. + + This function is expected to be extremely cheap and can be safely + called from anywhere in the training loop, loss function, or inside a + `torch.nn.Module`. + + Warning: The current implementation expects the set of unique names to + be consistent across processes. Please make sure that `report()` is + called at least once for each unique name by each process, and in the + same order. If a given process has no scalars to broadcast, it can do + `report(name, [])` (empty list). + + Args: + name: Arbitrary string specifying the name of the statistic. + Averages are accumulated separately for each unique name. + value: Arbitrary set of scalars. Can be a list, tuple, + NumPy array, PyTorch tensor, or Python scalar. + + Returns: + The same `value` that was passed in. + """ + if name not in _counters: + _counters[name] = dict() + + elems = torch.as_tensor(value) + if elems.numel() == 0: + return value + + elems = elems.detach().flatten().to(_reduce_dtype) + moments = torch.stack([ + torch.ones_like(elems).sum(), + elems.sum(), + elems.square().sum(), + ]) + assert moments.ndim == 1 and moments.shape[0] == _num_moments + moments = moments.to(_counter_dtype) + + device = moments.device + if device not in _counters[name]: + _counters[name][device] = torch.zeros_like(moments) + _counters[name][device].add_(moments) + return value + +#---------------------------------------------------------------------------- + +def report0(name, value): + r"""Broadcasts the given set of scalars by the first process (`rank = 0`), + but ignores any scalars provided by the other processes. + See `report()` for further details. + """ + report(name, value if _rank == 0 else []) + return value + +#---------------------------------------------------------------------------- + +class Collector: + r"""Collects the scalars broadcasted by `report()` and `report0()` and + computes their long-term averages (mean and standard deviation) over + user-defined periods of time. + + The averages are first collected into internal counters that are not + directly visible to the user. They are then copied to the user-visible + state as a result of calling `update()` and can then be queried using + `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the + internal counters for the next round, so that the user-visible state + effectively reflects averages collected between the last two calls to + `update()`. + + Args: + regex: Regular expression defining which statistics to + collect. The default is to collect everything. + keep_previous: Whether to retain the previous averages if no + scalars were collected on a given round + (default: True). + """ + def __init__(self, regex='.*', keep_previous=True): + self._regex = re.compile(regex) + self._keep_previous = keep_previous + self._cumulative = dict() + self._moments = dict() + self.update() + self._moments.clear() + + def names(self): + r"""Returns the names of all statistics broadcasted so far that + match the regular expression specified at construction time. + """ + return [name for name in _counters if self._regex.fullmatch(name)] + + def update(self): + r"""Copies current values of the internal counters to the + user-visible state and resets them for the next round. + + If `keep_previous=True` was specified at construction time, the + operation is skipped for statistics that have received no scalars + since the last update, retaining their previous averages. + + This method performs a number of GPU-to-CPU transfers and one + `torch.distributed.all_reduce()`. It is intended to be called + periodically in the main training loop, typically once every + N training steps. + """ + if not self._keep_previous: + self._moments.clear() + for name, cumulative in _sync(self.names()): + if name not in self._cumulative: + self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype) + delta = cumulative - self._cumulative[name] + self._cumulative[name].copy_(cumulative) + if float(delta[0]) != 0: + self._moments[name] = delta + + def _get_delta(self, name): + r"""Returns the raw moments that were accumulated for the given + statistic between the last two calls to `update()`, or zero if + no scalars were collected. + """ + assert self._regex.fullmatch(name) + if name not in self._moments: + self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype) + return self._moments[name] + + def num(self, name): + r"""Returns the number of scalars that were accumulated for the given + statistic between the last two calls to `update()`, or zero if + no scalars were collected. + """ + delta = self._get_delta(name) + return int(delta[0]) + + def mean(self, name): + r"""Returns the mean of the scalars that were accumulated for the + given statistic between the last two calls to `update()`, or NaN if + no scalars were collected. + """ + delta = self._get_delta(name) + if int(delta[0]) == 0: + return float('nan') + return float(delta[1] / delta[0]) + + def std(self, name): + r"""Returns the standard deviation of the scalars that were + accumulated for the given statistic between the last two calls to + `update()`, or NaN if no scalars were collected. + """ + delta = self._get_delta(name) + if int(delta[0]) == 0 or not np.isfinite(float(delta[1])): + return float('nan') + if int(delta[0]) == 1: + return float(0) + mean = float(delta[1] / delta[0]) + raw_var = float(delta[2] / delta[0]) + return np.sqrt(max(raw_var - np.square(mean), 0)) + + def as_dict(self): + r"""Returns the averages accumulated between the last two calls to + `update()` as an `dnnlib.EasyDict`. The contents are as follows: + + dnnlib.EasyDict( + NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT), + ... + ) + """ + stats = dnnlib.EasyDict() + for name in self.names(): + stats[name] = dnnlib.EasyDict(num=self.num(name), mean=self.mean(name), std=self.std(name)) + return stats + + def __getitem__(self, name): + r"""Convenience getter. + `collector[name]` is a synonym for `collector.mean(name)`. + """ + return self.mean(name) + +#---------------------------------------------------------------------------- + +def _sync(names): + r"""Synchronize the global cumulative counters across devices and + processes. Called internally by `Collector.update()`. + """ + if len(names) == 0: + return [] + global _sync_called + _sync_called = True + + # Collect deltas within current rank. + deltas = [] + device = _sync_device if _sync_device is not None else torch.device('cpu') + for name in names: + delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device) + for counter in _counters[name].values(): + delta.add_(counter.to(device)) + counter.copy_(torch.zeros_like(counter)) + deltas.append(delta) + deltas = torch.stack(deltas) + + # Sum deltas across ranks. + if _sync_device is not None: + torch.distributed.all_reduce(deltas) + + # Update cumulative values. + deltas = deltas.cpu() + for idx, name in enumerate(names): + if name not in _cumulative: + _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype) + _cumulative[name].add_(deltas[idx]) + + # Return name-value pairs. + return [(name, _cumulative[name]) for name in names] + +#---------------------------------------------------------------------------- diff --git a/PTI/training/__init__.py b/PTI/training/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/training/coaches/__init__.py b/PTI/training/coaches/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/training/coaches/base_coach.py b/PTI/training/coaches/base_coach.py new file mode 100644 index 0000000000000000000000000000000000000000..ccea133353df1f6b6737f9672ae7e2cb9438071d --- /dev/null +++ b/PTI/training/coaches/base_coach.py @@ -0,0 +1,158 @@ +import abc +import os +import pickle +from argparse import Namespace +import os.path +from PTI.criteria.localitly_regulizer import Space_Regulizer +import torch +from torchvision import transforms +from lpips import LPIPS +from PTI.training.projectors import w_projector +from PTI.configs import global_config, paths_config, hyperparameters +from PTI.criteria import l2_loss +from PTI.models.e4e.psp import pSp +from PTI.utils.log_utils import log_image_from_w +from PTI.utils.models_utils import toogle_grad, load_old_G + + +class BaseCoach: + def __init__(self, data_loader, use_wandb): + + self.use_wandb = use_wandb + self.data_loader = data_loader + self.w_pivots = {} + self.image_counter = 0 + + if hyperparameters.first_inv_type == 'w+': + self.initilize_e4e() + + self.e4e_image_transform = transforms.Compose([ + transforms.ToPILImage(), + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) + + # Initialize loss + self.lpips_loss = LPIPS(net=hyperparameters.lpips_type).to( + global_config.device).eval() + + self.restart_training() + + # Initialize checkpoint dir + self.checkpoint_dir = paths_config.checkpoints_dir + os.makedirs(self.checkpoint_dir, exist_ok=True) + + def restart_training(self): + + # Initialize networks + self.G = load_old_G() + toogle_grad(self.G, True) + + self.original_G = load_old_G() + + self.space_regulizer = Space_Regulizer( + self.original_G, self.lpips_loss) + self.optimizer = self.configure_optimizers() + + def get_inversion(self, w_path_dir, image_name, image): + embedding_dir = f'{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}' + os.makedirs(embedding_dir, exist_ok=True) + + w_pivot = None + if hyperparameters.use_last_w_pivots: + w_pivot = self.load_inversions(w_path_dir, image_name) + + if not hyperparameters.use_last_w_pivots or w_pivot is None: + w_pivot = self.calc_inversions(image, image_name) + torch.save(w_pivot, f'{embedding_dir}/0.pt') + + w_pivot = w_pivot.to(global_config.device) + return w_pivot + + def load_inversions(self, w_path_dir, image_name): + if image_name in self.w_pivots: + return self.w_pivots[image_name] + + if hyperparameters.first_inv_type == 'w+': + w_potential_path = f'{w_path_dir}/{paths_config.e4e_results_keyword}/{image_name}/0.pt' + else: + w_potential_path = f'{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}/0.pt' + if not os.path.isfile(w_potential_path): + return None + w = torch.load(w_potential_path).to(global_config.device) + self.w_pivots[image_name] = w + return w + + def calc_inversions(self, image, image_name): + if hyperparameters.first_inv_type == 'w+': + w = self.get_e4e_inversion(image) + + else: + id_image = torch.squeeze( + (image.to(global_config.device) + 1) / 2) * 255 + w = w_projector.project(self.G, id_image, device=torch.device(global_config.device), w_avg_samples=600, + num_steps=hyperparameters.first_inv_steps, w_name=image_name, + use_wandb=self.use_wandb) + + return w + + @abc.abstractmethod + def train(self): + pass + + def configure_optimizers(self): + optimizer = torch.optim.Adam( + self.G.parameters(), lr=hyperparameters.pti_learning_rate) + + return optimizer + + def calc_loss(self, generated_images, real_images, log_name, new_G, use_ball_holder, w_batch): + loss = 0.0 + + if hyperparameters.pt_l2_lambda > 0: + l2_loss_val = l2_loss.l2_loss(generated_images, real_images) + if self.use_wandb: + wandb.log({f'MSE_loss_val_{log_name}': l2_loss_val.detach( + ).cpu()}, step=global_config.training_step) + loss += l2_loss_val * hyperparameters.pt_l2_lambda + if hyperparameters.pt_lpips_lambda > 0: + loss_lpips = self.lpips_loss(generated_images, real_images) + loss_lpips = torch.squeeze(loss_lpips) + if self.use_wandb: + wandb.log({f'LPIPS_loss_val_{log_name}': loss_lpips.detach( + ).cpu()}, step=global_config.training_step) + loss += loss_lpips * hyperparameters.pt_lpips_lambda + + if use_ball_holder and hyperparameters.use_locality_regularization: + ball_holder_loss_val = self.space_regulizer.space_regulizer_loss( + new_G, w_batch, use_wandb=self.use_wandb) + loss += ball_holder_loss_val + + return loss, l2_loss_val, loss_lpips + + def forward(self, w): + generated_images = self.G.synthesis( + w, noise_mode='const', force_fp32=True) + + return generated_images + + def initilize_e4e(self): + ckpt = torch.load(paths_config.e4e, map_location='cpu') + opts = ckpt['opts'] + opts['batch_size'] = hyperparameters.train_batch_size + opts['checkpoint_path'] = paths_config.e4e + opts = Namespace(**opts) + self.e4e_inversion_net = pSp(opts) + self.e4e_inversion_net.eval() + self.e4e_inversion_net = self.e4e_inversion_net.to( + global_config.device) + toogle_grad(self.e4e_inversion_net, False) + + def get_e4e_inversion(self, image): + image = (image + 1) / 2 + new_image = self.e4e_image_transform(image[0]).to(global_config.device) + _, w = self.e4e_inversion_net(new_image.unsqueeze(0), randomize_noise=False, return_latents=True, resize=False, + input_code=False) + if self.use_wandb: + log_image_from_w(w, self.G, 'First e4e inversion') + return w diff --git a/PTI/training/coaches/multi_id_coach.py b/PTI/training/coaches/multi_id_coach.py new file mode 100644 index 0000000000000000000000000000000000000000..8e813fb9500d5e2fd610ef030b387abc7c542b62 --- /dev/null +++ b/PTI/training/coaches/multi_id_coach.py @@ -0,0 +1,73 @@ +import os +import sys +sys.path.append('.') +import torch +from tqdm import tqdm + +from PTI.configs import paths_config, hyperparameters, global_config +from PTI.training.coaches.base_coach import BaseCoach +from PTI.utils.log_utils import log_images_from_w + + +class MultiIDCoach(BaseCoach): + + def __init__(self, data_loader, use_wandb): + super().__init__(data_loader, use_wandb) + + def train(self): + self.G.synthesis.train() + self.G.mapping.train() + + w_path_dir = f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}' + os.makedirs(w_path_dir, exist_ok=True) + os.makedirs(f'{w_path_dir}/{paths_config.pti_results_keyword}', exist_ok=True) + + use_ball_holder = True + w_pivots = [] + images = [] + + for fname, image in self.data_loader: + if self.image_counter >= hyperparameters.max_images_to_invert: + break + + image_name = fname[0] + if hyperparameters.first_inv_type == 'w+': + embedding_dir = f'{w_path_dir}/{paths_config.e4e_results_keyword}/{image_name}' + else: + embedding_dir = f'{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}' + os.makedirs(embedding_dir, exist_ok=True) + + w_pivot = self.get_inversion(w_path_dir, image_name, image) + w_pivots.append(w_pivot) + images.append((image_name, image)) + self.image_counter += 1 + + for i in tqdm(range(hyperparameters.max_pti_steps)): + self.image_counter = 0 + + for data, w_pivot in zip(images, w_pivots): + image_name, image = data + + if self.image_counter >= hyperparameters.max_images_to_invert: + break + + real_images_batch = image.to(global_config.device) + + generated_images = self.forward(w_pivot) + loss, l2_loss_val, loss_lpips = self.calc_loss(generated_images, real_images_batch, image_name, + self.G, use_ball_holder, w_pivot) + + self.optimizer.zero_grad() + loss.backward() + self.optimizer.step() + + use_ball_holder = global_config.training_step % hyperparameters.locality_regularization_interval == 0 + + global_config.training_step += 1 + self.image_counter += 1 + + if self.use_wandb: + log_images_from_w(w_pivots, self.G, [image[0] for image in images]) + + torch.save(self.G, + f'{paths_config.checkpoints_dir}/model_{global_config.run_name}_multi_id.pt') diff --git a/PTI/training/coaches/single_id_coach.py b/PTI/training/coaches/single_id_coach.py new file mode 100644 index 0000000000000000000000000000000000000000..2ecab5bd53ac5343888314a38d682e9abcc1021d --- /dev/null +++ b/PTI/training/coaches/single_id_coach.py @@ -0,0 +1,80 @@ +import os +import torch +from tqdm import tqdm +from PTI.configs import paths_config, hyperparameters, global_config +from PTI.training.coaches.base_coach import BaseCoach +from PTI.utils.log_utils import log_images_from_w + + +class SingleIDCoach(BaseCoach): + def __init__(self, data_loader, use_wandb): + super().__init__(data_loader, use_wandb) + + def train(self): + w_path_dir = f"{paths_config.embedding_base_dir}/{paths_config.input_data_id}" + os.makedirs(w_path_dir, exist_ok=True) + os.makedirs(f"{w_path_dir}/{paths_config.pti_results_keyword}", exist_ok=True) + + use_ball_holder = True + w_pivot = None + fname, image = next(iter(self.data_loader)) + print("NANANAN", fname) + image_name = fname[0] + + self.restart_training() + + embedding_dir = f"{w_path_dir}/{paths_config.pti_results_keyword}/{image_name}" + os.makedirs(embedding_dir, exist_ok=True) + + if hyperparameters.use_last_w_pivots: + w_pivot = self.load_inversions(w_path_dir, image_name) + + elif not hyperparameters.use_last_w_pivots or w_pivot is None: + w_pivot = self.calc_inversions(image, image_name) + torch.save(w_pivot, f"{embedding_dir}/0.pt") + # w_pivot = w_pivot.detach().clone().to(global_config.device) + w_pivot = w_pivot.to(global_config.device) + + log_images_counter = 0 + real_images_batch = image.to(global_config.device) + + for i in tqdm(range(hyperparameters.max_pti_steps)): + generated_images = self.forward(w_pivot) + loss, l2_loss_val, loss_lpips = self.calc_loss( + generated_images, + real_images_batch, + image_name, + self.G, + use_ball_holder, + w_pivot, + ) + + self.optimizer.zero_grad() + + if loss_lpips <= hyperparameters.LPIPS_value_threshold: + break + + loss.backward() + self.optimizer.step() + + use_ball_holder = ( + global_config.training_step + % hyperparameters.locality_regularization_interval + == 0 + ) + + if ( + self.use_wandb + and log_images_counter % global_config.image_rec_result_log_snapshot + == 0 + ): + log_images_from_w([w_pivot], self.G, [image_name]) + + global_config.training_step += 1 + log_images_counter += 1 + + torch.save( + self.G, + f"{paths_config.checkpoints_dir}/model_{global_config.run_name}_{image_name}.pt", + ) + return self.G, w_pivot diff --git a/PTI/training/projectors/__init__.py b/PTI/training/projectors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/training/projectors/w_plus_projector.py b/PTI/training/projectors/w_plus_projector.py new file mode 100644 index 0000000000000000000000000000000000000000..b9cce427e5374c5ddce90199e1184f84a13d30c5 --- /dev/null +++ b/PTI/training/projectors/w_plus_projector.py @@ -0,0 +1,145 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Project given image to the latent space of pretrained network pickle.""" + +import copy +import wandb +import numpy as np +import torch +import torch.nn.functional as F +from tqdm import tqdm +from configs import global_config, hyperparameters +import dnnlib +from utils.log_utils import log_image_from_w + + +def project( + G, + target: torch.Tensor, # [C,H,W] and dynamic range [0,255], W & H must match G output resolution + *, + num_steps=1000, + w_avg_samples=10000, + initial_learning_rate=0.01, + initial_noise_factor=0.05, + lr_rampdown_length=0.25, + lr_rampup_length=0.05, + noise_ramp_length=0.75, + regularize_noise_weight=1e5, + verbose=False, + device: torch.device, + use_wandb=False, + initial_w=None, + image_log_step=global_config.image_rec_result_log_snapshot, + w_name: str +): + assert target.shape == (G.img_channels, G.img_resolution, G.img_resolution) + + def logprint(*args): + if verbose: + print(*args) + + G = copy.deepcopy(G).eval().requires_grad_(False).to(device).float() # type: ignore + + # Compute w stats. + logprint(f'Computing W midpoint and stddev using {w_avg_samples} samples...') + z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim) + w_samples = G.mapping(torch.from_numpy(z_samples).to(device), None) # [N, L, C] + w_samples = w_samples[:, :1, :].cpu().numpy().astype(np.float32) # [N, 1, C] + w_avg = np.mean(w_samples, axis=0, keepdims=True) # [1, 1, C] + w_avg_tensor = torch.from_numpy(w_avg).to(global_config.device) + w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5 + + start_w = initial_w if initial_w is not None else w_avg + + # Setup noise inputs. + noise_bufs = {name: buf for (name, buf) in G.synthesis.named_buffers() if 'noise_const' in name} + + # Load VGG16 feature detector. + url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt' + with dnnlib.util.open_url(url) as f: + vgg16 = torch.jit.load(f).eval().to(device) + + # Features for target image. + target_images = target.unsqueeze(0).to(device).to(torch.float32) + if target_images.shape[2] > 256: + target_images = F.interpolate(target_images, size=(256, 256), mode='area') + target_features = vgg16(target_images, resize_images=False, return_lpips=True) + + start_w = np.repeat(start_w, G.mapping.num_ws, axis=1) + w_opt = torch.tensor(start_w, dtype=torch.float32, device=device, + requires_grad=True) # pylint: disable=not-callable + + optimizer = torch.optim.Adam([w_opt] + list(noise_bufs.values()), betas=(0.9, 0.999), + lr=hyperparameters.first_inv_lr) + + # Init noise. + for buf in noise_bufs.values(): + buf[:] = torch.randn_like(buf) + buf.requires_grad = True + + for step in tqdm(range(num_steps)): + + # Learning rate schedule. + t = step / num_steps + w_noise_scale = w_std * initial_noise_factor * max(0.0, 1.0 - t / noise_ramp_length) ** 2 + lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length) + lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi) + lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length) + lr = initial_learning_rate * lr_ramp + for param_group in optimizer.param_groups: + param_group['lr'] = lr + + # Synth images from opt_w. + w_noise = torch.randn_like(w_opt) * w_noise_scale + ws = (w_opt + w_noise) + + synth_images = G.synthesis(ws, noise_mode='const', force_fp32=True) + + # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images. + synth_images = (synth_images + 1) * (255 / 2) + if synth_images.shape[2] > 256: + synth_images = F.interpolate(synth_images, size=(256, 256), mode='area') + + # Features for synth images. + synth_features = vgg16(synth_images, resize_images=False, return_lpips=True) + dist = (target_features - synth_features).square().sum() + + # Noise regularization. + reg_loss = 0.0 + for v in noise_bufs.values(): + noise = v[None, None, :, :] # must be [1,1,H,W] for F.avg_pool2d() + while True: + reg_loss += (noise * torch.roll(noise, shifts=1, dims=3)).mean() ** 2 + reg_loss += (noise * torch.roll(noise, shifts=1, dims=2)).mean() ** 2 + if noise.shape[2] <= 8: + break + noise = F.avg_pool2d(noise, kernel_size=2) + loss = dist + reg_loss * regularize_noise_weight + + if step % image_log_step == 0: + with torch.no_grad(): + if use_wandb: + global_config.training_step += 1 + wandb.log({f'first projection _{w_name}': loss.detach().cpu()}, step=global_config.training_step) + log_image_from_w(w_opt, G, w_name) + + # Step + optimizer.zero_grad(set_to_none=True) + loss.backward() + optimizer.step() + logprint(f'step {step + 1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}') + + # Normalize noise. + with torch.no_grad(): + for buf in noise_bufs.values(): + buf -= buf.mean() + buf *= buf.square().mean().rsqrt() + + del G + return w_opt diff --git a/PTI/training/projectors/w_projector.py b/PTI/training/projectors/w_projector.py new file mode 100644 index 0000000000000000000000000000000000000000..a4caffc368f87e06b41eaac2807a273079708840 --- /dev/null +++ b/PTI/training/projectors/w_projector.py @@ -0,0 +1,142 @@ +# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. +# +# NVIDIA CORPORATION and its licensors retain all intellectual property +# and proprietary rights in and to this software, related documentation +# and any modifications thereto. Any use, reproduction, disclosure or +# distribution of this software and related documentation without an express +# license agreement from NVIDIA CORPORATION is strictly prohibited. + +"""Project given image to the latent space of pretrained network pickle.""" + +import copy +import wandb +import numpy as np +import torch +import torch.nn.functional as F +from tqdm import tqdm +from PTI.configs import global_config, hyperparameters +from PTI.utils import log_utils +import dnnlib + + +def project( + G, + target: torch.Tensor, # [C,H,W] and dynamic range [0,255], W & H must match G output resolution + *, + num_steps=1000, + w_avg_samples=10000, + initial_learning_rate=0.01, + initial_noise_factor=0.05, + lr_rampdown_length=0.25, + lr_rampup_length=0.05, + noise_ramp_length=0.75, + regularize_noise_weight=1e5, + verbose=False, + device: torch.device, + use_wandb=False, + initial_w=None, + image_log_step=global_config.image_rec_result_log_snapshot, + w_name: str +): + assert target.shape == (G.img_channels, G.img_resolution, G.img_resolution),print(target.shape,G.img_resolution) + + def logprint(*args): + if verbose: + print(*args) + + G = copy.deepcopy(G).eval().requires_grad_(False).to(device).float() # type: ignore + + # Compute w stats. + logprint(f'Computing W midpoint and stddev using {w_avg_samples} samples...') + z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim) + w_samples = G.mapping(torch.from_numpy(z_samples).to(device), None) # [N, L, C] + w_samples = w_samples[:, :1, :].cpu().numpy().astype(np.float32) # [N, 1, C] + w_avg = np.mean(w_samples, axis=0, keepdims=True) # [1, 1, C] + w_avg_tensor = torch.from_numpy(w_avg).to(global_config.device) + w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5 + + start_w = initial_w if initial_w is not None else w_avg + + # Setup noise inputs. + noise_bufs = {name: buf for (name, buf) in G.synthesis.named_buffers() if 'noise_const' in name} + + # Load VGG16 feature detector. + url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt' + with dnnlib.util.open_url(url) as f: + vgg16 = torch.jit.load(f).eval().to(device) + + # Features for target image. + target_images = target.unsqueeze(0).to(device).to(torch.float32) + if target_images.shape[2] > 256: + target_images = F.interpolate(target_images, size=(256, 256), mode='area') + target_features = vgg16(target_images, resize_images=False, return_lpips=True) + + w_opt = torch.tensor(start_w, dtype=torch.float32, device=device, + requires_grad=True) # pylint: disable=not-callable + optimizer = torch.optim.Adam([w_opt] + list(noise_bufs.values()), betas=(0.9, 0.999), + lr=hyperparameters.first_inv_lr) + + # Init noise. + for buf in noise_bufs.values(): + buf[:] = torch.randn_like(buf) + buf.requires_grad = True + + for step in tqdm(range(num_steps)): + + # Learning rate schedule. + t = step / num_steps + w_noise_scale = w_std * initial_noise_factor * max(0.0, 1.0 - t / noise_ramp_length) ** 2 + lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length) + lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi) + lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length) + lr = initial_learning_rate * lr_ramp + for param_group in optimizer.param_groups: + param_group['lr'] = lr + + # Synth images from opt_w. + w_noise = torch.randn_like(w_opt) * w_noise_scale + ws = (w_opt + w_noise).repeat([1, G.mapping.num_ws, 1]) + synth_images = G.synthesis(ws, noise_mode='const', force_fp32=True) + + # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images. + synth_images = (synth_images + 1) * (255 / 2) + if synth_images.shape[2] > 256: + synth_images = F.interpolate(synth_images, size=(256, 256), mode='area') + + # Features for synth images. + synth_features = vgg16(synth_images, resize_images=False, return_lpips=True) + dist = (target_features - synth_features).square().sum() + + # Noise regularization. + reg_loss = 0.0 + for v in noise_bufs.values(): + noise = v[None, None, :, :] # must be [1,1,H,W] for F.avg_pool2d() + while True: + reg_loss += (noise * torch.roll(noise, shifts=1, dims=3)).mean() ** 2 + reg_loss += (noise * torch.roll(noise, shifts=1, dims=2)).mean() ** 2 + if noise.shape[2] <= 8: + break + noise = F.avg_pool2d(noise, kernel_size=2) + loss = dist + reg_loss * regularize_noise_weight + + if step % image_log_step == 0: + with torch.no_grad(): + if use_wandb: + global_config.training_step += 1 + wandb.log({f'first projection _{w_name}': loss.detach().cpu()}, step=global_config.training_step) + log_utils.log_image_from_w(w_opt.repeat([1, G.mapping.num_ws, 1]), G, w_name) + + # Step + optimizer.zero_grad(set_to_none=True) + loss.backward() + optimizer.step() + logprint(f'step {step + 1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}') + + # Normalize noise. + with torch.no_grad(): + for buf in noise_bufs.values(): + buf -= buf.mean() + buf *= buf.square().mean().rsqrt() + + del G + return w_opt.repeat([1, 18, 1]) diff --git a/PTI/utils/ImagesDataset.py b/PTI/utils/ImagesDataset.py new file mode 100644 index 0000000000000000000000000000000000000000..4d36e8665270f4f6dee5a2d58a36c564e1543771 --- /dev/null +++ b/PTI/utils/ImagesDataset.py @@ -0,0 +1,43 @@ +import os + +from torch.utils.data import Dataset +from PIL import Image + +from PTI.utils.data_utils import make_dataset +from torchvision import transforms + + +class Image2Dataset(Dataset): + def __init__(self, image) -> None: + super().__init__() + self.image = image + self.transform = transforms.Compose( + [ + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), + ] + ) + + def __len__(self): + return 1 + + def __getitem__(self, index): + return "customIMG", self.transform(self.image) + + +class ImagesDataset(Dataset): + def __init__(self, source_root, source_transform=None): + self.source_paths = sorted(make_dataset(source_root)) + self.source_transform = source_transform + + def __len__(self): + return len(self.source_paths) + + def __getitem__(self, index): + fname, from_path = self.source_paths[index] + from_im = Image.open(from_path).convert("RGB").resize([1024, 1024]) + + if self.source_transform: + from_im = self.source_transform(from_im) + + return fname, from_im diff --git a/PTI/utils/__init__.py b/PTI/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/PTI/utils/align_data.py b/PTI/utils/align_data.py new file mode 100644 index 0000000000000000000000000000000000000000..12b59bf5ce294972252876a714631e05cde5630c --- /dev/null +++ b/PTI/utils/align_data.py @@ -0,0 +1,37 @@ +import sys +sys.path.append('.') +from configs import paths_config +import dlib +import glob +import os +from tqdm import tqdm +from utils.alignment import align_face + + +def pre_process_images(raw_images_path): + current_directory = os.getcwd() + + IMAGE_SIZE = 1024 + predictor = dlib.shape_predictor(paths_config.dlib) + os.chdir(raw_images_path) + images_names = glob.glob(f'*') + + aligned_images = [] + for image_name in tqdm(images_names): + try: + aligned_image = align_face(filepath=f'{raw_images_path}/{image_name}', + predictor=predictor, output_size=IMAGE_SIZE) + aligned_images.append(aligned_image) + except Exception as e: + print(e) + + os.makedirs(paths_config.input_data_path, exist_ok=True) + for image, name in zip(aligned_images, images_names): + real_name = name.split('.')[0] + image.save(f'{paths_config.input_data_path}/{real_name}.jpeg') + + os.chdir(current_directory) + + +if __name__ == "__main__": + pre_process_images('/home/zhizizhang/Documents2/projects/PTI/docs') diff --git a/PTI/utils/alignment.py b/PTI/utils/alignment.py new file mode 100644 index 0000000000000000000000000000000000000000..d1e13a0d70eb0827abca405401f83b9939122f2d --- /dev/null +++ b/PTI/utils/alignment.py @@ -0,0 +1,113 @@ +import numpy as np +import PIL +import PIL.Image +import scipy +import scipy.ndimage +import dlib + +def get_landmark(img, predictor): + """get landmark with dlib + :return: np.array shape=(68, 2) + """ + detector = dlib.get_frontal_face_detector() + + img = np.array(img) + dets = detector(img, 1) + + for k, d in enumerate(dets): + shape = predictor(img, d) + + t = list(shape.parts()) + a = [] + for tt in t: + a.append([tt.x, tt.y]) + lm = np.array(a) + return lm + + +def align_face(img, predictor, output_size): + """ + :param img: PIL Image + :return: PIL Image + """ + + lm = get_landmark(img, predictor) + + lm_chin = lm[0: 17] # left-right + lm_eyebrow_left = lm[17: 22] # left-right + lm_eyebrow_right = lm[22: 27] # left-right + lm_nose = lm[27: 31] # top-down + lm_nostrils = lm[31: 36] # top-down + lm_eye_left = lm[36: 42] # left-clockwise + lm_eye_right = lm[42: 48] # left-clockwise + lm_mouth_outer = lm[48: 60] # left-clockwise + lm_mouth_inner = lm[60: 68] # left-clockwise + + # Calculate auxiliary vectors. + eye_left = np.mean(lm_eye_left, axis=0) + eye_right = np.mean(lm_eye_right, axis=0) + eye_avg = (eye_left + eye_right) * 0.5 + eye_to_eye = eye_right - eye_left + mouth_left = lm_mouth_outer[0] + mouth_right = lm_mouth_outer[6] + mouth_avg = (mouth_left + mouth_right) * 0.5 + eye_to_mouth = mouth_avg - eye_avg + + # Choose oriented crop rectangle. + x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] + x /= np.hypot(*x) + x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8) + y = np.flipud(x) * [-1, 1] + c = eye_avg + eye_to_mouth * 0.1 + quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) + qsize = np.hypot(*x) * 2 + + # read image + # img = img + + transform_size = output_size + enable_padding = True + + # Shrink. + shrink = int(np.floor(qsize / output_size * 0.5)) + if shrink > 1: + rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink))) + img = img.resize(rsize, PIL.Image.ANTIALIAS) + quad /= shrink + qsize /= shrink + + # Crop. + border = max(int(np.rint(qsize * 0.1)), 3) + crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), + int(np.ceil(max(quad[:, 1])))) + crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), + min(crop[3] + border, img.size[1])) + if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]: + img = img.crop(crop) + quad -= crop[0:2] + + # Pad. + pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), + int(np.ceil(max(quad[:, 1])))) + pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), + max(pad[3] - img.size[1] + border, 0)) + if enable_padding and max(pad) > border - 4: + pad = np.maximum(pad, int(np.rint(qsize * 0.3))) + img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') + h, w, _ = img.shape + y, x, _ = np.ogrid[:h, :w, :1] + mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]), + 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3])) + blur = qsize * 0.02 + img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) + img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0) + img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB') + quad += pad[:2] + + # Transform. + img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR) + if output_size < transform_size: + img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS) + + # Return aligned image. + return img diff --git a/PTI/utils/data_utils.py b/PTI/utils/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a477bb62396989bf1000a9a46c695687b5c15f59 --- /dev/null +++ b/PTI/utils/data_utils.py @@ -0,0 +1,34 @@ +import os + +from PIL import Image + +IMG_EXTENSIONS = [ + '.jpg', '.JPG', '.jpeg', '.JPEG', + '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tiff' +] + + +def is_image_file(filename): + return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) + + +def tensor2im(var): + # var shape: (3, H, W) + var = var.cpu().detach().transpose(0, 2).transpose(0, 1).numpy() + var = ((var + 1) / 2) + var[var < 0] = 0 + var[var > 1] = 1 + var = var * 255 + return Image.fromarray(var.astype('uint8')) + + +def make_dataset(dir): + images = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if is_image_file(fname): + path = os.path.join(root, fname) + fname = fname.split('.')[0] + images.append((fname, path)) + return images diff --git a/PTI/utils/log_utils.py b/PTI/utils/log_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b29eae05f1c3ba34df60c074373b417c5420e836 --- /dev/null +++ b/PTI/utils/log_utils.py @@ -0,0 +1,79 @@ +import numpy as np +from PIL import Image +import wandb +from PTI.configs import global_config +import torch +import matplotlib.pyplot as plt + + +def log_image_from_w(w, G, name): + img = get_image_from_w(w, G) + pillow_image = Image.fromarray(img) + wandb.log( + {f"{name}": [ + wandb.Image(pillow_image, caption=f"current inversion {name}")]}, + step=global_config.training_step) + + +def log_images_from_w(ws, G, names): + for name, w in zip(names, ws): + w = w.to(global_config.device) + log_image_from_w(w, G, name) + + +def plot_image_from_w(w, G): + img = get_image_from_w(w, G) + pillow_image = Image.fromarray(img) + plt.imshow(pillow_image) + plt.show() + + +def plot_image(img): + img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).detach().cpu().numpy() + pillow_image = Image.fromarray(img[0]) + plt.imshow(pillow_image) + plt.show() + + +def save_image(name, method_type, results_dir, image, run_id): + image.save(f'{results_dir}/{method_type}_{name}_{run_id}.jpg') + + +def save_w(w, G, name, method_type, results_dir): + im = get_image_from_w(w, G) + im = Image.fromarray(im, mode='RGB') + save_image(name, method_type, results_dir, im) + + +def save_concat_image(base_dir, image_latents, new_inv_image_latent, new_G, + old_G, + file_name, + extra_image=None): + images_to_save = [] + if extra_image is not None: + images_to_save.append(extra_image) + for latent in image_latents: + images_to_save.append(get_image_from_w(latent, old_G)) + images_to_save.append(get_image_from_w(new_inv_image_latent, new_G)) + result_image = create_alongside_images(images_to_save) + result_image.save(f'{base_dir}/{file_name}.jpg') + + +def save_single_image(base_dir, image_latent, G, file_name): + image_to_save = get_image_from_w(image_latent, G) + image_to_save = Image.fromarray(image_to_save, mode='RGB') + image_to_save.save(f'{base_dir}/{file_name}.jpg') + + +def create_alongside_images(images): + res = np.concatenate([np.array(image) for image in images], axis=1) + return Image.fromarray(res, mode='RGB') + + +def get_image_from_w(w, G): + if len(w.size()) <= 2: + w = w.unsqueeze(0) + with torch.no_grad(): + img = G.synthesis(w, noise_mode='const') + img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).detach().cpu().numpy() + return img[0] diff --git a/PTI/utils/models_utils.py b/PTI/utils/models_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..836151dcc405d62fa435a3cc3b3a0bd3472eeb03 --- /dev/null +++ b/PTI/utils/models_utils.py @@ -0,0 +1,25 @@ +import pickle +import functools +import torch +from PTI.configs import paths_config, global_config + + +def toogle_grad(model, flag=True): + for p in model.parameters(): + p.requires_grad = flag + + +def load_tuned_G(run_id, type): + new_G_path = f'{paths_config.checkpoints_dir}/model_{run_id}_{type}.pt' + with open(new_G_path, 'rb') as f: + new_G = torch.load(f).to(global_config.device).eval() + new_G = new_G.float() + toogle_grad(new_G, False) + return new_G + + +def load_old_G(): + with open(paths_config.stylegan2_ada_ffhq, 'rb') as f: + old_G = pickle.load(f)['G_ema'].to(global_config.device).eval() + old_G = old_G.float() + return old_G diff --git a/README.md b/README.md index a8663c08e47f9b51037955aeb8425166b230eef9..7ea7dcbb011aebb97612f542ec5a20a8e412cb0d 100644 --- a/README.md +++ b/README.md @@ -1,17 +1,21 @@ --- -title: DragGan -emoji: 👀 +title: DragGan - Drag Your GAN - Inversion +emoji: 🔄🐉 colorFrom: purple colorTo: pink sdk: gradio -sdk_version: 3.35.2 -app_file: visualizer_drag_gradio.py +python_version: 3.8.17 +sdk_version: 3.36.1 +app_file: visualizer_drag_gradio_inversion.py pinned: false --- # Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold +https://arxiv.org/abs/2305.10973 +https://huggingface.co/DragGan/DragGan-Models +

diff --git a/dnnlib/util.py b/dnnlib/util.py index 6bbdf3bd8fe1c138cd969d37dcc52190b45c4c16..90f91e1085239fd9672b2cbe83cbd8e85b27ec0e 100644 --- a/dnnlib/util.py +++ b/dnnlib/util.py @@ -79,7 +79,7 @@ class Logger(object): """Write text to stdout (and a file) and optionally flush.""" if isinstance(text, bytes): text = text.decode() - if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash + if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash return if self.file is not None: @@ -117,10 +117,12 @@ class Logger(object): _dnnlib_cache_dir = None + def set_cache_dir(path: str) -> None: global _dnnlib_cache_dir _dnnlib_cache_dir = path + def make_cache_dir_path(*paths: str) -> str: if _dnnlib_cache_dir is not None: return os.path.join(_dnnlib_cache_dir, *paths) @@ -243,13 +245,16 @@ def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]: # list alternatives for (module_name, local_obj_name) parts = obj_name.split(".") - name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)] + name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) + for i in range(len(parts), 0, -1)] # try each alternative in turn for module_name, local_obj_name in name_pairs: try: - module = importlib.import_module(module_name) # may raise ImportError - get_obj_from_module(module, local_obj_name) # may raise AttributeError + module = importlib.import_module( + module_name) # may raise ImportError + # may raise AttributeError + get_obj_from_module(module, local_obj_name) return module, local_obj_name except: pass @@ -257,7 +262,7 @@ def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]: # maybe some of the modules themselves contain errors? for module_name, _local_obj_name in name_pairs: try: - importlib.import_module(module_name) # may raise ImportError + importlib.import_module(module_name) # may raise ImportError except ImportError: if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"): raise @@ -265,8 +270,10 @@ def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]: # maybe the requested attribute is missing? for module_name, local_obj_name in name_pairs: try: - module = importlib.import_module(module_name) # may raise ImportError - get_obj_from_module(module, local_obj_name) # may raise AttributeError + module = importlib.import_module( + module_name) # may raise ImportError + # may raise AttributeError + get_obj_from_module(module, local_obj_name) except ImportError: pass @@ -319,7 +326,8 @@ def get_top_level_function_name(obj: Any) -> str: assert is_top_level_function(obj) module = obj.__module__ if module == '__main__': - module = os.path.splitext(os.path.basename(sys.modules[module].__file__))[0] + module = os.path.splitext(os.path.basename( + sys.modules[module].__file__))[0] return module + "." + obj.__name__ @@ -351,7 +359,8 @@ def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, a relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths] if add_base_to_relative: - relative_paths = [os.path.join(base_name, p) for p in relative_paths] + relative_paths = [os.path.join(base_name, p) + for p in relative_paths] assert len(absolute_paths) == len(relative_paths) result += zip(absolute_paths, relative_paths) @@ -451,14 +460,17 @@ def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: b if len(res.content) < 8192: content_str = res.content.decode("utf-8") if "download_warning" in res.headers.get("Set-Cookie", ""): - links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link] + links = [html.unescape(link) for link in content_str.split( + '"') if "export=download" in link] if len(links) == 1: url = requests.compat.urljoin(url, links[0]) raise IOError("Google Drive virus checker nag") if "Google Drive - Quota exceeded" in content_str: - raise IOError("Google Drive download quota exceeded -- please try again later") + raise IOError( + "Google Drive download quota exceeded -- please try again later") - match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", "")) + match = re.search( + r'filename="([^"]*)"', res.headers.get("Content-Disposition", "")) url_name = match[1] if match else url url_data = res.content if verbose: @@ -478,11 +490,12 @@ def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: b if cache: safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name) cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name) - temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name) + temp_file = os.path.join( + cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name) os.makedirs(cache_dir, exist_ok=True) with open(temp_file, "wb") as f: f.write(url_data) - os.replace(temp_file, cache_file) # atomic + os.replace(temp_file, cache_file) # atomic if return_filename: return cache_file diff --git a/gen_images.py b/gen_images.py index fdd7dfa6dc159ab80b527918a1505accd91d30a3..996bc12f4cde6ee9d0076446250ed076a04b2641 100644 --- a/gen_images.py +++ b/gen_images.py @@ -20,14 +20,16 @@ import torch import legacy -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def parse_range(s: Union[str, List]) -> List[int]: '''Parse a comma separated list of numbers or ranges and return a list of ints. Example: '1,2,5-10' returns [1, 2, 5, 6, 7] ''' - if isinstance(s, list): return s + if isinstance(s, list): + return s ranges = [] range_re = re.compile(r'^(\d+)-(\d+)$') for p in s.split(','): @@ -38,7 +40,8 @@ def parse_range(s: Union[str, List]) -> List[int]: ranges.append(int(p)) return ranges -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def parse_vec2(s: Union[str, Tuple[float, float]]) -> Tuple[float, float]: '''Parse a floating point 2-vector of syntax 'a,b'. @@ -46,15 +49,17 @@ def parse_vec2(s: Union[str, Tuple[float, float]]) -> Tuple[float, float]: Example: '0,1' returns (0,1) ''' - if isinstance(s, tuple): return s + if isinstance(s, tuple): + return s parts = s.split(',') if len(parts) == 2: return (float(parts[0]), float(parts[1])) raise ValueError(f'cannot parse 2-vector {s}') -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- -def make_transform(translate: Tuple[float,float], angle: float): + +def make_transform(translate: Tuple[float, float], angle: float): m = np.eye(3) s = np.sin(angle/360.0*np.pi*2) c = np.cos(angle/360.0*np.pi*2) @@ -66,7 +71,8 @@ def make_transform(translate: Tuple[float,float], angle: float): m[1][2] = translate[1] return m -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @click.command() @click.option('--network', 'network_pkl', help='Network pickle filename', required=True) @@ -83,7 +89,7 @@ def generate_images( truncation_psi: float, noise_mode: str, outdir: str, - translate: Tuple[float,float], + translate: Tuple[float, float], rotate: float, class_idx: Optional[int] ): @@ -105,7 +111,7 @@ def generate_images( print('Loading networks from "%s"...' % network_pkl) device = torch.device('cuda') with dnnlib.util.open_url(network_pkl) as f: - G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore + G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore # import pickle # G = legacy.load_network_pkl(f) # output = open('checkpoints/stylegan2-car-config-f-pt.pkl', 'wb') @@ -117,16 +123,19 @@ def generate_images( label = torch.zeros([1, G.c_dim], device=device) if G.c_dim != 0: if class_idx is None: - raise click.ClickException('Must specify class label with --class when using a conditional network') + raise click.ClickException( + 'Must specify class label with --class when using a conditional network') label[:, class_idx] = 1 else: if class_idx is not None: - print ('warn: --class=lbl ignored when running on an unconditional network') + print('warn: --class=lbl ignored when running on an unconditional network') # Generate images. for seed_idx, seed in enumerate(seeds): - print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds))) - z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device) + print('Generating image for seed %d (%d/%d) ...' % + (seed, seed_idx, len(seeds))) + z = torch.from_numpy(np.random.RandomState( + seed).randn(1, G.z_dim)).to(device) # Construct an inverse rotation/translation matrix and pass to the generator. The # generator expects this matrix as an inverse to avoid potentially failing numerical @@ -137,13 +146,15 @@ def generate_images( G.synthesis.input.transform.copy_(torch.from_numpy(m)) img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode) - img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) - PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/seed{seed:04d}.png') + img = (img.permute(0, 2, 3, 1) * 127.5 + + 128).clamp(0, 255).to(torch.uint8) + PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save( + f'{outdir}/seed{seed:04d}.png') -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- if __name__ == "__main__": - generate_images() # pylint: disable=no-value-for-parameter + generate_images() # pylint: disable=no-value-for-parameter -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/gui_utils/gl_utils.py b/gui_utils/gl_utils.py index fd8841c328d33a0f3c4376eb07dd9845ad660ebe..922db6ff7c8643352334c36b83039b8d2dad8a0f 100644 --- a/gui_utils/gl_utils.py +++ b/gui_utils/gl_utils.py @@ -15,10 +15,12 @@ import OpenGL.GL as gl import OpenGL.GL.ARB.texture_float import dnnlib -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def init_egl(): - assert os.environ['PYOPENGL_PLATFORM'] == 'egl' # Must be set before importing OpenGL. + # Must be set before importing OpenGL. + assert os.environ['PYOPENGL_PLATFORM'] == 'egl' import OpenGL.EGL as egl import ctypes @@ -61,7 +63,8 @@ def init_egl(): ok = egl.eglMakeCurrent(display, surface, surface, context) assert ok -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + _texture_formats = { ('uint8', 1): dnnlib.EasyDict(type=gl.GL_UNSIGNED_BYTE, format=gl.GL_LUMINANCE, internalformat=gl.GL_LUMINANCE8), @@ -74,10 +77,12 @@ _texture_formats = { ('float32', 4): dnnlib.EasyDict(type=gl.GL_FLOAT, format=gl.GL_RGBA, internalformat=gl.GL_RGBA32F), } + def get_texture_format(dtype, channels): return _texture_formats[(np.dtype(dtype).name, int(channels))] -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def prepare_texture_data(image): image = np.asarray(image) @@ -87,7 +92,8 @@ def prepare_texture_data(image): image = image.astype('float32') return image -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def draw_pixels(image, *, pos=0, zoom=1, align=0, rint=True): pos = np.broadcast_to(np.asarray(pos, dtype='float32'), [2]) @@ -110,7 +116,8 @@ def draw_pixels(image, *, pos=0, zoom=1, align=0, rint=True): gl.glPopClientAttrib() gl.glPopAttrib() -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def read_pixels(width, height, *, pos=0, dtype='uint8', channels=3): pos = np.broadcast_to(np.asarray(pos, dtype='float32'), [2]) @@ -120,11 +127,13 @@ def read_pixels(width, height, *, pos=0, dtype='uint8', channels=3): gl.glPushClientAttrib(gl.GL_CLIENT_PIXEL_STORE_BIT) gl.glPixelStorei(gl.GL_PACK_ALIGNMENT, 1) - gl.glReadPixels(int(np.round(pos[0])), int(np.round(pos[1])), width, height, fmt.format, fmt.type, image) + gl.glReadPixels(int(np.round(pos[0])), int( + np.round(pos[1])), width, height, fmt.format, fmt.type, image) gl.glPopClientAttrib() return np.flipud(image) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class Texture: def __init__(self, *, image=None, width=None, height=None, channels=None, dtype=None, bilinear=True, mipmap=True): @@ -148,15 +157,20 @@ class Texture: assert isinstance(self.width, int) and self.width >= 0 assert isinstance(self.height, int) and self.height >= 0 assert isinstance(self.channels, int) and self.channels >= 1 - assert self.is_compatible(width=width, height=height, channels=channels, dtype=dtype) + assert self.is_compatible( + width=width, height=height, channels=channels, dtype=dtype) # Create texture object. self.gl_id = gl.glGenTextures(1) with self.bind(): - gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_S, gl.GL_CLAMP_TO_EDGE) - gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_T, gl.GL_CLAMP_TO_EDGE) - gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR if self.bilinear else gl.GL_NEAREST) - gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR_MIPMAP_LINEAR if self.mipmap else gl.GL_NEAREST) + gl.glTexParameterf( + gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_S, gl.GL_CLAMP_TO_EDGE) + gl.glTexParameterf( + gl.GL_TEXTURE_2D, gl.GL_TEXTURE_WRAP_T, gl.GL_CLAMP_TO_EDGE) + gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, + gl.GL_LINEAR if self.bilinear else gl.GL_NEAREST) + gl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, + gl.GL_LINEAR_MIPMAP_LINEAR if self.mipmap else gl.GL_NEAREST) self.update(image) def delete(self): @@ -185,7 +199,8 @@ class Texture: fmt = get_texture_format(self.dtype, self.channels) gl.glPushClientAttrib(gl.GL_CLIENT_PIXEL_STORE_BIT) gl.glPixelStorei(gl.GL_UNPACK_ALIGNMENT, 1) - gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, fmt.internalformat, self.width, self.height, 0, fmt.format, fmt.type, image) + gl.glTexImage2D(gl.GL_TEXTURE_2D, 0, fmt.internalformat, + self.width, self.height, 0, fmt.format, fmt.type, image) if self.mipmap: gl.glGenerateMipmap(gl.GL_TEXTURE_2D) gl.glPopClientAttrib() @@ -196,10 +211,11 @@ class Texture: with self.bind(): gl.glPushAttrib(gl.GL_ENABLE_BIT) gl.glEnable(gl.GL_TEXTURE_2D) - draw_rect(pos=pos, size=size, align=align, rint=rint, color=color, alpha=alpha, rounding=rounding) + draw_rect(pos=pos, size=size, align=align, rint=rint, + color=color, alpha=alpha, rounding=rounding) gl.glPopAttrib() - def is_compatible(self, *, image=None, width=None, height=None, channels=None, dtype=None): # pylint: disable=too-many-return-statements + def is_compatible(self, *, image=None, width=None, height=None, channels=None, dtype=None): # pylint: disable=too-many-return-statements if image is not None: if image.ndim != 3: return False @@ -216,7 +232,8 @@ class Texture: return False return True -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class Framebuffer: def __init__(self, *, texture=None, width=None, height=None, channels=None, dtype=None, msaa=0): @@ -256,19 +273,24 @@ class Framebuffer: # Setup color buffer. if self.texture is not None: assert self.msaa == 0 - gl.glFramebufferTexture2D(gl.GL_FRAMEBUFFER, gl.GL_COLOR_ATTACHMENT0, gl.GL_TEXTURE_2D, self.texture.gl_id, 0) + gl.glFramebufferTexture2D( + gl.GL_FRAMEBUFFER, gl.GL_COLOR_ATTACHMENT0, gl.GL_TEXTURE_2D, self.texture.gl_id, 0) else: fmt = get_texture_format(self.dtype, self.channels) self.gl_color = gl.glGenRenderbuffers(1) gl.glBindRenderbuffer(gl.GL_RENDERBUFFER, self.gl_color) - gl.glRenderbufferStorageMultisample(gl.GL_RENDERBUFFER, self.msaa, fmt.internalformat, self.width, self.height) - gl.glFramebufferRenderbuffer(gl.GL_FRAMEBUFFER, gl.GL_COLOR_ATTACHMENT0, gl.GL_RENDERBUFFER, self.gl_color) + gl.glRenderbufferStorageMultisample( + gl.GL_RENDERBUFFER, self.msaa, fmt.internalformat, self.width, self.height) + gl.glFramebufferRenderbuffer( + gl.GL_FRAMEBUFFER, gl.GL_COLOR_ATTACHMENT0, gl.GL_RENDERBUFFER, self.gl_color) # Setup depth/stencil buffer. self.gl_depth_stencil = gl.glGenRenderbuffers(1) gl.glBindRenderbuffer(gl.GL_RENDERBUFFER, self.gl_depth_stencil) - gl.glRenderbufferStorageMultisample(gl.GL_RENDERBUFFER, self.msaa, gl.GL_DEPTH24_STENCIL8, self.width, self.height) - gl.glFramebufferRenderbuffer(gl.GL_FRAMEBUFFER, gl.GL_DEPTH_STENCIL_ATTACHMENT, gl.GL_RENDERBUFFER, self.gl_depth_stencil) + gl.glRenderbufferStorageMultisample( + gl.GL_RENDERBUFFER, self.msaa, gl.GL_DEPTH24_STENCIL8, self.width, self.height) + gl.glFramebufferRenderbuffer( + gl.GL_FRAMEBUFFER, gl.GL_DEPTH_STENCIL_ATTACHMENT, gl.GL_RENDERBUFFER, self.gl_depth_stencil) def delete(self): if self.gl_id is not None: @@ -301,17 +323,21 @@ class Framebuffer: def blit(self, dst=None): assert dst is None or isinstance(dst, Framebuffer) with self.bind(): - gl.glBindFramebuffer(gl.GL_DRAW_FRAMEBUFFER, 0 if dst is None else dst.fbo) - gl.glBlitFramebuffer(0, 0, self.width, self.height, 0, 0, self.width, self.height, gl.GL_COLOR_BUFFER_BIT, gl.GL_NEAREST) + gl.glBindFramebuffer(gl.GL_DRAW_FRAMEBUFFER, + 0 if dst is None else dst.fbo) + gl.glBlitFramebuffer(0, 0, self.width, self.height, 0, 0, + self.width, self.height, gl.GL_COLOR_BUFFER_BIT, gl.GL_NEAREST) + +# ---------------------------------------------------------------------------- -#---------------------------------------------------------------------------- def draw_shape(vertices, *, mode=gl.GL_TRIANGLE_FAN, pos=0, size=1, color=1, alpha=1): assert vertices.ndim == 2 and vertices.shape[1] == 2 pos = np.broadcast_to(np.asarray(pos, dtype='float32'), [2]) size = np.broadcast_to(np.asarray(size, dtype='float32'), [2]) color = np.broadcast_to(np.asarray(color, dtype='float32'), [3]) - alpha = np.clip(np.broadcast_to(np.asarray(alpha, dtype='float32'), []), 0, 1) + alpha = np.clip(np.broadcast_to( + np.asarray(alpha, dtype='float32'), []), 0, 1) gl.glPushClientAttrib(gl.GL_CLIENT_VERTEX_ARRAY_BIT) gl.glPushAttrib(gl.GL_CURRENT_BIT | gl.GL_TRANSFORM_BIT) @@ -331,7 +357,8 @@ def draw_shape(vertices, *, mode=gl.GL_TRIANGLE_FAN, pos=0, size=1, color=1, alp gl.glPopAttrib() gl.glPopClientAttrib() -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def draw_arrow(x1, y1, x2, y2, l=10, width=1.0): # Compute the length and angle of the arrow @@ -341,76 +368,88 @@ def draw_arrow(x1, y1, x2, y2, l=10, width=1.0): if length < l: return angle = math.atan2(dy, dx) - + # Save the current modelview matrix gl.glPushMatrix() - + # Translate and rotate the coordinate system gl.glTranslatef(x1, y1, 0.0) gl.glRotatef(angle * 180.0 / math.pi, 0.0, 0.0, 1.0) - + # Set the line width gl.glLineWidth(width) # gl.glColor3f(0.75, 0.75, 0.75) - + # Begin drawing lines gl.glBegin(gl.GL_LINES) - + # Draw the shaft of the arrow gl.glVertex2f(0.0, 0.0) gl.glVertex2f(length, 0.0) - + # Draw the head of the arrow gl.glVertex2f(length, 0.0) gl.glVertex2f(length - 2 * l, l) gl.glVertex2f(length, 0.0) gl.glVertex2f(length - 2 * l, -l) - + # End drawing lines gl.glEnd() - + # Restore the modelview matrix gl.glPopMatrix() -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def draw_rect(*, pos=0, pos2=None, size=None, align=0, rint=False, color=1, alpha=1, rounding=0): assert pos2 is None or size is None pos = np.broadcast_to(np.asarray(pos, dtype='float32'), [2]) - pos2 = np.broadcast_to(np.asarray(pos2, dtype='float32'), [2]) if pos2 is not None else None - size = np.broadcast_to(np.asarray(size, dtype='float32'), [2]) if size is not None else None - size = size if size is not None else pos2 - pos if pos2 is not None else np.array([1, 1], dtype='float32') + pos2 = np.broadcast_to(np.asarray(pos2, dtype='float32'), [ + 2]) if pos2 is not None else None + size = np.broadcast_to(np.asarray(size, dtype='float32'), [ + 2]) if size is not None else None + size = size if size is not None else pos2 - \ + pos if pos2 is not None else np.array([1, 1], dtype='float32') pos = pos - size * align if rint: pos = np.rint(pos) rounding = np.broadcast_to(np.asarray(rounding, dtype='float32'), [2]) - rounding = np.minimum(np.abs(rounding) / np.maximum(np.abs(size), 1e-8), 0.5) + rounding = np.minimum( + np.abs(rounding) / np.maximum(np.abs(size), 1e-8), 0.5) if np.min(rounding) == 0: rounding *= 0 vertices = _setup_rect(float(rounding[0]), float(rounding[1])) - draw_shape(vertices, mode=gl.GL_TRIANGLE_FAN, pos=pos, size=size, color=color, alpha=alpha) + draw_shape(vertices, mode=gl.GL_TRIANGLE_FAN, pos=pos, + size=size, color=color, alpha=alpha) + @functools.lru_cache(maxsize=10000) def _setup_rect(rx, ry): t = np.linspace(0, np.pi / 2, 1 if max(rx, ry) == 0 else 64) - s = 1 - np.sin(t); c = 1 - np.cos(t) + s = 1 - np.sin(t) + c = 1 - np.cos(t) x = [c * rx, 1 - s * rx, 1 - c * rx, s * rx] y = [s * ry, c * ry, 1 - s * ry, 1 - c * ry] v = np.stack([x, y], axis=-1).reshape(-1, 2) return v.astype('float32') -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def draw_circle(*, center=0, radius=100, hole=0, color=1, alpha=1): hole = np.broadcast_to(np.asarray(hole, dtype='float32'), []) vertices = _setup_circle(float(hole)) - draw_shape(vertices, mode=gl.GL_TRIANGLE_STRIP, pos=center, size=radius, color=color, alpha=alpha) + draw_shape(vertices, mode=gl.GL_TRIANGLE_STRIP, pos=center, + size=radius, color=color, alpha=alpha) + @functools.lru_cache(maxsize=10000) def _setup_circle(hole): t = np.linspace(0, np.pi * 2, 128) - s = np.sin(t); c = np.cos(t) + s = np.sin(t) + c = np.cos(t) v = np.stack([c, s, c * hole, s * hole], axis=-1).reshape(-1, 2) return v.astype('float32') -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/gui_utils/glfw_window.py b/gui_utils/glfw_window.py index 83264eb89a855ec5038cf255994ee2b4b3ddb5ee..69c96ff72ccff6a42bcf6ab1dbdbb8cfb8005921 100644 --- a/gui_utils/glfw_window.py +++ b/gui_utils/glfw_window.py @@ -11,28 +11,30 @@ import glfw import OpenGL.GL as gl from . import gl_utils -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- -class GlfwWindow: # pylint: disable=too-many-public-methods + +class GlfwWindow: # pylint: disable=too-many-public-methods def __init__(self, *, title='GlfwWindow', window_width=1920, window_height=1080, deferred_show=True, close_on_esc=True): - self._glfw_window = None - self._drawing_frame = False - self._frame_start_time = None - self._frame_delta = 0 - self._fps_limit = None - self._vsync = None - self._skip_frames = 0 - self._deferred_show = deferred_show - self._close_on_esc = close_on_esc - self._esc_pressed = False - self._drag_and_drop_paths = None - self._capture_next_frame = False - self._captured_frame = None + self._glfw_window = None + self._drawing_frame = False + self._frame_start_time = None + self._frame_delta = 0 + self._fps_limit = None + self._vsync = None + self._skip_frames = 0 + self._deferred_show = deferred_show + self._close_on_esc = close_on_esc + self._esc_pressed = False + self._drag_and_drop_paths = None + self._capture_next_frame = False + self._captured_frame = None # Create window. glfw.init() glfw.window_hint(glfw.VISIBLE, False) - self._glfw_window = glfw.create_window(width=window_width, height=window_height, title=title, monitor=None, share=None) + self._glfw_window = glfw.create_window( + width=window_width, height=window_height, title=title, monitor=None, share=None) self._attach_glfw_callbacks() self.make_context_current() @@ -48,7 +50,7 @@ class GlfwWindow: # pylint: disable=too-many-public-methods if self._glfw_window is not None: glfw.destroy_window(self._glfw_window) self._glfw_window = None - #glfw.terminate() # Commented out to play it nice with other glfw clients. + # glfw.terminate() # Commented out to play it nice with other glfw clients. def __del__(self): try: @@ -76,17 +78,20 @@ class GlfwWindow: # pylint: disable=too-many-public-methods @property def title_bar_height(self): - _left, top, _right, _bottom = glfw.get_window_frame_size(self._glfw_window) + _left, top, _right, _bottom = glfw.get_window_frame_size( + self._glfw_window) return top @property def monitor_width(self): - _, _, width, _height = glfw.get_monitor_workarea(glfw.get_primary_monitor()) + _, _, width, _height = glfw.get_monitor_workarea( + glfw.get_primary_monitor()) return width @property def monitor_height(self): - _, _, _width, height = glfw.get_monitor_workarea(glfw.get_primary_monitor()) + _, _, _width, height = glfw.get_monitor_workarea( + glfw.get_primary_monitor()) return height @property @@ -99,7 +104,8 @@ class GlfwWindow: # pylint: disable=too-many-public-methods def set_window_size(self, width, height): width = min(width, self.monitor_width) height = min(height, self.monitor_height) - glfw.set_window_size(self._glfw_window, width, max(height - self.title_bar_height, 0)) + glfw.set_window_size(self._glfw_window, width, max( + height - self.title_bar_height, 0)) if width == self.monitor_width and height == self.monitor_height: self.maximize() @@ -113,7 +119,8 @@ class GlfwWindow: # pylint: disable=too-many-public-methods glfw.set_window_pos(self._glfw_window, x, y + self.title_bar_height) def center(self): - self.set_position((self.monitor_width - self.window_width) // 2, (self.monitor_height - self.window_height) // 2) + self.set_position((self.monitor_width - self.window_width) // + 2, (self.monitor_height - self.window_height) // 2) def set_vsync(self, vsync): vsync = bool(vsync) @@ -130,7 +137,7 @@ class GlfwWindow: # pylint: disable=too-many-public-methods def skip_frame(self): self.skip_frames(1) - def skip_frames(self, num): # Do not update window for the next N frames. + def skip_frames(self, num): # Do not update window for the next N frames. self._skip_frames = max(self._skip_frames, int(num)) def is_skipping_frames(self): @@ -149,7 +156,7 @@ class GlfwWindow: # pylint: disable=too-many-public-methods self._drag_and_drop_paths = None return paths - def draw_frame(self): # To be overridden by subclass. + def draw_frame(self): # To be overridden by subclass. self.begin_frame() # Rendering code goes here. self.end_frame() @@ -185,11 +192,13 @@ class GlfwWindow: # pylint: disable=too-many-public-methods gl.glMatrixMode(gl.GL_PROJECTION) gl.glLoadIdentity() gl.glTranslate(-1, 1, 0) - gl.glScale(2 / max(self.content_width, 1), -2 / max(self.content_height, 1), 1) + gl.glScale(2 / max(self.content_width, 1), - + 2 / max(self.content_height, 1), 1) gl.glMatrixMode(gl.GL_MODELVIEW) gl.glLoadIdentity() gl.glEnable(gl.GL_BLEND) - gl.glBlendFunc(gl.GL_ONE, gl.GL_ONE_MINUS_SRC_ALPHA) # Pre-multiplied alpha. + # Pre-multiplied alpha. + gl.glBlendFunc(gl.GL_ONE, gl.GL_ONE_MINUS_SRC_ALPHA) # Clear. gl.glClearColor(0, 0, 0, 1) @@ -206,7 +215,8 @@ class GlfwWindow: # pylint: disable=too-many-public-methods # Capture frame if requested. if self._capture_next_frame: - self._captured_frame = gl_utils.read_pixels(self.content_width, self.content_height) + self._captured_frame = gl_utils.read_pixels( + self.content_width, self.content_height) self._capture_next_frame = False # Update window. @@ -226,4 +236,4 @@ class GlfwWindow: # pylint: disable=too-many-public-methods def _glfw_drop_callback(self, _window, paths): self._drag_and_drop_paths = paths -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/gui_utils/imgui_utils.py b/gui_utils/imgui_utils.py index 5924abe272e45855044929bb2a8c886029f4f261..6dbc8baaa2b9d1b23a7d9d6bb0cf11465bd236ec 100644 --- a/gui_utils/imgui_utils.py +++ b/gui_utils/imgui_utils.py @@ -9,34 +9,37 @@ import contextlib import imgui -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def set_default_style(color_scheme='dark', spacing=9, indent=23, scrollbar=27): s = imgui.get_style() - s.window_padding = [spacing, spacing] - s.item_spacing = [spacing, spacing] - s.item_inner_spacing = [spacing, spacing] - s.columns_min_spacing = spacing - s.indent_spacing = indent - s.scrollbar_size = scrollbar - s.frame_padding = [4, 3] - s.window_border_size = 1 - s.child_border_size = 1 - s.popup_border_size = 1 - s.frame_border_size = 1 - s.window_rounding = 0 - s.child_rounding = 0 - s.popup_rounding = 3 - s.frame_rounding = 3 - s.scrollbar_rounding = 3 - s.grab_rounding = 3 + s.window_padding = [spacing, spacing] + s.item_spacing = [spacing, spacing] + s.item_inner_spacing = [spacing, spacing] + s.columns_min_spacing = spacing + s.indent_spacing = indent + s.scrollbar_size = scrollbar + s.frame_padding = [4, 3] + s.window_border_size = 1 + s.child_border_size = 1 + s.popup_border_size = 1 + s.frame_border_size = 1 + s.window_rounding = 0 + s.child_rounding = 0 + s.popup_rounding = 3 + s.frame_rounding = 3 + s.scrollbar_rounding = 3 + s.grab_rounding = 3 getattr(imgui, f'style_colors_{color_scheme}')(s) c0 = s.colors[imgui.COLOR_MENUBAR_BACKGROUND] c1 = s.colors[imgui.COLOR_FRAME_BACKGROUND] - s.colors[imgui.COLOR_POPUP_BACKGROUND] = [x * 0.7 + y * 0.3 for x, y in zip(c0, c1)][:3] + [1] + s.colors[imgui.COLOR_POPUP_BACKGROUND] = [ + x * 0.7 + y * 0.3 for x, y in zip(c0, c1)][:3] + [1] + +# ---------------------------------------------------------------------------- -#---------------------------------------------------------------------------- @contextlib.contextmanager def grayed_out(cond=True): @@ -64,7 +67,8 @@ def grayed_out(cond=True): else: yield -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @contextlib.contextmanager def item_width(width=None): @@ -75,7 +79,8 @@ def item_width(width=None): else: yield -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def scoped_by_object_id(method): def decorator(self, *args, **kwargs): @@ -85,7 +90,8 @@ def scoped_by_object_id(method): return res return decorator -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def button(label, width=0, enabled=True): with grayed_out(not enabled): @@ -93,7 +99,8 @@ def button(label, width=0, enabled=True): clicked = clicked and enabled return clicked -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def collapsing_header(text, visible=None, flags=0, default=False, enabled=True, show=True): expanded = False @@ -103,11 +110,13 @@ def collapsing_header(text, visible=None, flags=0, default=False, enabled=True, if not enabled: flags |= imgui.TREE_NODE_LEAF with grayed_out(not enabled): - expanded, visible = imgui.collapsing_header(text, visible=visible, flags=flags) + expanded, visible = imgui.collapsing_header( + text, visible=visible, flags=flags) expanded = expanded and enabled return expanded, visible -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def popup_button(label, width=0, enabled=True): if button(label, width, enabled): @@ -115,7 +124,8 @@ def popup_button(label, width=0, enabled=True): opened = imgui.begin_popup(label) return opened -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def input_text(label, value, buffer_length, flags, width=None, help_text=''): old_value = value @@ -132,7 +142,8 @@ def input_text(label, value, buffer_length, flags, width=None, help_text=''): changed = (value != old_value) return changed, value -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def drag_previous_control(enabled=True): dragging = False @@ -146,34 +157,39 @@ def drag_previous_control(enabled=True): imgui.end_drag_drop_source() return dragging, dx, dy -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def drag_button(label, width=0, enabled=True): clicked = button(label, width=width, enabled=enabled) dragging, dx, dy = drag_previous_control(enabled=enabled) return clicked, dragging, dx, dy -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def drag_hidden_window(label, x, y, width, height, enabled=True): imgui.push_style_color(imgui.COLOR_WINDOW_BACKGROUND, 0, 0, 0, 0) imgui.push_style_color(imgui.COLOR_BORDER, 0, 0, 0, 0) imgui.set_next_window_position(x, y) imgui.set_next_window_size(width, height) - imgui.begin(label, closable=False, flags=(imgui.WINDOW_NO_TITLE_BAR | imgui.WINDOW_NO_RESIZE | imgui.WINDOW_NO_MOVE)) + imgui.begin(label, closable=False, flags=( + imgui.WINDOW_NO_TITLE_BAR | imgui.WINDOW_NO_RESIZE | imgui.WINDOW_NO_MOVE)) dragging, dx, dy = drag_previous_control(enabled=enabled) imgui.end() imgui.pop_style_color(2) return dragging, dx, dy -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def click_hidden_window(label, x, y, width, height, img_w, img_h, enabled=True): imgui.push_style_color(imgui.COLOR_WINDOW_BACKGROUND, 0, 0, 0, 0) imgui.push_style_color(imgui.COLOR_BORDER, 0, 0, 0, 0) imgui.set_next_window_position(x, y) imgui.set_next_window_size(width, height) - imgui.begin(label, closable=False, flags=(imgui.WINDOW_NO_TITLE_BAR | imgui.WINDOW_NO_RESIZE | imgui.WINDOW_NO_MOVE)) + imgui.begin(label, closable=False, flags=( + imgui.WINDOW_NO_TITLE_BAR | imgui.WINDOW_NO_RESIZE | imgui.WINDOW_NO_MOVE)) clicked, down = False, False img_x, img_y = 0, 0 if imgui.is_mouse_down(): @@ -188,4 +204,4 @@ def click_hidden_window(label, x, y, width, height, img_w, img_h, enabled=True): imgui.pop_style_color(2) return clicked, down, img_x, img_y -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/gui_utils/imgui_window.py b/gui_utils/imgui_window.py index 30d539a1382def526050c83978d1118348ac77ad..5937788f2e8e51772677ab12c67038f5ccd37b42 100644 --- a/gui_utils/imgui_window.py +++ b/gui_utils/imgui_window.py @@ -14,20 +14,21 @@ from . import glfw_window from . import imgui_utils from . import text_utils -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class ImguiWindow(glfw_window.GlfwWindow): - def __init__(self, *, title='ImguiWindow', font=None, font_sizes=range(14,24), **glfw_kwargs): + def __init__(self, *, title='ImguiWindow', font=None, font_sizes=range(14, 24), **glfw_kwargs): if font is None: font = text_utils.get_default_font() font_sizes = {int(size) for size in font_sizes} super().__init__(title=title, **glfw_kwargs) # Init fields. - self._imgui_context = None + self._imgui_context = None self._imgui_renderer = None - self._imgui_fonts = None - self._cur_font_size = max(font_sizes) + self._imgui_fonts = None + self._cur_font_size = max(font_sizes) # Delete leftover imgui.ini to avoid unexpected behavior. if os.path.isfile('imgui.ini'): @@ -37,9 +38,12 @@ class ImguiWindow(glfw_window.GlfwWindow): self._imgui_context = imgui.create_context() self._imgui_renderer = _GlfwRenderer(self._glfw_window) self._attach_glfw_callbacks() - imgui.get_io().ini_saving_rate = 0 # Disable creating imgui.ini at runtime. - imgui.get_io().mouse_drag_threshold = 0 # Improve behavior with imgui_utils.drag_custom(). - self._imgui_fonts = {size: imgui.get_io().fonts.add_font_from_file_ttf(font, size) for size in font_sizes} + # Disable creating imgui.ini at runtime. + imgui.get_io().ini_saving_rate = 0 + # Improve behavior with imgui_utils.drag_custom(). + imgui.get_io().mouse_drag_threshold = 0 + self._imgui_fonts = {size: imgui.get_io().fonts.add_font_from_file_ttf( + font, size) for size in font_sizes} self._imgui_renderer.refresh_font_texture() def close(self): @@ -49,7 +53,7 @@ class ImguiWindow(glfw_window.GlfwWindow): self._imgui_renderer.shutdown() self._imgui_renderer = None if self._imgui_context is not None: - #imgui.destroy_context(self._imgui_context) # Commented out to avoid creating imgui.ini at the end. + # imgui.destroy_context(self._imgui_context) # Commented out to avoid creating imgui.ini at the end. self._imgui_context = None super().close() @@ -65,8 +69,9 @@ class ImguiWindow(glfw_window.GlfwWindow): def spacing(self): return round(self._cur_font_size * 0.4) - def set_font_size(self, target): # Applied on next frame. - self._cur_font_size = min((abs(key - target), key) for key in self._imgui_fonts.keys())[1] + def set_font_size(self, target): # Applied on next frame. + self._cur_font_size = min((abs(key - target), key) + for key in self._imgui_fonts.keys())[1] def begin_frame(self): # Begin glfw frame. @@ -80,7 +85,8 @@ class ImguiWindow(glfw_window.GlfwWindow): # Begin imgui frame. imgui.new_frame() imgui.push_font(self._imgui_fonts[self._cur_font_size]) - imgui_utils.set_default_style(spacing=self.spacing, indent=self.font_size, scrollbar=self.font_size+4) + imgui_utils.set_default_style( + spacing=self.spacing, indent=self.font_size, scrollbar=self.font_size+4) def end_frame(self): imgui.pop_font() @@ -89,9 +95,10 @@ class ImguiWindow(glfw_window.GlfwWindow): self._imgui_renderer.render(imgui.get_draw_data()) super().end_frame() -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Wrapper class for GlfwRenderer to fix a mouse wheel bug on Linux. + class _GlfwRenderer(imgui.integrations.glfw.GlfwRenderer): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @@ -100,4 +107,4 @@ class _GlfwRenderer(imgui.integrations.glfw.GlfwRenderer): def scroll_callback(self, window, x_offset, y_offset): self.io.mouse_wheel += y_offset * self.mouse_wheel_multiplier -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/gui_utils/text_utils.py b/gui_utils/text_utils.py index 35e5e4a16dc62c4be80df5432208bce5d386bf16..d1d971d9defa9a223d5b4b19def17f351a262833 100644 --- a/gui_utils/text_utils.py +++ b/gui_utils/text_utils.py @@ -17,13 +17,16 @@ import scipy.ndimage from . import gl_utils -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def get_default_font(): - url = 'http://fonts.gstatic.com/s/opensans/v17/mem8YaGs126MiZpBA-U1UpcaXcl0Aw.ttf' # Open Sans regular + # Open Sans regular + url = 'http://fonts.gstatic.com/s/opensans/v17/mem8YaGs126MiZpBA-U1UpcaXcl0Aw.ttf' return dnnlib.util.open_url(url, return_filename=True) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @functools.lru_cache(maxsize=None) def get_pil_font(font=None, size=32): @@ -31,9 +34,10 @@ def get_pil_font(font=None, size=32): font = get_default_font() return PIL.ImageFont.truetype(font=font, size=size) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- -def get_array(string, *, dropshadow_radius: int=None, **kwargs): + +def get_array(string, *, dropshadow_radius: int = None, **kwargs): if dropshadow_radius is not None: offset_x = int(np.ceil(dropshadow_radius*2/3)) offset_y = int(np.ceil(dropshadow_radius*2/3)) @@ -41,17 +45,18 @@ def get_array(string, *, dropshadow_radius: int=None, **kwargs): else: return _get_array_priv(string, **kwargs) + @functools.lru_cache(maxsize=10000) def _get_array_priv( string: str, *, size: int = 32, - max_width: Optional[int]=None, - max_height: Optional[int]=None, + max_width: Optional[int] = None, + max_height: Optional[int] = None, min_size=10, shrink_coef=0.8, - dropshadow_radius: int=None, - offset_x: int=None, - offset_y: int=None, + dropshadow_radius: int = None, + offset_x: int = None, + offset_y: int = None, **kwargs ): cur_size = size @@ -59,7 +64,8 @@ def _get_array_priv( while True: if dropshadow_radius is not None: # separate implementation for dropshadow text rendering - array = _get_array_impl_dropshadow(string, size=cur_size, radius=dropshadow_radius, offset_x=offset_x, offset_y=offset_y, **kwargs) + array = _get_array_impl_dropshadow( + string, size=cur_size, radius=dropshadow_radius, offset_x=offset_x, offset_y=offset_y, **kwargs) else: array = _get_array_impl(string, size=cur_size, **kwargs) height, width, _ = array.shape @@ -68,21 +74,26 @@ def _get_array_priv( cur_size = max(int(cur_size * shrink_coef), min_size) return array -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @functools.lru_cache(maxsize=10000) -def _get_array_impl(string, *, font=None, size=32, outline=0, outline_pad=3, outline_coef=3, outline_exp=2, line_pad: int=None): +def _get_array_impl(string, *, font=None, size=32, outline=0, outline_pad=3, outline_coef=3, outline_exp=2, line_pad: int = None): pil_font = get_pil_font(font=font, size=size) lines = [pil_font.getmask(line, 'L') for line in string.split('\n')] - lines = [np.array(line, dtype=np.uint8).reshape([line.size[1], line.size[0]]) for line in lines] + lines = [np.array(line, dtype=np.uint8).reshape( + [line.size[1], line.size[0]]) for line in lines] width = max(line.shape[1] for line in lines) - lines = [np.pad(line, ((0, 0), (0, width - line.shape[1])), mode='constant') for line in lines] + lines = [np.pad(line, ((0, 0), (0, width - line.shape[1])), + mode='constant') for line in lines] line_spacing = line_pad if line_pad is not None else size // 2 - lines = [np.pad(line, ((0, line_spacing), (0, 0)), mode='constant') for line in lines[:-1]] + lines[-1:] + lines = [np.pad(line, ((0, line_spacing), (0, 0)), mode='constant') + for line in lines[:-1]] + lines[-1:] mask = np.concatenate(lines, axis=0) alpha = mask if outline > 0: - mask = np.pad(mask, int(np.ceil(outline * outline_pad)), mode='constant', constant_values=0) + mask = np.pad(mask, int(np.ceil(outline * outline_pad)), + mode='constant', constant_values=0) alpha = mask.astype(np.float32) / 255 alpha = scipy.ndimage.gaussian_filter(alpha, outline) alpha = 1 - np.maximum(1 - alpha * outline_coef, 0) ** outline_exp @@ -90,34 +101,41 @@ def _get_array_impl(string, *, font=None, size=32, outline=0, outline_pad=3, out alpha = np.maximum(alpha, mask) return np.stack([mask, alpha], axis=-1) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @functools.lru_cache(maxsize=10000) -def _get_array_impl_dropshadow(string, *, font=None, size=32, radius: int, offset_x: int, offset_y: int, line_pad: int=None, **kwargs): +def _get_array_impl_dropshadow(string, *, font=None, size=32, radius: int, offset_x: int, offset_y: int, line_pad: int = None, **kwargs): assert (offset_x > 0) and (offset_y > 0) pil_font = get_pil_font(font=font, size=size) lines = [pil_font.getmask(line, 'L') for line in string.split('\n')] - lines = [np.array(line, dtype=np.uint8).reshape([line.size[1], line.size[0]]) for line in lines] + lines = [np.array(line, dtype=np.uint8).reshape( + [line.size[1], line.size[0]]) for line in lines] width = max(line.shape[1] for line in lines) - lines = [np.pad(line, ((0, 0), (0, width - line.shape[1])), mode='constant') for line in lines] + lines = [np.pad(line, ((0, 0), (0, width - line.shape[1])), + mode='constant') for line in lines] line_spacing = line_pad if line_pad is not None else size // 2 - lines = [np.pad(line, ((0, line_spacing), (0, 0)), mode='constant') for line in lines[:-1]] + lines[-1:] + lines = [np.pad(line, ((0, line_spacing), (0, 0)), mode='constant') + for line in lines[:-1]] + lines[-1:] mask = np.concatenate(lines, axis=0) alpha = mask - mask = np.pad(mask, 2*radius + max(abs(offset_x), abs(offset_y)), mode='constant', constant_values=0) + mask = np.pad(mask, 2*radius + max(abs(offset_x), abs(offset_y)), + mode='constant', constant_values=0) alpha = mask.astype(np.float32) / 255 alpha = scipy.ndimage.gaussian_filter(alpha, radius) alpha = 1 - np.maximum(1 - alpha * 1.5, 0) ** 1.4 alpha = (alpha * 255 + 0.5).clip(0, 255).astype(np.uint8) - alpha = np.pad(alpha, [(offset_y, 0), (offset_x, 0)], mode='constant')[:-offset_y, :-offset_x] + alpha = np.pad(alpha, [(offset_y, 0), (offset_x, 0)], + mode='constant')[:-offset_y, :-offset_x] alpha = np.maximum(alpha, mask) return np.stack([mask, alpha], axis=-1) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @functools.lru_cache(maxsize=10000) def get_texture(string, bilinear=True, mipmap=True, **kwargs): return gl_utils.Texture(image=get_array(string, **kwargs), bilinear=bilinear, mipmap=mipmap) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/legacy.py b/legacy.py index 8cf53cb9396a639261bbcadb4e264e39415c1a56..a874c38c2c943e632badb8e12f5a4297071827df 100644 --- a/legacy.py +++ b/legacy.py @@ -17,7 +17,8 @@ import torch import dnnlib from torch_utils import misc -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def load_network_pkl(f, force_fp16=False): data = _LegacyUnpickler(f).load() @@ -57,22 +58,26 @@ def load_network_pkl(f, force_fp16=False): data[key] = new return data -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class _TFNetworkStub(dnnlib.EasyDict): pass + class _LegacyUnpickler(pickle.Unpickler): def find_class(self, module, name): if module == 'dnnlib.tflib.network' and name == 'Network': return _TFNetworkStub return super().find_class(module, name) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _collect_tf_params(tf_net): # pylint: disable=protected-access tf_params = dict() + def recurse(prefix, tf_net): for name, value in tf_net.variables: tf_params[prefix + name] = value @@ -81,7 +86,8 @@ def _collect_tf_params(tf_net): recurse('', tf_net) return tf_params -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _populate_module_params(module, *patterns): for name, tensor in misc.named_params_and_buffers(module): @@ -102,7 +108,8 @@ def _populate_module_params(module, *patterns): print(name, list(tensor.shape)) raise -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def convert_tf_generator(tf_G): if tf_G.version < 4: @@ -111,6 +118,7 @@ def convert_tf_generator(tf_G): # Collect kwargs. tf_kwargs = tf_G.static_kwargs known_kwargs = set() + def kwarg(tf_name, default=None, none=None): known_kwargs.add(tf_name) val = tf_kwargs.get(tf_name, default) @@ -120,26 +128,26 @@ def convert_tf_generator(tf_G): from training import networks_stylegan2 network_class = networks_stylegan2.Generator kwargs = dnnlib.EasyDict( - z_dim = kwarg('latent_size', 512), - c_dim = kwarg('label_size', 0), - w_dim = kwarg('dlatent_size', 512), - img_resolution = kwarg('resolution', 1024), - img_channels = kwarg('num_channels', 3), - channel_base = kwarg('fmap_base', 16384) * 2, - channel_max = kwarg('fmap_max', 512), - num_fp16_res = kwarg('num_fp16_res', 0), - conv_clamp = kwarg('conv_clamp', None), - architecture = kwarg('architecture', 'skip'), - resample_filter = kwarg('resample_kernel', [1,3,3,1]), - use_noise = kwarg('use_noise', True), - activation = kwarg('nonlinearity', 'lrelu'), - mapping_kwargs = dnnlib.EasyDict( - num_layers = kwarg('mapping_layers', 8), - embed_features = kwarg('label_fmaps', None), - layer_features = kwarg('mapping_fmaps', None), - activation = kwarg('mapping_nonlinearity', 'lrelu'), - lr_multiplier = kwarg('mapping_lrmul', 0.01), - w_avg_beta = kwarg('w_avg_beta', 0.995, none=1), + z_dim=kwarg('latent_size', 512), + c_dim=kwarg('label_size', 0), + w_dim=kwarg('dlatent_size', 512), + img_resolution=kwarg('resolution', 1024), + img_channels=kwarg('num_channels', 3), + channel_base=kwarg('fmap_base', 16384) * 2, + channel_max=kwarg('fmap_max', 512), + num_fp16_res=kwarg('num_fp16_res', 0), + conv_clamp=kwarg('conv_clamp', None), + architecture=kwarg('architecture', 'skip'), + resample_filter=kwarg('resample_kernel', [1, 3, 3, 1]), + use_noise=kwarg('use_noise', True), + activation=kwarg('nonlinearity', 'lrelu'), + mapping_kwargs=dnnlib.EasyDict( + num_layers=kwarg('mapping_layers', 8), + embed_features=kwarg('label_fmaps', None), + layer_features=kwarg('mapping_fmaps', None), + activation=kwarg('mapping_nonlinearity', 'lrelu'), + lr_multiplier=kwarg('mapping_lrmul', 0.01), + w_avg_beta=kwarg('w_avg_beta', 0.995, none=1), ), ) @@ -162,48 +170,74 @@ def convert_tf_generator(tf_G): r = kwargs.img_resolution // (2 ** int(match.group(1))) tf_params[f'{r}x{r}/ToRGB/{match.group(2)}'] = value kwargs.synthesis.kwargs.architecture = 'orig' - #for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}') + # for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}') # Convert params. G = network_class(**kwargs).eval().requires_grad_(False) # pylint: disable=unnecessary-lambda # pylint: disable=f-string-without-interpolation _populate_module_params(G, - r'mapping\.w_avg', lambda: tf_params[f'dlatent_avg'], - r'mapping\.embed\.weight', lambda: tf_params[f'mapping/LabelEmbed/weight'].transpose(), - r'mapping\.embed\.bias', lambda: tf_params[f'mapping/LabelEmbed/bias'], - r'mapping\.fc(\d+)\.weight', lambda i: tf_params[f'mapping/Dense{i}/weight'].transpose(), - r'mapping\.fc(\d+)\.bias', lambda i: tf_params[f'mapping/Dense{i}/bias'], - r'synthesis\.b4\.const', lambda: tf_params[f'synthesis/4x4/Const/const'][0], - r'synthesis\.b4\.conv1\.weight', lambda: tf_params[f'synthesis/4x4/Conv/weight'].transpose(3, 2, 0, 1), - r'synthesis\.b4\.conv1\.bias', lambda: tf_params[f'synthesis/4x4/Conv/bias'], - r'synthesis\.b4\.conv1\.noise_const', lambda: tf_params[f'synthesis/noise0'][0, 0], - r'synthesis\.b4\.conv1\.noise_strength', lambda: tf_params[f'synthesis/4x4/Conv/noise_strength'], - r'synthesis\.b4\.conv1\.affine\.weight', lambda: tf_params[f'synthesis/4x4/Conv/mod_weight'].transpose(), - r'synthesis\.b4\.conv1\.affine\.bias', lambda: tf_params[f'synthesis/4x4/Conv/mod_bias'] + 1, - r'synthesis\.b(\d+)\.conv0\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/weight'][::-1, ::-1].transpose(3, 2, 0, 1), - r'synthesis\.b(\d+)\.conv0\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/bias'], - r'synthesis\.b(\d+)\.conv0\.noise_const', lambda r: tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-5}'][0, 0], - r'synthesis\.b(\d+)\.conv0\.noise_strength', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/noise_strength'], - r'synthesis\.b(\d+)\.conv0\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_weight'].transpose(), - r'synthesis\.b(\d+)\.conv0\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_bias'] + 1, - r'synthesis\.b(\d+)\.conv1\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/weight'].transpose(3, 2, 0, 1), - r'synthesis\.b(\d+)\.conv1\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/bias'], - r'synthesis\.b(\d+)\.conv1\.noise_const', lambda r: tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-4}'][0, 0], - r'synthesis\.b(\d+)\.conv1\.noise_strength', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/noise_strength'], - r'synthesis\.b(\d+)\.conv1\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/mod_weight'].transpose(), - r'synthesis\.b(\d+)\.conv1\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/mod_bias'] + 1, - r'synthesis\.b(\d+)\.torgb\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/weight'].transpose(3, 2, 0, 1), - r'synthesis\.b(\d+)\.torgb\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/bias'], - r'synthesis\.b(\d+)\.torgb\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/mod_weight'].transpose(), - r'synthesis\.b(\d+)\.torgb\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/mod_bias'] + 1, - r'synthesis\.b(\d+)\.skip\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Skip/weight'][::-1, ::-1].transpose(3, 2, 0, 1), - r'.*\.resample_filter', None, - r'.*\.act_filter', None, - ) + r'mapping\.w_avg', lambda: tf_params[f'dlatent_avg'], + r'mapping\.embed\.weight', lambda: tf_params[f'mapping/LabelEmbed/weight'].transpose( + ), + r'mapping\.embed\.bias', lambda: tf_params[f'mapping/LabelEmbed/bias'], + r'mapping\.fc(\d+)\.weight', lambda i: tf_params[f'mapping/Dense{i}/weight'].transpose( + ), + r'mapping\.fc(\d+)\.bias', lambda i: tf_params[f'mapping/Dense{i}/bias'], + r'synthesis\.b4\.const', lambda: tf_params[f'synthesis/4x4/Const/const'][0], + r'synthesis\.b4\.conv1\.weight', lambda: tf_params[f'synthesis/4x4/Conv/weight'].transpose( + 3, 2, 0, 1), + r'synthesis\.b4\.conv1\.bias', lambda: tf_params[ + f'synthesis/4x4/Conv/bias'], + r'synthesis\.b4\.conv1\.noise_const', lambda: tf_params[ + f'synthesis/noise0'][0, 0], + r'synthesis\.b4\.conv1\.noise_strength', lambda: tf_params[ + f'synthesis/4x4/Conv/noise_strength'], + r'synthesis\.b4\.conv1\.affine\.weight', lambda: tf_params[ + f'synthesis/4x4/Conv/mod_weight'].transpose(), + r'synthesis\.b4\.conv1\.affine\.bias', lambda: tf_params[ + f'synthesis/4x4/Conv/mod_bias'] + 1, + r'synthesis\.b(\d+)\.conv0\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/weight'][::-1, ::-1].transpose( + 3, 2, 0, 1), + r'synthesis\.b(\d+)\.conv0\.bias', lambda r: tf_params[ + f'synthesis/{r}x{r}/Conv0_up/bias'], + r'synthesis\.b(\d+)\.conv0\.noise_const', lambda r: tf_params[ + f'synthesis/noise{int(np.log2(int(r)))*2-5}'][0, 0], + r'synthesis\.b(\d+)\.conv0\.noise_strength', lambda r: tf_params[ + f'synthesis/{r}x{r}/Conv0_up/noise_strength'], + r'synthesis\.b(\d+)\.conv0\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_weight'].transpose( + ), + r'synthesis\.b(\d+)\.conv0\.affine\.bias', lambda r: tf_params[ + f'synthesis/{r}x{r}/Conv0_up/mod_bias'] + 1, + r'synthesis\.b(\d+)\.conv1\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/weight'].transpose( + 3, 2, 0, 1), + r'synthesis\.b(\d+)\.conv1\.bias', lambda r: tf_params[ + f'synthesis/{r}x{r}/Conv1/bias'], + r'synthesis\.b(\d+)\.conv1\.noise_const', lambda r: tf_params[ + f'synthesis/noise{int(np.log2(int(r)))*2-4}'][0, 0], + r'synthesis\.b(\d+)\.conv1\.noise_strength', lambda r: tf_params[ + f'synthesis/{r}x{r}/Conv1/noise_strength'], + r'synthesis\.b(\d+)\.conv1\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/mod_weight'].transpose( + ), + r'synthesis\.b(\d+)\.conv1\.affine\.bias', lambda r: tf_params[ + f'synthesis/{r}x{r}/Conv1/mod_bias'] + 1, + r'synthesis\.b(\d+)\.torgb\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/weight'].transpose( + 3, 2, 0, 1), + r'synthesis\.b(\d+)\.torgb\.bias', lambda r: tf_params[ + f'synthesis/{r}x{r}/ToRGB/bias'], + r'synthesis\.b(\d+)\.torgb\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/mod_weight'].transpose( + ), + r'synthesis\.b(\d+)\.torgb\.affine\.bias', lambda r: tf_params[ + f'synthesis/{r}x{r}/ToRGB/mod_bias'] + 1, + r'synthesis\.b(\d+)\.skip\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Skip/weight'][::-1, ::-1].transpose( + 3, 2, 0, 1), + r'.*\.resample_filter', None, + r'.*\.act_filter', None, + ) return G -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def convert_tf_discriminator(tf_D): if tf_D.version < 4: @@ -212,37 +246,38 @@ def convert_tf_discriminator(tf_D): # Collect kwargs. tf_kwargs = tf_D.static_kwargs known_kwargs = set() + def kwarg(tf_name, default=None): known_kwargs.add(tf_name) return tf_kwargs.get(tf_name, default) # Convert kwargs. kwargs = dnnlib.EasyDict( - c_dim = kwarg('label_size', 0), - img_resolution = kwarg('resolution', 1024), - img_channels = kwarg('num_channels', 3), - architecture = kwarg('architecture', 'resnet'), - channel_base = kwarg('fmap_base', 16384) * 2, - channel_max = kwarg('fmap_max', 512), - num_fp16_res = kwarg('num_fp16_res', 0), - conv_clamp = kwarg('conv_clamp', None), - cmap_dim = kwarg('mapping_fmaps', None), - block_kwargs = dnnlib.EasyDict( - activation = kwarg('nonlinearity', 'lrelu'), - resample_filter = kwarg('resample_kernel', [1,3,3,1]), - freeze_layers = kwarg('freeze_layers', 0), + c_dim=kwarg('label_size', 0), + img_resolution=kwarg('resolution', 1024), + img_channels=kwarg('num_channels', 3), + architecture=kwarg('architecture', 'resnet'), + channel_base=kwarg('fmap_base', 16384) * 2, + channel_max=kwarg('fmap_max', 512), + num_fp16_res=kwarg('num_fp16_res', 0), + conv_clamp=kwarg('conv_clamp', None), + cmap_dim=kwarg('mapping_fmaps', None), + block_kwargs=dnnlib.EasyDict( + activation=kwarg('nonlinearity', 'lrelu'), + resample_filter=kwarg('resample_kernel', [1, 3, 3, 1]), + freeze_layers=kwarg('freeze_layers', 0), ), - mapping_kwargs = dnnlib.EasyDict( - num_layers = kwarg('mapping_layers', 0), - embed_features = kwarg('mapping_fmaps', None), - layer_features = kwarg('mapping_fmaps', None), - activation = kwarg('nonlinearity', 'lrelu'), - lr_multiplier = kwarg('mapping_lrmul', 0.1), + mapping_kwargs=dnnlib.EasyDict( + num_layers=kwarg('mapping_layers', 0), + embed_features=kwarg('mapping_fmaps', None), + layer_features=kwarg('mapping_fmaps', None), + activation=kwarg('nonlinearity', 'lrelu'), + lr_multiplier=kwarg('mapping_lrmul', 0.1), ), - epilogue_kwargs = dnnlib.EasyDict( - mbstd_group_size = kwarg('mbstd_group_size', None), - mbstd_num_channels = kwarg('mbstd_num_features', 1), - activation = kwarg('nonlinearity', 'lrelu'), + epilogue_kwargs=dnnlib.EasyDict( + mbstd_group_size=kwarg('mbstd_group_size', None), + mbstd_num_channels=kwarg('mbstd_num_features', 1), + activation=kwarg('nonlinearity', 'lrelu'), ), ) @@ -261,7 +296,7 @@ def convert_tf_discriminator(tf_D): r = kwargs.img_resolution // (2 ** int(match.group(1))) tf_params[f'{r}x{r}/FromRGB/{match.group(2)}'] = value kwargs.architecture = 'orig' - #for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}') + # for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}') # Convert params. from training import networks_stylegan2 @@ -269,26 +304,36 @@ def convert_tf_discriminator(tf_D): # pylint: disable=unnecessary-lambda # pylint: disable=f-string-without-interpolation _populate_module_params(D, - r'b(\d+)\.fromrgb\.weight', lambda r: tf_params[f'{r}x{r}/FromRGB/weight'].transpose(3, 2, 0, 1), - r'b(\d+)\.fromrgb\.bias', lambda r: tf_params[f'{r}x{r}/FromRGB/bias'], - r'b(\d+)\.conv(\d+)\.weight', lambda r, i: tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/weight'].transpose(3, 2, 0, 1), - r'b(\d+)\.conv(\d+)\.bias', lambda r, i: tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/bias'], - r'b(\d+)\.skip\.weight', lambda r: tf_params[f'{r}x{r}/Skip/weight'].transpose(3, 2, 0, 1), - r'mapping\.embed\.weight', lambda: tf_params[f'LabelEmbed/weight'].transpose(), - r'mapping\.embed\.bias', lambda: tf_params[f'LabelEmbed/bias'], - r'mapping\.fc(\d+)\.weight', lambda i: tf_params[f'Mapping{i}/weight'].transpose(), - r'mapping\.fc(\d+)\.bias', lambda i: tf_params[f'Mapping{i}/bias'], - r'b4\.conv\.weight', lambda: tf_params[f'4x4/Conv/weight'].transpose(3, 2, 0, 1), - r'b4\.conv\.bias', lambda: tf_params[f'4x4/Conv/bias'], - r'b4\.fc\.weight', lambda: tf_params[f'4x4/Dense0/weight'].transpose(), - r'b4\.fc\.bias', lambda: tf_params[f'4x4/Dense0/bias'], - r'b4\.out\.weight', lambda: tf_params[f'Output/weight'].transpose(), - r'b4\.out\.bias', lambda: tf_params[f'Output/bias'], - r'.*\.resample_filter', None, - ) + r'b(\d+)\.fromrgb\.weight', lambda r: tf_params[f'{r}x{r}/FromRGB/weight'].transpose( + 3, 2, 0, 1), + r'b(\d+)\.fromrgb\.bias', lambda r: tf_params[f'{r}x{r}/FromRGB/bias'], + r'b(\d+)\.conv(\d+)\.weight', lambda r, i: tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/weight'].transpose( + 3, 2, 0, 1), + r'b(\d+)\.conv(\d+)\.bias', lambda r, i: tf_params[ + f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/bias'], + r'b(\d+)\.skip\.weight', lambda r: tf_params[f'{r}x{r}/Skip/weight'].transpose( + 3, 2, 0, 1), + r'mapping\.embed\.weight', lambda: tf_params[f'LabelEmbed/weight'].transpose( + ), + r'mapping\.embed\.bias', lambda: tf_params[f'LabelEmbed/bias'], + r'mapping\.fc(\d+)\.weight', lambda i: tf_params[f'Mapping{i}/weight'].transpose( + ), + r'mapping\.fc(\d+)\.bias', lambda i: tf_params[f'Mapping{i}/bias'], + r'b4\.conv\.weight', lambda: tf_params[f'4x4/Conv/weight'].transpose( + 3, 2, 0, 1), + r'b4\.conv\.bias', lambda: tf_params[f'4x4/Conv/bias'], + r'b4\.fc\.weight', lambda: tf_params[f'4x4/Dense0/weight'].transpose( + ), + r'b4\.fc\.bias', lambda: tf_params[f'4x4/Dense0/bias'], + r'b4\.out\.weight', lambda: tf_params[f'Output/weight'].transpose( + ), + r'b4\.out\.bias', lambda: tf_params[f'Output/bias'], + r'.*\.resample_filter', None, + ) return D -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @click.command() @click.option('--source', help='Input pickle', required=True, metavar='PATH') @@ -315,9 +360,10 @@ def convert_network_pickle(source, dest, force_fp16): pickle.dump(data, f) print('Done.') -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + if __name__ == "__main__": - convert_network_pickle() # pylint: disable=no-value-for-parameter + convert_network_pickle() # pylint: disable=no-value-for-parameter -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/pre-requirements.txt b/pre-requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..4d6aa0d777e5fb9031fa5e74cd6e6892b9602848 --- /dev/null +++ b/pre-requirements.txt @@ -0,0 +1 @@ +pip \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 6602deab1b686562b396641efa3023e57343d2e9..b10e7c119185b02429f6e95beb854dd4115b068b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,20 +1,13 @@ torch -pip -numpy>=1.20 -click>=8.0 -pillow==8.3.1 -scipy==1.7.1 -pytorch==1.9.1 -cudatoolkit==11.1 -requests==2.26.0 -tqdm==4.62.2 -ninja==1.10.2 -matplotlib==3.4.2 -imageio==2.9.0 -imgui==1.3.0 -glfw==2.2.0 -pyopengl==3.1.5 -imageio-ffmpeg==0.4.3 -pyspng +torchvision Ninja -gradio \ No newline at end of file +gradio +huggingface_hub +hf_transfer +Pillow==9.5.0 +psutil +imageio +scikit-image +lpips +wandb +dlib \ No newline at end of file diff --git a/stylegan_human/PP_HumanSeg/deploy/infer.py b/stylegan_human/PP_HumanSeg/deploy/infer.py index df4dd04762fec4a78c423075fc5f45d892171af9..0c92735486d90de96c7dfaa006b80fd98c169b20 100644 --- a/stylegan_human/PP_HumanSeg/deploy/infer.py +++ b/stylegan_human/PP_HumanSeg/deploy/infer.py @@ -113,7 +113,6 @@ class Predictor: output = output_handle.copy_to_cpu() return self.postprocess(output, img, ori_shapes[0], bg) - def postprocess(self, pred, img, ori_shape, bg): if not os.path.exists(self.args.save_dir): os.makedirs(self.args.save_dir) @@ -125,8 +124,8 @@ class Predictor: score_map = 255 * score_map cur_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) cur_gray = cv2.resize(cur_gray, (resize_w, resize_h)) - optflow_map = optic_flow_process(cur_gray, score_map, self.prev_gray, self.prev_cfd, \ - self.disflow, self.is_init) + optflow_map = optic_flow_process(cur_gray, score_map, self.prev_gray, self.prev_cfd, + self.disflow, self.is_init) self.prev_gray = cur_gray.copy() self.prev_cfd = optflow_map.copy() self.is_init = False diff --git a/stylegan_human/PP_HumanSeg/export_model/download_export_model.py b/stylegan_human/PP_HumanSeg/export_model/download_export_model.py index ea4fc64c53441d068fe56dd24db153cf5cb92d45..152f598bd48724fe13c5f650f809fdac06c2bae2 100644 --- a/stylegan_human/PP_HumanSeg/export_model/download_export_model.py +++ b/stylegan_human/PP_HumanSeg/export_model/download_export_model.py @@ -13,6 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +from paddleseg.utils.download import download_file_and_uncompress import sys import os @@ -20,7 +21,6 @@ LOCAL_PATH = os.path.dirname(os.path.abspath(__file__)) TEST_PATH = os.path.join(LOCAL_PATH, "../../../", "test") sys.path.append(TEST_PATH) -from paddleseg.utils.download import download_file_and_uncompress model_urls = { "pphumanseg_lite_portrait_398x224_with_softmax": diff --git a/stylegan_human/PP_HumanSeg/pretrained_model/download_pretrained_model.py b/stylegan_human/PP_HumanSeg/pretrained_model/download_pretrained_model.py index 69e3f0910e5c553cc59a3067ac02881a720a474f..363dc74adda3a3cebc7f610dc51eccda35fcd083 100644 --- a/stylegan_human/PP_HumanSeg/pretrained_model/download_pretrained_model.py +++ b/stylegan_human/PP_HumanSeg/pretrained_model/download_pretrained_model.py @@ -13,6 +13,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +from paddleseg.utils.download import download_file_and_uncompress import sys import os @@ -20,7 +21,6 @@ LOCAL_PATH = os.path.dirname(os.path.abspath(__file__)) TEST_PATH = os.path.join(LOCAL_PATH, "../../../", "test") sys.path.append(TEST_PATH) -from paddleseg.utils.download import download_file_and_uncompress model_urls = { "pphumanseg_lite_portrait_398x224": diff --git a/stylegan_human/alignment.py b/stylegan_human/alignment.py index 012336ee3485599e3c4a314d718dc444e4c93864..46f58c79061ed8030562300f131f97f04e5ea42f 100644 --- a/stylegan_human/alignment.py +++ b/stylegan_human/alignment.py @@ -2,7 +2,7 @@ import os -import argparse +import argparse import numpy as np import torch from torch.utils.data import DataLoader @@ -22,44 +22,46 @@ from openpose.src.body import Body from PP_HumanSeg.deploy.infer import Predictor as PP_HumenSeg_Predictor import math -def angle_between_points(p0,p1,p2): - if p0[1]==-1 or p1[1]==-1 or p2[1]==-1: + + +def angle_between_points(p0, p1, p2): + if p0[1] == -1 or p1[1] == -1 or p2[1] == -1: return -1 a = (p1[0]-p0[0])**2 + (p1[1]-p0[1])**2 b = (p1[0]-p2[0])**2 + (p1[1]-p2[1])**2 - c = (p2[0]-p0[0])**2 + (p2[1]-p0[1])**2 + c = (p2[0]-p0[0])**2 + (p2[1]-p0[1])**2 if a * b == 0: return -1 return math.acos((a+b-c) / math.sqrt(4*a*b)) * 180 / math.pi def crop_img_with_padding(img, keypoints, rect): - person_xmin,person_xmax, ymin, ymax= rect - img_h,img_w,_ = img.shape ## find body center using keypoints + person_xmin, person_xmax, ymin, ymax = rect + img_h, img_w, _ = img.shape # find body center using keypoints middle_shoulder_x = keypoints[1][0] middle_hip_x = (keypoints[8][0] + keypoints[11][0]) // 2 - mid_x = (middle_hip_x + middle_shoulder_x) // 2 + mid_x = (middle_hip_x + middle_shoulder_x) // 2 mid_y = (ymin + ymax) // 2 - ## find which side (l or r) is further than center x, use the further side - if abs(mid_x-person_xmin) > abs(person_xmax-mid_x): #left further + # find which side (l or r) is further than center x, use the further side + if abs(mid_x-person_xmin) > abs(person_xmax-mid_x): # left further xmin = person_xmin xmax = mid_x + (mid_x-person_xmin) else: - ############### may be negtive - ### in this case, the script won't output any image, leave the case like this - ### since we don't want to pad human body - xmin = mid_x - (person_xmax-mid_x) - xmax = person_xmax + # may be negtive + # in this case, the script won't output any image, leave the case like this + # since we don't want to pad human body + xmin = mid_x - (person_xmax-mid_x) + xmax = person_xmax w = xmax - xmin h = ymax - ymin - ## pad rectangle to w:h = 1:2 ## calculate desired border length - if h / w >= 2: #pad horizontally + # pad rectangle to w:h = 1:2 ## calculate desired border length + if h / w >= 2: # pad horizontally target_w = h // 2 xmin_prime = int(mid_x - target_w / 2) xmax_prime = int(mid_x + target_w / 2) if xmin_prime < 0: - pad_left = abs(xmin_prime)# - xmin + pad_left = abs(xmin_prime) # - xmin xmin = 0 else: pad_left = 0 @@ -72,13 +74,14 @@ def crop_img_with_padding(img, keypoints, rect): xmax = xmax_prime cropped_img = img[int(ymin):int(ymax), int(xmin):int(xmax)] - im_pad = cv2.copyMakeBorder(cropped_img, 0, 0, int(pad_left), int(pad_right), cv2.BORDER_REPLICATE) - else: #pad vertically + im_pad = cv2.copyMakeBorder(cropped_img, 0, 0, int( + pad_left), int(pad_right), cv2.BORDER_REPLICATE) + else: # pad vertically target_h = w * 2 ymin_prime = mid_y - (target_h / 2) - ymax_prime = mid_y + (target_h / 2) - if ymin_prime < 0: - pad_up = abs(ymin_prime)# - ymin + ymax_prime = mid_y + (target_h / 2) + if ymin_prime < 0: + pad_up = abs(ymin_prime) # - ymin ymin = 0 else: pad_up = 0 @@ -89,18 +92,19 @@ def crop_img_with_padding(img, keypoints, rect): else: pad_down = 0 ymax = ymax_prime - print(ymin,ymax, xmin,xmax, img.shape) + print(ymin, ymax, xmin, xmax, img.shape) cropped_img = img[int(ymin):int(ymax), int(xmin):int(xmax)] im_pad = cv2.copyMakeBorder(cropped_img, int(pad_up), int(pad_down), 0, - 0, cv2.BORDER_REPLICATE) - result = cv2.resize(im_pad,(512,1024),interpolation = cv2.INTER_AREA) + 0, cv2.BORDER_REPLICATE) + result = cv2.resize(im_pad, (512, 1024), interpolation=cv2.INTER_AREA) return result def run(args): os.makedirs(args.output_folder, exist_ok=True) - dataset = ImagesDataset(args.image_folder, transforms.Compose([transforms.ToTensor()])) + dataset = ImagesDataset( + args.image_folder, transforms.Compose([transforms.ToTensor()])) dataloader = DataLoader(dataset, batch_size=1, shuffle=False) body_estimation = Body('openpose/model/body_pose_model.pth') @@ -109,47 +113,48 @@ def run(args): print('Num of dataloader : ', total) os.makedirs(f'{args.output_folder}', exist_ok=True) # os.makedirs(f'{args.output_folder}/middle_result', exist_ok=True) - - ## initialzide HumenSeg + + # initialzide HumenSeg human_seg_args = {} human_seg_args['cfg'] = 'PP_HumanSeg/export_model/deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax/deploy.yaml' - human_seg_args['input_shape'] = [1024,512] + human_seg_args['input_shape'] = [1024, 512] human_seg_args['save_dir'] = args.output_folder human_seg_args['soft_predict'] = False human_seg_args['use_gpu'] = True human_seg_args['test_speed'] = False human_seg_args['use_optic_flow'] = False human_seg_args['add_argmax'] = True - human_seg_args= argparse.Namespace(**human_seg_args) + human_seg_args = argparse.Namespace(**human_seg_args) human_seg = PP_HumenSeg_Predictor(human_seg_args) from tqdm import tqdm for fname, image in tqdm(dataloader): # try: - ## tensor to numpy image + # tensor to numpy image fname = fname[0] print(f'Processing \'{fname}\'.') - + image = (image.permute(0, 2, 3, 1) * 255).clamp(0, 255) - image = image.squeeze(0).numpy() # --> tensor to numpy, (H,W,C) + image = image.squeeze(0).numpy() # --> tensor to numpy, (H,W,C) # avoid super high res img - if image.shape[0] >= 2000: # height ### for shein image - ratio = image.shape[0]/1200 #height - dim = (int(image.shape[1]/ratio),1200)#(width, height) - image = cv2.resize(image, dim, interpolation = cv2.INTER_AREA) + if image.shape[0] >= 2000: # height ### for shein image + ratio = image.shape[0]/1200 # height + dim = (int(image.shape[1]/ratio), 1200) # (width, height) + image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA) image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) - ## create segmentation - # mybg = cv2.imread('mybg.png') - comb, segmentation, bg, ori_img = human_seg.run(image,None) #mybg) + # create segmentation + # mybg = cv2.imread('mybg.png') + comb, segmentation, bg, ori_img = human_seg.run(image, None) # mybg) # cv2.imwrite('comb.png',comb) # [0,255] # cv2.imwrite('alpha.png',segmentation*255) # segmentation [0,1] --> [0.255] # cv2.imwrite('bg.png',bg) #[0,255] # cv2.imwrite('ori_img.png',ori_img) # [0,255] - masks_np = (segmentation* 255)# .byte().cpu().numpy() #1024,512,1 - mask0_np = masks_np[:,:,0].astype(np.uint8)#[0, :, :] - contours = cv2.findContours(mask0_np, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + masks_np = (segmentation * 255) # .byte().cpu().numpy() #1024,512,1 + mask0_np = masks_np[:, :, 0].astype(np.uint8) # [0, :, :] + contours = cv2.findContours( + mask0_np, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = imutils.grab_contours(contours) c = max(cnts, key=cv2.contourArea) extTop = tuple(c[c[:, :, 1].argmin()][0]) @@ -157,14 +162,18 @@ def run(args): extBot = list(extBot) extTop = list(extTop) pad_range = int((extBot[1]-extTop[1])*0.05) - if (int(extTop[1])<=5 and int(extTop[1])>0) and (comb.shape[0]>int(extBot[1]) and int(extBot[1])>=comb.shape[0]-5): #seg mask already reaches to the edge - #pad with pure white, top 100 px, bottom 100 px - comb= cv2.copyMakeBorder(comb,pad_range+5,pad_range+5,0,0,cv2.BORDER_CONSTANT,value=[255,255,255]) - elif int(extTop[1])<=0 or int(extBot[1])>=comb.shape[0]: - print('PAD: body out of boundary', fname) #should not happened + # seg mask already reaches to the edge + if (int(extTop[1]) <= 5 and int(extTop[1]) > 0) and (comb.shape[0] > int(extBot[1]) and int(extBot[1]) >= comb.shape[0]-5): + # pad with pure white, top 100 px, bottom 100 px + comb = cv2.copyMakeBorder( + comb, pad_range+5, pad_range+5, 0, 0, cv2.BORDER_CONSTANT, value=[255, 255, 255]) + elif int(extTop[1]) <= 0 or int(extBot[1]) >= comb.shape[0]: + print('PAD: body out of boundary', fname) # should not happened return {} else: - comb = cv2.copyMakeBorder(comb, pad_range+5, pad_range+5, 0, 0, cv2.BORDER_REPLICATE) #105 instead of 100: give some extra space + # 105 instead of 100: give some extra space + comb = cv2.copyMakeBorder( + comb, pad_range+5, pad_range+5, 0, 0, cv2.BORDER_REPLICATE) extBot[1] = extBot[1] + pad_range+5 extTop[1] = extTop[1] + pad_range+5 @@ -172,22 +181,23 @@ def run(args): extRight = tuple(c[c[:, :, 0].argmax()][0]) extLeft = list(extLeft) extRight = list(extRight) - person_ymin = int(extTop[1])-pad_range # 100 - person_ymax = int(extBot[1])+pad_range # 100 #height - if person_ymin<0 or person_ymax>comb.shape[0]: # out of range + person_ymin = int(extTop[1])-pad_range # 100 + person_ymax = int(extBot[1])+pad_range # 100 #height + if person_ymin < 0 or person_ymax > comb.shape[0]: # out of range return {} person_xmin = int(extLeft[0]) person_xmax = int(extRight[0]) - rect = [person_xmin,person_xmax,person_ymin, person_ymax] + rect = [person_xmin, person_xmax, person_ymin, person_ymax] # recimg = copy.deepcopy(comb) # cv2.rectangle(recimg,(person_xmin,person_ymin),(person_xmax,person_ymax),(0,255,0),2) # cv2.imwrite(f'{args.output_folder}/middle_result/{fname}_rec.png',recimg) - ## detect keypoints + # detect keypoints keypoints, subset = body_estimation(comb) # print(keypoints, subset, len(subset)) - if len(subset) != 1 or (len(subset)==1 and subset[0][-1]<15): - print(f'Processing \'{fname}\'. Please import image contains one person only. Also can check segmentation mask. ') + if len(subset) != 1 or (len(subset) == 1 and subset[0][-1] < 15): + print( + f'Processing \'{fname}\'. Please import image contains one person only. Also can check segmentation mask. ') continue # canvas = copy.deepcopy(comb) @@ -196,28 +206,28 @@ def run(args): comb = crop_img_with_padding(comb, keypoints, rect) - cv2.imwrite(f'{args.output_folder}/{fname}.png', comb) print(f' -- Finished processing \'{fname}\'. --') # except: # print(f'Processing \'{fname}\'. Not satisfied the alignment strategy.') - - + + if __name__ == '__main__': torch.backends.cudnn.benchmark = True torch.backends.cudnn.deterministic = False - + t1 = time.time() arg_formatter = argparse.ArgumentDefaultsHelpFormatter description = 'StyleGAN-Human data process' parser = argparse.ArgumentParser(formatter_class=arg_formatter, description=description) parser.add_argument('--image-folder', type=str, dest='image_folder') - parser.add_argument('--output-folder', dest='output_folder', default='results', type=str) + parser.add_argument('--output-folder', + dest='output_folder', default='results', type=str) # parser.add_argument('--cfg', dest='cfg for segmentation', default='PP_HumanSeg/export_model/ppseg_lite_portrait_398x224_with_softmax/deploy.yaml', type=str) print('parsing arguments') cmd_args = parser.parse_args() run(cmd_args) - print('total time elapsed: ', str(time.time() - t1)) \ No newline at end of file + print('total time elapsed: ', str(time.time() - t1)) diff --git a/stylegan_human/bg_white.py b/stylegan_human/bg_white.py index f9bd13169baf5e000599b5b45d22e6d76726518c..9dc181fecbd30b0fcd08cdea87930bd09f4c51fc 100644 --- a/stylegan_human/bg_white.py +++ b/stylegan_human/bg_white.py @@ -5,18 +5,20 @@ import click import cv2 import numpy as np + def bg_white(seg, raw, blur_level=3, gaussian=81): seg = cv2.blur(seg, (blur_level, blur_level)) empty = np.ones_like(seg) - seg_bg = (empty - seg) * 255 - seg_bg = cv2.GaussianBlur(seg_bg,(gaussian,gaussian),0) + seg_bg = (empty - seg) * 255 + seg_bg = cv2.GaussianBlur(seg_bg, (gaussian, gaussian), 0) - background_mask = cv2.cvtColor(255 - cv2.cvtColor(seg, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) + background_mask = cv2.cvtColor( + 255 - cv2.cvtColor(seg, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR) masked_fg = (raw * (1 / 255)) * (seg * (1 / 255)) masked_bg = (seg_bg * (1 / 255)) * (background_mask * (1 / 255)) - frame = np.uint8(cv2.add(masked_bg,masked_fg)*255) + frame = np.uint8(cv2.add(masked_bg, masked_fg)*255) return frame @@ -31,12 +33,12 @@ python bg_white.py --raw_img_dir=./SHHQ-1.0/no_segment/ --raw_seg_dir=./SHHQ-1. --outdir=./SHHQ-1.0/bg_white/ """ + @click.command() @click.pass_context @click.option('--raw_img_dir', default="./SHHQ-1.0/no_segment/", help='folder of raw image', required=True) @click.option('--raw_seg_dir', default='./SHHQ-1.0/segments/', help='folder of segmentation masks', required=True) -@click.option('--outdir', help='Where to save the output images', default= "./SHHQ-1.0/bg_white/" , type=str, required=True, metavar='DIR') - +@click.option('--outdir', help='Where to save the output images', default="./SHHQ-1.0/bg_white/", type=str, required=True, metavar='DIR') def main( ctx: click.Context, raw_img_dir: str, @@ -44,14 +46,15 @@ def main( outdir: str): os.makedirs(outdir, exist_ok=True) files = os.listdir(raw_img_dir) - for file in files: + for file in files: print(file) raw = cv2.imread(os.path.join(raw_img_dir, file)) seg = cv2.imread(os.path.join(raw_seg_dir, file)) assert raw is not None assert seg is not None white_frame = bg_white(seg, raw) - cv2.imwrite(os.path.join(outdir,file), white_frame) + cv2.imwrite(os.path.join(outdir, file), white_frame) + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/stylegan_human/dnnlib/tflib/autosummary.py b/stylegan_human/dnnlib/tflib/autosummary.py index ede0f23dc3106112d241c70a8d4c17b2fa2af50d..272f054eea659e7191c7c71ae3745eefe5f82411 100755 --- a/stylegan_human/dnnlib/tflib/autosummary.py +++ b/stylegan_human/dnnlib/tflib/autosummary.py @@ -63,11 +63,14 @@ def _create_var(name: str, value_expr: TfExpression) -> TfExpression: v = [size_expr, v, tf.square(v)] else: v = [size_expr, tf.reduce_sum(v), tf.reduce_sum(tf.square(v))] - v = tf.cond(tf.is_finite(v[1]), lambda: tf.stack(v), lambda: tf.zeros(3, dtype=_dtype)) + v = tf.cond(tf.is_finite(v[1]), lambda: tf.stack( + v), lambda: tf.zeros(3, dtype=_dtype)) with tfutil.absolute_name_scope("Autosummary/" + name_id), tf.control_dependencies(None): - var = tf.Variable(tf.zeros(3, dtype=_dtype), trainable=False) # [sum(1), sum(x), sum(x**2)] - update_op = tf.cond(tf.is_variable_initialized(var), lambda: tf.assign_add(var, v), lambda: tf.assign(var, v)) + # [sum(1), sum(x), sum(x**2)] + var = tf.Variable(tf.zeros(3, dtype=_dtype), trainable=False) + update_op = tf.cond(tf.is_variable_initialized( + var), lambda: tf.assign_add(var, v), lambda: tf.assign(var, v)) if name in _vars: _vars[name].append(var) @@ -99,7 +102,8 @@ def autosummary(name: str, value: TfExpressionEx, passthru: TfExpressionEx = Non if tfutil.is_tf_expression(value): with tf.name_scope("summary_" + name_id), tf.device(value.device): condition = tf.convert_to_tensor(condition, name='condition') - update_op = tf.cond(condition, lambda: tf.group(_create_var(name, value)), tf.no_op) + update_op = tf.cond(condition, lambda: tf.group( + _create_var(name, value)), tf.no_op) with tf.control_dependencies([update_op]): return tf.identity(value if passthru is None else passthru) @@ -128,7 +132,8 @@ def finalize_autosummaries() -> None: return None _finalized = True - tfutil.init_uninitialized_vars([var for vars_list in _vars.values() for var in vars_list]) + tfutil.init_uninitialized_vars( + [var for vars_list in _vars.values() for var in vars_list]) # Create summary ops. with tf.device(None), tf.control_dependencies(None): @@ -137,15 +142,20 @@ def finalize_autosummaries() -> None: with tfutil.absolute_name_scope("Autosummary/" + name_id): moments = tf.add_n(vars_list) moments /= moments[0] - with tf.control_dependencies([moments]): # read before resetting - reset_ops = [tf.assign(var, tf.zeros(3, dtype=_dtype)) for var in vars_list] - with tf.name_scope(None), tf.control_dependencies(reset_ops): # reset before reporting + # read before resetting + with tf.control_dependencies([moments]): + reset_ops = [tf.assign(var, tf.zeros( + 3, dtype=_dtype)) for var in vars_list] + # reset before reporting + with tf.name_scope(None), tf.control_dependencies(reset_ops): mean = moments[1] std = tf.sqrt(moments[2] - tf.square(moments[1])) tf.summary.scalar(name, mean) if enable_custom_scalars: - tf.summary.scalar("xCustomScalars/" + name + "/margin_lo", mean - std) - tf.summary.scalar("xCustomScalars/" + name + "/margin_hi", mean + std) + tf.summary.scalar( + "xCustomScalars/" + name + "/margin_lo", mean - std) + tf.summary.scalar( + "xCustomScalars/" + name + "/margin_hi", mean + std) # Setup layout for custom scalars. layout = None @@ -171,11 +181,15 @@ def finalize_autosummaries() -> None: lower="xCustomScalars/" + series_name + "/margin_lo", upper="xCustomScalars/" + series_name + "/margin_hi")) margin = layout_pb2.MarginChartContent(series=series) - charts.append(layout_pb2.Chart(title=chart_name, margin=margin)) - categories.append(layout_pb2.Category(title=cat_name, chart=charts)) - layout = summary_lib.custom_scalar_pb(layout_pb2.Layout(category=categories)) + charts.append(layout_pb2.Chart( + title=chart_name, margin=margin)) + categories.append(layout_pb2.Category( + title=cat_name, chart=charts)) + layout = summary_lib.custom_scalar_pb( + layout_pb2.Layout(category=categories)) return layout + def save_summaries(file_writer, global_step=None): """Call FileWriter.add_summary() with all summaries in the default graph, automatically finalizing and merging them on the first call. diff --git a/stylegan_human/dnnlib/tflib/custom_ops.py b/stylegan_human/dnnlib/tflib/custom_ops.py index a09ac5dc2a5de80d22a5593ed7725551737d59af..3e2498b04f4a5c950dae0ff77b85f8372df1b5b9 100755 --- a/stylegan_human/dnnlib/tflib/custom_ops.py +++ b/stylegan_human/dnnlib/tflib/custom_ops.py @@ -16,15 +16,16 @@ import hashlib import tempfile import shutil import tensorflow as tf -from tensorflow.python.client import device_lib # pylint: disable=no-name-in-module +from tensorflow.python.client import device_lib # pylint: disable=no-name-in-module -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Global options. cuda_cache_path = os.path.join(os.path.dirname(__file__), '_cudacache') cuda_cache_version_tag = 'v1' -do_not_hash_included_headers = False # Speed up compilation by assuming that headers included by the CUDA code never change. Unsafe! -verbose = True # Print status messages to stdout. +# Speed up compilation by assuming that headers included by the CUDA code never change. Unsafe! +do_not_hash_included_headers = False +verbose = True # Print status messages to stdout. compiler_bindir_search_path = [ 'C:/Program Files (x86)/Microsoft Visual Studio/2017/Community/VC/Tools/MSVC/14.14.26428/bin/Hostx64/x64', @@ -32,15 +33,17 @@ compiler_bindir_search_path = [ 'C:/Program Files (x86)/Microsoft Visual Studio 14.0/vc/bin', ] -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Internal helper funcs. + def _find_compiler_bindir(): for compiler_path in compiler_bindir_search_path: if os.path.isdir(compiler_path): return compiler_path return None + def _get_compute_cap(device): caps_str = device.physical_device_desc m = re.search('compute capability: (\\d+).(\\d+)', caps_str) @@ -48,6 +51,7 @@ def _get_compute_cap(device): minor = m.group(2) return (major, minor) + def _get_cuda_gpu_arch_string(): gpus = [x for x in device_lib.list_local_devices() if x.device_type == 'GPU'] if len(gpus) == 0: @@ -55,37 +59,46 @@ def _get_cuda_gpu_arch_string(): (major, minor) = _get_compute_cap(gpus[0]) return 'sm_%s%s' % (major, minor) + def _run_cmd(cmd): with os.popen(cmd) as pipe: output = pipe.read() status = pipe.close() if status is not None: - raise RuntimeError('NVCC returned an error. See below for full command line and output log:\n\n%s\n\n%s' % (cmd, output)) + raise RuntimeError( + 'NVCC returned an error. See below for full command line and output log:\n\n%s\n\n%s' % (cmd, output)) + def _prepare_nvcc_cli(opts): cmd = 'nvcc ' + opts.strip() cmd += ' --disable-warnings' cmd += ' --include-path "%s"' % tf.sysconfig.get_include() - cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'protobuf_archive', 'src') - cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'com_google_absl') - cmd += ' --include-path "%s"' % os.path.join(tf.sysconfig.get_include(), 'external', 'eigen_archive') + cmd += ' --include-path "%s"' % os.path.join( + tf.sysconfig.get_include(), 'external', 'protobuf_archive', 'src') + cmd += ' --include-path "%s"' % os.path.join( + tf.sysconfig.get_include(), 'external', 'com_google_absl') + cmd += ' --include-path "%s"' % os.path.join( + tf.sysconfig.get_include(), 'external', 'eigen_archive') compiler_bindir = _find_compiler_bindir() if compiler_bindir is None: # Require that _find_compiler_bindir succeeds on Windows. Allow # nvcc to use whatever is the default on Linux. if os.name == 'nt': - raise RuntimeError('Could not find MSVC/GCC/CLANG installation on this computer. Check compiler_bindir_search_path list in "%s".' % __file__) + raise RuntimeError( + 'Could not find MSVC/GCC/CLANG installation on this computer. Check compiler_bindir_search_path list in "%s".' % __file__) else: cmd += ' --compiler-bindir "%s"' % compiler_bindir cmd += ' 2>&1' return cmd -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Main entry point. + _plugin_cache = dict() + def get_plugin(cuda_file): cuda_file_base = os.path.basename(cuda_file) cuda_file_name, cuda_file_ext = os.path.splitext(cuda_file_base) @@ -96,7 +109,8 @@ def get_plugin(cuda_file): # Setup plugin. if verbose: - print('Setting up TensorFlow plugin "%s": ' % cuda_file_base, end='', flush=True) + print('Setting up TensorFlow plugin "%s": ' % + cuda_file_base, end='', flush=True) try: # Hash CUDA source. md5 = hashlib.md5() @@ -109,13 +123,19 @@ def get_plugin(cuda_file): if verbose: print('Preprocessing... ', end='', flush=True) with tempfile.TemporaryDirectory() as tmp_dir: - tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + cuda_file_ext) - _run_cmd(_prepare_nvcc_cli('"%s" --preprocess -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir))) + tmp_file = os.path.join( + tmp_dir, cuda_file_name + '_tmp' + cuda_file_ext) + _run_cmd(_prepare_nvcc_cli( + '"%s" --preprocess -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir))) with open(tmp_file, 'rb') as f: - bad_file_str = ('"' + cuda_file.replace('\\', '/') + '"').encode('utf-8') # __FILE__ in error check macros - good_file_str = ('"' + cuda_file_base + '"').encode('utf-8') + # __FILE__ in error check macros + bad_file_str = ( + '"' + cuda_file.replace('\\', '/') + '"').encode('utf-8') + good_file_str = ('"' + cuda_file_base + + '"').encode('utf-8') for ln in f: - if not ln.startswith(b'# ') and not ln.startswith(b'#line '): # ignore line number pragmas + # ignore line number pragmas + if not ln.startswith(b'# ') and not ln.startswith(b'#line '): ln = ln.replace(bad_file_str, good_file_str) md5.update(ln) md5.update(b'\n') @@ -123,12 +143,14 @@ def get_plugin(cuda_file): # Select compiler options. compile_opts = '' if os.name == 'nt': - compile_opts += '"%s"' % os.path.join(tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.lib') + compile_opts += '"%s"' % os.path.join( + tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.lib') elif os.name == 'posix': - compile_opts += '"%s"' % os.path.join(tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.so') + compile_opts += '"%s"' % os.path.join( + tf.sysconfig.get_lib(), 'python', '_pywrap_tensorflow_internal.so') compile_opts += ' --compiler-options \'-fPIC -D_GLIBCXX_USE_CXX11_ABI=0\'' else: - assert False # not Windows or Linux, w00t? + assert False # not Windows or Linux, w00t? compile_opts += ' --gpu-architecture=%s' % _get_cuda_gpu_arch_string() compile_opts += ' --use_fast_math' nvcc_cmd = _prepare_nvcc_cli(compile_opts) @@ -136,21 +158,26 @@ def get_plugin(cuda_file): # Hash build configuration. md5.update(('nvcc_cmd: ' + nvcc_cmd).encode('utf-8') + b'\n') md5.update(('tf.VERSION: ' + tf.VERSION).encode('utf-8') + b'\n') - md5.update(('cuda_cache_version_tag: ' + cuda_cache_version_tag).encode('utf-8') + b'\n') + md5.update(('cuda_cache_version_tag: ' + + cuda_cache_version_tag).encode('utf-8') + b'\n') # Compile if not already compiled. bin_file_ext = '.dll' if os.name == 'nt' else '.so' - bin_file = os.path.join(cuda_cache_path, cuda_file_name + '_' + md5.hexdigest() + bin_file_ext) + bin_file = os.path.join( + cuda_cache_path, cuda_file_name + '_' + md5.hexdigest() + bin_file_ext) if not os.path.isfile(bin_file): if verbose: print('Compiling... ', end='', flush=True) with tempfile.TemporaryDirectory() as tmp_dir: - tmp_file = os.path.join(tmp_dir, cuda_file_name + '_tmp' + bin_file_ext) - _run_cmd(nvcc_cmd + ' "%s" --shared -o "%s" --keep --keep-dir "%s"' % (cuda_file, tmp_file, tmp_dir)) + tmp_file = os.path.join( + tmp_dir, cuda_file_name + '_tmp' + bin_file_ext) + _run_cmd(nvcc_cmd + ' "%s" --shared -o "%s" --keep --keep-dir "%s"' % + (cuda_file, tmp_file, tmp_dir)) os.makedirs(cuda_cache_path, exist_ok=True) - intermediate_file = os.path.join(cuda_cache_path, cuda_file_name + '_' + uuid.uuid4().hex + '_tmp' + bin_file_ext) + intermediate_file = os.path.join( + cuda_cache_path, cuda_file_name + '_' + uuid.uuid4().hex + '_tmp' + bin_file_ext) shutil.copyfile(tmp_file, intermediate_file) - os.rename(intermediate_file, bin_file) # atomic + os.rename(intermediate_file, bin_file) # atomic # Load. if verbose: @@ -168,4 +195,4 @@ def get_plugin(cuda_file): print('Failed!', flush=True) raise -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/dnnlib/tflib/network.py b/stylegan_human/dnnlib/tflib/network.py index bfa73dc5ff2051689d16159871d2bc7e31294502..f663a9a117661a56438d8a033903f18941319a83 100755 --- a/stylegan_human/dnnlib/tflib/network.py +++ b/stylegan_human/dnnlib/tflib/network.py @@ -24,8 +24,10 @@ from .. import util from .tfutil import TfExpression, TfExpressionEx -_import_handlers = [] # Custom import handlers for dealing with legacy data in pickle import. -_import_module_src = dict() # Source code for temporary modules created during pickle import. +# Custom import handlers for dealing with legacy data in pickle import. +_import_handlers = [] +# Source code for temporary modules created during pickle import. +_import_module_src = dict() def import_handler(handler_func): @@ -76,7 +78,8 @@ class Network: tfutil.assert_tf_initialized() assert isinstance(name, str) or name is None assert func_name is not None - assert isinstance(func_name, str) or util.is_top_level_function(func_name) + assert isinstance( + func_name, str) or util.is_top_level_function(func_name) assert util.is_pickleable(static_kwargs) self._init_fields() @@ -86,8 +89,10 @@ class Network: # Locate the user-specified network build function. if util.is_top_level_function(func_name): func_name = util.get_top_level_function_name(func_name) - module, self._build_func_name = util.get_module_from_obj_name(func_name) - self._build_func = util.get_obj_from_module(module, self._build_func_name) + module, self._build_func_name = util.get_module_from_obj_name( + func_name) + self._build_func = util.get_obj_from_module( + module, self._build_func_name) assert callable(self._build_func) # Dig up source code for the module containing the build function. @@ -119,9 +124,11 @@ class Network: self.trainables = OrderedDict() self.var_global_to_local = OrderedDict() - self._build_func = None # User-supplied build function that constructs the network. + # User-supplied build function that constructs the network. + self._build_func = None self._build_func_name = None # Name of the build function. - self._build_module_src = None # Full source code of the module containing the build function. + # Full source code of the module containing the build function. + self._build_module_src = None self._run_cache = dict() # Cached graph data for Network.run(). def _init_graph(self) -> None: @@ -148,27 +155,35 @@ class Network: build_kwargs["components"] = self.components # Build template graph. - with tfutil.absolute_variable_scope(self.scope, reuse=False), tfutil.absolute_name_scope(self.scope): # ignore surrounding scopes + # ignore surrounding scopes + with tfutil.absolute_variable_scope(self.scope, reuse=False), tfutil.absolute_name_scope(self.scope): assert tf.get_variable_scope().name == self.scope assert tf.get_default_graph().get_name_scope() == self.scope - with tf.control_dependencies(None): # ignore surrounding control dependencies - self.input_templates = [tf.placeholder(tf.float32, name=name) for name in self.input_names] - out_expr = self._build_func(*self.input_templates, **build_kwargs) + # ignore surrounding control dependencies + with tf.control_dependencies(None): + self.input_templates = [tf.placeholder( + tf.float32, name=name) for name in self.input_names] + out_expr = self._build_func( + *self.input_templates, **build_kwargs) # Collect outputs. assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple) - self.output_templates = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr) + self.output_templates = [out_expr] if tfutil.is_tf_expression( + out_expr) else list(out_expr) self.num_outputs = len(self.output_templates) assert self.num_outputs >= 1 assert all(tfutil.is_tf_expression(t) for t in self.output_templates) # Perform sanity checks. if any(t.shape.ndims is None for t in self.input_templates): - raise ValueError("Network input shapes not defined. Please call x.set_shape() for each input.") + raise ValueError( + "Network input shapes not defined. Please call x.set_shape() for each input.") if any(t.shape.ndims is None for t in self.output_templates): - raise ValueError("Network output shapes not defined. Please call x.set_shape() where applicable.") + raise ValueError( + "Network output shapes not defined. Please call x.set_shape() where applicable.") if any(not isinstance(comp, Network) for comp in self.components.values()): - raise ValueError("Components of a Network must be Networks themselves.") + raise ValueError( + "Components of a Network must be Networks themselves.") if len(self.components) != len(set(comp.name for comp in self.components.values())): raise ValueError("Components of a Network must have unique names.") @@ -177,14 +192,19 @@ class Network: self.output_shapes = [t.shape.as_list() for t in self.output_templates] self.input_shape = self.input_shapes[0] self.output_shape = self.output_shapes[0] - self.output_names = [t.name.split("/")[-1].split(":")[0] for t in self.output_templates] + self.output_names = [t.name.split( + "/")[-1].split(":")[0] for t in self.output_templates] # List variables. - self.own_vars = OrderedDict((var.name[len(self.scope) + 1:].split(":")[0], var) for var in tf.global_variables(self.scope + "/")) + self.own_vars = OrderedDict((var.name[len( + self.scope) + 1:].split(":")[0], var) for var in tf.global_variables(self.scope + "/")) self.vars = OrderedDict(self.own_vars) - self.vars.update((comp.name + "/" + name, var) for comp in self.components.values() for name, var in comp.vars.items()) - self.trainables = OrderedDict((name, var) for name, var in self.vars.items() if var.trainable) - self.var_global_to_local = OrderedDict((var.name.split(":")[0], name) for name, var in self.vars.items()) + self.vars.update((comp.name + "/" + name, var) + for comp in self.components.values() for name, var in comp.vars.items()) + self.trainables = OrderedDict( + (name, var) for name, var in self.vars.items() if var.trainable) + self.var_global_to_local = OrderedDict( + (var.name.split(":")[0], name) for name, var in self.vars.items()) def reset_own_vars(self) -> None: """Re-initialize all variables of this network, excluding sub-networks.""" @@ -218,7 +238,8 @@ class Network: if expr is not None: expr = tf.identity(expr, name=name) else: - expr = tf.zeros([tf.shape(valid_inputs[0])[0]] + shape[1:], name=name) + expr = tf.zeros([tf.shape(valid_inputs[0])[ + 0]] + shape[1:], name=name) final_inputs.append(expr) out_expr = self._build_func(*final_inputs, **build_kwargs) @@ -230,18 +251,22 @@ class Network: # Express outputs in the desired format. assert tfutil.is_tf_expression(out_expr) or isinstance(out_expr, tuple) if return_as_list: - out_expr = [out_expr] if tfutil.is_tf_expression(out_expr) else list(out_expr) + out_expr = [out_expr] if tfutil.is_tf_expression( + out_expr) else list(out_expr) return out_expr def get_var_local_name(self, var_or_global_name: Union[TfExpression, str]) -> str: """Get the local name of a given variable, without any surrounding name scopes.""" - assert tfutil.is_tf_expression(var_or_global_name) or isinstance(var_or_global_name, str) - global_name = var_or_global_name if isinstance(var_or_global_name, str) else var_or_global_name.name + assert tfutil.is_tf_expression( + var_or_global_name) or isinstance(var_or_global_name, str) + global_name = var_or_global_name if isinstance( + var_or_global_name, str) else var_or_global_name.name return self.var_global_to_local[global_name] def find_var(self, var_or_local_name: Union[TfExpression, str]) -> TfExpression: """Find variable by local or global name.""" - assert tfutil.is_tf_expression(var_or_local_name) or isinstance(var_or_local_name, str) + assert tfutil.is_tf_expression( + var_or_local_name) or isinstance(var_or_local_name, str) return self.vars[var_or_local_name] if isinstance(var_or_local_name, str) else var_or_local_name def get_var(self, var_or_local_name: Union[TfExpression, str]) -> np.ndarray: @@ -257,13 +282,14 @@ class Network: def __getstate__(self) -> dict: """Pickle export.""" state = dict() - state["version"] = 4 - state["name"] = self.name - state["static_kwargs"] = dict(self.static_kwargs) - state["components"] = dict(self.components) - state["build_module_src"] = self._build_module_src - state["build_func_name"] = self._build_func_name - state["variables"] = list(zip(self.own_vars.keys(), tfutil.run(list(self.own_vars.values())))) + state["version"] = 4 + state["name"] = self.name + state["static_kwargs"] = dict(self.static_kwargs) + state["components"] = dict(self.components) + state["build_module_src"] = self._build_module_src + state["build_func_name"] = self._build_func_name + state["variables"] = list( + zip(self.own_vars.keys(), tfutil.run(list(self.own_vars.values())))) return state def __setstate__(self, state: dict) -> None: @@ -289,16 +315,18 @@ class Network: module = types.ModuleType(module_name) sys.modules[module_name] = module _import_module_src[module] = self._build_module_src - exec(self._build_module_src, module.__dict__) # pylint: disable=exec-used + exec(self._build_module_src, module.__dict__) # pylint: disable=exec-used # Locate network build function in the temporary module. - self._build_func = util.get_obj_from_module(module, self._build_func_name) + self._build_func = util.get_obj_from_module( + module, self._build_func_name) assert callable(self._build_func) # Init TensorFlow graph. self._init_graph() self.reset_own_vars() - tfutil.set_vars({self.find_var(name): value for name, value in state["variables"]}) + tfutil.set_vars({self.find_var(name): value for name, + value in state["variables"]}) def clone(self, name: str = None, **new_static_kwargs) -> "Network": """Create a clone of this network with its own copy of the variables.""" @@ -317,18 +345,23 @@ class Network: def copy_own_vars_from(self, src_net: "Network") -> None: """Copy the values of all variables from the given network, excluding sub-networks.""" - names = [name for name in self.own_vars.keys() if name in src_net.own_vars] - tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names})) + names = [name for name in self.own_vars.keys() + if name in src_net.own_vars] + tfutil.set_vars(tfutil.run( + {self.vars[name]: src_net.vars[name] for name in names})) def copy_vars_from(self, src_net: "Network") -> None: """Copy the values of all variables from the given network, including sub-networks.""" names = [name for name in self.vars.keys() if name in src_net.vars] - tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names})) + tfutil.set_vars(tfutil.run( + {self.vars[name]: src_net.vars[name] for name in names})) def copy_trainables_from(self, src_net: "Network") -> None: """Copy the values of all trainable variables from the given network, including sub-networks.""" - names = [name for name in self.trainables.keys() if name in src_net.trainables] - tfutil.set_vars(tfutil.run({self.vars[name]: src_net.vars[name] for name in names})) + names = [name for name in self.trainables.keys() + if name in src_net.trainables] + tfutil.set_vars(tfutil.run( + {self.vars[name]: src_net.vars[name] for name in names})) def convert(self, new_func_name: str, new_name: str = None, **new_static_kwargs) -> "Network": """Create new network with the given parameters, and copy all variables from this network.""" @@ -380,15 +413,20 @@ class Network: """ assert len(in_arrays) == self.num_inputs assert not all(arr is None for arr in in_arrays) - assert input_transform is None or util.is_top_level_function(input_transform["func"]) - assert output_transform is None or util.is_top_level_function(output_transform["func"]) - output_transform, dynamic_kwargs = _handle_legacy_output_transforms(output_transform, dynamic_kwargs) + assert input_transform is None or util.is_top_level_function( + input_transform["func"]) + assert output_transform is None or util.is_top_level_function( + output_transform["func"]) + output_transform, dynamic_kwargs = _handle_legacy_output_transforms( + output_transform, dynamic_kwargs) num_items = in_arrays[0].shape[0] if minibatch_size is None: minibatch_size = num_items # Construct unique hash key from all arguments that affect the TensorFlow graph. - key = dict(input_transform=input_transform, output_transform=output_transform, num_gpus=num_gpus, assume_frozen=assume_frozen, dynamic_kwargs=dynamic_kwargs) + key = dict(input_transform=input_transform, output_transform=output_transform, + num_gpus=num_gpus, assume_frozen=assume_frozen, dynamic_kwargs=dynamic_kwargs) + def unwind_key(obj): if isinstance(obj, dict): return [(key, unwind_key(value)) for key, value in sorted(obj.items())] @@ -401,8 +439,10 @@ class Network: if key not in self._run_cache: with tfutil.absolute_name_scope(self.scope + "/_Run"), tf.control_dependencies(None): with tf.device("/cpu:0"): - in_expr = [tf.placeholder(tf.float32, name=name) for name in self.input_names] - in_split = list(zip(*[tf.split(x, num_gpus) for x in in_expr])) + in_expr = [tf.placeholder(tf.float32, name=name) + for name in self.input_names] + in_split = list( + zip(*[tf.split(x, num_gpus) for x in in_expr])) out_split = [] for gpu in range(num_gpus): @@ -412,27 +452,34 @@ class Network: if input_transform is not None: in_kwargs = dict(input_transform) - in_gpu = in_kwargs.pop("func")(*in_gpu, **in_kwargs) - in_gpu = [in_gpu] if tfutil.is_tf_expression(in_gpu) else list(in_gpu) + in_gpu = in_kwargs.pop("func")( + *in_gpu, **in_kwargs) + in_gpu = [in_gpu] if tfutil.is_tf_expression( + in_gpu) else list(in_gpu) assert len(in_gpu) == self.num_inputs - out_gpu = net_gpu.get_output_for(*in_gpu, return_as_list=True, **dynamic_kwargs) + out_gpu = net_gpu.get_output_for( + *in_gpu, return_as_list=True, **dynamic_kwargs) if output_transform is not None: out_kwargs = dict(output_transform) - out_gpu = out_kwargs.pop("func")(*out_gpu, **out_kwargs) - out_gpu = [out_gpu] if tfutil.is_tf_expression(out_gpu) else list(out_gpu) + out_gpu = out_kwargs.pop("func")( + *out_gpu, **out_kwargs) + out_gpu = [out_gpu] if tfutil.is_tf_expression( + out_gpu) else list(out_gpu) assert len(out_gpu) == self.num_outputs out_split.append(out_gpu) with tf.device("/cpu:0"): - out_expr = [tf.concat(outputs, axis=0) for outputs in zip(*out_split)] + out_expr = [tf.concat(outputs, axis=0) + for outputs in zip(*out_split)] self._run_cache[key] = in_expr, out_expr # Run minibatches. in_expr, out_expr = self._run_cache[key] - out_arrays = [np.empty([num_items] + expr.shape.as_list()[1:], expr.dtype.name) for expr in out_expr] + out_arrays = [np.empty( + [num_items] + expr.shape.as_list()[1:], expr.dtype.name) for expr in out_expr] for mb_begin in range(0, num_items, minibatch_size): if print_progress: @@ -440,7 +487,8 @@ class Network: mb_end = min(mb_begin + minibatch_size, num_items) mb_num = mb_end - mb_begin - mb_in = [src[mb_begin : mb_end] if src is not None else np.zeros([mb_num] + shape[1:]) for src, shape in zip(in_arrays, self.input_shapes)] + mb_in = [src[mb_begin: mb_end] if src is not None else np.zeros( + [mb_num] + shape[1:]) for src, shape in zip(in_arrays, self.input_shapes)] mb_out = tf.get_default_session().run(out_expr, dict(zip(in_expr, mb_in))) for dst, src in zip(out_arrays, mb_out): @@ -451,7 +499,8 @@ class Network: print("\r%d / %d" % (num_items, num_items)) if not return_as_list: - out_arrays = out_arrays[0] if len(out_arrays) == 1 else tuple(out_arrays) + out_arrays = out_arrays[0] if len( + out_arrays) == 1 else tuple(out_arrays) return out_arrays def list_ops(self) -> List[TfExpression]: @@ -475,31 +524,37 @@ class Network: # Filter ops and vars by scope. global_prefix = scope + "/" local_prefix = global_prefix[len(self.scope) + 1:] - cur_ops = [op for op in parent_ops if op.name.startswith(global_prefix) or op.name == global_prefix[:-1]] - cur_vars = [(name, var) for name, var in parent_vars if name.startswith(local_prefix) or name == local_prefix[:-1]] + cur_ops = [op for op in parent_ops if op.name.startswith( + global_prefix) or op.name == global_prefix[:-1]] + cur_vars = [(name, var) for name, var in parent_vars if name.startswith( + local_prefix) or name == local_prefix[:-1]] if not cur_ops and not cur_vars: return # Filter out all ops related to variables. for var in [op for op in cur_ops if op.type.startswith("Variable")]: var_prefix = var.name + "/" - cur_ops = [op for op in cur_ops if not op.name.startswith(var_prefix)] + cur_ops = [ + op for op in cur_ops if not op.name.startswith(var_prefix)] # Scope does not contain ops as immediate children => recurse deeper. - contains_direct_ops = any("/" not in op.name[len(global_prefix):] and op.type not in ["Identity", "Cast", "Transpose"] for op in cur_ops) + contains_direct_ops = any("/" not in op.name[len(global_prefix):] and op.type not in [ + "Identity", "Cast", "Transpose"] for op in cur_ops) if (level == 0 or not contains_direct_ops) and (len(cur_ops) + len(cur_vars)) > 1: visited = set() for rel_name in [op.name[len(global_prefix):] for op in cur_ops] + [name[len(local_prefix):] for name, _var in cur_vars]: token = rel_name.split("/")[0] if token not in visited: - recurse(global_prefix + token, cur_ops, cur_vars, level + 1) + recurse(global_prefix + token, + cur_ops, cur_vars, level + 1) visited.add(token) return # Report layer. layer_name = scope[len(self.scope) + 1:] layer_output = cur_ops[-1].outputs[0] if cur_ops else cur_vars[-1][1] - layer_trainables = [var for _name, var in cur_vars if var.trainable] + layer_trainables = [var for _name, + var in cur_vars if var.trainable] layers.append((layer_name, layer_output, layer_trainables)) recurse(self.scope, self.list_ops(), list(self.vars.items()), 0) @@ -507,13 +562,16 @@ class Network: def print_layers(self, title: str = None, hide_layers_with_no_params: bool = False) -> None: """Print a summary table of the network structure.""" - rows = [[title if title is not None else self.name, "Params", "OutputShape", "WeightShape"]] + rows = [[title if title is not None else self.name, + "Params", "OutputShape", "WeightShape"]] rows += [["---"] * 4] total_params = 0 for layer_name, layer_output, layer_trainables in self.list_layers(): - num_params = sum(int(np.prod(var.shape.as_list())) for var in layer_trainables) - weights = [var for var in layer_trainables if var.name.endswith("/weight:0")] + num_params = sum(int(np.prod(var.shape.as_list())) + for var in layer_trainables) + weights = [ + var for var in layer_trainables if var.name.endswith("/weight:0")] weights.sort(key=lambda x: len(x.name)) if len(weights) == 0 and len(layer_trainables) == 1: weights = layer_trainables @@ -522,8 +580,10 @@ class Network: if not hide_layers_with_no_params or num_params != 0: num_params_str = str(num_params) if num_params > 0 else "-" output_shape_str = str(layer_output.shape) - weight_shape_str = str(weights[0].shape) if len(weights) >= 1 else "-" - rows += [[layer_name, num_params_str, output_shape_str, weight_shape_str]] + weight_shape_str = str(weights[0].shape) if len( + weights) >= 1 else "-" + rows += [[layer_name, num_params_str, + output_shape_str, weight_shape_str]] rows += [["---"] * 4] rows += [["Total", str(total_params), "", ""]] @@ -531,7 +591,8 @@ class Network: widths = [max(len(cell) for cell in column) for column in zip(*rows)] print() for row in rows: - print(" ".join(cell + " " * (width - len(cell)) for cell, width in zip(row, widths))) + print(" ".join(cell + " " * (width - len(cell)) + for cell, width in zip(row, widths))) print() def setup_weight_histograms(self, title: str = None) -> None: @@ -549,11 +610,13 @@ class Network: tf.summary.histogram(name, var) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Backwards-compatible emulation of legacy output transformation in Network.run(). + _print_legacy_warning = True + def _handle_legacy_output_transforms(output_transform, dynamic_kwargs): global _print_legacy_warning legacy_kwargs = ["out_mul", "out_add", "out_shrink", "out_dtype"] @@ -570,10 +633,12 @@ def _handle_legacy_output_transforms(output_transform, dynamic_kwargs): assert output_transform is None new_kwargs = dict(dynamic_kwargs) - new_transform = {kwarg: new_kwargs.pop(kwarg) for kwarg in legacy_kwargs if kwarg in dynamic_kwargs} + new_transform = {kwarg: new_kwargs.pop( + kwarg) for kwarg in legacy_kwargs if kwarg in dynamic_kwargs} new_transform["func"] = _legacy_output_transform_func return new_transform, new_kwargs + def _legacy_output_transform_func(*expr, out_mul=1.0, out_add=0.0, out_shrink=1, out_dtype=None): if out_mul != 1.0: expr = [x * out_mul for x in expr] @@ -583,7 +648,8 @@ def _legacy_output_transform_func(*expr, out_mul=1.0, out_add=0.0, out_shrink=1, if out_shrink > 1: ksize = [1, 1, out_shrink, out_shrink] - expr = [tf.nn.avg_pool(x, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW") for x in expr] + expr = [tf.nn.avg_pool(x, ksize=ksize, strides=ksize, + padding="VALID", data_format="NCHW") for x in expr] if out_dtype is not None: if tf.as_dtype(out_dtype).is_integer: diff --git a/stylegan_human/dnnlib/tflib/ops/fused_bias_act.py b/stylegan_human/dnnlib/tflib/ops/fused_bias_act.py index 6b0dfd08d475f4d6759fd4bbdc133aef85f3bb24..9aeddfa257cf6148b7336644cbac7de276e31700 100755 --- a/stylegan_human/dnnlib/tflib/ops/fused_bias_act.py +++ b/stylegan_human/dnnlib/tflib/ops/fused_bias_act.py @@ -14,10 +14,12 @@ import tensorflow as tf from .. import custom_ops from ...util import EasyDict + def _get_plugin(): return custom_ops.get_plugin(os.path.splitext(__file__)[0] + '.cu') -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + activation_funcs = { 'linear': EasyDict(func=lambda x, **_: x, def_alpha=None, def_gain=1.0, cuda_idx=1, ref='y', zero_2nd_grad=True), @@ -31,7 +33,8 @@ activation_funcs = { 'swish': EasyDict(func=lambda x, **_: tf.nn.sigmoid(x) * x, def_alpha=None, def_gain=np.sqrt(2), cuda_idx=9, ref='x', zero_2nd_grad=False), } -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def fused_bias_act(x, b=None, axis=1, act='linear', alpha=None, gain=None, impl='cuda'): r"""Fused bias and activation function. @@ -69,16 +72,19 @@ def fused_bias_act(x, b=None, axis=1, act='linear', alpha=None, gain=None, impl= } return impl_dict[impl](x=x, b=b, axis=axis, act=act, alpha=alpha, gain=gain) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _fused_bias_act_ref(x, b, axis, act, alpha, gain): """Slow reference implementation of `fused_bias_act()` using standard TensorFlow ops.""" # Validate arguments. x = tf.convert_to_tensor(x) - b = tf.convert_to_tensor(b) if b is not None else tf.constant([], dtype=x.dtype) + b = tf.convert_to_tensor( + b) if b is not None else tf.constant([], dtype=x.dtype) act_spec = activation_funcs[act] - assert b.shape.rank == 1 and (b.shape[0] == 0 or b.shape[0] == x.shape[axis]) + assert b.shape.rank == 1 and ( + b.shape[0] == 0 or b.shape[0] == x.shape[axis]) assert b.shape[0] == 0 or 0 <= axis < x.shape.rank if alpha is None: alpha = act_spec.def_alpha @@ -87,7 +93,8 @@ def _fused_bias_act_ref(x, b, axis, act, alpha, gain): # Add bias. if b.shape[0] != 0: - x += tf.reshape(b, [-1 if i == axis else 1 for i in range(x.shape.rank)]) + x += tf.reshape(b, [-1 if i == + axis else 1 for i in range(x.shape.rank)]) # Evaluate activation function. x = act_spec.func(x, alpha=alpha) @@ -97,7 +104,8 @@ def _fused_bias_act_ref(x, b, axis, act, alpha, gain): x *= gain return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _fused_bias_act_cuda(x, b, axis, act, alpha, gain): """Fast CUDA implementation of `fused_bias_act()` using custom ops.""" @@ -107,7 +115,8 @@ def _fused_bias_act_cuda(x, b, axis, act, alpha, gain): empty_tensor = tf.constant([], dtype=x.dtype) b = tf.convert_to_tensor(b) if b is not None else empty_tensor act_spec = activation_funcs[act] - assert b.shape.rank == 1 and (b.shape[0] == 0 or b.shape[0] == x.shape[axis]) + assert b.shape.rank == 1 and ( + b.shape[0] == 0 or b.shape[0] == x.shape[axis]) assert b.shape[0] == 0 or 0 <= axis < x.shape.rank if alpha is None: alpha = act_spec.def_alpha @@ -122,7 +131,8 @@ def _fused_bias_act_cuda(x, b, axis, act, alpha, gain): # CUDA kernel. cuda_kernel = _get_plugin().fused_bias_act - cuda_kwargs = dict(axis=axis, act=act_spec.cuda_idx, alpha=alpha, gain=gain) + cuda_kwargs = dict(axis=axis, act=act_spec.cuda_idx, + alpha=alpha, gain=gain) # Forward pass: y = func(x, b). def func_y(x, b): @@ -136,6 +146,7 @@ def _fused_bias_act_cuda(x, b, axis, act, alpha, gain): dx = cuda_kernel(x=dy, b=empty_tensor, ref=ref, grad=1, **cuda_kwargs) dx.set_shape(x.shape) return dx + def grad_db(dx): if b.shape[0] == 0: return empty_tensor @@ -153,6 +164,7 @@ def _fused_bias_act_cuda(x, b, axis, act, alpha, gain): d_dy = cuda_kernel(x=d_dx, b=d_db, ref=ref, grad=1, **cuda_kwargs) d_dy.set_shape(x.shape) return d_dy + def grad2_d_x(d_dx, d_db, x, y): ref = {'x': x, 'y': y}[act_spec.ref] d_x = cuda_kernel(x=d_dx, b=d_db, ref=ref, grad=2, **cuda_kwargs) @@ -163,10 +175,12 @@ def _fused_bias_act_cuda(x, b, axis, act, alpha, gain): @tf.custom_gradient def func_zero_2nd_grad(x, b): y = func_y(x, b) + @tf.custom_gradient def grad(dy): dx = grad_dx(dy, x, y) db = grad_db(dx) + def grad2(d_dx, d_db): d_dy = grad2_d_dy(d_dx, d_db, x, y) return d_dy @@ -177,11 +191,13 @@ def _fused_bias_act_cuda(x, b, axis, act, alpha, gain): @tf.custom_gradient def func_nonzero_2nd_grad(x, b): y = func_y(x, b) + def grad_wrap(dy): @tf.custom_gradient def grad_impl(dy, x): dx = grad_dx(dy, x, y) db = grad_db(dx) + def grad2(d_dx, d_db): d_dy = grad2_d_dy(d_dx, d_db, x, y) d_x = grad2_d_x(d_dx, d_db, x, y) @@ -195,4 +211,4 @@ def _fused_bias_act_cuda(x, b, axis, act, alpha, gain): return func_zero_2nd_grad(x, b) return func_nonzero_2nd_grad(x, b) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/dnnlib/tflib/ops/upfirdn_2d.py b/stylegan_human/dnnlib/tflib/ops/upfirdn_2d.py index 22e4b14fd5436e42336d3dd82f6135876076c518..89413336877e0b6fcc5aff0a215cae3a7fe558cf 100755 --- a/stylegan_human/dnnlib/tflib/ops/upfirdn_2d.py +++ b/stylegan_human/dnnlib/tflib/ops/upfirdn_2d.py @@ -13,10 +13,12 @@ import numpy as np import tensorflow as tf from .. import custom_ops + def _get_plugin(): return custom_ops.get_plugin(os.path.splitext(__file__)[0] + '.cu') -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def upfirdn_2d(x, k, upx=1, upy=1, downx=1, downy=1, padx0=0, padx1=0, pady0=0, pady1=0, impl='cuda'): r"""Pad, upsample, FIR filter, and downsample a batch of 2D images. @@ -63,7 +65,8 @@ def upfirdn_2d(x, k, upx=1, upy=1, downx=1, downy=1, padx0=0, padx1=0, pady0=0, } return impl_dict[impl](x=x, k=k, upx=upx, upy=upy, downx=downx, downy=downy, padx0=padx0, padx1=padx1, pady0=pady0, pady1=pady1) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _upfirdn_2d_ref(x, k, upx, upy, downx, downy, padx0, padx1, pady0, pady1): """Slow reference implementation of `upfirdn_2d()` using standard TensorFlow ops.""" @@ -88,21 +91,27 @@ def _upfirdn_2d_ref(x, k, upx, upy, downx, downy, padx0, padx1, pady0, pady1): x = tf.reshape(x, [-1, inH * upy, inW * upx, minorDim]) # Pad (crop if negative). - x = tf.pad(x, [[0, 0], [max(pady0, 0), max(pady1, 0)], [max(padx0, 0), max(padx1, 0)], [0, 0]]) - x = x[:, max(-pady0, 0) : x.shape[1].value - max(-pady1, 0), max(-padx0, 0) : x.shape[2].value - max(-padx1, 0), :] + x = tf.pad(x, [[0, 0], [max(pady0, 0), max(pady1, 0)], + [max(padx0, 0), max(padx1, 0)], [0, 0]]) + x = x[:, max(-pady0, 0): x.shape[1].value - max(-pady1, 0), + max(-padx0, 0): x.shape[2].value - max(-padx1, 0), :] # Convolve with filter. x = tf.transpose(x, [0, 3, 1, 2]) - x = tf.reshape(x, [-1, 1, inH * upy + pady0 + pady1, inW * upx + padx0 + padx1]) + x = tf.reshape(x, [-1, 1, inH * upy + pady0 + + pady1, inW * upx + padx0 + padx1]) w = tf.constant(k[::-1, ::-1, np.newaxis, np.newaxis], dtype=x.dtype) - x = tf.nn.conv2d(x, w, strides=[1,1,1,1], padding='VALID', data_format='NCHW') - x = tf.reshape(x, [-1, minorDim, inH * upy + pady0 + pady1 - kernelH + 1, inW * upx + padx0 + padx1 - kernelW + 1]) + x = tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], + padding='VALID', data_format='NCHW') + x = tf.reshape(x, [-1, minorDim, inH * upy + pady0 + pady1 - + kernelH + 1, inW * upx + padx0 + padx1 - kernelW + 1]) x = tf.transpose(x, [0, 2, 3, 1]) # Downsample (throw away pixels). return x[:, ::downy, ::downx, :] -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _upfirdn_2d_cuda(x, k, upx, upy, downx, downy, padx0, padx1, pady0, pady1): """Fast CUDA implementation of `upfirdn_2d()` using custom ops.""" @@ -131,17 +140,21 @@ def _upfirdn_2d_cuda(x, k, upx, upy, downx, downy, padx0, padx1, pady0, pady1): @tf.custom_gradient def func(x): - y = _get_plugin().up_fir_dn2d(x=x, k=kc, upx=upx, upy=upy, downx=downx, downy=downy, padx0=padx0, padx1=padx1, pady0=pady0, pady1=pady1) + y = _get_plugin().up_fir_dn2d(x=x, k=kc, upx=upx, upy=upy, downx=downx, + downy=downy, padx0=padx0, padx1=padx1, pady0=pady0, pady1=pady1) y.set_shape([majorDim, outH, outW, minorDim]) + @tf.custom_gradient def grad(dy): - dx = _get_plugin().up_fir_dn2d(x=dy, k=gkc, upx=downx, upy=downy, downx=upx, downy=upy, padx0=gpadx0, padx1=gpadx1, pady0=gpady0, pady1=gpady1) + dx = _get_plugin().up_fir_dn2d(x=dy, k=gkc, upx=downx, upy=downy, downx=upx, + downy=upy, padx0=gpadx0, padx1=gpadx1, pady0=gpady0, pady1=gpady1) dx.set_shape([majorDim, inH, inW, minorDim]) return dx, func return y, grad return func(x) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def filter_2d(x, k, gain=1, data_format='NCHW', impl='cuda'): r"""Filter a batch of 2D images with the given FIR filter. @@ -166,7 +179,8 @@ def filter_2d(x, k, gain=1, data_format='NCHW', impl='cuda'): p = k.shape[0] - 1 return _simple_upfirdn_2d(x, k, pad0=(p+1)//2, pad1=p//2, data_format=data_format, impl=impl) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def upsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'): r"""Upsample a batch of 2D images with the given filter. @@ -199,7 +213,8 @@ def upsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'): p = k.shape[0] - factor return _simple_upfirdn_2d(x, k, up=factor, pad0=(p+1)//2+factor-1, pad1=p//2, data_format=data_format, impl=impl) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def downsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'): r"""Downsample a batch of 2D images with the given filter. @@ -231,7 +246,8 @@ def downsample_2d(x, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'): p = k.shape[0] - factor return _simple_upfirdn_2d(x, k, down=factor, pad0=(p+1)//2, pad1=p//2, data_format=data_format, impl=impl) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def upsample_conv_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'): r"""Fused `upsample_2d()` followed by `tf.nn.conv2d()`. @@ -277,11 +293,13 @@ def upsample_conv_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW', impl='c # Determine data dimensions. if data_format == 'NCHW': stride = [1, 1, factor, factor] - output_shape = [_shape(x, 0), outC, (_shape(x, 2) - 1) * factor + convH, (_shape(x, 3) - 1) * factor + convW] + output_shape = [_shape(x, 0), outC, (_shape( + x, 2) - 1) * factor + convH, (_shape(x, 3) - 1) * factor + convW] num_groups = _shape(x, 1) // inC else: stride = [1, factor, factor, 1] - output_shape = [_shape(x, 0), (_shape(x, 1) - 1) * factor + convH, (_shape(x, 2) - 1) * factor + convW, outC] + output_shape = [_shape(x, 0), (_shape( + x, 1) - 1) * factor + convH, (_shape(x, 2) - 1) * factor + convW, outC] num_groups = _shape(x, 3) // inC # Transpose weights. @@ -290,10 +308,12 @@ def upsample_conv_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW', impl='c w = tf.reshape(w, [convH, convW, -1, num_groups * inC]) # Execute. - x = tf.nn.conv2d_transpose(x, w, output_shape=output_shape, strides=stride, padding='VALID', data_format=data_format) + x = tf.nn.conv2d_transpose(x, w, output_shape=output_shape, + strides=stride, padding='VALID', data_format=data_format) return _simple_upfirdn_2d(x, k, pad0=(p+1)//2+factor-1, pad1=p//2+1, data_format=data_format, impl=impl) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def conv_downsample_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'): r"""Fused `tf.nn.conv2d()` followed by `downsample_2d()`. @@ -330,12 +350,14 @@ def conv_downsample_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW', impl= s = [1, 1, factor, factor] else: s = [1, factor, factor, 1] - x = _simple_upfirdn_2d(x, k, pad0=(p+1)//2, pad1=p//2, data_format=data_format, impl=impl) + x = _simple_upfirdn_2d(x, k, pad0=(p+1)//2, pad1=p // + 2, data_format=data_format, impl=impl) return tf.nn.conv2d(x, w, strides=s, padding='VALID', data_format=data_format) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Internal helper funcs. + def _shape(tf_expr, dim_idx): if tf_expr.shape.rank is not None: dim = tf_expr.shape[dim_idx].value @@ -343,6 +365,7 @@ def _shape(tf_expr, dim_idx): return dim return tf.shape(tf_expr)[dim_idx] + def _setup_kernel(k): k = np.asarray(k, dtype=np.float32) if k.ndim == 1: @@ -352,15 +375,17 @@ def _setup_kernel(k): assert k.shape[0] == k.shape[1] return k + def _simple_upfirdn_2d(x, k, up=1, down=1, pad0=0, pad1=0, data_format='NCHW', impl='cuda'): assert data_format in ['NCHW', 'NHWC'] assert x.shape.rank == 4 y = x if data_format == 'NCHW': y = tf.reshape(y, [-1, _shape(y, 2), _shape(y, 3), 1]) - y = upfirdn_2d(y, k, upx=up, upy=up, downx=down, downy=down, padx0=pad0, padx1=pad1, pady0=pad0, pady1=pad1, impl=impl) + y = upfirdn_2d(y, k, upx=up, upy=up, downx=down, downy=down, + padx0=pad0, padx1=pad1, pady0=pad0, pady1=pad1, impl=impl) if data_format == 'NCHW': y = tf.reshape(y, [-1, _shape(x, 1), _shape(y, 1), _shape(y, 2)]) return y -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/dnnlib/tflib/optimizer.py b/stylegan_human/dnnlib/tflib/optimizer.py index 93d5dcc6172209985308784c9b9e590759612a0b..cd130a8b5ca8e1af555365620fd01104a3be13ce 100755 --- a/stylegan_human/dnnlib/tflib/optimizer.py +++ b/stylegan_human/dnnlib/tflib/optimizer.py @@ -27,6 +27,7 @@ except: # Older TensorFlow versions import tensorflow.contrib.nccl as nccl_ops + class Optimizer: """A Wrapper for tf.train.Optimizer. @@ -40,37 +41,47 @@ class Optimizer: """ def __init__(self, - name: str = "Train", # Name string that will appear in TensorFlow graph. - tf_optimizer: str = "tf.train.AdamOptimizer", # Underlying optimizer class. - learning_rate: TfExpressionEx = 0.001, # Learning rate. Can vary over time. - minibatch_multiplier: TfExpressionEx = None, # Treat N consecutive minibatches as one by accumulating gradients. - share: "Optimizer" = None, # Share internal state with a previously created optimizer? - use_loss_scaling: bool = False, # Enable dynamic loss scaling for robust mixed-precision training? - loss_scaling_init: float = 64.0, # Log2 of initial loss scaling factor. - loss_scaling_inc: float = 0.0005, # Log2 of per-minibatch loss scaling increment when there is no overflow. - loss_scaling_dec: float = 1.0, # Log2 of per-minibatch loss scaling decrement when there is an overflow. - report_mem_usage: bool = False, # Report fine-grained memory usage statistics in TensorBoard? - **kwargs): + # Name string that will appear in TensorFlow graph. + name: str = "Train", + # Underlying optimizer class. + tf_optimizer: str = "tf.train.AdamOptimizer", + # Learning rate. Can vary over time. + learning_rate: TfExpressionEx = 0.001, + # Treat N consecutive minibatches as one by accumulating gradients. + minibatch_multiplier: TfExpressionEx = None, + # Share internal state with a previously created optimizer? + share: "Optimizer" = None, + # Enable dynamic loss scaling for robust mixed-precision training? + use_loss_scaling: bool = False, + # Log2 of initial loss scaling factor. + loss_scaling_init: float = 64.0, + # Log2 of per-minibatch loss scaling increment when there is no overflow. + loss_scaling_inc: float = 0.0005, + # Log2 of per-minibatch loss scaling decrement when there is an overflow. + loss_scaling_dec: float = 1.0, + # Report fine-grained memory usage statistics in TensorBoard? + report_mem_usage: bool = False, + **kwargs): # Public fields. - self.name = name - self.learning_rate = learning_rate - self.minibatch_multiplier = minibatch_multiplier - self.id = self.name.replace("/", ".") - self.scope = tf.get_default_graph().unique_name(self.id) - self.optimizer_class = util.get_obj_by_name(tf_optimizer) - self.optimizer_kwargs = dict(kwargs) - self.use_loss_scaling = use_loss_scaling - self.loss_scaling_init = loss_scaling_init - self.loss_scaling_inc = loss_scaling_inc - self.loss_scaling_dec = loss_scaling_dec + self.name = name + self.learning_rate = learning_rate + self.minibatch_multiplier = minibatch_multiplier + self.id = self.name.replace("/", ".") + self.scope = tf.get_default_graph().unique_name(self.id) + self.optimizer_class = util.get_obj_by_name(tf_optimizer) + self.optimizer_kwargs = dict(kwargs) + self.use_loss_scaling = use_loss_scaling + self.loss_scaling_init = loss_scaling_init + self.loss_scaling_inc = loss_scaling_inc + self.loss_scaling_dec = loss_scaling_dec # Private fields. - self._updates_applied = False - self._devices = OrderedDict() # device_name => EasyDict() - self._shared_optimizers = OrderedDict() # device_name => optimizer_class - self._gradient_shapes = None # [shape, ...] - self._report_mem_usage = report_mem_usage + self._updates_applied = False + self._devices = OrderedDict() # device_name => EasyDict() + self._shared_optimizers = OrderedDict() # device_name => optimizer_class + self._gradient_shapes = None # [shape, ...] + self._report_mem_usage = report_mem_usage # Validate arguments. assert callable(self.optimizer_class) @@ -81,7 +92,7 @@ class Optimizer: assert self.optimizer_class is share.optimizer_class assert self.learning_rate is share.learning_rate assert self.optimizer_kwargs == share.optimizer_kwargs - self._shared_optimizers = share._shared_optimizers # pylint: disable=protected-access + self._shared_optimizers = share._shared_optimizers # pylint: disable=protected-access def _get_device(self, device_name: str): """Get internal state for the given TensorFlow device.""" @@ -91,23 +102,28 @@ class Optimizer: # Initialize fields. device = util.EasyDict() - device.name = device_name - device.optimizer = None # Underlying optimizer: optimizer_class + device.name = device_name + device.optimizer = None # Underlying optimizer: optimizer_class device.loss_scaling_var = None # Log2 of loss scaling: tf.Variable - device.grad_raw = OrderedDict() # Raw gradients: var => [grad, ...] - device.grad_clean = OrderedDict() # Clean gradients: var => grad - device.grad_acc_vars = OrderedDict() # Accumulation sums: var => tf.Variable - device.grad_acc_count = None # Accumulation counter: tf.Variable - device.grad_acc = OrderedDict() # Accumulated gradients: var => grad + # Raw gradients: var => [grad, ...] + device.grad_raw = OrderedDict() + device.grad_clean = OrderedDict() # Clean gradients: var => grad + # Accumulation sums: var => tf.Variable + device.grad_acc_vars = OrderedDict() + device.grad_acc_count = None # Accumulation counter: tf.Variable + device.grad_acc = OrderedDict() # Accumulated gradients: var => grad # Setup TensorFlow objects. with tfutil.absolute_name_scope(self.scope + "/Devices"), tf.device(device_name), tf.control_dependencies(None): if device_name not in self._shared_optimizers: - optimizer_name = self.scope.replace("/", "_") + "_opt%d" % len(self._shared_optimizers) - self._shared_optimizers[device_name] = self.optimizer_class(name=optimizer_name, learning_rate=self.learning_rate, **self.optimizer_kwargs) + optimizer_name = self.scope.replace( + "/", "_") + "_opt%d" % len(self._shared_optimizers) + self._shared_optimizers[device_name] = self.optimizer_class( + name=optimizer_name, learning_rate=self.learning_rate, **self.optimizer_kwargs) device.optimizer = self._shared_optimizers[device_name] if self.use_loss_scaling: - device.loss_scaling_var = tf.Variable(np.float32(self.loss_scaling_init), trainable=False, name="loss_scaling_var") + device.loss_scaling_var = tf.Variable(np.float32( + self.loss_scaling_init), trainable=False, name="loss_scaling_var") # Register device. self._devices[device_name] = device @@ -122,16 +138,20 @@ class Optimizer: # Validate trainables. if isinstance(trainable_vars, dict): - trainable_vars = list(trainable_vars.values()) # allow passing in Network.trainables as vars + # allow passing in Network.trainables as vars + trainable_vars = list(trainable_vars.values()) assert isinstance(trainable_vars, list) and len(trainable_vars) >= 1 - assert all(tfutil.is_tf_expression(expr) for expr in trainable_vars + [loss]) + assert all(tfutil.is_tf_expression(expr) + for expr in trainable_vars + [loss]) assert all(var.device == device.name for var in trainable_vars) # Validate shapes. if self._gradient_shapes is None: - self._gradient_shapes = [var.shape.as_list() for var in trainable_vars] + self._gradient_shapes = [var.shape.as_list() + for var in trainable_vars] assert len(trainable_vars) == len(self._gradient_shapes) - assert all(var.shape.as_list() == var_shape for var, var_shape in zip(trainable_vars, self._gradient_shapes)) + assert all(var.shape.as_list() == var_shape for var, + var_shape in zip(trainable_vars, self._gradient_shapes)) # Report memory usage if requested. deps = [] @@ -139,7 +159,8 @@ class Optimizer: self._report_mem_usage = False try: with tf.name_scope(self.id + '_mem'), tf.device(device.name), tf.control_dependencies([loss]): - deps.append(autosummary.autosummary(self.id + "/mem_usage_gb", tf.contrib.memory_stats.BytesInUse() / 2**30)) + deps.append(autosummary.autosummary( + self.id + "/mem_usage_gb", tf.contrib.memory_stats.BytesInUse() / 2**30)) except tf.errors.NotFoundError: pass @@ -147,7 +168,8 @@ class Optimizer: with tf.name_scope(self.id + "_grad"), tf.device(device.name), tf.control_dependencies(deps): loss = self.apply_loss_scaling(tf.cast(loss, tf.float32)) gate = tf.train.Optimizer.GATE_NONE # disable gating to reduce memory usage - grad_list = device.optimizer.compute_gradients(loss=loss, var_list=trainable_vars, gate_gradients=gate) + grad_list = device.optimizer.compute_gradients( + loss=loss, var_list=trainable_vars, gate_gradients=gate) # Register gradients. for grad, var in grad_list: @@ -180,12 +202,15 @@ class Optimizer: if len(grad) == 0: grad = tf.zeros(var.shape) # No gradients => zero. elif len(grad) == 1: - grad = grad[0] # Single gradient => use as is. + # Single gradient => use as is. + grad = grad[0] else: - grad = tf.add_n(grad) # Multiple gradients => sum. + # Multiple gradients => sum. + grad = tf.add_n(grad) # Scale as needed. - scale = 1.0 / len(device.grad_raw[var]) / len(self._devices) + scale = 1.0 / \ + len(device.grad_raw[var]) / len(self._devices) scale = tf.constant(scale, dtype=tf.float32, name="scale") if self.minibatch_multiplier is not None: scale /= tf.cast(self.minibatch_multiplier, tf.float32) @@ -196,8 +221,10 @@ class Optimizer: if len(self._devices) > 1: with tfutil.absolute_name_scope(self.scope + "/Broadcast"), tf.device(None): for all_vars in zip(*[device.grad_clean.keys() for device in self._devices.values()]): - if len(all_vars) > 0 and all(dim > 0 for dim in all_vars[0].shape.as_list()): # NCCL does not support zero-sized tensors. - all_grads = [device.grad_clean[var] for device, var in zip(self._devices.values(), all_vars)] + # NCCL does not support zero-sized tensors. + if len(all_vars) > 0 and all(dim > 0 for dim in all_vars[0].shape.as_list()): + all_grads = [device.grad_clean[var] for device, var in zip( + self._devices.values(), all_vars)] all_grads = nccl_ops.all_sum(all_grads) for device, var, grad in zip(self._devices.values(), all_vars, all_grads): device.grad_clean[var] = grad @@ -215,15 +242,19 @@ class Optimizer: # Create variables. with tf.control_dependencies(None): for var in device.grad_clean.keys(): - device.grad_acc_vars[var] = tf.Variable(tf.zeros(var.shape), trainable=False, name="grad_acc_var") - device.grad_acc_count = tf.Variable(tf.zeros([]), trainable=False, name="grad_acc_count") + device.grad_acc_vars[var] = tf.Variable( + tf.zeros(var.shape), trainable=False, name="grad_acc_var") + device.grad_acc_count = tf.Variable( + tf.zeros([]), trainable=False, name="grad_acc_count") # Track counter. count_cur = device.grad_acc_count + 1.0 - count_inc_op = lambda: tf.assign(device.grad_acc_count, count_cur) - count_reset_op = lambda: tf.assign(device.grad_acc_count, tf.zeros([])) - acc_ok = (count_cur >= tf.cast(self.minibatch_multiplier, tf.float32)) - all_ops.append(tf.cond(acc_ok, count_reset_op, count_inc_op)) + def count_inc_op(): return tf.assign(device.grad_acc_count, count_cur) + def count_reset_op(): return tf.assign(device.grad_acc_count, tf.zeros([])) + acc_ok = (count_cur >= tf.cast( + self.minibatch_multiplier, tf.float32)) + all_ops.append( + tf.cond(acc_ok, count_reset_op, count_inc_op)) # Track gradients. for var, grad in device.grad_clean.items(): @@ -231,35 +262,47 @@ class Optimizer: acc_cur = acc_var + grad device.grad_acc[var] = acc_cur with tf.control_dependencies([acc_cur]): - acc_inc_op = lambda: tf.assign(acc_var, acc_cur) - acc_reset_op = lambda: tf.assign(acc_var, tf.zeros(var.shape)) - all_ops.append(tf.cond(acc_ok, acc_reset_op, acc_inc_op)) + def acc_inc_op(): return tf.assign(acc_var, acc_cur) + def acc_reset_op(): return tf.assign(acc_var, tf.zeros(var.shape)) + all_ops.append( + tf.cond(acc_ok, acc_reset_op, acc_inc_op)) # No overflow => apply gradients. - all_ok = tf.reduce_all(tf.stack([acc_ok] + [tf.reduce_all(tf.is_finite(g)) for g in device.grad_acc.values()])) - apply_op = lambda: device.optimizer.apply_gradients([(tf.cast(grad, var.dtype), var) for var, grad in device.grad_acc.items()]) + all_ok = tf.reduce_all(tf.stack( + [acc_ok] + [tf.reduce_all(tf.is_finite(g)) for g in device.grad_acc.values()])) + + def apply_op(): return device.optimizer.apply_gradients( + [(tf.cast(grad, var.dtype), var) for var, grad in device.grad_acc.items()]) all_ops.append(tf.cond(all_ok, apply_op, tf.no_op)) # Adjust loss scaling. if self.use_loss_scaling: - ls_inc_op = lambda: tf.assign_add(device.loss_scaling_var, self.loss_scaling_inc) - ls_dec_op = lambda: tf.assign_sub(device.loss_scaling_var, self.loss_scaling_dec) - ls_update_op = lambda: tf.group(tf.cond(all_ok, ls_inc_op, ls_dec_op)) + def ls_inc_op(): return tf.assign_add( + device.loss_scaling_var, self.loss_scaling_inc) + def ls_dec_op(): return tf.assign_sub( + device.loss_scaling_var, self.loss_scaling_dec) + + def ls_update_op(): return tf.group(tf.cond(all_ok, ls_inc_op, ls_dec_op)) all_ops.append(tf.cond(acc_ok, ls_update_op, tf.no_op)) # Last device => report statistics. if device_idx == len(self._devices) - 1: - all_ops.append(autosummary.autosummary(self.id + "/learning_rate", self.learning_rate)) - all_ops.append(autosummary.autosummary(self.id + "/overflow_frequency", tf.where(all_ok, 0, 1), condition=acc_ok)) + all_ops.append(autosummary.autosummary( + self.id + "/learning_rate", self.learning_rate)) + all_ops.append(autosummary.autosummary( + self.id + "/overflow_frequency", tf.where(all_ok, 0, 1), condition=acc_ok)) if self.use_loss_scaling: - all_ops.append(autosummary.autosummary(self.id + "/loss_scaling_log2", device.loss_scaling_var)) + all_ops.append(autosummary.autosummary( + self.id + "/loss_scaling_log2", device.loss_scaling_var)) # Initialize variables. self.reset_optimizer_state() if self.use_loss_scaling: - tfutil.init_uninitialized_vars([device.loss_scaling_var for device in self._devices.values()]) + tfutil.init_uninitialized_vars( + [device.loss_scaling_var for device in self._devices.values()]) if self.minibatch_multiplier is not None: - tfutil.run([var.initializer for device in self._devices.values() for var in list(device.grad_acc_vars.values()) + [device.grad_acc_count]]) + tfutil.run([var.initializer for device in self._devices.values() for var in list( + device.grad_acc_vars.values()) + [device.grad_acc_count]]) # Group everything into a single op. with tfutil.absolute_name_scope(self.scope): @@ -268,7 +311,8 @@ class Optimizer: def reset_optimizer_state(self) -> None: """Reset internal state of the underlying optimizer.""" tfutil.assert_tf_initialized() - tfutil.run([var.initializer for device in self._devices.values() for var in device.optimizer.variables()]) + tfutil.run([var.initializer for device in self._devices.values() + for var in device.optimizer.variables()]) def get_loss_scaling_var(self, device: str) -> Union[tf.Variable, None]: """Get or create variable representing log2 of the current dynamic loss scaling factor.""" @@ -286,7 +330,7 @@ class Optimizer: assert tfutil.is_tf_expression(value) if not self.use_loss_scaling: return value - return value * tfutil.exp2(-self.get_loss_scaling_var(value.device)) # pylint: disable=invalid-unary-operand-type + return value * tfutil.exp2(-self.get_loss_scaling_var(value.device)) # pylint: disable=invalid-unary-operand-type class SimpleAdam: @@ -314,24 +358,31 @@ class SimpleAdam: # Adjust learning rate to deal with startup bias. with tf.control_dependencies(None): - b1pow_var = tf.Variable(dtype=tf.float32, initial_value=1, trainable=False) - b2pow_var = tf.Variable(dtype=tf.float32, initial_value=1, trainable=False) + b1pow_var = tf.Variable( + dtype=tf.float32, initial_value=1, trainable=False) + b2pow_var = tf.Variable( + dtype=tf.float32, initial_value=1, trainable=False) state_vars += [b1pow_var, b2pow_var] b1pow_new = b1pow_var * self.beta1 b2pow_new = b2pow_var * self.beta2 - update_ops += [tf.assign(b1pow_var, b1pow_new), tf.assign(b2pow_var, b2pow_new)] - lr_new = self.learning_rate * tf.sqrt(1 - b2pow_new) / (1 - b1pow_new) + update_ops += [tf.assign(b1pow_var, b1pow_new), + tf.assign(b2pow_var, b2pow_new)] + lr_new = self.learning_rate * \ + tf.sqrt(1 - b2pow_new) / (1 - b1pow_new) # Construct ops to update each variable. for grad, var in grads_and_vars: with tf.control_dependencies(None): - m_var = tf.Variable(dtype=tf.float32, initial_value=tf.zeros_like(var), trainable=False) - v_var = tf.Variable(dtype=tf.float32, initial_value=tf.zeros_like(var), trainable=False) + m_var = tf.Variable( + dtype=tf.float32, initial_value=tf.zeros_like(var), trainable=False) + v_var = tf.Variable( + dtype=tf.float32, initial_value=tf.zeros_like(var), trainable=False) state_vars += [m_var, v_var] m_new = self.beta1 * m_var + (1 - self.beta1) * grad v_new = self.beta2 * v_var + (1 - self.beta2) * tf.square(grad) var_delta = lr_new * m_new / (tf.sqrt(v_new) + self.epsilon) - update_ops += [tf.assign(m_var, m_new), tf.assign(v_var, v_new), tf.assign_sub(var, var_delta)] + update_ops += [tf.assign(m_var, m_new), tf.assign(v_var, + v_new), tf.assign_sub(var, var_delta)] # Group everything together. self.all_state_vars += state_vars diff --git a/stylegan_human/dnnlib/tflib/tfutil.py b/stylegan_human/dnnlib/tflib/tfutil.py index 7b04c59e41a1b1548bc798379ceb551a488ed2a6..396525e184d6d4a6c935244b7677e8ba84144ea0 100755 --- a/stylegan_human/dnnlib/tflib/tfutil.py +++ b/stylegan_human/dnnlib/tflib/tfutil.py @@ -8,6 +8,8 @@ """Miscellaneous helper utils for Tensorflow.""" +from typing import Any, Iterable, List, Union +import tensorflow.contrib # requires TensorFlow 1.x! import os import numpy as np import tensorflow as tf @@ -15,10 +17,8 @@ import tensorflow as tf # Silence deprecation warnings from TensorFlow 1.13 onwards import logging logging.getLogger('tensorflow').setLevel(logging.ERROR) -import tensorflow.contrib # requires TensorFlow 1.x! tf.contrib = tensorflow.contrib -from typing import Any, Iterable, List, Union TfExpression = Union[tf.Tensor, tf.Variable, tf.Operation] """A type that represents a valid Tensorflow expression.""" @@ -86,11 +86,16 @@ def absolute_variable_scope(scope: str, **kwargs) -> tf.variable_scope: def _sanitize_tf_config(config_dict: dict = None) -> dict: # Defaults. cfg = dict() - cfg["rnd.np_random_seed"] = None # Random seed for NumPy. None = keep as is. - cfg["rnd.tf_random_seed"] = "auto" # Random seed for TensorFlow. 'auto' = derive from NumPy random state. None = keep as is. - cfg["env.TF_CPP_MIN_LOG_LEVEL"] = "1" # 0 = Print all available debug info from TensorFlow. 1 = Print warnings and errors, but disable debug info. - cfg["graph_options.place_pruned_graph"] = True # False = Check that all ops are available on the designated device. True = Skip the check for ops that are not used. - cfg["gpu_options.allow_growth"] = True # False = Allocate all GPU memory at the beginning. True = Allocate only as much GPU memory as needed. + # Random seed for NumPy. None = keep as is. + cfg["rnd.np_random_seed"] = None + # Random seed for TensorFlow. 'auto' = derive from NumPy random state. None = keep as is. + cfg["rnd.tf_random_seed"] = "auto" + # 0 = Print all available debug info from TensorFlow. 1 = Print warnings and errors, but disable debug info. + cfg["env.TF_CPP_MIN_LOG_LEVEL"] = "1" + # False = Check that all ops are available on the designated device. True = Skip the check for ops that are not used. + cfg["graph_options.place_pruned_graph"] = True + # False = Allocate all GPU memory at the beginning. True = Allocate only as much GPU memory as needed. + cfg["gpu_options.allow_growth"] = True # Remove defaults for environment variables that are already set. for key in list(cfg): @@ -137,7 +142,8 @@ def init_tf(config_dict: dict = None) -> None: def assert_tf_initialized(): """Check that TensorFlow session has been initialized.""" if tf.get_default_session() is None: - raise RuntimeError("No default TensorFlow session found. Please call dnnlib.tflib.init_tf().") + raise RuntimeError( + "No default TensorFlow session found. Please call dnnlib.tflib.init_tf().") def create_session(config_dict: dict = None, force_as_default: bool = False) -> tf.Session: @@ -176,12 +182,14 @@ def init_uninitialized_vars(target_vars: List[tf.Variable] = None) -> None: test_vars = [] test_ops = [] - with tf.control_dependencies(None): # ignore surrounding control_dependencies + # ignore surrounding control_dependencies + with tf.control_dependencies(None): for var in target_vars: assert is_tf_expression(var) try: - tf.get_default_graph().get_tensor_by_name(var.name.replace(":0", "/IsVariableInitialized:0")) + tf.get_default_graph().get_tensor_by_name( + var.name.replace(":0", "/IsVariableInitialized:0")) except KeyError: # Op does not exist => variable may be uninitialized. test_vars.append(var) @@ -189,7 +197,8 @@ def init_uninitialized_vars(target_vars: List[tf.Variable] = None) -> None: with absolute_name_scope(var.name.split(":")[0]): test_ops.append(tf.is_variable_initialized(var)) - init_vars = [var for var, inited in zip(test_vars, run(test_ops)) if not inited] + init_vars = [var for var, inited in zip( + test_vars, run(test_ops)) if not inited] run([var.initializer for var in init_vars]) @@ -207,11 +216,14 @@ def set_vars(var_to_value_dict: dict) -> None: assert is_tf_expression(var) try: - setter = tf.get_default_graph().get_tensor_by_name(var.name.replace(":0", "/setter:0")) # look for existing op + setter = tf.get_default_graph().get_tensor_by_name( + var.name.replace(":0", "/setter:0")) # look for existing op except KeyError: with absolute_name_scope(var.name.split(":")[0]): - with tf.control_dependencies(None): # ignore surrounding control_dependencies - setter = tf.assign(var, tf.placeholder(var.dtype, var.shape, "new_value"), name="setter") # create new setter + # ignore surrounding control_dependencies + with tf.control_dependencies(None): + setter = tf.assign(var, tf.placeholder( + var.dtype, var.shape, "new_value"), name="setter") # create new setter ops.append(setter) feed_dict[setter.op.inputs[1]] = value @@ -229,7 +241,7 @@ def create_var_with_large_initial_value(initial_value: np.ndarray, *args, **kwar return var -def convert_images_from_uint8(images, drange=[-1,1], nhwc_to_nchw=False): +def convert_images_from_uint8(images, drange=[-1, 1], nhwc_to_nchw=False): """Convert a minibatch of images from uint8 to float32 with configurable dynamic range. Can be used as an input transformation for Network.run(). """ @@ -239,14 +251,15 @@ def convert_images_from_uint8(images, drange=[-1,1], nhwc_to_nchw=False): return images * ((drange[1] - drange[0]) / 255) + drange[0] -def convert_images_to_uint8(images, drange=[-1,1], nchw_to_nhwc=False, shrink=1): +def convert_images_to_uint8(images, drange=[-1, 1], nchw_to_nhwc=False, shrink=1): """Convert a minibatch of images from float32 to uint8 with configurable dynamic range. Can be used as an output transformation for Network.run(). """ images = tf.cast(images, tf.float32) if shrink > 1: ksize = [1, 1, shrink, shrink] - images = tf.nn.avg_pool(images, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW") + images = tf.nn.avg_pool( + images, ksize=ksize, strides=ksize, padding="VALID", data_format="NCHW") if nchw_to_nhwc: images = tf.transpose(images, [0, 2, 3, 1]) scale = 255 / (drange[1] - drange[0]) diff --git a/stylegan_human/dnnlib/util.py b/stylegan_human/dnnlib/util.py index c2bf7a73d546895ac6eb73d9c56db2a04b096f3e..27bce0ab18a69f142db54084c0be2c014e60c20d 100644 --- a/stylegan_human/dnnlib/util.py +++ b/stylegan_human/dnnlib/util.py @@ -80,7 +80,7 @@ class Logger(object): """Write text to stdout (and a file) and optionally flush.""" if isinstance(text, bytes): text = text.decode() - if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash + if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash return if self.file is not None: @@ -118,10 +118,12 @@ class Logger(object): _dnnlib_cache_dir = None + def set_cache_dir(path: str) -> None: global _dnnlib_cache_dir _dnnlib_cache_dir = path + def make_cache_dir_path(*paths: str) -> str: if _dnnlib_cache_dir is not None: return os.path.join(_dnnlib_cache_dir, *paths) @@ -230,13 +232,16 @@ def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]: # list alternatives for (module_name, local_obj_name) parts = obj_name.split(".") - name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)] + name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) + for i in range(len(parts), 0, -1)] # try each alternative in turn for module_name, local_obj_name in name_pairs: try: - module = importlib.import_module(module_name) # may raise ImportError - get_obj_from_module(module, local_obj_name) # may raise AttributeError + module = importlib.import_module( + module_name) # may raise ImportError + # may raise AttributeError + get_obj_from_module(module, local_obj_name) return module, local_obj_name except: pass @@ -244,7 +249,7 @@ def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]: # maybe some of the modules themselves contain errors? for module_name, _local_obj_name in name_pairs: try: - importlib.import_module(module_name) # may raise ImportError + importlib.import_module(module_name) # may raise ImportError except ImportError: if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"): raise @@ -252,8 +257,10 @@ def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]: # maybe the requested attribute is missing? for module_name, local_obj_name in name_pairs: try: - module = importlib.import_module(module_name) # may raise ImportError - get_obj_from_module(module, local_obj_name) # may raise AttributeError + module = importlib.import_module( + module_name) # may raise ImportError + # may raise AttributeError + get_obj_from_module(module, local_obj_name) except ImportError: pass @@ -281,7 +288,7 @@ def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any: """Finds the python object with the given name and calls it as a function.""" assert func_name is not None # print('func_name: ', func_name) #'training.dataset.ImageFolderDataset' - func_obj = get_obj_by_name(func_name) + func_obj = get_obj_by_name(func_name) assert callable(func_obj) return func_obj(*args, **kwargs) @@ -307,7 +314,8 @@ def get_top_level_function_name(obj: Any) -> str: assert is_top_level_function(obj) module = obj.__module__ if module == '__main__': - module = os.path.splitext(os.path.basename(sys.modules[module].__file__))[0] + module = os.path.splitext(os.path.basename( + sys.modules[module].__file__))[0] return module + "." + obj.__name__ @@ -339,7 +347,8 @@ def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, a relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths] if add_base_to_relative: - relative_paths = [os.path.join(base_name, p) for p in relative_paths] + relative_paths = [os.path.join(base_name, p) + for p in relative_paths] assert len(absolute_paths) == len(relative_paths) result += zip(absolute_paths, relative_paths) @@ -439,14 +448,17 @@ def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: b if len(res.content) < 8192: content_str = res.content.decode("utf-8") if "download_warning" in res.headers.get("Set-Cookie", ""): - links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link] + links = [html.unescape(link) for link in content_str.split( + '"') if "export=download" in link] if len(links) == 1: url = requests.compat.urljoin(url, links[0]) raise IOError("Google Drive virus checker nag") if "Google Drive - Quota exceeded" in content_str: - raise IOError("Google Drive download quota exceeded -- please try again later") + raise IOError( + "Google Drive download quota exceeded -- please try again later") - match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", "")) + match = re.search( + r'filename="([^"]*)"', res.headers.get("Content-Disposition", "")) url_name = match[1] if match else url url_data = res.content if verbose: @@ -466,11 +478,12 @@ def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: b if cache: safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name) cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name) - temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name) + temp_file = os.path.join( + cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name) os.makedirs(cache_dir, exist_ok=True) with open(temp_file, "wb") as f: f.write(url_data) - os.replace(temp_file, cache_file) # atomic + os.replace(temp_file, cache_file) # atomic if return_filename: return cache_file diff --git a/stylegan_human/edit.py b/stylegan_human/edit.py index e91e625a21c914e42430b7b8779bd5335d76e037..778cb3283bde9466d53ad4605d5a6426869cb584 100644 --- a/stylegan_human/edit.py +++ b/stylegan_human/edit.py @@ -1,17 +1,17 @@ # Copyright (c) SenseTime Research. All rights reserved. +from edit.edit_helper import conv_warper, decoder, encoder_ifg, encoder_ss, encoder_sefa +import legacy +import subprocess +from typing import List, Optional +import cv2 +import click +from torch_utils.models import Generator import os import sys import torch import numpy as np sys.path.append(".") -from torch_utils.models import Generator -import click -import cv2 -from typing import List, Optional -import subprocess -import legacy -from edit.edit_helper import conv_warper, decoder, encoder_ifg, encoder_ss, encoder_sefa """ @@ -36,7 +36,6 @@ python edit.py ---network outputs/pti/checkpoints/model_test.pkl --attr_name upp """ - @click.command() @click.pass_context @click.option('--network', 'ckpt_path', help='Network pickle filename', required=True) @@ -49,9 +48,6 @@ python edit.py ---network outputs/pti/checkpoints/model_test.pkl --attr_name upp @click.option('--real', type=bool, help='True for editing real image', default=False) @click.option('--real_w_path', help='Path of latent code for real image') @click.option('--real_img_path', help='Path of real image, this just concat real image with inverted and edited results together') - - - def main( ctx: click.Context, ckpt_path: str, @@ -65,30 +61,30 @@ def main( real_w_path: str, real_img_path: str ): - ## convert pkl to pth + # convert pkl to pth # if not os.path.exists(ckpt_path.replace('.pkl','.pth')): - legacy.convert(ckpt_path, ckpt_path.replace('.pkl','.pth'), G_only=real) - ckpt_path = ckpt_path.replace('.pkl','.pth') + legacy.convert(ckpt_path, ckpt_path.replace('.pkl', '.pth'), G_only=real) + ckpt_path = ckpt_path.replace('.pkl', '.pth') print("start...", flush=True) - config = {"latent" : 512, "n_mlp" : 8, "channel_multiplier": 2} + config = {"latent": 512, "n_mlp": 8, "channel_multiplier": 2} generator = Generator( - size = 1024, - style_dim=config["latent"], - n_mlp=config["n_mlp"], - channel_multiplier=config["channel_multiplier"] - ) - + size=1024, + style_dim=config["latent"], + n_mlp=config["n_mlp"], + channel_multiplier=config["channel_multiplier"] + ) + generator.load_state_dict(torch.load(ckpt_path)['g_ema']) generator.eval().cuda() with torch.no_grad(): - mean_path = os.path.join('edit','mean_latent.pkl') + mean_path = os.path.join('edit', 'mean_latent.pkl') if not os.path.exists(mean_path): mean_n = 3000 mean_latent = generator.mean_latent(mean_n).detach() legacy.save_obj(mean_latent, mean_path) else: - mean_latent = legacy.load_pkl(mean_path).cuda() + mean_latent = legacy.load_pkl(mean_path).cuda() finals = [] ## -- selected sample seeds -- ## @@ -96,38 +92,46 @@ def main( # [60941,61064,61103,61313,61531,61570,61571] # bottom -> short # [60941,60965,61064,61103,6117461210,61531,61570,61571,61610] # upper --> long # [60948,61313,61511,61598] # upper --> short - if real: seeds = [0] + if real: + seeds = [0] for t in seeds: - if real: # now assume process single real image only + if real: # now assume process single real image only if real_img_path: real_image = cv2.imread(real_img_path) real_image = cv2.cvtColor(real_image, cv2.COLOR_BGR2RGB) import torchvision.transforms as transforms - transform = transforms.Compose( # normalize to (-1, 1) + transform = transforms.Compose( # normalize to (-1, 1) [transforms.ToTensor(), - transforms.Normalize(mean=(.5,.5,.5), std=(.5,.5,.5))] + transforms.Normalize(mean=(.5, .5, .5), std=(.5, .5, .5))] ) - real_image = transform(real_image).unsqueeze(0).cuda() + real_image = transform(real_image).unsqueeze(0).cuda() test_input = torch.load(real_w_path) - output, _ = generator(test_input, False, truncation=1,input_is_latent=True, real=True) + output, _ = generator( + test_input, False, truncation=1, input_is_latent=True, real=True) + + else: # generate image from random seeds + test_input = torch.from_numpy(np.random.RandomState( + t).randn(1, 512)).float().cuda() # torch.Size([1, 512]) + output, _ = generator( + [test_input], False, truncation=truncation, truncation_latent=mean_latent, real=real) - else: # generate image from random seeds - test_input = torch.from_numpy(np.random.RandomState(t).randn(1, 512)).float().cuda() # torch.Size([1, 512]) - output, _ = generator([test_input], False, truncation=truncation, truncation_latent=mean_latent, real=real) - # interfacegan - style_space, latent, noise = encoder_ifg(generator, test_input, attr_name, truncation, mean_latent,real=real) + style_space, latent, noise = encoder_ifg( + generator, test_input, attr_name, truncation, mean_latent, real=real) image1 = decoder(generator, style_space, latent, noise) # stylespace - style_space, latent, noise = encoder_ss(generator, test_input, attr_name, truncation, mean_latent,real=real) + style_space, latent, noise = encoder_ss( + generator, test_input, attr_name, truncation, mean_latent, real=real) image2 = decoder(generator, style_space, latent, noise) # sefa - latent, noise = encoder_sefa(generator, test_input, attr_name, truncation, mean_latent,real=real) + latent, noise = encoder_sefa( + generator, test_input, attr_name, truncation, mean_latent, real=real) image3, _ = generator([latent], noise=noise, input_is_latent=True) if real_img_path: - final = torch.cat((real_image, output, image1, image2, image3), 3) + final = torch.cat( + (real_image, output, image1, image2, image3), 3) else: final = torch.cat((output, image1, image2, image3), 3) @@ -146,7 +150,7 @@ def main( video_ifg_path = f"{outdir}/video/ifg_{attr_name}_{t:05d}/" video_ss_path = f"{outdir}/video/ss_{attr_name}_{t:05d}/" video_sefa_path = f"{outdir}/video/ss_{attr_name}_{t:05d}/" - video_comb_path = f"{outdir}/video/tmp" + video_comb_path = f"{outdir}/video/tmp" if combine: if not os.path.exists(video_comb_path): @@ -159,36 +163,45 @@ def main( if not os.path.exists(video_sefa_path): os.makedirs(video_sefa_path) for i in range(total_step): - style_space, latent, noise = encoder_ifg(generator, test_input, attr_name, truncation, mean_latent, step=i, total=total_step,real=real) + style_space, latent, noise = encoder_ifg( + generator, test_input, attr_name, truncation, mean_latent, step=i, total=total_step, real=real) image1 = decoder(generator, style_space, latent, noise) - style_space, latent, noise = encoder_ss(generator, test_input, attr_name, truncation, mean_latent, step=i, total=total_step,real=real) + style_space, latent, noise = encoder_ss( + generator, test_input, attr_name, truncation, mean_latent, step=i, total=total_step, real=real) image2 = decoder(generator, style_space, latent, noise) - latent, noise = encoder_sefa(generator, test_input, attr_name, truncation, mean_latent, step=i, total=total_step,real=real) - image3, _ = generator([latent], noise=noise, input_is_latent=True) + latent, noise = encoder_sefa( + generator, test_input, attr_name, truncation, mean_latent, step=i, total=total_step, real=real) + image3, _ = generator( + [latent], noise=noise, input_is_latent=True) if combine: if real_img_path: - comb_img = torch.cat((real_image, output, image1, image2, image3), 3) + comb_img = torch.cat( + (real_image, output, image1, image2, image3), 3) else: - comb_img = torch.cat((output, image1, image2, image3), 3) - legacy.visual(comb_img, os.path.join(video_comb_path, f'{i:05d}.jpg')) + comb_img = torch.cat( + (output, image1, image2, image3), 3) + legacy.visual(comb_img, os.path.join( + video_comb_path, f'{i:05d}.jpg')) else: - legacy.visual(image1, os.path.join(video_ifg_path, f'{i:05d}.jpg')) - legacy.visual(image2, os.path.join(video_ss_path, f'{i:05d}.jpg')) + legacy.visual(image1, os.path.join( + video_ifg_path, f'{i:05d}.jpg')) + legacy.visual(image2, os.path.join( + video_ss_path, f'{i:05d}.jpg')) if combine: - cmd=f"ffmpeg -hide_banner -loglevel error -y -r 30 -i {video_comb_path}/%05d.jpg -vcodec libx264 -pix_fmt yuv420p {video_ifg_path.replace('ifg_', '')[:-1] + '.mp4'}" + cmd = f"ffmpeg -hide_banner -loglevel error -y -r 30 -i {video_comb_path}/%05d.jpg -vcodec libx264 -pix_fmt yuv420p {video_ifg_path.replace('ifg_', '')[:-1] + '.mp4'}" subprocess.call(cmd, shell=True) else: - cmd=f"ffmpeg -hide_banner -loglevel error -y -r 30 -i {video_ifg_path}/%05d.jpg -vcodec libx264 -pix_fmt yuv420p {video_ifg_path[:-1] + '.mp4'}" + cmd = f"ffmpeg -hide_banner -loglevel error -y -r 30 -i {video_ifg_path}/%05d.jpg -vcodec libx264 -pix_fmt yuv420p {video_ifg_path[:-1] + '.mp4'}" subprocess.call(cmd, shell=True) - cmd=f"ffmpeg -hide_banner -loglevel error -y -r 30 -i {video_ss_path}/%05d.jpg -vcodec libx264 -pix_fmt yuv420p {video_ss_path[:-1] + '.mp4'}" + cmd = f"ffmpeg -hide_banner -loglevel error -y -r 30 -i {video_ss_path}/%05d.jpg -vcodec libx264 -pix_fmt yuv420p {video_ss_path[:-1] + '.mp4'}" subprocess.call(cmd, shell=True) # interfacegan, stylespace, sefa finals.append(final) final = torch.cat(finals, 2) - legacy.visual(final, os.path.join(outdir,'final.jpg')) + legacy.visual(final, os.path.join(outdir, 'final.jpg')) if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/stylegan_human/edit/__init__.py b/stylegan_human/edit/__init__.py index 864cbcfc7b3ac4df80cedd74c3f6cde9685434fb..416fc1a1fd281d321e854053390b94563a160cfd 100644 --- a/stylegan_human/edit/__init__.py +++ b/stylegan_human/edit/__init__.py @@ -1,3 +1,3 @@ # Copyright (c) SenseTime Research. All rights reserved. -# empty \ No newline at end of file +# empty diff --git a/stylegan_human/edit/edit_config.py b/stylegan_human/edit/edit_config.py index e5c6c5fc234c696fb161874f972ca2b83ec9896a..25fb4e500f5ce6ec6ec07631899b851492b08bb9 100644 --- a/stylegan_human/edit/edit_config.py +++ b/stylegan_human/edit/edit_config.py @@ -1,16 +1,19 @@ # Copyright (c) SenseTime Research. All rights reserved. attr_dict = dict( - interface_gan={ # strength - 'upper_length': [-1], # strength: negative for shorter, positive for longer + interface_gan={ # strength + # strength: negative for shorter, positive for longer + 'upper_length': [-1], 'bottom_length': [1] }, - stylespace={ # layer, strength, threshold - 'upper_length': [5, -5, 0.0028], # strength: negative for shorter, positive for longer - 'bottom_length': [3, 5, 0.003] + stylespace={ # layer, strength, threshold + # strength: negative for shorter, positive for longer + 'upper_length': [5, -5, 0.0028], + 'bottom_length': [3, 5, 0.003] }, - sefa={ # layer, strength - 'upper_length': [[4, 5, 6, 7], 5], #-5 # strength: negative for longer, positive for shorter + sefa={ # layer, strength + # -5 # strength: negative for longer, positive for shorter + 'upper_length': [[4, 5, 6, 7], 5], 'bottom_length': [[4, 5, 6, 7], 5] } -) \ No newline at end of file +) diff --git a/stylegan_human/edit/edit_helper.py b/stylegan_human/edit/edit_helper.py index 137a3dbd7cf1f48b21545673c422668b4d20765d..047e4d29d296306a008f7bb240c18e38e9757500 100644 --- a/stylegan_human/edit/edit_helper.py +++ b/stylegan_human/edit/edit_helper.py @@ -7,6 +7,7 @@ import pandas as pd from .edit_config import attr_dict import os + def conv_warper(layer, input, style, noise): # the conv should change conv = layer.conv @@ -31,7 +32,8 @@ def conv_warper(layer, input, style, noise): weight = weight.transpose(1, 2).reshape( batch * in_channel, conv.out_channel, conv.kernel_size, conv.kernel_size ) - out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch) + out = F.conv_transpose2d( + input, weight, padding=0, stride=2, groups=batch) _, _, height, width = out.shape out = out.view(batch, conv.out_channel, height, width) out = conv.blur(out) @@ -49,12 +51,13 @@ def conv_warper(layer, input, style, noise): out = F.conv2d(input, weight, padding=conv.padding, groups=batch) _, _, height, width = out.shape out = out.view(batch, conv.out_channel, height, width) - + out = layer.noise(out, noise=noise) out = layer.activate(out) - + return out + def decoder(G, style_space, latent, noise): # an decoder warper for G out = G.input(latent) @@ -73,30 +76,35 @@ def decoder(G, style_space, latent, noise): return image -def encoder_ifg(G, noise, attr_name, truncation=1, truncation_latent=None, - latent_dir='latent_direction/ss/', - step=0, total=0, real=False): + +def encoder_ifg(G, noise, attr_name, truncation=1, truncation_latent=None, + latent_dir='latent_direction/ss/', + step=0, total=0, real=False): if not real: styles = [noise] styles = [G.style(s) for s in styles] style_space = [] - - if truncation<1: - if not real: + + if truncation < 1: + if not real: style_t = [] for style in styles: - style_t.append(truncation_latent + truncation * (style - truncation_latent)) + style_t.append(truncation_latent + truncation * + (style - truncation_latent)) styles = style_t - else: # styles are latent (tensor: 1,18,512), for real PTI output - truncation_latent = truncation_latent.repeat(18,1).unsqueeze(0) # (1,512) --> (1,18,512) - styles = torch.add(truncation_latent,torch.mul(torch.sub(noise,truncation_latent),truncation)) - - - noise = [getattr(G.noises, 'noise_{}'.format(i)) for i in range(G.num_layers)] + else: # styles are latent (tensor: 1,18,512), for real PTI output + truncation_latent = truncation_latent.repeat( + 18, 1).unsqueeze(0) # (1,512) --> (1,18,512) + styles = torch.add(truncation_latent, torch.mul( + torch.sub(noise, truncation_latent), truncation)) + + noise = [getattr(G.noises, 'noise_{}'.format(i)) + for i in range(G.num_layers)] if not real: inject_index = G.n_latent latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: latent=styles + else: + latent = styles style_space.append(G.conv1.conv.modulation(latent[:, 0])) i = 1 @@ -113,39 +121,46 @@ def encoder_ifg(G, noise, attr_name, truncation=1, truncation_latent=None, if step != 0 and total != 0: strength = step / total * strength for i in range(15): - style_vect = load_pkl(os.path.join(latent_dir, '{}/style_vect_mean_{}.pkl'.format(attr_name, i))) + style_vect = load_pkl(os.path.join( + latent_dir, '{}/style_vect_mean_{}.pkl'.format(attr_name, i))) style_vect = torch.from_numpy(style_vect).to(latent.device).float() style_space[i] += style_vect * strength - + return style_space, latent, noise -def encoder_ss(G, noise, attr_name, truncation=1, truncation_latent=None, + +def encoder_ss(G, noise, attr_name, truncation=1, truncation_latent=None, statics_dir="latent_direction/ss_statics", latent_dir="latent_direction/ss/", - step=0, total=0,real=False): + step=0, total=0, real=False): if not real: styles = [noise] styles = [G.style(s) for s in styles] style_space = [] - if truncation<1: + if truncation < 1: if not real: style_t = [] for style in styles: style_t.append( - truncation_latent + truncation * (style - truncation_latent) + truncation_latent + truncation * + (style - truncation_latent) ) styles = style_t - else: # styles are latent (tensor: 1,18,512), for real PTI output - truncation_latent = truncation_latent.repeat(18,1).unsqueeze(0) # (1,512) --> (1,18,512) - styles = torch.add(truncation_latent,torch.mul(torch.sub(noise,truncation_latent),truncation)) + else: # styles are latent (tensor: 1,18,512), for real PTI output + truncation_latent = truncation_latent.repeat( + 18, 1).unsqueeze(0) # (1,512) --> (1,18,512) + styles = torch.add(truncation_latent, torch.mul( + torch.sub(noise, truncation_latent), truncation)) + + noise = [getattr(G.noises, 'noise_{}'.format(i)) + for i in range(G.num_layers)] - noise = [getattr(G.noises, 'noise_{}'.format(i)) for i in range(G.num_layers)] - if not real: inject_index = G.n_latent latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: latent = styles + else: + latent = styles style_space.append(G.conv1.conv.modulation(latent[:, 0])) i = 1 @@ -156,16 +171,18 @@ def encoder_ss(G, noise, attr_name, truncation=1, truncation_latent=None, style_space.append(conv2.conv.modulation(latent[:, i+1])) i += 2 # get threshold, layer, strength by dict - layer, strength, threshold = attr_dict['stylespace'][attr_name] + layer, strength, threshold = attr_dict['stylespace'][attr_name] - statis_dir = os.path.join(statics_dir, "{}_statis/{}".format(attr_name, layer)) + statis_dir = os.path.join( + statics_dir, "{}_statis/{}".format(attr_name, layer)) statis_csv_path = os.path.join(statis_dir, "statis.csv") statis_df = pd.read_csv(statis_csv_path) statis_df = statis_df.sort_values(by='channel', ascending=True) ch_mask = statis_df['strength'].values ch_mask = torch.from_numpy(ch_mask).to(latent.device).float() - ch_mask = (ch_mask.abs()>threshold).float() - style_vect = load_pkl(os.path.join(latent_dir, '{}/style_vect_mean_{}.pkl'.format(attr_name, layer))) + ch_mask = (ch_mask.abs() > threshold).float() + style_vect = load_pkl(os.path.join( + latent_dir, '{}/style_vect_mean_{}.pkl'.format(attr_name, layer))) style_vect = torch.from_numpy(style_vect).to(latent.device).float() style_vect = style_vect * ch_mask @@ -174,42 +191,47 @@ def encoder_ss(G, noise, attr_name, truncation=1, truncation_latent=None, strength = step / total * strength style_space[layer] += style_vect * strength - + return style_space, latent, noise -def encoder_sefa(G, noise, attr_name, truncation=1, truncation_latent=None, - latent_dir='latent_direction/sefa/', - step=0, total=0, real=False): - if not real: + +def encoder_sefa(G, noise, attr_name, truncation=1, truncation_latent=None, + latent_dir='latent_direction/sefa/', + step=0, total=0, real=False): + if not real: styles = [noise] styles = [G.style(s) for s in styles] - - if truncation<1: + + if truncation < 1: if not real: style_t = [] for style in styles: style_t.append( - truncation_latent + truncation * (style - truncation_latent) + truncation_latent + truncation * + (style - truncation_latent) ) styles = style_t else: - truncation_latent = truncation_latent.repeat(18,1).unsqueeze(0) # (1,512) --> (1,18,512) - styles = torch.add(truncation_latent,torch.mul(torch.sub(noise,truncation_latent),truncation)) - + truncation_latent = truncation_latent.repeat( + 18, 1).unsqueeze(0) # (1,512) --> (1,18,512) + styles = torch.add(truncation_latent, torch.mul( + torch.sub(noise, truncation_latent), truncation)) - noise = [getattr(G.noises, 'noise_{}'.format(i)) for i in range(G.num_layers)] + noise = [getattr(G.noises, 'noise_{}'.format(i)) + for i in range(G.num_layers)] if not real: inject_index = G.n_latent latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - else: latent = styles - - layer, strength = attr_dict['sefa'][attr_name] + else: + latent = styles + + layer, strength = attr_dict['sefa'][attr_name] - sefa_vect = torch.load(os.path.join(latent_dir, '{}.pt'.format(attr_name))).to(latent.device).float() + sefa_vect = torch.load(os.path.join( + latent_dir, '{}.pt'.format(attr_name))).to(latent.device).float() if step != 0 and total != 0: strength = step / total * strength for l in layer: latent[:, l, :] += (sefa_vect * strength * 2) - return latent, noise diff --git a/stylegan_human/generate.py b/stylegan_human/generate.py index e2d90c603dd3168d423cd19ae1626d86fec19df1..a8b7d55e6d190c193e427bd8d623c583b2dcdeda 100644 --- a/stylegan_human/generate.py +++ b/stylegan_human/generate.py @@ -43,7 +43,7 @@ python generate.py --outdir=outputs/generate/stylegan_human_v2_1024 --trunc=0.8 @click.option('--seeds', type=legacy.num_range, help='List of random seeds') @click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True) @click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True) -@click.option('--outdir', help='Where to save the output images', default= 'outputs/generate/' , type=str, required=True, metavar='DIR') +@click.option('--outdir', help='Where to save the output images', default='outputs/generate/', type=str, required=True, metavar='DIR') @click.option('--version', help="stylegan version, 1, 2 or 3", type=int, default=2) def generate_images( ctx: click.Context, @@ -56,64 +56,70 @@ def generate_images( ): print('Loading networks from "%s"...' % network_pkl) - if version == 1: + if version == 1: import dnnlib.tflib as tflib tflib.init_tf() G, D, Gs = legacy.load_pkl(network_pkl) - else: + else: import torch device = torch.device('cuda') with dnnlib.util.open_url(network_pkl) as f: - G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore + G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore os.makedirs(outdir, exist_ok=True) - if seeds is None: ctx.fail('--seeds option is required.') - # Generate images. target_z = np.array([]) target_w = np.array([]) - latent_out = outdir.replace('/images/','') + latent_out = outdir.replace('/images/', '') for seed_idx, seed in enumerate(seeds): if seed % 5000 == 0: - print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds))) + print('Generating image for seed %d (%d/%d) ...' % + (seed, seed_idx, len(seeds))) - if version == 1: ## stylegan v1 - z = np.random.RandomState(seed).randn(1, Gs.input_shape[1]) + if version == 1: # stylegan v1 + z = np.random.RandomState(seed).randn(1, Gs.input_shape[1]) # Generate image. fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True) - if noise_mode == 'const': randomize_noise=False - else: randomize_noise = True - images = Gs.run(z, None, truncation_psi=truncation_psi, randomize_noise=randomize_noise, output_transform=fmt) - PIL.Image.fromarray(images[0], 'RGB').save(f'{outdir}/seed{seed:04d}.png') + if noise_mode == 'const': + randomize_noise = False + else: + randomize_noise = True + images = Gs.run(z, None, truncation_psi=truncation_psi, + randomize_noise=randomize_noise, output_transform=fmt) + PIL.Image.fromarray(images[0], 'RGB').save( + f'{outdir}/seed{seed:04d}.png') - else: ## stylegan v2/v3 + else: # stylegan v2/v3 label = torch.zeros([1, G.c_dim], device=device) - z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device) - if target_z.size==0: - target_z= z.cpu() + z = torch.from_numpy(np.random.RandomState( + seed).randn(1, G.z_dim)).to(device) + if target_z.size == 0: + target_z = z.cpu() else: - target_z=np.append(target_z, z.cpu(), axis=0) + target_z = np.append(target_z, z.cpu(), axis=0) - w = G.mapping(z, label,truncation_psi=truncation_psi) - img = G.synthesis(w, noise_mode=noise_mode,force_fp32 = True) - if target_w.size==0: - target_w= w.cpu() + w = G.mapping(z, label, truncation_psi=truncation_psi) + img = G.synthesis(w, noise_mode=noise_mode, force_fp32=True) + if target_w.size == 0: + target_w = w.cpu() else: - target_w=np.append(target_w, w.cpu(), axis=0) + target_w = np.append(target_w, w.cpu(), axis=0) - img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) - PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/seed{seed:04d}.png') + img = (img.permute(0, 2, 3, 1) * 127.5 + + 128).clamp(0, 255).to(torch.uint8) + PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save( + f'{outdir}/seed{seed:04d}.png') # print(target_z) # print(target_z.shape,target_w.shape) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- if __name__ == "__main__": - generate_images() + generate_images() -#---------------------------------------------------------------------------- \ No newline at end of file +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/insetgan.py b/stylegan_human/insetgan.py index 06c29decec875b8f128014237eda3fd2f8094bc9..722a6e3a6cc02af06faa2f825d1ddef2532bd2cc 100644 --- a/stylegan_human/insetgan.py +++ b/stylegan_human/insetgan.py @@ -10,7 +10,7 @@ from torch_utils.models import Generator as bodyGAN from torch_utils.models_face import Generator as FaceGAN import dlib from utils.face_alignment import align_face_for_insetgan -from utils.util import visual,tensor_to_numpy, numpy_to_tensor +from utils.util import visual, tensor_to_numpy, numpy_to_tensor import legacy import os import click @@ -19,43 +19,49 @@ import click class InsetGAN(torch.nn.Module): def __init__(self, stylebody_ckpt, styleface_ckpt): super().__init__() - - ## convert pkl to pth - if not os.path.exists(stylebody_ckpt.replace('.pkl','.pth')): - legacy.convert(stylebody_ckpt, stylebody_ckpt.replace('.pkl','.pth')) - stylebody_ckpt = stylebody_ckpt.replace('.pkl','.pth') - - if not os.path.exists(styleface_ckpt.replace('.pkl','.pth')): - legacy.convert(styleface_ckpt, styleface_ckpt.replace('.pkl','.pth')) - styleface_ckpt = styleface_ckpt.replace('.pkl','.pth') - + + # convert pkl to pth + if not os.path.exists(stylebody_ckpt.replace('.pkl', '.pth')): + legacy.convert( + stylebody_ckpt, stylebody_ckpt.replace('.pkl', '.pth')) + stylebody_ckpt = stylebody_ckpt.replace('.pkl', '.pth') + + if not os.path.exists(styleface_ckpt.replace('.pkl', '.pth')): + legacy.convert( + styleface_ckpt, styleface_ckpt.replace('.pkl', '.pth')) + styleface_ckpt = styleface_ckpt.replace('.pkl', '.pth') + # dual generator - config = {"latent" : 512, "n_mlp" : 8, "channel_multiplier": 2} + config = {"latent": 512, "n_mlp": 8, "channel_multiplier": 2} self.body_generator = bodyGAN( - size = 1024, - style_dim=config["latent"], - n_mlp=config["n_mlp"], - channel_multiplier=config["channel_multiplier"] - ) - self.body_generator.load_state_dict(torch.load(stylebody_ckpt)['g_ema']) + size=1024, + style_dim=config["latent"], + n_mlp=config["n_mlp"], + channel_multiplier=config["channel_multiplier"] + ) + self.body_generator.load_state_dict( + torch.load(stylebody_ckpt)['g_ema']) self.body_generator.eval().requires_grad_(False).cuda() self.face_generator = FaceGAN( - size = 1024, - style_dim=config["latent"], - n_mlp=config["n_mlp"], - channel_multiplier=config["channel_multiplier"] - ) - self.face_generator.load_state_dict(torch.load(styleface_ckpt)['g_ema']) + size=1024, + style_dim=config["latent"], + n_mlp=config["n_mlp"], + channel_multiplier=config["channel_multiplier"] + ) + self.face_generator.load_state_dict( + torch.load(styleface_ckpt)['g_ema']) self.face_generator.eval().requires_grad_(False).cuda() # crop function - self.dlib_predictor = dlib.shape_predictor('./pretrained_models/shape_predictor_68_face_landmarks.dat') - self.dlib_cnn_face_detector = dlib.cnn_face_detection_model_v1("pretrained_models/mmod_human_face_detector.dat") + self.dlib_predictor = dlib.shape_predictor( + './pretrained_models/shape_predictor_68_face_landmarks.dat') + self.dlib_cnn_face_detector = dlib.cnn_face_detection_model_v1( + "pretrained_models/mmod_human_face_detector.dat") # criterion self.lpips_loss = LPIPS(net='alex').cuda().eval() self.l1_loss = torch.nn.L1Loss(reduction='mean') - + def loss_coarse(self, A_face, B, p1=500, p2=0.05): A_face = F.interpolate(A_face, size=(64, 64), mode='area') B = F.interpolate(B, size=(64, 64), mode='area') @@ -68,14 +74,15 @@ class InsetGAN(torch.nn.Module): mask = torch.zeros_like(A) mask[:, :, :x, ] = 1 mask[:, :, -x:, ] = 1 - mask[:, :, :, :x ] = 1 + mask[:, :, :, :x] = 1 mask[:, :, :, -x:] = 1 return mask - + @staticmethod def get_body_mask(A, crop, padding=4): mask = torch.ones_like(A) - mask[:, :, crop[1]-padding:crop[3]+padding, crop[0]-padding:crop[2]+padding] = 0 + mask[:, :, crop[1]-padding:crop[3]+padding, + crop[0]-padding:crop[2]+padding] = 0 return mask def loss_border(self, A_face, B, p1=10000, p2=2, spec=None): @@ -96,25 +103,25 @@ class InsetGAN(torch.nn.Module): loss_l1 = p1 * self.l1_loss(A*mask, B*mask) loss_lpips = p2 * self.lpips_loss(A*mask, B*mask) return loss_l1+loss_lpips - + def loss_reg(self, w, w_mean, p1, w_plus_delta=None, p2=None): return p1 * torch.mean(((w - w_mean) ** 2)) + p2 * torch.mean(w_plus_delta ** 2) - - # FFHQ type + + # FFHQ type def detect_face_dlib(self, img): # tensor to numpy array rgb uint8 img = tensor_to_numpy(img) - aligned_image, crop, rect = align_face_for_insetgan(img=img, - detector=self.dlib_cnn_face_detector, - predictor=self.dlib_predictor, - output_size=256) + aligned_image, crop, rect = align_face_for_insetgan(img=img, + detector=self.dlib_cnn_face_detector, + predictor=self.dlib_predictor, + output_size=256) aligned_image = np.array(aligned_image) aligned_image = numpy_to_tensor(aligned_image) return aligned_image, crop, rect - + # joint optimization - def dual_optimizer(self, + def dual_optimizer(self, face_w, body_w, joint_steps=500, @@ -124,23 +131,24 @@ class InsetGAN(torch.nn.Module): lr_rampup_length=0.05, seed=None, output_path=None, - video=0): + video=0): ''' Given a face_w, optimize a body_w with suitable body pose & shape for face_w ''' def visual_(path, synth_body, synth_face, body_crop, step, both=False, init_body_with_face=None): tmp = synth_body.clone().detach() - tmp[:, :, body_crop[1]:body_crop[3], body_crop[0]:body_crop[2]] = synth_face + tmp[:, :, body_crop[1]:body_crop[3], + body_crop[0]:body_crop[2]] = synth_face if both: tmp = torch.cat([synth_body, tmp], dim=3) save_path = os.path.join(path, f"{step:04d}.jpg") visual(tmp, save_path) - - def forward(face_w_opt, - body_w_opt, + + def forward(face_w_opt, + body_w_opt, face_w_delta, - body_w_delta, - body_crop, + body_w_delta, + body_crop, update_crop=False ): if face_w_opt.shape[1] != 18: @@ -148,25 +156,31 @@ class InsetGAN(torch.nn.Module): else: face_ws = face_w_opt.clone() face_ws = face_ws + face_w_delta - synth_face, _ = self.face_generator([face_ws], input_is_latent=True, randomize_noise=False) - + synth_face, _ = self.face_generator( + [face_ws], input_is_latent=True, randomize_noise=False) + body_ws = (body_w_opt).repeat([1, 18, 1]) body_ws = body_ws + body_w_delta - synth_body, _ = self.body_generator([body_ws], input_is_latent=True, randomize_noise=False) - + synth_body, _ = self.body_generator( + [body_ws], input_is_latent=True, randomize_noise=False) + if update_crop: - old_r = (body_crop[3]-body_crop[1]) // 2, (body_crop[2]-body_crop[0]) // 2 + old_r = (body_crop[3]-body_crop[1] + ) // 2, (body_crop[2]-body_crop[0]) // 2 _, body_crop, _ = self.detect_face_dlib(synth_body) - center = (body_crop[1] + body_crop[3]) // 2, (body_crop[0] + body_crop[2]) // 2 - body_crop = (center[1] - old_r[1], center[0] - old_r[0], center[1] + old_r[1], center[0] + old_r[0]) - + center = (body_crop[1] + body_crop[3] + ) // 2, (body_crop[0] + body_crop[2]) // 2 + body_crop = (center[1] - old_r[1], center[0] - old_r[0], + center[1] + old_r[1], center[0] + old_r[0]) + synth_body_face = synth_body[:, :, body_crop[1]:body_crop[3], body_crop[0]:body_crop[2]] - + if synth_face.shape[2] > body_crop[3]-body_crop[1]: - synth_face_resize = F.interpolate(synth_face, size=(body_crop[3]-body_crop[1], body_crop[2]-body_crop[0]), mode='area') - + synth_face_resize = F.interpolate(synth_face, size=( + body_crop[3]-body_crop[1], body_crop[2]-body_crop[0]), mode='area') + return synth_body, synth_body_face, synth_face, synth_face_resize, body_crop - + def update_lr(init_lr, step, num_steps, lr_rampdown_length, lr_rampup_length): t = step / num_steps lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length) @@ -174,94 +188,112 @@ class InsetGAN(torch.nn.Module): lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length) lr = init_lr * lr_ramp return lr - + # update output_path output_path = os.path.join(output_path, seed) os.makedirs(output_path, exist_ok=True) - + # define optimized params body_w_mean = self.body_generator.mean_latent(10000).detach() face_w_opt = face_w.clone().detach().requires_grad_(True) body_w_opt = body_w.clone().detach().requires_grad_(True) - face_w_delta = torch.zeros_like(face_w.repeat([1, 18, 1])).requires_grad_(True) - body_w_delta = torch.zeros_like(body_w.repeat([1, 18, 1])).requires_grad_(True) + face_w_delta = torch.zeros_like( + face_w.repeat([1, 18, 1])).requires_grad_(True) + body_w_delta = torch.zeros_like( + body_w.repeat([1, 18, 1])).requires_grad_(True) # generate ref face & body - ref_body, _ = self.body_generator([body_w.repeat([1, 18, 1])], input_is_latent=True, randomize_noise=False) + ref_body, _ = self.body_generator( + [body_w.repeat([1, 18, 1])], input_is_latent=True, randomize_noise=False) # for inversion - ref_face, _ = self.face_generator([face_w.repeat([1, 18, 1])], input_is_latent=True, randomize_noise=False) + ref_face, _ = self.face_generator( + [face_w.repeat([1, 18, 1])], input_is_latent=True, randomize_noise=False) # get initilized crop _, body_crop, _ = self.detect_face_dlib(ref_body) - _, _, face_crop = self.detect_face_dlib(ref_face) # NOTE: this is face rect only. no FFHQ type. + # NOTE: this is face rect only. no FFHQ type. + _, _, face_crop = self.detect_face_dlib(ref_face) # create optimizer - face_optimizer = torch.optim.Adam([face_w_opt, face_w_delta], betas=(0.9, 0.999), lr=face_initial_learning_rate) - body_optimizer = torch.optim.Adam([body_w_opt, body_w_delta], betas=(0.9, 0.999), lr=body_initial_learning_rate) - + face_optimizer = torch.optim.Adam([face_w_opt, face_w_delta], betas=( + 0.9, 0.999), lr=face_initial_learning_rate) + body_optimizer = torch.optim.Adam([body_w_opt, body_w_delta], betas=( + 0.9, 0.999), lr=body_initial_learning_rate) + global_step = 0 # Stage1: remove background of face image face_steps = 25 pbar = tqdm(range(face_steps)) for step in pbar: - face_lr = update_lr(face_initial_learning_rate / 2, step, face_steps, lr_rampdown_length, lr_rampup_length) + face_lr = update_lr(face_initial_learning_rate / 2, step, + face_steps, lr_rampdown_length, lr_rampup_length) for param_group in face_optimizer.param_groups: - param_group['lr'] =face_lr - synth_body, synth_body_face, synth_face_raw, synth_face, body_crop = forward(face_w_opt, - body_w_opt, - face_w_delta, - body_w_delta, - body_crop) - loss_face = self.loss_face(synth_face_raw, ref_face, face_crop, 5000, 1.75) - loss_coarse = self.loss_coarse(synth_face, synth_body_face, 50, 0.05) - loss_border = self.loss_border(synth_face, synth_body_face, 1000, 0.1) + param_group['lr'] = face_lr + synth_body, synth_body_face, synth_face_raw, synth_face, body_crop = forward(face_w_opt, + body_w_opt, + face_w_delta, + body_w_delta, + body_crop) + loss_face = self.loss_face( + synth_face_raw, ref_face, face_crop, 5000, 1.75) + loss_coarse = self.loss_coarse( + synth_face, synth_body_face, 50, 0.05) + loss_border = self.loss_border( + synth_face, synth_body_face, 1000, 0.1) loss = loss_coarse + loss_border + loss_face face_optimizer.zero_grad() loss.backward() face_optimizer.step() # visualization if video: - visual_(output_path, synth_body, synth_face, body_crop, global_step) + visual_(output_path, synth_body, + synth_face, body_crop, global_step) pbar.set_description( - ( + ( f"face: {step:.4f}, lr: {face_lr}, loss: {loss.item():.2f}, loss_coarse: {loss_coarse.item():.2f};" f"loss_border: {loss_border.item():.2f}, loss_face: {loss_face.item():.2f};" ) ) global_step += 1 - + # Stage2: find a suitable body body_steps = 150 pbar = tqdm(range(body_steps)) for step in pbar: - body_lr = update_lr(body_initial_learning_rate, step, body_steps, lr_rampdown_length, lr_rampup_length) + body_lr = update_lr(body_initial_learning_rate, step, + body_steps, lr_rampdown_length, lr_rampup_length) update_crop = True if (step % 50 == 0) else False # update_crop = False for param_group in body_optimizer.param_groups: - param_group['lr'] =body_lr - synth_body, synth_body_face, synth_face_raw, synth_face, body_crop = forward(face_w_opt, - body_w_opt, - face_w_delta, - body_w_delta, - body_crop, - update_crop=update_crop) - loss_coarse = self.loss_coarse(synth_face, synth_body_face, 500, 0.05) - loss_border = self.loss_border(synth_face, synth_body_face, 2500, 0) - loss_body = self.loss_body(synth_body, ref_body, body_crop, 9000, 0.1) - loss_reg = self.loss_reg(body_w_opt, body_w_mean, 15000, body_w_delta, 0) + param_group['lr'] = body_lr + synth_body, synth_body_face, synth_face_raw, synth_face, body_crop = forward(face_w_opt, + body_w_opt, + face_w_delta, + body_w_delta, + body_crop, + update_crop=update_crop) + loss_coarse = self.loss_coarse( + synth_face, synth_body_face, 500, 0.05) + loss_border = self.loss_border( + synth_face, synth_body_face, 2500, 0) + loss_body = self.loss_body( + synth_body, ref_body, body_crop, 9000, 0.1) + loss_reg = self.loss_reg( + body_w_opt, body_w_mean, 15000, body_w_delta, 0) loss = loss_coarse + loss_border + loss_body + loss_reg body_optimizer.zero_grad() loss.backward() - body_optimizer.step() - + body_optimizer.step() + # visualization if video: - visual_(output_path, synth_body, synth_face, body_crop, global_step) + visual_(output_path, synth_body, + synth_face, body_crop, global_step) pbar.set_description( ( f"body: {step:.4f}, lr: {body_lr}, loss: {loss.item():.2f}, loss_coarse: {loss_coarse.item():.2f};" f"loss_border: {loss_border.item():.2f}, loss_body: {loss_body.item():.2f}, loss_reg: {loss_reg:.2f}" ) - ) + ) global_step += 1 - + # Stage3: joint optimization interval = 50 joint_face_steps = joint_steps // 2 @@ -271,39 +303,49 @@ class InsetGAN(torch.nn.Module): pbar = tqdm(range(joint_steps)) flag = -1 for step in pbar: - if step % interval == 0: flag += 1 + if step % interval == 0: + flag += 1 text_flag = 'optimize_face' if flag % 2 == 0 else 'optimize_body' - synth_body, synth_body_face, synth_face_raw, synth_face, body_crop = forward(face_w_opt, - body_w_opt, - face_w_delta, - body_w_delta, - body_crop) + synth_body, synth_body_face, synth_face_raw, synth_face, body_crop = forward(face_w_opt, + body_w_opt, + face_w_delta, + body_w_delta, + body_crop) if text_flag == 'optimize_face': - face_lr = update_lr(face_initial_learning_rate, face_step, joint_face_steps, lr_rampdown_length, lr_rampup_length) + face_lr = update_lr(face_initial_learning_rate, face_step, + joint_face_steps, lr_rampdown_length, lr_rampup_length) for param_group in face_optimizer.param_groups: - param_group['lr'] =face_lr - loss_face = self.loss_face(synth_face_raw, ref_face, face_crop, 5000, 1.75) - loss_coarse = self.loss_coarse(synth_face, synth_body_face, 500, 0.05) - loss_border = self.loss_border(synth_face, synth_body_face, 25000, 0) + param_group['lr'] = face_lr + loss_face = self.loss_face( + synth_face_raw, ref_face, face_crop, 5000, 1.75) + loss_coarse = self.loss_coarse( + synth_face, synth_body_face, 500, 0.05) + loss_border = self.loss_border( + synth_face, synth_body_face, 25000, 0) loss = loss_coarse + loss_border + loss_face face_optimizer.zero_grad() loss.backward() face_optimizer.step() pbar.set_description( - ( + ( f"face: {step}, lr: {face_lr:.4f}, loss: {loss.item():.2f}, loss_coarse: {loss_coarse.item():.2f};" f"loss_border: {loss_border.item():.2f}, loss_face: {loss_face.item():.2f};" ) ) face_step += 1 else: - body_lr = update_lr(body_initial_learning_rate, body_step, joint_body_steps, lr_rampdown_length, lr_rampup_length) + body_lr = update_lr(body_initial_learning_rate, body_step, + joint_body_steps, lr_rampdown_length, lr_rampup_length) for param_group in body_optimizer.param_groups: - param_group['lr'] =body_lr - loss_coarse = self.loss_coarse(synth_face, synth_body_face, 500, 0.05) - loss_border = self.loss_border(synth_face, synth_body_face, 2500, 0) - loss_body = self.loss_body(synth_body, ref_body, body_crop, 9000, 0.1) - loss_reg = self.loss_reg(body_w_opt, body_w_mean, 25000, body_w_delta, 0) + param_group['lr'] = body_lr + loss_coarse = self.loss_coarse( + synth_face, synth_body_face, 500, 0.05) + loss_border = self.loss_border( + synth_face, synth_body_face, 2500, 0) + loss_body = self.loss_body( + synth_body, ref_body, body_crop, 9000, 0.1) + loss_reg = self.loss_reg( + body_w_opt, body_w_mean, 25000, body_w_delta, 0) loss = loss_coarse + loss_border + loss_body + loss_reg body_optimizer.zero_grad() loss.backward() @@ -316,13 +358,12 @@ class InsetGAN(torch.nn.Module): ) body_step += 1 if video: - visual_(output_path, synth_body, synth_face, body_crop, global_step) + visual_(output_path, synth_body, + synth_face, body_crop, global_step) global_step += 1 return face_w_opt.repeat([1, 18, 1])+face_w_delta, body_w_opt.repeat([1, 18, 1])+body_w_delta, body_crop - - """ Jointly combine and optimize generated faces and bodies . Examples: @@ -334,6 +375,7 @@ python insetgan.py --body_network=pretrained_models/stylegan_human_v2_1024.pkl - --body_seed=82 --face_seed=43 --trunc=0.6 --outdir=outputs/insetgan/ --video 1 """ + @click.command() @click.pass_context @click.option('--face_network', default="./pretrained_models/ffhq.pkl", help='Network pickle filename', required=True) @@ -342,7 +384,7 @@ python insetgan.py --body_network=pretrained_models/stylegan_human_v2_1024.pkl - @click.option('--body_seed', type=int, default=43, help='selected random seed') @click.option('--joint_steps', type=int, default=500, help='num steps for joint optimization') @click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=0.6, show_default=True) -@click.option('--outdir', help='Where to save the output images', default= "outputs/insetgan/" , type=str, required=True, metavar='DIR') +@click.option('--outdir', help='Where to save the output images', default="outputs/insetgan/", type=str, required=True, metavar='DIR') @click.option('--video', help="set to 1 if want to save video", type=int, default=0) def main( ctx: click.Context, @@ -359,40 +401,48 @@ def main( os.makedirs(outdir, exist_ok=True) face_z = np.random.RandomState(face_seed).randn(1, 512).astype(np.float32) face_mean = insgan.face_generator.mean_latent(3000) - face_w = insgan.face_generator.get_latent(torch.from_numpy(face_z).to(device)) # [N, L, C] + face_w = insgan.face_generator.get_latent( + torch.from_numpy(face_z).to(device)) # [N, L, C] face_w = truncation_psi * face_w + (1-truncation_psi) * face_mean face_img, _ = insgan.face_generator([face_w], input_is_latent=True) body_z = np.random.RandomState(body_seed).randn(1, 512).astype(np.float32) body_mean = insgan.body_generator.mean_latent(3000) - body_w = insgan.body_generator.get_latent(torch.from_numpy(body_z).to(device)) # [N, L, C] + body_w = insgan.body_generator.get_latent( + torch.from_numpy(body_z).to(device)) # [N, L, C] body_w = truncation_psi * body_w + (1-truncation_psi) * body_mean body_img, _ = insgan.body_generator([body_w], input_is_latent=True) _, body_crop, _ = insgan.detect_face_dlib(body_img) - face_img = F.interpolate(face_img, size=(body_crop[3]-body_crop[1], body_crop[2]-body_crop[0]), mode='area') + face_img = F.interpolate(face_img, size=( + body_crop[3]-body_crop[1], body_crop[2]-body_crop[0]), mode='area') cp_body = body_img.clone() - cp_body[:, :, body_crop[1]:body_crop[3], body_crop[0]:body_crop[2]] = face_img - + cp_body[:, :, body_crop[1]:body_crop[3], + body_crop[0]:body_crop[2]] = face_img + optim_face_w, optim_body_w, crop = insgan.dual_optimizer( - face_w, + face_w, body_w, joint_steps=joint_steps, seed=f'{face_seed:04d}_{body_seed:04d}', output_path=outdir, video=video ) - + if video: ffmpeg_cmd = f"ffmpeg -hide_banner -loglevel error -i ./{outdir}/{face_seed:04d}_{body_seed:04d}/%04d.jpg -c:v libx264 -vf fps=30 -pix_fmt yuv420p ./{outdir}/{face_seed:04d}_{body_seed:04d}.mp4" os.system(ffmpeg_cmd) - new_face_img, _ = insgan.face_generator([optim_face_w], input_is_latent=True) + new_face_img, _ = insgan.face_generator( + [optim_face_w], input_is_latent=True) new_shape = crop[3] - crop[1], crop[2] - crop[0] - new_face_img_crop = F.interpolate(new_face_img, size=new_shape, mode='area') - seamless_body, _ = insgan.body_generator([optim_body_w], input_is_latent=True) + new_face_img_crop = F.interpolate( + new_face_img, size=new_shape, mode='area') + seamless_body, _ = insgan.body_generator( + [optim_body_w], input_is_latent=True) seamless_body[:, :, crop[1]:crop[3], crop[0]:crop[2]] = new_face_img_crop temp = torch.cat([cp_body, seamless_body], dim=3) visual(temp, f"{outdir}/{face_seed:04d}_{body_seed:04d}.png") + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/stylegan_human/interpolation.py b/stylegan_human/interpolation.py index f95050479c5e085b0793f84fdcc09a8678af1379..b578881834f4333d7e386e5a8f142e3a98a3252c 100644 --- a/stylegan_human/interpolation.py +++ b/stylegan_human/interpolation.py @@ -1,7 +1,7 @@ # Copyright (c) SenseTime Research. All rights reserved. -## interpolate between two z code -## score all middle latent code +# interpolate between two z code +# score all middle latent code # https://www.aiuai.cn/aifarm1929.html import os @@ -24,7 +24,9 @@ def lerp(code1, code2, alpha): # Taken and adapted from wikipedia's slerp article # https://en.wikipedia.org/wiki/Slerp -def slerp(code1, code2, alpha, DOT_THRESHOLD=0.9995): # Spherical linear interpolation + + +def slerp(code1, code2, alpha, DOT_THRESHOLD=0.9995): # Spherical linear interpolation code1_copy = np.copy(code1) code2_copy = np.copy(code2) @@ -46,10 +48,11 @@ def slerp(code1, code2, alpha, DOT_THRESHOLD=0.9995): # Spherical linear interpo code3 = s0 * code1_copy + s1 * code2_copy return code3 + def generate_image_from_z(G, z, noise_mode, truncation_psi, device): label = torch.zeros([1, G.c_dim], device=device) - w = G.mapping(z, label,truncation_psi=truncation_psi) - img = G.synthesis(w, noise_mode=noise_mode,force_fp32 = True) + w = G.mapping(z, label, truncation_psi=truncation_psi) + img = G.synthesis(w, noise_mode=noise_mode, force_fp32=True) img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) img = PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB') return img @@ -62,26 +65,29 @@ def get_concat_h(im1, im2): return dst -def make_latent_interp_animation(G, code1, code2, img1, img2, num_interps, noise_mode, save_mid_image, truncation_psi,device, outdir,fps): +def make_latent_interp_animation(G, code1, code2, img1, img2, num_interps, noise_mode, save_mid_image, truncation_psi, device, outdir, fps): step_size = 1.0/num_interps - + all_imgs = [] amounts = np.arange(0, 1, step_size) for seed_idx, alpha in enumerate(tqdm(amounts)): interpolated_latent_code = lerp(code1, code2, alpha) - image = generate_image_from_z(G,interpolated_latent_code, noise_mode, truncation_psi, device) + image = generate_image_from_z( + G, interpolated_latent_code, noise_mode, truncation_psi, device) interp_latent_image = image.resize((512, 1024)) - if not os.path.exists(os.path.join(outdir,'img')): os.makedirs(os.path.join(outdir,'img'), exist_ok=True) - if save_mid_image: + if not os.path.exists(os.path.join(outdir, 'img')): + os.makedirs(os.path.join(outdir, 'img'), exist_ok=True) + if save_mid_image: interp_latent_image.save(f'{outdir}/img/seed{seed_idx:04d}.png') frame = get_concat_h(img2, interp_latent_image) frame = get_concat_h(frame, img1) all_imgs.append(frame) - save_name = os.path.join(outdir,'latent_space_traversal.gif') - all_imgs[0].save(save_name, save_all=True, append_images=all_imgs[1:], duration=1000/fps, loop=0) - + save_name = os.path.join(outdir, 'latent_space_traversal.gif') + all_imgs[0].save(save_name, save_all=True, + append_images=all_imgs[1:], duration=1000/fps, loop=0) + """ Create interpolated images between two given seeds using pretrained network pickle. @@ -93,16 +99,17 @@ python interpolation.py --network=pretrained_models/stylegan_human_v2_1024.pkl """ + @click.command() @click.pass_context @click.option('--network', 'network_pkl', help='Network pickle filename', required=True) @click.option('--seeds', type=legacy.num_range, help='List of 2 random seeds, e.g. 1,2') @click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=0.8, show_default=True) @click.option('--noise-mode', 'noise_mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True) -@click.option('--outdir', default= 'outputs/inter_gifs', help='Where to save the output images', type=str, required=True, metavar='DIR') +@click.option('--outdir', default='outputs/inter_gifs', help='Where to save the output images', type=str, required=True, metavar='DIR') @click.option('--save_mid_image', default=True, type=bool, help='select True if you want to save all interpolated images') -@click.option('--fps', default= 15, help='FPS for GIF', type=int) -@click.option('--num_interps', default= 100, help='Number of interpolation images', type=int) +@click.option('--fps', default=15, help='FPS for GIF', type=int) +@click.option('--num_interps', default=100, help='Number of interpolation images', type=int) def main( ctx: click.Context, network_pkl: str, @@ -111,36 +118,38 @@ def main( noise_mode: str, outdir: str, save_mid_image: bool, - fps:int, - num_interps:int + fps: int, + num_interps: int ): - device = torch.device('cuda') with dnnlib.util.open_url(network_pkl) as f: - G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore + G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore outdir = os.path.join(outdir) if not os.path.exists(outdir): - os.makedirs(outdir,exist_ok=True) - os.makedirs(os.path.join(outdir,'img'),exist_ok=True) + os.makedirs(outdir, exist_ok=True) + os.makedirs(os.path.join(outdir, 'img'), exist_ok=True) - if len(seeds) > 2: + if len(seeds) > 2: print("Receiving more than two seeds, only use the first two.") seeds = seeds[0:2] - elif len(seeds) == 1: + elif len(seeds) == 1: print('Require two seeds, randomly generate two now.') - seeds = [seeds[0],random.randint(0,10000)] + seeds = [seeds[0], random.randint(0, 10000)] - z1 = torch.from_numpy(np.random.RandomState(seeds[0]).randn(1, G.z_dim)).to(device) - z2 = torch.from_numpy(np.random.RandomState(seeds[1]).randn(1, G.z_dim)).to(device) + z1 = torch.from_numpy(np.random.RandomState( + seeds[0]).randn(1, G.z_dim)).to(device) + z2 = torch.from_numpy(np.random.RandomState( + seeds[1]).randn(1, G.z_dim)).to(device) img1 = generate_image_from_z(G, z1, noise_mode, truncation_psi, device) img2 = generate_image_from_z(G, z2, noise_mode, truncation_psi, device) img1.save(f'{outdir}/seed{seeds[0]:04d}.png') img2.save(f'{outdir}/seed{seeds[1]:04d}.png') - make_latent_interp_animation(G, z1, z2, img1, img2, num_interps, noise_mode, save_mid_image, truncation_psi, device, outdir, fps) + make_latent_interp_animation(G, z1, z2, img1, img2, num_interps, + noise_mode, save_mid_image, truncation_psi, device, outdir, fps) if __name__ == "__main__": - main() + main() diff --git a/stylegan_human/legacy.py b/stylegan_human/legacy.py index ef0e838df5426e5f01ba3b917244de553678ed0f..1f8b1a87fbf9a2c6b10227b9516a6851f6fabf12 100644 --- a/stylegan_human/legacy.py +++ b/stylegan_human/legacy.py @@ -6,7 +6,7 @@ # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. -# +# import pickle import dnnlib import re @@ -17,18 +17,19 @@ import numpy as np from torch_utils import misc -#---------------------------------------------------------------------------- -## loading torch pkl +# ---------------------------------------------------------------------------- +# loading torch pkl def load_network_pkl(f, force_fp16=False, G_only=False): data = _LegacyUnpickler(f).load() if G_only: - f = open('ori_model_Gonly.txt','a+') - else: f = open('ori_model.txt','a+') + f = open('ori_model_Gonly.txt', 'a+') + else: + f = open('ori_model.txt', 'a+') for key in data.keys(): f.write(str(data[key])) f.close() - ## We comment out this part, if you want to convert TF pickle, you can use the original script from StyleGAN2-ada-pytorch + # We comment out this part, if you want to convert TF pickle, you can use the original script from StyleGAN2-ada-pytorch # # Legacy TensorFlow pickle => convert. # if isinstance(data, tuple) and len(data) == 3 and all(isinstance(net, _TFNetworkStub) for net in data): # tf_G, tf_D, tf_Gs = data @@ -54,13 +55,15 @@ def load_network_pkl(f, force_fp16=False, G_only=False): # Force FP16. if force_fp16: if G_only: - convert_list = ['G_ema'] #'G' - else: convert_list = ['G', 'D', 'G_ema'] + convert_list = ['G_ema'] # 'G' + else: + convert_list = ['G', 'D', 'G_ema'] for key in convert_list: old = data[key] kwargs = copy.deepcopy(old.init_kwargs) if key.startswith('G'): - kwargs.synthesis_kwargs = dnnlib.EasyDict(kwargs.get('synthesis_kwargs', {})) + kwargs.synthesis_kwargs = dnnlib.EasyDict( + kwargs.get('synthesis_kwargs', {})) kwargs.synthesis_kwargs.num_fp16_res = 4 kwargs.synthesis_kwargs.conv_clamp = 256 if key.startswith('D'): @@ -70,18 +73,21 @@ def load_network_pkl(f, force_fp16=False, G_only=False): new = type(old)(**kwargs).eval().requires_grad_(False) misc.copy_params_and_buffers(old, new, require_all=True) data[key] = new - return data + return data + class _TFNetworkStub(dnnlib.EasyDict): pass + class _LegacyUnpickler(pickle.Unpickler): def find_class(self, module, name): if module == 'dnnlib.tflib.network' and name == 'Network': return _TFNetworkStub return super().find_class(module, name) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def num_range(s: str) -> List[int]: '''Accept either a comma separated list of numbers 'a,b,c' or a range 'a-c' and return as a list of ints.''' @@ -94,16 +100,17 @@ def num_range(s: str) -> List[int]: return [int(x) for x in vals] - -#---------------------------------------------------------------------------- -#### loading tf pkl +# ---------------------------------------------------------------------------- +# loading tf pkl def load_pkl(file_or_url): with open(file_or_url, 'rb') as file: return pickle.load(file, encoding='latin1') -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + +# For editing + -### For editing def visual(output, out_path): import torch import cv2 @@ -112,47 +119,57 @@ def visual(output, out_path): output = torch.clamp(output, 0, 1) if output.shape[1] == 1: output = torch.cat([output, output, output], 1) - output = output[0].detach().cpu().permute(1,2,0).numpy() + output = output[0].detach().cpu().permute(1, 2, 0).numpy() output = (output*255).astype(np.uint8) - output = output[:,:,::-1] + output = output[:, :, ::-1] cv2.imwrite(out_path, output) + def save_obj(obj, path): with open(path, 'wb+') as f: pickle.dump(obj, f, protocol=4) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + +# Converting pkl to pth, change dict info inside pickle -## Converting pkl to pth, change dict info inside pickle def convert_to_rgb(state_ros, state_nv, ros_name, nv_name): - state_ros[f"{ros_name}.conv.weight"] = state_nv[f"{nv_name}.torgb.weight"].unsqueeze(0) - state_ros[f"{ros_name}.bias"] = state_nv[f"{nv_name}.torgb.bias"].unsqueeze(0).unsqueeze(-1).unsqueeze(-1) + state_ros[f"{ros_name}.conv.weight"] = state_nv[f"{nv_name}.torgb.weight"].unsqueeze( + 0) + state_ros[f"{ros_name}.bias"] = state_nv[f"{nv_name}.torgb.bias"].unsqueeze( + 0).unsqueeze(-1).unsqueeze(-1) state_ros[f"{ros_name}.conv.modulation.weight"] = state_nv[f"{nv_name}.torgb.affine.weight"] state_ros[f"{ros_name}.conv.modulation.bias"] = state_nv[f"{nv_name}.torgb.affine.bias"] def convert_conv(state_ros, state_nv, ros_name, nv_name): - state_ros[f"{ros_name}.conv.weight"] = state_nv[f"{nv_name}.weight"].unsqueeze(0) + state_ros[f"{ros_name}.conv.weight"] = state_nv[f"{nv_name}.weight"].unsqueeze( + 0) state_ros[f"{ros_name}.activate.bias"] = state_nv[f"{nv_name}.bias"] state_ros[f"{ros_name}.conv.modulation.weight"] = state_nv[f"{nv_name}.affine.weight"] state_ros[f"{ros_name}.conv.modulation.bias"] = state_nv[f"{nv_name}.affine.bias"] - state_ros[f"{ros_name}.noise.weight"] = state_nv[f"{nv_name}.noise_strength"].unsqueeze(0) + state_ros[f"{ros_name}.noise.weight"] = state_nv[f"{nv_name}.noise_strength"].unsqueeze( + 0) def convert_blur_kernel(state_ros, state_nv, level): """Not quite sure why there is a factor of 4 here""" # They are all the same - state_ros[f"convs.{2*level}.conv.blur.kernel"] = 4*state_nv["synthesis.b4.resample_filter"] - state_ros[f"to_rgbs.{level}.upsample.kernel"] = 4*state_nv["synthesis.b4.resample_filter"] + state_ros[f"convs.{2*level}.conv.blur.kernel"] = 4 * \ + state_nv["synthesis.b4.resample_filter"] + state_ros[f"to_rgbs.{level}.upsample.kernel"] = 4 * \ + state_nv["synthesis.b4.resample_filter"] def determine_config(state_nv): mapping_names = [name for name in state_nv.keys() if "mapping.fc" in name] - sythesis_names = [name for name in state_nv.keys() if "synthesis.b" in name] + sythesis_names = [ + name for name in state_nv.keys() if "synthesis.b" in name] - n_mapping = max([int(re.findall("(\d+)", n)[0]) for n in mapping_names]) + 1 - resolution = max([int(re.findall("(\d+)", n)[0]) for n in sythesis_names]) + n_mapping = max([int(re.findall("(\d+)", n)[0]) + for n in mapping_names]) + 1 + resolution = max([int(re.findall("(\d+)", n)[0]) for n in sythesis_names]) n_layers = np.log(resolution/2)/np.log(2) return n_mapping, n_layers @@ -160,7 +177,7 @@ def determine_config(state_nv): def convert(network_pkl, output_file, G_only=False): with dnnlib.util.open_url(network_pkl) as f: - G_nvidia = load_network_pkl(f,G_only=G_only)['G_ema'] + G_nvidia = load_network_pkl(f, G_only=G_only)['G_ema'] state_nv = G_nvidia.state_dict() n_mapping, n_layers = determine_config(state_nv) @@ -174,17 +191,24 @@ def convert(network_pkl, output_file, G_only=False): for i in range(int(n_layers)): if i > 0: for conv_level in range(2): - convert_conv(state_ros, state_nv, f"convs.{2*i-2+conv_level}", f"synthesis.b{4*(2**i)}.conv{conv_level}") - state_ros[f"noises.noise_{2*i-1+conv_level}"] = state_nv[f"synthesis.b{4*(2**i)}.conv{conv_level}.noise_const"].unsqueeze(0).unsqueeze(0) + convert_conv( + state_ros, state_nv, f"convs.{2*i-2+conv_level}", f"synthesis.b{4*(2**i)}.conv{conv_level}") + state_ros[f"noises.noise_{2*i-1+conv_level}"] = state_nv[f"synthesis.b{4*(2**i)}.conv{conv_level}.noise_const"].unsqueeze( + 0).unsqueeze(0) - convert_to_rgb(state_ros, state_nv, f"to_rgbs.{i-1}", f"synthesis.b{4*(2**i)}") + convert_to_rgb(state_ros, state_nv, + f"to_rgbs.{i-1}", f"synthesis.b{4*(2**i)}") convert_blur_kernel(state_ros, state_nv, i-1) - + else: - state_ros[f"input.input"] = state_nv[f"synthesis.b{4*(2**i)}.const"].unsqueeze(0) - convert_conv(state_ros, state_nv, "conv1", f"synthesis.b{4*(2**i)}.conv1") - state_ros[f"noises.noise_{2*i}"] = state_nv[f"synthesis.b{4*(2**i)}.conv1.noise_const"].unsqueeze(0).unsqueeze(0) - convert_to_rgb(state_ros, state_nv, "to_rgb1", f"synthesis.b{4*(2**i)}") + state_ros[f"input.input"] = state_nv[f"synthesis.b{4*(2**i)}.const"].unsqueeze( + 0) + convert_conv(state_ros, state_nv, "conv1", + f"synthesis.b{4*(2**i)}.conv1") + state_ros[f"noises.noise_{2*i}"] = state_nv[f"synthesis.b{4*(2**i)}.conv1.noise_const"].unsqueeze( + 0).unsqueeze(0) + convert_to_rgb(state_ros, state_nv, "to_rgb1", + f"synthesis.b{4*(2**i)}") # https://github.com/yuval-alaluf/restyle-encoder/issues/1#issuecomment-828354736 latent_avg = state_nv['mapping.w_avg'] @@ -197,4 +221,3 @@ def convert(network_pkl, output_file, G_only=False): # f.write(str(key)+': '+str(state_dict['g_ema'][key].shape)+'\n') # f.close() torch.save(state_dict, output_file) - diff --git a/stylegan_human/openpose/src/body.py b/stylegan_human/openpose/src/body.py index 13dc2fb8215f2ad63ee6b54a6641800fddcc20a4..9a2c5024c3d70bc05deabcf3807e8ef77707a756 100644 --- a/stylegan_human/openpose/src/body.py +++ b/stylegan_human/openpose/src/body.py @@ -11,6 +11,7 @@ from torchvision import transforms from openpose.src import util from openpose.src.model import bodypose_model + class Body(object): def __init__(self, model_path): self.model = bodypose_model() @@ -34,9 +35,12 @@ class Body(object): for m in range(len(multiplier)): scale = multiplier[m] - imageToTest = cv2.resize(oriImg, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC) - imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue) - im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5 + imageToTest = cv2.resize( + oriImg, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC) + imageToTest_padded, pad = util.padRightDownCorner( + imageToTest, stride, padValue) + im = np.transpose(np.float32( + imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5 im = np.ascontiguousarray(im) data = torch.from_numpy(im).float() @@ -50,16 +54,24 @@ class Body(object): # extract outputs, resize, and remove padding # heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0)) # output 1 is heatmaps - heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0)) # output 1 is heatmaps - heatmap = cv2.resize(heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC) - heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :] - heatmap = cv2.resize(heatmap, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC) + # output 1 is heatmaps + heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0)) + heatmap = cv2.resize(heatmap, (0, 0), fx=stride, + fy=stride, interpolation=cv2.INTER_CUBIC) + heatmap = heatmap[:imageToTest_padded.shape[0] - + pad[2], :imageToTest_padded.shape[1] - pad[3], :] + heatmap = cv2.resize( + heatmap, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC) # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0)) # output 0 is PAFs - paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0)) # output 0 is PAFs - paf = cv2.resize(paf, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC) - paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :] - paf = cv2.resize(paf, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC) + paf = np.transpose(np.squeeze(Mconv7_stage6_L1), + (1, 2, 0)) # output 0 is PAFs + paf = cv2.resize(paf, (0, 0), fx=stride, fy=stride, + interpolation=cv2.INTER_CUBIC) + paf = paf[:imageToTest_padded.shape[0] - pad[2], + :imageToTest_padded.shape[1] - pad[3], :] + paf = cv2.resize( + paf, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC) heatmap_avg += heatmap_avg + heatmap / len(multiplier) paf_avg += + paf / len(multiplier) @@ -81,21 +93,25 @@ class Body(object): peaks_binary = np.logical_and.reduce( (one_heatmap >= map_left, one_heatmap >= map_right, one_heatmap >= map_up, one_heatmap >= map_down, one_heatmap > thre1)) - peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])) # note reverse + peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero( + peaks_binary)[0])) # note reverse peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks] peak_id = range(peak_counter, peak_counter + len(peaks)) - peaks_with_score_and_id = [peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))] + peaks_with_score_and_id = [ + peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))] all_peaks.append(peaks_with_score_and_id) peak_counter += len(peaks) # find connection in the specified sequence, center 29 is in the position 15 - limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \ - [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \ + limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], + [10, 11], [2, 12], [12, 13], [ + 13, 14], [2, 1], [1, 15], [15, 17], [1, 16], [16, 18], [3, 17], [6, 18]] # the middle joints heatmap correpondence - mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], \ - [23, 24], [25, 26], [27, 28], [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], \ + mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], + [23, 24], [25, 26], [27, 28], [29, 30], [ + 47, 48], [49, 50], [53, 54], [51, 52], [55, 56], [37, 38], [45, 46]] connection_all = [] @@ -118,29 +134,33 @@ class Body(object): norm = max(0.001, norm) vec = np.divide(vec, norm) - startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \ + startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), np.linspace(candA[i][1], candB[j][1], num=mid_num))) - vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \ + vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] for I in range(len(startend))]) - vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \ + vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] for I in range(len(startend))]) - score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1]) + score_midpts = np.multiply( + vec_x, vec[0]) + np.multiply(vec_y, vec[1]) score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min( 0.5 * oriImg.shape[0] / norm - 1, 0) - criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(score_midpts) + criterion1 = len(np.nonzero(score_midpts > thre2)[ + 0]) > 0.8 * len(score_midpts) criterion2 = score_with_dist_prior > 0 if criterion1 and criterion2: connection_candidate.append( [i, j, score_with_dist_prior, score_with_dist_prior + candA[i][2] + candB[j][2]]) - connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True) + connection_candidate = sorted( + connection_candidate, key=lambda x: x[2], reverse=True) connection = np.zeros((0, 5)) for c in range(len(connection_candidate)): i, j, s = connection_candidate[c][0:3] if (i not in connection[:, 3] and j not in connection[:, 4]): - connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]]) + connection = np.vstack( + [connection, [candA[i][3], candB[j][3], s, i, j]]) if (len(connection) >= min(nA, nB)): break @@ -152,7 +172,8 @@ class Body(object): # last number in each row is the total parts number of that person # the second last number in each row is the score of the overall configuration subset = -1 * np.ones((0, 20)) - candidate = np.array([item for sublist in all_peaks for item in sublist]) + candidate = np.array( + [item for sublist in all_peaks for item in sublist]) for k in range(len(mapIdx)): if k not in special_k: @@ -173,10 +194,12 @@ class Body(object): if subset[j][indexB] != partBs[i]: subset[j][indexB] = partBs[i] subset[j][-1] += 1 - subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2] + subset[j][-2] += candidate[partBs[i].astype( + int), 2] + connection_all[k][i][2] elif found == 2: # if found 2 and disjoint, merge them j1, j2 = subset_idx - membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2] + membership = ((subset[j1] >= 0).astype( + int) + (subset[j2] >= 0).astype(int))[:-2] if len(np.nonzero(membership == 2)[0]) == 0: # merge subset[j1][:-2] += (subset[j2][:-2] + 1) subset[j1][-2:] += subset[j2][-2:] @@ -185,7 +208,8 @@ class Body(object): else: # as like found == 1 subset[j1][indexB] = partBs[i] subset[j1][-1] += 1 - subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2] + subset[j1][-2] += candidate[partBs[i].astype( + int), 2] + connection_all[k][i][2] # if find no partA in the subset, create a new subset elif not found and k < 17: @@ -193,7 +217,8 @@ class Body(object): row[indexA] = partAs[i] row[indexB] = partBs[i] row[-1] = 2 - row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + connection_all[k][i][2] + row[-2] = sum(candidate[connection_all[k][i, + :2].astype(int), 2]) + connection_all[k][i][2] subset = np.vstack([subset, row]) # delete some rows of subset which has few parts occur deleteIdx = [] @@ -206,6 +231,7 @@ class Body(object): # candidate: x, y, score, id return candidate, subset + if __name__ == "__main__": body_estimation = Body('../model/body_pose_model.pth') diff --git a/stylegan_human/openpose/src/model.py b/stylegan_human/openpose/src/model.py index 5dfc80de827a17beccb9b0f3f7588545be78c9de..e5f67d39e3f8b1068ec1c3d27cee07670acbce91 100644 --- a/stylegan_human/openpose/src/model.py +++ b/stylegan_human/openpose/src/model.py @@ -4,12 +4,13 @@ from collections import OrderedDict import torch import torch.nn as nn + def make_layers(block, no_relu_layers): layers = [] for layer_name, v in block.items(): if 'pool' in layer_name: layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1], - padding=v[2]) + padding=v[2]) layers.append((layer_name, layer)) else: conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1], @@ -21,51 +22,51 @@ def make_layers(block, no_relu_layers): return nn.Sequential(OrderedDict(layers)) + class bodypose_model(nn.Module): def __init__(self): super(bodypose_model, self).__init__() # these layers have no relu layer - no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\ - 'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\ - 'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\ + no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1', + 'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2', + 'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1', 'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1'] blocks = {} block0 = OrderedDict([ - ('conv1_1', [3, 64, 3, 1, 1]), - ('conv1_2', [64, 64, 3, 1, 1]), - ('pool1_stage1', [2, 2, 0]), - ('conv2_1', [64, 128, 3, 1, 1]), - ('conv2_2', [128, 128, 3, 1, 1]), - ('pool2_stage1', [2, 2, 0]), - ('conv3_1', [128, 256, 3, 1, 1]), - ('conv3_2', [256, 256, 3, 1, 1]), - ('conv3_3', [256, 256, 3, 1, 1]), - ('conv3_4', [256, 256, 3, 1, 1]), - ('pool3_stage1', [2, 2, 0]), - ('conv4_1', [256, 512, 3, 1, 1]), - ('conv4_2', [512, 512, 3, 1, 1]), - ('conv4_3_CPM', [512, 256, 3, 1, 1]), - ('conv4_4_CPM', [256, 128, 3, 1, 1]) - ]) - + ('conv1_1', [3, 64, 3, 1, 1]), + ('conv1_2', [64, 64, 3, 1, 1]), + ('pool1_stage1', [2, 2, 0]), + ('conv2_1', [64, 128, 3, 1, 1]), + ('conv2_2', [128, 128, 3, 1, 1]), + ('pool2_stage1', [2, 2, 0]), + ('conv3_1', [128, 256, 3, 1, 1]), + ('conv3_2', [256, 256, 3, 1, 1]), + ('conv3_3', [256, 256, 3, 1, 1]), + ('conv3_4', [256, 256, 3, 1, 1]), + ('pool3_stage1', [2, 2, 0]), + ('conv4_1', [256, 512, 3, 1, 1]), + ('conv4_2', [512, 512, 3, 1, 1]), + ('conv4_3_CPM', [512, 256, 3, 1, 1]), + ('conv4_4_CPM', [256, 128, 3, 1, 1]) + ]) # Stage 1 block1_1 = OrderedDict([ - ('conv5_1_CPM_L1', [128, 128, 3, 1, 1]), - ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]), - ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]), - ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]), - ('conv5_5_CPM_L1', [512, 38, 1, 1, 0]) - ]) + ('conv5_1_CPM_L1', [128, 128, 3, 1, 1]), + ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]), + ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]), + ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]), + ('conv5_5_CPM_L1', [512, 38, 1, 1, 0]) + ]) block1_2 = OrderedDict([ - ('conv5_1_CPM_L2', [128, 128, 3, 1, 1]), - ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]), - ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]), - ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]), - ('conv5_5_CPM_L2', [512, 19, 1, 1, 0]) - ]) + ('conv5_1_CPM_L2', [128, 128, 3, 1, 1]), + ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]), + ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]), + ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]), + ('conv5_5_CPM_L2', [512, 19, 1, 1, 0]) + ]) blocks['block1_1'] = block1_1 blocks['block1_2'] = block1_2 @@ -74,24 +75,24 @@ class bodypose_model(nn.Module): # Stages 2 - 6 for i in range(2, 7): blocks['block%d_1' % i] = OrderedDict([ - ('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]), - ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]), - ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]), - ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]), - ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]), - ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]), - ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0]) - ]) + ('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]), + ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]), + ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0]) + ]) blocks['block%d_2' % i] = OrderedDict([ - ('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]), - ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]), - ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]), - ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]), - ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]), - ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]), - ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0]) - ]) + ('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]), + ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]), + ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0]) + ]) for k in blocks.keys(): blocks[k] = make_layers(blocks[k], no_relu_layers) @@ -110,7 +111,6 @@ class bodypose_model(nn.Module): self.model5_2 = blocks['block5_2'] self.model6_2 = blocks['block6_2'] - def forward(self, x): out1 = self.model0(x) @@ -140,34 +140,35 @@ class bodypose_model(nn.Module): return out6_1, out6_2 + class handpose_model(nn.Module): def __init__(self): super(handpose_model, self).__init__() # these layers have no relu layer - no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\ + no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3', 'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6'] # stage 1 block1_0 = OrderedDict([ - ('conv1_1', [3, 64, 3, 1, 1]), - ('conv1_2', [64, 64, 3, 1, 1]), - ('pool1_stage1', [2, 2, 0]), - ('conv2_1', [64, 128, 3, 1, 1]), - ('conv2_2', [128, 128, 3, 1, 1]), - ('pool2_stage1', [2, 2, 0]), - ('conv3_1', [128, 256, 3, 1, 1]), - ('conv3_2', [256, 256, 3, 1, 1]), - ('conv3_3', [256, 256, 3, 1, 1]), - ('conv3_4', [256, 256, 3, 1, 1]), - ('pool3_stage1', [2, 2, 0]), - ('conv4_1', [256, 512, 3, 1, 1]), - ('conv4_2', [512, 512, 3, 1, 1]), - ('conv4_3', [512, 512, 3, 1, 1]), - ('conv4_4', [512, 512, 3, 1, 1]), - ('conv5_1', [512, 512, 3, 1, 1]), - ('conv5_2', [512, 512, 3, 1, 1]), - ('conv5_3_CPM', [512, 128, 3, 1, 1]) - ]) + ('conv1_1', [3, 64, 3, 1, 1]), + ('conv1_2', [64, 64, 3, 1, 1]), + ('pool1_stage1', [2, 2, 0]), + ('conv2_1', [64, 128, 3, 1, 1]), + ('conv2_2', [128, 128, 3, 1, 1]), + ('pool2_stage1', [2, 2, 0]), + ('conv3_1', [128, 256, 3, 1, 1]), + ('conv3_2', [256, 256, 3, 1, 1]), + ('conv3_3', [256, 256, 3, 1, 1]), + ('conv3_4', [256, 256, 3, 1, 1]), + ('pool3_stage1', [2, 2, 0]), + ('conv4_1', [256, 512, 3, 1, 1]), + ('conv4_2', [512, 512, 3, 1, 1]), + ('conv4_3', [512, 512, 3, 1, 1]), + ('conv4_4', [512, 512, 3, 1, 1]), + ('conv5_1', [512, 512, 3, 1, 1]), + ('conv5_2', [512, 512, 3, 1, 1]), + ('conv5_3_CPM', [512, 128, 3, 1, 1]) + ]) block1_1 = OrderedDict([ ('conv6_1_CPM', [128, 512, 1, 1, 0]), @@ -181,14 +182,14 @@ class handpose_model(nn.Module): # stage 2-6 for i in range(2, 7): blocks['block%d' % i] = OrderedDict([ - ('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]), - ('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]), - ('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]), - ('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]), - ('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]), - ('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]), - ('Mconv7_stage%d' % i, [128, 22, 1, 1, 0]) - ]) + ('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]), + ('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]), + ('Mconv7_stage%d' % i, [128, 22, 1, 1, 0]) + ]) for k in blocks.keys(): blocks[k] = make_layers(blocks[k], no_relu_layers) @@ -215,5 +216,3 @@ class handpose_model(nn.Module): concat_stage6 = torch.cat([out_stage5, out1_0], 1) out_stage6 = self.model6(concat_stage6) return out_stage6 - - diff --git a/stylegan_human/openpose/src/util.py b/stylegan_human/openpose/src/util.py index 59819ee503d478e7ec2f4fcd3a967b04a3a1ea9f..d8f622d7e54c7103d4cc43a0cdcae96a0b3145d5 100644 --- a/stylegan_human/openpose/src/util.py +++ b/stylegan_human/openpose/src/util.py @@ -14,10 +14,10 @@ def padRightDownCorner(img, stride, padValue): w = img.shape[1] pad = 4 * [None] - pad[0] = 0 # up - pad[1] = 0 # left - pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down - pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right + pad[0] = 0 # up + pad[1] = 0 # left + pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down + pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right img_padded = img pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1)) @@ -32,21 +32,27 @@ def padRightDownCorner(img, stride, padValue): return img_padded, pad # transfer caffe model to pytorch which will match the layer name + + def transfer(model, model_weights): transfered_model_weights = {} for weights_name in model.state_dict().keys(): - transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])] + transfered_model_weights[weights_name] = model_weights['.'.join( + weights_name.split('.')[1:])] return transfered_model_weights # draw the body keypoint and lims -def draw_bodypose(canvas, candidate, subset,show_number=False): + + +def draw_bodypose(canvas, candidate, subset, show_number=False): stickwidth = 4 - limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \ - [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \ + limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], + [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], [1, 16], [16, 18], [3, 17], [6, 18]] - colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \ - [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \ + colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], + [0, 255, 85], [0, 255, 170], [0, 255, 255], [ + 0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]] for i in range(18): for n in range(len(subset)): @@ -56,9 +62,9 @@ def draw_bodypose(canvas, candidate, subset,show_number=False): x, y = candidate[index][0:2] cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1) if show_number: - cv2.putText(canvas, f'{index}', (int(x), int(y)),cv2.FONT_HERSHEY_SIMPLEX, 0.6, - (255,255,0), 1, cv2.LINE_AA) - ## calc and print average + cv2.putText(canvas, f'{index}', (int(x), int(y)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, + (255, 255, 0), 1, cv2.LINE_AA) + # calc and print average for i in range(17): for n in range(len(subset)): index = subset[n][np.array(limbSeq[i]) - 1] @@ -71,13 +77,16 @@ def draw_bodypose(canvas, candidate, subset,show_number=False): mY = np.mean(Y) length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) - polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1) + polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int( + length / 2), stickwidth), int(angle), 0, 360, 1) cv2.fillConvexPoly(cur_canvas, polygon, colors[i]) canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0) return canvas # get max index of 2d array + + def npmax(array): arrayindex = array.argmax(1) arrayvalue = array.max(1) @@ -86,10 +95,12 @@ def npmax(array): return i, j # get max index of 2d array + + def npmax_with_score(array): arrayindex = array.argmax(1) arrayvalue = array.max(1) i = arrayvalue.argmax() j = arrayindex[i] - score =array[i][j] - return i, j,score + score = array[i][j] + return i, j, score diff --git a/stylegan_human/pti/pti_configs/global_config.py b/stylegan_human/pti/pti_configs/global_config.py index ce793517213d2325a98b061314755c2edf69fbd9..bda8d2d08828aace7551db94847e2a1e039876df 100644 --- a/stylegan_human/pti/pti_configs/global_config.py +++ b/stylegan_human/pti/pti_configs/global_config.py @@ -1,12 +1,12 @@ -## Device +# Device cuda_visible_devices = '0' device = 'cuda:0' -## Logs +# Logs training_step = 1 image_rec_result_log_snapshot = 100 pivotal_training_steps = 0 model_snapshot_interval = 400 -## Run name to be updated during PTI +# Run name to be updated during PTI run_name = 'exp' diff --git a/stylegan_human/pti/pti_configs/hyperparameters.py b/stylegan_human/pti/pti_configs/hyperparameters.py index c1014875cc3d46871056cf17fdc8c778ac6139de..ca3a22302a7c5b31a6aa15492a860aa367776e4b 100644 --- a/stylegan_human/pti/pti_configs/hyperparameters.py +++ b/stylegan_human/pti/pti_configs/hyperparameters.py @@ -1,9 +1,9 @@ -## Architechture +# Architechture lpips_type = 'alex' -first_inv_type = 'w+'#'w+' +first_inv_type = 'w+' # 'w+' optim_type = 'adam' -## Locality regularization +# Locality regularization latent_ball_num_of_samples = 1 locality_regularization_interval = 1 use_locality_regularization = False @@ -11,17 +11,17 @@ regulizer_l2_lambda = 0.1 regulizer_lpips_lambda = 0.1 regulizer_alpha = 30 -## Loss +# Loss pt_l2_lambda = 1 pt_lpips_lambda = 1 -## Steps +# Steps LPIPS_value_threshold = 0.04 max_pti_steps = 350 first_inv_steps = 450 max_images_to_invert = 30 -## Optimization +# Optimization pti_learning_rate = 5e-4 first_inv_lr = 8e-3 train_batch_size = 1 diff --git a/stylegan_human/pti/pti_configs/paths_config.py b/stylegan_human/pti/pti_configs/paths_config.py index e4b6fce7bfcf285fb09ae4ddf9432fa6aa562ea5..282741a6c77bab1a0f6970eef590eeed16924b05 100644 --- a/stylegan_human/pti/pti_configs/paths_config.py +++ b/stylegan_human/pti/pti_configs/paths_config.py @@ -1,22 +1,22 @@ import os -## Pretrained models paths +# Pretrained models paths e4e = './pti/e4e_w+.pt' stylegan2_ada_shhq = './pretrained_models/stylegan_human_v2_1024.pkl' -ir_se50 = '' #'./model_ir_se50.pth' +ir_se50 = '' # './model_ir_se50.pth' -## Dirs for output files +# Dirs for output files checkpoints_dir = './outputs/pti/checkpoints/' embedding_base_dir = './outputs/pti/embeddings' experiments_output_dir = './outputs/pti/' -## Input info -### Input dir, where the images reside +# Input info +# Input dir, where the images reside input_data_path = 'aligned_image/' -### Inversion identifier, used to keeping track of the inversion results. Both the latent code and the generator +# Inversion identifier, used to keeping track of the inversion results. Both the latent code and the generator input_data_id = 'test' -## Keywords +# Keywords pti_results_keyword = 'PTI' e4e_results_keyword = 'e4e' sg2_results_keyword = 'SG2' diff --git a/stylegan_human/pti/pti_models/e4e/encoders/helpers.py b/stylegan_human/pti/pti_models/e4e/encoders/helpers.py index c4a58b34ea5ca6912fe53c63dede0a8696f5c024..c5e907be6703ccc43f263b4c40f7d1b84bc47755 100644 --- a/stylegan_human/pti/pti_models/e4e/encoders/helpers.py +++ b/stylegan_human/pti/pti_models/e4e/encoders/helpers.py @@ -50,7 +50,8 @@ def get_blocks(num_layers): get_block(in_channel=256, depth=512, num_units=3) ] else: - raise ValueError("Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers)) + raise ValueError( + "Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers)) return blocks @@ -58,9 +59,11 @@ class SEModule(Module): def __init__(self, channels, reduction): super(SEModule, self).__init__() self.avg_pool = AdaptiveAvgPool2d(1) - self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False) + self.fc1 = Conv2d(channels, channels // reduction, + kernel_size=1, padding=0, bias=False) self.relu = ReLU(inplace=True) - self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False) + self.fc2 = Conv2d(channels // reduction, channels, + kernel_size=1, padding=0, bias=False) self.sigmoid = Sigmoid() def forward(self, x): @@ -85,8 +88,10 @@ class bottleneck_IR(Module): ) self.res_layer = Sequential( BatchNorm2d(in_channel), - Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth), - Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth) + Conv2d(in_channel, depth, (3, 3), (1, 1), + 1, bias=False), PReLU(depth), + Conv2d(depth, depth, (3, 3), stride, 1, + bias=False), BatchNorm2d(depth) ) def forward(self, x): diff --git a/stylegan_human/pti/pti_models/e4e/encoders/model_irse.py b/stylegan_human/pti/pti_models/e4e/encoders/model_irse.py index 976ce2c61104efdc6b0015d895830346dd01bc10..daa7a98457de533545a16b2e09030d8414c5b00e 100644 --- a/stylegan_human/pti/pti_models/e4e/encoders/model_irse.py +++ b/stylegan_human/pti/pti_models/e4e/encoders/model_irse.py @@ -10,7 +10,8 @@ class Backbone(Module): def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True): super(Backbone, self).__init__() assert input_size in [112, 224], "input_size should be 112 or 224" - assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152" + assert num_layers in [ + 50, 100, 152], "num_layers should be 50, 100 or 152" assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se" blocks = get_blocks(num_layers) if mode == 'ir': @@ -50,35 +51,41 @@ class Backbone(Module): def IR_50(input_size): """Constructs a ir-50 model.""" - model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False) + model = Backbone(input_size, num_layers=50, mode='ir', + drop_ratio=0.4, affine=False) return model def IR_101(input_size): """Constructs a ir-101 model.""" - model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False) + model = Backbone(input_size, num_layers=100, mode='ir', + drop_ratio=0.4, affine=False) return model def IR_152(input_size): """Constructs a ir-152 model.""" - model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False) + model = Backbone(input_size, num_layers=152, mode='ir', + drop_ratio=0.4, affine=False) return model def IR_SE_50(input_size): """Constructs a ir_se-50 model.""" - model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False) + model = Backbone(input_size, num_layers=50, mode='ir_se', + drop_ratio=0.4, affine=False) return model def IR_SE_101(input_size): """Constructs a ir_se-101 model.""" - model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False) + model = Backbone(input_size, num_layers=100, mode='ir_se', + drop_ratio=0.4, affine=False) return model def IR_SE_152(input_size): """Constructs a ir_se-152 model.""" - model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False) + model = Backbone(input_size, num_layers=152, mode='ir_se', + drop_ratio=0.4, affine=False) return model diff --git a/stylegan_human/pti/pti_models/e4e/encoders/psp_encoders.py b/stylegan_human/pti/pti_models/e4e/encoders/psp_encoders.py index 85da8a41461e20170cc3f3afaff3f25be9f6b2d1..4bdfa8a5072770967f81ae1f8393b44368ffe42b 100644 --- a/stylegan_human/pti/pti_models/e4e/encoders/psp_encoders.py +++ b/stylegan_human/pti/pti_models/e4e/encoders/psp_encoders.py @@ -58,7 +58,8 @@ class GradualStyleBlock(Module): class GradualStyleEncoder(Module): def __init__(self, num_layers, mode='ir', opts=None): super(GradualStyleEncoder, self).__init__() - assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152' + assert num_layers in [ + 50, 100, 152], 'num_layers should be 50,100, or 152' assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se' blocks = get_blocks(num_layers) if mode == 'ir': @@ -89,8 +90,10 @@ class GradualStyleEncoder(Module): else: style = GradualStyleBlock(512, 512, 64) self.styles.append(style) - self.latlayer1 = nn.Conv2d(256, 512, kernel_size=1, stride=1, padding=0) - self.latlayer2 = nn.Conv2d(128, 512, kernel_size=1, stride=1, padding=0) + self.latlayer1 = nn.Conv2d( + 256, 512, kernel_size=1, stride=1, padding=0) + self.latlayer2 = nn.Conv2d( + 128, 512, kernel_size=1, stride=1, padding=0) def forward(self, x): x = self.input_layer(x) @@ -124,7 +127,8 @@ class GradualStyleEncoder(Module): class Encoder4Editing(Module): def __init__(self, num_layers, mode='ir', opts=None): super(Encoder4Editing, self).__init__() - assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152' + assert num_layers in [ + 50, 100, 152], 'num_layers should be 50,100, or 152' assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se' blocks = get_blocks(num_layers) if mode == 'ir': @@ -157,8 +161,10 @@ class Encoder4Editing(Module): style = GradualStyleBlock(512, 512, 64) self.styles.append(style) - self.latlayer1 = nn.Conv2d(256, 512, kernel_size=1, stride=1, padding=0) - self.latlayer2 = nn.Conv2d(128, 512, kernel_size=1, stride=1, padding=0) + self.latlayer1 = nn.Conv2d( + 256, 512, kernel_size=1, stride=1, padding=0) + self.latlayer2 = nn.Conv2d( + 128, 512, kernel_size=1, stride=1, padding=0) self.progressive_stage = ProgressiveStage.Inference @@ -190,10 +196,12 @@ class Encoder4Editing(Module): features = c3 for i in range(1, min(stage + 1, self.style_count)): # Infer additional deltas if i == self.coarse_ind: - p2 = _upsample_add(c3, self.latlayer1(c2)) # FPN's middle features + # FPN's middle features + p2 = _upsample_add(c3, self.latlayer1(c2)) features = p2 elif i == self.middle_ind: - p1 = _upsample_add(p2, self.latlayer2(c1)) # FPN's fine features + # FPN's fine features + p1 = _upsample_add(p2, self.latlayer2(c1)) features = p1 delta_i = self.styles[i](features) w[:, i] += delta_i diff --git a/stylegan_human/pti/pti_models/e4e/latent_codes_pool.py b/stylegan_human/pti/pti_models/e4e/latent_codes_pool.py index 0281d4b5e80f8eb26e824fa35b4f908dcb6634e6..626a798a8024e8dced8200038f6d397508ecd7c1 100644 --- a/stylegan_human/pti/pti_models/e4e/latent_codes_pool.py +++ b/stylegan_human/pti/pti_models/e4e/latent_codes_pool.py @@ -33,10 +33,12 @@ class LatentCodesPool: for w in ws: # ws.shape: (batch, 512) or (batch, n_latent, 512) # w = torch.unsqueeze(image.data, 0) if w.ndim == 2: - i = random.randint(0, len(w) - 1) # apply a random latent index as a candidate + # apply a random latent index as a candidate + i = random.randint(0, len(w) - 1) w = w[i] self.handle_w(w, return_ws) - return_ws = torch.stack(return_ws, 0) # collect all the images and return + # collect all the images and return + return_ws = torch.stack(return_ws, 0) return return_ws def handle_w(self, w, return_ws): @@ -47,7 +49,8 @@ class LatentCodesPool: else: p = random.uniform(0, 1) if p > 0.5: # by 50% chance, the buffer will return a previously stored latent code, and insert the current code into the buffer - random_id = random.randint(0, self.pool_size - 1) # randint is inclusive + random_id = random.randint( + 0, self.pool_size - 1) # randint is inclusive tmp = self.ws[random_id].clone() self.ws[random_id] = w return_ws.append(tmp) diff --git a/stylegan_human/pti/pti_models/e4e/psp.py b/stylegan_human/pti/pti_models/e4e/psp.py index da7f66099059792f6857a7a0c295be35563a9587..4fa715ae86a6280a7cdb8640ef9192608a5b7e30 100644 --- a/stylegan_human/pti/pti_models/e4e/psp.py +++ b/stylegan_human/pti/pti_models/e4e/psp.py @@ -1,16 +1,17 @@ +from pti.pti_models.e4e.stylegan2.model import Generator +from pti.pti_models.e4e.encoders import psp_encoders +from torch import nn +import torch import matplotlib from pti.pti_configs import paths_config matplotlib.use('Agg') -import torch -from torch import nn -from pti.pti_models.e4e.encoders import psp_encoders -from pti.pti_models.e4e.stylegan2.model import Generator def get_keys(d, name): if 'state_dict' in d: d = d['state_dict'] - d_filt = {k[len(name) + 1:]: v for k, v in d.items() if k[:len(name)] == name} + d_filt = {k[len(name) + 1:]: v for k, v in d.items() + if k[:len(name)] == name} return d_filt @@ -21,7 +22,8 @@ class pSp(nn.Module): self.opts = opts # Define architecture self.encoder = self.set_encoder() - self.decoder = Generator(opts.stylegan_size, 512, 8, channel_multiplier=2) + self.decoder = Generator( + opts.stylegan_size, 512, 8, channel_multiplier=2) self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256 // 2)) # Load weights if needed self.load_weights() @@ -32,17 +34,22 @@ class pSp(nn.Module): elif self.opts.encoder_type == 'Encoder4Editing': encoder = psp_encoders.Encoder4Editing(50, 'ir_se', self.opts) elif self.opts.encoder_type == 'SingleStyleCodeEncoder': - encoder = psp_encoders.BackboneEncoderUsingLastLayerIntoW(50, 'ir_se', self.opts) + encoder = psp_encoders.BackboneEncoderUsingLastLayerIntoW( + 50, 'ir_se', self.opts) else: - raise Exception('{} is not a valid encoders'.format(self.opts.encoder_type)) + raise Exception('{} is not a valid encoders'.format( + self.opts.encoder_type)) return encoder def load_weights(self): if self.opts.checkpoint_path is not None: - print('Loading e4e over the pSp framework from checkpoint: {}'.format(self.opts.checkpoint_path)) + print('Loading e4e over the pSp framework from checkpoint: {}'.format( + self.opts.checkpoint_path)) ckpt = torch.load(self.opts.checkpoint_path, map_location='cpu') - self.encoder.load_state_dict(get_keys(ckpt, 'encoder'), strict=True) - self.decoder.load_state_dict(get_keys(ckpt, 'decoder'), strict=True) + self.encoder.load_state_dict( + get_keys(ckpt, 'encoder'), strict=True) + self.decoder.load_state_dict( + get_keys(ckpt, 'decoder'), strict=True) self.__load_latent_avg(ckpt) else: print('Loading encoders weights from irse50!') @@ -62,15 +69,18 @@ class pSp(nn.Module): # normalize with respect to the center of an average face if self.opts.start_from_latent_avg: if codes.ndim == 2: - codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1)[:, 0, :] + codes = codes + \ + self.latent_avg.repeat(codes.shape[0], 1, 1)[:, 0, :] else: - codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1) + codes = codes + \ + self.latent_avg.repeat(codes.shape[0], 1, 1) if latent_mask is not None: for i in latent_mask: if inject_latent is not None: if alpha is not None: - codes[:, i] = alpha * inject_latent[:, i] + (1 - alpha) * codes[:, i] + codes[:, i] = alpha * inject_latent[:, i] + \ + (1 - alpha) * codes[:, i] else: codes[:, i] = inject_latent[:, i] else: diff --git a/stylegan_human/pti/pti_models/e4e/stylegan2/model.py b/stylegan_human/pti/pti_models/e4e/stylegan2/model.py index d8c8f16d6baf48d95082abba9aa72d30f2bc4377..6ca30efb2baa3159f1bc1954fe3b882ae4e48d12 100644 --- a/stylegan_human/pti/pti_models/e4e/stylegan2/model.py +++ b/stylegan_human/pti/pti_models/e4e/stylegan2/model.py @@ -43,7 +43,8 @@ class Upsample(nn.Module): self.pad = (pad0, pad1) def forward(self, input): - out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad) + out = upfirdn2d(input, self.kernel, up=self.factor, + down=1, pad=self.pad) return out @@ -64,7 +65,8 @@ class Downsample(nn.Module): self.pad = (pad0, pad1) def forward(self, input): - out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad) + out = upfirdn2d(input, self.kernel, up=1, + down=self.factor, pad=self.pad) return out @@ -202,7 +204,8 @@ class ModulatedConv2d(nn.Module): pad0 = (p + 1) // 2 + factor - 1 pad1 = p // 2 + 1 - self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor) + self.blur = Blur(blur_kernel, pad=( + pad0, pad1), upsample_factor=factor) if downsample: factor = 2 @@ -252,7 +255,8 @@ class ModulatedConv2d(nn.Module): weight = weight.transpose(1, 2).reshape( batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size ) - out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch) + out = F.conv_transpose2d( + input, weight, padding=0, stride=2, groups=batch) _, _, height, width = out.shape out = out.view(batch, self.out_channel, height, width) out = self.blur(out) @@ -345,7 +349,8 @@ class ToRGB(nn.Module): if upsample: self.upsample = Upsample(blur_kernel) - self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False) + self.conv = ModulatedConv2d( + in_channel, 3, 1, style_dim, demodulate=False) self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) def forward(self, input, style, skip=None): @@ -455,7 +460,8 @@ class Generator(nn.Module): for i in range(3, self.log_size + 1): for _ in range(2): - noises.append(torch.randn(1, 1, 2 ** i, 2 ** i // 2, device=device)) + noises.append(torch.randn( + 1, 1, 2 ** i, 2 ** i // 2, device=device)) return noises @@ -498,7 +504,8 @@ class Generator(nn.Module): for style in styles: style_t.append( - truncation_latent + truncation * (style - truncation_latent) + truncation_latent + truncation * + (style - truncation_latent) ) styles = style_t @@ -520,7 +527,8 @@ class Generator(nn.Module): # else: # latent = latent[:, :inject_index, :] latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1) + latent2 = styles[1].unsqueeze(1).repeat( + 1, self.n_latent - inject_index, 1) # latent = styles[0][:, :inject_index, :] # latent2 = styles[1][:, inject_index:, :] latent = torch.cat([latent, latent2], 1) @@ -655,7 +663,8 @@ class Discriminator(nn.Module): self.final_conv = ConvLayer(in_channel + 1, channels[4], 3) self.final_linear = nn.Sequential( - EqualLinear(channels[4] * 4 * 4 // 2, channels[4], activation='fused_lrelu'), + EqualLinear(channels[4] * 4 * 4 // 2, + channels[4], activation='fused_lrelu'), EqualLinear(channels[4], 1), ) diff --git a/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_act.py b/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_act.py index 90949545ba955dabf2e17d8cf5e524d5cb190a63..bd4aedd84977884683fb213e11f33ca493aef583 100644 --- a/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_act.py +++ b/stylegan_human/pti/pti_models/e4e/stylegan2/op/fused_act.py @@ -9,7 +9,6 @@ from torch.autograd import Function module_path = os.path.dirname(__file__) - class FusedLeakyReLU(nn.Module): def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5): super().__init__() @@ -31,4 +30,3 @@ def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5): ) * scale ) - diff --git a/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d.py b/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d.py index 02fc25af780868d9b883631eb6b03a25c225d745..6727f2bf0857c1f4e0d50de363de75e7b8d4de50 100644 --- a/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d.py +++ b/stylegan_human/pti/pti_models/e4e/stylegan2/op/upfirdn2d.py @@ -7,7 +7,6 @@ from torch.nn import functional as F module_path = os.path.dirname(__file__) - def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): out = upfirdn2d_native( input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1] @@ -30,12 +29,13 @@ def upfirdn2d_native( out = out.view(-1, in_h * up_y, in_w * up_x, minor) out = F.pad( - out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] + out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), + max(pad_y0, 0), max(pad_y1, 0)] ) out = out[ :, - max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), - max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), + max(-pad_y0, 0): out.shape[1] - max(-pad_y1, 0), + max(-pad_x0, 0): out.shape[2] - max(-pad_x1, 0), :, ] @@ -57,4 +57,4 @@ def upfirdn2d_native( out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 - return out.view(-1, channel, out_h, out_w) \ No newline at end of file + return out.view(-1, channel, out_h, out_w) diff --git a/stylegan_human/pti/training/coaches/base_coach.py b/stylegan_human/pti/training/coaches/base_coach.py index b429bd3cf186fc32bbf9491a39a889eebfe2110d..1d754dccd39786bcec2a6f08ad1e6fd845319bab 100644 --- a/stylegan_human/pti/training/coaches/base_coach.py +++ b/stylegan_human/pti/training/coaches/base_coach.py @@ -33,7 +33,8 @@ class BaseCoach: transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) # Initialize loss - self.lpips_loss = LPIPS(net=hyperparameters.lpips_type).to(global_config.device).eval() + self.lpips_loss = LPIPS(net=hyperparameters.lpips_type).to( + global_config.device).eval() self.restart_training() @@ -49,7 +50,8 @@ class BaseCoach: self.original_G = load_old_G() - self.space_regulizer = Space_Regulizer(self.original_G, self.lpips_loss) + self.space_regulizer = Space_Regulizer( + self.original_G, self.lpips_loss) self.optimizer = self.configure_optimizers() def get_inversion(self, w_path_dir, image_name, image): @@ -87,7 +89,8 @@ class BaseCoach: w = self.get_e4e_inversion(image) else: - id_image = torch.squeeze((image.to(global_config.device) + 1) / 2) * 255 + id_image = torch.squeeze( + (image.to(global_config.device) + 1) / 2) * 255 w = w_projector.project(self.G, id_image, device=torch.device(global_config.device), w_avg_samples=600, num_steps=hyperparameters.first_inv_steps, w_name=image_name, use_wandb=self.use_wandb) @@ -99,7 +102,8 @@ class BaseCoach: pass def configure_optimizers(self): - optimizer = torch.optim.Adam(self.G.parameters(), lr=hyperparameters.pti_learning_rate) + optimizer = torch.optim.Adam( + self.G.parameters(), lr=hyperparameters.pti_learning_rate) return optimizer @@ -109,23 +113,27 @@ class BaseCoach: if hyperparameters.pt_l2_lambda > 0: l2_loss_val = l2_loss(generated_images, real_images) if self.use_wandb: - wandb.log({f'MSE_loss_val_{log_name}': l2_loss_val.detach().cpu()}, step=global_config.training_step) + wandb.log({f'MSE_loss_val_{log_name}': l2_loss_val.detach( + ).cpu()}, step=global_config.training_step) loss += l2_loss_val * hyperparameters.pt_l2_lambda if hyperparameters.pt_lpips_lambda > 0: loss_lpips = self.lpips_loss(generated_images, real_images) loss_lpips = torch.squeeze(loss_lpips) if self.use_wandb: - wandb.log({f'LPIPS_loss_val_{log_name}': loss_lpips.detach().cpu()}, step=global_config.training_step) + wandb.log({f'LPIPS_loss_val_{log_name}': loss_lpips.detach( + ).cpu()}, step=global_config.training_step) loss += loss_lpips * hyperparameters.pt_lpips_lambda if use_ball_holder and hyperparameters.use_locality_regularization: - ball_holder_loss_val = self.space_regulizer.space_regulizer_loss(new_G, w_batch, use_wandb=self.use_wandb) + ball_holder_loss_val = self.space_regulizer.space_regulizer_loss( + new_G, w_batch, use_wandb=self.use_wandb) loss += ball_holder_loss_val return loss, l2_loss_val, loss_lpips def forward(self, w): - generated_images = self.G.synthesis(w, noise_mode='const', force_fp32=True) + generated_images = self.G.synthesis( + w, noise_mode='const', force_fp32=True) return generated_images @@ -137,7 +145,8 @@ class BaseCoach: opts = Namespace(**opts) self.e4e_inversion_net = pSp(opts) self.e4e_inversion_net.eval() - self.e4e_inversion_net = self.e4e_inversion_net.to(global_config.device) + self.e4e_inversion_net = self.e4e_inversion_net.to( + global_config.device) toogle_grad(self.e4e_inversion_net, False) def get_e4e_inversion(self, image): diff --git a/stylegan_human/pti/training/coaches/localitly_regulizer.py b/stylegan_human/pti/training/coaches/localitly_regulizer.py index 4a4edc3694dd4134d9caa6af0184909451032cc6..c4fe05cd2a77113b569587c1b4a5ec358646f7a4 100644 --- a/stylegan_human/pti/training/coaches/localitly_regulizer.py +++ b/stylegan_human/pti/training/coaches/localitly_regulizer.py @@ -19,9 +19,11 @@ class Space_Regulizer: def get_morphed_w_code(self, new_w_code, fixed_w): interpolation_direction = new_w_code - fixed_w interpolation_direction_norm = torch.norm(interpolation_direction, p=2) - direction_to_move = hyperparameters.regulizer_alpha * interpolation_direction / interpolation_direction_norm + direction_to_move = hyperparameters.regulizer_alpha * \ + interpolation_direction / interpolation_direction_norm result_w = fixed_w + direction_to_move - self.morphing_regulizer_alpha * fixed_w + (1 - self.morphing_regulizer_alpha) * new_w_code + self.morphing_regulizer_alpha * fixed_w + \ + (1 - self.morphing_regulizer_alpha) * new_w_code return result_w @@ -31,15 +33,19 @@ class Space_Regulizer: def ball_holder_loss_lazy(self, new_G, num_of_sampled_latents, w_batch, use_wandb=False): loss = 0.0 - z_samples = np.random.randn(num_of_sampled_latents, self.original_G.z_dim) + z_samples = np.random.randn( + num_of_sampled_latents, self.original_G.z_dim) w_samples = self.original_G.mapping(torch.from_numpy(z_samples).to(global_config.device), None, truncation_psi=0.5) - territory_indicator_ws = [self.get_morphed_w_code(w_code.unsqueeze(0), w_batch) for w_code in w_samples] + territory_indicator_ws = [self.get_morphed_w_code( + w_code.unsqueeze(0), w_batch) for w_code in w_samples] for w_code in territory_indicator_ws: - new_img = new_G.synthesis(w_code, noise_mode='none', force_fp32=True) + new_img = new_G.synthesis( + w_code, noise_mode='none', force_fp32=True) with torch.no_grad(): - old_img = self.original_G.synthesis(w_code, noise_mode='none', force_fp32=True) + old_img = self.original_G.synthesis( + w_code, noise_mode='none', force_fp32=True) if hyperparameters.regulizer_l2_lambda > 0: l2_loss_val = l2_loss.l2_loss(old_img, new_img) @@ -59,5 +65,6 @@ class Space_Regulizer: return loss / len(territory_indicator_ws) def space_regulizer_loss(self, new_G, w_batch, use_wandb): - ret_val = self.ball_holder_loss_lazy(new_G, hyperparameters.latent_ball_num_of_samples, w_batch, use_wandb) + ret_val = self.ball_holder_loss_lazy( + new_G, hyperparameters.latent_ball_num_of_samples, w_batch, use_wandb) return ret_val diff --git a/stylegan_human/pti/training/coaches/multi_id_coach.py b/stylegan_human/pti/training/coaches/multi_id_coach.py index 97a9cd796fef1c2989a57d6b821e269d274f2d24..50210f7086e6613e50c057d1503b97359ad3359f 100644 --- a/stylegan_human/pti/training/coaches/multi_id_coach.py +++ b/stylegan_human/pti/training/coaches/multi_id_coach.py @@ -21,7 +21,8 @@ class MultiIDCoach(BaseCoach): w_path_dir = f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}' os.makedirs(w_path_dir, exist_ok=True) - os.makedirs(f'{w_path_dir}/{paths_config.pti_results_keyword}', exist_ok=True) + os.makedirs( + f'{w_path_dir}/{paths_config.pti_results_keyword}', exist_ok=True) use_ball_holder = True w_pivots = [] @@ -56,7 +57,7 @@ class MultiIDCoach(BaseCoach): generated_images = self.forward(w_pivot) loss, l2_loss_val, loss_lpips = self.calc_loss(generated_images, real_images_batch, image_name, - self.G, use_ball_holder, w_pivot) + self.G, use_ball_holder, w_pivot) self.optimizer.zero_grad() loss.backward() @@ -75,5 +76,5 @@ class MultiIDCoach(BaseCoach): snapshot_data = dict() snapshot_data['G_ema'] = self.G import pickle - with open(f'{paths_config.checkpoints_dir}/model_{global_config.run_name}_multi_id.pkl', 'wb') as f: - pickle.dump(snapshot_data, f) + with open(f'{paths_config.checkpoints_dir}/model_{global_config.run_name}_multi_id.pkl', 'wb') as f: + pickle.dump(snapshot_data, f) diff --git a/stylegan_human/pti/training/coaches/single_id_coach.py b/stylegan_human/pti/training/coaches/single_id_coach.py index 7521a6eed000de76c14504f293efd2b6789eb5f1..f703573a522bdfc6fecd85f25fe2bfb2e0430e29 100644 --- a/stylegan_human/pti/training/coaches/single_id_coach.py +++ b/stylegan_human/pti/training/coaches/single_id_coach.py @@ -8,6 +8,7 @@ from pti.training.coaches.base_coach import BaseCoach from utils.log_utils import log_images_from_w from torchvision.utils import save_image + class SingleIDCoach(BaseCoach): def __init__(self, data_loader, use_wandb): @@ -17,7 +18,8 @@ class SingleIDCoach(BaseCoach): w_path_dir = f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}' os.makedirs(w_path_dir, exist_ok=True) - os.makedirs(f'{w_path_dir}/{paths_config.pti_results_keyword}', exist_ok=True) + os.makedirs( + f'{w_path_dir}/{paths_config.pti_results_keyword}', exist_ok=True) use_ball_holder = True @@ -74,9 +76,10 @@ class SingleIDCoach(BaseCoach): log_images_counter += 1 # save output image - tmp = torch.cat([real_images_batch, tmp1, generated_images], axis= 3) - save_image(tmp, f"{paths_config.experiments_output_dir}/{image_name}.png", normalize=True) - + tmp = torch.cat( + [real_images_batch, tmp1, generated_images], axis=3) + save_image( + tmp, f"{paths_config.experiments_output_dir}/{image_name}.png", normalize=True) self.image_counter += 1 @@ -86,4 +89,4 @@ class SingleIDCoach(BaseCoach): snapshot_data['G_ema'] = self.G import pickle with open(f'{paths_config.checkpoints_dir}/model_{image_name}.pkl', 'wb') as f: - pickle.dump(snapshot_data, f) + pickle.dump(snapshot_data, f) diff --git a/stylegan_human/pti/training/projectors/w_plus_projector.py b/stylegan_human/pti/training/projectors/w_plus_projector.py index 7d4abaf0ef32378504191559d7c95f3e58a63ffa..b61fa0159b02a052bc8a52341a53ec4b62ced657 100644 --- a/stylegan_human/pti/training/projectors/w_plus_projector.py +++ b/stylegan_human/pti/training/projectors/w_plus_projector.py @@ -21,7 +21,8 @@ from utils.log_utils import log_image_from_w def project( G, - target: torch.Tensor, # [C,H,W] and dynamic range [0,255], W & H must match G output resolution + # [C,H,W] and dynamic range [0,255], W & H must match G output resolution + target: torch.Tensor, *, num_steps=1000, w_avg_samples=10000, @@ -39,20 +40,25 @@ def project( w_name: str ): print('inside training/projectors/w_plus_projector') - print(target.shape, G.img_channels, G.img_resolution * 2 , G.img_resolution) - assert target.shape == (G.img_channels, G.img_resolution * 2, G.img_resolution) + print(target.shape, G.img_channels, G.img_resolution * 2, G.img_resolution) + assert target.shape == ( + G.img_channels, G.img_resolution * 2, G.img_resolution) def logprint(*args): if verbose: print(*args) - G = copy.deepcopy(G).eval().requires_grad_(False).to(device).float() # type: ignore + G = copy.deepcopy(G).eval().requires_grad_( + False).to(device).float() # type: ignore # Compute w stats. - logprint(f'Computing W midpoint and stddev using {w_avg_samples} samples...') + logprint( + f'Computing W midpoint and stddev using {w_avg_samples} samples...') z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim) - w_samples = G.mapping(torch.from_numpy(z_samples).to(device), None) # [N, L, C] - w_samples = w_samples[:, :1, :].cpu().numpy().astype(np.float32) # [N, 1, C] + w_samples = G.mapping(torch.from_numpy( + z_samples).to(device), None) # [N, L, C] + w_samples = w_samples[:, :1, :].cpu( + ).numpy().astype(np.float32) # [N, 1, C] w_avg = np.mean(w_samples, axis=0, keepdims=True) # [1, 1, C] w_avg_tensor = torch.from_numpy(w_avg).to(global_config.device) w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5 @@ -60,7 +66,8 @@ def project( start_w = initial_w if initial_w is not None else w_avg # Setup noise inputs. - noise_bufs = {name: buf for (name, buf) in G.synthesis.named_buffers() if 'noise_const' in name} + noise_bufs = {name: buf for ( + name, buf) in G.synthesis.named_buffers() if 'noise_const' in name} # Load VGG16 feature detector. url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt' @@ -70,8 +77,10 @@ def project( # Features for target image. target_images = target.unsqueeze(0).to(device).to(torch.float32) if target_images.shape[2] > 256: - target_images = F.interpolate(target_images, size=(256, 256), mode='area') - target_features = vgg16(target_images, resize_images=False, return_lpips=True) + target_images = F.interpolate( + target_images, size=(256, 256), mode='area') + target_features = vgg16( + target_images, resize_images=False, return_lpips=True) start_w = np.repeat(start_w, G.mapping.num_ws, axis=1) w_opt = torch.tensor(start_w, dtype=torch.float32, device=device, @@ -89,7 +98,8 @@ def project( # Learning rate schedule. t = step / num_steps - w_noise_scale = w_std * initial_noise_factor * max(0.0, 1.0 - t / noise_ramp_length) ** 2 + w_noise_scale = w_std * initial_noise_factor * \ + max(0.0, 1.0 - t / noise_ramp_length) ** 2 lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length) lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi) lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length) @@ -106,10 +116,12 @@ def project( # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images. synth_images = (synth_images + 1) * (255 / 2) if synth_images.shape[2] > 256: - synth_images = F.interpolate(synth_images, size=(256, 256), mode='area') + synth_images = F.interpolate( + synth_images, size=(256, 256), mode='area') # Features for synth images. - synth_features = vgg16(synth_images, resize_images=False, return_lpips=True) + synth_features = vgg16( + synth_images, resize_images=False, return_lpips=True) dist = (target_features - synth_features).square().sum() # Noise regularization. @@ -117,8 +129,10 @@ def project( for v in noise_bufs.values(): noise = v[None, None, :, :] # must be [1,1,H,W] for F.avg_pool2d() while True: - reg_loss += (noise * torch.roll(noise, shifts=1, dims=3)).mean() ** 2 - reg_loss += (noise * torch.roll(noise, shifts=1, dims=2)).mean() ** 2 + reg_loss += (noise * torch.roll(noise, + shifts=1, dims=3)).mean() ** 2 + reg_loss += (noise * torch.roll(noise, + shifts=1, dims=2)).mean() ** 2 if noise.shape[2] <= 8: break noise = F.avg_pool2d(noise, kernel_size=2) @@ -128,14 +142,16 @@ def project( with torch.no_grad(): if use_wandb: global_config.training_step += 1 - wandb.log({f'first projection _{w_name}': loss.detach().cpu()}, step=global_config.training_step) + wandb.log({f'first projection _{w_name}': loss.detach( + ).cpu()}, step=global_config.training_step) log_image_from_w(w_opt, G, w_name) # Step optimizer.zero_grad(set_to_none=True) loss.backward() optimizer.step() - logprint(f'step {step + 1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}') + logprint( + f'step {step + 1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}') # Normalize noise. with torch.no_grad(): diff --git a/stylegan_human/pti/training/projectors/w_projector.py b/stylegan_human/pti/training/projectors/w_projector.py index 36d93c1d61e5c6fe91e0ccdb68cd1e953a0a3807..12553b8c4450dc8bb605b0eab0f55d90ba2d051f 100644 --- a/stylegan_human/pti/training/projectors/w_projector.py +++ b/stylegan_human/pti/training/projectors/w_projector.py @@ -21,7 +21,8 @@ import dnnlib def project( G, - target: torch.Tensor, # [C,H,W] and dynamic range [0,255], W & H must match G output resolution + # [C,H,W] and dynamic range [0,255], W & H must match G output resolution + target: torch.Tensor, *, num_steps=1000, w_avg_samples=10000, @@ -38,20 +39,25 @@ def project( image_log_step=global_config.image_rec_result_log_snapshot, w_name: str ): - print(target.shape,G.img_channels, G.img_resolution, G.img_resolution//2) - assert target.shape == (G.img_channels, G.img_resolution, G.img_resolution // 2) + print(target.shape, G.img_channels, G.img_resolution, G.img_resolution//2) + assert target.shape == ( + G.img_channels, G.img_resolution, G.img_resolution // 2) def logprint(*args): if verbose: print(*args) - G = copy.deepcopy(G).eval().requires_grad_(False).to(device).float() # type: ignore + G = copy.deepcopy(G).eval().requires_grad_( + False).to(device).float() # type: ignore # Compute w stats. - logprint(f'Computing W midpoint and stddev using {w_avg_samples} samples...') + logprint( + f'Computing W midpoint and stddev using {w_avg_samples} samples...') z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim) - w_samples = G.mapping(torch.from_numpy(z_samples).to(device), None) # [N, L, C] - w_samples = w_samples[:, :1, :].cpu().numpy().astype(np.float32) # [N, 1, C] + w_samples = G.mapping(torch.from_numpy( + z_samples).to(device), None) # [N, L, C] + w_samples = w_samples[:, :1, :].cpu( + ).numpy().astype(np.float32) # [N, 1, C] w_avg = np.mean(w_samples, axis=0, keepdims=True) # [1, 1, C] w_avg_tensor = torch.from_numpy(w_avg).to(global_config.device) w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5 @@ -59,7 +65,8 @@ def project( start_w = initial_w if initial_w is not None else w_avg # Setup noise inputs. - noise_bufs = {name: buf for (name, buf) in G.synthesis.named_buffers() if 'noise_const' in name} + noise_bufs = {name: buf for ( + name, buf) in G.synthesis.named_buffers() if 'noise_const' in name} # Load VGG16 feature detector. url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt' @@ -69,8 +76,10 @@ def project( # Features for target image. target_images = target.unsqueeze(0).to(device).to(torch.float32) if target_images.shape[2] > 256: - target_images = F.interpolate(target_images, size=(256, 256), mode='area') - target_features = vgg16(target_images, resize_images=False, return_lpips=True) + target_images = F.interpolate( + target_images, size=(256, 256), mode='area') + target_features = vgg16( + target_images, resize_images=False, return_lpips=True) w_opt = torch.tensor(start_w, dtype=torch.float32, device=device, requires_grad=True) # pylint: disable=not-callable @@ -86,7 +95,8 @@ def project( # Learning rate schedule. t = step / num_steps - w_noise_scale = w_std * initial_noise_factor * max(0.0, 1.0 - t / noise_ramp_length) ** 2 + w_noise_scale = w_std * initial_noise_factor * \ + max(0.0, 1.0 - t / noise_ramp_length) ** 2 lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length) lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi) lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length) @@ -102,10 +112,12 @@ def project( # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images. synth_images = (synth_images + 1) * (255 / 2) if synth_images.shape[2] > 256: - synth_images = F.interpolate(synth_images, size=(256, 256), mode='area') + synth_images = F.interpolate( + synth_images, size=(256, 256), mode='area') # Features for synth images. - synth_features = vgg16(synth_images, resize_images=False, return_lpips=True) + synth_features = vgg16( + synth_images, resize_images=False, return_lpips=True) dist = (target_features - synth_features).square().sum() # Noise regularization. @@ -113,8 +125,10 @@ def project( for v in noise_bufs.values(): noise = v[None, None, :, :] # must be [1,1,H,W] for F.avg_pool2d() while True: - reg_loss += (noise * torch.roll(noise, shifts=1, dims=3)).mean() ** 2 - reg_loss += (noise * torch.roll(noise, shifts=1, dims=2)).mean() ** 2 + reg_loss += (noise * torch.roll(noise, + shifts=1, dims=3)).mean() ** 2 + reg_loss += (noise * torch.roll(noise, + shifts=1, dims=2)).mean() ** 2 if noise.shape[2] <= 8: break noise = F.avg_pool2d(noise, kernel_size=2) @@ -125,14 +139,17 @@ def project( with torch.no_grad(): if use_wandb: global_config.training_step += 1 - wandb.log({f'first projection _{w_name}': loss.detach().cpu()}, step=global_config.training_step) - log_utils.log_image_from_w(w_opt.repeat([1, G.mapping.num_ws, 1]), G, w_name) + wandb.log({f'first projection _{w_name}': loss.detach( + ).cpu()}, step=global_config.training_step) + log_utils.log_image_from_w(w_opt.repeat( + [1, G.mapping.num_ws, 1]), G, w_name) # Step optimizer.zero_grad(set_to_none=True) loss.backward() optimizer.step() - logprint(f'step {step + 1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}') + logprint( + f'step {step + 1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}') # Normalize noise. with torch.no_grad(): diff --git a/stylegan_human/run_pti.py b/stylegan_human/run_pti.py index c335c5b156554ea1069af0c153ec6edbea70d931..1be91596fa768240020a2e9af03cb5f24ca1072e 100644 --- a/stylegan_human/run_pti.py +++ b/stylegan_human/run_pti.py @@ -18,17 +18,19 @@ def run_PTI(run_name='', use_wandb=False, use_multi_id_training=False): os.environ['CUDA_VISIBLE_DEVICES'] = global_config.cuda_visible_devices if run_name == '': - global_config.run_name = ''.join(choice(ascii_uppercase) for i in range(12)) + global_config.run_name = ''.join( + choice(ascii_uppercase) for i in range(12)) else: global_config.run_name = run_name if use_wandb: - run = wandb.init(project=paths_config.pti_results_keyword, reinit=True, name=global_config.run_name) + run = wandb.init(project=paths_config.pti_results_keyword, + reinit=True, name=global_config.run_name) global_config.pivotal_training_steps = 1 global_config.training_step = 1 embedding_dir_path = f'{paths_config.embedding_base_dir}/{paths_config.input_data_id}/{paths_config.pti_results_keyword}' - # print('embedding_dir_path: ', embedding_dir_path) #./embeddings/barcelona/PTI + # print('embedding_dir_path: ', embedding_dir_path) #./embeddings/barcelona/PTI os.makedirs(embedding_dir_path, exist_ok=True) dataset = ImagesDataset(paths_config.input_data_path, transforms.Compose([ diff --git a/stylegan_human/style_mixing.py b/stylegan_human/style_mixing.py index e23b60fa321ab6e19cdbdd9b5206cf595f045fe8..022912df133bd977364786f90d6ae635292dc135 100644 --- a/stylegan_human/style_mixing.py +++ b/stylegan_human/style_mixing.py @@ -6,7 +6,7 @@ # and any modifications thereto. Any use, reproduction, disclosure or # distribution of this software and related documentation without an express # license agreement from NVIDIA CORPORATION is strictly prohibited. -# +# import os @@ -30,6 +30,7 @@ python style_mixing.py --network=pretrained_models/stylegan_human_v2_1024.pkl -- --cols=55,821,1789,293 --styles=0-3 --outdir=outputs/stylemixing """ + @click.command() @click.option('--network', 'network_pkl', help='Network pickle filename', required=True) @click.option('--rows', 'row_seeds', type=legacy.num_range, help='Random seeds to use for image rows', required=True) @@ -47,7 +48,7 @@ def generate_style_mix( noise_mode: str, outdir: str ): - + print('Loading networks from "%s"...' % network_pkl) device = torch.device('cuda') with dnnlib.util.open_url(network_pkl) as f: @@ -57,7 +58,8 @@ def generate_style_mix( print('Generating W vectors...') all_seeds = list(set(row_seeds + col_seeds)) - all_z = np.stack([np.random.RandomState(seed).randn(G.z_dim) for seed in all_seeds]) + all_z = np.stack([np.random.RandomState(seed).randn(G.z_dim) + for seed in all_seeds]) all_w = G.mapping(torch.from_numpy(all_z).to(device), None) w_avg = G.mapping.w_avg all_w = w_avg + (all_w - w_avg) * truncation_psi @@ -65,8 +67,10 @@ def generate_style_mix( print('Generating images...') all_images = G.synthesis(all_w, noise_mode=noise_mode) - all_images = (all_images.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).cpu().numpy() - image_dict = {(seed, seed): image for seed, image in zip(all_seeds, list(all_images))} + all_images = (all_images.permute(0, 2, 3, 1) * 127.5 + + 128).clamp(0, 255).to(torch.uint8).cpu().numpy() + image_dict = {(seed, seed): image for seed, + image in zip(all_seeds, list(all_images))} print('Generating style-mixed images...') for row_seed in row_seeds: @@ -74,9 +78,10 @@ def generate_style_mix( w = w_dict[row_seed].clone() w[col_styles] = w_dict[col_seed][col_styles] image = G.synthesis(w[np.newaxis], noise_mode=noise_mode) - image = (image.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) + image = (image.permute(0, 2, 3, 1) * 127.5 + + 128).clamp(0, 255).to(torch.uint8) image_dict[(row_seed, col_seed)] = image[0].cpu().numpy() - + os.makedirs(outdir, exist_ok=True) # print('Saving images...') # for (row_seed, col_seed), image in image_dict.items(): @@ -84,8 +89,9 @@ def generate_style_mix( print('Saving image grid...') W = G.img_resolution // 2 - H = G.img_resolution - canvas = PIL.Image.new('RGB', (W * (len(col_seeds) + 1), H * (len(row_seeds) + 1)), 'black') + H = G.img_resolution + canvas = PIL.Image.new( + 'RGB', (W * (len(col_seeds) + 1), H * (len(row_seeds) + 1)), 'black') for row_idx, row_seed in enumerate([0] + row_seeds): for col_idx, col_seed in enumerate([0] + col_seeds): if row_idx == 0 and col_idx == 0: @@ -95,13 +101,14 @@ def generate_style_mix( key = (col_seed, col_seed) if col_idx == 0: key = (row_seed, row_seed) - canvas.paste(PIL.Image.fromarray(image_dict[key], 'RGB'), (W * col_idx, H * row_idx)) + canvas.paste(PIL.Image.fromarray( + image_dict[key], 'RGB'), (W * col_idx, H * row_idx)) canvas.save(f'{outdir}/grid.png') -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- if __name__ == "__main__": - generate_style_mix() # pylint: disable=no-value-for-parameter + generate_style_mix() # pylint: disable=no-value-for-parameter -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/stylemixing_video.py b/stylegan_human/stylemixing_video.py index 734f3439d118e652810b3692967a50fd15c174e2..b917189ea0530163c5d3d61c17a78b92fcc99bb4 100755 --- a/stylegan_human/stylemixing_video.py +++ b/stylegan_human/stylemixing_video.py @@ -5,6 +5,7 @@ Script reference: https://github.com/PDillis/stylegan2-fun """ +import moviepy.editor import argparse import legacy @@ -22,7 +23,6 @@ import click import torch os.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = "hide" -import moviepy.editor """ @@ -34,24 +34,28 @@ python stylemixing_video.py --network=pretrained_models/stylegan_human_v2_1024.p --col-seeds=3098,31759,3791 --col-styles=8-12 --trunc=0.8 --outdir=outputs/stylemixing_video """ + @click.command() @click.option('--network', 'network_pkl', help='Path to network pickle filename', required=True) @click.option('--row-seed', 'src_seed', type=legacy.num_range, help='Random seed to use for image source row', required=True) @click.option('--col-seeds', 'dst_seeds', type=legacy.num_range, help='Random seeds to use for image columns (style)', required=True) @click.option('--col-styles', 'col_styles', type=legacy.num_range, help='Style layer range (default: %(default)s)', default='0-6') -@click.option('--only-stylemix', 'only_stylemix', help='Add flag to only show the style mxied images in the video',default=False) +@click.option('--only-stylemix', 'only_stylemix', help='Add flag to only show the style mxied images in the video', default=False) @click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi (default: %(default)s)', default=1) @click.option('--duration-sec', 'duration_sec', type=float, help='Duration of video (default: %(default)s)', default=10) @click.option('--fps', 'mp4_fps', type=int, help='FPS of generated video (default: %(default)s)', default=10) @click.option('--indent-range', 'indent_range', type=int, default=30) @click.option('--outdir', help='Root directory for run results (default: %(default)s)', default='outputs/stylemixing_video', metavar='DIR') - def style_mixing_video(network_pkl: str, - src_seed: List[int], # Seed of the source image style (row) - dst_seeds: List[int], # Seeds of the destination image styles (columns) - col_styles: List[int], # Styles to transfer from first row to first column - truncation_psi=float, - only_stylemix=bool, # True if user wishes to show only thre style transferred result + # Seed of the source image style (row) + src_seed: List[int], + # Seeds of the destination image styles (columns) + dst_seeds: List[int], + # Styles to transfer from first row to first column + col_styles: List[int], + truncation_psi=float, + # True if user wishes to show only thre style transferred result + only_stylemix=bool, duration_sec=float, smoothing_sec=1.0, mp4_fps=int, @@ -67,17 +71,20 @@ def style_mixing_video(network_pkl: str, print('Loading networks from "%s"...' % network_pkl) device = torch.device('cuda') with dnnlib.util.open_url(network_pkl) as f: - Gs = legacy.load_network_pkl(f)['G_ema'].to(device) + Gs = legacy.load_network_pkl(f)['G_ema'].to(device) - print(Gs.num_ws, Gs.w_dim, Gs.img_resolution) + print(Gs.num_ws, Gs.w_dim, Gs.img_resolution) max_style = int(2 * np.log2(Gs.img_resolution)) - 3 - assert max(col_styles) <= max_style, f"Maximum col-style allowed: {max_style}" + assert max( + col_styles) <= max_style, f"Maximum col-style allowed: {max_style}" # Left col latents print('Generating Source W vectors...') - src_shape = [num_frames] + [Gs.z_dim] - src_z = np.random.RandomState(*src_seed).randn(*src_shape).astype(np.float32) # [frames, src, component] - src_z = scipy.ndimage.gaussian_filter(src_z, [smoothing_sec * mp4_fps] + [0] * (2- 1), mode="wrap") + src_shape = [num_frames] + [Gs.z_dim] + src_z = np.random.RandomState( + *src_seed).randn(*src_shape).astype(np.float32) # [frames, src, component] + src_z = scipy.ndimage.gaussian_filter( + src_z, [smoothing_sec * mp4_fps] + [0] * (2 - 1), mode="wrap") src_z /= np.sqrt(np.mean(np.square(src_z))) # Map into the detangled latent space W and do truncation trick src_w = Gs.mapping(torch.from_numpy(src_z).to(device), None) @@ -85,8 +92,9 @@ def style_mixing_video(network_pkl: str, src_w = w_avg + (src_w - w_avg) * truncation_psi # Top row latents (fixed reference) - print('Generating Destination W vectors...') - dst_z = np.stack([np.random.RandomState(seed).randn(Gs.z_dim) for seed in dst_seeds]) + print('Generating Destination W vectors...') + dst_z = np.stack([np.random.RandomState(seed).randn(Gs.z_dim) + for seed in dst_seeds]) dst_w = Gs.mapping(torch.from_numpy(dst_z).to(device), None) dst_w = w_avg + (dst_w - w_avg) * truncation_psi # Get the width and height of each image: @@ -95,37 +103,44 @@ def style_mixing_video(network_pkl: str, # Generate ALL the source images: src_images = Gs.synthesis(src_w, noise_mode=noise_mode) - src_images = (src_images.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) + src_images = (src_images.permute(0, 2, 3, 1) * 127.5 + + 128).clamp(0, 255).to(torch.uint8) # Generate the column images: dst_images = Gs.synthesis(dst_w, noise_mode=noise_mode) - dst_images = (dst_images.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) - + dst_images = (dst_images.permute(0, 2, 3, 1) * 127.5 + + 128).clamp(0, 255).to(torch.uint8) print('Generating full video (including source and destination images)') # Generate our canvas where we will paste all the generated images: - canvas = PIL.Image.new("RGB", ((W-indent_range) * (len(dst_seeds) + 1), H * (len(src_seed) + 1)), "white") # W, H + canvas = PIL.Image.new("RGB", (( + W-indent_range) * (len(dst_seeds) + 1), H * (len(src_seed) + 1)), "white") # W, H - for col, dst_image in enumerate(list(dst_images)): #dst_image:[3,1024,512] - canvas.paste(PIL.Image.fromarray(dst_image.cpu().numpy(), "RGB"), ((col + 1) * (W-indent_range), 0)) #H + # dst_image:[3,1024,512] + for col, dst_image in enumerate(list(dst_images)): + canvas.paste(PIL.Image.fromarray(dst_image.cpu().numpy(), + "RGB"), ((col + 1) * (W-indent_range), 0)) # H # Aux functions: Frame generation func for moviepy. + def make_frame(t): # Get the frame number according to time t: frame_idx = int(np.clip(np.round(t * mp4_fps), 0, num_frames - 1)) # We wish the image belonging to the frame at time t: - src_image = src_images[frame_idx] # always in the same place - canvas.paste(PIL.Image.fromarray(src_image.cpu().numpy(), "RGB"), (0-indent_range, H)) # Paste it to the lower left + src_image = src_images[frame_idx] # always in the same place + canvas.paste(PIL.Image.fromarray(src_image.cpu().numpy(), "RGB"), + (0-indent_range, H)) # Paste it to the lower left # Now, for each of the column images: for col, dst_image in enumerate(list(dst_images)): # Select the pertinent latent w column: - w_col = np.stack([dst_w[col].cpu()]) # [18, 512] -> [1, 18, 512] + w_col = np.stack([dst_w[col].cpu()]) # [18, 512] -> [1, 18, 512] w_col = torch.from_numpy(w_col).to(device) # Replace the values defined by col_styles: - w_col[:, col_styles] = src_w[frame_idx, col_styles]#.cpu() + w_col[:, col_styles] = src_w[frame_idx, col_styles] # .cpu() # Generate these synthesized images: col_images = Gs.synthesis(w_col, noise_mode=noise_mode) - col_images = (col_images.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) + col_images = (col_images.permute(0, 2, 3, 1) * + 127.5 + 128).clamp(0, 255).to(torch.uint8) # Paste them in their respective spot: for row, image in enumerate(list(col_images)): canvas.paste( @@ -133,14 +148,16 @@ def style_mixing_video(network_pkl: str, ((col + 1) * (W - indent_range), (row + 1) * H), ) return np.array(canvas) - + # Generate video using make_frame: print('Generating style-mixed video...') videoclip = moviepy.editor.VideoClip(make_frame, duration=duration_sec) grid_size = [len(dst_seeds), len(src_seed)] - mp4 = "{}x{}-style-mixing_{}_{}.mp4".format(*grid_size,min(col_styles),max(col_styles)) - if not os.path.exists(outdir): os.makedirs(outdir) - videoclip.write_videofile(os.path.join(outdir,mp4), + mp4 = "{}x{}-style-mixing_{}_{}.mp4".format( + *grid_size, min(col_styles), max(col_styles)) + if not os.path.exists(outdir): + os.makedirs(outdir) + videoclip.write_videofile(os.path.join(outdir, mp4), fps=mp4_fps, codec=mp4_codec, bitrate=mp4_bitrate) @@ -148,6 +165,3 @@ def style_mixing_video(network_pkl: str, if __name__ == "__main__": style_mixing_video() - - - diff --git a/stylegan_human/torch_utils/custom_ops.py b/stylegan_human/torch_utils/custom_ops.py index fda77a69777a69bd3eda96713c29f66fe3b016b9..6509d70bbcd49a24628e3e90258ccb0d8c5a3b39 100644 --- a/stylegan_human/torch_utils/custom_ops.py +++ b/stylegan_human/torch_utils/custom_ops.py @@ -21,14 +21,15 @@ import uuid from torch.utils.file_baton import FileBaton -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Global options. -verbosity = 'brief' # Verbosity level: 'none', 'brief', 'full' +verbosity = 'brief' # Verbosity level: 'none', 'brief', 'full' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Internal helper funcs. + def _find_compiler_bindir(): patterns = [ 'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64', @@ -42,6 +43,7 @@ def _find_compiler_bindir(): return matches[-1] return None + def _get_mangled_gpu_name(): name = torch.cuda.get_device_name().lower() out = [] @@ -53,11 +55,12 @@ def _get_mangled_gpu_name(): return ''.join(out) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Main entry point for compiling and loading C++/CUDA plugins. _cached_plugins = dict() + def get_plugin(module_name, sources, **build_kwargs): assert verbosity in ['none', 'brief', 'full'] @@ -69,14 +72,16 @@ def get_plugin(module_name, sources, **build_kwargs): if verbosity == 'full': print(f'Setting up PyTorch plugin "{module_name}"...') elif verbosity == 'brief': - print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True) + print( + f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True) - try: # pylint: disable=too-many-nested-blocks + try: # pylint: disable=too-many-nested-blocks # Make sure we can find the necessary compiler binaries. if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0: compiler_bindir = _find_compiler_bindir() if compiler_bindir is None: - raise RuntimeError(f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".') + raise RuntimeError( + f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".') os.environ['PATH'] += ';' + compiler_bindir # Compile and load. @@ -94,7 +99,8 @@ def get_plugin(module_name, sources, **build_kwargs): # actually cares about this.) source_dirs_set = set(os.path.dirname(source) for source in sources) if len(source_dirs_set) == 1 and ('TORCH_EXTENSIONS_DIR' in os.environ): - all_source_files = sorted(list(x for x in Path(list(source_dirs_set)[0]).iterdir() if x.is_file())) + all_source_files = sorted(list(x for x in Path( + list(source_dirs_set)[0]).iterdir() if x.is_file())) # Compute a combined hash digest for all source files in the same # custom op directory (usually .cu, .cpp, .py and .h files). @@ -102,7 +108,8 @@ def get_plugin(module_name, sources, **build_kwargs): for src in all_source_files: with open(src, 'rb') as f: hash_md5.update(f.read()) - build_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) # pylint: disable=protected-access + build_dir = torch.utils.cpp_extension._get_build_directory( + module_name, verbose=verbose_build) # pylint: disable=protected-access digest_build_dir = os.path.join(build_dir, hash_md5.hexdigest()) if not os.path.isdir(digest_build_dir): @@ -111,18 +118,21 @@ def get_plugin(module_name, sources, **build_kwargs): if baton.try_acquire(): try: for src in all_source_files: - shutil.copyfile(src, os.path.join(digest_build_dir, os.path.basename(src))) + shutil.copyfile(src, os.path.join( + digest_build_dir, os.path.basename(src))) finally: baton.release() else: # Someone else is copying source files under the digest dir, # wait until done and continue. baton.wait() - digest_sources = [os.path.join(digest_build_dir, os.path.basename(x)) for x in sources] + digest_sources = [os.path.join( + digest_build_dir, os.path.basename(x)) for x in sources] torch.utils.cpp_extension.load(name=module_name, build_directory=build_dir, - verbose=verbose_build, sources=digest_sources, **build_kwargs) + verbose=verbose_build, sources=digest_sources, **build_kwargs) else: - torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs) + torch.utils.cpp_extension.load( + name=module_name, verbose=verbose_build, sources=sources, **build_kwargs) module = importlib.import_module(module_name) except: @@ -138,7 +148,9 @@ def get_plugin(module_name, sources, **build_kwargs): _cached_plugins[module_name] = module return module -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + + def get_plugin_v3(module_name, sources, headers=None, source_dir=None, **build_kwargs): assert verbosity in ['none', 'brief', 'full'] if headers is None: @@ -155,16 +167,18 @@ def get_plugin_v3(module_name, sources, headers=None, source_dir=None, **build_k if verbosity == 'full': print(f'Setting up PyTorch plugin "{module_name}"...') elif verbosity == 'brief': - print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True) + print( + f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True) verbose_build = (verbosity == 'full') # Compile and load. - try: # pylint: disable=too-many-nested-blocks + try: # pylint: disable=too-many-nested-blocks # Make sure we can find the necessary compiler binaries. if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0: compiler_bindir = _find_compiler_bindir() if compiler_bindir is None: - raise RuntimeError(f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".') + raise RuntimeError( + f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".') os.environ['PATH'] += ';' + compiler_bindir # Some containers set TORCH_CUDA_ARCH_LIST to a list that can either @@ -188,8 +202,10 @@ def get_plugin_v3(module_name, sources, headers=None, source_dir=None, **build_k # around the *.cu dependency bug in ninja config. # all_source_files = sorted(sources + headers) - all_source_dirs = set(os.path.dirname(fname) for fname in all_source_files) - if len(all_source_dirs) == 1: # and ('TORCH_EXTENSIONS_DIR' in os.environ): + all_source_dirs = set(os.path.dirname(fname) + for fname in all_source_files) + # and ('TORCH_EXTENSIONS_DIR' in os.environ): + if len(all_source_dirs) == 1: # Compute combined hash digest for all source files. hash_md5 = hashlib.md5() @@ -199,27 +215,33 @@ def get_plugin_v3(module_name, sources, headers=None, source_dir=None, **build_k # Select cached build directory name. source_digest = hash_md5.hexdigest() - build_top_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) # pylint: disable=protected-access - cached_build_dir = os.path.join(build_top_dir, f'{source_digest}-{_get_mangled_gpu_name()}') + build_top_dir = torch.utils.cpp_extension._get_build_directory( + module_name, verbose=verbose_build) # pylint: disable=protected-access + cached_build_dir = os.path.join( + build_top_dir, f'{source_digest}-{_get_mangled_gpu_name()}') if not os.path.isdir(cached_build_dir): tmpdir = f'{build_top_dir}/srctmp-{uuid.uuid4().hex}' os.makedirs(tmpdir) for src in all_source_files: - shutil.copyfile(src, os.path.join(tmpdir, os.path.basename(src))) + shutil.copyfile(src, os.path.join( + tmpdir, os.path.basename(src))) try: - os.replace(tmpdir, cached_build_dir) # atomic + os.replace(tmpdir, cached_build_dir) # atomic except OSError: # source directory already exists, delete tmpdir and its contents. shutil.rmtree(tmpdir) - if not os.path.isdir(cached_build_dir): raise + if not os.path.isdir(cached_build_dir): + raise # Compile. - cached_sources = [os.path.join(cached_build_dir, os.path.basename(fname)) for fname in sources] + cached_sources = [os.path.join( + cached_build_dir, os.path.basename(fname)) for fname in sources] torch.utils.cpp_extension.load(name=module_name, build_directory=cached_build_dir, - verbose=verbose_build, sources=cached_sources, **build_kwargs) + verbose=verbose_build, sources=cached_sources, **build_kwargs) else: - torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs) + torch.utils.cpp_extension.load( + name=module_name, verbose=verbose_build, sources=sources, **build_kwargs) # Load. module = importlib.import_module(module_name) @@ -235,4 +257,4 @@ def get_plugin_v3(module_name, sources, headers=None, source_dir=None, **build_k elif verbosity == 'brief': print('Done.') _cached_plugins[module_name] = module - return module \ No newline at end of file + return module diff --git a/stylegan_human/torch_utils/misc.py b/stylegan_human/torch_utils/misc.py index cd512ab8b61ece35d81ec35f43948a843efbbce1..5470dcfc5e59e6bc4484ca3075cd09a708e43467 100644 --- a/stylegan_human/torch_utils/misc.py +++ b/stylegan_human/torch_utils/misc.py @@ -15,12 +15,13 @@ import torch import warnings import dnnlib -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Cached construction of constant tensors. Avoids CPU=>GPU copy when the # same constant is used multiple times. _constant_cache = dict() + def constant(value, shape=None, dtype=None, device=None, memory_format=None): value = np.asarray(value) if shape is not None: @@ -32,7 +33,8 @@ def constant(value, shape=None, dtype=None, device=None, memory_format=None): if memory_format is None: memory_format = torch.contiguous_format - key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format) + key = (value.shape, value.dtype, value.tobytes(), + shape, dtype, device, memory_format) tensor = _constant_cache.get(key, None) if tensor is None: tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device) @@ -42,13 +44,14 @@ def constant(value, shape=None, dtype=None, device=None, memory_format=None): _constant_cache[key] = tensor return tensor -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Replace NaN/Inf with specified numerical values. + try: - nan_to_num = torch.nan_to_num # 1.8.0a0 + nan_to_num = torch.nan_to_num # 1.8.0a0 except AttributeError: - def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin + def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin assert isinstance(input, torch.Tensor) if posinf is None: posinf = torch.finfo(input.dtype).max @@ -57,46 +60,53 @@ except AttributeError: assert nan == 0 return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Symbolic assert. try: - symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access + symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access except AttributeError: - symbolic_assert = torch.Assert # 1.7.0 + symbolic_assert = torch.Assert # 1.7.0 -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Context manager to suppress known warnings in torch.jit.trace(). + class suppress_tracer_warnings(warnings.catch_warnings): def __enter__(self): super().__enter__() warnings.simplefilter('ignore', category=torch.jit.TracerWarning) return self -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Assert that the shape of a tensor matches the given list of integers. # None indicates that the size of a dimension is allowed to vary. # Performs symbolic assertion when used in torch.jit.trace(). + def assert_shape(tensor, ref_shape): if tensor.ndim != len(ref_shape): - raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}') + raise AssertionError( + f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}') for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)): if ref_size is None: pass elif isinstance(ref_size, torch.Tensor): - with suppress_tracer_warnings(): # as_tensor results are registered as constants - symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}') + with suppress_tracer_warnings(): # as_tensor results are registered as constants + symbolic_assert(torch.equal(torch.as_tensor( + size), ref_size), f'Wrong size for dimension {idx}') elif isinstance(size, torch.Tensor): - with suppress_tracer_warnings(): # as_tensor results are registered as constants - symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}') + with suppress_tracer_warnings(): # as_tensor results are registered as constants + symbolic_assert(torch.equal(size, torch.as_tensor( + ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}') elif size != ref_size: - raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}') + raise AssertionError( + f'Wrong size for dimension {idx}: got {size}, expected {ref_size}') -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Function decorator that calls torch.autograd.profiler.record_function(). + def profiled_function(fn): def decorator(*args, **kwargs): with torch.autograd.profiler.record_function(fn.__name__): @@ -104,10 +114,11 @@ def profiled_function(fn): decorator.__name__ = fn.__name__ return decorator -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Sampler for torch.utils.data.DataLoader that loops over the dataset # indefinitely, shuffling items as it goes. + class InfiniteSampler(torch.utils.data.Sampler): def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5): assert len(dataset) > 0 @@ -141,30 +152,36 @@ class InfiniteSampler(torch.utils.data.Sampler): order[i], order[j] = order[j], order[i] idx += 1 -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Utilities for operating with torch.nn.Module parameters and buffers. + def params_and_buffers(module): assert isinstance(module, torch.nn.Module) return list(module.parameters()) + list(module.buffers()) + def named_params_and_buffers(module): assert isinstance(module, torch.nn.Module) return list(module.named_parameters()) + list(module.named_buffers()) + def copy_params_and_buffers(src_module, dst_module, require_all=False): assert isinstance(src_module, torch.nn.Module) assert isinstance(dst_module, torch.nn.Module) - src_tensors = {name: tensor for name, tensor in named_params_and_buffers(src_module)} + src_tensors = {name: tensor for name, + tensor in named_params_and_buffers(src_module)} for name, tensor in named_params_and_buffers(dst_module): assert (name in src_tensors) or (not require_all) if name in src_tensors: - tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad) + tensor.copy_(src_tensors[name].detach()).requires_grad_( + tensor.requires_grad) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Context manager for easily enabling/disabling DistributedDataParallel # synchronization. + @contextlib.contextmanager def ddp_sync(module, sync): assert isinstance(module, torch.nn.Module) @@ -174,9 +191,10 @@ def ddp_sync(module, sync): with module.no_sync(): yield -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Check DistributedDataParallel consistency across processes. + def check_ddp_consistency(module, ignore_regex=None): assert isinstance(module, torch.nn.Module) for name, tensor in named_params_and_buffers(module): @@ -188,9 +206,10 @@ def check_ddp_consistency(module, ignore_regex=None): torch.distributed.broadcast(tensor=other, src=0) assert (nan_to_num(tensor) == nan_to_num(other)).all(), fullname -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Print summary table of module hierarchy. + def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True): assert isinstance(module, torch.nn.Module) assert not isinstance(module, torch.jit.ScriptModule) @@ -199,15 +218,19 @@ def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True): # Register hooks. entries = [] nesting = [0] + def pre_hook(_mod, _inputs): nesting[0] += 1 + def post_hook(mod, _inputs, outputs): nesting[0] -= 1 if nesting[0] <= max_nesting: - outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs] + outputs = list(outputs) if isinstance( + outputs, (tuple, list)) else [outputs] outputs = [t for t in outputs if isinstance(t, torch.Tensor)] entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs)) - hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()] + hooks = [mod.register_forward_pre_hook( + pre_hook) for mod in module.modules()] hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()] # Run module. @@ -218,17 +241,22 @@ def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True): # Identify unique outputs, parameters, and buffers. tensors_seen = set() for e in entries: - e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen] - e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen] + e.unique_params = [ + t for t in e.mod.parameters() if id(t) not in tensors_seen] + e.unique_buffers = [ + t for t in e.mod.buffers() if id(t) not in tensors_seen] e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen] - tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs} + tensors_seen |= {id(t) for t in e.unique_params + + e.unique_buffers + e.unique_outputs} # Filter out redundant entries. if skip_redundant: - entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)] + entries = [e for e in entries if len(e.unique_params) or len( + e.unique_buffers) or len(e.unique_outputs)] # Construct table. - rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']] + rows = [[type(module).__name__, 'Parameters', + 'Buffers', 'Output shape', 'Datatype']] rows += [['---'] * len(rows[0])] param_total = 0 buffer_total = 0 @@ -247,7 +275,8 @@ def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True): (output_dtypes + ['-'])[0], ]] for idx in range(1, len(e.outputs)): - rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]] + rows += [[name + f':{idx}', '-', '-', + output_shapes[idx], output_dtypes[idx]]] param_total += param_size buffer_total += buffer_size rows += [['---'] * len(rows[0])] @@ -257,8 +286,9 @@ def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True): widths = [max(len(cell) for cell in column) for column in zip(*rows)] print() for row in rows: - print(' '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths))) + print(' '.join(cell + ' ' * (width - len(cell)) + for cell, width in zip(row, widths))) print() return outputs -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/torch_utils/models.py b/stylegan_human/torch_utils/models.py index 762550239ba6f1e09f4887bf1b27fd421745a589..936e16ad992fce3faf868d974274b5cd7c6a6be9 100644 --- a/stylegan_human/torch_utils/models.py +++ b/stylegan_human/torch_utils/models.py @@ -48,7 +48,8 @@ class Upsample(nn.Module): self.pad = (pad0, pad1) def forward(self, input): - out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad) + out = upfirdn2d(input, self.kernel, up=self.factor, + down=1, pad=self.pad) return out @@ -68,7 +69,8 @@ class Downsample(nn.Module): self.pad = (pad0, pad1) def forward(self, input): - out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad) + out = upfirdn2d(input, self.kernel, up=1, + down=self.factor, pad=self.pad) return out @@ -197,7 +199,8 @@ class ModulatedConv2d(nn.Module): p = (len(blur_kernel) - factor) - (kernel_size - 1) pad0 = (p + 1) // 2 + factor - 1 pad1 = p // 2 + 1 - self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor) + self.blur = Blur(blur_kernel, pad=( + pad0, pad1), upsample_factor=factor) if downsample: factor = 2 @@ -243,7 +246,8 @@ class ModulatedConv2d(nn.Module): weight = weight.transpose(1, 2).reshape( batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size ) - out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch) + out = F.conv_transpose2d( + input, weight, padding=0, stride=2, groups=batch) _, _, height, width = out.shape out = out.view(batch, self.out_channel, height, width) out = self.blur(out) @@ -325,7 +329,8 @@ class ToRGB(nn.Module): if upsample: self.upsample = Upsample(blur_kernel) - self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False) + self.conv = ModulatedConv2d( + in_channel, 3, 1, style_dim, demodulate=False) self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) def forward(self, input, style, skip=None): @@ -379,7 +384,8 @@ class Generator(nn.Module): 64: 64 * channel_multiplier, } elif small_isaac: - self.channels = {4: 256, 8: 256, 16: 256, 32: 256, 64: 128, 128: 128} + self.channels = {4: 256, 8: 256, + 16: 256, 32: 256, 64: 128, 128: 128} else: self.channels = { 4: 512, @@ -448,7 +454,8 @@ class Generator(nn.Module): for i in range(3, self.log_size + 1): for _ in range(2): - noises.append(torch.randn(1, 1, 2 ** i, 2 ** i // 2, device=device)) + noises.append(torch.randn( + 1, 1, 2 ** i, 2 ** i // 2, device=device)) return noises @@ -489,18 +496,21 @@ class Generator(nn.Module): if truncation < 1: # print('truncation_latent: ', truncation_latent.shape) - if not real: #if type(styles) == list: + if not real: # if type(styles) == list: style_t = [] for style in styles: style_t.append( - truncation_latent + truncation * (style - truncation_latent) - ) # (-1.1162e-03-(-1.0914e-01))*0.8+(-1.0914e-01) + truncation_latent + truncation * + (style - truncation_latent) + ) # (-1.1162e-03-(-1.0914e-01))*0.8+(-1.0914e-01) styles = style_t - else: # styles are latent (tensor: 1,18,512), for real PTI output - truncation_latent = truncation_latent.repeat(18,1).unsqueeze(0) # (1,512) --> (1,18,512) - styles = torch.add(truncation_latent,torch.mul(torch.sub(styles,truncation_latent),truncation)) + else: # styles are latent (tensor: 1,18,512), for real PTI output + truncation_latent = truncation_latent.repeat( + 18, 1).unsqueeze(0) # (1,512) --> (1,18,512) + styles = torch.add(truncation_latent, torch.mul( + torch.sub(styles, truncation_latent), truncation)) # print('now styles after truncation : ', styles) - #if type(styles) == list and len(styles) < 2: # this if for input as list of [(1,512)] + # if type(styles) == list and len(styles) < 2: # this if for input as list of [(1,512)] if not real: if len(styles) < 2: inject_index = self.n_latent @@ -511,15 +521,17 @@ class Generator(nn.Module): elif type(styles) == list: if inject_index is None: inject_index = 4 - + latent = styles[0].unsqueeze(0) if latent.shape[1] == 1: latent = latent.repeat(1, inject_index, 1) else: latent = latent[:, :inject_index, :] - latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1) + latent2 = styles[1].unsqueeze(1).repeat( + 1, self.n_latent - inject_index, 1) latent = torch.cat([latent, latent2], 1) - else: # input is tensor of size with torch.Size([1, 18, 512]), for real PTI output + # input is tensor of size with torch.Size([1, 18, 512]), for real PTI output + else: latent = styles # print(f'processed latent: {latent.shape}') @@ -665,19 +677,19 @@ class StyleDiscriminator(nn.Module): self.final_conv = ConvLayer(in_channel + 1, channels[4], 3) self.final_linear = nn.Sequential( - EqualLinear(channels[4] * 4 * 4, channels[4], activation="fused_lrelu"), + EqualLinear(channels[4] * 4 * 4, channels[4], + activation="fused_lrelu"), EqualLinear(channels[4], 1), ) - def forward(self, input): h = input h_list = [] - + for index, blocklist in enumerate(self.convs): h = blocklist(h) h_list.append(h) - + out = h batch, channel, height, width = out.shape group = min(batch, self.stddev_group) @@ -691,17 +703,17 @@ class StyleDiscriminator(nn.Module): out = self.final_conv(out) h_list.append(out) - + out = out.view(batch, -1) out = self.final_linear(out) - + return out, h_list class StyleEncoder(nn.Module): def __init__(self, size, w_dim=512): super().__init__() - + channels = { 4: 512, 8: 512, @@ -712,8 +724,8 @@ class StyleEncoder(nn.Module): 256: 64, 512: 32, 1024: 16 - } - + } + self.w_dim = w_dim log_size = int(math.log(size, 2)) convs = [ConvLayer(3, channels[size], 1)] @@ -724,15 +736,17 @@ class StyleEncoder(nn.Module): convs.append(ResBlock(in_channel, out_channel)) in_channel = out_channel - convs.append(EqualConv2d(in_channel,2*self.w_dim, 4, padding=0, bias=False)) + convs.append(EqualConv2d( + in_channel, 2*self.w_dim, 4, padding=0, bias=False)) self.convs = nn.Sequential(*convs) def forward(self, input): out = self.convs(input) # return out.view(len(input), self.n_latents, self.w_dim) - reshaped = out.view(len(input), 2*self.w_dim) - return reshaped[:,:self.w_dim], reshaped[:,self.w_dim:] + reshaped = out.view(len(input), 2*self.w_dim) + return reshaped[:, :self.w_dim], reshaped[:, self.w_dim:] + def kaiming_init(m): if isinstance(m, (nn.Linear, nn.Conv2d)): @@ -753,4 +767,4 @@ def normal_init(m): elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)): m.weight.data.fill_(1) if m.bias is not None: - m.bias.data.fill_(0) \ No newline at end of file + m.bias.data.fill_(0) diff --git a/stylegan_human/torch_utils/models_face.py b/stylegan_human/torch_utils/models_face.py index ce3f5d2f3c41206c18a9dba973c8e5999ddf47fd..f9ba50f96041a163ac974b0c54b4985069b554f3 100644 --- a/stylegan_human/torch_utils/models_face.py +++ b/stylegan_human/torch_utils/models_face.py @@ -49,7 +49,8 @@ class Upsample(nn.Module): self.pad = (pad0, pad1) def forward(self, input): - out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad) + out = upfirdn2d(input, self.kernel, up=self.factor, + down=1, pad=self.pad) return out @@ -70,7 +71,8 @@ class Downsample(nn.Module): self.pad = (pad0, pad1) def forward(self, input): - out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad) + out = upfirdn2d(input, self.kernel, up=1, + down=self.factor, pad=self.pad) return out @@ -208,7 +210,8 @@ class ModulatedConv2d(nn.Module): pad0 = (p + 1) // 2 + factor - 1 pad1 = p // 2 + 1 - self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor) + self.blur = Blur(blur_kernel, pad=( + pad0, pad1), upsample_factor=factor) if downsample: factor = 2 @@ -258,7 +261,8 @@ class ModulatedConv2d(nn.Module): weight = weight.transpose(1, 2).reshape( batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size ) - out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch) + out = F.conv_transpose2d( + input, weight, padding=0, stride=2, groups=batch) _, _, height, width = out.shape out = out.view(batch, self.out_channel, height, width) out = self.blur(out) @@ -351,7 +355,8 @@ class ToRGB(nn.Module): if upsample: self.upsample = Upsample(blur_kernel) - self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False) + self.conv = ModulatedConv2d( + in_channel, 3, 1, style_dim, demodulate=False) self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) def forward(self, input, style, skip=None): @@ -407,7 +412,8 @@ class Generator(nn.Module): 64: 64 * channel_multiplier, } elif small_isaac: - self.channels = {4: 256, 8: 256, 16: 256, 32: 256, 64: 128, 128: 128} + self.channels = {4: 256, 8: 256, + 16: 256, 32: 256, 64: 128, 128: 128} else: self.channels = { 4: 512, @@ -521,14 +527,15 @@ class Generator(nn.Module): for style in styles: style_t.append( - truncation_latent + truncation * (style - truncation_latent) + truncation_latent + truncation * + (style - truncation_latent) ) styles = style_t # print(styles) if len(styles) < 2: inject_index = self.n_latent - + if styles[0].ndim < 3: latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) # print("a") @@ -541,13 +548,14 @@ class Generator(nn.Module): # print("c") if inject_index is None: inject_index = 4 - + latent = styles[0].unsqueeze(0) if latent.shape[1] == 1: latent = latent.repeat(1, inject_index, 1) else: latent = latent[:, :inject_index, :] - latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1) + latent2 = styles[1].unsqueeze(1).repeat( + 1, self.n_latent - inject_index, 1) latent = torch.cat([latent, latent2], 1) @@ -694,7 +702,8 @@ class StyleDiscriminator(nn.Module): self.final_conv = ConvLayer(in_channel + 1, channels[4], 3) self.final_linear = nn.Sequential( - EqualLinear(channels[4] * 4 * 4, channels[4], activation="fused_lrelu"), + EqualLinear(channels[4] * 4 * 4, channels[4], + activation="fused_lrelu"), EqualLinear(channels[4], 1), ) @@ -717,15 +726,15 @@ class StyleDiscriminator(nn.Module): # out = self.final_linear(out) # return out - + def forward(self, input): h = input h_list = [] - + for index, blocklist in enumerate(self.convs): h = blocklist(h) h_list.append(h) - + out = h batch, channel, height, width = out.shape group = min(batch, self.stddev_group) @@ -739,17 +748,17 @@ class StyleDiscriminator(nn.Module): out = self.final_conv(out) h_list.append(out) - + out = out.view(batch, -1) out = self.final_linear(out) - + return out, h_list class StyleEncoder(nn.Module): def __init__(self, size, w_dim=512): super().__init__() - + channels = { 4: 512, 8: 512, @@ -760,13 +769,13 @@ class StyleEncoder(nn.Module): 256: 64, 512: 32, 1024: 16 - } - + } + self.w_dim = w_dim log_size = int(math.log(size, 2)) - + # self.n_latents = log_size*2 - 2 - + convs = [ConvLayer(3, channels[size], 1)] in_channel = channels[size] @@ -776,16 +785,17 @@ class StyleEncoder(nn.Module): in_channel = out_channel # convs.append(EqualConv2d(in_channel, self.n_latents*self.w_dim, 4, padding=0, bias=False)) - convs.append(EqualConv2d(in_channel,2*self.w_dim, 4, padding=0, bias=False)) - + convs.append(EqualConv2d( + in_channel, 2*self.w_dim, 4, padding=0, bias=False)) self.convs = nn.Sequential(*convs) def forward(self, input): out = self.convs(input) # return out.view(len(input), self.n_latents, self.w_dim) - reshaped = out.view(len(input), 2*self.w_dim) - return reshaped[:,:self.w_dim], reshaped[:,self.w_dim:] + reshaped = out.view(len(input), 2*self.w_dim) + return reshaped[:, :self.w_dim], reshaped[:, self.w_dim:] + def kaiming_init(m): if isinstance(m, (nn.Linear, nn.Conv2d)): @@ -806,4 +816,4 @@ def normal_init(m): elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)): m.weight.data.fill_(1) if m.bias is not None: - m.bias.data.fill_(0) \ No newline at end of file + m.bias.data.fill_(0) diff --git a/stylegan_human/torch_utils/op_edit/fused_act.py b/stylegan_human/torch_utils/op_edit/fused_act.py index 138f090bc67b94b363c346cbf405990f1bbdff68..c9c7b6f0e2b16b78dd81c174cf139a4bd848648a 100644 --- a/stylegan_human/torch_utils/op_edit/fused_act.py +++ b/stylegan_human/torch_utils/op_edit/fused_act.py @@ -55,7 +55,8 @@ class FusedLeakyReLUFunction(Function): @staticmethod def forward(ctx, input, bias, negative_slope, scale): empty = input.new_empty(0) - out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale) + out = fused.fused_bias_act( + input, bias, empty, 3, 0, negative_slope, scale) ctx.save_for_backward(out) ctx.negative_slope = negative_slope ctx.scale = scale diff --git a/stylegan_human/torch_utils/op_edit/upfirdn2d.py b/stylegan_human/torch_utils/op_edit/upfirdn2d.py index 874c09c5e98bee1ace64408aa31ec547dfe695a4..ecdcabbe20d2405b71d049d0bf94ae576fe58493 100644 --- a/stylegan_human/torch_utils/op_edit/upfirdn2d.py +++ b/stylegan_human/torch_utils/op_edit/upfirdn2d.py @@ -42,7 +42,8 @@ class UpFirDn2dBackward(Function): g_pad_y0, g_pad_y1, ) - grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3]) + grad_input = grad_input.view( + in_size[0], in_size[1], in_size[2], in_size[3]) ctx.save_for_backward(kernel) @@ -65,7 +66,8 @@ class UpFirDn2dBackward(Function): def backward(ctx, gradgrad_input): (kernel,) = ctx.saved_tensors - gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1) + gradgrad_input = gradgrad_input.reshape(-1, + ctx.in_size[2], ctx.in_size[3], 1) gradgrad_out = upfirdn2d_op.upfirdn2d( gradgrad_input, @@ -152,7 +154,8 @@ def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): else: out = UpFirDn2d.apply( - input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1]) + input, kernel, (up, up), (down, + down), (pad[0], pad[1], pad[0], pad[1]) ) return out @@ -172,12 +175,13 @@ def upfirdn2d_native( out = out.view(-1, in_h * up_y, in_w * up_x, minor) out = F.pad( - out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] + out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), + max(pad_y0, 0), max(pad_y1, 0)] ) out = out[ :, - max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), - max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), + max(-pad_y0, 0): out.shape[1] - max(-pad_y1, 0), + max(-pad_x0, 0): out.shape[2] - max(-pad_x1, 0), :, ] diff --git a/stylegan_human/torch_utils/ops/__init__.py b/stylegan_human/torch_utils/ops/__init__.py index 9c46c314cf2ff24fff74d7308dd8cc50767dd870..55929854a284626862af6666d3d981e83ad486fa 100644 --- a/stylegan_human/torch_utils/ops/__init__.py +++ b/stylegan_human/torch_utils/ops/__init__.py @@ -1,3 +1,3 @@ # Copyright (c) SenseTime Research. All rights reserved. -#empty \ No newline at end of file +# empty diff --git a/stylegan_human/torch_utils/ops/bias_act.py b/stylegan_human/torch_utils/ops/bias_act.py index 8041208be7680ddeceb1a87a9db9faae7101e7bf..cea981875cddec8c961dda4a96554558bca1e073 100644 --- a/stylegan_human/torch_utils/ops/bias_act.py +++ b/stylegan_human/torch_utils/ops/bias_act.py @@ -20,7 +20,7 @@ import traceback from .. import custom_ops from .. import misc -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- activation_funcs = { 'linear': dnnlib.EasyDict(func=lambda x, **_: x, def_alpha=0, def_gain=1, cuda_idx=1, ref='', has_2nd_grad=False), @@ -34,12 +34,13 @@ activation_funcs = { 'swish': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x) * x, def_alpha=0, def_gain=np.sqrt(2), cuda_idx=9, ref='x', has_2nd_grad=True), } -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- _inited = False _plugin = None _null_tensor = torch.empty([0]) + def _init(): global _inited, _plugin if not _inited: @@ -47,12 +48,15 @@ def _init(): sources = ['bias_act.cpp', 'bias_act.cu'] sources = [os.path.join(os.path.dirname(__file__), s) for s in sources] try: - _plugin = custom_ops.get_plugin('bias_act_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math']) + _plugin = custom_ops.get_plugin( + 'bias_act_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math']) except: - warnings.warn('Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc()) + warnings.warn( + 'Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc()) return _plugin is not None -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'): r"""Fused bias and activation function. @@ -90,7 +94,8 @@ def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b) return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None): @@ -121,13 +126,15 @@ def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=N # Clamp. if clamp >= 0: - x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type + x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + _bias_act_cuda_cache = dict() + def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): """Fast CUDA implementation of `bias_act()` using custom ops. """ @@ -146,13 +153,15 @@ def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): # Forward op. class BiasActCuda(torch.autograd.Function): @staticmethod - def forward(ctx, x, b): # pylint: disable=arguments-differ - ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride()[1] == 1 else torch.contiguous_format + def forward(ctx, x, b): # pylint: disable=arguments-differ + ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride()[ + 1] == 1 else torch.contiguous_format x = x.contiguous(memory_format=ctx.memory_format) b = b.contiguous() if b is not None else _null_tensor y = x if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor: - y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp) + y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, + _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp) ctx.save_for_backward( x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor, b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor, @@ -160,7 +169,7 @@ def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): return y @staticmethod - def backward(ctx, dy): # pylint: disable=arguments-differ + def backward(ctx, dy): # pylint: disable=arguments-differ dy = dy.contiguous(memory_format=ctx.memory_format) x, b, y = ctx.saved_tensors dx = None @@ -179,16 +188,18 @@ def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): # Backward op. class BiasActCudaGrad(torch.autograd.Function): @staticmethod - def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ - ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride()[1] == 1 else torch.contiguous_format - dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp) + def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ + ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride()[ + 1] == 1 else torch.contiguous_format + dx = _plugin.bias_act(dy, b, x, y, _null_tensor, + 1, dim, spec.cuda_idx, alpha, gain, clamp) ctx.save_for_backward( dy if spec.has_2nd_grad else _null_tensor, x, b, y) return dx @staticmethod - def backward(ctx, d_dx): # pylint: disable=arguments-differ + def backward(ctx, d_dx): # pylint: disable=arguments-differ d_dx = d_dx.contiguous(memory_format=ctx.memory_format) dy, x, b, y = ctx.saved_tensors d_dy = None @@ -200,7 +211,8 @@ def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): d_dy = BiasActCudaGrad.apply(d_dx, x, b, y) if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]): - d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp) + d_x = _plugin.bias_act( + d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp) if spec.has_2nd_grad and ctx.needs_input_grad[2]: d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim]) @@ -211,4 +223,4 @@ def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): _bias_act_cuda_cache[key] = BiasActCuda return BiasActCuda -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/torch_utils/ops/conv2d_gradfix.py b/stylegan_human/torch_utils/ops/conv2d_gradfix.py index 093036b728336d6f2f593aaea187054a8af8d523..8fd6a8dd22661d42899c7dba5047b3a466eecfc7 100644 --- a/stylegan_human/torch_utils/ops/conv2d_gradfix.py +++ b/stylegan_human/torch_utils/ops/conv2d_gradfix.py @@ -19,10 +19,13 @@ import torch # pylint: disable=arguments-differ # pylint: disable=protected-access -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + +# Enable the custom op by setting this to true. +enabled = False +# Forcefully disable computation of gradients with respect to the weights. +weight_gradients_disabled = False -enabled = False # Enable the custom op by setting this to true. -weight_gradients_disabled = False # Forcefully disable computation of gradients with respect to the weights. @contextlib.contextmanager def no_weight_gradients(): @@ -32,19 +35,22 @@ def no_weight_gradients(): yield weight_gradients_disabled = old -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1): if _should_use_custom_op(input): return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias) return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups) + def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1): if _should_use_custom_op(input): return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias) return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _should_use_custom_op(input): assert isinstance(input, torch.Tensor) @@ -54,19 +60,23 @@ def _should_use_custom_op(input): return False if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']): return True - warnings.warn(f'conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d().') + warnings.warn( + f'conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d().') return False + def _tuple_of_ints(xs, ndim): xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim assert len(xs) == ndim assert all(isinstance(x, int) for x in xs) return xs -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + _conv2d_gradfix_cache = dict() + def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups): # Parse arguments. ndim = 2 @@ -77,7 +87,8 @@ def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, di dilation = _tuple_of_ints(dilation, ndim) # Lookup from cache. - key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups) + key = (transpose, weight_shape, stride, padding, + output_padding, dilation, groups) if key in _conv2d_gradfix_cache: return _conv2d_gradfix_cache[key] @@ -89,11 +100,14 @@ def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, di assert all(dilation[i] >= 0 for i in range(ndim)) if not transpose: assert all(output_padding[i] == 0 for i in range(ndim)) - else: # transpose - assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim)) + else: # transpose + assert all(0 <= output_padding[i] < max( + stride[i], dilation[i]) for i in range(ndim)) # Helpers. - common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups) + common_kwargs = dict(stride=stride, padding=padding, + dilation=dilation, groups=groups) + def calc_output_padding(input_shape, output_shape): if transpose: return [0, 0] @@ -111,9 +125,11 @@ def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, di def forward(ctx, input, weight, bias): assert weight.shape == weight_shape if not transpose: - output = torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs) - else: # transpose - output = torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs) + output = torch.nn.functional.conv2d( + input=input, weight=weight, bias=bias, **common_kwargs) + else: # transpose + output = torch.nn.functional.conv_transpose2d( + input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs) ctx.save_for_backward(input, weight) return output @@ -125,8 +141,10 @@ def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, di grad_bias = None if ctx.needs_input_grad[0]: - p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape) - grad_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, weight, None) + p = calc_output_padding( + input_shape=input.shape, output_shape=grad_output.shape) + grad_input = _conv2d_gradfix(transpose=( + not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, weight, None) assert grad_input.shape == input.shape if ctx.needs_input_grad[1] and not weight_gradients_disabled: @@ -142,9 +160,12 @@ def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, di class Conv2dGradWeight(torch.autograd.Function): @staticmethod def forward(ctx, grad_output, input): - op = torch._C._jit_get_operation('aten::cudnn_convolution_backward_weight' if not transpose else 'aten::cudnn_convolution_transpose_backward_weight') - flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32] - grad_weight = op(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags) + op = torch._C._jit_get_operation( + 'aten::cudnn_convolution_backward_weight' if not transpose else 'aten::cudnn_convolution_transpose_backward_weight') + flags = [torch.backends.cudnn.benchmark, + torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32] + grad_weight = op(weight_shape, grad_output, input, + padding, stride, dilation, groups, *flags) assert grad_weight.shape == weight_shape ctx.save_for_backward(grad_output, input) return grad_weight @@ -156,12 +177,15 @@ def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, di grad2_input = None if ctx.needs_input_grad[0]: - grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None) + grad2_grad_output = Conv2d.apply( + input, grad2_grad_weight, None) assert grad2_grad_output.shape == grad_output.shape if ctx.needs_input_grad[1]: - p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape) - grad2_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, grad2_grad_weight, None) + p = calc_output_padding( + input_shape=input.shape, output_shape=grad_output.shape) + grad2_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, + output_padding=p, **common_kwargs).apply(grad_output, grad2_grad_weight, None) assert grad2_input.shape == input.shape return grad2_grad_output, grad2_input @@ -169,4 +193,4 @@ def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, di _conv2d_gradfix_cache[key] = Conv2d return Conv2d -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/torch_utils/ops/conv2d_resample.py b/stylegan_human/torch_utils/ops/conv2d_resample.py index 44a0883f731156af72ec19829ef0bfb8026682be..11c0d1c313bd400a76d4d8aed496c4f31d8c6724 100644 --- a/stylegan_human/torch_utils/ops/conv2d_resample.py +++ b/stylegan_human/torch_utils/ops/conv2d_resample.py @@ -18,15 +18,17 @@ from . import upfirdn2d from .upfirdn2d import _parse_padding from .upfirdn2d import _get_filter_size -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _get_weight_shape(w): - with misc.suppress_tracer_warnings(): # this value will be treated as a constant + with misc.suppress_tracer_warnings(): # this value will be treated as a constant shape = [int(sz) for sz in w.shape] misc.assert_shape(w, shape) return shape -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True): """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations. @@ -34,7 +36,8 @@ def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_w out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) # Flip weight if requested. - if not flip_weight: # conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False). + # conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False). + if not flip_weight: w = w.flip([2, 3]) # Workaround performance pitfall in cuDNN 8.0.5, triggered when using @@ -43,8 +46,10 @@ def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_w if x.stride()[1] == 1 and min(out_channels, in_channels_per_group) < 64: if out_channels <= 4 and groups == 1: in_shape = x.shape - x = w.squeeze(3).squeeze(2) @ x.reshape([in_shape[0], in_channels_per_group, -1]) - x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]]) + x = w.squeeze(3).squeeze( + 2) @ x.reshape([in_shape[0], in_channels_per_group, -1]) + x = x.reshape([in_shape[0], out_channels, + in_shape[2], in_shape[3]]) else: x = x.to(memory_format=torch.contiguous_format) w = w.to(memory_format=torch.contiguous_format) @@ -55,7 +60,8 @@ def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_w op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d return op(x, w, stride=stride, padding=padding, groups=groups) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False): @@ -84,8 +90,10 @@ def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight """ # Validate arguments. assert isinstance(x, torch.Tensor) and (x.ndim == 4) - assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype) - assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32) + assert isinstance(w, torch.Tensor) and ( + w.ndim == 4) and (w.dtype == x.dtype) + assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [ + 1, 2] and f.dtype == torch.float32) assert isinstance(up, int) and (up >= 1) assert isinstance(down, int) and (down >= 1) assert isinstance(groups, int) and (groups >= 1) @@ -107,20 +115,24 @@ def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve. if kw == 1 and kh == 1 and (down > 1 and up == 1): - x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter) + x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[ + px0, px1, py0, py1], flip_filter=flip_filter) x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) return x # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample. if kw == 1 and kh == 1 and (up > 1 and down == 1): x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) - x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) + x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[ + px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) return x # Fast path: downsampling only => use strided convolution. if down > 1 and up == 1: - x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter) - x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight) + x = upfirdn2d.upfirdn2d( + x=x, f=f, padding=[px0, px1, py0, py1], flip_filter=flip_filter) + x = _conv2d_wrapper(x=x, w=w, stride=down, + groups=groups, flip_weight=flip_weight) return x # Fast path: upsampling with optional downsampling => use transpose strided convolution. @@ -128,31 +140,37 @@ def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight if groups == 1: w = w.transpose(0, 1) else: - w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw) + w = w.reshape(groups, out_channels // groups, + in_channels_per_group, kh, kw) w = w.transpose(1, 2) - w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw) + w = w.reshape(groups * in_channels_per_group, + out_channels // groups, kh, kw) px0 -= kw - 1 px1 -= kw - up py0 -= kh - 1 py1 -= kh - up pxt = max(min(-px0, -px1), 0) pyt = max(min(-py0, -py1), 0) - x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight)) - x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter) + x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[ + pyt, pxt], groups=groups, transpose=True, flip_weight=(not flip_weight)) + x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[ + px0+pxt, px1+pxt, py0+pyt, py1+pyt], gain=up**2, flip_filter=flip_filter) if down > 1: - x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) + x = upfirdn2d.upfirdn2d( + x=x, f=f, down=down, flip_filter=flip_filter) return x # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d. if up == 1 and down == 1: if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0: - return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight) + return _conv2d_wrapper(x=x, w=w, padding=[py0, px0], groups=groups, flip_weight=flip_weight) # Fallback: Generic reference implementation. - x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) + x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[ + px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) if down > 1: x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/torch_utils/ops/filtered_lrelu.py b/stylegan_human/torch_utils/ops/filtered_lrelu.py index f5e3748fb725884b18b7e8119f569722b5bbe67f..9ec83ece49d60cb9f60295c46f64f69f7493f5ca 100644 --- a/stylegan_human/torch_utils/ops/filtered_lrelu.py +++ b/stylegan_human/torch_utils/ops/filtered_lrelu.py @@ -16,10 +16,11 @@ from .. import misc from . import upfirdn2d from . import bias_act -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- _plugin = None + def _init(): global _plugin if _plugin is None: @@ -33,19 +34,23 @@ def _init(): _plugin = custom_ops.get_plugin_v3( module_name='filtered_lrelu_plugin', - sources=['filtered_lrelu.cpp', 'filtered_lrelu_wr.cu', 'filtered_lrelu_rd.cu', 'filtered_lrelu_ns.cu'], + sources=['filtered_lrelu.cpp', 'filtered_lrelu_wr.cu', + 'filtered_lrelu_rd.cu', 'filtered_lrelu_ns.cu'], headers=['filtered_lrelu.h', 'filtered_lrelu.cu'], source_dir=os.path.dirname(__file__), - extra_cuda_cflags=['--use_fast_math', '--allow-unsupported-compiler'], + extra_cuda_cflags=['--use_fast_math', + '--allow-unsupported-compiler'], ) return True + def _get_filter_size(f): if f is None: return 1, 1 assert isinstance(f, torch.Tensor) assert 1 <= f.ndim <= 2 - return f.shape[-1], f.shape[0] # width, height + return f.shape[-1], f.shape[0] # width, height + def _parse_padding(padding): if isinstance(padding, int): @@ -59,7 +64,8 @@ def _parse_padding(padding): px0, px1, py0, py1 = padding return px0, px1, py0, py1 -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def filtered_lrelu(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False, impl='cuda'): r"""Filtered leaky ReLU for a batch of 2D images. @@ -123,7 +129,8 @@ def filtered_lrelu(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np return _filtered_lrelu_cuda(up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter).apply(x, fu, fd, b, None, 0, 0) return _filtered_lrelu_ref(x, fu=fu, fd=fd, b=b, up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def _filtered_lrelu_ref(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False): @@ -146,24 +153,33 @@ def _filtered_lrelu_ref(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, ga # Calculate output size. batch_size, channels, in_h, in_w = x.shape in_dtype = x.dtype - out_w = (in_w * up + (px0 + px1) - (fu_w - 1) - (fd_w - 1) + (down - 1)) // down - out_h = (in_h * up + (py0 + py1) - (fu_h - 1) - (fd_h - 1) + (down - 1)) // down + out_w = (in_w * up + (px0 + px1) - (fu_w - 1) - + (fd_w - 1) + (down - 1)) // down + out_h = (in_h * up + (py0 + py1) - (fu_h - 1) - + (fd_h - 1) + (down - 1)) // down # Compute using existing ops. - x = bias_act.bias_act(x=x, b=b) # Apply bias. - x = upfirdn2d.upfirdn2d(x=x, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample. - x = bias_act.bias_act(x=x, act='lrelu', alpha=slope, gain=gain, clamp=clamp) # Bias, leaky ReLU, clamp. - x = upfirdn2d.upfirdn2d(x=x, f=fd, down=down, flip_filter=flip_filter) # Downsample. + x = bias_act.bias_act(x=x, b=b) # Apply bias. + # Upsample. + x = upfirdn2d.upfirdn2d(x=x, f=fu, up=up, padding=[ + px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) + # Bias, leaky ReLU, clamp. + x = bias_act.bias_act(x=x, act='lrelu', alpha=slope, + gain=gain, clamp=clamp) + # Downsample. + x = upfirdn2d.upfirdn2d(x=x, f=fd, down=down, flip_filter=flip_filter) # Check output shape & dtype. misc.assert_shape(x, [batch_size, channels, out_h, out_w]) assert x.dtype == in_dtype return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + _filtered_lrelu_cuda_cache = dict() + def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False): """Fast CUDA implementation of `filtered_lrelu()` using custom ops. """ @@ -185,7 +201,7 @@ def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, cl # Forward op. class FilteredLReluCuda(torch.autograd.Function): @staticmethod - def forward(ctx, x, fu, fd, b, si, sx, sy): # pylint: disable=arguments-differ + def forward(ctx, x, fu, fd, b, si, sx, sy): # pylint: disable=arguments-differ assert isinstance(x, torch.Tensor) and x.ndim == 4 # Replace empty up/downsample kernels with full 1x1 kernels (faster than separable). @@ -211,30 +227,41 @@ def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, cl b = torch.zeros([x.shape[1]], dtype=x.dtype, device=x.device) # Construct internal sign tensor only if gradients are needed. - write_signs = (si.numel() == 0) and (x.requires_grad or b.requires_grad) + write_signs = (si.numel() == 0) and ( + x.requires_grad or b.requires_grad) # Warn if input storage strides are not in decreasing order due to e.g. channels-last layout. strides = [x.stride(i) for i in range(x.ndim) if x.size(i) > 1] if any(a < b for a, b in zip(strides[:-1], strides[1:])): - warnings.warn("low-performance memory layout detected in filtered_lrelu input", RuntimeWarning) + warnings.warn( + "low-performance memory layout detected in filtered_lrelu input", RuntimeWarning) # Call C++/Cuda plugin if datatype is supported. if x.dtype in [torch.float16, torch.float32]: if torch.cuda.current_stream(x.device) != torch.cuda.default_stream(x.device): - warnings.warn("filtered_lrelu called with non-default cuda stream but concurrent execution is not supported", RuntimeWarning) - y, so, return_code = _plugin.filtered_lrelu(x, fu, fd, b, si, up, down, px0, px1, py0, py1, sx, sy, gain, slope, clamp, flip_filter, write_signs) + warnings.warn( + "filtered_lrelu called with non-default cuda stream but concurrent execution is not supported", RuntimeWarning) + y, so, return_code = _plugin.filtered_lrelu( + x, fu, fd, b, si, up, down, px0, px1, py0, py1, sx, sy, gain, slope, clamp, flip_filter, write_signs) else: return_code = -1 # No Cuda kernel found? Fall back to generic implementation. Still more memory efficient than the reference implementation because # only the bit-packed sign tensor is retained for gradient computation. if return_code < 0: - warnings.warn("filtered_lrelu called with parameters that have no optimized CUDA kernel, using generic fallback", RuntimeWarning) - - y = x.add(b.unsqueeze(-1).unsqueeze(-1)) # Add bias. - y = upfirdn2d.upfirdn2d(x=y, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample. - so = _plugin.filtered_lrelu_act_(y, si, sx, sy, gain, slope, clamp, write_signs) # Activation function and sign handling. Modifies y in-place. - y = upfirdn2d.upfirdn2d(x=y, f=fd, down=down, flip_filter=flip_filter) # Downsample. + warnings.warn( + "filtered_lrelu called with parameters that have no optimized CUDA kernel, using generic fallback", RuntimeWarning) + + y = x.add(b.unsqueeze(-1).unsqueeze(-1)) # Add bias. + # Upsample. + y = upfirdn2d.upfirdn2d(x=y, f=fu, up=up, padding=[ + px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) + # Activation function and sign handling. Modifies y in-place. + so = _plugin.filtered_lrelu_act_( + y, si, sx, sy, gain, slope, clamp, write_signs) + # Downsample. + y = upfirdn2d.upfirdn2d( + x=y, f=fd, down=down, flip_filter=flip_filter) # Prepare for gradient computation. ctx.save_for_backward(fu, fd, (si if si.numel() else so)) @@ -244,18 +271,23 @@ def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, cl return y @staticmethod - def backward(ctx, dy): # pylint: disable=arguments-differ + def backward(ctx, dy): # pylint: disable=arguments-differ fu, fd, si = ctx.saved_tensors _, _, xh, xw = ctx.x_shape _, _, yh, yw = ctx.y_shape sx, sy = ctx.s_ofs - dx = None # 0 - dfu = None; assert not ctx.needs_input_grad[1] - dfd = None; assert not ctx.needs_input_grad[2] - db = None # 3 - dsi = None; assert not ctx.needs_input_grad[4] - dsx = None; assert not ctx.needs_input_grad[5] - dsy = None; assert not ctx.needs_input_grad[6] + dx = None # 0 + dfu = None + assert not ctx.needs_input_grad[1] + dfd = None + assert not ctx.needs_input_grad[2] + db = None # 3 + dsi = None + assert not ctx.needs_input_grad[4] + dsx = None + assert not ctx.needs_input_grad[5] + dsy = None + assert not ctx.needs_input_grad[6] if ctx.needs_input_grad[0] or ctx.needs_input_grad[3]: pp = [ @@ -267,8 +299,9 @@ def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, cl gg = gain * (up ** 2) / (down ** 2) ff = (not flip_filter) sx = sx - (fu.shape[-1] - 1) + px0 - sy = sy - (fu.shape[0] - 1) + py0 - dx = _filtered_lrelu_cuda(up=down, down=up, padding=pp, gain=gg, slope=slope, clamp=None, flip_filter=ff).apply(dy, fd, fu, None, si, sx, sy) + sy = sy - (fu.shape[0] - 1) + py0 + dx = _filtered_lrelu_cuda(up=down, down=up, padding=pp, gain=gg, slope=slope, + clamp=None, flip_filter=ff).apply(dy, fd, fu, None, si, sx, sy) if ctx.needs_input_grad[3]: db = dx.sum([0, 2, 3]) @@ -279,4 +312,4 @@ def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, cl _filtered_lrelu_cuda_cache[key] = FilteredLReluCuda return FilteredLReluCuda -#---------------------------------------------------------------------------- \ No newline at end of file +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/torch_utils/ops/fma.py b/stylegan_human/torch_utils/ops/fma.py index 06530ed5e0731b1355b18c7fe1526786dc683d26..a934ea1137d2ade6caefcbdb0476fca40fed8f0c 100644 --- a/stylegan_human/torch_utils/ops/fma.py +++ b/stylegan_human/torch_utils/ops/fma.py @@ -12,23 +12,25 @@ import torch -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- -def fma(a, b, c): # => a * b + c + +def fma(a, b, c): # => a * b + c return _FusedMultiplyAdd.apply(a, b, c) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + -class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c +class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c @staticmethod - def forward(ctx, a, b, c): # pylint: disable=arguments-differ + def forward(ctx, a, b, c): # pylint: disable=arguments-differ out = torch.addcmul(c, a, b) ctx.save_for_backward(a, b) ctx.c_shape = c.shape return out @staticmethod - def backward(ctx, dout): # pylint: disable=arguments-differ + def backward(ctx, dout): # pylint: disable=arguments-differ a, b = ctx.saved_tensors c_shape = ctx.c_shape da = None @@ -46,12 +48,14 @@ class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c return da, db, dc -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _unbroadcast(x, shape): extra_dims = x.ndim - len(shape) assert extra_dims >= 0 - dim = [i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)] + dim = [i for i in range(x.ndim) if x.shape[i] > 1 and ( + i < extra_dims or shape[i - extra_dims] == 1)] if len(dim): x = x.sum(dim=dim, keepdim=True) if extra_dims: @@ -59,4 +63,4 @@ def _unbroadcast(x, shape): assert x.shape == shape return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/torch_utils/ops/grid_sample_gradfix.py b/stylegan_human/torch_utils/ops/grid_sample_gradfix.py index 4f69aad7510d49d55cd865b5e2554703f979b185..c522ae9b6f36a89203ce62f3d4487514523b5b00 100644 --- a/stylegan_human/torch_utils/ops/grid_sample_gradfix.py +++ b/stylegan_human/torch_utils/ops/grid_sample_gradfix.py @@ -20,45 +20,52 @@ import torch # pylint: disable=arguments-differ # pylint: disable=protected-access -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- enabled = False # Enable the custom op by setting this to true. -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def grid_sample(input, grid): if _should_use_custom_op(): return _GridSample2dForward.apply(input, grid) return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _should_use_custom_op(): if not enabled: return False if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']): return True - warnings.warn(f'grid_sample_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.grid_sample().') + warnings.warn( + f'grid_sample_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.grid_sample().') return False -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class _GridSample2dForward(torch.autograd.Function): @staticmethod def forward(ctx, input, grid): assert input.ndim == 4 assert grid.ndim == 4 - output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False) + output = torch.nn.functional.grid_sample( + input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False) ctx.save_for_backward(input, grid) return output @staticmethod def backward(ctx, grad_output): input, grid = ctx.saved_tensors - grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid) + grad_input, grad_grid = _GridSample2dBackward.apply( + grad_output, input, grid) return grad_input, grad_grid -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class _GridSample2dBackward(torch.autograd.Function): @staticmethod @@ -70,16 +77,17 @@ class _GridSample2dBackward(torch.autograd.Function): @staticmethod def backward(ctx, grad2_grad_input, grad2_grad_grid): - _ = grad2_grad_grid # unused + _ = grad2_grad_grid # unused grid, = ctx.saved_tensors grad2_grad_output = None grad2_input = None grad2_grid = None if ctx.needs_input_grad[0]: - grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid) + grad2_grad_output = _GridSample2dForward.apply( + grad2_grad_input, grid) assert not ctx.needs_input_grad[2] return grad2_grad_output, grad2_input, grad2_grid -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/torch_utils/ops/upfirdn2d.py b/stylegan_human/torch_utils/ops/upfirdn2d.py index a14c15fe8737cf047338d2de795d7e40a1f4e9cc..5fa95018f961f1aaa8013befcae7471995eee505 100644 --- a/stylegan_human/torch_utils/ops/upfirdn2d.py +++ b/stylegan_human/torch_utils/ops/upfirdn2d.py @@ -20,22 +20,26 @@ from .. import custom_ops from .. import misc from . import conv2d_gradfix -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- _inited = False _plugin = None + def _init(): global _inited, _plugin if not _inited: sources = ['upfirdn2d.cpp', 'upfirdn2d.cu'] sources = [os.path.join(os.path.dirname(__file__), s) for s in sources] try: - _plugin = custom_ops.get_plugin('upfirdn2d_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math']) + _plugin = custom_ops.get_plugin( + 'upfirdn2d_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math']) except: - warnings.warn('Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc()) + warnings.warn( + 'Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc()) return _plugin is not None + def _parse_scaling(scaling): if isinstance(scaling, int): scaling = [scaling, scaling] @@ -45,6 +49,7 @@ def _parse_scaling(scaling): assert sx >= 1 and sy >= 1 return sx, sy + def _parse_padding(padding): if isinstance(padding, int): padding = [padding, padding] @@ -56,6 +61,7 @@ def _parse_padding(padding): padx0, padx1, pady0, pady1 = padding return padx0, padx1, pady0, pady1 + def _get_filter_size(f): if f is None: return 1, 1 @@ -69,7 +75,8 @@ def _get_filter_size(f): assert fw >= 1 and fh >= 1 return fw, fh -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None): r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`. @@ -117,7 +124,8 @@ def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=Fals f = f.to(device=device) return f -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'): r"""Pad, upsample, filter, and downsample a batch of 2D images. @@ -165,7 +173,8 @@ def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cu return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f) return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1): @@ -188,8 +197,10 @@ def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1): x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx]) # Pad or crop. - x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)]) - x = x[:, :, max(-pady0, 0) : x.shape[2] - max(-pady1, 0), max(-padx0, 0) : x.shape[3] - max(-padx1, 0)] + x = torch.nn.functional.pad( + x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)]) + x = x[:, :, max(-pady0, 0): x.shape[2] - max(-pady1, 0), + max(-padx0, 0): x.shape[3] - max(-padx1, 0)] # Setup filter. f = f * (gain ** (f.ndim / 2)) @@ -202,17 +213,21 @@ def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1): if f.ndim == 4: x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels) else: - x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels) - x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels) + x = conv2d_gradfix.conv2d( + input=x, weight=f.unsqueeze(2), groups=num_channels) + x = conv2d_gradfix.conv2d( + input=x, weight=f.unsqueeze(3), groups=num_channels) # Downsample by throwing away pixels. x = x[:, :, ::downy, ::downx] return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + _upfirdn2d_cuda_cache = dict() + def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1): """Fast CUDA implementation of `upfirdn2d()` using custom ops. """ @@ -222,30 +237,34 @@ def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1): padx0, padx1, pady0, pady1 = _parse_padding(padding) # Lookup from cache. - key = (upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain) + key = (upx, upy, downx, downy, padx0, padx1, + pady0, pady1, flip_filter, gain) if key in _upfirdn2d_cuda_cache: return _upfirdn2d_cuda_cache[key] # Forward op. class Upfirdn2dCuda(torch.autograd.Function): @staticmethod - def forward(ctx, x, f): # pylint: disable=arguments-differ + def forward(ctx, x, f): # pylint: disable=arguments-differ assert isinstance(x, torch.Tensor) and x.ndim == 4 if f is None: f = torch.ones([1, 1], dtype=torch.float32, device=x.device) assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] y = x if f.ndim == 2: - y = _plugin.upfirdn2d(y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain) + y = _plugin.upfirdn2d( + y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain) else: - y = _plugin.upfirdn2d(y, f.unsqueeze(0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, np.sqrt(gain)) - y = _plugin.upfirdn2d(y, f.unsqueeze(1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, np.sqrt(gain)) + y = _plugin.upfirdn2d(y, f.unsqueeze( + 0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, np.sqrt(gain)) + y = _plugin.upfirdn2d(y, f.unsqueeze( + 1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, np.sqrt(gain)) ctx.save_for_backward(f) ctx.x_shape = x.shape return y @staticmethod - def backward(ctx, dy): # pylint: disable=arguments-differ + def backward(ctx, dy): # pylint: disable=arguments-differ f, = ctx.saved_tensors _, _, ih, iw = ctx.x_shape _, _, oh, ow = dy.shape @@ -260,7 +279,8 @@ def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1): df = None if ctx.needs_input_grad[0]: - dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(not flip_filter), gain=gain).apply(dy, f) + dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=( + not flip_filter), gain=gain).apply(dy, f) assert not ctx.needs_input_grad[1] return dx, df @@ -269,7 +289,8 @@ def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1): _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda return Upfirdn2dCuda -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'): r"""Filter a batch of 2D images using the given 2D FIR filter. @@ -305,7 +326,8 @@ def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'): ] return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'): r"""Upsample a batch of 2D images using the given 2D FIR filter. @@ -344,7 +366,8 @@ def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'): ] return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'): r"""Downsample a batch of 2D images using the given 2D FIR filter. @@ -383,4 +406,4 @@ def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda' ] return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/torch_utils/persistence.py b/stylegan_human/torch_utils/persistence.py index 50269409c8d9f7c38d7870ee7c8e4660bfb4115c..718e480d44479029d224be2629acda269070491f 100644 --- a/stylegan_human/torch_utils/persistence.py +++ b/stylegan_human/torch_utils/persistence.py @@ -24,15 +24,16 @@ import uuid import types import dnnlib -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- -_version = 6 # internal version number -_decorators = set() # {decorator_class, ...} -_import_hooks = [] # [hook_function, ...] +_version = 6 # internal version number +_decorators = set() # {decorator_class, ...} +_import_hooks = [] # [hook_function, ...] _module_to_src_dict = dict() # {module: src, ...} _src_to_module_dict = dict() # {src: module, ...} -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def persistent_class(orig_class): r"""Class decorator that extends a given class to save its source code @@ -121,17 +122,19 @@ def persistent_class(orig_class): fields = list(super().__reduce__()) fields += [None] * max(3 - len(fields), 0) if fields[0] is not _reconstruct_persistent_obj: - meta = dict(type='class', version=_version, module_src=self._orig_module_src, class_name=self._orig_class_name, state=fields[2]) - fields[0] = _reconstruct_persistent_obj # reconstruct func - fields[1] = (meta,) # reconstruct args - fields[2] = None # state dict + meta = dict(type='class', version=_version, module_src=self._orig_module_src, + class_name=self._orig_class_name, state=fields[2]) + fields[0] = _reconstruct_persistent_obj # reconstruct func + fields[1] = (meta,) # reconstruct args + fields[2] = None # state dict return tuple(fields) Decorator.__name__ = orig_class.__name__ _decorators.add(Decorator) return Decorator -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def is_persistent(obj): r"""Test whether the given object or class is persistent, i.e., @@ -142,9 +145,10 @@ def is_persistent(obj): return True except TypeError: pass - return type(obj) in _decorators # pylint: disable=unidiomatic-typecheck + return type(obj) in _decorators # pylint: disable=unidiomatic-typecheck + +# ---------------------------------------------------------------------------- -#---------------------------------------------------------------------------- def import_hook(hook): r"""Register an import hook that is called whenever a persistent object @@ -176,7 +180,8 @@ def import_hook(hook): assert callable(hook) _import_hooks.append(hook) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _reconstruct_persistent_obj(meta): r"""Hook that is called internally by the `pickle` module to unpickle @@ -198,12 +203,13 @@ def _reconstruct_persistent_obj(meta): setstate = getattr(obj, '__setstate__', None) if callable(setstate): - setstate(meta.state) # pylint: disable=not-callable + setstate(meta.state) # pylint: disable=not-callable else: obj.__dict__.update(meta.state) return obj -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _module_to_src(module): r"""Query the source code of a given Python module. @@ -215,6 +221,7 @@ def _module_to_src(module): _src_to_module_dict[src] = module return src + def _src_to_module(src): r"""Get or create a Python module for the given source code. """ @@ -225,10 +232,11 @@ def _src_to_module(src): sys.modules[module_name] = module _module_to_src_dict[module] = src _src_to_module_dict[src] = module - exec(src, module.__dict__) # pylint: disable=exec-used + exec(src, module.__dict__) # pylint: disable=exec-used return module -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _check_pickleable(obj): r"""Check that the given object is pickleable, raising an exception if @@ -241,13 +249,14 @@ def _check_pickleable(obj): if isinstance(obj, dict): return [[recurse(x), recurse(y)] for x, y in obj.items()] if isinstance(obj, (str, int, float, bool, bytes, bytearray)): - return None # Python primitive types are pickleable. + return None # Python primitive types are pickleable. if f'{type(obj).__module__}.{type(obj).__name__}' in ['numpy.ndarray', 'torch.Tensor']: - return None # NumPy arrays and PyTorch tensors are pickleable. + return None # NumPy arrays and PyTorch tensors are pickleable. if is_persistent(obj): - return None # Persistent objects are pickleable, by virtue of the constructor check. + # Persistent objects are pickleable, by virtue of the constructor check. + return None return obj with io.BytesIO() as f: pickle.dump(recurse(obj), f) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/torch_utils/training_stats.py b/stylegan_human/torch_utils/training_stats.py index 3eb94d95286d8aeffe40ad32ca667e53b4622c4f..7017b775bd47056e7daf2b098a1dd29372342f0a 100644 --- a/stylegan_human/torch_utils/training_stats.py +++ b/stylegan_human/torch_utils/training_stats.py @@ -20,18 +20,23 @@ import dnnlib from . import misc -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- -_num_moments = 3 # [num_scalars, sum_of_scalars, sum_of_squares] -_reduce_dtype = torch.float32 # Data type to use for initial per-tensor reduction. -_counter_dtype = torch.float64 # Data type to use for the internal counters. -_rank = 0 # Rank of the current process. -_sync_device = None # Device to use for multiprocess communication. None = single-process. -_sync_called = False # Has _sync() been called yet? -_counters = dict() # Running counters on each device, updated by report(): name => device => torch.Tensor -_cumulative = dict() # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor +_num_moments = 3 # [num_scalars, sum_of_scalars, sum_of_squares] +# Data type to use for initial per-tensor reduction. +_reduce_dtype = torch.float32 +_counter_dtype = torch.float64 # Data type to use for the internal counters. +_rank = 0 # Rank of the current process. +# Device to use for multiprocess communication. None = single-process. +_sync_device = None +_sync_called = False # Has _sync() been called yet? +# Running counters on each device, updated by report(): name => device => torch.Tensor +_counters = dict() +# Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor +_cumulative = dict() + +# ---------------------------------------------------------------------------- -#---------------------------------------------------------------------------- def init_multiprocessing(rank, sync_device): r"""Initializes `torch_utils.training_stats` for collecting statistics @@ -52,7 +57,8 @@ def init_multiprocessing(rank, sync_device): _rank = rank _sync_device = sync_device -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def report(name, value): @@ -100,7 +106,8 @@ def report(name, value): _counters[name][device].add_(moments) return value -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def report0(name, value): r"""Broadcasts the given set of scalars by the first process (`rank = 0`), @@ -110,7 +117,8 @@ def report0(name, value): report(name, value if _rank == 0 else []) return value -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class Collector: r"""Collects the scalars broadcasted by `report()` and `report0()` and @@ -132,6 +140,7 @@ class Collector: scalars were collected on a given round (default: True). """ + def __init__(self, regex='.*', keep_previous=True): self._regex = re.compile(regex) self._keep_previous = keep_previous @@ -163,7 +172,8 @@ class Collector: self._moments.clear() for name, cumulative in _sync(self.names()): if name not in self._cumulative: - self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype) + self._cumulative[name] = torch.zeros( + [_num_moments], dtype=_counter_dtype) delta = cumulative - self._cumulative[name] self._cumulative[name].copy_(cumulative) if float(delta[0]) != 0: @@ -176,7 +186,8 @@ class Collector: """ assert self._regex.fullmatch(name) if name not in self._moments: - self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype) + self._moments[name] = torch.zeros( + [_num_moments], dtype=_counter_dtype) return self._moments[name] def num(self, name): @@ -222,7 +233,8 @@ class Collector: """ stats = dnnlib.EasyDict() for name in self.names(): - stats[name] = dnnlib.EasyDict(num=self.num(name), mean=self.mean(name), std=self.std(name)) + stats[name] = dnnlib.EasyDict(num=self.num( + name), mean=self.mean(name), std=self.std(name)) return stats def __getitem__(self, name): @@ -231,7 +243,8 @@ class Collector: """ return self.mean(name) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _sync(names): r"""Synchronize the global cumulative counters across devices and @@ -246,7 +259,8 @@ def _sync(names): deltas = [] device = _sync_device if _sync_device is not None else torch.device('cpu') for name in names: - delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device) + delta = torch.zeros( + [_num_moments], dtype=_counter_dtype, device=device) for counter in _counters[name].values(): delta.add_(counter.to(device)) counter.copy_(torch.zeros_like(counter)) @@ -261,10 +275,11 @@ def _sync(names): deltas = deltas.cpu() for idx, name in enumerate(names): if name not in _cumulative: - _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype) + _cumulative[name] = torch.zeros( + [_num_moments], dtype=_counter_dtype) _cumulative[name].add_(deltas[idx]) # Return name-value pairs. return [(name, _cumulative[name]) for name in names] -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/training/augment.py b/stylegan_human/training/augment.py index d68e35c96ef9fa9c18bbb6668f03b9463098710e..8067f4e3fec058c9025edaa7a9a0442afe859ae5 100644 --- a/stylegan_human/training/augment.py +++ b/stylegan_human/training/augment.py @@ -20,7 +20,7 @@ from torch_utils.ops import upfirdn2d from torch_utils.ops import grid_sample_gradfix from torch_utils.ops import conv2d_gradfix -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Coefficients of various wavelet decomposition low-pass filters. wavelets = { @@ -42,9 +42,10 @@ wavelets = { 'sym8': [-0.0033824159510061256, -0.0005421323317911481, 0.03169508781149298, 0.007607487324917605, -0.1432942383508097, -0.061273359067658524, 0.4813596512583722, 0.7771857517005235, 0.3644418948353314, -0.05194583810770904, -0.027219029917056003, 0.049137179673607506, 0.003808752013890615, -0.01495225833704823, -0.0003029205147213668, 0.0018899503327594609], } -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Helpers for constructing transformation matrices. + def matrix(*rows, device=None): assert all(len(row) == len(rows[0]) for row in rows) elems = [x for row in rows for x in row] @@ -52,9 +53,11 @@ def matrix(*rows, device=None): if len(ref) == 0: return misc.constant(np.asarray(rows), device=device) assert device is None or device == ref[0].device - elems = [x if isinstance(x, torch.Tensor) else misc.constant(x, shape=ref[0].shape, device=ref[0].device) for x in elems] + elems = [x if isinstance(x, torch.Tensor) else misc.constant( + x, shape=ref[0].shape, device=ref[0].device) for x in elems] return torch.stack(elems, dim=-1).reshape(ref[0].shape + (len(rows), -1)) + def translate2d(tx, ty, **kwargs): return matrix( [1, 0, tx], @@ -62,6 +65,7 @@ def translate2d(tx, ty, **kwargs): [0, 0, 1], **kwargs) + def translate3d(tx, ty, tz, **kwargs): return matrix( [1, 0, 0, tx], @@ -70,6 +74,7 @@ def translate3d(tx, ty, tz, **kwargs): [0, 0, 0, 1], **kwargs) + def scale2d(sx, sy, **kwargs): return matrix( [sx, 0, 0], @@ -77,6 +82,7 @@ def scale2d(sx, sy, **kwargs): [0, 0, 1], **kwargs) + def scale3d(sx, sy, sz, **kwargs): return matrix( [sx, 0, 0, 0], @@ -85,6 +91,7 @@ def scale3d(sx, sy, sz, **kwargs): [0, 0, 0, 1], **kwargs) + def rotate2d(theta, **kwargs): return matrix( [torch.cos(theta), torch.sin(-theta), 0], @@ -92,9 +99,14 @@ def rotate2d(theta, **kwargs): [0, 0, 1], **kwargs) + def rotate3d(v, theta, **kwargs): - vx = v[..., 0]; vy = v[..., 1]; vz = v[..., 2] - s = torch.sin(theta); c = torch.cos(theta); cc = 1 - c + vx = v[..., 0] + vy = v[..., 1] + vz = v[..., 2] + s = torch.sin(theta) + c = torch.cos(theta) + cc = 1 - c return matrix( [vx*vx*cc+c, vx*vy*cc-vz*s, vx*vz*cc+vy*s, 0], [vy*vx*cc+vz*s, vy*vy*cc+c, vy*vz*cc-vx*s, 0], @@ -102,93 +114,131 @@ def rotate3d(v, theta, **kwargs): [0, 0, 0, 1], **kwargs) + def translate2d_inv(tx, ty, **kwargs): return translate2d(-tx, -ty, **kwargs) + def scale2d_inv(sx, sy, **kwargs): return scale2d(1 / sx, 1 / sy, **kwargs) + def rotate2d_inv(theta, **kwargs): return rotate2d(-theta, **kwargs) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Versatile image augmentation pipeline from the paper # "Training Generative Adversarial Networks with Limited Data". # # All augmentations are disabled by default; individual augmentations can # be enabled by setting their probability multipliers to 1. + @persistence.persistent_class class AugmentPipe(torch.nn.Module): def __init__(self, - xflip=0, rotate90=0, xint=0, xint_max=0.125, - scale=0, rotate=0, aniso=0, xfrac=0, scale_std=0.2, rotate_max=1, aniso_std=0.2, xfrac_std=0.125, - brightness=0, contrast=0, lumaflip=0, hue=0, saturation=0, brightness_std=0.2, contrast_std=0.5, hue_max=1, saturation_std=1, - imgfilter=0, imgfilter_bands=[1,1,1,1], imgfilter_std=1, - noise=0, cutout=0, noise_std=0.1, cutout_size=0.5, - ): + xflip=0, rotate90=0, xint=0, xint_max=0.125, + scale=0, rotate=0, aniso=0, xfrac=0, scale_std=0.2, rotate_max=1, aniso_std=0.2, xfrac_std=0.125, + brightness=0, contrast=0, lumaflip=0, hue=0, saturation=0, brightness_std=0.2, contrast_std=0.5, hue_max=1, saturation_std=1, + imgfilter=0, imgfilter_bands=[1, 1, 1, 1], imgfilter_std=1, + noise=0, cutout=0, noise_std=0.1, cutout_size=0.5, + ): super().__init__() - self.register_buffer('p', torch.ones([])) # Overall multiplier for augmentation probability. + # Overall multiplier for augmentation probability. + self.register_buffer('p', torch.ones([])) # Pixel blitting. - self.xflip = float(xflip) # Probability multiplier for x-flip. - self.rotate90 = float(rotate90) # Probability multiplier for 90 degree rotations. - self.xint = float(xint) # Probability multiplier for integer translation. - self.xint_max = float(xint_max) # Range of integer translation, relative to image dimensions. + # Probability multiplier for x-flip. + self.xflip = float(xflip) + # Probability multiplier for 90 degree rotations. + self.rotate90 = float(rotate90) + # Probability multiplier for integer translation. + self.xint = float(xint) + # Range of integer translation, relative to image dimensions. + self.xint_max = float(xint_max) # General geometric transformations. - self.scale = float(scale) # Probability multiplier for isotropic scaling. - self.rotate = float(rotate) # Probability multiplier for arbitrary rotation. - self.aniso = float(aniso) # Probability multiplier for anisotropic scaling. - self.xfrac = float(xfrac) # Probability multiplier for fractional translation. - self.scale_std = float(scale_std) # Log2 standard deviation of isotropic scaling. - self.rotate_max = float(rotate_max) # Range of arbitrary rotation, 1 = full circle. - self.aniso_std = float(aniso_std) # Log2 standard deviation of anisotropic scaling. - self.xfrac_std = float(xfrac_std) # Standard deviation of frational translation, relative to image dimensions. + # Probability multiplier for isotropic scaling. + self.scale = float(scale) + # Probability multiplier for arbitrary rotation. + self.rotate = float(rotate) + # Probability multiplier for anisotropic scaling. + self.aniso = float(aniso) + # Probability multiplier for fractional translation. + self.xfrac = float(xfrac) + # Log2 standard deviation of isotropic scaling. + self.scale_std = float(scale_std) + # Range of arbitrary rotation, 1 = full circle. + self.rotate_max = float(rotate_max) + # Log2 standard deviation of anisotropic scaling. + self.aniso_std = float(aniso_std) + # Standard deviation of frational translation, relative to image dimensions. + self.xfrac_std = float(xfrac_std) # Color transformations. - self.brightness = float(brightness) # Probability multiplier for brightness. - self.contrast = float(contrast) # Probability multiplier for contrast. - self.lumaflip = float(lumaflip) # Probability multiplier for luma flip. - self.hue = float(hue) # Probability multiplier for hue rotation. - self.saturation = float(saturation) # Probability multiplier for saturation. - self.brightness_std = float(brightness_std) # Standard deviation of brightness. - self.contrast_std = float(contrast_std) # Log2 standard deviation of contrast. - self.hue_max = float(hue_max) # Range of hue rotation, 1 = full circle. - self.saturation_std = float(saturation_std) # Log2 standard deviation of saturation. + # Probability multiplier for brightness. + self.brightness = float(brightness) + # Probability multiplier for contrast. + self.contrast = float(contrast) + # Probability multiplier for luma flip. + self.lumaflip = float(lumaflip) + # Probability multiplier for hue rotation. + self.hue = float(hue) + # Probability multiplier for saturation. + self.saturation = float(saturation) + # Standard deviation of brightness. + self.brightness_std = float(brightness_std) + # Log2 standard deviation of contrast. + self.contrast_std = float(contrast_std) + # Range of hue rotation, 1 = full circle. + self.hue_max = float(hue_max) + # Log2 standard deviation of saturation. + self.saturation_std = float(saturation_std) # Image-space filtering. - self.imgfilter = float(imgfilter) # Probability multiplier for image-space filtering. - self.imgfilter_bands = list(imgfilter_bands) # Probability multipliers for individual frequency bands. - self.imgfilter_std = float(imgfilter_std) # Log2 standard deviation of image-space filter amplification. + # Probability multiplier for image-space filtering. + self.imgfilter = float(imgfilter) + # Probability multipliers for individual frequency bands. + self.imgfilter_bands = list(imgfilter_bands) + # Log2 standard deviation of image-space filter amplification. + self.imgfilter_std = float(imgfilter_std) # Image-space corruptions. - self.noise = float(noise) # Probability multiplier for additive RGB noise. - self.cutout = float(cutout) # Probability multiplier for cutout. - self.noise_std = float(noise_std) # Standard deviation of additive RGB noise. - self.cutout_size = float(cutout_size) # Size of the cutout rectangle, relative to image dimensions. + # Probability multiplier for additive RGB noise. + self.noise = float(noise) + # Probability multiplier for cutout. + self.cutout = float(cutout) + # Standard deviation of additive RGB noise. + self.noise_std = float(noise_std) + # Size of the cutout rectangle, relative to image dimensions. + self.cutout_size = float(cutout_size) # Setup orthogonal lowpass filter for geometric augmentations. - self.register_buffer('Hz_geom', upfirdn2d.setup_filter(wavelets['sym6'])) + self.register_buffer( + 'Hz_geom', upfirdn2d.setup_filter(wavelets['sym6'])) # Construct filter bank for image-space filtering. Hz_lo = np.asarray(wavelets['sym2']) # H(z) - Hz_hi = Hz_lo * ((-1) ** np.arange(Hz_lo.size)) # H(-z) + Hz_hi = Hz_lo * ((-1) ** np.arange(Hz_lo.size)) # H(-z) Hz_lo2 = np.convolve(Hz_lo, Hz_lo[::-1]) / 2 # H(z) * H(z^-1) / 2 Hz_hi2 = np.convolve(Hz_hi, Hz_hi[::-1]) / 2 # H(-z) * H(-z^-1) / 2 Hz_fbank = np.eye(4, 1) # Bandpass(H(z), b_i) for i in range(1, Hz_fbank.shape[0]): - Hz_fbank = np.dstack([Hz_fbank, np.zeros_like(Hz_fbank)]).reshape(Hz_fbank.shape[0], -1)[:, :-1] + Hz_fbank = np.dstack([Hz_fbank, np.zeros_like(Hz_fbank)]).reshape( + Hz_fbank.shape[0], -1)[:, :-1] Hz_fbank = scipy.signal.convolve(Hz_fbank, [Hz_lo2]) - Hz_fbank[i, (Hz_fbank.shape[1] - Hz_hi2.size) // 2 : (Hz_fbank.shape[1] + Hz_hi2.size) // 2] += Hz_hi2 - self.register_buffer('Hz_fbank', torch.as_tensor(Hz_fbank, dtype=torch.float32)) + Hz_fbank[i, (Hz_fbank.shape[1] - Hz_hi2.size) // + 2: (Hz_fbank.shape[1] + Hz_hi2.size) // 2] += Hz_hi2 + self.register_buffer('Hz_fbank', torch.as_tensor( + Hz_fbank, dtype=torch.float32)) def forward(self, images, debug_percentile=None): assert isinstance(images, torch.Tensor) and images.ndim == 4 batch_size, num_channels, height, width = images.shape device = images.device if debug_percentile is not None: - debug_percentile = torch.as_tensor(debug_percentile, dtype=torch.float32, device=device) + debug_percentile = torch.as_tensor( + debug_percentile, dtype=torch.float32, device=device) # ------------------------------------- # Select parameters for pixel blitting. @@ -201,7 +251,8 @@ class AugmentPipe(torch.nn.Module): # Apply x-flip with probability (xflip * strength). if self.xflip > 0: i = torch.floor(torch.rand([batch_size], device=device) * 2) - i = torch.where(torch.rand([batch_size], device=device) < self.xflip * self.p, i, torch.zeros_like(i)) + i = torch.where(torch.rand( + [batch_size], device=device) < self.xflip * self.p, i, torch.zeros_like(i)) if debug_percentile is not None: i = torch.full_like(i, torch.floor(debug_percentile * 2)) G_inv = G_inv @ scale2d_inv(1 - 2 * i, 1) @@ -209,18 +260,23 @@ class AugmentPipe(torch.nn.Module): # Apply 90 degree rotations with probability (rotate90 * strength). if self.rotate90 > 0: i = torch.floor(torch.rand([batch_size], device=device) * 4) - i = torch.where(torch.rand([batch_size], device=device) < self.rotate90 * self.p, i, torch.zeros_like(i)) + i = torch.where(torch.rand( + [batch_size], device=device) < self.rotate90 * self.p, i, torch.zeros_like(i)) if debug_percentile is not None: i = torch.full_like(i, torch.floor(debug_percentile * 4)) G_inv = G_inv @ rotate2d_inv(-np.pi / 2 * i) # Apply integer translation with probability (xint * strength). if self.xint > 0: - t = (torch.rand([batch_size, 2], device=device) * 2 - 1) * self.xint_max - t = torch.where(torch.rand([batch_size, 1], device=device) < self.xint * self.p, t, torch.zeros_like(t)) + t = (torch.rand([batch_size, 2], device=device) + * 2 - 1) * self.xint_max + t = torch.where(torch.rand( + [batch_size, 1], device=device) < self.xint * self.p, t, torch.zeros_like(t)) if debug_percentile is not None: - t = torch.full_like(t, (debug_percentile * 2 - 1) * self.xint_max) - G_inv = G_inv @ translate2d_inv(torch.round(t[:,0] * width), torch.round(t[:,1] * height)) + t = torch.full_like( + t, (debug_percentile * 2 - 1) * self.xint_max) + G_inv = G_inv @ translate2d_inv(torch.round( + t[:, 0] * width), torch.round(t[:, 1] * height)) # -------------------------------------------------------- # Select parameters for general geometric transformations. @@ -228,44 +284,58 @@ class AugmentPipe(torch.nn.Module): # Apply isotropic scaling with probability (scale * strength). if self.scale > 0: - s = torch.exp2(torch.randn([batch_size], device=device) * self.scale_std) - s = torch.where(torch.rand([batch_size], device=device) < self.scale * self.p, s, torch.ones_like(s)) + s = torch.exp2(torch.randn( + [batch_size], device=device) * self.scale_std) + s = torch.where(torch.rand( + [batch_size], device=device) < self.scale * self.p, s, torch.ones_like(s)) if debug_percentile is not None: - s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.scale_std)) + s = torch.full_like(s, torch.exp2(torch.erfinv( + debug_percentile * 2 - 1) * self.scale_std)) G_inv = G_inv @ scale2d_inv(s, s) # Apply pre-rotation with probability p_rot. - p_rot = 1 - torch.sqrt((1 - self.rotate * self.p).clamp(0, 1)) # P(pre OR post) = p + # P(pre OR post) = p + p_rot = 1 - torch.sqrt((1 - self.rotate * self.p).clamp(0, 1)) if self.rotate > 0: - theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max - theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta)) + theta = (torch.rand([batch_size], device=device) + * 2 - 1) * np.pi * self.rotate_max + theta = torch.where(torch.rand( + [batch_size], device=device) < p_rot, theta, torch.zeros_like(theta)) if debug_percentile is not None: - theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.rotate_max) - G_inv = G_inv @ rotate2d_inv(-theta) # Before anisotropic scaling. + theta = torch.full_like( + theta, (debug_percentile * 2 - 1) * np.pi * self.rotate_max) + G_inv = G_inv @ rotate2d_inv(-theta) # Before anisotropic scaling. # Apply anisotropic scaling with probability (aniso * strength). if self.aniso > 0: - s = torch.exp2(torch.randn([batch_size], device=device) * self.aniso_std) - s = torch.where(torch.rand([batch_size], device=device) < self.aniso * self.p, s, torch.ones_like(s)) + s = torch.exp2(torch.randn( + [batch_size], device=device) * self.aniso_std) + s = torch.where(torch.rand( + [batch_size], device=device) < self.aniso * self.p, s, torch.ones_like(s)) if debug_percentile is not None: - s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.aniso_std)) + s = torch.full_like(s, torch.exp2(torch.erfinv( + debug_percentile * 2 - 1) * self.aniso_std)) G_inv = G_inv @ scale2d_inv(s, 1 / s) # Apply post-rotation with probability p_rot. if self.rotate > 0: - theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max - theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta)) + theta = (torch.rand([batch_size], device=device) + * 2 - 1) * np.pi * self.rotate_max + theta = torch.where(torch.rand( + [batch_size], device=device) < p_rot, theta, torch.zeros_like(theta)) if debug_percentile is not None: theta = torch.zeros_like(theta) - G_inv = G_inv @ rotate2d_inv(-theta) # After anisotropic scaling. + G_inv = G_inv @ rotate2d_inv(-theta) # After anisotropic scaling. # Apply fractional translation with probability (xfrac * strength). if self.xfrac > 0: t = torch.randn([batch_size, 2], device=device) * self.xfrac_std - t = torch.where(torch.rand([batch_size, 1], device=device) < self.xfrac * self.p, t, torch.zeros_like(t)) + t = torch.where(torch.rand( + [batch_size, 1], device=device) < self.xfrac * self.p, t, torch.zeros_like(t)) if debug_percentile is not None: - t = torch.full_like(t, torch.erfinv(debug_percentile * 2 - 1) * self.xfrac_std) - G_inv = G_inv @ translate2d_inv(t[:,0] * width, t[:,1] * height) + t = torch.full_like(t, torch.erfinv( + debug_percentile * 2 - 1) * self.xfrac_std) + G_inv = G_inv @ translate2d_inv(t[:, 0] * width, t[:, 1] * height) # ---------------------------------- # Execute geometric transformations. @@ -277,33 +347,46 @@ class AugmentPipe(torch.nn.Module): # Calculate padding. cx = (width - 1) / 2 cy = (height - 1) / 2 - cp = matrix([-cx, -cy, 1], [cx, -cy, 1], [cx, cy, 1], [-cx, cy, 1], device=device) # [idx, xyz] - cp = G_inv @ cp.t() # [batch, xyz, idx] + cp = matrix([-cx, -cy, 1], [cx, -cy, 1], [cx, cy, 1], + [-cx, cy, 1], device=device) # [idx, xyz] + cp = G_inv @ cp.t() # [batch, xyz, idx] Hz_pad = self.Hz_geom.shape[0] // 4 - margin = cp[:, :2, :].permute(1, 0, 2).flatten(1) # [xy, batch * idx] - margin = torch.cat([-margin, margin]).max(dim=1).values # [x0, y0, x1, y1] - margin = margin + misc.constant([Hz_pad * 2 - cx, Hz_pad * 2 - cy] * 2, device=device) + margin = cp[:, :2, :].permute( + 1, 0, 2).flatten(1) # [xy, batch * idx] + # [x0, y0, x1, y1] + margin = torch.cat([-margin, margin]).max(dim=1).values + margin = margin + \ + misc.constant([Hz_pad * 2 - cx, Hz_pad * 2 - cy] + * 2, device=device) margin = margin.max(misc.constant([0, 0] * 2, device=device)) - margin = margin.min(misc.constant([width-1, height-1] * 2, device=device)) + margin = margin.min(misc.constant( + [width-1, height-1] * 2, device=device)) mx0, my0, mx1, my1 = margin.ceil().to(torch.int32) # Pad image and adjust origin. - images = torch.nn.functional.pad(input=images, pad=[mx0,mx1,my0,my1], mode='reflect') + images = torch.nn.functional.pad( + input=images, pad=[mx0, mx1, my0, my1], mode='reflect') G_inv = translate2d((mx0 - mx1) / 2, (my0 - my1) / 2) @ G_inv # Upsample. images = upfirdn2d.upsample2d(x=images, f=self.Hz_geom, up=2) - G_inv = scale2d(2, 2, device=device) @ G_inv @ scale2d_inv(2, 2, device=device) - G_inv = translate2d(-0.5, -0.5, device=device) @ G_inv @ translate2d_inv(-0.5, -0.5, device=device) + G_inv = scale2d( + 2, 2, device=device) @ G_inv @ scale2d_inv(2, 2, device=device) + G_inv = translate2d(-0.5, -0.5, + device=device) @ G_inv @ translate2d_inv(-0.5, -0.5, device=device) # Execute transformation. - shape = [batch_size, num_channels, (height + Hz_pad * 2) * 2, (width + Hz_pad * 2) * 2] - G_inv = scale2d(2 / images.shape[3], 2 / images.shape[2], device=device) @ G_inv @ scale2d_inv(2 / shape[3], 2 / shape[2], device=device) - grid = torch.nn.functional.affine_grid(theta=G_inv[:,:2,:], size=shape, align_corners=False) + shape = [batch_size, num_channels, + (height + Hz_pad * 2) * 2, (width + Hz_pad * 2) * 2] + G_inv = scale2d(2 / images.shape[3], 2 / images.shape[2], device=device) @ G_inv @ scale2d_inv( + 2 / shape[3], 2 / shape[2], device=device) + grid = torch.nn.functional.affine_grid( + theta=G_inv[:, :2, :], size=shape, align_corners=False) images = grid_sample_gradfix.grid_sample(images, grid) # Downsample and crop. - images = upfirdn2d.downsample2d(x=images, f=self.Hz_geom, down=2, padding=-Hz_pad*2, flip_filter=True) + images = upfirdn2d.downsample2d( + x=images, f=self.Hz_geom, down=2, padding=-Hz_pad*2, flip_filter=True) # -------------------------------------------- # Select parameters for color transformations. @@ -316,42 +399,55 @@ class AugmentPipe(torch.nn.Module): # Apply brightness with probability (brightness * strength). if self.brightness > 0: b = torch.randn([batch_size], device=device) * self.brightness_std - b = torch.where(torch.rand([batch_size], device=device) < self.brightness * self.p, b, torch.zeros_like(b)) + b = torch.where(torch.rand( + [batch_size], device=device) < self.brightness * self.p, b, torch.zeros_like(b)) if debug_percentile is not None: - b = torch.full_like(b, torch.erfinv(debug_percentile * 2 - 1) * self.brightness_std) + b = torch.full_like(b, torch.erfinv( + debug_percentile * 2 - 1) * self.brightness_std) C = translate3d(b, b, b) @ C # Apply contrast with probability (contrast * strength). if self.contrast > 0: - c = torch.exp2(torch.randn([batch_size], device=device) * self.contrast_std) - c = torch.where(torch.rand([batch_size], device=device) < self.contrast * self.p, c, torch.ones_like(c)) + c = torch.exp2(torch.randn( + [batch_size], device=device) * self.contrast_std) + c = torch.where(torch.rand( + [batch_size], device=device) < self.contrast * self.p, c, torch.ones_like(c)) if debug_percentile is not None: - c = torch.full_like(c, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.contrast_std)) + c = torch.full_like(c, torch.exp2(torch.erfinv( + debug_percentile * 2 - 1) * self.contrast_std)) C = scale3d(c, c, c) @ C # Apply luma flip with probability (lumaflip * strength). - v = misc.constant(np.asarray([1, 1, 1, 0]) / np.sqrt(3), device=device) # Luma axis. + # Luma axis. + v = misc.constant(np.asarray([1, 1, 1, 0]) / np.sqrt(3), device=device) if self.lumaflip > 0: i = torch.floor(torch.rand([batch_size, 1, 1], device=device) * 2) - i = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.lumaflip * self.p, i, torch.zeros_like(i)) + i = torch.where(torch.rand( + [batch_size, 1, 1], device=device) < self.lumaflip * self.p, i, torch.zeros_like(i)) if debug_percentile is not None: i = torch.full_like(i, torch.floor(debug_percentile * 2)) - C = (I_4 - 2 * v.ger(v) * i) @ C # Householder reflection. + C = (I_4 - 2 * v.ger(v) * i) @ C # Householder reflection. # Apply hue rotation with probability (hue * strength). if self.hue > 0 and num_channels > 1: - theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.hue_max - theta = torch.where(torch.rand([batch_size], device=device) < self.hue * self.p, theta, torch.zeros_like(theta)) + theta = (torch.rand([batch_size], device=device) + * 2 - 1) * np.pi * self.hue_max + theta = torch.where(torch.rand( + [batch_size], device=device) < self.hue * self.p, theta, torch.zeros_like(theta)) if debug_percentile is not None: - theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.hue_max) - C = rotate3d(v, theta) @ C # Rotate around v. + theta = torch.full_like( + theta, (debug_percentile * 2 - 1) * np.pi * self.hue_max) + C = rotate3d(v, theta) @ C # Rotate around v. # Apply saturation with probability (saturation * strength). if self.saturation > 0 and num_channels > 1: - s = torch.exp2(torch.randn([batch_size, 1, 1], device=device) * self.saturation_std) - s = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.saturation * self.p, s, torch.ones_like(s)) + s = torch.exp2(torch.randn( + [batch_size, 1, 1], device=device) * self.saturation_std) + s = torch.where(torch.rand( + [batch_size, 1, 1], device=device) < self.saturation * self.p, s, torch.ones_like(s)) if debug_percentile is not None: - s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.saturation_std)) + s = torch.full_like(s, torch.exp2(torch.erfinv( + debug_percentile * 2 - 1) * self.saturation_std)) C = (v.ger(v) + (I_4 - v.ger(v)) * s) @ C # ------------------------------ @@ -365,9 +461,11 @@ class AugmentPipe(torch.nn.Module): images = C[:, :3, :3] @ images + C[:, :3, 3:] elif num_channels == 1: C = C[:, :3, :].mean(dim=1, keepdims=True) - images = images * C[:, :, :3].sum(dim=2, keepdims=True) + C[:, :, 3:] + images = images * \ + C[:, :, :3].sum(dim=2, keepdims=True) + C[:, :, 3:] else: - raise ValueError('Image must be RGB (3 channels) or L (1 channel)') + raise ValueError( + 'Image must be RGB (3 channels) or L (1 channel)') images = images.reshape([batch_size, num_channels, height, width]) # ---------------------- @@ -377,31 +475,49 @@ class AugmentPipe(torch.nn.Module): if self.imgfilter > 0: num_bands = self.Hz_fbank.shape[0] assert len(self.imgfilter_bands) == num_bands - expected_power = misc.constant(np.array([10, 1, 1, 1]) / 13, device=device) # Expected power spectrum (1/f). + # Expected power spectrum (1/f). + expected_power = misc.constant( + np.array([10, 1, 1, 1]) / 13, device=device) # Apply amplification for each band with probability (imgfilter * strength * band_strength). - g = torch.ones([batch_size, num_bands], device=device) # Global gain vector (identity). + # Global gain vector (identity). + g = torch.ones([batch_size, num_bands], device=device) for i, band_strength in enumerate(self.imgfilter_bands): - t_i = torch.exp2(torch.randn([batch_size], device=device) * self.imgfilter_std) - t_i = torch.where(torch.rand([batch_size], device=device) < self.imgfilter * self.p * band_strength, t_i, torch.ones_like(t_i)) + t_i = torch.exp2(torch.randn( + [batch_size], device=device) * self.imgfilter_std) + t_i = torch.where(torch.rand( + [batch_size], device=device) < self.imgfilter * self.p * band_strength, t_i, torch.ones_like(t_i)) if debug_percentile is not None: - t_i = torch.full_like(t_i, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.imgfilter_std)) if band_strength > 0 else torch.ones_like(t_i) - t = torch.ones([batch_size, num_bands], device=device) # Temporary gain vector. - t[:, i] = t_i # Replace i'th element. - t = t / (expected_power * t.square()).sum(dim=-1, keepdims=True).sqrt() # Normalize power. - g = g * t # Accumulate into global gain. + t_i = torch.full_like(t_i, torch.exp2(torch.erfinv( + debug_percentile * 2 - 1) * self.imgfilter_std)) if band_strength > 0 else torch.ones_like(t_i) + # Temporary gain vector. + t = torch.ones([batch_size, num_bands], device=device) + # Replace i'th element. + t[:, i] = t_i + # Normalize power. + t = t / (expected_power * t.square() + ).sum(dim=-1, keepdims=True).sqrt() + # Accumulate into global gain. + g = g * t # Construct combined amplification filter. - Hz_prime = g @ self.Hz_fbank # [batch, tap] - Hz_prime = Hz_prime.unsqueeze(1).repeat([1, num_channels, 1]) # [batch, channels, tap] - Hz_prime = Hz_prime.reshape([batch_size * num_channels, 1, -1]) # [batch * channels, 1, tap] + # [batch, tap] + Hz_prime = g @ self.Hz_fbank + Hz_prime = Hz_prime.unsqueeze(1).repeat( + [1, num_channels, 1]) # [batch, channels, tap] + # [batch * channels, 1, tap] + Hz_prime = Hz_prime.reshape([batch_size * num_channels, 1, -1]) # Apply filter. p = self.Hz_fbank.shape[1] // 2 - images = images.reshape([1, batch_size * num_channels, height, width]) - images = torch.nn.functional.pad(input=images, pad=[p,p,p,p], mode='reflect') - images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(2), groups=batch_size*num_channels) - images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(3), groups=batch_size*num_channels) + images = images.reshape( + [1, batch_size * num_channels, height, width]) + images = torch.nn.functional.pad( + input=images, pad=[p, p, p, p], mode='reflect') + images = conv2d_gradfix.conv2d( + input=images, weight=Hz_prime.unsqueeze(2), groups=batch_size*num_channels) + images = conv2d_gradfix.conv2d( + input=images, weight=Hz_prime.unsqueeze(3), groups=batch_size*num_channels) images = images.reshape([batch_size, num_channels, height, width]) # ------------------------ @@ -410,27 +526,37 @@ class AugmentPipe(torch.nn.Module): # Apply additive RGB noise with probability (noise * strength). if self.noise > 0: - sigma = torch.randn([batch_size, 1, 1, 1], device=device).abs() * self.noise_std - sigma = torch.where(torch.rand([batch_size, 1, 1, 1], device=device) < self.noise * self.p, sigma, torch.zeros_like(sigma)) + sigma = torch.randn([batch_size, 1, 1, 1], + device=device).abs() * self.noise_std + sigma = torch.where(torch.rand( + [batch_size, 1, 1, 1], device=device) < self.noise * self.p, sigma, torch.zeros_like(sigma)) if debug_percentile is not None: - sigma = torch.full_like(sigma, torch.erfinv(debug_percentile) * self.noise_std) - images = images + torch.randn([batch_size, num_channels, height, width], device=device) * sigma + sigma = torch.full_like(sigma, torch.erfinv( + debug_percentile) * self.noise_std) + images = images + \ + torch.randn([batch_size, num_channels, height, + width], device=device) * sigma # Apply cutout with probability (cutout * strength). if self.cutout > 0: - size = torch.full([batch_size, 2, 1, 1, 1], self.cutout_size, device=device) - size = torch.where(torch.rand([batch_size, 1, 1, 1, 1], device=device) < self.cutout * self.p, size, torch.zeros_like(size)) + size = torch.full([batch_size, 2, 1, 1, 1], + self.cutout_size, device=device) + size = torch.where(torch.rand( + [batch_size, 1, 1, 1, 1], device=device) < self.cutout * self.p, size, torch.zeros_like(size)) center = torch.rand([batch_size, 2, 1, 1, 1], device=device) if debug_percentile is not None: size = torch.full_like(size, self.cutout_size) center = torch.full_like(center, debug_percentile) coord_x = torch.arange(width, device=device).reshape([1, 1, 1, -1]) - coord_y = torch.arange(height, device=device).reshape([1, 1, -1, 1]) - mask_x = (((coord_x + 0.5) / width - center[:, 0]).abs() >= size[:, 0] / 2) - mask_y = (((coord_y + 0.5) / height - center[:, 1]).abs() >= size[:, 1] / 2) + coord_y = torch.arange( + height, device=device).reshape([1, 1, -1, 1]) + mask_x = (((coord_x + 0.5) / width - + center[:, 0]).abs() >= size[:, 0] / 2) + mask_y = (((coord_y + 0.5) / height - + center[:, 1]).abs() >= size[:, 1] / 2) mask = torch.logical_or(mask_x, mask_y).to(torch.float32) images = images * mask return images -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/training/dataset.py b/stylegan_human/training/dataset.py index 68c356e3b89b63211e0b4bdde88babcffd26d59e..f04842155f754b0aac49b91b1de1de6db017a776 100644 --- a/stylegan_human/training/dataset.py +++ b/stylegan_human/training/dataset.py @@ -21,17 +21,22 @@ try: except ImportError: pyspng = None -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class Dataset(torch.utils.data.Dataset): def __init__(self, - name, # Name of the dataset. - raw_shape, # Shape of the raw image data (NCHW). - max_size = None, # Artificially limit the size of the dataset. None = no limit. Applied before xflip. - use_labels = False, # Enable conditioning labels? False = label dimension is zero. - xflip = False, # Artificially double the size of the dataset via x-flips. Applied after max_size. - random_seed = 0, # Random seed to use when applying max_size. - ): + name, # Name of the dataset. + raw_shape, # Shape of the raw image data (NCHW). + # Artificially limit the size of the dataset. None = no limit. Applied before xflip. + max_size=None, + # Enable conditioning labels? False = label dimension is zero. + use_labels=False, + # Artificially double the size of the dataset via x-flips. Applied after max_size. + xflip=False, + # Random seed to use when applying max_size. + random_seed=0, + ): self._name = name self._raw_shape = list(raw_shape) self._use_labels = use_labels @@ -48,13 +53,15 @@ class Dataset(torch.utils.data.Dataset): self._xflip = np.zeros(self._raw_idx.size, dtype=np.uint8) if xflip: self._raw_idx = np.tile(self._raw_idx, 2) - self._xflip = np.concatenate([self._xflip, np.ones_like(self._xflip)]) + self._xflip = np.concatenate( + [self._xflip, np.ones_like(self._xflip)]) def _get_raw_labels(self): if self._raw_labels is None: self._raw_labels = self._load_raw_labels() if self._use_labels else None if self._raw_labels is None: - self._raw_labels = np.zeros([self._raw_shape[0], 0], dtype=np.float32) + self._raw_labels = np.zeros( + [self._raw_shape[0], 0], dtype=np.float32) assert isinstance(self._raw_labels, np.ndarray) assert self._raw_labels.shape[0] == self._raw_shape[0] assert self._raw_labels.dtype in [np.float32, np.int64] @@ -63,13 +70,13 @@ class Dataset(torch.utils.data.Dataset): assert np.all(self._raw_labels >= 0) return self._raw_labels - def close(self): # to be overridden by subclass + def close(self): # to be overridden by subclass pass - def _load_raw_image(self, raw_idx): # to be overridden by subclass + def _load_raw_image(self, raw_idx): # to be overridden by subclass raise NotImplementedError - def _load_raw_labels(self): # to be overridden by subclass + def _load_raw_labels(self): # to be overridden by subclass raise NotImplementedError def __getstate__(self): @@ -90,7 +97,7 @@ class Dataset(torch.utils.data.Dataset): assert list(image.shape) == self.image_shape assert image.dtype == np.uint8 if self._xflip[idx]: - assert image.ndim == 3 # CHW + assert image.ndim == 3 # CHW image = image[:, :, ::-1] return image.copy(), self.get_label(idx) @@ -119,12 +126,12 @@ class Dataset(torch.utils.data.Dataset): @property def num_channels(self): - assert len(self.image_shape) == 3 # CHW + assert len(self.image_shape) == 3 # CHW return self.image_shape[0] @property def resolution(self): - assert len(self.image_shape) == 3 # CHW + assert len(self.image_shape) == 3 # CHW assert self.image_shape[1] == self.image_shape[2] return self.image_shape[1] @@ -151,20 +158,24 @@ class Dataset(torch.utils.data.Dataset): def has_onehot_labels(self): return self._get_raw_labels().dtype == np.int64 -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class ImageFolderDataset(Dataset): def __init__(self, - path, # Path to directory or zip. - resolution = None, # Ensure specific resolution, None = highest available. - **super_kwargs, # Additional arguments for the Dataset base class. - ): + path, # Path to directory or zip. + # Ensure specific resolution, None = highest available. + resolution=None, + # Additional arguments for the Dataset base class. + **super_kwargs, + ): self._path = path self._zipfile = None if os.path.isdir(self._path): self._type = 'dir' - self._all_fnames = {os.path.relpath(os.path.join(root, fname), start=self._path) for root, _dirs, files in os.walk(self._path) for fname in files} + self._all_fnames = {os.path.relpath(os.path.join( + root, fname), start=self._path) for root, _dirs, files in os.walk(self._path) for fname in files} elif self._file_ext(self._path) == '.zip': self._type = 'zip' self._all_fnames = set(self._get_zipfile().namelist()) @@ -172,12 +183,14 @@ class ImageFolderDataset(Dataset): raise IOError('Path must point to a directory or zip') PIL.Image.init() - self._image_fnames = sorted(fname for fname in self._all_fnames if self._file_ext(fname) in PIL.Image.EXTENSION) + self._image_fnames = sorted( + fname for fname in self._all_fnames if self._file_ext(fname) in PIL.Image.EXTENSION) if len(self._image_fnames) == 0: raise IOError('No image files found in the specified path') name = os.path.splitext(os.path.basename(self._path))[0] - raw_shape = [len(self._image_fnames)] + list(self._load_raw_image(0).shape) + raw_shape = [len(self._image_fnames)] + \ + list(self._load_raw_image(0).shape) if resolution is not None and (raw_shape[2] != resolution or raw_shape[3] != resolution): raise IOError('Image files do not match the specified resolution') super().__init__(name=name, raw_shape=raw_shape, **super_kwargs) @@ -217,8 +230,8 @@ class ImageFolderDataset(Dataset): else: image = np.array(PIL.Image.open(f)) if image.ndim == 2: - image = image[:, :, np.newaxis] # HW => HWC - image = image.transpose(2, 0, 1) # HWC => CHW + image = image[:, :, np.newaxis] # HW => HWC + image = image.transpose(2, 0, 1) # HWC => CHW return image def _load_raw_labels(self): @@ -230,9 +243,10 @@ class ImageFolderDataset(Dataset): if labels is None: return None labels = dict(labels) - labels = [labels[fname.replace('\\', '/')] for fname in self._image_fnames] + labels = [labels[fname.replace('\\', '/')] + for fname in self._image_fnames] labels = np.array(labels) labels = labels.astype({1: np.int64, 2: np.float32}[labels.ndim]) return labels -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/training/loss.py b/stylegan_human/training/loss.py index 56748095c1fb409fedbf87b2375075440440f0b4..3b6d0833ca639bb3b08f216419dfa25f1e657da2 100644 --- a/stylegan_human/training/loss.py +++ b/stylegan_human/training/loss.py @@ -14,38 +14,44 @@ from torch_utils import training_stats from torch_utils.ops import conv2d_gradfix from torch_utils.ops import upfirdn2d -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class Loss: - def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg): # to be overridden by subclass + # to be overridden by subclass + def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg): raise NotImplementedError() -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class StyleGAN2Loss(Loss): def __init__(self, device, G, D, augment_pipe=None, r1_gamma=10, style_mixing_prob=0, pl_weight=0, pl_batch_shrink=2, pl_decay=0.01, pl_no_weight_grad=False, blur_init_sigma=0, blur_fade_kimg=0): super().__init__() - self.device = device - self.G = G - self.D = D - self.augment_pipe = augment_pipe - self.r1_gamma = r1_gamma - self.style_mixing_prob = style_mixing_prob - self.pl_weight = pl_weight - self.pl_batch_shrink = pl_batch_shrink - self.pl_decay = pl_decay - self.pl_no_weight_grad = pl_no_weight_grad - self.pl_mean = torch.zeros([], device=device) - self.blur_init_sigma = blur_init_sigma - self.blur_fade_kimg = blur_fade_kimg + self.device = device + self.G = G + self.D = D + self.augment_pipe = augment_pipe + self.r1_gamma = r1_gamma + self.style_mixing_prob = style_mixing_prob + self.pl_weight = pl_weight + self.pl_batch_shrink = pl_batch_shrink + self.pl_decay = pl_decay + self.pl_no_weight_grad = pl_no_weight_grad + self.pl_mean = torch.zeros([], device=device) + self.blur_init_sigma = blur_init_sigma + self.blur_fade_kimg = blur_fade_kimg def run_G(self, z, c, update_emas=False): ws = self.G.mapping(z, c, update_emas=update_emas) if self.style_mixing_prob > 0: with torch.autograd.profiler.record_function('style_mixing'): - cutoff = torch.empty([], dtype=torch.int64, device=ws.device).random_(1, ws.shape[1]) - cutoff = torch.where(torch.rand([], device=ws.device) < self.style_mixing_prob, cutoff, torch.full_like(cutoff, ws.shape[1])) - ws[:, cutoff:] = self.G.mapping(torch.randn_like(z), c, update_emas=False)[:, cutoff:] + cutoff = torch.empty([], dtype=torch.int64, + device=ws.device).random_(1, ws.shape[1]) + cutoff = torch.where(torch.rand( + [], device=ws.device) < self.style_mixing_prob, cutoff, torch.full_like(cutoff, ws.shape[1])) + ws[:, cutoff:] = self.G.mapping( + torch.randn_like(z), c, update_emas=False)[:, cutoff:] img = self.G.synthesis(ws, update_emas=update_emas) return img, ws @@ -53,7 +59,8 @@ class StyleGAN2Loss(Loss): blur_size = np.floor(blur_sigma * 3) if blur_size > 0: with torch.autograd.profiler.record_function('blur'): - f = torch.arange(-blur_size, blur_size + 1, device=img.device).div(blur_sigma).square().neg().exp2() + f = torch.arange(-blur_size, blur_size + 1, + device=img.device).div(blur_sigma).square().neg().exp2() img = upfirdn2d.filter2d(img, f / f.sum()) if self.augment_pipe is not None: img = self.augment_pipe(img) @@ -66,7 +73,8 @@ class StyleGAN2Loss(Loss): phase = {'Greg': 'none', 'Gboth': 'Gmain'}.get(phase, phase) if self.r1_gamma == 0: phase = {'Dreg': 'none', 'Dboth': 'Dmain'}.get(phase, phase) - blur_sigma = max(1 - cur_nimg / (self.blur_fade_kimg * 1e3), 0) * self.blur_init_sigma if self.blur_fade_kimg > 0 else 0 + blur_sigma = max(1 - cur_nimg / (self.blur_fade_kimg * 1e3), 0) * \ + self.blur_init_sigma if self.blur_fade_kimg > 0 else 0 # Gmain: Maximize logits for generated images. if phase in ['Gmain', 'Gboth']: @@ -75,7 +83,8 @@ class StyleGAN2Loss(Loss): gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma) training_stats.report('Loss/scores/fake', gen_logits) training_stats.report('Loss/signs/fake', gen_logits.sign()) - loss_Gmain = torch.nn.functional.softplus(-gen_logits) # -log(sigmoid(gen_logits)) + # -log(sigmoid(gen_logits)) + loss_Gmain = torch.nn.functional.softplus(-gen_logits) training_stats.report('Loss/G/loss', loss_Gmain) with torch.autograd.profiler.record_function('Gmain_backward'): loss_Gmain.mean().mul(gain).backward() @@ -84,10 +93,13 @@ class StyleGAN2Loss(Loss): if phase in ['Greg', 'Gboth']: with torch.autograd.profiler.record_function('Gpl_forward'): batch_size = gen_z.shape[0] // self.pl_batch_shrink - gen_img, gen_ws = self.run_G(gen_z[:batch_size], gen_c[:batch_size]) - pl_noise = torch.randn_like(gen_img) / np.sqrt(gen_img.shape[2] * gen_img.shape[3]) + gen_img, gen_ws = self.run_G( + gen_z[:batch_size], gen_c[:batch_size]) + pl_noise = torch.randn_like( + gen_img) / np.sqrt(gen_img.shape[2] * gen_img.shape[3]) with torch.autograd.profiler.record_function('pl_grads'), conv2d_gradfix.no_weight_gradients(self.pl_no_weight_grad): - pl_grads = torch.autograd.grad(outputs=[(gen_img * pl_noise).sum()], inputs=[gen_ws], create_graph=True, only_inputs=True)[0] + pl_grads = torch.autograd.grad(outputs=[( + gen_img * pl_noise).sum()], inputs=[gen_ws], create_graph=True, only_inputs=True)[0] pl_lengths = pl_grads.square().sum(2).mean(1).sqrt() pl_mean = self.pl_mean.lerp(pl_lengths.mean(), self.pl_decay) self.pl_mean.copy_(pl_mean.detach()) @@ -103,10 +115,12 @@ class StyleGAN2Loss(Loss): if phase in ['Dmain', 'Dboth']: with torch.autograd.profiler.record_function('Dgen_forward'): gen_img, _gen_ws = self.run_G(gen_z, gen_c, update_emas=True) - gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma, update_emas=True) + gen_logits = self.run_D( + gen_img, gen_c, blur_sigma=blur_sigma, update_emas=True) training_stats.report('Loss/scores/fake', gen_logits) training_stats.report('Loss/signs/fake', gen_logits.sign()) - loss_Dgen = torch.nn.functional.softplus(gen_logits) # -log(1 - sigmoid(gen_logits)) + loss_Dgen = torch.nn.functional.softplus( + gen_logits) # -log(1 - sigmoid(gen_logits)) with torch.autograd.profiler.record_function('Dgen_backward'): loss_Dgen.mean().mul(gain).backward() @@ -115,21 +129,26 @@ class StyleGAN2Loss(Loss): if phase in ['Dmain', 'Dreg', 'Dboth']: name = 'Dreal' if phase == 'Dmain' else 'Dr1' if phase == 'Dreg' else 'Dreal_Dr1' with torch.autograd.profiler.record_function(name + '_forward'): - real_img_tmp = real_img.detach().requires_grad_(phase in ['Dreg', 'Dboth']) - real_logits = self.run_D(real_img_tmp, real_c, blur_sigma=blur_sigma) + real_img_tmp = real_img.detach().requires_grad_( + phase in ['Dreg', 'Dboth']) + real_logits = self.run_D( + real_img_tmp, real_c, blur_sigma=blur_sigma) training_stats.report('Loss/scores/real', real_logits) training_stats.report('Loss/signs/real', real_logits.sign()) loss_Dreal = 0 if phase in ['Dmain', 'Dboth']: - loss_Dreal = torch.nn.functional.softplus(-real_logits) # -log(sigmoid(real_logits)) - training_stats.report('Loss/D/loss', loss_Dgen + loss_Dreal) + # -log(sigmoid(real_logits)) + loss_Dreal = torch.nn.functional.softplus(-real_logits) + training_stats.report( + 'Loss/D/loss', loss_Dgen + loss_Dreal) loss_Dr1 = 0 if phase in ['Dreg', 'Dboth']: with torch.autograd.profiler.record_function('r1_grads'), conv2d_gradfix.no_weight_gradients(): - r1_grads = torch.autograd.grad(outputs=[real_logits.sum()], inputs=[real_img_tmp], create_graph=True, only_inputs=True)[0] - r1_penalty = r1_grads.square().sum([1,2,3]) + r1_grads = torch.autograd.grad(outputs=[real_logits.sum()], inputs=[ + real_img_tmp], create_graph=True, only_inputs=True)[0] + r1_penalty = r1_grads.square().sum([1, 2, 3]) loss_Dr1 = r1_penalty * (self.r1_gamma / 2) training_stats.report('Loss/r1_penalty', r1_penalty) training_stats.report('Loss/D/reg', loss_Dr1) @@ -137,4 +156,4 @@ class StyleGAN2Loss(Loss): with torch.autograd.profiler.record_function(name + '_backward'): (loss_Dreal + loss_Dr1).mean().mul(gain).backward() -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/training/networks_stylegan2.py b/stylegan_human/training/networks_stylegan2.py index 14c717927b2ad41681f5471511e428504808f4fe..923f150ef7352ed85b4be2ff8d9f8a6193aac1e9 100644 --- a/stylegan_human/training/networks_stylegan2.py +++ b/stylegan_human/training/networks_stylegan2.py @@ -21,56 +21,68 @@ from torch_utils.ops import upfirdn2d from torch_utils.ops import bias_act from torch_utils.ops import fma -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def normalize_2nd_moment(x, dim=1, eps=1e-8): return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt() -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def modulated_conv2d( - x, # Input tensor of shape [batch_size, in_channels, in_height, in_width]. - weight, # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width]. - styles, # Modulation coefficients of shape [batch_size, in_channels]. - noise = None, # Optional noise tensor to add to the output activations. - up = 1, # Integer upsampling factor. - down = 1, # Integer downsampling factor. - padding = 0, # Padding with respect to the upsampled image. - resample_filter = None, # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter(). - demodulate = True, # Apply weight demodulation? - flip_weight = True, # False = convolution, True = correlation (matches torch.nn.functional.conv2d). - fused_modconv = True, # Perform modulation, convolution, and demodulation as a single fused operation? + # Input tensor of shape [batch_size, in_channels, in_height, in_width]. + x, + # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width]. + weight, + # Modulation coefficients of shape [batch_size, in_channels]. + styles, + noise=None, # Optional noise tensor to add to the output activations. + up=1, # Integer upsampling factor. + down=1, # Integer downsampling factor. + padding=0, # Padding with respect to the upsampled image. + # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter(). + resample_filter=None, + demodulate=True, # Apply weight demodulation? + # False = convolution, True = correlation (matches torch.nn.functional.conv2d). + flip_weight=True, + # Perform modulation, convolution, and demodulation as a single fused operation? + fused_modconv=True, ): batch_size = x.shape[0] out_channels, in_channels, kh, kw = weight.shape - misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk] - misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] - misc.assert_shape(styles, [batch_size, in_channels]) # [NI] + misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk] + misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] + misc.assert_shape(styles, [batch_size, in_channels]) # [NI] # Pre-normalize inputs to avoid FP16 overflow. if x.dtype == torch.float16 and demodulate: - weight = weight * (1 / np.sqrt(in_channels * kh * kw) / weight.norm(float('inf'), dim=[1,2,3], keepdim=True)) # max_Ikk - styles = styles / styles.norm(float('inf'), dim=1, keepdim=True) # max_I + weight = weight * (1 / np.sqrt(in_channels * kh * kw) / + weight.norm(float('inf'), dim=[1, 2, 3], keepdim=True)) # max_Ikk + styles = styles / \ + styles.norm(float('inf'), dim=1, keepdim=True) # max_I # Calculate per-sample weights and demodulation coefficients. w = None dcoefs = None if demodulate or fused_modconv: - w = weight.unsqueeze(0) # [NOIkk] - w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk] + w = weight.unsqueeze(0) # [NOIkk] + w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk] if demodulate: - dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO] + dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt() # [NO] if demodulate and fused_modconv: - w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk] + w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk] # Execute by scaling the activations before and after the convolution. if not fused_modconv: x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1) - x = conv2d_resample.conv2d_resample(x=x, w=weight.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight) + x = conv2d_resample.conv2d_resample(x=x, w=weight.to( + x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight) if demodulate and noise is not None: - x = fma.fma(x, dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1), noise.to(x.dtype)) + x = fma.fma(x, dcoefs.to(x.dtype).reshape( + batch_size, -1, 1, 1), noise.to(x.dtype)) elif demodulate: x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1) elif noise is not None: @@ -78,35 +90,40 @@ def modulated_conv2d( return x # Execute as one fused op using grouped convolution. - with misc.suppress_tracer_warnings(): # this value will be treated as a constant + with misc.suppress_tracer_warnings(): # this value will be treated as a constant batch_size = int(batch_size) misc.assert_shape(x, [batch_size, in_channels, None, None]) x = x.reshape(1, -1, *x.shape[2:]) w = w.reshape(-1, in_channels, kh, kw) - x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight) + x = conv2d_resample.conv2d_resample(x=x, w=w.to( + x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight) x = x.reshape(batch_size, -1, *x.shape[2:]) if noise is not None: x = x.add_(noise) return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class FullyConnectedLayer(torch.nn.Module): def __init__(self, - in_features, # Number of input features. - out_features, # Number of output features. - bias = True, # Apply additive bias before the activation function? - activation = 'linear', # Activation function: 'relu', 'lrelu', etc. - lr_multiplier = 1, # Learning rate multiplier. - bias_init = 0, # Initial value for the additive bias. - ): + in_features, # Number of input features. + out_features, # Number of output features. + bias=True, # Apply additive bias before the activation function? + # Activation function: 'relu', 'lrelu', etc. + activation='linear', + lr_multiplier=1, # Learning rate multiplier. + bias_init=0, # Initial value for the additive bias. + ): super().__init__() self.in_features = in_features self.out_features = out_features self.activation = activation - self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) / lr_multiplier) - self.bias = torch.nn.Parameter(torch.full([out_features], np.float32(bias_init))) if bias else None + self.weight = torch.nn.Parameter(torch.randn( + [out_features, in_features]) / lr_multiplier) + self.bias = torch.nn.Parameter(torch.full( + [out_features], np.float32(bias_init))) if bias else None self.weight_gain = lr_multiplier / np.sqrt(in_features) self.bias_gain = lr_multiplier @@ -128,23 +145,28 @@ class FullyConnectedLayer(torch.nn.Module): def extra_repr(self): return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class Conv2dLayer(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels. - out_channels, # Number of output channels. - kernel_size, # Width and height of the convolution kernel. - bias = True, # Apply additive bias before the activation function? - activation = 'linear', # Activation function: 'relu', 'lrelu', etc. - up = 1, # Integer upsampling factor. - down = 1, # Integer downsampling factor. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = None, # Clamp the output to +-X, None = disable clamping. - channels_last = False, # Expect the input to have memory_format=channels_last? - trainable = True, # Update the weights of this layer during training? - ): + in_channels, # Number of input channels. + out_channels, # Number of output channels. + # Width and height of the convolution kernel. + kernel_size, + bias=True, # Apply additive bias before the activation function? + # Activation function: 'relu', 'lrelu', etc. + activation='linear', + up=1, # Integer upsampling factor. + down=1, # Integer downsampling factor. + # Low-pass filter to apply when resampling activations. + resample_filter=[1, 3, 3, 1], + # Clamp the output to +-X, None = disable clamping. + conv_clamp=None, + channels_last=False, # Expect the input to have memory_format=channels_last? + trainable=True, # Update the weights of this layer during training? + ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels @@ -152,13 +174,15 @@ class Conv2dLayer(torch.nn.Module): self.up = up self.down = down self.conv_clamp = conv_clamp - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.register_buffer( + 'resample_filter', upfirdn2d.setup_filter(resample_filter)) self.padding = kernel_size // 2 self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) self.act_gain = bias_act.activation_funcs[activation].def_gain memory_format = torch.channels_last if channels_last else torch.contiguous_format - weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format) + weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to( + memory_format=memory_format) bias = torch.zeros([out_channels]) if bias else None if trainable: self.weight = torch.nn.Parameter(weight) @@ -173,12 +197,14 @@ class Conv2dLayer(torch.nn.Module): def forward(self, x, gain=1): w = self.weight * self.weight_gain b = self.bias.to(x.dtype) if self.bias is not None else None - flip_weight = (self.up == 1) # slightly faster - x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight) + flip_weight = (self.up == 1) # slightly faster + x = conv2d_resample.conv2d_resample(x=x, w=w.to( + x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight) act_gain = self.act_gain * gain act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None - x = bias_act.bias_act(x, b, act=self.activation, gain=act_gain, clamp=act_clamp) + x = bias_act.bias_act(x, b, act=self.activation, + gain=act_gain, clamp=act_clamp) return x def extra_repr(self): @@ -186,22 +212,32 @@ class Conv2dLayer(torch.nn.Module): f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, activation={self.activation:s},', f'up={self.up}, down={self.down}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class MappingNetwork(torch.nn.Module): def __init__(self, - z_dim, # Input latent (Z) dimensionality, 0 = no latent. - c_dim, # Conditioning label (C) dimensionality, 0 = no label. - w_dim, # Intermediate latent (W) dimensionality. - num_ws, # Number of intermediate latents to output, None = do not broadcast. - num_layers = 8, # Number of mapping layers. - embed_features = None, # Label embedding dimensionality, None = same as w_dim. - layer_features = None, # Number of intermediate features in the mapping layers, None = same as w_dim. - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers. - w_avg_beta = 0.998, # Decay for tracking the moving average of W during training, None = do not track. - ): + # Input latent (Z) dimensionality, 0 = no latent. + z_dim, + # Conditioning label (C) dimensionality, 0 = no label. + c_dim, + # Intermediate latent (W) dimensionality. + w_dim, + # Number of intermediate latents to output, None = do not broadcast. + num_ws, + num_layers=8, # Number of mapping layers. + # Label embedding dimensionality, None = same as w_dim. + embed_features=None, + # Number of intermediate features in the mapping layers, None = same as w_dim. + layer_features=None, + # Activation function: 'relu', 'lrelu', etc. + activation='lrelu', + # Learning rate multiplier for the mapping layers. + lr_multiplier=0.01, + # Decay for tracking the moving average of W during training, None = do not track. + w_avg_beta=0.998, + ): super().__init__() self.z_dim = z_dim self.c_dim = c_dim @@ -216,14 +252,16 @@ class MappingNetwork(torch.nn.Module): embed_features = 0 if layer_features is None: layer_features = w_dim - features_list = [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim] + features_list = [z_dim + embed_features] + \ + [layer_features] * (num_layers - 1) + [w_dim] if c_dim > 0: self.embed = FullyConnectedLayer(c_dim, embed_features) for idx in range(num_layers): in_features = features_list[idx] out_features = features_list[idx + 1] - layer = FullyConnectedLayer(in_features, out_features, activation=activation, lr_multiplier=lr_multiplier) + layer = FullyConnectedLayer( + in_features, out_features, activation=activation, lr_multiplier=lr_multiplier) setattr(self, f'fc{idx}', layer) if num_ws is not None and w_avg_beta is not None: @@ -249,7 +287,8 @@ class MappingNetwork(torch.nn.Module): # Update moving average of W. if update_emas and self.w_avg_beta is not None: with torch.autograd.profiler.record_function('update_w_avg'): - self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta)) + self.w_avg.copy_(x.detach().mean( + dim=0).lerp(self.w_avg, self.w_avg_beta)) # Broadcast. if self.num_ws is not None: @@ -263,29 +302,35 @@ class MappingNetwork(torch.nn.Module): if self.num_ws is None or truncation_cutoff is None: x = self.w_avg.lerp(x, truncation_psi) else: - x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi) + x[:, :truncation_cutoff] = self.w_avg.lerp( + x[:, :truncation_cutoff], truncation_psi) return x def extra_repr(self): return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisLayer(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels. - out_channels, # Number of output channels. - w_dim, # Intermediate latent (W) dimensionality. - resolution, # Resolution of this layer. - kernel_size = 3, # Convolution kernel size. - up = 1, # Integer upsampling factor. - use_noise = True, # Enable noise input? - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. - channels_last = False, # Use channels_last format for the weights? - ): + in_channels, # Number of input channels. + out_channels, # Number of output channels. + # Intermediate latent (W) dimensionality. + w_dim, + resolution, # Resolution of this layer. + kernel_size=3, # Convolution kernel size. + up=1, # Integer upsampling factor. + use_noise=True, # Enable noise input? + # Activation function: 'relu', 'lrelu', etc. + activation='lrelu', + # Low-pass filter to apply when resampling activations. + resample_filter=[1, 3, 3, 1], + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=None, + channels_last=False, # Use channels_last format for the weights? + ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels @@ -295,37 +340,43 @@ class SynthesisLayer(torch.nn.Module): self.use_noise = use_noise self.activation = activation self.conv_clamp = conv_clamp - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.register_buffer( + 'resample_filter', upfirdn2d.setup_filter(resample_filter)) self.padding = kernel_size // 2 self.act_gain = bias_act.activation_funcs[activation].def_gain self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) memory_format = torch.channels_last if channels_last else torch.contiguous_format - self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) + self.weight = torch.nn.Parameter(torch.randn( + [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) if use_noise: - self.register_buffer('noise_const', torch.randn([resolution, resolution])) + self.register_buffer( + 'noise_const', torch.randn([resolution, resolution])) self.noise_strength = torch.nn.Parameter(torch.zeros([])) self.bias = torch.nn.Parameter(torch.zeros([out_channels])) def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1): assert noise_mode in ['random', 'const', 'none'] in_resolution = self.resolution // self.up - misc.assert_shape(x, [None, self.in_channels, in_resolution, in_resolution]) + misc.assert_shape(x, [None, self.in_channels, + in_resolution, in_resolution]) styles = self.affine(w) noise = None if self.use_noise and noise_mode == 'random': - noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength + noise = torch.randn([x.shape[0], 1, self.resolution, + self.resolution], device=x.device) * self.noise_strength if self.use_noise and noise_mode == 'const': noise = self.noise_const * self.noise_strength - flip_weight = (self.up == 1) # slightly faster + flip_weight = (self.up == 1) # slightly faster x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up, - padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv) + padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv) act_gain = self.act_gain * gain act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None - x = bias_act.bias_act(x, self.bias.to(x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp) + x = bias_act.bias_act(x, self.bias.to( + x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp) return x def extra_repr(self): @@ -333,7 +384,8 @@ class SynthesisLayer(torch.nn.Module): f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d},', f'resolution={self.resolution:d}, up={self.up}, activation={self.activation:s}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class ToRGBLayer(torch.nn.Module): @@ -345,38 +397,51 @@ class ToRGBLayer(torch.nn.Module): self.conv_clamp = conv_clamp self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) memory_format = torch.channels_last if channels_last else torch.contiguous_format - self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) + self.weight = torch.nn.Parameter(torch.randn( + [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) self.bias = torch.nn.Parameter(torch.zeros([out_channels])) self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) def forward(self, x, w, fused_modconv=True): styles = self.affine(w) * self.weight_gain - x = modulated_conv2d(x=x, weight=self.weight, styles=styles, demodulate=False, fused_modconv=fused_modconv) + x = modulated_conv2d(x=x, weight=self.weight, styles=styles, + demodulate=False, fused_modconv=fused_modconv) x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp) return x def extra_repr(self): return f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisBlock(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels, 0 = first block. - out_channels, # Number of output channels. - w_dim, # Intermediate latent (W) dimensionality. - resolution, # Resolution of this block. - img_channels, # Number of output color channels. - is_last, # Is this the last block? - architecture = 'skip', # Architecture: 'orig', 'skip', 'resnet'. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = 256, # Clamp the output of convolution layers to +-X, None = disable clamping. - use_fp16 = False, # Use FP16 for this block? - fp16_channels_last = False, # Use channels-last memory format with FP16? - fused_modconv_default = True, # Default value of fused_modconv. 'inference_only' = True for inference, False for training. - **layer_kwargs, # Arguments for SynthesisLayer. - ): + # Number of input channels, 0 = first block. + in_channels, + # Number of output channels. + out_channels, + # Intermediate latent (W) dimensionality. + w_dim, + # Resolution of this block. + resolution, + # Number of output color channels. + img_channels, + is_last, # Is this the last block? + # Architecture: 'orig', 'skip', 'resnet'. + architecture='skip', + # Low-pass filter to apply when resampling activations. + resample_filter=[1, 3, 3, 1], + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=256, + use_fp16=False, # Use FP16 for this block? + fp16_channels_last=False, # Use channels-last memory format with FP16? + # Default value of fused_modconv. 'inference_only' = True for inference, False for training. + fused_modconv_default=True, + # Arguments for SynthesisLayer. + **layer_kwargs, + ): assert architecture in ['orig', 'skip', 'resnet'] super().__init__() self.in_channels = in_channels @@ -388,34 +453,37 @@ class SynthesisBlock(torch.nn.Module): self.use_fp16 = use_fp16 self.channels_last = (use_fp16 and fp16_channels_last) self.fused_modconv_default = fused_modconv_default - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.register_buffer( + 'resample_filter', upfirdn2d.setup_filter(resample_filter)) self.num_conv = 0 self.num_torgb = 0 if in_channels == 0: - self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution])) + self.const = torch.nn.Parameter(torch.randn( + [out_channels, resolution, resolution])) if in_channels != 0: self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2, - resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs) + resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs) self.num_conv += 1 self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution, - conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs) + conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs) self.num_conv += 1 if is_last or architecture == 'skip': self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim, - conv_clamp=conv_clamp, channels_last=self.channels_last) + conv_clamp=conv_clamp, channels_last=self.channels_last) self.num_torgb += 1 if in_channels != 0 and architecture == 'resnet': self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2, - resample_filter=resample_filter, channels_last=self.channels_last) + resample_filter=resample_filter, channels_last=self.channels_last) def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, update_emas=False, **layer_kwargs): - _ = update_emas # unused - misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim]) + _ = update_emas # unused + misc.assert_shape( + ws, [None, self.num_conv + self.num_torgb, self.w_dim]) w_iter = iter(ws.unbind(dim=1)) if ws.device.type != 'cuda': force_fp32 = True @@ -431,28 +499,36 @@ class SynthesisBlock(torch.nn.Module): x = self.const.to(dtype=dtype, memory_format=memory_format) x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1]) else: - misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2]) + misc.assert_shape(x, [None, self.in_channels, + self.resolution // 2, self.resolution // 2]) x = x.to(dtype=dtype, memory_format=memory_format) # Main layers. if self.in_channels == 0: - x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv1(x, next(w_iter), + fused_modconv=fused_modconv, **layer_kwargs) elif self.architecture == 'resnet': y = self.skip(x, gain=np.sqrt(0.5)) - x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) - x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs) + x = self.conv0(x, next(w_iter), + fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, + gain=np.sqrt(0.5), **layer_kwargs) x = y.add_(x) else: - x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) - x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv0(x, next(w_iter), + fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv1(x, next(w_iter), + fused_modconv=fused_modconv, **layer_kwargs) # ToRGB. if img is not None: - misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2]) + misc.assert_shape( + img, [None, self.img_channels, self.resolution // 2, self.resolution // 2]) img = upfirdn2d.upsample2d(img, self.resample_filter) if self.is_last or self.architecture == 'skip': y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv) - y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format) + y = y.to(dtype=torch.float32, + memory_format=torch.contiguous_format) img = img.add_(y) if img is not None else y assert x.dtype == dtype @@ -462,29 +538,38 @@ class SynthesisBlock(torch.nn.Module): def extra_repr(self): return f'resolution={self.resolution:d}, architecture={self.architecture:s}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisNetwork(torch.nn.Module): def __init__(self, - w_dim, # Intermediate latent (W) dimensionality. - img_resolution, # Output image resolution. - img_channels, # Number of color channels. - channel_base = 32768, # Overall multiplier for the number of channels. - channel_max = 512, # Maximum number of channels in any layer. - num_fp16_res = 4, # Use FP16 for the N highest resolutions. - **block_kwargs, # Arguments for SynthesisBlock. - ): - assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0 + # Intermediate latent (W) dimensionality. + w_dim, + img_resolution, # Output image resolution. + img_channels, # Number of color channels. + # Overall multiplier for the number of channels. + channel_base=32768, + # Maximum number of channels in any layer. + channel_max=512, + # Use FP16 for the N highest resolutions. + num_fp16_res=4, + **block_kwargs, # Arguments for SynthesisBlock. + ): + assert img_resolution >= 4 and img_resolution & ( + img_resolution - 1) == 0 super().__init__() self.w_dim = w_dim self.img_resolution = img_resolution self.img_resolution_log2 = int(np.log2(img_resolution)) self.img_channels = img_channels self.num_fp16_res = num_fp16_res - self.block_resolutions = [2 ** i for i in range(2, self.img_resolution_log2 + 1)] - channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions} - fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) + self.block_resolutions = [ + 2 ** i for i in range(2, self.img_resolution_log2 + 1)] + channels_dict = {res: min(channel_base // res, channel_max) + for res in self.block_resolutions} + fp16_resolution = max( + 2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) self.num_ws = 0 for res in self.block_resolutions: @@ -493,7 +578,7 @@ class SynthesisNetwork(torch.nn.Module): use_fp16 = (res >= fp16_resolution) is_last = (res == self.img_resolution) block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res, - img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, **block_kwargs) + img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, **block_kwargs) self.num_ws += block.num_conv if is_last: self.num_ws += block.num_torgb @@ -508,7 +593,8 @@ class SynthesisNetwork(torch.nn.Module): w_idx = 0 for res in self.block_resolutions: block = getattr(self, f'b{res}') - block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb)) + block_ws.append( + ws.narrow(1, w_idx, block.num_conv + block.num_torgb)) w_idx += block.num_conv x = img = None @@ -527,30 +613,35 @@ class SynthesisNetwork(torch.nn.Module): f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},', f'num_fp16_res={self.num_fp16_res:d}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class Generator(torch.nn.Module): def __init__(self, - z_dim, # Input latent (Z) dimensionality. - c_dim, # Conditioning label (C) dimensionality. - w_dim, # Intermediate latent (W) dimensionality. - img_resolution, # Output resolution. - img_channels, # Number of output color channels. - mapping_kwargs = {}, # Arguments for MappingNetwork. - synthesis_kwargs = {}, # Arguments for SynthesisNetwork. - resize=None, - # **synthesis_kwargs, # Arguments for SynthesisNetwork. - ): + z_dim, # Input latent (Z) dimensionality. + # Conditioning label (C) dimensionality. + c_dim, + # Intermediate latent (W) dimensionality. + w_dim, + img_resolution, # Output resolution. + img_channels, # Number of output color channels. + mapping_kwargs={}, # Arguments for MappingNetwork. + synthesis_kwargs={}, # Arguments for SynthesisNetwork. + resize=None, + # **synthesis_kwargs, # Arguments for SynthesisNetwork. + ): super().__init__() self.z_dim = z_dim self.c_dim = c_dim self.w_dim = w_dim self.img_resolution = img_resolution self.img_channels = img_channels - self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs) + self.synthesis = SynthesisNetwork( + w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs) self.num_ws = self.synthesis.num_ws - self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) + self.mapping = MappingNetwork( + z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) self.resize = resize def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, input_is_w=False, return_feature=False, **synthesis_kwargs): @@ -559,8 +650,10 @@ class Generator(torch.nn.Module): if ws.dim() == 2: ws = ws.unsqueeze(1).repeat([1, self.mapping.num_ws, 1]) else: - ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas) - img = self.synthesis(ws, update_emas=update_emas, return_feature=return_feature, **synthesis_kwargs) + ws = self.mapping(z, c, truncation_psi=truncation_psi, + truncation_cutoff=truncation_cutoff, update_emas=update_emas) + img = self.synthesis(ws, update_emas=update_emas, + return_feature=return_feature, **synthesis_kwargs) if self.resize is not None: img = imresize(img, [self.resize, self.resize]) return img @@ -579,25 +672,37 @@ def imresize(image, size): image = image.squeeze(1) return image -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class DiscriminatorBlock(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels, 0 = first block. - tmp_channels, # Number of intermediate channels. - out_channels, # Number of output channels. - resolution, # Resolution of this block. - img_channels, # Number of input color channels. - first_layer_idx, # Index of the first layer. - architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. - use_fp16 = False, # Use FP16 for this block? - fp16_channels_last = False, # Use channels-last memory format with FP16? - freeze_layers = 0, # Freeze-D: Number of layers to freeze. - ): + # Number of input channels, 0 = first block. + in_channels, + # Number of intermediate channels. + tmp_channels, + # Number of output channels. + out_channels, + # Resolution of this block. + resolution, + # Number of input color channels. + img_channels, + # Index of the first layer. + first_layer_idx, + # Architecture: 'orig', 'skip', 'resnet'. + architecture='resnet', + # Activation function: 'relu', 'lrelu', etc. + activation='lrelu', + # Low-pass filter to apply when resampling activations. + resample_filter=[1, 3, 3, 1], + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=None, + use_fp16=False, # Use FP16 for this block? + fp16_channels_last=False, # Use channels-last memory format with FP16? + # Freeze-D: Number of layers to freeze. + freeze_layers=0, + ): assert in_channels in [0, tmp_channels] assert architecture in ['orig', 'skip', 'resnet'] super().__init__() @@ -608,9 +713,11 @@ class DiscriminatorBlock(torch.nn.Module): self.architecture = architecture self.use_fp16 = use_fp16 self.channels_last = (use_fp16 and fp16_channels_last) - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.register_buffer( + 'resample_filter', upfirdn2d.setup_filter(resample_filter)) self.num_layers = 0 + def trainable_gen(): while True: layer_idx = self.first_layer_idx + self.num_layers @@ -621,17 +728,17 @@ class DiscriminatorBlock(torch.nn.Module): if in_channels == 0 or architecture == 'skip': self.fromrgb = Conv2dLayer(img_channels, tmp_channels, kernel_size=1, activation=activation, - trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) + trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) self.conv0 = Conv2dLayer(tmp_channels, tmp_channels, kernel_size=3, activation=activation, - trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) + trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) self.conv1 = Conv2dLayer(tmp_channels, out_channels, kernel_size=3, activation=activation, down=2, - trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last) + trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last) if architecture == 'resnet': self.skip = Conv2dLayer(tmp_channels, out_channels, kernel_size=1, bias=False, down=2, - trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last) + trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last) def forward(self, x, img, force_fp32=False): if (x if x is not None else img).device.type != 'cuda': @@ -641,16 +748,19 @@ class DiscriminatorBlock(torch.nn.Module): # Input. if x is not None: - misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) + misc.assert_shape(x, [None, self.in_channels, + self.resolution, self.resolution]) x = x.to(dtype=dtype, memory_format=memory_format) # FromRGB. if self.in_channels == 0 or self.architecture == 'skip': - misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution]) + misc.assert_shape( + img, [None, self.img_channels, self.resolution, self.resolution]) img = img.to(dtype=dtype, memory_format=memory_format) y = self.fromrgb(img) x = x + y if x is not None else y - img = upfirdn2d.downsample2d(img, self.resample_filter) if self.architecture == 'skip' else None + img = upfirdn2d.downsample2d( + img, self.resample_filter) if self.architecture == 'skip' else None # Main layers. if self.architecture == 'resnet': @@ -668,7 +778,8 @@ class DiscriminatorBlock(torch.nn.Module): def extra_repr(self): return f'resolution={self.resolution:d}, architecture={self.architecture:s}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class MinibatchStdLayer(torch.nn.Module): @@ -679,39 +790,54 @@ class MinibatchStdLayer(torch.nn.Module): def forward(self, x): N, C, H, W = x.shape - with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants - G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor(N)) if self.group_size is not None else N + with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants + G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor( + N)) if self.group_size is not None else N F = self.num_channels c = C // F - y = x.reshape(G, -1, F, c, H, W) # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c. - y = y - y.mean(dim=0) # [GnFcHW] Subtract mean over group. - y = y.square().mean(dim=0) # [nFcHW] Calc variance over group. + # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c. + y = x.reshape(G, -1, F, c, H, W) + # [GnFcHW] Subtract mean over group. + y = y - y.mean(dim=0) + # [nFcHW] Calc variance over group. + y = y.square().mean(dim=0) y = (y + 1e-8).sqrt() # [nFcHW] Calc stddev over group. - y = y.mean(dim=[2,3,4]) # [nF] Take average over channels and pixels. + # [nF] Take average over channels and pixels. + y = y.mean(dim=[2, 3, 4]) y = y.reshape(-1, F, 1, 1) # [nF11] Add missing dimensions. - y = y.repeat(G, 1, H, W) # [NFHW] Replicate over group and pixels. - x = torch.cat([x, y], dim=1) # [NCHW] Append to input as new channels. + # [NFHW] Replicate over group and pixels. + y = y.repeat(G, 1, H, W) + # [NCHW] Append to input as new channels. + x = torch.cat([x, y], dim=1) return x def extra_repr(self): return f'group_size={self.group_size}, num_channels={self.num_channels:d}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class DiscriminatorEpilogue(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels. - cmap_dim, # Dimensionality of mapped conditioning label, 0 = no label. - resolution, # Resolution of this block. - img_channels, # Number of input color channels. - architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. - mbstd_group_size = 4, # Group size for the minibatch standard deviation layer, None = entire minibatch. - mbstd_num_channels = 1, # Number of features for the minibatch standard deviation layer, 0 = disable. - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. - ): + in_channels, # Number of input channels. + # Dimensionality of mapped conditioning label, 0 = no label. + cmap_dim, + resolution, # Resolution of this block. + # Number of input color channels. + img_channels, + # Architecture: 'orig', 'skip', 'resnet'. + architecture='resnet', + # Group size for the minibatch standard deviation layer, None = entire minibatch. + mbstd_group_size=4, + # Number of features for the minibatch standard deviation layer, 0 = disable. + mbstd_num_channels=1, + # Activation function: 'relu', 'lrelu', etc. + activation='lrelu', + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=None, + ): assert architecture in ['orig', 'skip', 'resnet'] super().__init__() self.in_channels = in_channels @@ -721,22 +847,29 @@ class DiscriminatorEpilogue(torch.nn.Module): self.architecture = architecture if architecture == 'skip': - self.fromrgb = Conv2dLayer(img_channels, in_channels, kernel_size=1, activation=activation) - self.mbstd = MinibatchStdLayer(group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None - self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels, kernel_size=3, activation=activation, conv_clamp=conv_clamp) - self.fc = FullyConnectedLayer(in_channels * (resolution ** 2), in_channels, activation=activation) - self.out = FullyConnectedLayer(in_channels, 1 if cmap_dim == 0 else cmap_dim) + self.fromrgb = Conv2dLayer( + img_channels, in_channels, kernel_size=1, activation=activation) + self.mbstd = MinibatchStdLayer( + group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None + self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels, + kernel_size=3, activation=activation, conv_clamp=conv_clamp) + self.fc = FullyConnectedLayer( + in_channels * (resolution ** 2), in_channels, activation=activation) + self.out = FullyConnectedLayer( + in_channels, 1 if cmap_dim == 0 else cmap_dim) def forward(self, x, img, cmap, force_fp32=False): - misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) # [NCHW] - _ = force_fp32 # unused + misc.assert_shape(x, [None, self.in_channels, + self.resolution, self.resolution]) # [NCHW] + _ = force_fp32 # unused dtype = torch.float32 memory_format = torch.contiguous_format # FromRGB. x = x.to(dtype=dtype, memory_format=memory_format) if self.architecture == 'skip': - misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution]) + misc.assert_shape( + img, [None, self.img_channels, self.resolution, self.resolution]) img = img.to(dtype=dtype, memory_format=memory_format) x = x + self.fromrgb(img) @@ -750,7 +883,8 @@ class DiscriminatorEpilogue(torch.nn.Module): # Conditioning. if self.cmap_dim > 0: misc.assert_shape(cmap, [None, self.cmap_dim]) - x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim)) + x = (x * cmap).sum(dim=1, keepdim=True) * \ + (1 / np.sqrt(self.cmap_dim)) assert x.dtype == dtype return x @@ -758,39 +892,53 @@ class DiscriminatorEpilogue(torch.nn.Module): def extra_repr(self): return f'resolution={self.resolution:d}, architecture={self.architecture:s}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class Discriminator(torch.nn.Module): def __init__(self, - c_dim, # Conditioning label (C) dimensionality. - img_resolution, # Input resolution. - img_channels, # Number of input color channels. - architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. - channel_base = 32768, # Overall multiplier for the number of channels. - channel_max = 512, # Maximum number of channels in any layer. - num_fp16_res = 4, # Use FP16 for the N highest resolutions. - conv_clamp = 256, # Clamp the output of convolution layers to +-X, None = disable clamping. - cmap_dim = None, # Dimensionality of mapped conditioning label, None = default. - block_kwargs = {}, # Arguments for DiscriminatorBlock. - mapping_kwargs = {}, # Arguments for MappingNetwork. - epilogue_kwargs = {}, # Arguments for DiscriminatorEpilogue. - ): + # Conditioning label (C) dimensionality. + c_dim, + img_resolution, # Input resolution. + # Number of input color channels. + img_channels, + # Architecture: 'orig', 'skip', 'resnet'. + architecture='resnet', + # Overall multiplier for the number of channels. + channel_base=32768, + # Maximum number of channels in any layer. + channel_max=512, + # Use FP16 for the N highest resolutions. + num_fp16_res=4, + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=256, + # Dimensionality of mapped conditioning label, None = default. + cmap_dim=None, + block_kwargs={}, # Arguments for DiscriminatorBlock. + mapping_kwargs={}, # Arguments for MappingNetwork. + # Arguments for DiscriminatorEpilogue. + epilogue_kwargs={}, + ): super().__init__() self.c_dim = c_dim self.img_resolution = img_resolution self.img_resolution_log2 = int(np.log2(img_resolution)) self.img_channels = img_channels - self.block_resolutions = [2 ** i for i in range(self.img_resolution_log2, 2, -1)] - channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions + [4]} - fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) + self.block_resolutions = [ + 2 ** i for i in range(self.img_resolution_log2, 2, -1)] + channels_dict = {res: min(channel_base // res, channel_max) + for res in self.block_resolutions + [4]} + fp16_resolution = max( + 2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) if cmap_dim is None: cmap_dim = channels_dict[4] if c_dim == 0: cmap_dim = 0 - common_kwargs = dict(img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp) + common_kwargs = dict(img_channels=img_channels, + architecture=architecture, conv_clamp=conv_clamp) cur_layer_idx = 0 for res in self.block_resolutions: in_channels = channels_dict[res] if res < img_resolution else 0 @@ -798,15 +946,17 @@ class Discriminator(torch.nn.Module): out_channels = channels_dict[res // 2] use_fp16 = (res >= fp16_resolution) block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res, - first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs) + first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs) setattr(self, f'b{res}', block) cur_layer_idx += block.num_layers if c_dim > 0: - self.mapping = MappingNetwork(z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs) - self.b4 = DiscriminatorEpilogue(channels_dict[4], cmap_dim=cmap_dim, resolution=4, **epilogue_kwargs, **common_kwargs) + self.mapping = MappingNetwork( + z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs) + self.b4 = DiscriminatorEpilogue( + channels_dict[4], cmap_dim=cmap_dim, resolution=4, **epilogue_kwargs, **common_kwargs) def forward(self, img, c, update_emas=False, **block_kwargs): - _ = update_emas # unused + _ = update_emas # unused x = None for res in self.block_resolutions: block = getattr(self, f'b{res}') @@ -821,4 +971,4 @@ class Discriminator(torch.nn.Module): def extra_repr(self): return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/training/networks_stylegan3.py b/stylegan_human/training/networks_stylegan3.py index 70d0ebce100b504b39791dbf3e1dfea4c9473f2b..d4b5b6ae121c6e8b89283f0763108b5471ea4af1 100644 --- a/stylegan_human/training/networks_stylegan3.py +++ b/stylegan_human/training/networks_stylegan3.py @@ -20,70 +20,80 @@ from torch_utils.ops import conv2d_gradfix from torch_utils.ops import filtered_lrelu from torch_utils.ops import bias_act -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def modulated_conv2d( - x, # Input tensor: [batch_size, in_channels, in_height, in_width] - w, # Weight tensor: [out_channels, in_channels, kernel_height, kernel_width] + # Input tensor: [batch_size, in_channels, in_height, in_width] + x, + # Weight tensor: [out_channels, in_channels, kernel_height, kernel_width] + w, s, # Style tensor: [batch_size, in_channels] - demodulate = True, # Apply weight demodulation? - padding = 0, # Padding: int or [padH, padW] - input_gain = None, # Optional scale factors for the input channels: [], [in_channels], or [batch_size, in_channels] + demodulate=True, # Apply weight demodulation? + padding=0, # Padding: int or [padH, padW] + input_gain=None, # Optional scale factors for the input channels: [], [in_channels], or [batch_size, in_channels] ): - with misc.suppress_tracer_warnings(): # this value will be treated as a constant + with misc.suppress_tracer_warnings(): # this value will be treated as a constant batch_size = int(x.shape[0]) out_channels, in_channels, kh, kw = w.shape - misc.assert_shape(w, [out_channels, in_channels, kh, kw]) # [OIkk] - misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] - misc.assert_shape(s, [batch_size, in_channels]) # [NI] + misc.assert_shape(w, [out_channels, in_channels, kh, kw]) # [OIkk] + misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] + misc.assert_shape(s, [batch_size, in_channels]) # [NI] # Pre-normalize inputs. if demodulate: - w = w * w.square().mean([1,2,3], keepdim=True).rsqrt() + w = w * w.square().mean([1, 2, 3], keepdim=True).rsqrt() s = s * s.square().mean().rsqrt() # Modulate weights. - w = w.unsqueeze(0) # [NOIkk] - w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk] + w = w.unsqueeze(0) # [NOIkk] + w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk] # Demodulate weights. if demodulate: - dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO] - w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4) # [NOIkk] + dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt() # [NO] + w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4) # [NOIkk] # Apply input scaling. if input_gain is not None: - input_gain = input_gain.expand(batch_size, in_channels) # [NI] - w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk] + input_gain = input_gain.expand(batch_size, in_channels) # [NI] + w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk] # Execute as one fused op using grouped convolution. x = x.reshape(1, -1, *x.shape[2:]) w = w.reshape(-1, in_channels, kh, kw) - x = conv2d_gradfix.conv2d(input=x, weight=w.to(x.dtype), padding=padding, groups=batch_size) + x = conv2d_gradfix.conv2d(input=x, weight=w.to( + x.dtype), padding=padding, groups=batch_size) x = x.reshape(batch_size, -1, *x.shape[2:]) return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class FullyConnectedLayer(torch.nn.Module): def __init__(self, - in_features, # Number of input features. - out_features, # Number of output features. - activation = 'linear', # Activation function: 'relu', 'lrelu', etc. - bias = True, # Apply additive bias before the activation function? - lr_multiplier = 1, # Learning rate multiplier. - weight_init = 1, # Initial standard deviation of the weight tensor. - bias_init = 0, # Initial value of the additive bias. - ): + in_features, # Number of input features. + out_features, # Number of output features. + # Activation function: 'relu', 'lrelu', etc. + activation='linear', + bias=True, # Apply additive bias before the activation function? + lr_multiplier=1, # Learning rate multiplier. + # Initial standard deviation of the weight tensor. + weight_init=1, + bias_init=0, # Initial value of the additive bias. + ): super().__init__() self.in_features = in_features self.out_features = out_features self.activation = activation - self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) * (weight_init / lr_multiplier)) - bias_init = np.broadcast_to(np.asarray(bias_init, dtype=np.float32), [out_features]) - self.bias = torch.nn.Parameter(torch.from_numpy(bias_init / lr_multiplier)) if bias else None + self.weight = torch.nn.Parameter(torch.randn( + [out_features, in_features]) * (weight_init / lr_multiplier)) + bias_init = np.broadcast_to(np.asarray( + bias_init, dtype=np.float32), [out_features]) + self.bias = torch.nn.Parameter(torch.from_numpy( + bias_init / lr_multiplier)) if bias else None self.weight_gain = lr_multiplier / np.sqrt(in_features) self.bias_gain = lr_multiplier @@ -104,19 +114,25 @@ class FullyConnectedLayer(torch.nn.Module): def extra_repr(self): return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class MappingNetwork(torch.nn.Module): def __init__(self, - z_dim, # Input latent (Z) dimensionality. - c_dim, # Conditioning label (C) dimensionality, 0 = no labels. - w_dim, # Intermediate latent (W) dimensionality. - num_ws, # Number of intermediate latents to output. - num_layers = 2, # Number of mapping layers. - lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers. - w_avg_beta = 0.998, # Decay for tracking the moving average of W during training. - ): + z_dim, # Input latent (Z) dimensionality. + # Conditioning label (C) dimensionality, 0 = no labels. + c_dim, + # Intermediate latent (W) dimensionality. + w_dim, + # Number of intermediate latents to output. + num_ws, + num_layers=2, # Number of mapping layers. + # Learning rate multiplier for the mapping layers. + lr_multiplier=0.01, + # Decay for tracking the moving average of W during training. + w_avg_beta=0.998, + ): super().__init__() self.z_dim = z_dim self.c_dim = c_dim @@ -126,10 +142,13 @@ class MappingNetwork(torch.nn.Module): self.w_avg_beta = w_avg_beta # Construct layers. - self.embed = FullyConnectedLayer(self.c_dim, self.w_dim) if self.c_dim > 0 else None - features = [self.z_dim + (self.w_dim if self.c_dim > 0 else 0)] + [self.w_dim] * self.num_layers + self.embed = FullyConnectedLayer( + self.c_dim, self.w_dim) if self.c_dim > 0 else None + features = [self.z_dim + (self.w_dim if self.c_dim > + 0 else 0)] + [self.w_dim] * self.num_layers for idx, in_features, out_features in zip(range(num_layers), features[:-1], features[1:]): - layer = FullyConnectedLayer(in_features, out_features, activation='lrelu', lr_multiplier=lr_multiplier) + layer = FullyConnectedLayer( + in_features, out_features, activation='lrelu', lr_multiplier=lr_multiplier) setattr(self, f'fc{idx}', layer) self.register_buffer('w_avg', torch.zeros([w_dim])) @@ -153,28 +172,31 @@ class MappingNetwork(torch.nn.Module): # Update moving average of W. if update_emas: - self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta)) + self.w_avg.copy_(x.detach().mean( + dim=0).lerp(self.w_avg, self.w_avg_beta)) # Broadcast and apply truncation. x = x.unsqueeze(1).repeat([1, self.num_ws, 1]) if truncation_psi != 1: - x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi) + x[:, :truncation_cutoff] = self.w_avg.lerp( + x[:, :truncation_cutoff], truncation_psi) return x def extra_repr(self): return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisInput(torch.nn.Module): def __init__(self, - w_dim, # Intermediate latent (W) dimensionality. - channels, # Number of output channels. - size, # Output spatial size: int or [width, height]. - sampling_rate, # Output sampling rate. - bandwidth, # Output bandwidth. - ): + w_dim, # Intermediate latent (W) dimensionality. + channels, # Number of output channels. + size, # Output spatial size: int or [width, height]. + sampling_rate, # Output sampling rate. + bandwidth, # Output bandwidth. + ): super().__init__() self.w_dim = w_dim self.channels = channels @@ -190,46 +212,58 @@ class SynthesisInput(torch.nn.Module): phases = torch.rand([self.channels]) - 0.5 # Setup parameters and buffers. - self.weight = torch.nn.Parameter(torch.randn([self.channels, self.channels])) - self.affine = FullyConnectedLayer(w_dim, 4, weight_init=0, bias_init=[1,0,0,0]) - self.register_buffer('transform', torch.eye(3, 3)) # User-specified inverse transform wrt. resulting image. + self.weight = torch.nn.Parameter( + torch.randn([self.channels, self.channels])) + self.affine = FullyConnectedLayer( + w_dim, 4, weight_init=0, bias_init=[1, 0, 0, 0]) + # User-specified inverse transform wrt. resulting image. + self.register_buffer('transform', torch.eye(3, 3)) self.register_buffer('freqs', freqs) self.register_buffer('phases', phases) def forward(self, w): # Introduce batch dimension. - transforms = self.transform.unsqueeze(0) # [batch, row, col] - freqs = self.freqs.unsqueeze(0) # [batch, channel, xy] - phases = self.phases.unsqueeze(0) # [batch, channel] + transforms = self.transform.unsqueeze(0) # [batch, row, col] + freqs = self.freqs.unsqueeze(0) # [batch, channel, xy] + phases = self.phases.unsqueeze(0) # [batch, channel] # Apply learned transformation. - t = self.affine(w) # t = (r_c, r_s, t_x, t_y) - t = t / t[:, :2].norm(dim=1, keepdim=True) # t' = (r'_c, r'_s, t'_x, t'_y) - m_r = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse rotation wrt. resulting image. + t = self.affine(w) # t = (r_c, r_s, t_x, t_y) + # t' = (r'_c, r'_s, t'_x, t'_y) + t = t / t[:, :2].norm(dim=1, keepdim=True) + # Inverse rotation wrt. resulting image. + m_r = torch.eye(3, device=w.device).unsqueeze( + 0).repeat([w.shape[0], 1, 1]) m_r[:, 0, 0] = t[:, 0] # r'_c - m_r[:, 0, 1] = -t[:, 1] # r'_s + m_r[:, 0, 1] = -t[:, 1] # r'_s m_r[:, 1, 0] = t[:, 1] # r'_s m_r[:, 1, 1] = t[:, 0] # r'_c - m_t = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse translation wrt. resulting image. - m_t[:, 0, 2] = -t[:, 2] # t'_x - m_t[:, 1, 2] = -t[:, 3] # t'_y - transforms = m_r @ m_t @ transforms # First rotate resulting image, then translate, and finally apply user-specified transform. + # Inverse translation wrt. resulting image. + m_t = torch.eye(3, device=w.device).unsqueeze( + 0).repeat([w.shape[0], 1, 1]) + m_t[:, 0, 2] = -t[:, 2] # t'_x + m_t[:, 1, 2] = -t[:, 3] # t'_y + # First rotate resulting image, then translate, and finally apply user-specified transform. + transforms = m_r @ m_t @ transforms # Transform frequencies. phases = phases + (freqs @ transforms[:, :2, 2:]).squeeze(2) freqs = freqs @ transforms[:, :2, :2] # Dampen out-of-band frequencies that may occur due to the user-specified transform. - amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) / (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1) + amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) / + (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1) # Construct sampling grid. theta = torch.eye(2, 3, device=w.device) theta[0, 0] = 0.5 * self.size[0] / self.sampling_rate theta[1, 1] = 0.5 * self.size[1] / self.sampling_rate - grids = torch.nn.functional.affine_grid(theta.unsqueeze(0), [1, 1, self.size[1], self.size[0]], align_corners=False) + grids = torch.nn.functional.affine_grid(theta.unsqueeze( + 0), [1, 1, self.size[1], self.size[0]], align_corners=False) # Compute Fourier features. - x = (grids.unsqueeze(3) @ freqs.permute(0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(3) # [batch, height, width, channel] + x = (grids.unsqueeze(3) @ freqs.permute(0, 2, 1).unsqueeze(1).unsqueeze(2) + ).squeeze(3) # [batch, height, width, channel] x = x + phases.unsqueeze(1).unsqueeze(2) x = torch.sin(x * (np.pi * 2)) x = x * amplitudes.unsqueeze(1).unsqueeze(2) @@ -239,8 +273,9 @@ class SynthesisInput(torch.nn.Module): x = x @ weight.t() # Ensure correct shape. - x = x.permute(0, 3, 1, 2) # [batch, channel, height, width] - misc.assert_shape(x, [w.shape[0], self.channels, int(self.size[1]), int(self.size[0])]) + x = x.permute(0, 3, 1, 2) # [batch, channel, height, width] + misc.assert_shape(x, [w.shape[0], self.channels, + int(self.size[1]), int(self.size[0])]) return x def extra_repr(self): @@ -248,36 +283,50 @@ class SynthesisInput(torch.nn.Module): f'w_dim={self.w_dim:d}, channels={self.channels:d}, size={list(self.size)},', f'sampling_rate={self.sampling_rate:g}, bandwidth={self.bandwidth:g}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisLayer(torch.nn.Module): def __init__(self, - w_dim, # Intermediate latent (W) dimensionality. - is_torgb, # Is this the final ToRGB layer? - is_critically_sampled, # Does this layer use critical sampling? - use_fp16, # Does this layer use FP16? - - # Input & output specifications. - in_channels, # Number of input channels. - out_channels, # Number of output channels. - in_size, # Input spatial size: int or [width, height]. - out_size, # Output spatial size: int or [width, height]. - in_sampling_rate, # Input sampling rate (s). - out_sampling_rate, # Output sampling rate (s). - in_cutoff, # Input cutoff frequency (f_c). - out_cutoff, # Output cutoff frequency (f_c). - in_half_width, # Input transition band half-width (f_h). - out_half_width, # Output Transition band half-width (f_h). - - # Hyperparameters. - conv_kernel = 3, # Convolution kernel size. Ignored for final the ToRGB layer. - filter_size = 6, # Low-pass filter size relative to the lower resolution when up/downsampling. - lrelu_upsampling = 2, # Relative sampling rate for leaky ReLU. Ignored for final the ToRGB layer. - use_radial_filters = False, # Use radially symmetric downsampling filter? Ignored for critically sampled layers. - conv_clamp = 256, # Clamp the output to [-X, +X], None = disable clamping. - magnitude_ema_beta = 0.999, # Decay rate for the moving average of input magnitudes. - ): + # Intermediate latent (W) dimensionality. + w_dim, + is_torgb, # Is this the final ToRGB layer? + is_critically_sampled, # Does this layer use critical sampling? + use_fp16, # Does this layer use FP16? + + # Input & output specifications. + in_channels, # Number of input channels. + out_channels, # Number of output channels. + # Input spatial size: int or [width, height]. + in_size, + # Output spatial size: int or [width, height]. + out_size, + in_sampling_rate, # Input sampling rate (s). + out_sampling_rate, # Output sampling rate (s). + # Input cutoff frequency (f_c). + in_cutoff, + # Output cutoff frequency (f_c). + out_cutoff, + # Input transition band half-width (f_h). + in_half_width, + # Output Transition band half-width (f_h). + out_half_width, + + # Hyperparameters. + # Convolution kernel size. Ignored for final the ToRGB layer. + conv_kernel=3, + # Low-pass filter size relative to the lower resolution when up/downsampling. + filter_size=6, + # Relative sampling rate for leaky ReLU. Ignored for final the ToRGB layer. + lrelu_upsampling=2, + # Use radially symmetric downsampling filter? Ignored for critically sampled layers. + use_radial_filters=False, + # Clamp the output to [-X, +X], None = disable clamping. + conv_clamp=256, + # Decay rate for the moving average of input magnitudes. + magnitude_ema_beta=0.999, + ): super().__init__() self.w_dim = w_dim self.is_torgb = is_torgb @@ -289,7 +338,8 @@ class SynthesisLayer(torch.nn.Module): self.out_size = np.broadcast_to(np.asarray(out_size), [2]) self.in_sampling_rate = in_sampling_rate self.out_sampling_rate = out_sampling_rate - self.tmp_sampling_rate = max(in_sampling_rate, out_sampling_rate) * (1 if is_torgb else lrelu_upsampling) + self.tmp_sampling_rate = max( + in_sampling_rate, out_sampling_rate) * (1 if is_torgb else lrelu_upsampling) self.in_cutoff = in_cutoff self.out_cutoff = out_cutoff self.in_half_width = in_half_width @@ -299,65 +349,81 @@ class SynthesisLayer(torch.nn.Module): self.magnitude_ema_beta = magnitude_ema_beta # Setup parameters and buffers. - self.affine = FullyConnectedLayer(self.w_dim, self.in_channels, bias_init=1) - self.weight = torch.nn.Parameter(torch.randn([self.out_channels, self.in_channels, self.conv_kernel, self.conv_kernel])) + self.affine = FullyConnectedLayer( + self.w_dim, self.in_channels, bias_init=1) + self.weight = torch.nn.Parameter(torch.randn( + [self.out_channels, self.in_channels, self.conv_kernel, self.conv_kernel])) self.bias = torch.nn.Parameter(torch.zeros([self.out_channels])) self.register_buffer('magnitude_ema', torch.ones([])) # Design upsampling filter. - self.up_factor = int(np.rint(self.tmp_sampling_rate / self.in_sampling_rate)) + self.up_factor = int( + np.rint(self.tmp_sampling_rate / self.in_sampling_rate)) assert self.in_sampling_rate * self.up_factor == self.tmp_sampling_rate - self.up_taps = filter_size * self.up_factor if self.up_factor > 1 and not self.is_torgb else 1 + self.up_taps = filter_size * \ + self.up_factor if self.up_factor > 1 and not self.is_torgb else 1 self.register_buffer('up_filter', self.design_lowpass_filter( numtaps=self.up_taps, cutoff=self.in_cutoff, width=self.in_half_width*2, fs=self.tmp_sampling_rate)) # Design downsampling filter. - self.down_factor = int(np.rint(self.tmp_sampling_rate / self.out_sampling_rate)) + self.down_factor = int( + np.rint(self.tmp_sampling_rate / self.out_sampling_rate)) assert self.out_sampling_rate * self.down_factor == self.tmp_sampling_rate - self.down_taps = filter_size * self.down_factor if self.down_factor > 1 and not self.is_torgb else 1 + self.down_taps = filter_size * \ + self.down_factor if self.down_factor > 1 and not self.is_torgb else 1 self.down_radial = use_radial_filters and not self.is_critically_sampled self.register_buffer('down_filter', self.design_lowpass_filter( numtaps=self.down_taps, cutoff=self.out_cutoff, width=self.out_half_width*2, fs=self.tmp_sampling_rate, radial=self.down_radial)) # Compute padding. - pad_total = (self.out_size - 1) * self.down_factor + 1 # Desired output size before downsampling. - pad_total -= (self.in_size + self.conv_kernel - 1) * self.up_factor # Input size after upsampling. - pad_total += self.up_taps + self.down_taps - 2 # Size reduction caused by the filters. - pad_lo = (pad_total + self.up_factor) // 2 # Shift sample locations according to the symmetric interpretation (Appendix C.3). + # Desired output size before downsampling. + pad_total = (self.out_size - 1) * self.down_factor + 1 + # Input size after upsampling. + pad_total -= (self.in_size + self.conv_kernel - 1) * self.up_factor + # Size reduction caused by the filters. + pad_total += self.up_taps + self.down_taps - 2 + # Shift sample locations according to the symmetric interpretation (Appendix C.3). + pad_lo = (pad_total + self.up_factor) // 2 pad_hi = pad_total - pad_lo - self.padding = [int(pad_lo[0]), int(pad_hi[0]), int(pad_lo[1]), int(pad_hi[1])] + self.padding = [int(pad_lo[0]), int(pad_hi[0]), + int(pad_lo[1]), int(pad_hi[1])] def forward(self, x, w, noise_mode='random', force_fp32=False, update_emas=False): - assert noise_mode in ['random', 'const', 'none'] # unused - misc.assert_shape(x, [None, self.in_channels, int(self.in_size[1]), int(self.in_size[0])]) + assert noise_mode in ['random', 'const', 'none'] # unused + misc.assert_shape(x, [None, self.in_channels, int( + self.in_size[1]), int(self.in_size[0])]) misc.assert_shape(w, [x.shape[0], self.w_dim]) # Track input magnitude. if update_emas: with torch.autograd.profiler.record_function('update_magnitude_ema'): magnitude_cur = x.detach().to(torch.float32).square().mean() - self.magnitude_ema.copy_(magnitude_cur.lerp(self.magnitude_ema, self.magnitude_ema_beta)) + self.magnitude_ema.copy_(magnitude_cur.lerp( + self.magnitude_ema, self.magnitude_ema_beta)) input_gain = self.magnitude_ema.rsqrt() # Execute affine layer. styles = self.affine(w) if self.is_torgb: - weight_gain = 1 / np.sqrt(self.in_channels * (self.conv_kernel ** 2)) + weight_gain = 1 / \ + np.sqrt(self.in_channels * (self.conv_kernel ** 2)) styles = styles * weight_gain # Execute modulated conv2d. - dtype = torch.float16 if (self.use_fp16 and not force_fp32 and x.device.type == 'cuda') else torch.float32 + dtype = torch.float16 if ( + self.use_fp16 and not force_fp32 and x.device.type == 'cuda') else torch.float32 x = modulated_conv2d(x=x.to(dtype), w=self.weight, s=styles, - padding=self.conv_kernel-1, demodulate=(not self.is_torgb), input_gain=input_gain) + padding=self.conv_kernel-1, demodulate=(not self.is_torgb), input_gain=input_gain) # Execute bias, filtered leaky ReLU, and clamping. gain = 1 if self.is_torgb else np.sqrt(2) slope = 1 if self.is_torgb else 0.2 x = filtered_lrelu.filtered_lrelu(x=x, fu=self.up_filter, fd=self.down_filter, b=self.bias.to(x.dtype), - up=self.up_factor, down=self.down_factor, padding=self.padding, gain=gain, slope=slope, clamp=self.conv_clamp) + up=self.up_factor, down=self.down_factor, padding=self.padding, gain=gain, slope=slope, clamp=self.conv_clamp) # Ensure correct shape and dtype. - misc.assert_shape(x, [None, self.out_channels, int(self.out_size[1]), int(self.out_size[0])]) + misc.assert_shape(x, [None, self.out_channels, int( + self.out_size[1]), int(self.out_size[0])]) assert x.dtype == dtype return x @@ -371,14 +437,16 @@ class SynthesisLayer(torch.nn.Module): # Separable Kaiser low-pass filter. if not radial: - f = scipy.signal.firwin(numtaps=numtaps, cutoff=cutoff, width=width, fs=fs) + f = scipy.signal.firwin( + numtaps=numtaps, cutoff=cutoff, width=width, fs=fs) return torch.as_tensor(f, dtype=torch.float32) # Radially symmetric jinc-based filter. x = (np.arange(numtaps) - (numtaps - 1) / 2) / fs r = np.hypot(*np.meshgrid(x, x)) f = scipy.special.j1(2 * cutoff * (np.pi * r)) / (np.pi * r) - beta = scipy.signal.kaiser_beta(scipy.signal.kaiser_atten(numtaps, width / (fs / 2))) + beta = scipy.signal.kaiser_beta( + scipy.signal.kaiser_atten(numtaps, width / (fs / 2))) w = np.kaiser(numtaps, beta) f *= np.outer(w, w) f /= np.sum(f) @@ -394,26 +462,38 @@ class SynthesisLayer(torch.nn.Module): f'in_size={list(self.in_size)}, out_size={list(self.out_size)},', f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisNetwork(torch.nn.Module): def __init__(self, - w_dim, # Intermediate latent (W) dimensionality. - img_resolution, # Output image resolution. - img_channels, # Number of color channels. - channel_base = 32768, # Overall multiplier for the number of channels. - channel_max = 512, # Maximum number of channels in any layer. - num_layers = 14, # Total number of layers, excluding Fourier features and ToRGB. - num_critical = 2, # Number of critically sampled layers at the end. - first_cutoff = 2, # Cutoff frequency of the first layer (f_{c,0}). - first_stopband = 2**2.1, # Minimum stopband of the first layer (f_{t,0}). - last_stopband_rel = 2**0.3, # Minimum stopband of the last layer, expressed relative to the cutoff. - margin_size = 10, # Number of additional pixels outside the image. - output_scale = 0.25, # Scale factor for the output image. - num_fp16_res = 4, # Use FP16 for the N highest resolutions. - **layer_kwargs, # Arguments for SynthesisLayer. - ): + # Intermediate latent (W) dimensionality. + w_dim, + img_resolution, # Output image resolution. + img_channels, # Number of color channels. + # Overall multiplier for the number of channels. + channel_base=32768, + # Maximum number of channels in any layer. + channel_max=512, + # Total number of layers, excluding Fourier features and ToRGB. + num_layers=14, + # Number of critically sampled layers at the end. + num_critical=2, + # Cutoff frequency of the first layer (f_{c,0}). + first_cutoff=2, + # Minimum stopband of the first layer (f_{t,0}). + first_stopband=2**2.1, + # Minimum stopband of the last layer, expressed relative to the cutoff. + last_stopband_rel=2**0.3, + # Number of additional pixels outside the image. + margin_size=10, + output_scale=0.25, # Scale factor for the output image. + # Use FP16 for the N highest resolutions. + num_fp16_res=4, + # Arguments for SynthesisLayer. + **layer_kwargs, + ): super().__init__() self.w_dim = w_dim self.num_ws = num_layers + 2 @@ -426,18 +506,24 @@ class SynthesisNetwork(torch.nn.Module): self.num_fp16_res = num_fp16_res # Geometric progression of layer cutoffs and min. stopbands. - last_cutoff = self.img_resolution / 2 # f_{c,N} - last_stopband = last_cutoff * last_stopband_rel # f_{t,N} - exponents = np.minimum(np.arange(self.num_layers + 1) / (self.num_layers - self.num_critical), 1) - cutoffs = first_cutoff * (last_cutoff / first_cutoff) ** exponents # f_c[i] - stopbands = first_stopband * (last_stopband / first_stopband) ** exponents # f_t[i] + last_cutoff = self.img_resolution / 2 # f_{c,N} + last_stopband = last_cutoff * last_stopband_rel # f_{t,N} + exponents = np.minimum( + np.arange(self.num_layers + 1) / (self.num_layers - self.num_critical), 1) + cutoffs = first_cutoff * \ + (last_cutoff / first_cutoff) ** exponents # f_c[i] + stopbands = first_stopband * \ + (last_stopband / first_stopband) ** exponents # f_t[i] # Compute remaining layer parameters. - sampling_rates = np.exp2(np.ceil(np.log2(np.minimum(stopbands * 2, self.img_resolution)))) # s[i] - half_widths = np.maximum(stopbands, sampling_rates / 2) - cutoffs # f_h[i] + sampling_rates = np.exp2( + np.ceil(np.log2(np.minimum(stopbands * 2, self.img_resolution)))) # s[i] + half_widths = np.maximum( + stopbands, sampling_rates / 2) - cutoffs # f_h[i] sizes = sampling_rates + self.margin_size * 2 sizes[-2:] = self.img_resolution - channels = np.rint(np.minimum((channel_base / 2) / cutoffs, channel_max)) + channels = np.rint(np.minimum( + (channel_base / 2) / cutoffs, channel_max)) channels[-1] = self.img_channels # Construct layers. @@ -448,11 +534,13 @@ class SynthesisNetwork(torch.nn.Module): for idx in range(self.num_layers + 1): prev = max(idx - 1, 0) is_torgb = (idx == self.num_layers) - is_critically_sampled = (idx >= self.num_layers - self.num_critical) - use_fp16 = (sampling_rates[idx] * (2 ** self.num_fp16_res) > self.img_resolution) + is_critically_sampled = ( + idx >= self.num_layers - self.num_critical) + use_fp16 = (sampling_rates[idx] * (2 ** + self.num_fp16_res) > self.img_resolution) layer = SynthesisLayer( w_dim=self.w_dim, is_torgb=is_torgb, is_critically_sampled=is_critically_sampled, use_fp16=use_fp16, - in_channels=int(channels[prev]), out_channels= int(channels[idx]), + in_channels=int(channels[prev]), out_channels=int(channels[idx]), in_size=int(sizes[prev]), out_size=int(sizes[idx]), in_sampling_rate=int(sampling_rates[prev]), out_sampling_rate=int(sampling_rates[idx]), in_cutoff=cutoffs[prev], out_cutoff=cutoffs[idx], @@ -474,7 +562,8 @@ class SynthesisNetwork(torch.nn.Module): x = x * self.output_scale # Ensure correct shape and dtype. - misc.assert_shape(x, [None, self.img_channels, self.img_resolution, self.img_resolution]) + misc.assert_shape(x, [None, self.img_channels, + self.img_resolution, self.img_resolution]) x = x.to(torch.float32) return x @@ -485,29 +574,34 @@ class SynthesisNetwork(torch.nn.Module): f'num_layers={self.num_layers:d}, num_critical={self.num_critical:d},', f'margin_size={self.margin_size:d}, num_fp16_res={self.num_fp16_res:d}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class Generator(torch.nn.Module): def __init__(self, - z_dim, # Input latent (Z) dimensionality. - c_dim, # Conditioning label (C) dimensionality. - w_dim, # Intermediate latent (W) dimensionality. - img_resolution, # Output resolution. - img_channels, # Number of output color channels. - mapping_kwargs = {}, # Arguments for MappingNetwork. - resize=None, - **synthesis_kwargs, # Arguments for SynthesisNetwork. - ): + z_dim, # Input latent (Z) dimensionality. + # Conditioning label (C) dimensionality. + c_dim, + # Intermediate latent (W) dimensionality. + w_dim, + img_resolution, # Output resolution. + img_channels, # Number of output color channels. + mapping_kwargs={}, # Arguments for MappingNetwork. + resize=None, + **synthesis_kwargs, # Arguments for SynthesisNetwork. + ): super().__init__() self.z_dim = z_dim self.c_dim = c_dim self.w_dim = w_dim self.img_resolution = img_resolution self.img_channels = img_channels - self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs) + self.synthesis = SynthesisNetwork( + w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs) self.num_ws = self.synthesis.num_ws - self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) + self.mapping = MappingNetwork( + z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) self.resize = resize def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, input_is_w=False, **synthesis_kwargs): @@ -516,13 +610,15 @@ class Generator(torch.nn.Module): if ws.dim() == 2: ws = ws.unsqueeze(1).repeat([1, self.mapping.num_ws, 1]) else: - ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas) + ws = self.mapping(z, c, truncation_psi=truncation_psi, + truncation_cutoff=truncation_cutoff, update_emas=update_emas) img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs) if self.resize is not None: img = imresize(img, [self.resize, self.resize]) return img -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def imresize(image, size): dim = image.dim() diff --git a/stylegan_human/training/training_loop.py b/stylegan_human/training/training_loop.py index ddd0c15e226b0436048fee4469341e3fb653c71b..b1643b2d96a597d236af29053878191859a74cb7 100644 --- a/stylegan_human/training/training_loop.py +++ b/stylegan_human/training/training_loop.py @@ -26,7 +26,8 @@ from torch_utils.ops import grid_sample_gradfix import legacy from metrics import metric_main -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def setup_snapshot_image_grid(training_set, random_seed=0): rnd = np.random.RandomState(random_seed) @@ -37,11 +38,12 @@ def setup_snapshot_image_grid(training_set, random_seed=0): if not training_set.has_labels: all_indices = list(range(len(training_set))) rnd.shuffle(all_indices) - grid_indices = [all_indices[i % len(all_indices)] for i in range(gw * gh)] + grid_indices = [all_indices[i % + len(all_indices)] for i in range(gw * gh)] else: # Group training samples by label. - label_groups = dict() # label => [idx, ...] + label_groups = dict() # label => [idx, ...] for idx in range(len(training_set)): label = tuple(training_set.get_details(idx).raw_label.flat[::-1]) if label not in label_groups: @@ -59,13 +61,15 @@ def setup_snapshot_image_grid(training_set, random_seed=0): label = label_order[y % len(label_order)] indices = label_groups[label] grid_indices += [indices[x % len(indices)] for x in range(gw)] - label_groups[label] = [indices[(i + gw) % len(indices)] for i in range(len(indices))] + label_groups[label] = [ + indices[(i + gw) % len(indices)] for i in range(len(indices))] # Load data. images, labels = zip(*[training_set[i] for i in grid_indices]) return (gw, gh), np.stack(images), np.stack(labels) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def save_image_grid(img, fname, drange, grid_size): lo, hi = drange @@ -85,59 +89,79 @@ def save_image_grid(img, fname, drange, grid_size): if C == 3: PIL.Image.fromarray(img, 'RGB').save(fname) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def training_loop( - run_dir = '.', # Output directory. - training_set_kwargs = {}, # Options for training set. - data_loader_kwargs = {}, # Options for torch.utils.data.DataLoader. - G_kwargs = {}, # Options for generator network. - D_kwargs = {}, # Options for discriminator network. - G_opt_kwargs = {}, # Options for generator optimizer. - D_opt_kwargs = {}, # Options for discriminator optimizer. - augment_kwargs = None, # Options for augmentation pipeline. None = disable. - loss_kwargs = {}, # Options for loss function. - metrics = [], # Metrics to evaluate during training. - random_seed = 0, # Global random seed. - num_gpus = 1, # Number of GPUs participating in the training. - rank = 0, # Rank of the current process in [0, num_gpus[. - batch_size = 4, # Total batch size for one training iteration. Can be larger than batch_gpu * num_gpus. - batch_gpu = 4, # Number of samples processed at a time by one GPU. - ema_kimg = 10, # Half-life of the exponential moving average (EMA) of generator weights. - ema_rampup = 0.05, # EMA ramp-up coefficient. None = no rampup. - G_reg_interval = None, # How often to perform regularization for G? None = disable lazy regularization. - D_reg_interval = 16, # How often to perform regularization for D? None = disable lazy regularization. - augment_p = 0, # Initial value of augmentation probability. - ada_target = None, # ADA target value. None = fixed p. - ada_interval = 4, # How often to perform ADA adjustment? - ada_kimg = 500, # ADA adjustment speed, measured in how many kimg it takes for p to increase/decrease by one unit. - total_kimg = 25000, # Total length of the training, measured in thousands of real images. - kimg_per_tick = 4, # Progress snapshot interval. - image_snapshot_ticks = 50, # How often to save image snapshots? None = disable. - network_snapshot_ticks = 50, # How often to save network snapshots? None = disable. - resume_pkl = None, # Network pickle to resume training from. - resume_kimg = 0, # First kimg to report when resuming training. - cudnn_benchmark = True, # Enable torch.backends.cudnn.benchmark? - abort_fn = None, # Callback function for determining whether to abort training. Must return consistent results across ranks. - progress_fn = None, # Callback function for updating training progress. Called for all ranks. + run_dir='.', # Output directory. + training_set_kwargs={}, # Options for training set. + data_loader_kwargs={}, # Options for torch.utils.data.DataLoader. + G_kwargs={}, # Options for generator network. + D_kwargs={}, # Options for discriminator network. + G_opt_kwargs={}, # Options for generator optimizer. + D_opt_kwargs={}, # Options for discriminator optimizer. + # Options for augmentation pipeline. None = disable. + augment_kwargs=None, + loss_kwargs={}, # Options for loss function. + metrics=[], # Metrics to evaluate during training. + random_seed=0, # Global random seed. + num_gpus=1, # Number of GPUs participating in the training. + rank=0, # Rank of the current process in [0, num_gpus[. + # Total batch size for one training iteration. Can be larger than batch_gpu * num_gpus. + batch_size=4, + batch_gpu=4, # Number of samples processed at a time by one GPU. + # Half-life of the exponential moving average (EMA) of generator weights. + ema_kimg=10, + ema_rampup=0.05, # EMA ramp-up coefficient. None = no rampup. + # How often to perform regularization for G? None = disable lazy regularization. + G_reg_interval=None, + # How often to perform regularization for D? None = disable lazy regularization. + D_reg_interval=16, + augment_p=0, # Initial value of augmentation probability. + ada_target=None, # ADA target value. None = fixed p. + ada_interval=4, # How often to perform ADA adjustment? + # ADA adjustment speed, measured in how many kimg it takes for p to increase/decrease by one unit. + ada_kimg=500, + # Total length of the training, measured in thousands of real images. + total_kimg=25000, + kimg_per_tick=4, # Progress snapshot interval. + # How often to save image snapshots? None = disable. + image_snapshot_ticks=50, + # How often to save network snapshots? None = disable. + network_snapshot_ticks=50, + resume_pkl=None, # Network pickle to resume training from. + resume_kimg=0, # First kimg to report when resuming training. + cudnn_benchmark=True, # Enable torch.backends.cudnn.benchmark? + # Callback function for determining whether to abort training. Must return consistent results across ranks. + abort_fn=None, + # Callback function for updating training progress. Called for all ranks. + progress_fn=None, ): # Initialize. start_time = time.time() device = torch.device('cuda', rank) np.random.seed(random_seed * num_gpus + rank) torch.manual_seed(random_seed * num_gpus + rank) - torch.backends.cudnn.benchmark = cudnn_benchmark # Improves training speed. - torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy. - torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy. - conv2d_gradfix.enabled = True # Improves training speed. - grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. + # Improves training speed. + torch.backends.cudnn.benchmark = cudnn_benchmark + # Improves numerical accuracy. + torch.backends.cuda.matmul.allow_tf32 = False + # Improves numerical accuracy. + torch.backends.cudnn.allow_tf32 = False + # Improves training speed. + conv2d_gradfix.enabled = True + # Avoids errors with the augmentation pipe. + grid_sample_gradfix.enabled = True # Load training set. if rank == 0: print('Loading training set...') - training_set = dnnlib.util.construct_class_by_name(**training_set_kwargs) # subclass of training.dataset.Dataset - training_set_sampler = misc.InfiniteSampler(dataset=training_set, rank=rank, num_replicas=num_gpus, seed=random_seed) - training_set_iterator = iter(torch.utils.data.DataLoader(dataset=training_set, sampler=training_set_sampler, batch_size=batch_size//num_gpus, **data_loader_kwargs)) + training_set = dnnlib.util.construct_class_by_name( + **training_set_kwargs) # subclass of training.dataset.Dataset + training_set_sampler = misc.InfiniteSampler( + dataset=training_set, rank=rank, num_replicas=num_gpus, seed=random_seed) + training_set_iterator = iter(torch.utils.data.DataLoader( + dataset=training_set, sampler=training_set_sampler, batch_size=batch_size//num_gpus, **data_loader_kwargs)) if rank == 0: print() print('Num images: ', len(training_set)) @@ -148,9 +172,12 @@ def training_loop( # Construct networks. if rank == 0: print('Constructing networks...') - common_kwargs = dict(c_dim=training_set.label_dim, img_resolution=training_set.resolution, img_channels=training_set.num_channels) - G = dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module - D = dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module + common_kwargs = dict(c_dim=training_set.label_dim, + img_resolution=training_set.resolution, img_channels=training_set.num_channels) + G = dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs).train( + ).requires_grad_(False).to(device) # subclass of torch.nn.Module + D = dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs).train( + ).requires_grad_(False).to(device) # subclass of torch.nn.Module G_ema = copy.deepcopy(G).eval() # Resume from existing pickle. @@ -159,7 +186,8 @@ def training_loop( with dnnlib.util.open_url(resume_pkl) as f: resume_data = legacy.load_network_pkl(f) for name, module in [('G', G), ('D', D), ('G_ema', G_ema)]: - misc.copy_params_and_buffers(resume_data[name], module, require_all=False) + misc.copy_params_and_buffers( + resume_data[name], module, require_all=False) # Print network summary tables. if rank == 0: @@ -174,7 +202,8 @@ def training_loop( augment_pipe = None ada_stats = None if (augment_kwargs is not None) and (augment_p > 0 or ada_target is not None): - augment_pipe = dnnlib.util.construct_class_by_name(**augment_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module + augment_pipe = dnnlib.util.construct_class_by_name( + **augment_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module augment_pipe.p.copy_(torch.as_tensor(augment_p)) if ada_target is not None: ada_stats = training_stats.Collector(regex='Loss/signs/real') @@ -190,20 +219,26 @@ def training_loop( # Setup training phases. if rank == 0: print('Setting up training phases...') - loss = dnnlib.util.construct_class_by_name(device=device, G=G, D=D, augment_pipe=augment_pipe, **loss_kwargs) # subclass of training.loss.Loss + loss = dnnlib.util.construct_class_by_name( + device=device, G=G, D=D, augment_pipe=augment_pipe, **loss_kwargs) # subclass of training.loss.Loss phases = [] for name, module, opt_kwargs, reg_interval in [('G', G, G_opt_kwargs, G_reg_interval), ('D', D, D_opt_kwargs, D_reg_interval)]: if reg_interval is None: - opt = dnnlib.util.construct_class_by_name(params=module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer - phases += [dnnlib.EasyDict(name=name+'both', module=module, opt=opt, interval=1)] - else: # Lazy regularization. + opt = dnnlib.util.construct_class_by_name( + params=module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer + phases += [dnnlib.EasyDict(name=name+'both', + module=module, opt=opt, interval=1)] + else: # Lazy regularization. mb_ratio = reg_interval / (reg_interval + 1) opt_kwargs = dnnlib.EasyDict(opt_kwargs) opt_kwargs.lr = opt_kwargs.lr * mb_ratio opt_kwargs.betas = [beta ** mb_ratio for beta in opt_kwargs.betas] - opt = dnnlib.util.construct_class_by_name(module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer - phases += [dnnlib.EasyDict(name=name+'main', module=module, opt=opt, interval=1)] - phases += [dnnlib.EasyDict(name=name+'reg', module=module, opt=opt, interval=reg_interval)] + opt = dnnlib.util.construct_class_by_name( + module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer + phases += [dnnlib.EasyDict(name=name+'main', + module=module, opt=opt, interval=1)] + phases += [dnnlib.EasyDict(name=name+'reg', + module=module, opt=opt, interval=reg_interval)] for phase in phases: phase.start_event = None phase.end_event = None @@ -217,12 +252,17 @@ def training_loop( grid_c = None if rank == 0: print('Exporting sample images...') - grid_size, images, labels = setup_snapshot_image_grid(training_set=training_set) - save_image_grid(images, os.path.join(run_dir, 'reals.png'), drange=[0,255], grid_size=grid_size) - grid_z = torch.randn([labels.shape[0], G.z_dim], device=device).split(batch_gpu) + grid_size, images, labels = setup_snapshot_image_grid( + training_set=training_set) + save_image_grid(images, os.path.join(run_dir, 'reals.png'), + drange=[0, 255], grid_size=grid_size) + grid_z = torch.randn([labels.shape[0], G.z_dim], + device=device).split(batch_gpu) grid_c = torch.from_numpy(labels).to(device).split(batch_gpu) - images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy() - save_image_grid(images, os.path.join(run_dir, 'fakes_init.png'), drange=[-1,1], grid_size=grid_size) + images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() + for z, c in zip(grid_z, grid_c)]).numpy() + save_image_grid(images, os.path.join( + run_dir, 'fakes_init.png'), drange=[-1, 1], grid_size=grid_size) # Initialize logs. if rank == 0: @@ -256,13 +296,19 @@ def training_loop( # Fetch training data. with torch.autograd.profiler.record_function('data_fetch'): phase_real_img, phase_real_c = next(training_set_iterator) - phase_real_img = (phase_real_img.to(device).to(torch.float32) / 127.5 - 1).split(batch_gpu) + phase_real_img = (phase_real_img.to(device).to( + torch.float32) / 127.5 - 1).split(batch_gpu) phase_real_c = phase_real_c.to(device).split(batch_gpu) - all_gen_z = torch.randn([len(phases) * batch_size, G.z_dim], device=device) - all_gen_z = [phase_gen_z.split(batch_gpu) for phase_gen_z in all_gen_z.split(batch_size)] - all_gen_c = [training_set.get_label(np.random.randint(len(training_set))) for _ in range(len(phases) * batch_size)] - all_gen_c = torch.from_numpy(np.stack(all_gen_c)).pin_memory().to(device) - all_gen_c = [phase_gen_c.split(batch_gpu) for phase_gen_c in all_gen_c.split(batch_size)] + all_gen_z = torch.randn( + [len(phases) * batch_size, G.z_dim], device=device) + all_gen_z = [phase_gen_z.split( + batch_gpu) for phase_gen_z in all_gen_z.split(batch_size)] + all_gen_c = [training_set.get_label(np.random.randint( + len(training_set))) for _ in range(len(phases) * batch_size)] + all_gen_c = torch.from_numpy( + np.stack(all_gen_c)).pin_memory().to(device) + all_gen_c = [phase_gen_c.split( + batch_gpu) for phase_gen_c in all_gen_c.split(batch_size)] # Execute training phases. for phase, phase_gen_z, phase_gen_c in zip(phases, all_gen_z, all_gen_c): @@ -275,18 +321,22 @@ def training_loop( phase.opt.zero_grad(set_to_none=True) phase.module.requires_grad_(True) for real_img, real_c, gen_z, gen_c in zip(phase_real_img, phase_real_c, phase_gen_z, phase_gen_c): - loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c, gen_z=gen_z, gen_c=gen_c, gain=phase.interval, cur_nimg=cur_nimg) + loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c, + gen_z=gen_z, gen_c=gen_c, gain=phase.interval, cur_nimg=cur_nimg) phase.module.requires_grad_(False) # Update weights. with torch.autograd.profiler.record_function(phase.name + '_opt'): - params = [param for param in phase.module.parameters() if param.grad is not None] + params = [param for param in phase.module.parameters() + if param.grad is not None] if len(params) > 0: - flat = torch.cat([param.grad.flatten() for param in params]) + flat = torch.cat([param.grad.flatten() + for param in params]) if num_gpus > 1: torch.distributed.all_reduce(flat) flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) + misc.nan_to_num(flat, nan=0, posinf=1e5, + neginf=-1e5, out=flat) grads = flat.split([param.numel() for param in params]) for param, grad in zip(params, grads): param.grad = grad.reshape(param.shape) @@ -314,8 +364,10 @@ def training_loop( # Execute ADA heuristic. if (ada_stats is not None) and (batch_idx % ada_interval == 0): ada_stats.update() - adjust = np.sign(ada_stats['Loss/signs/real'] - ada_target) * (batch_size * ada_interval) / (ada_kimg * 1000) - augment_pipe.p.copy_((augment_pipe.p + adjust).max(misc.constant(0, device=device))) + adjust = np.sign(ada_stats['Loss/signs/real'] - ada_target) * \ + (batch_size * ada_interval) / (ada_kimg * 1000) + augment_pipe.p.copy_( + (augment_pipe.p + adjust).max(misc.constant(0, device=device))) # Perform maintenance tasks once per tick. done = (cur_nimg >= total_kimg * 1000) @@ -325,19 +377,31 @@ def training_loop( # Print status line, accumulating the same information in training_stats. tick_end_time = time.time() fields = [] - fields += [f"tick {training_stats.report0('Progress/tick', cur_tick):<5d}"] - fields += [f"kimg {training_stats.report0('Progress/kimg', cur_nimg / 1e3):<8.1f}"] - fields += [f"time {dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}"] - fields += [f"sec/tick {training_stats.report0('Timing/sec_per_tick', tick_end_time - tick_start_time):<7.1f}"] - fields += [f"sec/kimg {training_stats.report0('Timing/sec_per_kimg', (tick_end_time - tick_start_time) / (cur_nimg - tick_start_nimg) * 1e3):<7.2f}"] - fields += [f"maintenance {training_stats.report0('Timing/maintenance_sec', maintenance_time):<6.1f}"] - fields += [f"cpumem {training_stats.report0('Resources/cpu_mem_gb', psutil.Process(os.getpid()).memory_info().rss / 2**30):<6.2f}"] - fields += [f"gpumem {training_stats.report0('Resources/peak_gpu_mem_gb', torch.cuda.max_memory_allocated(device) / 2**30):<6.2f}"] - fields += [f"reserved {training_stats.report0('Resources/peak_gpu_mem_reserved_gb', torch.cuda.max_memory_reserved(device) / 2**30):<6.2f}"] + fields += [ + f"tick {training_stats.report0('Progress/tick', cur_tick):<5d}"] + fields += [ + f"kimg {training_stats.report0('Progress/kimg', cur_nimg / 1e3):<8.1f}"] + fields += [ + f"time {dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}"] + fields += [ + f"sec/tick {training_stats.report0('Timing/sec_per_tick', tick_end_time - tick_start_time):<7.1f}"] + fields += [ + f"sec/kimg {training_stats.report0('Timing/sec_per_kimg', (tick_end_time - tick_start_time) / (cur_nimg - tick_start_nimg) * 1e3):<7.2f}"] + fields += [ + f"maintenance {training_stats.report0('Timing/maintenance_sec', maintenance_time):<6.1f}"] + fields += [ + f"cpumem {training_stats.report0('Resources/cpu_mem_gb', psutil.Process(os.getpid()).memory_info().rss / 2**30):<6.2f}"] + fields += [ + f"gpumem {training_stats.report0('Resources/peak_gpu_mem_gb', torch.cuda.max_memory_allocated(device) / 2**30):<6.2f}"] + fields += [ + f"reserved {training_stats.report0('Resources/peak_gpu_mem_reserved_gb', torch.cuda.max_memory_reserved(device) / 2**30):<6.2f}"] torch.cuda.reset_peak_memory_stats() - fields += [f"augment {training_stats.report0('Progress/augment', float(augment_pipe.p.cpu()) if augment_pipe is not None else 0):.3f}"] - training_stats.report0('Timing/total_hours', (tick_end_time - start_time) / (60 * 60)) - training_stats.report0('Timing/total_days', (tick_end_time - start_time) / (24 * 60 * 60)) + fields += [ + f"augment {training_stats.report0('Progress/augment', float(augment_pipe.p.cpu()) if augment_pipe is not None else 0):.3f}"] + training_stats.report0('Timing/total_hours', + (tick_end_time - start_time) / (60 * 60)) + training_stats.report0('Timing/total_days', + (tick_end_time - start_time) / (24 * 60 * 60)) if rank == 0: print(' '.join(fields)) @@ -350,24 +414,29 @@ def training_loop( # Save image snapshot. if (rank == 0) and (image_snapshot_ticks is not None) and (done or cur_tick % image_snapshot_ticks == 0): - images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy() - save_image_grid(images, os.path.join(run_dir, f'fakes{cur_nimg//1000:06d}.png'), drange=[-1,1], grid_size=grid_size) + images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() + for z, c in zip(grid_z, grid_c)]).numpy() + save_image_grid(images, os.path.join( + run_dir, f'fakes{cur_nimg//1000:06d}.png'), drange=[-1, 1], grid_size=grid_size) # Save network snapshot. snapshot_pkl = None snapshot_data = None if (network_snapshot_ticks is not None) and (done or cur_tick % network_snapshot_ticks == 0): - snapshot_data = dict(G=G, D=D, G_ema=G_ema, augment_pipe=augment_pipe, training_set_kwargs=dict(training_set_kwargs)) + snapshot_data = dict(G=G, D=D, G_ema=G_ema, augment_pipe=augment_pipe, + training_set_kwargs=dict(training_set_kwargs)) for key, value in snapshot_data.items(): if isinstance(value, torch.nn.Module): value = copy.deepcopy(value).eval().requires_grad_(False) if num_gpus > 1: - misc.check_ddp_consistency(value, ignore_regex=r'.*\.[^.]+_(avg|ema)') + misc.check_ddp_consistency( + value, ignore_regex=r'.*\.[^.]+_(avg|ema)') for param in misc.params_and_buffers(value): torch.distributed.broadcast(param, src=0) snapshot_data[key] = value.cpu() - del value # conserve memory - snapshot_pkl = os.path.join(run_dir, f'network-snapshot-{cur_nimg//1000:06d}.pkl') + del value # conserve memory + snapshot_pkl = os.path.join( + run_dir, f'network-snapshot-{cur_nimg//1000:06d}.pkl') if rank == 0: with open(snapshot_pkl, 'wb') as f: pickle.dump(snapshot_data, f) @@ -378,11 +447,12 @@ def training_loop( print('Evaluating metrics...') for metric in metrics: result_dict = metric_main.calc_metric(metric=metric, G=snapshot_data['G_ema'], - dataset_kwargs=training_set_kwargs, num_gpus=num_gpus, rank=rank, device=device) + dataset_kwargs=training_set_kwargs, num_gpus=num_gpus, rank=rank, device=device) if rank == 0: - metric_main.report_metric(result_dict, run_dir=run_dir, snapshot_pkl=snapshot_pkl) + metric_main.report_metric( + result_dict, run_dir=run_dir, snapshot_pkl=snapshot_pkl) stats_metrics.update(result_dict.results) - del snapshot_data # conserve memory + del snapshot_data # conserve memory # Collect statistics. for phase in phases: @@ -404,9 +474,11 @@ def training_loop( global_step = int(cur_nimg / 1e3) walltime = timestamp - start_time for name, value in stats_dict.items(): - stats_tfevents.add_scalar(name, value.mean, global_step=global_step, walltime=walltime) + stats_tfevents.add_scalar( + name, value.mean, global_step=global_step, walltime=walltime) for name, value in stats_metrics.items(): - stats_tfevents.add_scalar(f'Metrics/{name}', value, global_step=global_step, walltime=walltime) + stats_tfevents.add_scalar( + f'Metrics/{name}', value, global_step=global_step, walltime=walltime) stats_tfevents.flush() if progress_fn is not None: progress_fn(cur_nimg // 1000, total_kimg) @@ -424,4 +496,4 @@ def training_loop( print() print('Exiting...') -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/training_scripts/sg2/train.py b/stylegan_human/training_scripts/sg2/train.py index 64253fe6b67fee6a28494b32ac781be7630f619b..74d016a65caedb70806c490b6ebbcad665de51b9 100644 --- a/stylegan_human/training_scripts/sg2/train.py +++ b/stylegan_human/training_scripts/sg2/train.py @@ -25,49 +25,55 @@ from metrics import metric_main from torch_utils import training_stats from torch_utils import custom_ops -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class UserError(Exception): pass -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def setup_training_loop_kwargs( # General options (not included in desc). - gpus = None, # Number of GPUs: , default = 1 gpu - snap = None, # Snapshot interval: , default = 50 ticks - metrics = None, # List of metric names: [], ['fid50k_full'] (default), ... - seed = None, # Random seed: , default = 0 + gpus=None, # Number of GPUs: , default = 1 gpu + snap=None, # Snapshot interval: , default = 50 ticks + metrics=None, # List of metric names: [], ['fid50k_full'] (default), ... + seed=None, # Random seed: , default = 0 # Dataset. - data = None, # Training dataset (required): - cond = None, # Train conditional model based on dataset labels: , default = False - subset = None, # Train with only N images: , default = all - mirror = None, # Augment dataset with x-flips: , default = False - square = None, + data=None, # Training dataset (required): + cond=None, # Train conditional model based on dataset labels: , default = False + subset=None, # Train with only N images: , default = all + mirror=None, # Augment dataset with x-flips: , default = False + square=None, # Base config. - cfg = None, # Base config: 'auto' (default), 'stylegan2', 'paper256', 'paper512', 'paper1024', 'cifar', 'shhq' - gamma = None, # Override R1 gamma: - kimg = None, # Override training duration: - batch = None, # Override batch size: + # Base config: 'auto' (default), 'stylegan2', 'paper256', 'paper512', 'paper1024', 'cifar', 'shhq' + cfg=None, + gamma=None, # Override R1 gamma: + kimg=None, # Override training duration: + batch=None, # Override batch size: # Discriminator augmentation. - aug = None, # Augmentation mode: 'ada' (default), 'noaug', 'fixed' - p = None, # Specify p for 'fixed' (required): - target = None, # Override ADA target for 'ada': , default = depends on aug - augpipe = None, # Augmentation pipeline: 'blit', 'geom', 'color', 'filter', 'noise', 'cutout', 'bg', 'bgc' (default), ..., 'bgcfnc' + aug=None, # Augmentation mode: 'ada' (default), 'noaug', 'fixed' + p=None, # Specify p for 'fixed' (required): + target=None, # Override ADA target for 'ada': , default = depends on aug + # Augmentation pipeline: 'blit', 'geom', 'color', 'filter', 'noise', 'cutout', 'bg', 'bgc' (default), ..., 'bgcfnc' + augpipe=None, # Transfer learning. - resume = None, # Load previous network: 'noresume' (default), 'ffhq256', 'ffhq512', 'ffhq1024', 'celebahq256', 'lsundog256', , - freezed = None, # Freeze-D: , default = 0 discriminator layers + # Load previous network: 'noresume' (default), 'ffhq256', 'ffhq512', 'ffhq1024', 'celebahq256', 'lsundog256', , + resume=None, + freezed=None, # Freeze-D: , default = 0 discriminator layers # Performance options (not included in desc). - fp32 = None, # Disable mixed-precision training: , default = False - nhwc = None, # Use NHWC memory format with FP16: , default = False - allow_tf32 = None, # Allow PyTorch to use TF32 for matmul and convolutions: , default = False - nobench = None, # Disable cuDNN benchmarking: , default = False - workers = None, # Override number of DataLoader workers: , default = 3 + fp32=None, # Disable mixed-precision training: , default = False + nhwc=None, # Use NHWC memory format with FP16: , default = False + # Allow PyTorch to use TF32 for matmul and convolutions: , default = False + allow_tf32=None, + nobench=None, # Disable cuDNN benchmarking: , default = False + workers=None, # Override number of DataLoader workers: , default = 3 ): args = dnnlib.EasyDict() @@ -95,7 +101,8 @@ def setup_training_loop_kwargs( metrics = ['fid50k_full'] assert isinstance(metrics, list) if not all(metric_main.is_valid_metric(metric) for metric in metrics): - raise UserError('\n'.join(['--metrics can only contain the following values:'] + metric_main.list_valid_metrics())) + raise UserError('\n'.join( + ['--metrics can only contain the following values:'] + metric_main.list_valid_metrics())) args.metrics = metrics if seed is None: @@ -112,29 +119,37 @@ def setup_training_loop_kwargs( assert data is not None assert isinstance(data, str) - args.training_set_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=data, use_labels=True, max_size=None, xflip=False, square=square) - args.data_loader_kwargs = dnnlib.EasyDict(pin_memory=True, num_workers=3, prefetch_factor=2) + args.training_set_kwargs = dnnlib.EasyDict( + class_name='training.dataset.ImageFolderDataset', path=data, use_labels=True, max_size=None, xflip=False, square=square) + args.data_loader_kwargs = dnnlib.EasyDict( + pin_memory=True, num_workers=3, prefetch_factor=2) try: - training_set = dnnlib.util.construct_class_by_name(**args.training_set_kwargs) # subclass of training.dataset.Dataset - args.training_set_kwargs.resolution = training_set.resolution # be explicit about resolution - args.training_set_kwargs.use_labels = training_set.has_labels # be explicit about labels - args.training_set_kwargs.max_size = len(training_set) # be explicit about dataset size + training_set = dnnlib.util.construct_class_by_name( + **args.training_set_kwargs) # subclass of training.dataset.Dataset + # be explicit about resolution + args.training_set_kwargs.resolution = training_set.resolution + # be explicit about labels + args.training_set_kwargs.use_labels = training_set.has_labels + args.training_set_kwargs.max_size = len( + training_set) # be explicit about dataset size desc = training_set.name print('desc: ', desc) - del training_set # conserve memory + del training_set # conserve memory except IOError as err: raise UserError(f'--data: {err}') - - if square: desc += '-square' - else: desc += '-rectangle' + if square: + desc += '-square' + else: + desc += '-rectangle' if cond is None: cond = False assert isinstance(cond, bool) if cond: if not args.training_set_kwargs.use_labels: - raise UserError('--cond=True requires labels specified in dataset.json') + raise UserError( + '--cond=True requires labels specified in dataset.json') desc += '-cond' else: args.training_set_kwargs.use_labels = False @@ -142,7 +157,8 @@ def setup_training_loop_kwargs( if subset is not None: assert isinstance(subset, int) if not 1 <= subset <= args.training_set_kwargs.max_size: - raise UserError(f'--subset must be between 1 and {args.training_set_kwargs.max_size}') + raise UserError( + f'--subset must be between 1 and {args.training_set_kwargs.max_size}') desc += f'-subset{subset}' if subset < args.training_set_kwargs.max_size: args.training_set_kwargs.max_size = subset @@ -166,8 +182,10 @@ def setup_training_loop_kwargs( cfg_specs = { 'auto': dict(ref_gpus=-1, kimg=25000, mb=-1, mbstd=-1, fmaps=-1, lrate=-1, gamma=-1, ema=-1, ramp=0.05, map=2), - 'shhq': dict(ref_gpus=-1, kimg=25000, mb=-1, mbstd=-1, fmaps=-1, lrate=-1, gamma=-1, ema=-1, ramp=0.05, map=8), # Populated dynamically based on resolution and GPU count. - 'stylegan2': dict(ref_gpus=8, kimg=25000, mb=32, mbstd=4, fmaps=1, lrate=0.002, gamma=10, ema=10, ramp=None, map=8), # Uses mixed-precision, unlike the original StyleGAN2. + # Populated dynamically based on resolution and GPU count. + 'shhq': dict(ref_gpus=-1, kimg=25000, mb=-1, mbstd=-1, fmaps=-1, lrate=-1, gamma=-1, ema=-1, ramp=0.05, map=8), + # Uses mixed-precision, unlike the original StyleGAN2. + 'stylegan2': dict(ref_gpus=8, kimg=25000, mb=32, mbstd=4, fmaps=1, lrate=0.002, gamma=10, ema=10, ramp=None, map=8), 'paper256': dict(ref_gpus=8, kimg=25000, mb=64, mbstd=8, fmaps=0.5, lrate=0.0025, gamma=1, ema=20, ramp=None, map=8), 'paper512': dict(ref_gpus=8, kimg=25000, mb=64, mbstd=8, fmaps=1, lrate=0.0025, gamma=0.5, ema=20, ramp=None, map=8), 'paper1024': dict(ref_gpus=8, kimg=25000, mb=32, mbstd=4, fmaps=1, lrate=0.002, gamma=2, ema=10, ramp=None, map=8), @@ -180,25 +198,35 @@ def setup_training_loop_kwargs( desc += f'{gpus:d}' spec.ref_gpus = gpus res = args.training_set_kwargs.resolution - spec.mb = max(min(gpus * min(4096 // res, 32), 64), gpus) # keep gpu memory consumption at bay - spec.mbstd = min(spec.mb // gpus, 4) # other hyperparams behave more predictably if mbstd group size remains fixed + # keep gpu memory consumption at bay + spec.mb = max(min(gpus * min(4096 // res, 32), 64), gpus) + # other hyperparams behave more predictably if mbstd group size remains fixed + spec.mbstd = min(spec.mb // gpus, 4) spec.fmaps = 1 if res >= 512 else 0.5 spec.lrate = 0.002 if res >= 1024 else 0.0025 - spec.gamma = 0.0002 * (res ** 2) / spec.mb # heuristic formula + spec.gamma = 0.0002 * (res ** 2) / spec.mb # heuristic formula spec.ema = spec.mb * 10 / 32 - args.G_kwargs = dnnlib.EasyDict(class_name='training.networks.Generator', z_dim=512, w_dim=512, mapping_kwargs=dnnlib.EasyDict(), synthesis_kwargs=dnnlib.EasyDict(),square=square) - args.D_kwargs = dnnlib.EasyDict(class_name='training.networks.Discriminator', block_kwargs=dnnlib.EasyDict(), mapping_kwargs=dnnlib.EasyDict(), epilogue_kwargs=dnnlib.EasyDict(),square=square) - args.G_kwargs.synthesis_kwargs.channel_base = args.D_kwargs.channel_base = int(spec.fmaps * 32768) + args.G_kwargs = dnnlib.EasyDict(class_name='training.networks.Generator', z_dim=512, w_dim=512, + mapping_kwargs=dnnlib.EasyDict(), synthesis_kwargs=dnnlib.EasyDict(), square=square) + args.D_kwargs = dnnlib.EasyDict(class_name='training.networks.Discriminator', block_kwargs=dnnlib.EasyDict( + ), mapping_kwargs=dnnlib.EasyDict(), epilogue_kwargs=dnnlib.EasyDict(), square=square) + args.G_kwargs.synthesis_kwargs.channel_base = args.D_kwargs.channel_base = int( + spec.fmaps * 32768) args.G_kwargs.synthesis_kwargs.channel_max = args.D_kwargs.channel_max = 512 args.G_kwargs.mapping_kwargs.num_layers = spec.map - args.G_kwargs.synthesis_kwargs.num_fp16_res = args.D_kwargs.num_fp16_res = 4 # enable mixed-precision training - args.G_kwargs.synthesis_kwargs.conv_clamp = args.D_kwargs.conv_clamp = 256 # clamp activations to avoid float16 overflow + # enable mixed-precision training + args.G_kwargs.synthesis_kwargs.num_fp16_res = args.D_kwargs.num_fp16_res = 4 + # clamp activations to avoid float16 overflow + args.G_kwargs.synthesis_kwargs.conv_clamp = args.D_kwargs.conv_clamp = 256 args.D_kwargs.epilogue_kwargs.mbstd_group_size = spec.mbstd - args.G_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', lr=spec.lrate, betas=[0,0.99], eps=1e-8) - args.D_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', lr=spec.lrate, betas=[0,0.99], eps=1e-8) - args.loss_kwargs = dnnlib.EasyDict(class_name='training.loss.StyleGAN2Loss', r1_gamma=spec.gamma) + args.G_opt_kwargs = dnnlib.EasyDict( + class_name='torch.optim.Adam', lr=spec.lrate, betas=[0, 0.99], eps=1e-8) + args.D_opt_kwargs = dnnlib.EasyDict( + class_name='torch.optim.Adam', lr=spec.lrate, betas=[0, 0.99], eps=1e-8) + args.loss_kwargs = dnnlib.EasyDict( + class_name='training.loss.StyleGAN2Loss', r1_gamma=spec.gamma) args.total_kimg = spec.kimg args.batch_size = spec.mb @@ -207,9 +235,9 @@ def setup_training_loop_kwargs( args.ema_rampup = spec.ramp if cfg == 'cifar': - args.loss_kwargs.pl_weight = 0 # disable path length regularization - args.loss_kwargs.style_mixing_prob = 0 # disable style mixing - args.D_kwargs.architecture = 'orig' # disable residual skip connections + args.loss_kwargs.pl_weight = 0 # disable path length regularization + args.loss_kwargs.style_mixing_prob = 0 # disable style mixing + args.D_kwargs.architecture = 'orig' # disable residual skip connections if gamma is not None: assert isinstance(gamma, float) @@ -228,7 +256,8 @@ def setup_training_loop_kwargs( if batch is not None: assert isinstance(batch, int) if not (batch >= 1 and batch % gpus == 0): - raise UserError('--batch must be at least 1 and divisible by --gpus') + raise UserError( + '--batch must be at least 1 and divisible by --gpus') desc += f'-batch{batch}' args.batch_size = batch args.batch_gpu = batch // gpus @@ -299,7 +328,8 @@ def setup_training_loop_kwargs( assert augpipe in augpipe_specs if aug != 'noaug': - args.augment_kwargs = dnnlib.EasyDict(class_name='training.augment.AugmentPipe', **augpipe_specs[augpipe]) + args.augment_kwargs = dnnlib.EasyDict( + class_name='training.augment.AugmentPipe', **augpipe_specs[augpipe]) # ---------------------------------- # Transfer learning: resume, freezed @@ -320,14 +350,14 @@ def setup_training_loop_kwargs( desc += '-noresume' elif resume in resume_specs: desc += f'-resume{resume}' - args.resume_pkl = resume_specs[resume] # predefined url + args.resume_pkl = resume_specs[resume] # predefined url else: desc += '-resumecustom' - args.resume_pkl = resume # custom path or url + args.resume_pkl = resume # custom path or url if resume != 'noresume': - args.ada_kimg = 100 # make ADA react faster at the beginning - args.ema_rampup = None # disable EMA rampup + args.ada_kimg = 100 # make ADA react faster at the beginning + args.ema_rampup = None # disable EMA rampup if freezed is not None: assert isinstance(freezed, int) @@ -373,20 +403,25 @@ def setup_training_loop_kwargs( return desc, args -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def subprocess_fn(rank, args, temp_dir): - dnnlib.util.Logger(file_name=os.path.join(args.run_dir, 'log.txt'), file_mode='a', should_flush=True) + dnnlib.util.Logger(file_name=os.path.join( + args.run_dir, 'log.txt'), file_mode='a', should_flush=True) # Init torch.distributed. if args.num_gpus > 1: - init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init')) + init_file = os.path.abspath(os.path.join( + temp_dir, '.torch_distributed_init')) if os.name == 'nt': init_method = 'file:///' + init_file.replace('\\', '/') - torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=args.num_gpus) + torch.distributed.init_process_group( + backend='gloo', init_method=init_method, rank=rank, world_size=args.num_gpus) else: init_method = f'file://{init_file}' - torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=args.num_gpus) + torch.distributed.init_process_group( + backend='nccl', init_method=init_method, rank=rank, world_size=args.num_gpus) # Init torch_utils. sync_device = torch.device('cuda', rank) if args.num_gpus > 1 else None @@ -394,11 +429,11 @@ def subprocess_fn(rank, args, temp_dir): if rank != 0: custom_ops.verbosity = 'none' - # Execute training loop. training_loop.training_loop(rank=rank, **args) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class CommaSeparatedList(click.ParamType): name = 'list' @@ -409,11 +444,11 @@ class CommaSeparatedList(click.ParamType): return [] return value.split(',') -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @click.command() @click.pass_context - # General options. @click.option('--outdir', help='Where to save the results', required=True, metavar='DIR') @click.option('--gpus', help='Number of GPUs to use [default: 1]', type=int, metavar='INT') @@ -421,40 +456,31 @@ class CommaSeparatedList(click.ParamType): @click.option('--metrics', help='Comma-separated list or "none" [default: fid50k_full]', type=CommaSeparatedList()) @click.option('--seed', help='Random seed [default: 0]', type=int, metavar='INT') @click.option('-n', '--dry-run', help='Print training options and exit', is_flag=True) - # Dataset. @click.option('--data', help='Training data (directory or zip)', metavar='PATH', required=True) @click.option('--cond', help='Train conditional model based on dataset labels [default: false]', type=bool, metavar='BOOL') @click.option('--subset', help='Train with only N images [default: all]', type=int, metavar='INT') @click.option('--mirror', help='Enable dataset x-flips [default: false]', type=bool, metavar='BOOL') @click.option('--square', help='True for square, False for rectangle', type=bool, metavar='BOOL', default=False) - # Base config. -@click.option('--cfg', help='Base config [default: auto]', type=click.Choice(['auto', 'stylegan2', 'paper256', 'paper512', 'paper1024', 'cifar','shhq'])) +@click.option('--cfg', help='Base config [default: auto]', type=click.Choice(['auto', 'stylegan2', 'paper256', 'paper512', 'paper1024', 'cifar', 'shhq'])) @click.option('--gamma', help='Override R1 gamma', type=float) @click.option('--kimg', help='Override training duration', type=int, metavar='INT') @click.option('--batch', help='Override batch size', type=int, metavar='INT') - # Discriminator augmentation. @click.option('--aug', help='Augmentation mode [default: ada]', type=click.Choice(['noaug', 'ada', 'fixed'])) @click.option('--p', help='Augmentation probability for --aug=fixed', type=float) @click.option('--target', help='ADA target value for --aug=ada', type=float) @click.option('--augpipe', help='Augmentation pipeline [default: bgc]', type=click.Choice(['blit', 'geom', 'color', 'filter', 'noise', 'cutout', 'bg', 'bgc', 'bgcf', 'bgcfn', 'bgcfnc', 'body'])) - # Transfer learning. @click.option('--resume', help='Resume training [default: noresume]', metavar='PKL') @click.option('--freezed', help='Freeze-D [default: 0 layers]', type=int, metavar='INT') - # Performance options. @click.option('--fp32', help='Disable mixed-precision training', type=bool, metavar='BOOL') @click.option('--nhwc', help='Use NHWC memory format with FP16', type=bool, metavar='BOOL') @click.option('--nobench', help='Disable cuDNN benchmarking', type=bool, metavar='BOOL') @click.option('--allow-tf32', help='Allow PyTorch to use TF32 internally', type=bool, metavar='BOOL') @click.option('--workers', help='Override number of DataLoader workers', type=int, metavar='INT') - - - - def main(ctx, outdir, dry_run, **config_kwargs): """Train a GAN using the techniques described in the paper "Training Generative Adversarial Networks with Limited Data". @@ -510,7 +536,8 @@ def main(ctx, outdir, dry_run, **config_kwargs): # Pick output directory. prev_run_dirs = [] if os.path.isdir(outdir): - prev_run_dirs = [x for x in os.listdir(outdir) if os.path.isdir(os.path.join(outdir, x))] + prev_run_dirs = [x for x in os.listdir( + outdir) if os.path.isdir(os.path.join(outdir, x))] prev_run_ids = [re.match(r'^\d+', x) for x in prev_run_dirs] prev_run_ids = [int(x.group()) for x in prev_run_ids if x is not None] cur_run_id = max(prev_run_ids, default=-1) + 1 @@ -550,11 +577,13 @@ def main(ctx, outdir, dry_run, **config_kwargs): if args.num_gpus == 1: subprocess_fn(rank=0, args=args, temp_dir=temp_dir) else: - torch.multiprocessing.spawn(fn=subprocess_fn, args=(args, temp_dir), nprocs=args.num_gpus) + torch.multiprocessing.spawn(fn=subprocess_fn, args=( + args, temp_dir), nprocs=args.num_gpus) + +# ---------------------------------------------------------------------------- -#---------------------------------------------------------------------------- if __name__ == "__main__": - main() # pylint: disable=no-value-for-parameter + main() # pylint: disable=no-value-for-parameter -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/training_scripts/sg2/training/dataset.py b/stylegan_human/training_scripts/sg2/training/dataset.py index 8c6b05e09d9b1d8c386e36e0f47a44cb1290e1d9..cfb6ff76e18cb42a9493e2ddae1d843895acdadc 100644 --- a/stylegan_human/training_scripts/sg2/training/dataset.py +++ b/stylegan_human/training_scripts/sg2/training/dataset.py @@ -24,18 +24,23 @@ try: except ImportError: pyspng = None -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class Dataset(torch.utils.data.Dataset): def __init__(self, - name, # Name of the dataset. - raw_shape, # Shape of the raw image data (NCHW). - max_size = None, # Artificially limit the size of the dataset. None = no limit. Applied before xflip. - use_labels = False, # Enable conditioning labels? False = label dimension is zero. - xflip = False, # Artificially double the size of the dataset via x-flips. Applied after max_size. - random_seed = 0, # Random seed to use when applying max_size. - square = False, - ): + name, # Name of the dataset. + raw_shape, # Shape of the raw image data (NCHW). + # Artificially limit the size of the dataset. None = no limit. Applied before xflip. + max_size=None, + # Enable conditioning labels? False = label dimension is zero. + use_labels=False, + # Artificially double the size of the dataset via x-flips. Applied after max_size. + xflip=False, + # Random seed to use when applying max_size. + random_seed=0, + square=False, + ): # print(' Inside Dataset ') self._name = name self._raw_shape = list(raw_shape) @@ -54,13 +59,15 @@ class Dataset(torch.utils.data.Dataset): self._xflip = np.zeros(self._raw_idx.size, dtype=np.uint8) if xflip: self._raw_idx = np.tile(self._raw_idx, 2) - self._xflip = np.concatenate([self._xflip, np.ones_like(self._xflip)]) + self._xflip = np.concatenate( + [self._xflip, np.ones_like(self._xflip)]) def _get_raw_labels(self): if self._raw_labels is None: self._raw_labels = self._load_raw_labels() if self._use_labels else None if self._raw_labels is None: - self._raw_labels = np.zeros([self._raw_shape[0], 0], dtype=np.float32) + self._raw_labels = np.zeros( + [self._raw_shape[0], 0], dtype=np.float32) assert isinstance(self._raw_labels, np.ndarray) assert self._raw_labels.shape[0] == self._raw_shape[0] assert self._raw_labels.dtype in [np.float32, np.int64] @@ -69,13 +76,13 @@ class Dataset(torch.utils.data.Dataset): assert np.all(self._raw_labels >= 0) return self._raw_labels - def close(self): # to be overridden by subclass + def close(self): # to be overridden by subclass pass - def _load_raw_image(self, raw_idx): # to be overridden by subclass + def _load_raw_image(self, raw_idx): # to be overridden by subclass raise NotImplementedError - def _load_raw_labels(self): # to be overridden by subclass + def _load_raw_labels(self): # to be overridden by subclass raise NotImplementedError def __getstate__(self): @@ -96,7 +103,7 @@ class Dataset(torch.utils.data.Dataset): assert list(image.shape) == self.image_shape assert image.dtype == np.uint8 if self._xflip[idx]: - assert image.ndim == 3 # CHW + assert image.ndim == 3 # CHW image = image[:, :, ::-1] return image.copy(), self.get_label(idx) @@ -125,12 +132,12 @@ class Dataset(torch.utils.data.Dataset): @property def num_channels(self): - assert len(self.image_shape) == 3 # CHW + assert len(self.image_shape) == 3 # CHW return self.image_shape[0] @property def resolution(self): - assert len(self.image_shape) == 3 # CHW + assert len(self.image_shape) == 3 # CHW if self._square: assert self.image_shape[1] == self.image_shape[2] else: @@ -160,22 +167,26 @@ class Dataset(torch.utils.data.Dataset): def has_onehot_labels(self): return self._get_raw_labels().dtype == np.int64 -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class ImageFolderDataset(Dataset): def __init__(self, - path, # Path to directory or zip. - resolution = None, # Ensure specific resolution, None = highest available. - square = False, - **super_kwargs, # Additional arguments for the Dataset base class. - ): - self._path = path + path, # Path to directory or zip. + # Ensure specific resolution, None = highest available. + resolution=None, + square=False, + # Additional arguments for the Dataset base class. + **super_kwargs, + ): + self._path = path self._zipfile = None self._square = square if os.path.isdir(self._path): self._type = 'dir' - self._all_fnames = {os.path.relpath(os.path.join(root, fname), start=self._path) for root, _dirs, files in os.walk(self._path) for fname in files} + self._all_fnames = {os.path.relpath(os.path.join( + root, fname), start=self._path) for root, _dirs, files in os.walk(self._path) for fname in files} elif self._file_ext(self._path) == '.zip': self._type = 'zip' self._all_fnames = set(self._get_zipfile().namelist()) @@ -183,22 +194,24 @@ class ImageFolderDataset(Dataset): raise IOError('Path must point to a directory or zip') PIL.Image.init() - self._image_fnames = sorted(fname for fname in self._all_fnames if self._file_ext(fname) in PIL.Image.EXTENSION) + self._image_fnames = sorted( + fname for fname in self._all_fnames if self._file_ext(fname) in PIL.Image.EXTENSION) if len(self._image_fnames) == 0: raise IOError('No image files found in the specified path') name = os.path.splitext(os.path.basename(self._path))[0] - raw_shape = [len(self._image_fnames)] + list(self._load_raw_image(0).shape) + raw_shape = [len(self._image_fnames)] + \ + list(self._load_raw_image(0).shape) # if resolution is not None and (raw_shape[2] != resolution or raw_shape[3] != resolution): - # raise IOError('Image files do not match the specified resolution') + # raise IOError('Image files do not match the specified resolution') if resolution is not None: - if self._square: - raw_shape[2] = raw_shape[3] = resolution + if self._square: + raw_shape[2] = raw_shape[3] = resolution else: - raw_shape[2] = resolution - raw_shape[3] = resolution // 2 + raw_shape[2] = resolution + raw_shape[3] = resolution // 2 # print(raw_shape) - super().__init__(name=name, raw_shape=raw_shape,square=square, **super_kwargs) + super().__init__(name=name, raw_shape=raw_shape, square=square, **super_kwargs) @staticmethod def _file_ext(fname): @@ -227,7 +240,7 @@ class ImageFolderDataset(Dataset): def __getstate__(self): return dict(super().__getstate__(), _zipfile=None) - def _load_raw_image(self, raw_idx): #load single image + def _load_raw_image(self, raw_idx): # load single image fname = self._image_fnames[raw_idx] with self._open_file(fname) as f: if pyspng is not None and self._file_ext(fname) == '.png': @@ -235,8 +248,8 @@ class ImageFolderDataset(Dataset): else: image = np.array(PIL.Image.open(f)) if image.ndim == 2: - image = image[:, :, np.newaxis] # HW => HWC - image = image.transpose(2, 0, 1) # HWC => CHW + image = image[:, :, np.newaxis] # HW => HWC + image = image.transpose(2, 0, 1) # HWC => CHW return image def _load_raw_labels(self): @@ -248,10 +261,11 @@ class ImageFolderDataset(Dataset): if labels is None: return None labels = dict(labels) - labels = [labels[fname.replace('\\', '/')] for fname in self._image_fnames] + labels = [labels[fname.replace('\\', '/')] + for fname in self._image_fnames] labels = np.array(labels) labels = labels.astype({1: np.int64, 2: np.float32}[labels.ndim]) return labels -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/training_scripts/sg2/training/networks.py b/stylegan_human/training_scripts/sg2/training/networks.py index 1284716d220a6af44d8cce53c0694b276a617ec3..291d1f6d157aeab10896bc106c15fe4d03fcb145 100644 --- a/stylegan_human/training_scripts/sg2/training/networks.py +++ b/stylegan_human/training_scripts/sg2/training/networks.py @@ -17,56 +17,68 @@ from torch_utils.ops import upfirdn2d from torch_utils.ops import bias_act from torch_utils.ops import fma -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def normalize_2nd_moment(x, dim=1, eps=1e-8): return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt() -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def modulated_conv2d( - x, # Input tensor of shape [batch_size, in_channels, in_height, in_width]. - weight, # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width]. - styles, # Modulation coefficients of shape [batch_size, in_channels]. - noise = None, # Optional noise tensor to add to the output activations. - up = 1, # Integer upsampling factor. - down = 1, # Integer downsampling factor. - padding = 0, # Padding with respect to the upsampled image. - resample_filter = None, # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter(). - demodulate = True, # Apply weight demodulation? - flip_weight = True, # False = convolution, True = correlation (matches torch.nn.functional.conv2d). - fused_modconv = True, # Perform modulation, convolution, and demodulation as a single fused operation? + # Input tensor of shape [batch_size, in_channels, in_height, in_width]. + x, + # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width]. + weight, + # Modulation coefficients of shape [batch_size, in_channels]. + styles, + noise=None, # Optional noise tensor to add to the output activations. + up=1, # Integer upsampling factor. + down=1, # Integer downsampling factor. + padding=0, # Padding with respect to the upsampled image. + # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter(). + resample_filter=None, + demodulate=True, # Apply weight demodulation? + # False = convolution, True = correlation (matches torch.nn.functional.conv2d). + flip_weight=True, + # Perform modulation, convolution, and demodulation as a single fused operation? + fused_modconv=True, ): batch_size = x.shape[0] out_channels, in_channels, kh, kw = weight.shape - misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk] - misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] - misc.assert_shape(styles, [batch_size, in_channels]) # [NI] + misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk] + misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] + misc.assert_shape(styles, [batch_size, in_channels]) # [NI] # Pre-normalize inputs to avoid FP16 overflow. if x.dtype == torch.float16 and demodulate: - weight = weight * (1 / np.sqrt(in_channels * kh * kw) / weight.norm(float('inf'), dim=[1,2,3], keepdim=True)) # max_Ikk - styles = styles / styles.norm(float('inf'), dim=1, keepdim=True) # max_I + weight = weight * (1 / np.sqrt(in_channels * kh * kw) / + weight.norm(float('inf'), dim=[1, 2, 3], keepdim=True)) # max_Ikk + styles = styles / \ + styles.norm(float('inf'), dim=1, keepdim=True) # max_I # Calculate per-sample weights and demodulation coefficients. w = None dcoefs = None if demodulate or fused_modconv: - w = weight.unsqueeze(0) # [NOIkk] - w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk] + w = weight.unsqueeze(0) # [NOIkk] + w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk] if demodulate: - dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO] + dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt() # [NO] if demodulate and fused_modconv: - w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk] + w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk] # Execute by scaling the activations before and after the convolution. if not fused_modconv: x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1) - x = conv2d_resample.conv2d_resample(x=x, w=weight.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight) + x = conv2d_resample.conv2d_resample(x=x, w=weight.to( + x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight) if demodulate and noise is not None: - x = fma.fma(x, dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1), noise.to(x.dtype)) + x = fma.fma(x, dcoefs.to(x.dtype).reshape( + batch_size, -1, 1, 1), noise.to(x.dtype)) elif demodulate: x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1) elif noise is not None: @@ -74,33 +86,38 @@ def modulated_conv2d( return x # Execute as one fused op using grouped convolution. - with misc.suppress_tracer_warnings(): # this value will be treated as a constant + with misc.suppress_tracer_warnings(): # this value will be treated as a constant batch_size = int(batch_size) misc.assert_shape(x, [batch_size, in_channels, None, None]) x = x.reshape(1, -1, *x.shape[2:]) w = w.reshape(-1, in_channels, kh, kw) - x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight) + x = conv2d_resample.conv2d_resample(x=x, w=w.to( + x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight) x = x.reshape(batch_size, -1, *x.shape[2:]) if noise is not None: x = x.add_(noise) return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class FullyConnectedLayer(torch.nn.Module): def __init__(self, - in_features, # Number of input features. - out_features, # Number of output features. - bias = True, # Apply additive bias before the activation function? - activation = 'linear', # Activation function: 'relu', 'lrelu', etc. - lr_multiplier = 1, # Learning rate multiplier. - bias_init = 0, # Initial value for the additive bias. - ): + in_features, # Number of input features. + out_features, # Number of output features. + bias=True, # Apply additive bias before the activation function? + # Activation function: 'relu', 'lrelu', etc. + activation='linear', + lr_multiplier=1, # Learning rate multiplier. + bias_init=0, # Initial value for the additive bias. + ): super().__init__() self.activation = activation - self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) / lr_multiplier) - self.bias = torch.nn.Parameter(torch.full([out_features], np.float32(bias_init))) if bias else None + self.weight = torch.nn.Parameter(torch.randn( + [out_features, in_features]) / lr_multiplier) + self.bias = torch.nn.Parameter(torch.full( + [out_features], np.float32(bias_init))) if bias else None self.weight_gain = lr_multiplier / np.sqrt(in_features) self.bias_gain = lr_multiplier @@ -119,35 +136,42 @@ class FullyConnectedLayer(torch.nn.Module): x = bias_act.bias_act(x, b, act=self.activation) return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class Conv2dLayer(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels. - out_channels, # Number of output channels. - kernel_size, # Width and height of the convolution kernel. - bias = True, # Apply additive bias before the activation function? - activation = 'linear', # Activation function: 'relu', 'lrelu', etc. - up = 1, # Integer upsampling factor. - down = 1, # Integer downsampling factor. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = None, # Clamp the output to +-X, None = disable clamping. - channels_last = False, # Expect the input to have memory_format=channels_last? - trainable = True, # Update the weights of this layer during training? - ): + in_channels, # Number of input channels. + out_channels, # Number of output channels. + # Width and height of the convolution kernel. + kernel_size, + bias=True, # Apply additive bias before the activation function? + # Activation function: 'relu', 'lrelu', etc. + activation='linear', + up=1, # Integer upsampling factor. + down=1, # Integer downsampling factor. + # Low-pass filter to apply when resampling activations. + resample_filter=[1, 3, 3, 1], + # Clamp the output to +-X, None = disable clamping. + conv_clamp=None, + channels_last=False, # Expect the input to have memory_format=channels_last? + trainable=True, # Update the weights of this layer during training? + ): super().__init__() self.activation = activation self.up = up self.down = down self.conv_clamp = conv_clamp - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.register_buffer( + 'resample_filter', upfirdn2d.setup_filter(resample_filter)) self.padding = kernel_size // 2 self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) self.act_gain = bias_act.activation_funcs[activation].def_gain memory_format = torch.channels_last if channels_last else torch.contiguous_format - weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format) + weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to( + memory_format=memory_format) bias = torch.zeros([out_channels]) if bias else None if trainable: self.weight = torch.nn.Parameter(weight) @@ -162,30 +186,42 @@ class Conv2dLayer(torch.nn.Module): def forward(self, x, gain=1): w = self.weight * self.weight_gain b = self.bias.to(x.dtype) if self.bias is not None else None - flip_weight = (self.up == 1) # slightly faster - x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight) - + flip_weight = (self.up == 1) # slightly faster + x = conv2d_resample.conv2d_resample(x=x, w=w.to( + x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight) + act_gain = self.act_gain * gain act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None - x = bias_act.bias_act(x, b, act=self.activation, gain=act_gain, clamp=act_clamp) + x = bias_act.bias_act(x, b, act=self.activation, + gain=act_gain, clamp=act_clamp) return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class MappingNetwork(torch.nn.Module): def __init__(self, - z_dim, # Input latent (Z) dimensionality, 0 = no latent. - c_dim, # Conditioning label (C) dimensionality, 0 = no label. - w_dim, # Intermediate latent (W) dimensionality. - num_ws, # Number of intermediate latents to output, None = do not broadcast. - num_layers = 8, # Number of mapping layers. - embed_features = None, # Label embedding dimensionality, None = same as w_dim. - layer_features = None, # Number of intermediate features in the mapping layers, None = same as w_dim. - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers. - w_avg_beta = 0.995, # Decay for tracking the moving average of W during training, None = do not track. - ): + # Input latent (Z) dimensionality, 0 = no latent. + z_dim, + # Conditioning label (C) dimensionality, 0 = no label. + c_dim, + # Intermediate latent (W) dimensionality. + w_dim, + # Number of intermediate latents to output, None = do not broadcast. + num_ws, + num_layers=8, # Number of mapping layers. + # Label embedding dimensionality, None = same as w_dim. + embed_features=None, + # Number of intermediate features in the mapping layers, None = same as w_dim. + layer_features=None, + # Activation function: 'relu', 'lrelu', etc. + activation='lrelu', + # Learning rate multiplier for the mapping layers. + lr_multiplier=0.01, + # Decay for tracking the moving average of W during training, None = do not track. + w_avg_beta=0.995, + ): super().__init__() self.z_dim = z_dim self.c_dim = c_dim @@ -200,14 +236,16 @@ class MappingNetwork(torch.nn.Module): embed_features = 0 if layer_features is None: layer_features = w_dim - features_list = [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim] + features_list = [z_dim + embed_features] + \ + [layer_features] * (num_layers - 1) + [w_dim] if c_dim > 0: self.embed = FullyConnectedLayer(c_dim, embed_features) for idx in range(num_layers): in_features = features_list[idx] out_features = features_list[idx + 1] - layer = FullyConnectedLayer(in_features, out_features, activation=activation, lr_multiplier=lr_multiplier) + layer = FullyConnectedLayer( + in_features, out_features, activation=activation, lr_multiplier=lr_multiplier) setattr(self, f'fc{idx}', layer) if num_ws is not None and w_avg_beta is not None: @@ -233,7 +271,8 @@ class MappingNetwork(torch.nn.Module): # Update moving average of W. if self.w_avg_beta is not None and self.training and not skip_w_avg_update: with torch.autograd.profiler.record_function('update_w_avg'): - self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta)) + self.w_avg.copy_(x.detach().mean( + dim=0).lerp(self.w_avg, self.w_avg_beta)) # Broadcast. if self.num_ws is not None: @@ -247,46 +286,56 @@ class MappingNetwork(torch.nn.Module): if self.num_ws is None or truncation_cutoff is None: x = self.w_avg.lerp(x, truncation_psi) else: - x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi) + x[:, :truncation_cutoff] = self.w_avg.lerp( + x[:, :truncation_cutoff], truncation_psi) return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisLayer(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels. - out_channels, # Number of output channels. - w_dim, # Intermediate latent (W) dimensionality. - resolution, # Resolution of this layer. - kernel_size = 3, # Convolution kernel size. - up = 1, # Integer upsampling factor. - use_noise = True, # Enable noise input? - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. - channels_last = False, # Use channels_last format for the weights? - square = False, # default if for rectangle images - ): + in_channels, # Number of input channels. + out_channels, # Number of output channels. + # Intermediate latent (W) dimensionality. + w_dim, + resolution, # Resolution of this layer. + kernel_size=3, # Convolution kernel size. + up=1, # Integer upsampling factor. + use_noise=True, # Enable noise input? + # Activation function: 'relu', 'lrelu', etc. + activation='lrelu', + # Low-pass filter to apply when resampling activations. + resample_filter=[1, 3, 3, 1], + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=None, + channels_last=False, # Use channels_last format for the weights? + square=False, # default if for rectangle images + ): super().__init__() self.resolution = resolution self.up = up self.use_noise = use_noise self.activation = activation self.conv_clamp = conv_clamp - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.register_buffer( + 'resample_filter', upfirdn2d.setup_filter(resample_filter)) self.padding = kernel_size // 2 self.act_gain = bias_act.activation_funcs[activation].def_gain - self.square=square + self.square = square self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) memory_format = torch.channels_last if channels_last else torch.contiguous_format - self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) + self.weight = torch.nn.Parameter(torch.randn( + [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) if use_noise: if self.square: - self.register_buffer('noise_const', torch.randn([resolution, resolution])) + self.register_buffer( + 'noise_const', torch.randn([resolution, resolution])) else: - self.register_buffer('noise_const', torch.randn([resolution, resolution // 2])) + self.register_buffer('noise_const', torch.randn( + [resolution, resolution // 2])) self.noise_strength = torch.nn.Parameter(torch.zeros([])) self.bias = torch.nn.Parameter(torch.zeros([out_channels])) @@ -294,30 +343,36 @@ class SynthesisLayer(torch.nn.Module): assert noise_mode in ['random', 'const', 'none'] in_resolution = self.resolution // self.up if self.square: - misc.assert_shape(x, [None, self.weight.shape[1], in_resolution, in_resolution]) + misc.assert_shape( + x, [None, self.weight.shape[1], in_resolution, in_resolution]) else: - misc.assert_shape(x, [None, self.weight.shape[1], in_resolution, in_resolution // 2]) + misc.assert_shape( + x, [None, self.weight.shape[1], in_resolution, in_resolution // 2]) styles = self.affine(w) noise = None if self.use_noise and noise_mode == 'random': if self.square: - noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength + noise = torch.randn( + [x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength else: - noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution // 2], device=x.device) * self.noise_strength + noise = torch.randn( + [x.shape[0], 1, self.resolution, self.resolution // 2], device=x.device) * self.noise_strength if self.use_noise and noise_mode == 'const': noise = self.noise_const * self.noise_strength - flip_weight = (self.up == 1) # slightly faster + flip_weight = (self.up == 1) # slightly faster x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up, - padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv) + padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv) act_gain = self.act_gain * gain act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None - x = bias_act.bias_act(x, self.bias.to(x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp) + x = bias_act.bias_act(x, self.bias.to( + x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp) return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class ToRGBLayer(torch.nn.Module): @@ -326,35 +381,47 @@ class ToRGBLayer(torch.nn.Module): self.conv_clamp = conv_clamp self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) memory_format = torch.channels_last if channels_last else torch.contiguous_format - self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) + self.weight = torch.nn.Parameter(torch.randn( + [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) self.bias = torch.nn.Parameter(torch.zeros([out_channels])) self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) def forward(self, x, w, fused_modconv=True): styles = self.affine(w) * self.weight_gain - x = modulated_conv2d(x=x, weight=self.weight, styles=styles, demodulate=False, fused_modconv=fused_modconv) + x = modulated_conv2d(x=x, weight=self.weight, styles=styles, + demodulate=False, fused_modconv=fused_modconv) x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp) return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisBlock(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels, 0 = first block. - out_channels, # Number of output channels. - w_dim, # Intermediate latent (W) dimensionality. - resolution, # Resolution of this block. - img_channels, # Number of output color channels. - is_last, # Is this the last block? - architecture = 'skip', # Architecture: 'orig', 'skip', 'resnet'. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. - use_fp16 = False, # Use FP16 for this block? - fp16_channels_last = False, # Use channels-last memory format with FP16? - square = False, # default is for rectangle images - **layer_kwargs, # Arguments for SynthesisLayer. - ): + # Number of input channels, 0 = first block. + in_channels, + # Number of output channels. + out_channels, + # Intermediate latent (W) dimensionality. + w_dim, + # Resolution of this block. + resolution, + # Number of output color channels. + img_channels, + is_last, # Is this the last block? + # Architecture: 'orig', 'skip', 'resnet'. + architecture='skip', + # Low-pass filter to apply when resampling activations. + resample_filter=[1, 3, 3, 1], + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=None, + use_fp16=False, # Use FP16 for this block? + fp16_channels_last=False, # Use channels-last memory format with FP16? + square=False, # default is for rectangle images + # Arguments for SynthesisLayer. + **layer_kwargs, + ): assert architecture in ['orig', 'skip', 'resnet'] super().__init__() self.in_channels = in_channels @@ -365,43 +432,48 @@ class SynthesisBlock(torch.nn.Module): self.architecture = architecture self.use_fp16 = use_fp16 self.channels_last = (use_fp16 and fp16_channels_last) - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.register_buffer( + 'resample_filter', upfirdn2d.setup_filter(resample_filter)) self.num_conv = 0 self.num_torgb = 0 self.square = square if in_channels == 0: if self.square: - self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution])) - else: # rectangle - self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution // 2])) + self.const = torch.nn.Parameter(torch.randn( + [out_channels, resolution, resolution])) + else: # rectangle + self.const = torch.nn.Parameter(torch.randn( + [out_channels, resolution, resolution // 2])) if in_channels != 0: self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2, - resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, square=square,**layer_kwargs) + resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, square=square, **layer_kwargs) self.num_conv += 1 self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution, - conv_clamp=conv_clamp, channels_last=self.channels_last, square=square, **layer_kwargs) + conv_clamp=conv_clamp, channels_last=self.channels_last, square=square, **layer_kwargs) self.num_conv += 1 if is_last or architecture == 'skip': self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim, - conv_clamp=conv_clamp, channels_last=self.channels_last) + conv_clamp=conv_clamp, channels_last=self.channels_last) self.num_torgb += 1 if in_channels != 0 and architecture == 'resnet': self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2, - resample_filter=resample_filter, channels_last=self.channels_last) + resample_filter=resample_filter, channels_last=self.channels_last) def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, **layer_kwargs): - misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim]) + misc.assert_shape( + ws, [None, self.num_conv + self.num_torgb, self.w_dim]) w_iter = iter(ws.unbind(dim=1)) dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32 memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format if fused_modconv is None: - with misc.suppress_tracer_warnings(): # this value will be treated as a constant - fused_modconv = (not self.training) and (dtype == torch.float32 or int(x.shape[0]) == 1) + with misc.suppress_tracer_warnings(): # this value will be treated as a constant + fused_modconv = (not self.training) and ( + dtype == torch.float32 or int(x.shape[0]) == 1) # Input. if self.in_channels == 0: @@ -409,63 +481,82 @@ class SynthesisBlock(torch.nn.Module): x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1]) else: if self.square: - misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2]) - else: # rectangle - misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 4]) + misc.assert_shape( + x, [None, self.in_channels, self.resolution // 2, self.resolution // 2]) + else: # rectangle + misc.assert_shape( + x, [None, self.in_channels, self.resolution // 2, self.resolution // 4]) x = x.to(dtype=dtype, memory_format=memory_format) # Main layers. if self.in_channels == 0: - x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv1(x, next(w_iter), + fused_modconv=fused_modconv, **layer_kwargs) elif self.architecture == 'resnet': y = self.skip(x, gain=np.sqrt(0.5)) - x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) - x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs) + x = self.conv0(x, next(w_iter), + fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, + gain=np.sqrt(0.5), **layer_kwargs) x = y.add_(x) else: - x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) - x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv0(x, next(w_iter), + fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv1(x, next(w_iter), + fused_modconv=fused_modconv, **layer_kwargs) # ToRGB. if img is not None: if self.square: - misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2]) + misc.assert_shape( + img, [None, self.img_channels, self.resolution // 2, self.resolution // 2]) else: - misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 4]) + misc.assert_shape( + img, [None, self.img_channels, self.resolution // 2, self.resolution // 4]) img = upfirdn2d.upsample2d(img, self.resample_filter) if self.is_last or self.architecture == 'skip': y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv) - y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format) + y = y.to(dtype=torch.float32, + memory_format=torch.contiguous_format) img = img.add_(y) if img is not None else y assert x.dtype == dtype assert img is None or img.dtype == torch.float32 return x, img -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisNetwork(torch.nn.Module): def __init__(self, - w_dim, # Intermediate latent (W) dimensionality. - img_resolution, # Output image resolution. - img_channels, # Number of color channels. - square, - channel_base = 32768, # Overall multiplier for the number of channels. - channel_max = 512, # Maximum number of channels in any layer. - num_fp16_res = 0, # Use FP16 for the N highest resolutions. - **block_kwargs, # Arguments for SynthesisBlock. - ): - assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0 + # Intermediate latent (W) dimensionality. + w_dim, + img_resolution, # Output image resolution. + img_channels, # Number of color channels. + square, + # Overall multiplier for the number of channels. + channel_base=32768, + # Maximum number of channels in any layer. + channel_max=512, + # Use FP16 for the N highest resolutions. + num_fp16_res=0, + **block_kwargs, # Arguments for SynthesisBlock. + ): + assert img_resolution >= 4 and img_resolution & ( + img_resolution - 1) == 0 super().__init__() self.w_dim = w_dim self.img_resolution = img_resolution self.img_resolution_log2 = int(np.log2(img_resolution)) self.img_channels = img_channels - self.square=square - self.block_resolutions = [2 ** i for i in range(2, self.img_resolution_log2 + 1)] - channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions} - fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) + self.square = square + self.block_resolutions = [ + 2 ** i for i in range(2, self.img_resolution_log2 + 1)] + channels_dict = {res: min(channel_base // res, channel_max) + for res in self.block_resolutions} + fp16_resolution = max( + 2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) self.num_ws = 0 for res in self.block_resolutions: @@ -474,7 +565,7 @@ class SynthesisNetwork(torch.nn.Module): use_fp16 = (res >= fp16_resolution) is_last = (res == self.img_resolution) block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res, - img_channels=img_channels, is_last=is_last, use_fp16=use_fp16,square=square, **block_kwargs) + img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, square=square, **block_kwargs) self.num_ws += block.num_conv if is_last: self.num_ws += block.num_torgb @@ -489,7 +580,8 @@ class SynthesisNetwork(torch.nn.Module): w_idx = 0 for res in self.block_resolutions: block = getattr(self, f'b{res}') - block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb)) + block_ws.append( + ws.narrow(1, w_idx, block.num_conv + block.num_torgb)) w_idx += block.num_conv x = img = None @@ -502,21 +594,24 @@ class SynthesisNetwork(torch.nn.Module): else: return img -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class Generator(torch.nn.Module): def __init__(self, - z_dim, # Input latent (Z) dimensionality. - c_dim, # Conditioning label (C) dimensionality. - w_dim, # Intermediate latent (W) dimensionality. - img_resolution, # Output resolution. - square, - img_channels, # Number of output color channels. - mapping_kwargs = {}, # Arguments for MappingNetwork. - synthesis_kwargs = {}, # Arguments for SynthesisNetwork. - padding=False - ): + z_dim, # Input latent (Z) dimensionality. + # Conditioning label (C) dimensionality. + c_dim, + # Intermediate latent (W) dimensionality. + w_dim, + img_resolution, # Output resolution. + square, + img_channels, # Number of output color channels. + mapping_kwargs={}, # Arguments for MappingNetwork. + synthesis_kwargs={}, # Arguments for SynthesisNetwork. + padding=False + ): super().__init__() self.z_dim = z_dim self.c_dim = c_dim @@ -525,9 +620,11 @@ class Generator(torch.nn.Module): self.img_resolution = img_resolution self.img_channels = img_channels self.padding = padding - self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels,square=square,**synthesis_kwargs) + self.synthesis = SynthesisNetwork( + w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, square=square, **synthesis_kwargs) self.num_ws = self.synthesis.num_ws - self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) + self.mapping = MappingNetwork( + z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, input_is_w=False, return_feature=False, **synthesis_kwargs): if input_is_w: @@ -535,8 +632,10 @@ class Generator(torch.nn.Module): if ws.dim() == 2: ws = ws.unsqueeze(1).repeat([1, self.mapping.num_ws, 1]) else: - ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff) - img = self.synthesis(ws, return_feature=return_feature, **synthesis_kwargs) + ws = self.mapping(z, c, truncation_psi=truncation_psi, + truncation_cutoff=truncation_cutoff) + img = self.synthesis( + ws, return_feature=return_feature, **synthesis_kwargs) if return_feature: img, feature = img if self.padding: @@ -545,32 +644,45 @@ class Generator(torch.nn.Module): if return_feature: for i, feat in enumerate(feature): pad = (feat.size(2) - feat.size(3)) // 2 - feature[i] = torch.nn.functional.pad(feat, (pad, pad), "constant", 0) + feature[i] = torch.nn.functional.pad( + feat, (pad, pad), "constant", 0) if return_feature: return img, feature else: return img -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class DiscriminatorBlock(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels, 0 = first block. - tmp_channels, # Number of intermediate channels. - out_channels, # Number of output channels. - resolution, # Resolution of this block. - img_channels, # Number of input color channels. - first_layer_idx, # Index of the first layer. - architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. - use_fp16 = False, # Use FP16 for this block? - fp16_channels_last = False, # Use channels-last memory format with FP16? - freeze_layers = 0, # Freeze-D: Number of layers to freeze. - square = False, - ): + # Number of input channels, 0 = first block. + in_channels, + # Number of intermediate channels. + tmp_channels, + # Number of output channels. + out_channels, + # Resolution of this block. + resolution, + # Number of input color channels. + img_channels, + # Index of the first layer. + first_layer_idx, + # Architecture: 'orig', 'skip', 'resnet'. + architecture='resnet', + # Activation function: 'relu', 'lrelu', etc. + activation='lrelu', + # Low-pass filter to apply when resampling activations. + resample_filter=[1, 3, 3, 1], + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=None, + use_fp16=False, # Use FP16 for this block? + fp16_channels_last=False, # Use channels-last memory format with FP16? + # Freeze-D: Number of layers to freeze. + freeze_layers=0, + square=False, + ): assert in_channels in [0, tmp_channels] assert architecture in ['orig', 'skip', 'resnet'] super().__init__() @@ -581,10 +693,12 @@ class DiscriminatorBlock(torch.nn.Module): self.architecture = architecture self.use_fp16 = use_fp16 self.channels_last = (use_fp16 and fp16_channels_last) - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.register_buffer( + 'resample_filter', upfirdn2d.setup_filter(resample_filter)) self.square = square self.num_layers = 0 + def trainable_gen(): while True: layer_idx = self.first_layer_idx + self.num_layers @@ -595,17 +709,17 @@ class DiscriminatorBlock(torch.nn.Module): if in_channels == 0 or architecture == 'skip': self.fromrgb = Conv2dLayer(img_channels, tmp_channels, kernel_size=1, activation=activation, - trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) + trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) self.conv0 = Conv2dLayer(tmp_channels, tmp_channels, kernel_size=3, activation=activation, - trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) + trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) self.conv1 = Conv2dLayer(tmp_channels, out_channels, kernel_size=3, activation=activation, down=2, - trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last) + trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last) if architecture == 'resnet': self.skip = Conv2dLayer(tmp_channels, out_channels, kernel_size=1, bias=False, down=2, - trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last) + trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last) def forward(self, x, img, force_fp32=False): dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32 @@ -614,21 +728,26 @@ class DiscriminatorBlock(torch.nn.Module): # Input. if x is not None: if self.square: - misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) + misc.assert_shape( + x, [None, self.in_channels, self.resolution, self.resolution]) else: - misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution // 2]) + misc.assert_shape( + x, [None, self.in_channels, self.resolution, self.resolution // 2]) x = x.to(dtype=dtype, memory_format=memory_format) # FromRGB. if self.in_channels == 0 or self.architecture == 'skip': if self.square: - misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution]) - else: - misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution // 2]) + misc.assert_shape( + img, [None, self.img_channels, self.resolution, self.resolution]) + else: + misc.assert_shape( + img, [None, self.img_channels, self.resolution, self.resolution // 2]) img = img.to(dtype=dtype, memory_format=memory_format) y = self.fromrgb(img) x = x + y if x is not None else y - img = upfirdn2d.downsample2d(img, self.resample_filter) if self.architecture == 'skip' else None + img = upfirdn2d.downsample2d( + img, self.resample_filter) if self.architecture == 'skip' else None # Main layers. if self.architecture == 'resnet': @@ -643,7 +762,8 @@ class DiscriminatorBlock(torch.nn.Module): assert x.dtype == dtype return x, img -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class MinibatchStdLayer(torch.nn.Module): @@ -654,37 +774,52 @@ class MinibatchStdLayer(torch.nn.Module): def forward(self, x): N, C, H, W = x.shape - with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants - G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor(N)) if self.group_size is not None else N + with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants + G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor( + N)) if self.group_size is not None else N F = self.num_channels c = C // F - y = x.reshape(G, -1, F, c, H, W) # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c. - y = y - y.mean(dim=0) # [GnFcHW] Subtract mean over group. - y = y.square().mean(dim=0) # [nFcHW] Calc variance over group. + # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c. + y = x.reshape(G, -1, F, c, H, W) + # [GnFcHW] Subtract mean over group. + y = y - y.mean(dim=0) + # [nFcHW] Calc variance over group. + y = y.square().mean(dim=0) y = (y + 1e-8).sqrt() # [nFcHW] Calc stddev over group. - y = y.mean(dim=[2,3,4]) # [nF] Take average over channels and pixels. + # [nF] Take average over channels and pixels. + y = y.mean(dim=[2, 3, 4]) y = y.reshape(-1, F, 1, 1) # [nF11] Add missing dimensions. - y = y.repeat(G, 1, H, W) # [NFHW] Replicate over group and pixels. - x = torch.cat([x, y], dim=1) # [NCHW] Append to input as new channels. + # [NFHW] Replicate over group and pixels. + y = y.repeat(G, 1, H, W) + # [NCHW] Append to input as new channels. + x = torch.cat([x, y], dim=1) return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class DiscriminatorEpilogue(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels. - cmap_dim, # Dimensionality of mapped conditioning label, 0 = no label. - resolution, # Resolution of this block. - img_channels, # Number of input color channels. - architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. - mbstd_group_size = 4, # Group size for the minibatch standard deviation layer, None = entire minibatch. - mbstd_num_channels = 1, # Number of features for the minibatch standard deviation layer, 0 = disable. - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. - square = False, - ): + in_channels, # Number of input channels. + # Dimensionality of mapped conditioning label, 0 = no label. + cmap_dim, + resolution, # Resolution of this block. + # Number of input color channels. + img_channels, + # Architecture: 'orig', 'skip', 'resnet'. + architecture='resnet', + # Group size for the minibatch standard deviation layer, None = entire minibatch. + mbstd_group_size=4, + # Number of features for the minibatch standard deviation layer, 0 = disable. + mbstd_num_channels=1, + # Activation function: 'relu', 'lrelu', etc. + activation='lrelu', + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=None, + square=False, + ): assert architecture in ['orig', 'skip', 'resnet'] super().__init__() self.in_channels = in_channels @@ -695,23 +830,31 @@ class DiscriminatorEpilogue(torch.nn.Module): self.square = square if architecture == 'skip': - self.fromrgb = Conv2dLayer(img_channels, in_channels, kernel_size=1, activation=activation) - self.mbstd = MinibatchStdLayer(group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None - self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels, kernel_size=3, activation=activation, conv_clamp=conv_clamp) - + self.fromrgb = Conv2dLayer( + img_channels, in_channels, kernel_size=1, activation=activation) + self.mbstd = MinibatchStdLayer( + group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None + self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels, + kernel_size=3, activation=activation, conv_clamp=conv_clamp) + if self.square: - self.fc = FullyConnectedLayer(in_channels * (resolution ** 2), in_channels, activation=activation) + self.fc = FullyConnectedLayer( + in_channels * (resolution ** 2), in_channels, activation=activation) else: - self.fc = FullyConnectedLayer(in_channels * (resolution ** 2 // 2), in_channels, activation=activation) - - self.out = FullyConnectedLayer(in_channels, 1 if cmap_dim == 0 else cmap_dim) + self.fc = FullyConnectedLayer( + in_channels * (resolution ** 2 // 2), in_channels, activation=activation) + + self.out = FullyConnectedLayer( + in_channels, 1 if cmap_dim == 0 else cmap_dim) def forward(self, x, img, cmap, force_fp32=False): if self.square: - misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) + misc.assert_shape(x, [None, self.in_channels, + self.resolution, self.resolution]) else: - misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution // 2]) # [NCHW] - _ = force_fp32 # unused + misc.assert_shape( + x, [None, self.in_channels, self.resolution, self.resolution // 2]) # [NCHW] + _ = force_fp32 # unused dtype = torch.float32 memory_format = torch.contiguous_format @@ -719,9 +862,11 @@ class DiscriminatorEpilogue(torch.nn.Module): x = x.to(dtype=dtype, memory_format=memory_format) if self.architecture == 'skip': if self.square: - misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution]) + misc.assert_shape( + img, [None, self.img_channels, self.resolution, self.resolution]) else: - misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution // 2]) + misc.assert_shape( + img, [None, self.img_channels, self.resolution, self.resolution // 2]) img = img.to(dtype=dtype, memory_format=memory_format) x = x + self.fromrgb(img) @@ -735,46 +880,61 @@ class DiscriminatorEpilogue(torch.nn.Module): # Conditioning. if self.cmap_dim > 0: misc.assert_shape(cmap, [None, self.cmap_dim]) - x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim)) + x = (x * cmap).sum(dim=1, keepdim=True) * \ + (1 / np.sqrt(self.cmap_dim)) assert x.dtype == dtype return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class Discriminator(torch.nn.Module): def __init__(self, - c_dim, # Conditioning label (C) dimensionality. - img_resolution, # Input resolution. - img_channels, # Number of input color channels. - architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. - channel_base = 32768, # Overall multiplier for the number of channels. - channel_max = 512, # Maximum number of channels in any layer. - num_fp16_res = 0, # Use FP16 for the N highest resolutions. - conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. - cmap_dim = None, # Dimensionality of mapped conditioning label, None = default. - square = False, # default for rectangle images - block_kwargs = {}, # Arguments for DiscriminatorBlock. - mapping_kwargs = {}, # Arguments for MappingNetwork. - epilogue_kwargs = {}, # Arguments for DiscriminatorEpilogue. - ): + # Conditioning label (C) dimensionality. + c_dim, + img_resolution, # Input resolution. + # Number of input color channels. + img_channels, + # Architecture: 'orig', 'skip', 'resnet'. + architecture='resnet', + # Overall multiplier for the number of channels. + channel_base=32768, + # Maximum number of channels in any layer. + channel_max=512, + # Use FP16 for the N highest resolutions. + num_fp16_res=0, + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=None, + # Dimensionality of mapped conditioning label, None = default. + cmap_dim=None, + square=False, # default for rectangle images + block_kwargs={}, # Arguments for DiscriminatorBlock. + mapping_kwargs={}, # Arguments for MappingNetwork. + # Arguments for DiscriminatorEpilogue. + epilogue_kwargs={}, + ): super().__init__() self.c_dim = c_dim self.img_resolution = img_resolution self.img_resolution_log2 = int(np.log2(img_resolution)) self.img_channels = img_channels self.square = square - self.block_resolutions = [2 ** i for i in range(self.img_resolution_log2, 2, -1)] - channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions + [4]} - fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) + self.block_resolutions = [ + 2 ** i for i in range(self.img_resolution_log2, 2, -1)] + channels_dict = {res: min(channel_base // res, channel_max) + for res in self.block_resolutions + [4]} + fp16_resolution = max( + 2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) if cmap_dim is None: cmap_dim = channels_dict[4] if c_dim == 0: cmap_dim = 0 - common_kwargs = dict(img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp) + common_kwargs = dict(img_channels=img_channels, + architecture=architecture, conv_clamp=conv_clamp) cur_layer_idx = 0 for res in self.block_resolutions: in_channels = channels_dict[res] if res < img_resolution else 0 @@ -782,12 +942,14 @@ class Discriminator(torch.nn.Module): out_channels = channels_dict[res // 2] use_fp16 = (res >= fp16_resolution) block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res, - first_layer_idx=cur_layer_idx, use_fp16=use_fp16, square=square, **block_kwargs, **common_kwargs) + first_layer_idx=cur_layer_idx, use_fp16=use_fp16, square=square, **block_kwargs, **common_kwargs) setattr(self, f'b{res}', block) cur_layer_idx += block.num_layers if c_dim > 0: - self.mapping = MappingNetwork(z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs) - self.b4 = DiscriminatorEpilogue(channels_dict[4], cmap_dim=cmap_dim, resolution=4, square=square, **epilogue_kwargs, **common_kwargs) + self.mapping = MappingNetwork( + z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs) + self.b4 = DiscriminatorEpilogue( + channels_dict[4], cmap_dim=cmap_dim, resolution=4, square=square, **epilogue_kwargs, **common_kwargs) def forward(self, img, c, **block_kwargs): x = None @@ -801,4 +963,4 @@ class Discriminator(torch.nn.Module): x = self.b4(x, img, cmap) return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/training_scripts/sg3/train.py b/stylegan_human/training_scripts/sg3/train.py index 5c8d5e2be100495b906f36b93197935c42ec1528..afc4c934c6944b4333efa38a025f14888c67c59d 100644 --- a/stylegan_human/training_scripts/sg3/train.py +++ b/stylegan_human/training_scripts/sg3/train.py @@ -24,20 +24,25 @@ from metrics import metric_main from torch_utils import training_stats from torch_utils import custom_ops import ast -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def subprocess_fn(rank, c, temp_dir): - dnnlib.util.Logger(file_name=os.path.join(c.run_dir, 'log.txt'), file_mode='a', should_flush=True) + dnnlib.util.Logger(file_name=os.path.join( + c.run_dir, 'log.txt'), file_mode='a', should_flush=True) # Init torch.distributed. if c.num_gpus > 1: - init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init')) + init_file = os.path.abspath(os.path.join( + temp_dir, '.torch_distributed_init')) if os.name == 'nt': init_method = 'file:///' + init_file.replace('\\', '/') - torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=c.num_gpus) + torch.distributed.init_process_group( + backend='gloo', init_method=init_method, rank=rank, world_size=c.num_gpus) else: init_method = f'file://{init_file}' - torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=c.num_gpus) + torch.distributed.init_process_group( + backend='nccl', init_method=init_method, rank=rank, world_size=c.num_gpus) # Init torch_utils. sync_device = torch.device('cuda', rank) if c.num_gpus > 1 else None @@ -48,7 +53,8 @@ def subprocess_fn(rank, c, temp_dir): # Execute training loop. training_loop.training_loop(rank=rank, **c) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def launch_training(c, desc, outdir, dry_run): dnnlib.util.Logger(should_flush=True) @@ -56,7 +62,8 @@ def launch_training(c, desc, outdir, dry_run): # Pick output directory. prev_run_dirs = [] if os.path.isdir(outdir): - prev_run_dirs = [x for x in os.listdir(outdir) if os.path.isdir(os.path.join(outdir, x))] + prev_run_dirs = [x for x in os.listdir( + outdir) if os.path.isdir(os.path.join(outdir, x))] prev_run_ids = [re.match(r'^\d+', x) for x in prev_run_dirs] prev_run_ids = [int(x.group()) for x in prev_run_ids if x is not None] cur_run_id = max(prev_run_ids, default=-1) + 1 @@ -97,25 +104,33 @@ def launch_training(c, desc, outdir, dry_run): if c.num_gpus == 1: subprocess_fn(rank=0, c=c, temp_dir=temp_dir) else: - torch.multiprocessing.spawn(fn=subprocess_fn, args=(c, temp_dir), nprocs=c.num_gpus) + torch.multiprocessing.spawn( + fn=subprocess_fn, args=(c, temp_dir), nprocs=c.num_gpus) + +# ---------------------------------------------------------------------------- -#---------------------------------------------------------------------------- def init_dataset_kwargs(data, square=False): # dataset try: - dataset_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=data, use_labels=True, max_size=None, xflip=False, square=square) - dataset_obj = dnnlib.util.construct_class_by_name(**dataset_kwargs) # Subclass of training.dataset.Dataset. - dataset_kwargs.resolution = dataset_obj.resolution # Be explicit about resolution. - dataset_kwargs.use_labels = dataset_obj.has_labels # Be explicit about labels. - dataset_kwargs.max_size = len(dataset_obj) # Be explicit about dataset size. + dataset_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', + path=data, use_labels=True, max_size=None, xflip=False, square=square) + # Subclass of training.dataset.Dataset. + dataset_obj = dnnlib.util.construct_class_by_name(**dataset_kwargs) + # Be explicit about resolution. + dataset_kwargs.resolution = dataset_obj.resolution + # Be explicit about labels. + dataset_kwargs.use_labels = dataset_obj.has_labels + # Be explicit about dataset size. + dataset_kwargs.max_size = len(dataset_obj) return dataset_kwargs, dataset_obj.name except IOError as err: raise click.ClickException(f'--data: {err}') print("out of dataset") -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def parse_comma_separated_list(s): if isinstance(s, list): @@ -124,10 +139,10 @@ def parse_comma_separated_list(s): return [] return s.split(',') -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- -@click.command() +@click.command() # Required. @click.option('--outdir', help='Where to save the results', metavar='DIR', required=True) @click.option('--cfg', help='Base configuration', type=click.Choice(['stylegan3-t', 'stylegan3-r', 'stylegan2']), required=True) @@ -136,14 +151,12 @@ def parse_comma_separated_list(s): @click.option('--batch', help='Total batch size', metavar='INT', type=click.IntRange(min=1), required=True) @click.option('--gamma', help='R1 regularization weight', metavar='FLOAT', type=click.FloatRange(min=0), required=True) @click.option('--square', help='True for square, False for rectangle', type=bool, metavar='BOOL', default=False) - # Optional features. @click.option('--cond', help='Train conditional model', metavar='BOOL', type=bool, default=False, show_default=True) @click.option('--mirror', help='Enable dataset x-flips', metavar='BOOL', type=bool, default=False, show_default=True) @click.option('--aug', help='Augmentation mode', type=click.Choice(['noaug', 'ada', 'fixed']), default='ada', show_default=True) @click.option('--resume', help='Resume from given network pickle', metavar='[PATH|URL]', type=str) @click.option('--freezed', help='Freeze first layers of D', metavar='INT', type=click.IntRange(min=0), default=0, show_default=True) - # Misc hyperparameters. @click.option('--p', help='Probability for --aug=fixed', metavar='FLOAT', type=click.FloatRange(min=0, max=1), default=0.2, show_default=True) @click.option('--target', help='Target value for --aug=ada', metavar='FLOAT', type=click.FloatRange(min=0, max=1), default=0.6, show_default=True) @@ -154,7 +167,6 @@ def parse_comma_separated_list(s): @click.option('--dlr', help='D learning rate', metavar='FLOAT', type=click.FloatRange(min=0), default=0.002, show_default=True) @click.option('--map-depth', help='Mapping network depth [default: varies]', metavar='INT', type=click.IntRange(min=1)) @click.option('--mbstd-group', help='Minibatch std group size', metavar='INT', type=click.IntRange(min=1), default=4, show_default=True) - # Misc settings. @click.option('--desc', help='String to include in result dir name', metavar='STR', type=str) @click.option('--metrics', help='Quality metrics', metavar='[NAME|A,B,C|none]', type=parse_comma_separated_list, default='fid50k_full', show_default=True) @@ -165,8 +177,7 @@ def parse_comma_separated_list(s): @click.option('--fp32', help='Disable mixed-precision', metavar='BOOL', type=bool, default=False, show_default=True) @click.option('--nobench', help='Disable cuDNN benchmarking', metavar='BOOL', type=bool, default=False, show_default=True) @click.option('--workers', help='DataLoader worker processes', metavar='INT', type=click.IntRange(min=1), default=3, show_default=True) -@click.option('-n','--dry-run', help='Print training options and exit', is_flag=True) - +@click.option('-n', '--dry-run', help='Print training options and exit', is_flag=True) def main(**kwargs): """Train a GAN using the techniques described in the paper "Alias-Free Generative Adversarial Networks". @@ -191,20 +202,26 @@ def main(**kwargs): """ # Initialize config. - opts = dnnlib.EasyDict(kwargs) # Command line arguments. - c = dnnlib.EasyDict() # Main config dict. - print('---- square: ',opts.square) - c.G_kwargs = dnnlib.EasyDict(class_name=None, z_dim=512, w_dim=512, mapping_kwargs=dnnlib.EasyDict(),square=opts.square) - c.D_kwargs = dnnlib.EasyDict(class_name='training.networks_stylegan2.Discriminator', block_kwargs=dnnlib.EasyDict(), mapping_kwargs=dnnlib.EasyDict(), epilogue_kwargs=dnnlib.EasyDict(),square=opts.square) - c.G_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', betas=[0,0.99], eps=1e-8) - c.D_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', betas=[0,0.99], eps=1e-8) + opts = dnnlib.EasyDict(kwargs) # Command line arguments. + c = dnnlib.EasyDict() # Main config dict. + print('---- square: ', opts.square) + c.G_kwargs = dnnlib.EasyDict( + class_name=None, z_dim=512, w_dim=512, mapping_kwargs=dnnlib.EasyDict(), square=opts.square) + c.D_kwargs = dnnlib.EasyDict(class_name='training.networks_stylegan2.Discriminator', block_kwargs=dnnlib.EasyDict( + ), mapping_kwargs=dnnlib.EasyDict(), epilogue_kwargs=dnnlib.EasyDict(), square=opts.square) + c.G_opt_kwargs = dnnlib.EasyDict( + class_name='torch.optim.Adam', betas=[0, 0.99], eps=1e-8) + c.D_opt_kwargs = dnnlib.EasyDict( + class_name='torch.optim.Adam', betas=[0, 0.99], eps=1e-8) c.loss_kwargs = dnnlib.EasyDict(class_name='training.loss.StyleGAN2Loss') c.data_loader_kwargs = dnnlib.EasyDict(pin_memory=True, prefetch_factor=2) # Training set. - c.training_set_kwargs, dataset_name = init_dataset_kwargs(data=opts.data, square=opts.square) + c.training_set_kwargs, dataset_name = init_dataset_kwargs( + data=opts.data, square=opts.square) if opts.cond and not c.training_set_kwargs.use_labels: - raise click.ClickException('--cond=True requires labels specified in dataset.json') + raise click.ClickException( + '--cond=True requires labels specified in dataset.json') c.training_set_kwargs.use_labels = opts.cond c.training_set_kwargs.xflip = opts.mirror @@ -214,11 +231,13 @@ def main(**kwargs): c.batch_gpu = opts.batch_gpu or opts.batch // opts.gpus c.G_kwargs.channel_base = c.D_kwargs.channel_base = opts.cbase c.G_kwargs.channel_max = c.D_kwargs.channel_max = opts.cmax - c.G_kwargs.mapping_kwargs.num_layers = (8 if opts.cfg == 'stylegan2' else 2) if opts.map_depth is None else opts.map_depth + c.G_kwargs.mapping_kwargs.num_layers = ( + 8 if opts.cfg == 'stylegan2' else 2) if opts.map_depth is None else opts.map_depth c.D_kwargs.block_kwargs.freeze_layers = opts.freezed c.D_kwargs.epilogue_kwargs.mbstd_group_size = opts.mbstd_group c.loss_kwargs.r1_gamma = opts.gamma - c.G_opt_kwargs.lr = (0.002 if opts.cfg == 'stylegan2' else 0.0025) if opts.glr is None else opts.glr + c.G_opt_kwargs.lr = ( + 0.002 if opts.cfg == 'stylegan2' else 0.0025) if opts.glr is None else opts.glr c.D_opt_kwargs.lr = opts.dlr c.metrics = opts.metrics c.total_kimg = opts.kimg @@ -231,35 +250,45 @@ def main(**kwargs): if c.batch_size % c.num_gpus != 0: raise click.ClickException('--batch must be a multiple of --gpus') if c.batch_size % (c.num_gpus * c.batch_gpu) != 0: - raise click.ClickException('--batch must be a multiple of --gpus times --batch-gpu') + raise click.ClickException( + '--batch must be a multiple of --gpus times --batch-gpu') if c.batch_gpu < c.D_kwargs.epilogue_kwargs.mbstd_group_size: - raise click.ClickException('--batch-gpu cannot be smaller than --mbstd') + raise click.ClickException( + '--batch-gpu cannot be smaller than --mbstd') if any(not metric_main.is_valid_metric(metric) for metric in c.metrics): - raise click.ClickException('\n'.join(['--metrics can only contain the following values:'] + metric_main.list_valid_metrics())) + raise click.ClickException('\n'.join( + ['--metrics can only contain the following values:'] + metric_main.list_valid_metrics())) # Base configuration. c.ema_kimg = c.batch_size * 10 / 32 if opts.cfg == 'stylegan2': c.G_kwargs.class_name = 'training.networks_stylegan2.Generator' - c.loss_kwargs.style_mixing_prob = 0.9 # Enable style mixing regularization. - c.loss_kwargs.pl_weight = 2 # Enable path length regularization. - c.G_reg_interval = 4 # Enable lazy regularization for G. - c.G_kwargs.fused_modconv_default = 'inference_only' # Speed up training by using regular convolutions instead of grouped convolutions. - c.loss_kwargs.pl_no_weight_grad = True # Speed up path length regularization by skipping gradient computation wrt. conv2d weights. + # Enable style mixing regularization. + c.loss_kwargs.style_mixing_prob = 0.9 + c.loss_kwargs.pl_weight = 2 # Enable path length regularization. + c.G_reg_interval = 4 # Enable lazy regularization for G. + # Speed up training by using regular convolutions instead of grouped convolutions. + c.G_kwargs.fused_modconv_default = 'inference_only' + # Speed up path length regularization by skipping gradient computation wrt. conv2d weights. + c.loss_kwargs.pl_no_weight_grad = True else: c.G_kwargs.class_name = 'training.networks_stylegan3.Generator' c.G_kwargs.magnitude_ema_beta = 0.5 ** (c.batch_size / (20 * 1e3)) if opts.cfg == 'stylegan3-r': - c.G_kwargs.conv_kernel = 1 # Use 1x1 convolutions. - c.G_kwargs.channel_base *= 2 # Double the number of feature maps. + c.G_kwargs.conv_kernel = 1 # Use 1x1 convolutions. + c.G_kwargs.channel_base *= 2 # Double the number of feature maps. c.G_kwargs.channel_max *= 2 - c.G_kwargs.use_radial_filters = True # Use radially symmetric downsampling filters. - c.loss_kwargs.blur_init_sigma = 10 # Blur the images seen by the discriminator. - c.loss_kwargs.blur_fade_kimg = c.batch_size * 200 / 32 # Fade out the blur during the first N kimg. + # Use radially symmetric downsampling filters. + c.G_kwargs.use_radial_filters = True + # Blur the images seen by the discriminator. + c.loss_kwargs.blur_init_sigma = 10 + # Fade out the blur during the first N kimg. + c.loss_kwargs.blur_fade_kimg = c.batch_size * 200 / 32 # Augmentation. if opts.aug != 'noaug': - c.augment_kwargs = dnnlib.EasyDict(class_name='training.augment.AugmentPipe', xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1) + c.augment_kwargs = dnnlib.EasyDict(class_name='training.augment.AugmentPipe', xflip=1, rotate90=1, xint=1, + scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1) if opts.aug == 'ada': c.ada_target = opts.target if opts.aug == 'fixed': @@ -268,9 +297,9 @@ def main(**kwargs): # Resume. if opts.resume is not None: c.resume_pkl = opts.resume - c.ada_kimg = 100 # Make ADA react faster at the beginning. - c.ema_rampup = None # Disable EMA rampup. - c.loss_kwargs.blur_init_sigma = 0 # Disable blur rampup. + c.ada_kimg = 100 # Make ADA react faster at the beginning. + c.ema_rampup = None # Disable EMA rampup. + c.loss_kwargs.blur_init_sigma = 0 # Disable blur rampup. # Performance-related toggles. if opts.fp32: @@ -287,9 +316,10 @@ def main(**kwargs): # Launch. launch_training(c=c, desc=desc, outdir=opts.outdir, dry_run=opts.dry_run) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + if __name__ == "__main__": - main() # pylint: disable=no-value-for-parameter + main() # pylint: disable=no-value-for-parameter -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/training_scripts/sg3/training/dataset.py b/stylegan_human/training_scripts/sg3/training/dataset.py index df0aaec62a4771e63cbfd42b253790547c766baa..c4f10460a3c1d864d544fc7c9344cffd723312fe 100644 --- a/stylegan_human/training_scripts/sg3/training/dataset.py +++ b/stylegan_human/training_scripts/sg3/training/dataset.py @@ -26,18 +26,23 @@ try: except ImportError: pyspng = None -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class Dataset(torch.utils.data.Dataset): def __init__(self, - name, # Name of the dataset. - raw_shape, # Shape of the raw image data (NCHW). - max_size = None, # Artificially limit the size of the dataset. None = no limit. Applied before xflip. - use_labels = False, # Enable conditioning labels? False = label dimension is zero. - xflip = False, # Artificially double the size of the dataset via x-flips. Applied after max_size. - random_seed = 0, # Random seed to use when applying max_size. - square = False, - ): + name, # Name of the dataset. + raw_shape, # Shape of the raw image data (NCHW). + # Artificially limit the size of the dataset. None = no limit. Applied before xflip. + max_size=None, + # Enable conditioning labels? False = label dimension is zero. + use_labels=False, + # Artificially double the size of the dataset via x-flips. Applied after max_size. + xflip=False, + # Random seed to use when applying max_size. + random_seed=0, + square=False, + ): print('Inside Dataset') self._name = name self._raw_shape = list(raw_shape) @@ -57,13 +62,15 @@ class Dataset(torch.utils.data.Dataset): self._xflip = np.zeros(self._raw_idx.size, dtype=np.uint8) if xflip: self._raw_idx = np.tile(self._raw_idx, 2) - self._xflip = np.concatenate([self._xflip, np.ones_like(self._xflip)]) + self._xflip = np.concatenate( + [self._xflip, np.ones_like(self._xflip)]) def _get_raw_labels(self): if self._raw_labels is None: self._raw_labels = self._load_raw_labels() if self._use_labels else None if self._raw_labels is None: - self._raw_labels = np.zeros([self._raw_shape[0], 0], dtype=np.float32) + self._raw_labels = np.zeros( + [self._raw_shape[0], 0], dtype=np.float32) assert isinstance(self._raw_labels, np.ndarray) assert self._raw_labels.shape[0] == self._raw_shape[0] assert self._raw_labels.dtype in [np.float32, np.int64] @@ -72,13 +79,13 @@ class Dataset(torch.utils.data.Dataset): assert np.all(self._raw_labels >= 0) return self._raw_labels - def close(self): # to be overridden by subclass + def close(self): # to be overridden by subclass pass - def _load_raw_image(self, raw_idx): # to be overridden by subclass + def _load_raw_image(self, raw_idx): # to be overridden by subclass raise NotImplementedError - def _load_raw_labels(self): # to be overridden by subclass + def _load_raw_labels(self): # to be overridden by subclass raise NotImplementedError def __getstate__(self): @@ -99,7 +106,7 @@ class Dataset(torch.utils.data.Dataset): assert list(image.shape) == self.image_shape assert image.dtype == np.uint8 if self._xflip[idx]: - assert image.ndim == 3 # CHW + assert image.ndim == 3 # CHW image = image[:, :, ::-1] return image.copy(), self.get_label(idx) @@ -128,12 +135,12 @@ class Dataset(torch.utils.data.Dataset): @property def num_channels(self): - assert len(self.image_shape) == 3 # CHW + assert len(self.image_shape) == 3 # CHW return self.image_shape[0] @property def resolution(self): - assert len(self.image_shape) == 3 # CHW + assert len(self.image_shape) == 3 # CHW if self._square: assert self.image_shape[1] == self.image_shape[2] else: @@ -163,23 +170,27 @@ class Dataset(torch.utils.data.Dataset): def has_onehot_labels(self): return self._get_raw_labels().dtype == np.int64 -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class ImageFolderDataset(Dataset): def __init__(self, - path, # Path to directory or zip. - resolution = None, # Ensure specific resolution, None = highest available. - ceph = False, - square = False, - **super_kwargs, # Additional arguments for the Dataset base class. - ): - self._path = path + path, # Path to directory or zip. + # Ensure specific resolution, None = highest available. + resolution=None, + ceph=False, + square=False, + # Additional arguments for the Dataset base class. + **super_kwargs, + ): + self._path = path self._zipfile = None self._square = square if os.path.isdir(self._path): self._type = 'dir' - self._all_fnames = {os.path.relpath(os.path.join(root, fname), start=self._path) for root, _dirs, files in os.walk(self._path) for fname in files} + self._all_fnames = {os.path.relpath(os.path.join( + root, fname), start=self._path) for root, _dirs, files in os.walk(self._path) for fname in files} elif self._file_ext(self._path) == '.zip': self._type = 'zip' self._all_fnames = set(self._get_zipfile().namelist()) @@ -187,22 +198,24 @@ class ImageFolderDataset(Dataset): raise IOError('Path must point to a directory or zip') PIL.Image.init() - self._image_fnames = sorted(fname for fname in self._all_fnames if self._file_ext(fname) in PIL.Image.EXTENSION) + self._image_fnames = sorted( + fname for fname in self._all_fnames if self._file_ext(fname) in PIL.Image.EXTENSION) if len(self._image_fnames) == 0: raise IOError('No image files found in the specified path') name = os.path.splitext(os.path.basename(self._path))[0] - raw_shape = [len(self._image_fnames)] + list(self._load_raw_image(0).shape) + raw_shape = [len(self._image_fnames)] + \ + list(self._load_raw_image(0).shape) # if resolution is not None and (raw_shape[2] != resolution or raw_shape[3] != resolution): - # raise IOError('Image files do not match the specified resolution') + # raise IOError('Image files do not match the specified resolution') if resolution is not None: - if self._square: - raw_shape[2] = raw_shape[3] = resolution + if self._square: + raw_shape[2] = raw_shape[3] = resolution else: - raw_shape[2] = resolution - raw_shape[3] = resolution // 2 + raw_shape[2] = resolution + raw_shape[3] = resolution // 2 # print(raw_shape) - super().__init__(name=name, raw_shape=raw_shape,square=square, **super_kwargs) + super().__init__(name=name, raw_shape=raw_shape, square=square, **super_kwargs) @staticmethod def _file_ext(fname): @@ -239,8 +252,8 @@ class ImageFolderDataset(Dataset): else: image = np.array(PIL.Image.open(f)) if image.ndim == 2: - image = image[:, :, np.newaxis] # HW => HWC - image = image.transpose(2, 0, 1) # HWC => CHW + image = image[:, :, np.newaxis] # HW => HWC + image = image.transpose(2, 0, 1) # HWC => CHW return image def _load_raw_labels(self): @@ -252,9 +265,10 @@ class ImageFolderDataset(Dataset): if labels is None: return None labels = dict(labels) - labels = [labels[fname.replace('\\', '/')] for fname in self._image_fnames] + labels = [labels[fname.replace('\\', '/')] + for fname in self._image_fnames] labels = np.array(labels) labels = labels.astype({1: np.int64, 2: np.float32}[labels.ndim]) return labels -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/training_scripts/sg3/training/networks_stylegan2.py b/stylegan_human/training_scripts/sg3/training/networks_stylegan2.py index cb50599d59a5e49276b60f516b6aac0954bf6da3..832c7faf0baa0ddf6a1d39ad867a0b3d03bb47d2 100644 --- a/stylegan_human/training_scripts/sg3/training/networks_stylegan2.py +++ b/stylegan_human/training_scripts/sg3/training/networks_stylegan2.py @@ -22,56 +22,68 @@ from torch_utils.ops import upfirdn2d from torch_utils.ops import bias_act from torch_utils.ops import fma -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def normalize_2nd_moment(x, dim=1, eps=1e-8): return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt() -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def modulated_conv2d( - x, # Input tensor of shape [batch_size, in_channels, in_height, in_width]. - weight, # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width]. - styles, # Modulation coefficients of shape [batch_size, in_channels]. - noise = None, # Optional noise tensor to add to the output activations. - up = 1, # Integer upsampling factor. - down = 1, # Integer downsampling factor. - padding = 0, # Padding with respect to the upsampled image. - resample_filter = None, # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter(). - demodulate = True, # Apply weight demodulation? - flip_weight = True, # False = convolution, True = correlation (matches torch.nn.functional.conv2d). - fused_modconv = True, # Perform modulation, convolution, and demodulation as a single fused operation? + # Input tensor of shape [batch_size, in_channels, in_height, in_width]. + x, + # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width]. + weight, + # Modulation coefficients of shape [batch_size, in_channels]. + styles, + noise=None, # Optional noise tensor to add to the output activations. + up=1, # Integer upsampling factor. + down=1, # Integer downsampling factor. + padding=0, # Padding with respect to the upsampled image. + # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter(). + resample_filter=None, + demodulate=True, # Apply weight demodulation? + # False = convolution, True = correlation (matches torch.nn.functional.conv2d). + flip_weight=True, + # Perform modulation, convolution, and demodulation as a single fused operation? + fused_modconv=True, ): batch_size = x.shape[0] out_channels, in_channels, kh, kw = weight.shape - misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk] - misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] - misc.assert_shape(styles, [batch_size, in_channels]) # [NI] + misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk] + misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] + misc.assert_shape(styles, [batch_size, in_channels]) # [NI] # Pre-normalize inputs to avoid FP16 overflow. if x.dtype == torch.float16 and demodulate: - weight = weight * (1 / np.sqrt(in_channels * kh * kw) / weight.norm(float('inf'), dim=[1,2,3], keepdim=True)) # max_Ikk - styles = styles / styles.norm(float('inf'), dim=1, keepdim=True) # max_I + weight = weight * (1 / np.sqrt(in_channels * kh * kw) / + weight.norm(float('inf'), dim=[1, 2, 3], keepdim=True)) # max_Ikk + styles = styles / \ + styles.norm(float('inf'), dim=1, keepdim=True) # max_I # Calculate per-sample weights and demodulation coefficients. w = None dcoefs = None if demodulate or fused_modconv: - w = weight.unsqueeze(0) # [NOIkk] - w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk] + w = weight.unsqueeze(0) # [NOIkk] + w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk] if demodulate: - dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO] + dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt() # [NO] if demodulate and fused_modconv: - w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk] + w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk] # Execute by scaling the activations before and after the convolution. if not fused_modconv: x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1) - x = conv2d_resample.conv2d_resample(x=x, w=weight.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight) + x = conv2d_resample.conv2d_resample(x=x, w=weight.to( + x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight) if demodulate and noise is not None: - x = fma.fma(x, dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1), noise.to(x.dtype)) + x = fma.fma(x, dcoefs.to(x.dtype).reshape( + batch_size, -1, 1, 1), noise.to(x.dtype)) elif demodulate: x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1) elif noise is not None: @@ -79,35 +91,40 @@ def modulated_conv2d( return x # Execute as one fused op using grouped convolution. - with misc.suppress_tracer_warnings(): # this value will be treated as a constant + with misc.suppress_tracer_warnings(): # this value will be treated as a constant batch_size = int(batch_size) misc.assert_shape(x, [batch_size, in_channels, None, None]) x = x.reshape(1, -1, *x.shape[2:]) w = w.reshape(-1, in_channels, kh, kw) - x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight) + x = conv2d_resample.conv2d_resample(x=x, w=w.to( + x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight) x = x.reshape(batch_size, -1, *x.shape[2:]) if noise is not None: x = x.add_(noise) return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class FullyConnectedLayer(torch.nn.Module): def __init__(self, - in_features, # Number of input features. - out_features, # Number of output features. - bias = True, # Apply additive bias before the activation function? - activation = 'linear', # Activation function: 'relu', 'lrelu', etc. - lr_multiplier = 1, # Learning rate multiplier. - bias_init = 0, # Initial value for the additive bias. - ): + in_features, # Number of input features. + out_features, # Number of output features. + bias=True, # Apply additive bias before the activation function? + # Activation function: 'relu', 'lrelu', etc. + activation='linear', + lr_multiplier=1, # Learning rate multiplier. + bias_init=0, # Initial value for the additive bias. + ): super().__init__() self.in_features = in_features self.out_features = out_features self.activation = activation - self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) / lr_multiplier) - self.bias = torch.nn.Parameter(torch.full([out_features], np.float32(bias_init))) if bias else None + self.weight = torch.nn.Parameter(torch.randn( + [out_features, in_features]) / lr_multiplier) + self.bias = torch.nn.Parameter(torch.full( + [out_features], np.float32(bias_init))) if bias else None self.weight_gain = lr_multiplier / np.sqrt(in_features) self.bias_gain = lr_multiplier @@ -129,23 +146,28 @@ class FullyConnectedLayer(torch.nn.Module): def extra_repr(self): return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class Conv2dLayer(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels. - out_channels, # Number of output channels. - kernel_size, # Width and height of the convolution kernel. - bias = True, # Apply additive bias before the activation function? - activation = 'linear', # Activation function: 'relu', 'lrelu', etc. - up = 1, # Integer upsampling factor. - down = 1, # Integer downsampling factor. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = None, # Clamp the output to +-X, None = disable clamping. - channels_last = False, # Expect the input to have memory_format=channels_last? - trainable = True, # Update the weights of this layer during training? - ): + in_channels, # Number of input channels. + out_channels, # Number of output channels. + # Width and height of the convolution kernel. + kernel_size, + bias=True, # Apply additive bias before the activation function? + # Activation function: 'relu', 'lrelu', etc. + activation='linear', + up=1, # Integer upsampling factor. + down=1, # Integer downsampling factor. + # Low-pass filter to apply when resampling activations. + resample_filter=[1, 3, 3, 1], + # Clamp the output to +-X, None = disable clamping. + conv_clamp=None, + channels_last=False, # Expect the input to have memory_format=channels_last? + trainable=True, # Update the weights of this layer during training? + ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels @@ -153,13 +175,15 @@ class Conv2dLayer(torch.nn.Module): self.up = up self.down = down self.conv_clamp = conv_clamp - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.register_buffer( + 'resample_filter', upfirdn2d.setup_filter(resample_filter)) self.padding = kernel_size // 2 self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) self.act_gain = bias_act.activation_funcs[activation].def_gain memory_format = torch.channels_last if channels_last else torch.contiguous_format - weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format) + weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to( + memory_format=memory_format) bias = torch.zeros([out_channels]) if bias else None if trainable: self.weight = torch.nn.Parameter(weight) @@ -174,12 +198,14 @@ class Conv2dLayer(torch.nn.Module): def forward(self, x, gain=1): w = self.weight * self.weight_gain b = self.bias.to(x.dtype) if self.bias is not None else None - flip_weight = (self.up == 1) # slightly faster - x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight) + flip_weight = (self.up == 1) # slightly faster + x = conv2d_resample.conv2d_resample(x=x, w=w.to( + x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight) act_gain = self.act_gain * gain act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None - x = bias_act.bias_act(x, b, act=self.activation, gain=act_gain, clamp=act_clamp) + x = bias_act.bias_act(x, b, act=self.activation, + gain=act_gain, clamp=act_clamp) return x def extra_repr(self): @@ -187,22 +213,32 @@ class Conv2dLayer(torch.nn.Module): f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, activation={self.activation:s},', f'up={self.up}, down={self.down}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class MappingNetwork(torch.nn.Module): def __init__(self, - z_dim, # Input latent (Z) dimensionality, 0 = no latent. - c_dim, # Conditioning label (C) dimensionality, 0 = no label. - w_dim, # Intermediate latent (W) dimensionality. - num_ws, # Number of intermediate latents to output, None = do not broadcast. - num_layers = 8, # Number of mapping layers. - embed_features = None, # Label embedding dimensionality, None = same as w_dim. - layer_features = None, # Number of intermediate features in the mapping layers, None = same as w_dim. - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers. - w_avg_beta = 0.998, # Decay for tracking the moving average of W during training, None = do not track. - ): + # Input latent (Z) dimensionality, 0 = no latent. + z_dim, + # Conditioning label (C) dimensionality, 0 = no label. + c_dim, + # Intermediate latent (W) dimensionality. + w_dim, + # Number of intermediate latents to output, None = do not broadcast. + num_ws, + num_layers=8, # Number of mapping layers. + # Label embedding dimensionality, None = same as w_dim. + embed_features=None, + # Number of intermediate features in the mapping layers, None = same as w_dim. + layer_features=None, + # Activation function: 'relu', 'lrelu', etc. + activation='lrelu', + # Learning rate multiplier for the mapping layers. + lr_multiplier=0.01, + # Decay for tracking the moving average of W during training, None = do not track. + w_avg_beta=0.998, + ): super().__init__() self.z_dim = z_dim self.c_dim = c_dim @@ -217,14 +253,16 @@ class MappingNetwork(torch.nn.Module): embed_features = 0 if layer_features is None: layer_features = w_dim - features_list = [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim] + features_list = [z_dim + embed_features] + \ + [layer_features] * (num_layers - 1) + [w_dim] if c_dim > 0: self.embed = FullyConnectedLayer(c_dim, embed_features) for idx in range(num_layers): in_features = features_list[idx] out_features = features_list[idx + 1] - layer = FullyConnectedLayer(in_features, out_features, activation=activation, lr_multiplier=lr_multiplier) + layer = FullyConnectedLayer( + in_features, out_features, activation=activation, lr_multiplier=lr_multiplier) setattr(self, f'fc{idx}', layer) if num_ws is not None and w_avg_beta is not None: @@ -250,7 +288,8 @@ class MappingNetwork(torch.nn.Module): # Update moving average of W. if update_emas and self.w_avg_beta is not None: with torch.autograd.profiler.record_function('update_w_avg'): - self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta)) + self.w_avg.copy_(x.detach().mean( + dim=0).lerp(self.w_avg, self.w_avg_beta)) # Broadcast. if self.num_ws is not None: @@ -264,30 +303,36 @@ class MappingNetwork(torch.nn.Module): if self.num_ws is None or truncation_cutoff is None: x = self.w_avg.lerp(x, truncation_psi) else: - x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi) + x[:, :truncation_cutoff] = self.w_avg.lerp( + x[:, :truncation_cutoff], truncation_psi) return x def extra_repr(self): return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisLayer(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels. - out_channels, # Number of output channels. - w_dim, # Intermediate latent (W) dimensionality. - resolution, # Resolution of this layer. - kernel_size = 3, # Convolution kernel size. - up = 1, # Integer upsampling factor. - use_noise = True, # Enable noise input? - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. - channels_last = False, # Use channels_last format for the weights? - square = False, # default if for rectangle images - ): + in_channels, # Number of input channels. + out_channels, # Number of output channels. + # Intermediate latent (W) dimensionality. + w_dim, + resolution, # Resolution of this layer. + kernel_size=3, # Convolution kernel size. + up=1, # Integer upsampling factor. + use_noise=True, # Enable noise input? + # Activation function: 'relu', 'lrelu', etc. + activation='lrelu', + # Low-pass filter to apply when resampling activations. + resample_filter=[1, 3, 3, 1], + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=None, + channels_last=False, # Use channels_last format for the weights? + square=False, # default if for rectangle images + ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels @@ -297,19 +342,23 @@ class SynthesisLayer(torch.nn.Module): self.use_noise = use_noise self.activation = activation self.conv_clamp = conv_clamp - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.register_buffer( + 'resample_filter', upfirdn2d.setup_filter(resample_filter)) self.padding = kernel_size // 2 self.act_gain = bias_act.activation_funcs[activation].def_gain - self.square=square + self.square = square self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) memory_format = torch.channels_last if channels_last else torch.contiguous_format - self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) + self.weight = torch.nn.Parameter(torch.randn( + [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) if use_noise: if self.square: - self.register_buffer('noise_const', torch.randn([resolution, resolution])) + self.register_buffer( + 'noise_const', torch.randn([resolution, resolution])) else: - self.register_buffer('noise_const', torch.randn([resolution, resolution // 2])) + self.register_buffer('noise_const', torch.randn( + [resolution, resolution // 2])) self.noise_strength = torch.nn.Parameter(torch.zeros([])) self.bias = torch.nn.Parameter(torch.zeros([out_channels])) @@ -317,27 +366,32 @@ class SynthesisLayer(torch.nn.Module): assert noise_mode in ['random', 'const', 'none'] in_resolution = self.resolution // self.up if self.square: - misc.assert_shape(x, [None, self.weight.shape[1], in_resolution, in_resolution]) + misc.assert_shape( + x, [None, self.weight.shape[1], in_resolution, in_resolution]) else: - misc.assert_shape(x, [None, self.weight.shape[1], in_resolution, in_resolution // 2]) + misc.assert_shape( + x, [None, self.weight.shape[1], in_resolution, in_resolution // 2]) styles = self.affine(w) noise = None if self.use_noise and noise_mode == 'random': if self.square: - noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength + noise = torch.randn( + [x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength else: - noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution // 2], device=x.device) * self.noise_strength + noise = torch.randn( + [x.shape[0], 1, self.resolution, self.resolution // 2], device=x.device) * self.noise_strength if self.use_noise and noise_mode == 'const': noise = self.noise_const * self.noise_strength - flip_weight = (self.up == 1) # slightly faster + flip_weight = (self.up == 1) # slightly faster x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up, - padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv) + padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv) act_gain = self.act_gain * gain act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None - x = bias_act.bias_act(x, self.bias.to(x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp) + x = bias_act.bias_act(x, self.bias.to( + x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp) return x def extra_repr(self): @@ -345,7 +399,8 @@ class SynthesisLayer(torch.nn.Module): f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d},', f'resolution={self.resolution:d}, up={self.up}, activation={self.activation:s}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class ToRGBLayer(torch.nn.Module): @@ -357,39 +412,52 @@ class ToRGBLayer(torch.nn.Module): self.conv_clamp = conv_clamp self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) memory_format = torch.channels_last if channels_last else torch.contiguous_format - self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) + self.weight = torch.nn.Parameter(torch.randn( + [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) self.bias = torch.nn.Parameter(torch.zeros([out_channels])) self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) def forward(self, x, w, fused_modconv=True): styles = self.affine(w) * self.weight_gain - x = modulated_conv2d(x=x, weight=self.weight, styles=styles, demodulate=False, fused_modconv=fused_modconv) + x = modulated_conv2d(x=x, weight=self.weight, styles=styles, + demodulate=False, fused_modconv=fused_modconv) x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp) return x def extra_repr(self): return f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisBlock(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels, 0 = first block. - out_channels, # Number of output channels. - w_dim, # Intermediate latent (W) dimensionality. - resolution, # Resolution of this block. - img_channels, # Number of output color channels. - is_last, # Is this the last block? - architecture = 'skip', # Architecture: 'orig', 'skip', 'resnet'. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = 256, # Clamp the output of convolution layers to +-X, None = disable clamping. - use_fp16 = False, # Use FP16 for this block? - fp16_channels_last = False, # Use channels-last memory format with FP16? - square = False, # default is for rectangle images - fused_modconv_default = True, # Default value of fused_modconv. 'inference_only' = True for inference, False for training. - **layer_kwargs, # Arguments for SynthesisLayer. - ): + # Number of input channels, 0 = first block. + in_channels, + # Number of output channels. + out_channels, + # Intermediate latent (W) dimensionality. + w_dim, + # Resolution of this block. + resolution, + # Number of output color channels. + img_channels, + is_last, # Is this the last block? + # Architecture: 'orig', 'skip', 'resnet'. + architecture='skip', + # Low-pass filter to apply when resampling activations. + resample_filter=[1, 3, 3, 1], + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=256, + use_fp16=False, # Use FP16 for this block? + fp16_channels_last=False, # Use channels-last memory format with FP16? + square=False, # default is for rectangle images + # Default value of fused_modconv. 'inference_only' = True for inference, False for training. + fused_modconv_default=True, + # Arguments for SynthesisLayer. + **layer_kwargs, + ): assert architecture in ['orig', 'skip', 'resnet'] super().__init__() self.in_channels = in_channels @@ -401,38 +469,42 @@ class SynthesisBlock(torch.nn.Module): self.use_fp16 = use_fp16 self.channels_last = (use_fp16 and fp16_channels_last) self.fused_modconv_default = fused_modconv_default - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.register_buffer( + 'resample_filter', upfirdn2d.setup_filter(resample_filter)) self.num_conv = 0 self.num_torgb = 0 self.square = square if in_channels == 0: if self.square: - self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution])) - else: # rectangle - self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution // 2])) + self.const = torch.nn.Parameter(torch.randn( + [out_channels, resolution, resolution])) + else: # rectangle + self.const = torch.nn.Parameter(torch.randn( + [out_channels, resolution, resolution // 2])) if in_channels != 0: self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2, - resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, square=square, **layer_kwargs) + resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, square=square, **layer_kwargs) self.num_conv += 1 self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution, - conv_clamp=conv_clamp, channels_last=self.channels_last, square=square, **layer_kwargs) + conv_clamp=conv_clamp, channels_last=self.channels_last, square=square, **layer_kwargs) self.num_conv += 1 if is_last or architecture == 'skip': self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim, - conv_clamp=conv_clamp, channels_last=self.channels_last) + conv_clamp=conv_clamp, channels_last=self.channels_last) self.num_torgb += 1 if in_channels != 0 and architecture == 'resnet': self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2, - resample_filter=resample_filter, channels_last=self.channels_last) + resample_filter=resample_filter, channels_last=self.channels_last) def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, update_emas=False, **layer_kwargs): - _ = update_emas # unused - misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim]) + _ = update_emas # unused + misc.assert_shape( + ws, [None, self.num_conv + self.num_torgb, self.w_dim]) w_iter = iter(ws.unbind(dim=1)) if ws.device.type != 'cuda': force_fp32 = True @@ -449,33 +521,43 @@ class SynthesisBlock(torch.nn.Module): x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1]) else: if self.square: - misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2]) - else: # rectangle - misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 4]) + misc.assert_shape( + x, [None, self.in_channels, self.resolution // 2, self.resolution // 2]) + else: # rectangle + misc.assert_shape( + x, [None, self.in_channels, self.resolution // 2, self.resolution // 4]) x = x.to(dtype=dtype, memory_format=memory_format) # Main layers. if self.in_channels == 0: - x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv1(x, next(w_iter), + fused_modconv=fused_modconv, **layer_kwargs) elif self.architecture == 'resnet': y = self.skip(x, gain=np.sqrt(0.5)) - x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) - x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs) + x = self.conv0(x, next(w_iter), + fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, + gain=np.sqrt(0.5), **layer_kwargs) x = y.add_(x) else: - x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) - x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv0(x, next(w_iter), + fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv1(x, next(w_iter), + fused_modconv=fused_modconv, **layer_kwargs) # ToRGB. if img is not None: if self.square: - misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2]) + misc.assert_shape( + img, [None, self.img_channels, self.resolution // 2, self.resolution // 2]) else: - misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 4]) + misc.assert_shape( + img, [None, self.img_channels, self.resolution // 2, self.resolution // 4]) img = upfirdn2d.upsample2d(img, self.resample_filter) if self.is_last or self.architecture == 'skip': y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv) - y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format) + y = y.to(dtype=torch.float32, + memory_format=torch.contiguous_format) img = img.add_(y) if img is not None else y assert x.dtype == dtype @@ -485,31 +567,40 @@ class SynthesisBlock(torch.nn.Module): def extra_repr(self): return f'resolution={self.resolution:d}, architecture={self.architecture:s}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisNetwork(torch.nn.Module): def __init__(self, - w_dim, # Intermediate latent (W) dimensionality. - img_resolution, # Output image resolution. - img_channels, # Number of color channels. - square, - channel_base = 32768, # Overall multiplier for the number of channels. - channel_max = 512, # Maximum number of channels in any layer. - num_fp16_res = 4, # Use FP16 for the N highest resolutions. - **block_kwargs, # Arguments for SynthesisBlock. - ): - assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0 + # Intermediate latent (W) dimensionality. + w_dim, + img_resolution, # Output image resolution. + img_channels, # Number of color channels. + square, + # Overall multiplier for the number of channels. + channel_base=32768, + # Maximum number of channels in any layer. + channel_max=512, + # Use FP16 for the N highest resolutions. + num_fp16_res=4, + **block_kwargs, # Arguments for SynthesisBlock. + ): + assert img_resolution >= 4 and img_resolution & ( + img_resolution - 1) == 0 super().__init__() self.w_dim = w_dim self.img_resolution = img_resolution self.img_resolution_log2 = int(np.log2(img_resolution)) self.img_channels = img_channels - self.square=square + self.square = square self.num_fp16_res = num_fp16_res - self.block_resolutions = [2 ** i for i in range(2, self.img_resolution_log2 + 1)] - channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions} - fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) + self.block_resolutions = [ + 2 ** i for i in range(2, self.img_resolution_log2 + 1)] + channels_dict = {res: min(channel_base // res, channel_max) + for res in self.block_resolutions} + fp16_resolution = max( + 2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) self.num_ws = 0 for res in self.block_resolutions: @@ -518,7 +609,7 @@ class SynthesisNetwork(torch.nn.Module): use_fp16 = (res >= fp16_resolution) is_last = (res == self.img_resolution) block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res, - img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, square=square,**block_kwargs) + img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, square=square, **block_kwargs) self.num_ws += block.num_conv if is_last: self.num_ws += block.num_torgb @@ -532,7 +623,8 @@ class SynthesisNetwork(torch.nn.Module): w_idx = 0 for res in self.block_resolutions: block = getattr(self, f'b{res}') - block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb)) + block_ws.append( + ws.narrow(1, w_idx, block.num_conv + block.num_torgb)) w_idx += block.num_conv x = img = None @@ -547,20 +639,23 @@ class SynthesisNetwork(torch.nn.Module): f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},', f'num_fp16_res={self.num_fp16_res:d}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class Generator(torch.nn.Module): def __init__(self, - z_dim, # Input latent (Z) dimensionality. - c_dim, # Conditioning label (C) dimensionality. - w_dim, # Intermediate latent (W) dimensionality. - square, - img_resolution, # Output resolution. - img_channels, # Number of output color channels. - mapping_kwargs = {}, # Arguments for MappingNetwork. - **synthesis_kwargs, # Arguments for SynthesisNetwork. - ): + z_dim, # Input latent (Z) dimensionality. + # Conditioning label (C) dimensionality. + c_dim, + # Intermediate latent (W) dimensionality. + w_dim, + square, + img_resolution, # Output resolution. + img_channels, # Number of output color channels. + mapping_kwargs={}, # Arguments for MappingNetwork. + **synthesis_kwargs, # Arguments for SynthesisNetwork. + ): super().__init__() self.z_dim = z_dim self.c_dim = c_dim @@ -568,35 +663,50 @@ class Generator(torch.nn.Module): self.square = square self.img_resolution = img_resolution self.img_channels = img_channels - self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, square=square, **synthesis_kwargs) + self.synthesis = SynthesisNetwork( + w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, square=square, **synthesis_kwargs) self.num_ws = self.synthesis.num_ws - self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) + self.mapping = MappingNetwork( + z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs): - ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas) + ws = self.mapping(z, c, truncation_psi=truncation_psi, + truncation_cutoff=truncation_cutoff, update_emas=update_emas) img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs) return img -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class DiscriminatorBlock(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels, 0 = first block. - tmp_channels, # Number of intermediate channels. - out_channels, # Number of output channels. - resolution, # Resolution of this block. - img_channels, # Number of input color channels. - first_layer_idx, # Index of the first layer. - architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. - use_fp16 = False, # Use FP16 for this block? - fp16_channels_last = False, # Use channels-last memory format with FP16? - freeze_layers = 0, # Freeze-D: Number of layers to freeze. - square = False, - ): + # Number of input channels, 0 = first block. + in_channels, + # Number of intermediate channels. + tmp_channels, + # Number of output channels. + out_channels, + # Resolution of this block. + resolution, + # Number of input color channels. + img_channels, + # Index of the first layer. + first_layer_idx, + # Architecture: 'orig', 'skip', 'resnet'. + architecture='resnet', + # Activation function: 'relu', 'lrelu', etc. + activation='lrelu', + # Low-pass filter to apply when resampling activations. + resample_filter=[1, 3, 3, 1], + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=None, + use_fp16=False, # Use FP16 for this block? + fp16_channels_last=False, # Use channels-last memory format with FP16? + # Freeze-D: Number of layers to freeze. + freeze_layers=0, + square=False, + ): assert in_channels in [0, tmp_channels] assert architecture in ['orig', 'skip', 'resnet'] super().__init__() @@ -607,10 +717,12 @@ class DiscriminatorBlock(torch.nn.Module): self.architecture = architecture self.use_fp16 = use_fp16 self.channels_last = (use_fp16 and fp16_channels_last) - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.register_buffer( + 'resample_filter', upfirdn2d.setup_filter(resample_filter)) self.square = square self.num_layers = 0 + def trainable_gen(): while True: layer_idx = self.first_layer_idx + self.num_layers @@ -621,17 +733,17 @@ class DiscriminatorBlock(torch.nn.Module): if in_channels == 0 or architecture == 'skip': self.fromrgb = Conv2dLayer(img_channels, tmp_channels, kernel_size=1, activation=activation, - trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) + trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) self.conv0 = Conv2dLayer(tmp_channels, tmp_channels, kernel_size=3, activation=activation, - trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) + trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) self.conv1 = Conv2dLayer(tmp_channels, out_channels, kernel_size=3, activation=activation, down=2, - trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last) + trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last) if architecture == 'resnet': self.skip = Conv2dLayer(tmp_channels, out_channels, kernel_size=1, bias=False, down=2, - trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last) + trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last) def forward(self, x, img, force_fp32=False): if (x if x is not None else img).device.type != 'cuda': @@ -642,21 +754,26 @@ class DiscriminatorBlock(torch.nn.Module): # Input. if x is not None: if self.square: - misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) + misc.assert_shape( + x, [None, self.in_channels, self.resolution, self.resolution]) else: - misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution // 2]) + misc.assert_shape( + x, [None, self.in_channels, self.resolution, self.resolution // 2]) x = x.to(dtype=dtype, memory_format=memory_format) # FromRGB. if self.in_channels == 0 or self.architecture == 'skip': if self.square: - misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution]) - else: - misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution // 2]) + misc.assert_shape( + img, [None, self.img_channels, self.resolution, self.resolution]) + else: + misc.assert_shape( + img, [None, self.img_channels, self.resolution, self.resolution // 2]) img = img.to(dtype=dtype, memory_format=memory_format) y = self.fromrgb(img) x = x + y if x is not None else y - img = upfirdn2d.downsample2d(img, self.resample_filter) if self.architecture == 'skip' else None + img = upfirdn2d.downsample2d( + img, self.resample_filter) if self.architecture == 'skip' else None # Main layers. if self.architecture == 'resnet': @@ -674,7 +791,8 @@ class DiscriminatorBlock(torch.nn.Module): def extra_repr(self): return f'resolution={self.resolution:d}, architecture={self.architecture:s}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class MinibatchStdLayer(torch.nn.Module): @@ -685,40 +803,55 @@ class MinibatchStdLayer(torch.nn.Module): def forward(self, x): N, C, H, W = x.shape - with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants - G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor(N)) if self.group_size is not None else N + with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants + G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor( + N)) if self.group_size is not None else N F = self.num_channels c = C // F - y = x.reshape(G, -1, F, c, H, W) # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c. - y = y - y.mean(dim=0) # [GnFcHW] Subtract mean over group. - y = y.square().mean(dim=0) # [nFcHW] Calc variance over group. + # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c. + y = x.reshape(G, -1, F, c, H, W) + # [GnFcHW] Subtract mean over group. + y = y - y.mean(dim=0) + # [nFcHW] Calc variance over group. + y = y.square().mean(dim=0) y = (y + 1e-8).sqrt() # [nFcHW] Calc stddev over group. - y = y.mean(dim=[2,3,4]) # [nF] Take average over channels and pixels. + # [nF] Take average over channels and pixels. + y = y.mean(dim=[2, 3, 4]) y = y.reshape(-1, F, 1, 1) # [nF11] Add missing dimensions. - y = y.repeat(G, 1, H, W) # [NFHW] Replicate over group and pixels. - x = torch.cat([x, y], dim=1) # [NCHW] Append to input as new channels. + # [NFHW] Replicate over group and pixels. + y = y.repeat(G, 1, H, W) + # [NCHW] Append to input as new channels. + x = torch.cat([x, y], dim=1) return x def extra_repr(self): return f'group_size={self.group_size}, num_channels={self.num_channels:d}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class DiscriminatorEpilogue(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels. - cmap_dim, # Dimensionality of mapped conditioning label, 0 = no label. - resolution, # Resolution of this block. - img_channels, # Number of input color channels. - architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. - mbstd_group_size = 4, # Group size for the minibatch standard deviation layer, None = entire minibatch. - mbstd_num_channels = 1, # Number of features for the minibatch standard deviation layer, 0 = disable. - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. - square = False, - ): + in_channels, # Number of input channels. + # Dimensionality of mapped conditioning label, 0 = no label. + cmap_dim, + resolution, # Resolution of this block. + # Number of input color channels. + img_channels, + # Architecture: 'orig', 'skip', 'resnet'. + architecture='resnet', + # Group size for the minibatch standard deviation layer, None = entire minibatch. + mbstd_group_size=4, + # Number of features for the minibatch standard deviation layer, 0 = disable. + mbstd_num_channels=1, + # Activation function: 'relu', 'lrelu', etc. + activation='lrelu', + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=None, + square=False, + ): assert architecture in ['orig', 'skip', 'resnet'] super().__init__() self.in_channels = in_channels @@ -729,24 +862,32 @@ class DiscriminatorEpilogue(torch.nn.Module): self.square = square if architecture == 'skip': - self.fromrgb = Conv2dLayer(img_channels, in_channels, kernel_size=1, activation=activation) - self.mbstd = MinibatchStdLayer(group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None - self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels, kernel_size=3, activation=activation, conv_clamp=conv_clamp) + self.fromrgb = Conv2dLayer( + img_channels, in_channels, kernel_size=1, activation=activation) + self.mbstd = MinibatchStdLayer( + group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None + self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels, + kernel_size=3, activation=activation, conv_clamp=conv_clamp) if self.square: - self.fc = FullyConnectedLayer(in_channels * (resolution ** 2), in_channels, activation=activation) + self.fc = FullyConnectedLayer( + in_channels * (resolution ** 2), in_channels, activation=activation) else: - self.fc = FullyConnectedLayer(in_channels * (resolution ** 2 // 2), in_channels, activation=activation) - - self.out = FullyConnectedLayer(in_channels, 1 if cmap_dim == 0 else cmap_dim) + self.fc = FullyConnectedLayer( + in_channels * (resolution ** 2 // 2), in_channels, activation=activation) + + self.out = FullyConnectedLayer( + in_channels, 1 if cmap_dim == 0 else cmap_dim) def forward(self, x, img, cmap, force_fp32=False): if self.square: - misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) + misc.assert_shape(x, [None, self.in_channels, + self.resolution, self.resolution]) else: - misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution // 2]) # [NCHW] - - _ = force_fp32 # unused + misc.assert_shape( + x, [None, self.in_channels, self.resolution, self.resolution // 2]) # [NCHW] + + _ = force_fp32 # unused dtype = torch.float32 memory_format = torch.contiguous_format @@ -754,10 +895,12 @@ class DiscriminatorEpilogue(torch.nn.Module): x = x.to(dtype=dtype, memory_format=memory_format) if self.architecture == 'skip': if self.square: - misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution]) + misc.assert_shape( + img, [None, self.img_channels, self.resolution, self.resolution]) else: - misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution // 2]) - + misc.assert_shape( + img, [None, self.img_channels, self.resolution, self.resolution // 2]) + img = img.to(dtype=dtype, memory_format=memory_format) x = x + self.fromrgb(img) @@ -771,7 +914,8 @@ class DiscriminatorEpilogue(torch.nn.Module): # Conditioning. if self.cmap_dim > 0: misc.assert_shape(cmap, [None, self.cmap_dim]) - x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim)) + x = (x * cmap).sum(dim=1, keepdim=True) * \ + (1 / np.sqrt(self.cmap_dim)) assert x.dtype == dtype return x @@ -779,41 +923,55 @@ class DiscriminatorEpilogue(torch.nn.Module): def extra_repr(self): return f'resolution={self.resolution:d}, architecture={self.architecture:s}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class Discriminator(torch.nn.Module): def __init__(self, - c_dim, # Conditioning label (C) dimensionality. - img_resolution, # Input resolution. - img_channels, # Number of input color channels. - architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. - channel_base = 32768, # Overall multiplier for the number of channels. - channel_max = 512, # Maximum number of channels in any layer. - num_fp16_res = 4, # Use FP16 for the N highest resolutions. - conv_clamp = 256, # Clamp the output of convolution layers to +-X, None = disable clamping. - cmap_dim = None, # Dimensionality of mapped conditioning label, None = default. - square = False, # default for rectangle images - block_kwargs = {}, # Arguments for DiscriminatorBlock. - mapping_kwargs = {}, # Arguments for MappingNetwork. - epilogue_kwargs = {}, # Arguments for DiscriminatorEpilogue. - ): + # Conditioning label (C) dimensionality. + c_dim, + img_resolution, # Input resolution. + # Number of input color channels. + img_channels, + # Architecture: 'orig', 'skip', 'resnet'. + architecture='resnet', + # Overall multiplier for the number of channels. + channel_base=32768, + # Maximum number of channels in any layer. + channel_max=512, + # Use FP16 for the N highest resolutions. + num_fp16_res=4, + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=256, + # Dimensionality of mapped conditioning label, None = default. + cmap_dim=None, + square=False, # default for rectangle images + block_kwargs={}, # Arguments for DiscriminatorBlock. + mapping_kwargs={}, # Arguments for MappingNetwork. + # Arguments for DiscriminatorEpilogue. + epilogue_kwargs={}, + ): super().__init__() self.c_dim = c_dim self.img_resolution = img_resolution self.img_resolution_log2 = int(np.log2(img_resolution)) self.img_channels = img_channels self.square = square - self.block_resolutions = [2 ** i for i in range(self.img_resolution_log2, 2, -1)] - channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions + [4]} - fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) + self.block_resolutions = [ + 2 ** i for i in range(self.img_resolution_log2, 2, -1)] + channels_dict = {res: min(channel_base // res, channel_max) + for res in self.block_resolutions + [4]} + fp16_resolution = max( + 2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) if cmap_dim is None: cmap_dim = channels_dict[4] if c_dim == 0: cmap_dim = 0 - common_kwargs = dict(img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp) + common_kwargs = dict(img_channels=img_channels, + architecture=architecture, conv_clamp=conv_clamp) cur_layer_idx = 0 for res in self.block_resolutions: in_channels = channels_dict[res] if res < img_resolution else 0 @@ -821,15 +979,17 @@ class Discriminator(torch.nn.Module): out_channels = channels_dict[res // 2] use_fp16 = (res >= fp16_resolution) block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res, - first_layer_idx=cur_layer_idx, use_fp16=use_fp16, square=square, **block_kwargs, **common_kwargs) + first_layer_idx=cur_layer_idx, use_fp16=use_fp16, square=square, **block_kwargs, **common_kwargs) setattr(self, f'b{res}', block) cur_layer_idx += block.num_layers if c_dim > 0: - self.mapping = MappingNetwork(z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs) - self.b4 = DiscriminatorEpilogue(channels_dict[4], cmap_dim=cmap_dim, resolution=4, square=square, **epilogue_kwargs, **common_kwargs) + self.mapping = MappingNetwork( + z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs) + self.b4 = DiscriminatorEpilogue( + channels_dict[4], cmap_dim=cmap_dim, resolution=4, square=square, **epilogue_kwargs, **common_kwargs) def forward(self, img, c, update_emas=False, **block_kwargs): - _ = update_emas # unused + _ = update_emas # unused x = None for res in self.block_resolutions: block = getattr(self, f'b{res}') @@ -844,4 +1004,4 @@ class Discriminator(torch.nn.Module): def extra_repr(self): return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/training_scripts/sg3/training/networks_stylegan3.py b/stylegan_human/training_scripts/sg3/training/networks_stylegan3.py index 3c2391bea7511ee1e8c658ec90e5b67b4380a45e..31d3485accc72888a2cbb7d43bffeb8ae2f13c48 100644 --- a/stylegan_human/training_scripts/sg3/training/networks_stylegan3.py +++ b/stylegan_human/training_scripts/sg3/training/networks_stylegan3.py @@ -21,70 +21,80 @@ from torch_utils.ops import conv2d_gradfix from torch_utils.ops import filtered_lrelu from torch_utils.ops import bias_act -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def modulated_conv2d( - x, # Input tensor: [batch_size, in_channels, in_height, in_width] - w, # Weight tensor: [out_channels, in_channels, kernel_height, kernel_width] + # Input tensor: [batch_size, in_channels, in_height, in_width] + x, + # Weight tensor: [out_channels, in_channels, kernel_height, kernel_width] + w, s, # Style tensor: [batch_size, in_channels] - demodulate = True, # Apply weight demodulation? - padding = 0, # Padding: int or [padH, padW] - input_gain = None, # Optional scale factors for the input channels: [], [in_channels], or [batch_size, in_channels] + demodulate=True, # Apply weight demodulation? + padding=0, # Padding: int or [padH, padW] + input_gain=None, # Optional scale factors for the input channels: [], [in_channels], or [batch_size, in_channels] ): - with misc.suppress_tracer_warnings(): # this value will be treated as a constant + with misc.suppress_tracer_warnings(): # this value will be treated as a constant batch_size = int(x.shape[0]) out_channels, in_channels, kh, kw = w.shape - misc.assert_shape(w, [out_channels, in_channels, kh, kw]) # [OIkk] - misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] - misc.assert_shape(s, [batch_size, in_channels]) # [NI] + misc.assert_shape(w, [out_channels, in_channels, kh, kw]) # [OIkk] + misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] + misc.assert_shape(s, [batch_size, in_channels]) # [NI] # Pre-normalize inputs. if demodulate: - w = w * w.square().mean([1,2,3], keepdim=True).rsqrt() + w = w * w.square().mean([1, 2, 3], keepdim=True).rsqrt() s = s * s.square().mean().rsqrt() # Modulate weights. - w = w.unsqueeze(0) # [NOIkk] - w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk] + w = w.unsqueeze(0) # [NOIkk] + w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk] # Demodulate weights. if demodulate: - dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO] - w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4) # [NOIkk] + dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt() # [NO] + w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4) # [NOIkk] # Apply input scaling. if input_gain is not None: - input_gain = input_gain.expand(batch_size, in_channels) # [NI] - w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk] + input_gain = input_gain.expand(batch_size, in_channels) # [NI] + w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk] # Execute as one fused op using grouped convolution. x = x.reshape(1, -1, *x.shape[2:]) w = w.reshape(-1, in_channels, kh, kw) - x = conv2d_gradfix.conv2d(input=x, weight=w.to(x.dtype), padding=padding, groups=batch_size) + x = conv2d_gradfix.conv2d(input=x, weight=w.to( + x.dtype), padding=padding, groups=batch_size) x = x.reshape(batch_size, -1, *x.shape[2:]) return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class FullyConnectedLayer(torch.nn.Module): def __init__(self, - in_features, # Number of input features. - out_features, # Number of output features. - activation = 'linear', # Activation function: 'relu', 'lrelu', etc. - bias = True, # Apply additive bias before the activation function? - lr_multiplier = 1, # Learning rate multiplier. - weight_init = 1, # Initial standard deviation of the weight tensor. - bias_init = 0, # Initial value of the additive bias. - ): + in_features, # Number of input features. + out_features, # Number of output features. + # Activation function: 'relu', 'lrelu', etc. + activation='linear', + bias=True, # Apply additive bias before the activation function? + lr_multiplier=1, # Learning rate multiplier. + # Initial standard deviation of the weight tensor. + weight_init=1, + bias_init=0, # Initial value of the additive bias. + ): super().__init__() self.in_features = in_features self.out_features = out_features self.activation = activation - self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) * (weight_init / lr_multiplier)) - bias_init = np.broadcast_to(np.asarray(bias_init, dtype=np.float32), [out_features]) - self.bias = torch.nn.Parameter(torch.from_numpy(bias_init / lr_multiplier)) if bias else None + self.weight = torch.nn.Parameter(torch.randn( + [out_features, in_features]) * (weight_init / lr_multiplier)) + bias_init = np.broadcast_to(np.asarray( + bias_init, dtype=np.float32), [out_features]) + self.bias = torch.nn.Parameter(torch.from_numpy( + bias_init / lr_multiplier)) if bias else None self.weight_gain = lr_multiplier / np.sqrt(in_features) self.bias_gain = lr_multiplier @@ -105,19 +115,25 @@ class FullyConnectedLayer(torch.nn.Module): def extra_repr(self): return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class MappingNetwork(torch.nn.Module): def __init__(self, - z_dim, # Input latent (Z) dimensionality. - c_dim, # Conditioning label (C) dimensionality, 0 = no labels. - w_dim, # Intermediate latent (W) dimensionality. - num_ws, # Number of intermediate latents to output. - num_layers = 2, # Number of mapping layers. - lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers. - w_avg_beta = 0.998, # Decay for tracking the moving average of W during training. - ): + z_dim, # Input latent (Z) dimensionality. + # Conditioning label (C) dimensionality, 0 = no labels. + c_dim, + # Intermediate latent (W) dimensionality. + w_dim, + # Number of intermediate latents to output. + num_ws, + num_layers=2, # Number of mapping layers. + # Learning rate multiplier for the mapping layers. + lr_multiplier=0.01, + # Decay for tracking the moving average of W during training. + w_avg_beta=0.998, + ): super().__init__() self.z_dim = z_dim self.c_dim = c_dim @@ -127,10 +143,13 @@ class MappingNetwork(torch.nn.Module): self.w_avg_beta = w_avg_beta # Construct layers. - self.embed = FullyConnectedLayer(self.c_dim, self.w_dim) if self.c_dim > 0 else None - features = [self.z_dim + (self.w_dim if self.c_dim > 0 else 0)] + [self.w_dim] * self.num_layers + self.embed = FullyConnectedLayer( + self.c_dim, self.w_dim) if self.c_dim > 0 else None + features = [self.z_dim + (self.w_dim if self.c_dim > + 0 else 0)] + [self.w_dim] * self.num_layers for idx, in_features, out_features in zip(range(num_layers), features[:-1], features[1:]): - layer = FullyConnectedLayer(in_features, out_features, activation='lrelu', lr_multiplier=lr_multiplier) + layer = FullyConnectedLayer( + in_features, out_features, activation='lrelu', lr_multiplier=lr_multiplier) setattr(self, f'fc{idx}', layer) self.register_buffer('w_avg', torch.zeros([w_dim])) @@ -154,29 +173,32 @@ class MappingNetwork(torch.nn.Module): # Update moving average of W. if update_emas: - self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta)) + self.w_avg.copy_(x.detach().mean( + dim=0).lerp(self.w_avg, self.w_avg_beta)) # Broadcast and apply truncation. x = x.unsqueeze(1).repeat([1, self.num_ws, 1]) if truncation_psi != 1: - x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi) + x[:, :truncation_cutoff] = self.w_avg.lerp( + x[:, :truncation_cutoff], truncation_psi) return x def extra_repr(self): return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisInput(torch.nn.Module): def __init__(self, - w_dim, # Intermediate latent (W) dimensionality. - channels, # Number of output channels. - size, # Output spatial size: int or [width, height]. - sampling_rate, # Output sampling rate. - bandwidth, # Output bandwidth. - square, - ): + w_dim, # Intermediate latent (W) dimensionality. + channels, # Number of output channels. + size, # Output spatial size: int or [width, height]. + sampling_rate, # Output sampling rate. + bandwidth, # Output bandwidth. + square, + ): super().__init__() self.w_dim = w_dim self.channels = channels @@ -184,7 +206,7 @@ class SynthesisInput(torch.nn.Module): if self.square: self.size = np.broadcast_to(np.asarray(size), [2]) else: - self.size = np.array([size // 2, size]) # [width, height] + self.size = np.array([size // 2, size]) # [width, height] self.sampling_rate = sampling_rate self.bandwidth = bandwidth @@ -196,46 +218,58 @@ class SynthesisInput(torch.nn.Module): phases = torch.rand([self.channels]) - 0.5 # Setup parameters and buffers. - self.weight = torch.nn.Parameter(torch.randn([self.channels, self.channels])) - self.affine = FullyConnectedLayer(w_dim, 4, weight_init=0, bias_init=[1,0,0,0]) - self.register_buffer('transform', torch.eye(3, 3)) # User-specified inverse transform wrt. resulting image. + self.weight = torch.nn.Parameter( + torch.randn([self.channels, self.channels])) + self.affine = FullyConnectedLayer( + w_dim, 4, weight_init=0, bias_init=[1, 0, 0, 0]) + # User-specified inverse transform wrt. resulting image. + self.register_buffer('transform', torch.eye(3, 3)) self.register_buffer('freqs', freqs) self.register_buffer('phases', phases) def forward(self, w): # Introduce batch dimension. - transforms = self.transform.unsqueeze(0) # [batch, row, col] - freqs = self.freqs.unsqueeze(0) # [batch, channel, xy] - phases = self.phases.unsqueeze(0) # [batch, channel] + transforms = self.transform.unsqueeze(0) # [batch, row, col] + freqs = self.freqs.unsqueeze(0) # [batch, channel, xy] + phases = self.phases.unsqueeze(0) # [batch, channel] # Apply learned transformation. - t = self.affine(w) # t = (r_c, r_s, t_x, t_y) - t = t / t[:, :2].norm(dim=1, keepdim=True) # t' = (r'_c, r'_s, t'_x, t'_y) - m_r = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse rotation wrt. resulting image. + t = self.affine(w) # t = (r_c, r_s, t_x, t_y) + # t' = (r'_c, r'_s, t'_x, t'_y) + t = t / t[:, :2].norm(dim=1, keepdim=True) + # Inverse rotation wrt. resulting image. + m_r = torch.eye(3, device=w.device).unsqueeze( + 0).repeat([w.shape[0], 1, 1]) m_r[:, 0, 0] = t[:, 0] # r'_c - m_r[:, 0, 1] = -t[:, 1] # r'_s + m_r[:, 0, 1] = -t[:, 1] # r'_s m_r[:, 1, 0] = t[:, 1] # r'_s m_r[:, 1, 1] = t[:, 0] # r'_c - m_t = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse translation wrt. resulting image. - m_t[:, 0, 2] = -t[:, 2] # t'_x - m_t[:, 1, 2] = -t[:, 3] # t'_y - transforms = m_r @ m_t @ transforms # First rotate resulting image, then translate, and finally apply user-specified transform. + # Inverse translation wrt. resulting image. + m_t = torch.eye(3, device=w.device).unsqueeze( + 0).repeat([w.shape[0], 1, 1]) + m_t[:, 0, 2] = -t[:, 2] # t'_x + m_t[:, 1, 2] = -t[:, 3] # t'_y + # First rotate resulting image, then translate, and finally apply user-specified transform. + transforms = m_r @ m_t @ transforms # Transform frequencies. phases = phases + (freqs @ transforms[:, :2, 2:]).squeeze(2) freqs = freqs @ transforms[:, :2, :2] # Dampen out-of-band frequencies that may occur due to the user-specified transform. - amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) / (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1) + amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) / + (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1) # Construct sampling grid. theta = torch.eye(2, 3, device=w.device) theta[0, 0] = 0.5 * self.size[0] / self.sampling_rate theta[1, 1] = 0.5 * self.size[1] / self.sampling_rate - grids = torch.nn.functional.affine_grid(theta.unsqueeze(0), [1, 1, self.size[1], self.size[0]], align_corners=False) + grids = torch.nn.functional.affine_grid(theta.unsqueeze( + 0), [1, 1, self.size[1], self.size[0]], align_corners=False) # Compute Fourier features. - x = (grids.unsqueeze(3) @ freqs.permute(0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(3) # [batch, height, width, channel] + x = (grids.unsqueeze(3) @ freqs.permute(0, 2, 1).unsqueeze(1).unsqueeze(2) + ).squeeze(3) # [batch, height, width, channel] x = x + phases.unsqueeze(1).unsqueeze(2) x = torch.sin(x * (np.pi * 2)) x = x * amplitudes.unsqueeze(1).unsqueeze(2) @@ -245,8 +279,9 @@ class SynthesisInput(torch.nn.Module): x = x @ weight.t() # Ensure correct shape. - x = x.permute(0, 3, 1, 2) # [batch, channel, height, width] - misc.assert_shape(x, [w.shape[0], self.channels, int(self.size[1]), int(self.size[0])]) + x = x.permute(0, 3, 1, 2) # [batch, channel, height, width] + misc.assert_shape(x, [w.shape[0], self.channels, + int(self.size[1]), int(self.size[0])]) return x def extra_repr(self): @@ -254,37 +289,51 @@ class SynthesisInput(torch.nn.Module): f'w_dim={self.w_dim:d}, channels={self.channels:d}, size={list(self.size)},', f'sampling_rate={self.sampling_rate:g}, bandwidth={self.bandwidth:g}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisLayer(torch.nn.Module): def __init__(self, - w_dim, # Intermediate latent (W) dimensionality. - is_torgb, # Is this the final ToRGB layer? - is_critically_sampled, # Does this layer use critical sampling? - use_fp16, # Does this layer use FP16? - - # Input & output specifications. - in_channels, # Number of input channels. - out_channels, # Number of output channels. - in_size, # Input spatial size: int or [width, height]. - out_size, # Output spatial size: int or [width, height]. - in_sampling_rate, # Input sampling rate (s). - out_sampling_rate, # Output sampling rate (s). - in_cutoff, # Input cutoff frequency (f_c). - out_cutoff, # Output cutoff frequency (f_c). - in_half_width, # Input transition band half-width (f_h). - out_half_width, # Output Transition band half-width (f_h). - - # Hyperparameters. - conv_kernel = 3, # Convolution kernel size. Ignored for final the ToRGB layer. - filter_size = 6, # Low-pass filter size relative to the lower resolution when up/downsampling. - lrelu_upsampling = 2, # Relative sampling rate for leaky ReLU. Ignored for final the ToRGB layer. - use_radial_filters = False, # Use radially symmetric downsampling filter? Ignored for critically sampled layers. - conv_clamp = 256, # Clamp the output to [-X, +X], None = disable clamping. - magnitude_ema_beta = 0.999, # Decay rate for the moving average of input magnitudes. - square = False, # default if for rectangle images - ): + # Intermediate latent (W) dimensionality. + w_dim, + is_torgb, # Is this the final ToRGB layer? + is_critically_sampled, # Does this layer use critical sampling? + use_fp16, # Does this layer use FP16? + + # Input & output specifications. + in_channels, # Number of input channels. + out_channels, # Number of output channels. + # Input spatial size: int or [width, height]. + in_size, + # Output spatial size: int or [width, height]. + out_size, + in_sampling_rate, # Input sampling rate (s). + out_sampling_rate, # Output sampling rate (s). + # Input cutoff frequency (f_c). + in_cutoff, + # Output cutoff frequency (f_c). + out_cutoff, + # Input transition band half-width (f_h). + in_half_width, + # Output Transition band half-width (f_h). + out_half_width, + + # Hyperparameters. + # Convolution kernel size. Ignored for final the ToRGB layer. + conv_kernel=3, + # Low-pass filter size relative to the lower resolution when up/downsampling. + filter_size=6, + # Relative sampling rate for leaky ReLU. Ignored for final the ToRGB layer. + lrelu_upsampling=2, + # Use radially symmetric downsampling filter? Ignored for critically sampled layers. + use_radial_filters=False, + # Clamp the output to [-X, +X], None = disable clamping. + conv_clamp=256, + # Decay rate for the moving average of input magnitudes. + magnitude_ema_beta=0.999, + square=False, # default if for rectangle images + ): super().__init__() self.w_dim = w_dim self.is_torgb = is_torgb @@ -293,7 +342,7 @@ class SynthesisLayer(torch.nn.Module): self.in_channels = in_channels self.out_channels = out_channels self.square = square - if self.square: + if self.square: self.in_size = np.broadcast_to(np.asarray(in_size), [2]) self.out_size = np.broadcast_to(np.asarray(out_size), [2]) else: @@ -303,7 +352,8 @@ class SynthesisLayer(torch.nn.Module): self.out_size = np.array([out_size // 2, out_size]) self.in_sampling_rate = in_sampling_rate self.out_sampling_rate = out_sampling_rate - self.tmp_sampling_rate = max(in_sampling_rate, out_sampling_rate) * (1 if is_torgb else lrelu_upsampling) + self.tmp_sampling_rate = max( + in_sampling_rate, out_sampling_rate) * (1 if is_torgb else lrelu_upsampling) self.in_cutoff = in_cutoff self.out_cutoff = out_cutoff self.in_half_width = in_half_width @@ -313,65 +363,81 @@ class SynthesisLayer(torch.nn.Module): self.magnitude_ema_beta = magnitude_ema_beta # Setup parameters and buffers. - self.affine = FullyConnectedLayer(self.w_dim, self.in_channels, bias_init=1) - self.weight = torch.nn.Parameter(torch.randn([self.out_channels, self.in_channels, self.conv_kernel, self.conv_kernel])) + self.affine = FullyConnectedLayer( + self.w_dim, self.in_channels, bias_init=1) + self.weight = torch.nn.Parameter(torch.randn( + [self.out_channels, self.in_channels, self.conv_kernel, self.conv_kernel])) self.bias = torch.nn.Parameter(torch.zeros([self.out_channels])) self.register_buffer('magnitude_ema', torch.ones([])) # Design upsampling filter. - self.up_factor = int(np.rint(self.tmp_sampling_rate / self.in_sampling_rate)) + self.up_factor = int( + np.rint(self.tmp_sampling_rate / self.in_sampling_rate)) assert self.in_sampling_rate * self.up_factor == self.tmp_sampling_rate - self.up_taps = filter_size * self.up_factor if self.up_factor > 1 and not self.is_torgb else 1 + self.up_taps = filter_size * \ + self.up_factor if self.up_factor > 1 and not self.is_torgb else 1 self.register_buffer('up_filter', self.design_lowpass_filter( numtaps=self.up_taps, cutoff=self.in_cutoff, width=self.in_half_width*2, fs=self.tmp_sampling_rate)) # Design downsampling filter. - self.down_factor = int(np.rint(self.tmp_sampling_rate / self.out_sampling_rate)) + self.down_factor = int( + np.rint(self.tmp_sampling_rate / self.out_sampling_rate)) assert self.out_sampling_rate * self.down_factor == self.tmp_sampling_rate - self.down_taps = filter_size * self.down_factor if self.down_factor > 1 and not self.is_torgb else 1 + self.down_taps = filter_size * \ + self.down_factor if self.down_factor > 1 and not self.is_torgb else 1 self.down_radial = use_radial_filters and not self.is_critically_sampled self.register_buffer('down_filter', self.design_lowpass_filter( numtaps=self.down_taps, cutoff=self.out_cutoff, width=self.out_half_width*2, fs=self.tmp_sampling_rate, radial=self.down_radial)) # Compute padding. - pad_total = (self.out_size - 1) * self.down_factor + 1 # Desired output size before downsampling. - pad_total -= (self.in_size + self.conv_kernel - 1) * self.up_factor # Input size after upsampling. - pad_total += self.up_taps + self.down_taps - 2 # Size reduction caused by the filters. - pad_lo = (pad_total + self.up_factor) // 2 # Shift sample locations according to the symmetric interpretation (Appendix C.3). + # Desired output size before downsampling. + pad_total = (self.out_size - 1) * self.down_factor + 1 + # Input size after upsampling. + pad_total -= (self.in_size + self.conv_kernel - 1) * self.up_factor + # Size reduction caused by the filters. + pad_total += self.up_taps + self.down_taps - 2 + # Shift sample locations according to the symmetric interpretation (Appendix C.3). + pad_lo = (pad_total + self.up_factor) // 2 pad_hi = pad_total - pad_lo - self.padding = [int(pad_lo[0]), int(pad_hi[0]), int(pad_lo[1]), int(pad_hi[1])] + self.padding = [int(pad_lo[0]), int(pad_hi[0]), + int(pad_lo[1]), int(pad_hi[1])] def forward(self, x, w, noise_mode='random', force_fp32=False, update_emas=False): - assert noise_mode in ['random', 'const', 'none'] # unused - misc.assert_shape(x, [None, self.in_channels, int(self.in_size[1]), int(self.in_size[0])]) + assert noise_mode in ['random', 'const', 'none'] # unused + misc.assert_shape(x, [None, self.in_channels, int( + self.in_size[1]), int(self.in_size[0])]) misc.assert_shape(w, [x.shape[0], self.w_dim]) # Track input magnitude. if update_emas: with torch.autograd.profiler.record_function('update_magnitude_ema'): magnitude_cur = x.detach().to(torch.float32).square().mean() - self.magnitude_ema.copy_(magnitude_cur.lerp(self.magnitude_ema, self.magnitude_ema_beta)) + self.magnitude_ema.copy_(magnitude_cur.lerp( + self.magnitude_ema, self.magnitude_ema_beta)) input_gain = self.magnitude_ema.rsqrt() # Execute affine layer. styles = self.affine(w) if self.is_torgb: - weight_gain = 1 / np.sqrt(self.in_channels * (self.conv_kernel ** 2)) + weight_gain = 1 / \ + np.sqrt(self.in_channels * (self.conv_kernel ** 2)) styles = styles * weight_gain # Execute modulated conv2d. - dtype = torch.float16 if (self.use_fp16 and not force_fp32 and x.device.type == 'cuda') else torch.float32 + dtype = torch.float16 if ( + self.use_fp16 and not force_fp32 and x.device.type == 'cuda') else torch.float32 x = modulated_conv2d(x=x.to(dtype), w=self.weight, s=styles, - padding=self.conv_kernel-1, demodulate=(not self.is_torgb), input_gain=input_gain) + padding=self.conv_kernel-1, demodulate=(not self.is_torgb), input_gain=input_gain) # Execute bias, filtered leaky ReLU, and clamping. gain = 1 if self.is_torgb else np.sqrt(2) slope = 1 if self.is_torgb else 0.2 x = filtered_lrelu.filtered_lrelu(x=x, fu=self.up_filter, fd=self.down_filter, b=self.bias.to(x.dtype), - up=self.up_factor, down=self.down_factor, padding=self.padding, gain=gain, slope=slope, clamp=self.conv_clamp) + up=self.up_factor, down=self.down_factor, padding=self.padding, gain=gain, slope=slope, clamp=self.conv_clamp) # Ensure correct shape and dtype. - misc.assert_shape(x, [None, self.out_channels, int(self.out_size[1]), int(self.out_size[0])]) + misc.assert_shape(x, [None, self.out_channels, int( + self.out_size[1]), int(self.out_size[0])]) assert x.dtype == dtype return x @@ -385,14 +451,16 @@ class SynthesisLayer(torch.nn.Module): # Separable Kaiser low-pass filter. if not radial: - f = scipy.signal.firwin(numtaps=numtaps, cutoff=cutoff, width=width, fs=fs) + f = scipy.signal.firwin( + numtaps=numtaps, cutoff=cutoff, width=width, fs=fs) return torch.as_tensor(f, dtype=torch.float32) # Radially symmetric jinc-based filter. x = (np.arange(numtaps) - (numtaps - 1) / 2) / fs r = np.hypot(*np.meshgrid(x, x)) f = scipy.special.j1(2 * cutoff * (np.pi * r)) / (np.pi * r) - beta = scipy.signal.kaiser_beta(scipy.signal.kaiser_atten(numtaps, width / (fs / 2))) + beta = scipy.signal.kaiser_beta( + scipy.signal.kaiser_atten(numtaps, width / (fs / 2))) w = np.kaiser(numtaps, beta) f *= np.outer(w, w) f /= np.sum(f) @@ -408,28 +476,40 @@ class SynthesisLayer(torch.nn.Module): f'in_size={list(self.in_size)}, out_size={list(self.out_size)},', f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisNetwork(torch.nn.Module): def __init__(self, - w_dim, # Intermediate latent (W) dimensionality. - img_resolution, # Output image resolution. - img_channels, # Number of color channels. - square, - channel_base = 32768, # Overall multiplier for the number of channels. - channel_max = 512, # Maximum number of channels in any layer. - num_layers = 14, # Total number of layers, excluding Fourier features and ToRGB. - num_critical = 2, # Number of critically sampled layers at the end. - first_cutoff = 2, # Cutoff frequency of the first layer (f_{c,0}). - first_stopband = 2**2.1, # Minimum stopband of the first layer (f_{t,0}). - last_stopband_rel = 2**0.3, # Minimum stopband of the last layer, expressed relative to the cutoff. - margin_size = 10, # Number of additional pixels outside the image. - output_scale = 0.25, # Scale factor for the output image. - num_fp16_res = 4, # Use FP16 for the N highest resolutions. - **layer_kwargs, # Arguments for SynthesisLayer. - - ): + # Intermediate latent (W) dimensionality. + w_dim, + img_resolution, # Output image resolution. + img_channels, # Number of color channels. + square, + # Overall multiplier for the number of channels. + channel_base=32768, + # Maximum number of channels in any layer. + channel_max=512, + # Total number of layers, excluding Fourier features and ToRGB. + num_layers=14, + # Number of critically sampled layers at the end. + num_critical=2, + # Cutoff frequency of the first layer (f_{c,0}). + first_cutoff=2, + # Minimum stopband of the first layer (f_{t,0}). + first_stopband=2**2.1, + # Minimum stopband of the last layer, expressed relative to the cutoff. + last_stopband_rel=2**0.3, + # Number of additional pixels outside the image. + margin_size=10, + output_scale=0.25, # Scale factor for the output image. + # Use FP16 for the N highest resolutions. + num_fp16_res=4, + # Arguments for SynthesisLayer. + **layer_kwargs, + + ): super().__init__() self.w_dim = w_dim self.num_ws = num_layers + 2 @@ -443,18 +523,24 @@ class SynthesisNetwork(torch.nn.Module): self.square = square # Geometric progression of layer cutoffs and min. stopbands. - last_cutoff = self.img_resolution / 2 # f_{c,N} - last_stopband = last_cutoff * last_stopband_rel # f_{t,N} - exponents = np.minimum(np.arange(self.num_layers + 1) / (self.num_layers - self.num_critical), 1) - cutoffs = first_cutoff * (last_cutoff / first_cutoff) ** exponents # f_c[i] - stopbands = first_stopband * (last_stopband / first_stopband) ** exponents # f_t[i] + last_cutoff = self.img_resolution / 2 # f_{c,N} + last_stopband = last_cutoff * last_stopband_rel # f_{t,N} + exponents = np.minimum( + np.arange(self.num_layers + 1) / (self.num_layers - self.num_critical), 1) + cutoffs = first_cutoff * \ + (last_cutoff / first_cutoff) ** exponents # f_c[i] + stopbands = first_stopband * \ + (last_stopband / first_stopband) ** exponents # f_t[i] # Compute remaining layer parameters. - sampling_rates = np.exp2(np.ceil(np.log2(np.minimum(stopbands * 2, self.img_resolution)))) # s[i] - half_widths = np.maximum(stopbands, sampling_rates / 2) - cutoffs # f_h[i] + sampling_rates = np.exp2( + np.ceil(np.log2(np.minimum(stopbands * 2, self.img_resolution)))) # s[i] + half_widths = np.maximum( + stopbands, sampling_rates / 2) - cutoffs # f_h[i] sizes = sampling_rates + self.margin_size * 2 sizes[-2:] = self.img_resolution - channels = np.rint(np.minimum((channel_base / 2) / cutoffs, channel_max)) + channels = np.rint(np.minimum( + (channel_base / 2) / cutoffs, channel_max)) channels[-1] = self.img_channels # Construct layers. @@ -465,11 +551,13 @@ class SynthesisNetwork(torch.nn.Module): for idx in range(self.num_layers + 1): prev = max(idx - 1, 0) is_torgb = (idx == self.num_layers) - is_critically_sampled = (idx >= self.num_layers - self.num_critical) - use_fp16 = (sampling_rates[idx] * (2 ** self.num_fp16_res) > self.img_resolution) + is_critically_sampled = ( + idx >= self.num_layers - self.num_critical) + use_fp16 = (sampling_rates[idx] * (2 ** + self.num_fp16_res) > self.img_resolution) layer = SynthesisLayer( w_dim=self.w_dim, is_torgb=is_torgb, is_critically_sampled=is_critically_sampled, use_fp16=use_fp16, - in_channels=int(channels[prev]), out_channels= int(channels[idx]), + in_channels=int(channels[prev]), out_channels=int(channels[idx]), in_size=int(sizes[prev]), out_size=int(sizes[idx]), in_sampling_rate=int(sampling_rates[prev]), out_sampling_rate=int(sampling_rates[idx]), in_cutoff=cutoffs[prev], out_cutoff=cutoffs[idx], @@ -493,9 +581,11 @@ class SynthesisNetwork(torch.nn.Module): # Ensure correct shape and dtype. if self.square: - misc.assert_shape(x, [None, self.img_channels, self.img_resolution, self.img_resolution]) + misc.assert_shape( + x, [None, self.img_channels, self.img_resolution, self.img_resolution]) else: - misc.assert_shape(x, [None, self.img_channels, self.img_resolution, self.img_resolution // 2]) + misc.assert_shape( + x, [None, self.img_channels, self.img_resolution, self.img_resolution // 2]) x = x.to(torch.float32) return x @@ -506,20 +596,23 @@ class SynthesisNetwork(torch.nn.Module): f'num_layers={self.num_layers:d}, num_critical={self.num_critical:d},', f'margin_size={self.margin_size:d}, num_fp16_res={self.num_fp16_res:d}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class Generator(torch.nn.Module): def __init__(self, - z_dim, # Input latent (Z) dimensionality. - c_dim, # Conditioning label (C) dimensionality. - w_dim, # Intermediate latent (W) dimensionality. - img_resolution, # Output resolution. - square, - img_channels, # Number of output color channels. - mapping_kwargs = {}, # Arguments for MappingNetwork. - **synthesis_kwargs, # Arguments for SynthesisNetwork. - ): + z_dim, # Input latent (Z) dimensionality. + # Conditioning label (C) dimensionality. + c_dim, + # Intermediate latent (W) dimensionality. + w_dim, + img_resolution, # Output resolution. + square, + img_channels, # Number of output color channels. + mapping_kwargs={}, # Arguments for MappingNetwork. + **synthesis_kwargs, # Arguments for SynthesisNetwork. + ): super().__init__() self.z_dim = z_dim self.c_dim = c_dim @@ -527,13 +620,16 @@ class Generator(torch.nn.Module): self.img_resolution = img_resolution self.img_channels = img_channels self.square = square - self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, square=self.square, **synthesis_kwargs) + self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, + img_channels=img_channels, square=self.square, **synthesis_kwargs) self.num_ws = self.synthesis.num_ws - self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) + self.mapping = MappingNetwork( + z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs): - ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas) + ws = self.mapping(z, c, truncation_psi=truncation_psi, + truncation_cutoff=truncation_cutoff, update_emas=update_emas) img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs) return img -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/stylegan_human/utils/ImagesDataset.py b/stylegan_human/utils/ImagesDataset.py index d108f1a16cb8dec386d51ae3e9f18a860780e391..ffe6106ff3d52b1c6c13ecab48618f2786bbfee5 100644 --- a/stylegan_human/utils/ImagesDataset.py +++ b/stylegan_human/utils/ImagesDataset.py @@ -20,9 +20,8 @@ class ImagesDataset(Dataset): def __getitem__(self, index): fname, from_path = self.source_paths[index] from_im = Image.open(from_path).convert('RGB') - + if self.source_transform: from_im = self.source_transform(from_im) return fname, from_im - diff --git a/stylegan_human/utils/face_alignment.py b/stylegan_human/utils/face_alignment.py index ae10e3f8e1ff662ef0ad79cf8d623c1a26baaf46..5854599e963a79e852d57f396ea08c952f25440e 100644 --- a/stylegan_human/utils/face_alignment.py +++ b/stylegan_human/utils/face_alignment.py @@ -9,6 +9,7 @@ import dlib import copy from PIL import Image + def get_landmark(img, detector, predictor): """get landmark with dlib :return: np.array shape=(68, 2) @@ -23,14 +24,13 @@ def get_landmark(img, detector, predictor): for tt in t: a.append([tt.x, tt.y]) lm = np.array(a) - + # face rect - face_rect = [dets[0].rect.left(), dets[0].rect.top(), dets[0].rect.right(), dets[0].rect.bottom()] + face_rect = [dets[0].rect.left(), dets[0].rect.top(), + dets[0].rect.right(), dets[0].rect.bottom()] return lm, face_rect - - def align_face_for_insetgan(img, detector, predictor, output_size=256): """ :param img: numpy array rgb @@ -88,7 +88,7 @@ def align_face_for_insetgan(img, detector, predictor, output_size=256): border = max(int(np.rint(qsize * 0.1)), 3) crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1])))) - + # crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), # min(crop[3] + border, img.size[1])) # img.save("debug/raw.jpg") @@ -116,11 +116,11 @@ def align_face_for_insetgan(img, detector, predictor, output_size=256): # Transform. # crop shape to transform shape - # nw = + # nw = # print(img.size, quad+0.5, np.bound((quad+0.5).flatten())) # assert False # img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR) - + # img.save("debug/transform.jpg") # if output_size < transform_size: img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS) @@ -128,10 +128,8 @@ def align_face_for_insetgan(img, detector, predictor, output_size=256): # print((quad+crop[0:2]).flatten()) # assert False # Return aligned image. - - return img, crop, face_rect - + return img, crop, face_rect def align_face_for_projector(img, detector, predictor, output_size): @@ -143,7 +141,6 @@ def align_face_for_projector(img, detector, predictor, output_size): img_cp = copy.deepcopy(img) lm, face_rect = get_landmark(img, detector, predictor) - lm_chin = lm[0: 17] # left-right lm_eyebrow_left = lm[17: 22] # left-right lm_eyebrow_right = lm[22: 27] # left-right @@ -182,7 +179,8 @@ def align_face_for_projector(img, detector, predictor, output_size): # Shrink. shrink = int(np.floor(qsize / output_size * 0.5)) if shrink > 1: - rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink))) + rsize = (int(np.rint(float(img.size[0]) / shrink)), + int(np.rint(float(img.size[1]) / shrink))) img = img.resize(rsize, PIL.Image.ANTIALIAS) quad /= shrink qsize /= shrink @@ -204,19 +202,23 @@ def align_face_for_projector(img, detector, predictor, output_size): max(pad[3] - img.size[1] + border, 0)) if enable_padding and max(pad) > border - 4: pad = np.maximum(pad, int(np.rint(qsize * 0.3))) - img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') + img = np.pad(np.float32(img), + ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') h, w, _ = img.shape y, x, _ = np.ogrid[:h, :w, :1] mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]), 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3])) blur = qsize * 0.02 - img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) + img += (scipy.ndimage.gaussian_filter(img, + [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0) - img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB') + img = PIL.Image.fromarray( + np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB') quad += pad[:2] # Transform. - img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR) + img = img.transform((transform_size, transform_size), + PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR) if output_size < transform_size: img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS) @@ -227,42 +229,46 @@ def align_face_for_projector(img, detector, predictor, output_size): def reverse_quad_transform(image, quad_to_map_to, alpha): # forward mapping, for simplicity - result = Image.new("RGBA",image.size) + result = Image.new("RGBA", image.size) result_pixels = result.load() width, height = result.size for y in range(height): for x in range(width): - result_pixels[x,y] = (0,0,0,0) + result_pixels[x, y] = (0, 0, 0, 0) - p1 = (quad_to_map_to[0],quad_to_map_to[1]) - p2 = (quad_to_map_to[2],quad_to_map_to[3]) - p3 = (quad_to_map_to[4],quad_to_map_to[5]) - p4 = (quad_to_map_to[6],quad_to_map_to[7]) + p1 = (quad_to_map_to[0], quad_to_map_to[1]) + p2 = (quad_to_map_to[2], quad_to_map_to[3]) + p3 = (quad_to_map_to[4], quad_to_map_to[5]) + p4 = (quad_to_map_to[6], quad_to_map_to[7]) - p1_p2_vec = (p2[0] - p1[0],p2[1] - p1[1]) - p4_p3_vec = (p3[0] - p4[0],p3[1] - p4[1]) + p1_p2_vec = (p2[0] - p1[0], p2[1] - p1[1]) + p4_p3_vec = (p3[0] - p4[0], p3[1] - p4[1]) for y in range(height): for x in range(width): - pixel = image.getpixel((x,y)) + pixel = image.getpixel((x, y)) y_percentage = y / float(height) x_percentage = x / float(width) # interpolate vertically - pa = (p1[0] + p1_p2_vec[0] * y_percentage, p1[1] + p1_p2_vec[1] * y_percentage) - pb = (p4[0] + p4_p3_vec[0] * y_percentage, p4[1] + p4_p3_vec[1] * y_percentage) + pa = (p1[0] + p1_p2_vec[0] * y_percentage, + p1[1] + p1_p2_vec[1] * y_percentage) + pb = (p4[0] + p4_p3_vec[0] * y_percentage, + p4[1] + p4_p3_vec[1] * y_percentage) - pa_to_pb_vec = (pb[0] - pa[0],pb[1] - pa[1]) + pa_to_pb_vec = (pb[0] - pa[0], pb[1] - pa[1]) # interpolate horizontally - p = (pa[0] + pa_to_pb_vec[0] * x_percentage, pa[1] + pa_to_pb_vec[1] * x_percentage) + p = (pa[0] + pa_to_pb_vec[0] * x_percentage, + pa[1] + pa_to_pb_vec[1] * x_percentage) try: - result_pixels[p[0],p[1]] = (pixel[0],pixel[1],pixel[2],min(int(alpha * 255),pixel[3])) + result_pixels[p[0], p[1]] = ( + pixel[0], pixel[1], pixel[2], min(int(alpha * 255), pixel[3])) except Exception: pass - return result \ No newline at end of file + return result diff --git a/stylegan_human/utils/log_utils.py b/stylegan_human/utils/log_utils.py index 12171ab4cb73659e163bafaf4491dd15218c7e06..7b4528dda762802b1161b7148c4348a5d360ad83 100644 --- a/stylegan_human/utils/log_utils.py +++ b/stylegan_human/utils/log_utils.py @@ -32,7 +32,8 @@ def plot_image_from_w(w, G): def plot_image(img): - img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).detach().cpu().numpy() + img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, + 255).to(torch.uint8).detach().cpu().numpy() pillow_image = Image.fromarray(img[0]) plt.imshow(pillow_image) plt.show() @@ -78,5 +79,6 @@ def get_image_from_w(w, G): w = w.unsqueeze(0) with torch.no_grad(): img = G.synthesis(w, noise_mode='const') - img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).detach().cpu().numpy() + img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, + 255).to(torch.uint8).detach().cpu().numpy() return img[0] diff --git a/stylegan_human/utils/util.py b/stylegan_human/utils/util.py index ca2b8681b7ad012a41394cc7bfcf56266e083669..544c94895dfc0bfcd1285fde7cd2c102b71113ed 100644 --- a/stylegan_human/utils/util.py +++ b/stylegan_human/utils/util.py @@ -12,26 +12,26 @@ def visual(output, out_path): output = torch.clamp(output, 0, 1) if output.shape[1] == 1: output = torch.cat([output, output, output], 1) - output = output[0].detach().cpu().permute(1,2,0).numpy() + output = output[0].detach().cpu().permute(1, 2, 0).numpy() output = (output*255).astype(np.uint8) - output = output[:,:,::-1] + output = output[:, :, ::-1] cv2.imwrite(out_path, output) - - + + def get_lr(t, initial_lr, rampdown=0.25, rampup=0.05): - + lr_ramp = min(1, (1 - t) / rampdown) lr_ramp = 0.5 - 0.5 * math.cos(lr_ramp * math.pi) lr_ramp = lr_ramp * min(1, t / rampup) return initial_lr * lr_ramp - def latent_noise(latent, strength): noise = torch.randn_like(latent) * strength return latent + noise + def noise_regularize_(noises): loss = 0 @@ -61,23 +61,24 @@ def noise_normalize_(noises): std = noise.std() noise.data.add_(-mean).div_(std) - + def tensor_to_numpy(x): x = x[0].permute(1, 2, 0) - x = torch.clamp(x, -1 ,1) + x = torch.clamp(x, -1, 1) x = (x+1) * 127.5 x = x.cpu().detach().numpy().astype(np.uint8) return x + def numpy_to_tensor(x): x = (x / 255 - 0.5) * 2 x = torch.from_numpy(x).unsqueeze(0).permute(0, 3, 1, 2) x = x.cuda().float() return x + def tensor_to_pil(x): - x = torch.clamp(x, -1 ,1) + x = torch.clamp(x, -1, 1) x = (x+1) * 127.5 return transforms.ToPILImage()(x.squeeze_(0)) - diff --git a/torch_utils/custom_ops.py b/torch_utils/custom_ops.py index 439e445b16da7ac985f7a1f2053e665385d47e87..6c11b863842a2e5ef1ee2da0b02c0733fe79e4b1 100644 --- a/torch_utils/custom_ops.py +++ b/torch_utils/custom_ops.py @@ -18,14 +18,15 @@ import torch import torch.utils.cpp_extension from torch.utils.file_baton import FileBaton -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Global options. -verbosity = 'brief' # Verbosity level: 'none', 'brief', 'full' +verbosity = 'brief' # Verbosity level: 'none', 'brief', 'full' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Internal helper funcs. + def _find_compiler_bindir(): patterns = [ 'C:/Program Files*/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64', @@ -39,7 +40,8 @@ def _find_compiler_bindir(): return matches[-1] return None -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _get_mangled_gpu_name(): name = torch.cuda.get_device_name().lower() @@ -51,11 +53,13 @@ def _get_mangled_gpu_name(): out.append('-') return ''.join(out) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Main entry point for compiling and loading C++/CUDA plugins. + _cached_plugins = dict() + def get_plugin(module_name, sources, headers=None, source_dir=None, **build_kwargs): assert verbosity in ['none', 'brief', 'full'] if headers is None: @@ -72,16 +76,18 @@ def get_plugin(module_name, sources, headers=None, source_dir=None, **build_kwar if verbosity == 'full': print(f'Setting up PyTorch plugin "{module_name}"...') elif verbosity == 'brief': - print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True) + print( + f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True) verbose_build = (verbosity == 'full') # Compile and load. - try: # pylint: disable=too-many-nested-blocks + try: # pylint: disable=too-many-nested-blocks # Make sure we can find the necessary compiler binaries. if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0: compiler_bindir = _find_compiler_bindir() if compiler_bindir is None: - raise RuntimeError(f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".') + raise RuntimeError( + f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".') os.environ['PATH'] += ';' + compiler_bindir # Some containers set TORCH_CUDA_ARCH_LIST to a list that can either @@ -105,8 +111,10 @@ def get_plugin(module_name, sources, headers=None, source_dir=None, **build_kwar # around the *.cu dependency bug in ninja config. # all_source_files = sorted(sources + headers) - all_source_dirs = set(os.path.dirname(fname) for fname in all_source_files) - if len(all_source_dirs) == 1: # and ('TORCH_EXTENSIONS_DIR' in os.environ): + all_source_dirs = set(os.path.dirname(fname) + for fname in all_source_files) + # and ('TORCH_EXTENSIONS_DIR' in os.environ): + if len(all_source_dirs) == 1: # Compute combined hash digest for all source files. hash_md5 = hashlib.md5() @@ -116,27 +124,33 @@ def get_plugin(module_name, sources, headers=None, source_dir=None, **build_kwar # Select cached build directory name. source_digest = hash_md5.hexdigest() - build_top_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) # pylint: disable=protected-access - cached_build_dir = os.path.join(build_top_dir, f'{source_digest}-{_get_mangled_gpu_name()}') + build_top_dir = torch.utils.cpp_extension._get_build_directory( + module_name, verbose=verbose_build) # pylint: disable=protected-access + cached_build_dir = os.path.join( + build_top_dir, f'{source_digest}-{_get_mangled_gpu_name()}') if not os.path.isdir(cached_build_dir): tmpdir = f'{build_top_dir}/srctmp-{uuid.uuid4().hex}' os.makedirs(tmpdir) for src in all_source_files: - shutil.copyfile(src, os.path.join(tmpdir, os.path.basename(src))) + shutil.copyfile(src, os.path.join( + tmpdir, os.path.basename(src))) try: - os.replace(tmpdir, cached_build_dir) # atomic + os.replace(tmpdir, cached_build_dir) # atomic except OSError: # source directory already exists, delete tmpdir and its contents. shutil.rmtree(tmpdir) - if not os.path.isdir(cached_build_dir): raise + if not os.path.isdir(cached_build_dir): + raise # Compile. - cached_sources = [os.path.join(cached_build_dir, os.path.basename(fname)) for fname in sources] + cached_sources = [os.path.join( + cached_build_dir, os.path.basename(fname)) for fname in sources] torch.utils.cpp_extension.load(name=module_name, build_directory=cached_build_dir, - verbose=verbose_build, sources=cached_sources, **build_kwargs) + verbose=verbose_build, sources=cached_sources, **build_kwargs) else: - torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs) + torch.utils.cpp_extension.load( + name=module_name, verbose=verbose_build, sources=sources, **build_kwargs) # Load. module = importlib.import_module(module_name) @@ -154,4 +168,4 @@ def get_plugin(module_name, sources, headers=None, source_dir=None, **build_kwar _cached_plugins[module_name] = module return module -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/torch_utils/misc.py b/torch_utils/misc.py index 335397dd1662d8f5bfd44e17899a00549867f4bc..d67d234396ca97b72d8549184fd1d2252bab466d 100644 --- a/torch_utils/misc.py +++ b/torch_utils/misc.py @@ -13,12 +13,13 @@ import torch import warnings import dnnlib -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Cached construction of constant tensors. Avoids CPU=>GPU copy when the # same constant is used multiple times. _constant_cache = dict() + def constant(value, shape=None, dtype=None, device=None, memory_format=None): value = np.asarray(value) if shape is not None: @@ -30,7 +31,8 @@ def constant(value, shape=None, dtype=None, device=None, memory_format=None): if memory_format is None: memory_format = torch.contiguous_format - key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format) + key = (value.shape, value.dtype, value.tobytes(), + shape, dtype, device, memory_format) tensor = _constant_cache.get(key, None) if tensor is None: tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device) @@ -40,13 +42,14 @@ def constant(value, shape=None, dtype=None, device=None, memory_format=None): _constant_cache[key] = tensor return tensor -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Replace NaN/Inf with specified numerical values. + try: - nan_to_num = torch.nan_to_num # 1.8.0a0 + nan_to_num = torch.nan_to_num # 1.8.0a0 except AttributeError: - def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin + def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin assert isinstance(input, torch.Tensor) if posinf is None: posinf = torch.finfo(input.dtype).max @@ -55,18 +58,19 @@ except AttributeError: assert nan == 0 return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Symbolic assert. try: - symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access + symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access except AttributeError: - symbolic_assert = torch.Assert # 1.7.0 + symbolic_assert = torch.Assert # 1.7.0 -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Context manager to temporarily suppress known warnings in torch.jit.trace(). # Note: Cannot use catch_warnings because of https://bugs.python.org/issue29672 + @contextlib.contextmanager def suppress_tracer_warnings(): flt = ('ignore', None, torch.jit.TracerWarning, None, 0) @@ -74,29 +78,35 @@ def suppress_tracer_warnings(): yield warnings.filters.remove(flt) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Assert that the shape of a tensor matches the given list of integers. # None indicates that the size of a dimension is allowed to vary. # Performs symbolic assertion when used in torch.jit.trace(). + def assert_shape(tensor, ref_shape): if tensor.ndim != len(ref_shape): - raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}') + raise AssertionError( + f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}') for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)): if ref_size is None: pass elif isinstance(ref_size, torch.Tensor): - with suppress_tracer_warnings(): # as_tensor results are registered as constants - symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}') + with suppress_tracer_warnings(): # as_tensor results are registered as constants + symbolic_assert(torch.equal(torch.as_tensor( + size), ref_size), f'Wrong size for dimension {idx}') elif isinstance(size, torch.Tensor): - with suppress_tracer_warnings(): # as_tensor results are registered as constants - symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}') + with suppress_tracer_warnings(): # as_tensor results are registered as constants + symbolic_assert(torch.equal(size, torch.as_tensor( + ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}') elif size != ref_size: - raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}') + raise AssertionError( + f'Wrong size for dimension {idx}: got {size}, expected {ref_size}') -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Function decorator that calls torch.autograd.profiler.record_function(). + def profiled_function(fn): def decorator(*args, **kwargs): with torch.autograd.profiler.record_function(fn.__name__): @@ -104,10 +114,11 @@ def profiled_function(fn): decorator.__name__ = fn.__name__ return decorator -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Sampler for torch.utils.data.DataLoader that loops over the dataset # indefinitely, shuffling items as it goes. + class InfiniteSampler(torch.utils.data.Sampler): def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5): assert len(dataset) > 0 @@ -141,17 +152,20 @@ class InfiniteSampler(torch.utils.data.Sampler): order[i], order[j] = order[j], order[i] idx += 1 -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Utilities for operating with torch.nn.Module parameters and buffers. + def params_and_buffers(module): assert isinstance(module, torch.nn.Module) return list(module.parameters()) + list(module.buffers()) + def named_params_and_buffers(module): assert isinstance(module, torch.nn.Module) return list(module.named_parameters()) + list(module.named_buffers()) + def copy_params_and_buffers(src_module, dst_module, require_all=False): assert isinstance(src_module, torch.nn.Module) assert isinstance(dst_module, torch.nn.Module) @@ -159,12 +173,14 @@ def copy_params_and_buffers(src_module, dst_module, require_all=False): for name, tensor in named_params_and_buffers(dst_module): assert (name in src_tensors) or (not require_all) if name in src_tensors: - tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad) + tensor.copy_(src_tensors[name].detach()).requires_grad_( + tensor.requires_grad) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Context manager for easily enabling/disabling DistributedDataParallel # synchronization. + @contextlib.contextmanager def ddp_sync(module, sync): assert isinstance(module, torch.nn.Module) @@ -174,9 +190,10 @@ def ddp_sync(module, sync): with module.no_sync(): yield -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Check DistributedDataParallel consistency across processes. + def check_ddp_consistency(module, ignore_regex=None): assert isinstance(module, torch.nn.Module) for name, tensor in named_params_and_buffers(module): @@ -190,9 +207,10 @@ def check_ddp_consistency(module, ignore_regex=None): torch.distributed.broadcast(tensor=other, src=0) assert (tensor == other).all(), fullname -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Print summary table of module hierarchy. + def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True): assert isinstance(module, torch.nn.Module) assert not isinstance(module, torch.jit.ScriptModule) @@ -201,15 +219,19 @@ def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True): # Register hooks. entries = [] nesting = [0] + def pre_hook(_mod, _inputs): nesting[0] += 1 + def post_hook(mod, _inputs, outputs): nesting[0] -= 1 if nesting[0] <= max_nesting: - outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs] + outputs = list(outputs) if isinstance( + outputs, (tuple, list)) else [outputs] outputs = [t for t in outputs if isinstance(t, torch.Tensor)] entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs)) - hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()] + hooks = [mod.register_forward_pre_hook( + pre_hook) for mod in module.modules()] hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()] # Run module. @@ -220,17 +242,22 @@ def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True): # Identify unique outputs, parameters, and buffers. tensors_seen = set() for e in entries: - e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen] - e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen] + e.unique_params = [ + t for t in e.mod.parameters() if id(t) not in tensors_seen] + e.unique_buffers = [ + t for t in e.mod.buffers() if id(t) not in tensors_seen] e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen] - tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs} + tensors_seen |= {id(t) for t in e.unique_params + + e.unique_buffers + e.unique_outputs} # Filter out redundant entries. if skip_redundant: - entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)] + entries = [e for e in entries if len(e.unique_params) or len( + e.unique_buffers) or len(e.unique_outputs)] # Construct table. - rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']] + rows = [[type(module).__name__, 'Parameters', + 'Buffers', 'Output shape', 'Datatype']] rows += [['---'] * len(rows[0])] param_total = 0 buffer_total = 0 @@ -249,7 +276,8 @@ def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True): (output_dtypes + ['-'])[0], ]] for idx in range(1, len(e.outputs)): - rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]] + rows += [[name + f':{idx}', '-', '-', + output_shapes[idx], output_dtypes[idx]]] param_total += param_size buffer_total += buffer_size rows += [['---'] * len(rows[0])] @@ -259,8 +287,9 @@ def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True): widths = [max(len(cell) for cell in column) for column in zip(*rows)] print() for row in rows: - print(' '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths))) + print(' '.join(cell + ' ' * (width - len(cell)) + for cell, width in zip(row, widths))) print() return outputs -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/torch_utils/ops/bias_act.py b/torch_utils/ops/bias_act.py index b2b53d7da34c76d53251bb9cbc2eb071c50af921..d809aa54ba33483a52b072345c5f090b85e21a3f 100644 --- a/torch_utils/ops/bias_act.py +++ b/torch_utils/ops/bias_act.py @@ -16,7 +16,7 @@ import dnnlib from .. import custom_ops from .. import misc -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- activation_funcs = { 'linear': dnnlib.EasyDict(func=lambda x, **_: x, def_alpha=0, def_gain=1, cuda_idx=1, ref='', has_2nd_grad=False), @@ -30,11 +30,12 @@ activation_funcs = { 'swish': dnnlib.EasyDict(func=lambda x, **_: torch.sigmoid(x) * x, def_alpha=0, def_gain=np.sqrt(2), cuda_idx=9, ref='x', has_2nd_grad=True), } -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- _plugin = None _null_tensor = torch.empty([0]) + def _init(): global _plugin if _plugin is None: @@ -43,11 +44,13 @@ def _init(): sources=['bias_act.cpp', 'bias_act.cu'], headers=['bias_act.h'], source_dir=os.path.dirname(__file__), - extra_cuda_cflags=['--use_fast_math', '--allow-unsupported-compiler'], + extra_cuda_cflags=['--use_fast_math', + '--allow-unsupported-compiler'], ) return True -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'): r"""Fused bias and activation function. @@ -85,7 +88,8 @@ def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b) return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None): @@ -116,13 +120,15 @@ def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=N # Clamp. if clamp >= 0: - x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type + x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + _bias_act_cuda_cache = dict() + def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): """Fast CUDA implementation of `bias_act()` using custom ops. """ @@ -141,13 +147,15 @@ def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): # Forward op. class BiasActCuda(torch.autograd.Function): @staticmethod - def forward(ctx, x, b): # pylint: disable=arguments-differ - ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride(1) == 1 else torch.contiguous_format + def forward(ctx, x, b): # pylint: disable=arguments-differ + ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride( + 1) == 1 else torch.contiguous_format x = x.contiguous(memory_format=ctx.memory_format) b = b.contiguous() if b is not None else _null_tensor y = x if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor: - y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp) + y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, + _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp) ctx.save_for_backward( x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor, b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor, @@ -155,7 +163,7 @@ def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): return y @staticmethod - def backward(ctx, dy): # pylint: disable=arguments-differ + def backward(ctx, dy): # pylint: disable=arguments-differ dy = dy.contiguous(memory_format=ctx.memory_format) x, b, y = ctx.saved_tensors dx = None @@ -174,16 +182,18 @@ def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): # Backward op. class BiasActCudaGrad(torch.autograd.Function): @staticmethod - def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ - ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride(1) == 1 else torch.contiguous_format - dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp) + def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ + ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride( + 1) == 1 else torch.contiguous_format + dx = _plugin.bias_act(dy, b, x, y, _null_tensor, + 1, dim, spec.cuda_idx, alpha, gain, clamp) ctx.save_for_backward( dy if spec.has_2nd_grad else _null_tensor, x, b, y) return dx @staticmethod - def backward(ctx, d_dx): # pylint: disable=arguments-differ + def backward(ctx, d_dx): # pylint: disable=arguments-differ d_dx = d_dx.contiguous(memory_format=ctx.memory_format) dy, x, b, y = ctx.saved_tensors d_dy = None @@ -195,7 +205,8 @@ def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): d_dy = BiasActCudaGrad.apply(d_dx, x, b, y) if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]): - d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp) + d_x = _plugin.bias_act( + d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp) if spec.has_2nd_grad and ctx.needs_input_grad[2]: d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim]) @@ -206,4 +217,4 @@ def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None): _bias_act_cuda_cache[key] = BiasActCuda return BiasActCuda -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/torch_utils/ops/conv2d_gradfix.py b/torch_utils/ops/conv2d_gradfix.py index 388778fa971d7bc5c64b5fd6c0e5492863ee1c5f..563543d23df5ae0432461a2c637aec71a4bee9ca 100644 --- a/torch_utils/ops/conv2d_gradfix.py +++ b/torch_utils/ops/conv2d_gradfix.py @@ -16,10 +16,13 @@ import torch # pylint: disable=arguments-differ # pylint: disable=protected-access -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + +# Enable the custom op by setting this to true. +enabled = False +# Forcefully disable computation of gradients with respect to the weights. +weight_gradients_disabled = False -enabled = False # Enable the custom op by setting this to true. -weight_gradients_disabled = False # Forcefully disable computation of gradients with respect to the weights. @contextlib.contextmanager def no_weight_gradients(disable=True): @@ -30,19 +33,22 @@ def no_weight_gradients(disable=True): yield weight_gradients_disabled = old -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1): if _should_use_custom_op(input): return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias) return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups) + def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1): if _should_use_custom_op(input): return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias) return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _should_use_custom_op(input): assert isinstance(input, torch.Tensor) @@ -52,17 +58,20 @@ def _should_use_custom_op(input): return False return True + def _tuple_of_ints(xs, ndim): xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim assert len(xs) == ndim assert all(isinstance(x, int) for x in xs) return xs -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + _conv2d_gradfix_cache = dict() _null_tensor = torch.empty([0]) + def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups): # Parse arguments. ndim = 2 @@ -73,7 +82,8 @@ def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, di dilation = _tuple_of_ints(dilation, ndim) # Lookup from cache. - key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups) + key = (transpose, weight_shape, stride, padding, + output_padding, dilation, groups) if key in _conv2d_gradfix_cache: return _conv2d_gradfix_cache[key] @@ -85,11 +95,14 @@ def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, di assert all(dilation[i] >= 0 for i in range(ndim)) if not transpose: assert all(output_padding[i] == 0 for i in range(ndim)) - else: # transpose - assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim)) + else: # transpose + assert all(0 <= output_padding[i] < max( + stride[i], dilation[i]) for i in range(ndim)) # Helpers. - common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups) + common_kwargs = dict(stride=stride, padding=padding, + dilation=dilation, groups=groups) + def calc_output_padding(input_shape, output_shape): if transpose: return [0, 0] @@ -114,11 +127,16 @@ def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, di # Simple 1x1 convolution => cuBLAS (only on Volta, not on Ampere). if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0) and torch.cuda.get_device_capability(input.device) < (8, 0): - a = weight.reshape(groups, weight_shape[0] // groups, weight_shape[1]) - b = input.reshape(input.shape[0], groups, input.shape[1] // groups, -1) - c = (a.transpose(1, 2) if transpose else a) @ b.permute(1, 2, 0, 3).flatten(2) - c = c.reshape(-1, input.shape[0], *input.shape[2:]).transpose(0, 1) - c = c if bias is None else c + bias.unsqueeze(0).unsqueeze(2).unsqueeze(3) + a = weight.reshape( + groups, weight_shape[0] // groups, weight_shape[1]) + b = input.reshape( + input.shape[0], groups, input.shape[1] // groups, -1) + c = (a.transpose(1, 2) if transpose else a) @ b.permute(1, + 2, 0, 3).flatten(2) + c = c.reshape(-1, input.shape[0], + *input.shape[2:]).transpose(0, 1) + c = c if bias is None else c + \ + bias.unsqueeze(0).unsqueeze(2).unsqueeze(3) return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format)) # General case => cuDNN. @@ -135,8 +153,10 @@ def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, di grad_bias = None if ctx.needs_input_grad[0]: - p = calc_output_padding(input_shape=input_shape, output_shape=grad_output.shape) - op = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs) + p = calc_output_padding( + input_shape=input_shape, output_shape=grad_output.shape) + op = _conv2d_gradfix(transpose=( + not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs) grad_input = op.apply(grad_output, weight, None) assert grad_input.shape == input_shape @@ -162,14 +182,18 @@ def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, di # Simple 1x1 convolution => cuBLAS (on both Volta and Ampere). if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0): - a = grad_output.reshape(grad_output.shape[0], groups, grad_output.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2) - b = input.reshape(input.shape[0], groups, input.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2) - c = (b @ a.transpose(1, 2) if transpose else a @ b.transpose(1, 2)).reshape(weight_shape) + a = grad_output.reshape( + grad_output.shape[0], groups, grad_output.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2) + b = input.reshape( + input.shape[0], groups, input.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2) + c = (b @ a.transpose(1, 2) if transpose else a @ + b.transpose(1, 2)).reshape(weight_shape) return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format)) # General case => cuDNN. name = 'aten::cudnn_convolution_transpose_backward_weight' if transpose else 'aten::cudnn_convolution_backward_weight' - flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32] + flags = [torch.backends.cudnn.benchmark, + torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32] return torch._C._jit_get_operation(name)(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags) @staticmethod @@ -181,12 +205,15 @@ def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, di grad2_input = None if ctx.needs_input_grad[0]: - grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None) + grad2_grad_output = Conv2d.apply( + input, grad2_grad_weight, None) assert grad2_grad_output.shape == grad_output_shape if ctx.needs_input_grad[1]: - p = calc_output_padding(input_shape=input_shape, output_shape=grad_output_shape) - op = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs) + p = calc_output_padding( + input_shape=input_shape, output_shape=grad_output_shape) + op = _conv2d_gradfix(transpose=( + not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs) grad2_input = op.apply(grad_output, grad2_grad_weight, None) assert grad2_input.shape == input_shape @@ -195,4 +222,4 @@ def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, di _conv2d_gradfix_cache[key] = Conv2d return Conv2d -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/torch_utils/ops/conv2d_resample.py b/torch_utils/ops/conv2d_resample.py index 5eb5877d7ffe4af74a2165f1d8d8c39dfac2476b..e89e1253094036046e326f3a6e57527c541fae8b 100644 --- a/torch_utils/ops/conv2d_resample.py +++ b/torch_utils/ops/conv2d_resample.py @@ -16,15 +16,17 @@ from . import upfirdn2d from .upfirdn2d import _parse_padding from .upfirdn2d import _get_filter_size -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _get_weight_shape(w): - with misc.suppress_tracer_warnings(): # this value will be treated as a constant + with misc.suppress_tracer_warnings(): # this value will be treated as a constant shape = [int(sz) for sz in w.shape] misc.assert_shape(w, shape) return shape -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True): """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations. @@ -40,7 +42,8 @@ def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_w op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d return op(x, w, stride=stride, padding=padding, groups=groups) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False): @@ -69,8 +72,10 @@ def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight """ # Validate arguments. assert isinstance(x, torch.Tensor) and (x.ndim == 4) - assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype) - assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32) + assert isinstance(w, torch.Tensor) and ( + w.ndim == 4) and (w.dtype == x.dtype) + assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [ + 1, 2] and f.dtype == torch.float32) assert isinstance(up, int) and (up >= 1) assert isinstance(down, int) and (down >= 1) assert isinstance(groups, int) and (groups >= 1) @@ -92,20 +97,24 @@ def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve. if kw == 1 and kh == 1 and (down > 1 and up == 1): - x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter) + x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[ + px0, px1, py0, py1], flip_filter=flip_filter) x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) return x # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample. if kw == 1 and kh == 1 and (up > 1 and down == 1): x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) - x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) + x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[ + px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) return x # Fast path: downsampling only => use strided convolution. if down > 1 and up == 1: - x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter) - x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight) + x = upfirdn2d.upfirdn2d( + x=x, f=f, padding=[px0, px1, py0, py1], flip_filter=flip_filter) + x = _conv2d_wrapper(x=x, w=w, stride=down, + groups=groups, flip_weight=flip_weight) return x # Fast path: upsampling with optional downsampling => use transpose strided convolution. @@ -113,31 +122,37 @@ def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight if groups == 1: w = w.transpose(0, 1) else: - w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw) + w = w.reshape(groups, out_channels // groups, + in_channels_per_group, kh, kw) w = w.transpose(1, 2) - w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw) + w = w.reshape(groups * in_channels_per_group, + out_channels // groups, kh, kw) px0 -= kw - 1 px1 -= kw - up py0 -= kh - 1 py1 -= kh - up pxt = max(min(-px0, -px1), 0) pyt = max(min(-py0, -py1), 0) - x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight)) - x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter) + x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[ + pyt, pxt], groups=groups, transpose=True, flip_weight=(not flip_weight)) + x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[ + px0+pxt, px1+pxt, py0+pyt, py1+pyt], gain=up**2, flip_filter=flip_filter) if down > 1: - x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) + x = upfirdn2d.upfirdn2d( + x=x, f=f, down=down, flip_filter=flip_filter) return x # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d. if up == 1 and down == 1: if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0: - return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight) + return _conv2d_wrapper(x=x, w=w, padding=[py0, px0], groups=groups, flip_weight=flip_weight) # Fallback: Generic reference implementation. - x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) + x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[ + px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) if down > 1: x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/torch_utils/ops/filtered_lrelu.py b/torch_utils/ops/filtered_lrelu.py index 6701cd72d1f0683a43f56b59ed3337dd3d6f0d3c..1a77f7e7a0ed0e951435cf6c7171d1baac8cf834 100644 --- a/torch_utils/ops/filtered_lrelu.py +++ b/torch_utils/ops/filtered_lrelu.py @@ -16,28 +16,33 @@ from .. import misc from . import upfirdn2d from . import bias_act -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- _plugin = None + def _init(): global _plugin if _plugin is None: _plugin = custom_ops.get_plugin( module_name='filtered_lrelu_plugin', - sources=['filtered_lrelu.cpp', 'filtered_lrelu_wr.cu', 'filtered_lrelu_rd.cu', 'filtered_lrelu_ns.cu'], + sources=['filtered_lrelu.cpp', 'filtered_lrelu_wr.cu', + 'filtered_lrelu_rd.cu', 'filtered_lrelu_ns.cu'], headers=['filtered_lrelu.h', 'filtered_lrelu.cu'], source_dir=os.path.dirname(__file__), - extra_cuda_cflags=['--use_fast_math', '--allow-unsupported-compiler'], + extra_cuda_cflags=['--use_fast_math', + '--allow-unsupported-compiler'], ) return True + def _get_filter_size(f): if f is None: return 1, 1 assert isinstance(f, torch.Tensor) assert 1 <= f.ndim <= 2 - return f.shape[-1], f.shape[0] # width, height + return f.shape[-1], f.shape[0] # width, height + def _parse_padding(padding): if isinstance(padding, int): @@ -51,7 +56,8 @@ def _parse_padding(padding): px0, px1, py0, py1 = padding return px0, px1, py0, py1 -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def filtered_lrelu(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False, impl='cuda'): r"""Filtered leaky ReLU for a batch of 2D images. @@ -115,7 +121,8 @@ def filtered_lrelu(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np return _filtered_lrelu_cuda(up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter).apply(x, fu, fd, b, None, 0, 0) return _filtered_lrelu_ref(x, fu=fu, fd=fd, b=b, up=up, down=down, padding=padding, gain=gain, slope=slope, clamp=clamp, flip_filter=flip_filter) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def _filtered_lrelu_ref(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False): @@ -138,24 +145,33 @@ def _filtered_lrelu_ref(x, fu=None, fd=None, b=None, up=1, down=1, padding=0, ga # Calculate output size. batch_size, channels, in_h, in_w = x.shape in_dtype = x.dtype - out_w = (in_w * up + (px0 + px1) - (fu_w - 1) - (fd_w - 1) + (down - 1)) // down - out_h = (in_h * up + (py0 + py1) - (fu_h - 1) - (fd_h - 1) + (down - 1)) // down + out_w = (in_w * up + (px0 + px1) - (fu_w - 1) - + (fd_w - 1) + (down - 1)) // down + out_h = (in_h * up + (py0 + py1) - (fu_h - 1) - + (fd_h - 1) + (down - 1)) // down # Compute using existing ops. - x = bias_act.bias_act(x=x, b=b) # Apply bias. - x = upfirdn2d.upfirdn2d(x=x, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample. - x = bias_act.bias_act(x=x, act='lrelu', alpha=slope, gain=gain, clamp=clamp) # Bias, leaky ReLU, clamp. - x = upfirdn2d.upfirdn2d(x=x, f=fd, down=down, flip_filter=flip_filter) # Downsample. + x = bias_act.bias_act(x=x, b=b) # Apply bias. + # Upsample. + x = upfirdn2d.upfirdn2d(x=x, f=fu, up=up, padding=[ + px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) + # Bias, leaky ReLU, clamp. + x = bias_act.bias_act(x=x, act='lrelu', alpha=slope, + gain=gain, clamp=clamp) + # Downsample. + x = upfirdn2d.upfirdn2d(x=x, f=fd, down=down, flip_filter=flip_filter) # Check output shape & dtype. misc.assert_shape(x, [batch_size, channels, out_h, out_w]) assert x.dtype == in_dtype return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + _filtered_lrelu_cuda_cache = dict() + def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, clamp=None, flip_filter=False): """Fast CUDA implementation of `filtered_lrelu()` using custom ops. """ @@ -177,7 +193,7 @@ def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, cl # Forward op. class FilteredLReluCuda(torch.autograd.Function): @staticmethod - def forward(ctx, x, fu, fd, b, si, sx, sy): # pylint: disable=arguments-differ + def forward(ctx, x, fu, fd, b, si, sx, sy): # pylint: disable=arguments-differ assert isinstance(x, torch.Tensor) and x.ndim == 4 # Replace empty up/downsample kernels with full 1x1 kernels (faster than separable). @@ -203,30 +219,41 @@ def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, cl b = torch.zeros([x.shape[1]], dtype=x.dtype, device=x.device) # Construct internal sign tensor only if gradients are needed. - write_signs = (si.numel() == 0) and (x.requires_grad or b.requires_grad) + write_signs = (si.numel() == 0) and ( + x.requires_grad or b.requires_grad) # Warn if input storage strides are not in decreasing order due to e.g. channels-last layout. strides = [x.stride(i) for i in range(x.ndim) if x.size(i) > 1] if any(a < b for a, b in zip(strides[:-1], strides[1:])): - warnings.warn("low-performance memory layout detected in filtered_lrelu input", RuntimeWarning) + warnings.warn( + "low-performance memory layout detected in filtered_lrelu input", RuntimeWarning) # Call C++/Cuda plugin if datatype is supported. if x.dtype in [torch.float16, torch.float32]: if torch.cuda.current_stream(x.device) != torch.cuda.default_stream(x.device): - warnings.warn("filtered_lrelu called with non-default cuda stream but concurrent execution is not supported", RuntimeWarning) - y, so, return_code = _plugin.filtered_lrelu(x, fu, fd, b, si, up, down, px0, px1, py0, py1, sx, sy, gain, slope, clamp, flip_filter, write_signs) + warnings.warn( + "filtered_lrelu called with non-default cuda stream but concurrent execution is not supported", RuntimeWarning) + y, so, return_code = _plugin.filtered_lrelu( + x, fu, fd, b, si, up, down, px0, px1, py0, py1, sx, sy, gain, slope, clamp, flip_filter, write_signs) else: return_code = -1 # No Cuda kernel found? Fall back to generic implementation. Still more memory efficient than the reference implementation because # only the bit-packed sign tensor is retained for gradient computation. if return_code < 0: - warnings.warn("filtered_lrelu called with parameters that have no optimized CUDA kernel, using generic fallback", RuntimeWarning) - - y = x.add(b.unsqueeze(-1).unsqueeze(-1)) # Add bias. - y = upfirdn2d.upfirdn2d(x=y, f=fu, up=up, padding=[px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) # Upsample. - so = _plugin.filtered_lrelu_act_(y, si, sx, sy, gain, slope, clamp, write_signs) # Activation function and sign handling. Modifies y in-place. - y = upfirdn2d.upfirdn2d(x=y, f=fd, down=down, flip_filter=flip_filter) # Downsample. + warnings.warn( + "filtered_lrelu called with parameters that have no optimized CUDA kernel, using generic fallback", RuntimeWarning) + + y = x.add(b.unsqueeze(-1).unsqueeze(-1)) # Add bias. + # Upsample. + y = upfirdn2d.upfirdn2d(x=y, f=fu, up=up, padding=[ + px0, px1, py0, py1], gain=up**2, flip_filter=flip_filter) + # Activation function and sign handling. Modifies y in-place. + so = _plugin.filtered_lrelu_act_( + y, si, sx, sy, gain, slope, clamp, write_signs) + # Downsample. + y = upfirdn2d.upfirdn2d( + x=y, f=fd, down=down, flip_filter=flip_filter) # Prepare for gradient computation. ctx.save_for_backward(fu, fd, (si if si.numel() else so)) @@ -236,18 +263,23 @@ def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, cl return y @staticmethod - def backward(ctx, dy): # pylint: disable=arguments-differ + def backward(ctx, dy): # pylint: disable=arguments-differ fu, fd, si = ctx.saved_tensors _, _, xh, xw = ctx.x_shape _, _, yh, yw = ctx.y_shape sx, sy = ctx.s_ofs - dx = None # 0 - dfu = None; assert not ctx.needs_input_grad[1] - dfd = None; assert not ctx.needs_input_grad[2] - db = None # 3 - dsi = None; assert not ctx.needs_input_grad[4] - dsx = None; assert not ctx.needs_input_grad[5] - dsy = None; assert not ctx.needs_input_grad[6] + dx = None # 0 + dfu = None + assert not ctx.needs_input_grad[1] + dfd = None + assert not ctx.needs_input_grad[2] + db = None # 3 + dsi = None + assert not ctx.needs_input_grad[4] + dsx = None + assert not ctx.needs_input_grad[5] + dsy = None + assert not ctx.needs_input_grad[6] if ctx.needs_input_grad[0] or ctx.needs_input_grad[3]: pp = [ @@ -259,8 +291,9 @@ def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, cl gg = gain * (up ** 2) / (down ** 2) ff = (not flip_filter) sx = sx - (fu.shape[-1] - 1) + px0 - sy = sy - (fu.shape[0] - 1) + py0 - dx = _filtered_lrelu_cuda(up=down, down=up, padding=pp, gain=gg, slope=slope, clamp=None, flip_filter=ff).apply(dy, fd, fu, None, si, sx, sy) + sy = sy - (fu.shape[0] - 1) + py0 + dx = _filtered_lrelu_cuda(up=down, down=up, padding=pp, gain=gg, slope=slope, + clamp=None, flip_filter=ff).apply(dy, fd, fu, None, si, sx, sy) if ctx.needs_input_grad[3]: db = dx.sum([0, 2, 3]) @@ -271,4 +304,4 @@ def _filtered_lrelu_cuda(up=1, down=1, padding=0, gain=np.sqrt(2), slope=0.2, cl _filtered_lrelu_cuda_cache[key] = FilteredLReluCuda return FilteredLReluCuda -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/torch_utils/ops/fma.py b/torch_utils/ops/fma.py index 51a45dfa0829987e8ee5214663e068cb3af2a8b9..5434c1845d1f9601277a1257fbd9907a2382ae73 100644 --- a/torch_utils/ops/fma.py +++ b/torch_utils/ops/fma.py @@ -10,23 +10,25 @@ import torch -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- -def fma(a, b, c): # => a * b + c + +def fma(a, b, c): # => a * b + c return _FusedMultiplyAdd.apply(a, b, c) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + -class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c +class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c @staticmethod - def forward(ctx, a, b, c): # pylint: disable=arguments-differ + def forward(ctx, a, b, c): # pylint: disable=arguments-differ out = torch.addcmul(c, a, b) ctx.save_for_backward(a, b) ctx.c_shape = c.shape return out @staticmethod - def backward(ctx, dout): # pylint: disable=arguments-differ + def backward(ctx, dout): # pylint: disable=arguments-differ a, b = ctx.saved_tensors c_shape = ctx.c_shape da = None @@ -44,12 +46,14 @@ class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c return da, db, dc -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _unbroadcast(x, shape): extra_dims = x.ndim - len(shape) assert extra_dims >= 0 - dim = [i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)] + dim = [i for i in range(x.ndim) if x.shape[i] > 1 and ( + i < extra_dims or shape[i - extra_dims] == 1)] if len(dim): x = x.sum(dim=dim, keepdim=True) if extra_dims: @@ -57,4 +61,4 @@ def _unbroadcast(x, shape): assert x.shape == shape return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/torch_utils/ops/grid_sample_gradfix.py b/torch_utils/ops/grid_sample_gradfix.py index 979ee831b232c68b8c271be9e376c70c57a31b02..5031a7720cbe25b68946b93928f41dc9fbeac0d5 100644 --- a/torch_utils/ops/grid_sample_gradfix.py +++ b/torch_utils/ops/grid_sample_gradfix.py @@ -17,40 +17,46 @@ import torch # pylint: disable=arguments-differ # pylint: disable=protected-access -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- enabled = False # Enable the custom op by setting this to true. -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def grid_sample(input, grid): if _should_use_custom_op(): return _GridSample2dForward.apply(input, grid) return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _should_use_custom_op(): return enabled -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class _GridSample2dForward(torch.autograd.Function): @staticmethod def forward(ctx, input, grid): assert input.ndim == 4 assert grid.ndim == 4 - output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False) + output = torch.nn.functional.grid_sample( + input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False) ctx.save_for_backward(input, grid) return output @staticmethod def backward(ctx, grad_output): input, grid = ctx.saved_tensors - grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid) + grad_input, grad_grid = _GridSample2dBackward.apply( + grad_output, input, grid) return grad_input, grad_grid -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class _GridSample2dBackward(torch.autograd.Function): @staticmethod @@ -62,16 +68,17 @@ class _GridSample2dBackward(torch.autograd.Function): @staticmethod def backward(ctx, grad2_grad_input, grad2_grad_grid): - _ = grad2_grad_grid # unused + _ = grad2_grad_grid # unused grid, = ctx.saved_tensors grad2_grad_output = None grad2_input = None grad2_grid = None if ctx.needs_input_grad[0]: - grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid) + grad2_grad_output = _GridSample2dForward.apply( + grad2_grad_input, grid) assert not ctx.needs_input_grad[2] return grad2_grad_output, grad2_input, grad2_grid -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/torch_utils/ops/upfirdn2d.py b/torch_utils/ops/upfirdn2d.py index 394f746e0096ececc7b6c83daf75c21cb808385f..2cccba1871e4ba8c84773a845544ff8fdf447c8c 100644 --- a/torch_utils/ops/upfirdn2d.py +++ b/torch_utils/ops/upfirdn2d.py @@ -16,10 +16,11 @@ from .. import custom_ops from .. import misc from . import conv2d_gradfix -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- _plugin = None + def _init(): global _plugin if _plugin is None: @@ -28,10 +29,12 @@ def _init(): sources=['upfirdn2d.cpp', 'upfirdn2d.cu'], headers=['upfirdn2d.h'], source_dir=os.path.dirname(__file__), - extra_cuda_cflags=['--use_fast_math', '--allow-unsupported-compiler'], + extra_cuda_cflags=['--use_fast_math', + '--allow-unsupported-compiler'], ) return True + def _parse_scaling(scaling): if isinstance(scaling, int): scaling = [scaling, scaling] @@ -41,6 +44,7 @@ def _parse_scaling(scaling): assert sx >= 1 and sy >= 1 return sx, sy + def _parse_padding(padding): if isinstance(padding, int): padding = [padding, padding] @@ -52,6 +56,7 @@ def _parse_padding(padding): padx0, padx1, pady0, pady1 = padding return padx0, padx1, pady0, pady1 + def _get_filter_size(f): if f is None: return 1, 1 @@ -65,7 +70,8 @@ def _get_filter_size(f): assert fw >= 1 and fh >= 1 return fw, fh -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None): r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`. @@ -113,7 +119,8 @@ def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=Fals f = f.to(device=device) return f -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'): r"""Pad, upsample, filter, and downsample a batch of 2D images. @@ -161,7 +168,8 @@ def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cu return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f) return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1): @@ -189,8 +197,10 @@ def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1): x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx]) # Pad or crop. - x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)]) - x = x[:, :, max(-pady0, 0) : x.shape[2] - max(-pady1, 0), max(-padx0, 0) : x.shape[3] - max(-padx1, 0)] + x = torch.nn.functional.pad( + x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)]) + x = x[:, :, max(-pady0, 0): x.shape[2] - max(-pady1, 0), + max(-padx0, 0): x.shape[3] - max(-padx1, 0)] # Setup filter. f = f * (gain ** (f.ndim / 2)) @@ -203,17 +213,21 @@ def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1): if f.ndim == 4: x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels) else: - x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels) - x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels) + x = conv2d_gradfix.conv2d( + input=x, weight=f.unsqueeze(2), groups=num_channels) + x = conv2d_gradfix.conv2d( + input=x, weight=f.unsqueeze(3), groups=num_channels) # Downsample by throwing away pixels. x = x[:, :, ::downy, ::downx] return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + _upfirdn2d_cuda_cache = dict() + def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1): """Fast CUDA implementation of `upfirdn2d()` using custom ops. """ @@ -223,32 +237,37 @@ def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1): padx0, padx1, pady0, pady1 = _parse_padding(padding) # Lookup from cache. - key = (upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain) + key = (upx, upy, downx, downy, padx0, padx1, + pady0, pady1, flip_filter, gain) if key in _upfirdn2d_cuda_cache: return _upfirdn2d_cuda_cache[key] # Forward op. class Upfirdn2dCuda(torch.autograd.Function): @staticmethod - def forward(ctx, x, f): # pylint: disable=arguments-differ + def forward(ctx, x, f): # pylint: disable=arguments-differ assert isinstance(x, torch.Tensor) and x.ndim == 4 if f is None: f = torch.ones([1, 1], dtype=torch.float32, device=x.device) if f.ndim == 1 and f.shape[0] == 1: - f = f.square().unsqueeze(0) # Convert separable-1 into full-1x1. + # Convert separable-1 into full-1x1. + f = f.square().unsqueeze(0) assert isinstance(f, torch.Tensor) and f.ndim in [1, 2] y = x if f.ndim == 2: - y = _plugin.upfirdn2d(y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain) + y = _plugin.upfirdn2d( + y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain) else: - y = _plugin.upfirdn2d(y, f.unsqueeze(0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, 1.0) - y = _plugin.upfirdn2d(y, f.unsqueeze(1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, gain) + y = _plugin.upfirdn2d(y, f.unsqueeze( + 0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, 1.0) + y = _plugin.upfirdn2d(y, f.unsqueeze( + 1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, gain) ctx.save_for_backward(f) ctx.x_shape = x.shape return y @staticmethod - def backward(ctx, dy): # pylint: disable=arguments-differ + def backward(ctx, dy): # pylint: disable=arguments-differ f, = ctx.saved_tensors _, _, ih, iw = ctx.x_shape _, _, oh, ow = dy.shape @@ -263,7 +282,8 @@ def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1): df = None if ctx.needs_input_grad[0]: - dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(not flip_filter), gain=gain).apply(dy, f) + dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=( + not flip_filter), gain=gain).apply(dy, f) assert not ctx.needs_input_grad[1] return dx, df @@ -272,7 +292,8 @@ def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1): _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda return Upfirdn2dCuda -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'): r"""Filter a batch of 2D images using the given 2D FIR filter. @@ -308,7 +329,8 @@ def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'): ] return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'): r"""Upsample a batch of 2D images using the given 2D FIR filter. @@ -347,7 +369,8 @@ def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'): ] return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'): r"""Downsample a batch of 2D images using the given 2D FIR filter. @@ -386,4 +409,4 @@ def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda' ] return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/torch_utils/persistence.py b/torch_utils/persistence.py index f90ce85e8ace0f44e839158b22c5790de448d82d..d03055014ea6ba7e8ba475f79c91da4907fb6c0b 100644 --- a/torch_utils/persistence.py +++ b/torch_utils/persistence.py @@ -22,15 +22,16 @@ import uuid import types import dnnlib -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- -_version = 6 # internal version number -_decorators = set() # {decorator_class, ...} -_import_hooks = [] # [hook_function, ...] +_version = 6 # internal version number +_decorators = set() # {decorator_class, ...} +_import_hooks = [] # [hook_function, ...] _module_to_src_dict = dict() # {module: src, ...} _src_to_module_dict = dict() # {src: module, ...} -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def persistent_class(orig_class): r"""Class decorator that extends a given class to save its source code @@ -119,17 +120,19 @@ def persistent_class(orig_class): fields = list(super().__reduce__()) fields += [None] * max(3 - len(fields), 0) if fields[0] is not _reconstruct_persistent_obj: - meta = dict(type='class', version=_version, module_src=self._orig_module_src, class_name=self._orig_class_name, state=fields[2]) - fields[0] = _reconstruct_persistent_obj # reconstruct func - fields[1] = (meta,) # reconstruct args - fields[2] = None # state dict + meta = dict(type='class', version=_version, module_src=self._orig_module_src, + class_name=self._orig_class_name, state=fields[2]) + fields[0] = _reconstruct_persistent_obj # reconstruct func + fields[1] = (meta,) # reconstruct args + fields[2] = None # state dict return tuple(fields) Decorator.__name__ = orig_class.__name__ _decorators.add(Decorator) return Decorator -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def is_persistent(obj): r"""Test whether the given object or class is persistent, i.e., @@ -140,9 +143,10 @@ def is_persistent(obj): return True except TypeError: pass - return type(obj) in _decorators # pylint: disable=unidiomatic-typecheck + return type(obj) in _decorators # pylint: disable=unidiomatic-typecheck + +# ---------------------------------------------------------------------------- -#---------------------------------------------------------------------------- def import_hook(hook): r"""Register an import hook that is called whenever a persistent object @@ -174,7 +178,8 @@ def import_hook(hook): assert callable(hook) _import_hooks.append(hook) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _reconstruct_persistent_obj(meta): r"""Hook that is called internally by the `pickle` module to unpickle @@ -196,12 +201,13 @@ def _reconstruct_persistent_obj(meta): setstate = getattr(obj, '__setstate__', None) if callable(setstate): - setstate(meta.state) # pylint: disable=not-callable + setstate(meta.state) # pylint: disable=not-callable else: obj.__dict__.update(meta.state) return obj -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _module_to_src(module): r"""Query the source code of a given Python module. @@ -213,6 +219,7 @@ def _module_to_src(module): _src_to_module_dict[src] = module return src + def _src_to_module(src): r"""Get or create a Python module for the given source code. """ @@ -223,10 +230,11 @@ def _src_to_module(src): sys.modules[module_name] = module _module_to_src_dict[module] = src _src_to_module_dict[src] = module - exec(src, module.__dict__) # pylint: disable=exec-used + exec(src, module.__dict__) # pylint: disable=exec-used return module -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _check_pickleable(obj): r"""Check that the given object is pickleable, raising an exception if @@ -239,13 +247,14 @@ def _check_pickleable(obj): if isinstance(obj, dict): return [[recurse(x), recurse(y)] for x, y in obj.items()] if isinstance(obj, (str, int, float, bool, bytes, bytearray)): - return None # Python primitive types are pickleable. + return None # Python primitive types are pickleable. if f'{type(obj).__module__}.{type(obj).__name__}' in ['numpy.ndarray', 'torch.Tensor', 'torch.nn.parameter.Parameter']: - return None # NumPy arrays and PyTorch tensors are pickleable. + return None # NumPy arrays and PyTorch tensors are pickleable. if is_persistent(obj): - return None # Persistent objects are pickleable, by virtue of the constructor check. + # Persistent objects are pickleable, by virtue of the constructor check. + return None return obj with io.BytesIO() as f: pickle.dump(recurse(obj), f) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/torch_utils/pti.py b/torch_utils/pti.py new file mode 100644 index 0000000000000000000000000000000000000000..23be1ee0a101a1c6ce14db7f66673946a0f7d612 --- /dev/null +++ b/torch_utils/pti.py @@ -0,0 +1,89 @@ +import pickle +from PTI.utils.ImagesDataset import ImagesDataset, Image2Dataset +import torch +from PTI.utils.models_utils import load_old_G +from PTI.utils.alignment import align_face + +from PTI.training.coaches.single_id_coach import SingleIDCoach +from PTI.configs import global_config, paths_config +import dlib + +import os +from torchvision.transforms import transforms +from torch.utils.data import DataLoader +from string import ascii_uppercase +import sys +from pathlib import Path + +sys.path.append(".") +# sys.path.append('PTI/') +# sys.path.append('PTI/training/') + + +def run_PTI(img, run_name): + # os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' + # os.environ['CUDA_VISIBLE_DEVICES'] = global_config.cuda_visible_devices + + global_config.run_name = run_name + + global_config.pivotal_training_steps = 1 + global_config.training_step = 1 + + embedding_dir_path = f"{paths_config.embedding_base_dir}/{paths_config.input_data_id}/{paths_config.pti_results_keyword}" + os.makedirs(embedding_dir_path, exist_ok=True) + + # dataset = ImagesDataset(paths_config.input_data_path, transforms.Compose([ + # transforms.ToTensor(), + # transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])) + + G = load_old_G() + IMAGE_SIZE = 1024 + predictor = dlib.shape_predictor(paths_config.dlib) + aligned_image = align_face(img, predictor=predictor, output_size=IMAGE_SIZE) + img = aligned_image.resize([G.img_resolution, G.img_resolution]) + dataset = Image2Dataset(img) + + dataloader = DataLoader(dataset, batch_size=1, shuffle=False) + + coach = SingleIDCoach(dataloader, use_wandb=False) + + new_G, w_pivot = coach.train() + return new_G, w_pivot + + +def export_updated_pickle(new_G, out_path, run_name): + image_name = "customIMG" + + with open(paths_config.stylegan2_ada_ffhq, "rb") as f: + old_G = pickle.load(f)["G_ema"].cuda() + + embedding = Path(f"{paths_config.checkpoints_dir}/model_{run_name}_{image_name}.pt") + with open(embedding, "rb") as f_new: + new_G = torch.load(f_new).cuda() + + print("Exporting large updated pickle based off new generator and ffhq.pkl") + with open(paths_config.stylegan2_ada_ffhq, "rb") as f: + d = pickle.load(f) + old_G = d["G_ema"].cuda() # tensor + old_D = d["D"].eval().requires_grad_(False).cpu() + + tmp = {} + tmp["G"] = old_G.eval().requires_grad_(False).cpu() + tmp["G_ema"] = new_G.eval().requires_grad_(False).cpu() + tmp["D"] = old_D + tmp["training_set_kwargs"] = None + tmp["augment_pipe"] = None + + with open(out_path, "wb") as f: + pickle.dump(tmp, f) + # delete + + embedding.unlink() + + +# if __name__ == '__main__': +# from PIL import Image +# img = Image.open('PTI/test/test.jpg') +# new_G, w_pivot = run_PTI(img, use_wandb=False, use_multi_id_training=False) +# out_path = f'checkpoints/stylegan2_custom_512_pytorch.pkl' +# export_updated_pickle(new_G, out_path) diff --git a/torch_utils/training_stats.py b/torch_utils/training_stats.py index 5de4134f1943e7c3104bbc926b2abaf828626525..aa5837c2948372ecdb3e34076f4b3f4f42c81fef 100644 --- a/torch_utils/training_stats.py +++ b/torch_utils/training_stats.py @@ -18,18 +18,23 @@ import dnnlib from . import misc -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- -_num_moments = 3 # [num_scalars, sum_of_scalars, sum_of_squares] -_reduce_dtype = torch.float32 # Data type to use for initial per-tensor reduction. -_counter_dtype = torch.float64 # Data type to use for the internal counters. -_rank = 0 # Rank of the current process. -_sync_device = None # Device to use for multiprocess communication. None = single-process. -_sync_called = False # Has _sync() been called yet? -_counters = dict() # Running counters on each device, updated by report(): name => device => torch.Tensor -_cumulative = dict() # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor +_num_moments = 3 # [num_scalars, sum_of_scalars, sum_of_squares] +# Data type to use for initial per-tensor reduction. +_reduce_dtype = torch.float32 +_counter_dtype = torch.float64 # Data type to use for the internal counters. +_rank = 0 # Rank of the current process. +# Device to use for multiprocess communication. None = single-process. +_sync_device = None +_sync_called = False # Has _sync() been called yet? +# Running counters on each device, updated by report(): name => device => torch.Tensor +_counters = dict() +# Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor +_cumulative = dict() + +# ---------------------------------------------------------------------------- -#---------------------------------------------------------------------------- def init_multiprocessing(rank, sync_device): r"""Initializes `torch_utils.training_stats` for collecting statistics @@ -50,7 +55,8 @@ def init_multiprocessing(rank, sync_device): _rank = rank _sync_device = sync_device -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def report(name, value): @@ -98,7 +104,8 @@ def report(name, value): _counters[name][device].add_(moments) return value -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def report0(name, value): r"""Broadcasts the given set of scalars by the first process (`rank = 0`), @@ -108,7 +115,8 @@ def report0(name, value): report(name, value if _rank == 0 else []) return value -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class Collector: r"""Collects the scalars broadcasted by `report()` and `report0()` and @@ -130,6 +138,7 @@ class Collector: scalars were collected on a given round (default: True). """ + def __init__(self, regex='.*', keep_previous=True): self._regex = re.compile(regex) self._keep_previous = keep_previous @@ -161,7 +170,8 @@ class Collector: self._moments.clear() for name, cumulative in _sync(self.names()): if name not in self._cumulative: - self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype) + self._cumulative[name] = torch.zeros( + [_num_moments], dtype=_counter_dtype) delta = cumulative - self._cumulative[name] self._cumulative[name].copy_(cumulative) if float(delta[0]) != 0: @@ -174,7 +184,8 @@ class Collector: """ assert self._regex.fullmatch(name) if name not in self._moments: - self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype) + self._moments[name] = torch.zeros( + [_num_moments], dtype=_counter_dtype) return self._moments[name] def num(self, name): @@ -220,7 +231,8 @@ class Collector: """ stats = dnnlib.EasyDict() for name in self.names(): - stats[name] = dnnlib.EasyDict(num=self.num(name), mean=self.mean(name), std=self.std(name)) + stats[name] = dnnlib.EasyDict(num=self.num( + name), mean=self.mean(name), std=self.std(name)) return stats def __getitem__(self, name): @@ -229,7 +241,8 @@ class Collector: """ return self.mean(name) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _sync(names): r"""Synchronize the global cumulative counters across devices and @@ -244,7 +257,8 @@ def _sync(names): deltas = [] device = _sync_device if _sync_device is not None else torch.device('cpu') for name in names: - delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device) + delta = torch.zeros( + [_num_moments], dtype=_counter_dtype, device=device) for counter in _counters[name].values(): delta.add_(counter.to(device)) counter.copy_(torch.zeros_like(counter)) @@ -259,10 +273,11 @@ def _sync(names): deltas = deltas.cpu() for idx, name in enumerate(names): if name not in _cumulative: - _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype) + _cumulative[name] = torch.zeros( + [_num_moments], dtype=_counter_dtype) _cumulative[name].add_(deltas[idx]) # Return name-value pairs. return [(name, _cumulative[name]) for name in names] -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/training/augment.py b/training/augment.py index d68e35c96ef9fa9c18bbb6668f03b9463098710e..8067f4e3fec058c9025edaa7a9a0442afe859ae5 100644 --- a/training/augment.py +++ b/training/augment.py @@ -20,7 +20,7 @@ from torch_utils.ops import upfirdn2d from torch_utils.ops import grid_sample_gradfix from torch_utils.ops import conv2d_gradfix -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Coefficients of various wavelet decomposition low-pass filters. wavelets = { @@ -42,9 +42,10 @@ wavelets = { 'sym8': [-0.0033824159510061256, -0.0005421323317911481, 0.03169508781149298, 0.007607487324917605, -0.1432942383508097, -0.061273359067658524, 0.4813596512583722, 0.7771857517005235, 0.3644418948353314, -0.05194583810770904, -0.027219029917056003, 0.049137179673607506, 0.003808752013890615, -0.01495225833704823, -0.0003029205147213668, 0.0018899503327594609], } -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Helpers for constructing transformation matrices. + def matrix(*rows, device=None): assert all(len(row) == len(rows[0]) for row in rows) elems = [x for row in rows for x in row] @@ -52,9 +53,11 @@ def matrix(*rows, device=None): if len(ref) == 0: return misc.constant(np.asarray(rows), device=device) assert device is None or device == ref[0].device - elems = [x if isinstance(x, torch.Tensor) else misc.constant(x, shape=ref[0].shape, device=ref[0].device) for x in elems] + elems = [x if isinstance(x, torch.Tensor) else misc.constant( + x, shape=ref[0].shape, device=ref[0].device) for x in elems] return torch.stack(elems, dim=-1).reshape(ref[0].shape + (len(rows), -1)) + def translate2d(tx, ty, **kwargs): return matrix( [1, 0, tx], @@ -62,6 +65,7 @@ def translate2d(tx, ty, **kwargs): [0, 0, 1], **kwargs) + def translate3d(tx, ty, tz, **kwargs): return matrix( [1, 0, 0, tx], @@ -70,6 +74,7 @@ def translate3d(tx, ty, tz, **kwargs): [0, 0, 0, 1], **kwargs) + def scale2d(sx, sy, **kwargs): return matrix( [sx, 0, 0], @@ -77,6 +82,7 @@ def scale2d(sx, sy, **kwargs): [0, 0, 1], **kwargs) + def scale3d(sx, sy, sz, **kwargs): return matrix( [sx, 0, 0, 0], @@ -85,6 +91,7 @@ def scale3d(sx, sy, sz, **kwargs): [0, 0, 0, 1], **kwargs) + def rotate2d(theta, **kwargs): return matrix( [torch.cos(theta), torch.sin(-theta), 0], @@ -92,9 +99,14 @@ def rotate2d(theta, **kwargs): [0, 0, 1], **kwargs) + def rotate3d(v, theta, **kwargs): - vx = v[..., 0]; vy = v[..., 1]; vz = v[..., 2] - s = torch.sin(theta); c = torch.cos(theta); cc = 1 - c + vx = v[..., 0] + vy = v[..., 1] + vz = v[..., 2] + s = torch.sin(theta) + c = torch.cos(theta) + cc = 1 - c return matrix( [vx*vx*cc+c, vx*vy*cc-vz*s, vx*vz*cc+vy*s, 0], [vy*vx*cc+vz*s, vy*vy*cc+c, vy*vz*cc-vx*s, 0], @@ -102,93 +114,131 @@ def rotate3d(v, theta, **kwargs): [0, 0, 0, 1], **kwargs) + def translate2d_inv(tx, ty, **kwargs): return translate2d(-tx, -ty, **kwargs) + def scale2d_inv(sx, sy, **kwargs): return scale2d(1 / sx, 1 / sy, **kwargs) + def rotate2d_inv(theta, **kwargs): return rotate2d(-theta, **kwargs) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- # Versatile image augmentation pipeline from the paper # "Training Generative Adversarial Networks with Limited Data". # # All augmentations are disabled by default; individual augmentations can # be enabled by setting their probability multipliers to 1. + @persistence.persistent_class class AugmentPipe(torch.nn.Module): def __init__(self, - xflip=0, rotate90=0, xint=0, xint_max=0.125, - scale=0, rotate=0, aniso=0, xfrac=0, scale_std=0.2, rotate_max=1, aniso_std=0.2, xfrac_std=0.125, - brightness=0, contrast=0, lumaflip=0, hue=0, saturation=0, brightness_std=0.2, contrast_std=0.5, hue_max=1, saturation_std=1, - imgfilter=0, imgfilter_bands=[1,1,1,1], imgfilter_std=1, - noise=0, cutout=0, noise_std=0.1, cutout_size=0.5, - ): + xflip=0, rotate90=0, xint=0, xint_max=0.125, + scale=0, rotate=0, aniso=0, xfrac=0, scale_std=0.2, rotate_max=1, aniso_std=0.2, xfrac_std=0.125, + brightness=0, contrast=0, lumaflip=0, hue=0, saturation=0, brightness_std=0.2, contrast_std=0.5, hue_max=1, saturation_std=1, + imgfilter=0, imgfilter_bands=[1, 1, 1, 1], imgfilter_std=1, + noise=0, cutout=0, noise_std=0.1, cutout_size=0.5, + ): super().__init__() - self.register_buffer('p', torch.ones([])) # Overall multiplier for augmentation probability. + # Overall multiplier for augmentation probability. + self.register_buffer('p', torch.ones([])) # Pixel blitting. - self.xflip = float(xflip) # Probability multiplier for x-flip. - self.rotate90 = float(rotate90) # Probability multiplier for 90 degree rotations. - self.xint = float(xint) # Probability multiplier for integer translation. - self.xint_max = float(xint_max) # Range of integer translation, relative to image dimensions. + # Probability multiplier for x-flip. + self.xflip = float(xflip) + # Probability multiplier for 90 degree rotations. + self.rotate90 = float(rotate90) + # Probability multiplier for integer translation. + self.xint = float(xint) + # Range of integer translation, relative to image dimensions. + self.xint_max = float(xint_max) # General geometric transformations. - self.scale = float(scale) # Probability multiplier for isotropic scaling. - self.rotate = float(rotate) # Probability multiplier for arbitrary rotation. - self.aniso = float(aniso) # Probability multiplier for anisotropic scaling. - self.xfrac = float(xfrac) # Probability multiplier for fractional translation. - self.scale_std = float(scale_std) # Log2 standard deviation of isotropic scaling. - self.rotate_max = float(rotate_max) # Range of arbitrary rotation, 1 = full circle. - self.aniso_std = float(aniso_std) # Log2 standard deviation of anisotropic scaling. - self.xfrac_std = float(xfrac_std) # Standard deviation of frational translation, relative to image dimensions. + # Probability multiplier for isotropic scaling. + self.scale = float(scale) + # Probability multiplier for arbitrary rotation. + self.rotate = float(rotate) + # Probability multiplier for anisotropic scaling. + self.aniso = float(aniso) + # Probability multiplier for fractional translation. + self.xfrac = float(xfrac) + # Log2 standard deviation of isotropic scaling. + self.scale_std = float(scale_std) + # Range of arbitrary rotation, 1 = full circle. + self.rotate_max = float(rotate_max) + # Log2 standard deviation of anisotropic scaling. + self.aniso_std = float(aniso_std) + # Standard deviation of frational translation, relative to image dimensions. + self.xfrac_std = float(xfrac_std) # Color transformations. - self.brightness = float(brightness) # Probability multiplier for brightness. - self.contrast = float(contrast) # Probability multiplier for contrast. - self.lumaflip = float(lumaflip) # Probability multiplier for luma flip. - self.hue = float(hue) # Probability multiplier for hue rotation. - self.saturation = float(saturation) # Probability multiplier for saturation. - self.brightness_std = float(brightness_std) # Standard deviation of brightness. - self.contrast_std = float(contrast_std) # Log2 standard deviation of contrast. - self.hue_max = float(hue_max) # Range of hue rotation, 1 = full circle. - self.saturation_std = float(saturation_std) # Log2 standard deviation of saturation. + # Probability multiplier for brightness. + self.brightness = float(brightness) + # Probability multiplier for contrast. + self.contrast = float(contrast) + # Probability multiplier for luma flip. + self.lumaflip = float(lumaflip) + # Probability multiplier for hue rotation. + self.hue = float(hue) + # Probability multiplier for saturation. + self.saturation = float(saturation) + # Standard deviation of brightness. + self.brightness_std = float(brightness_std) + # Log2 standard deviation of contrast. + self.contrast_std = float(contrast_std) + # Range of hue rotation, 1 = full circle. + self.hue_max = float(hue_max) + # Log2 standard deviation of saturation. + self.saturation_std = float(saturation_std) # Image-space filtering. - self.imgfilter = float(imgfilter) # Probability multiplier for image-space filtering. - self.imgfilter_bands = list(imgfilter_bands) # Probability multipliers for individual frequency bands. - self.imgfilter_std = float(imgfilter_std) # Log2 standard deviation of image-space filter amplification. + # Probability multiplier for image-space filtering. + self.imgfilter = float(imgfilter) + # Probability multipliers for individual frequency bands. + self.imgfilter_bands = list(imgfilter_bands) + # Log2 standard deviation of image-space filter amplification. + self.imgfilter_std = float(imgfilter_std) # Image-space corruptions. - self.noise = float(noise) # Probability multiplier for additive RGB noise. - self.cutout = float(cutout) # Probability multiplier for cutout. - self.noise_std = float(noise_std) # Standard deviation of additive RGB noise. - self.cutout_size = float(cutout_size) # Size of the cutout rectangle, relative to image dimensions. + # Probability multiplier for additive RGB noise. + self.noise = float(noise) + # Probability multiplier for cutout. + self.cutout = float(cutout) + # Standard deviation of additive RGB noise. + self.noise_std = float(noise_std) + # Size of the cutout rectangle, relative to image dimensions. + self.cutout_size = float(cutout_size) # Setup orthogonal lowpass filter for geometric augmentations. - self.register_buffer('Hz_geom', upfirdn2d.setup_filter(wavelets['sym6'])) + self.register_buffer( + 'Hz_geom', upfirdn2d.setup_filter(wavelets['sym6'])) # Construct filter bank for image-space filtering. Hz_lo = np.asarray(wavelets['sym2']) # H(z) - Hz_hi = Hz_lo * ((-1) ** np.arange(Hz_lo.size)) # H(-z) + Hz_hi = Hz_lo * ((-1) ** np.arange(Hz_lo.size)) # H(-z) Hz_lo2 = np.convolve(Hz_lo, Hz_lo[::-1]) / 2 # H(z) * H(z^-1) / 2 Hz_hi2 = np.convolve(Hz_hi, Hz_hi[::-1]) / 2 # H(-z) * H(-z^-1) / 2 Hz_fbank = np.eye(4, 1) # Bandpass(H(z), b_i) for i in range(1, Hz_fbank.shape[0]): - Hz_fbank = np.dstack([Hz_fbank, np.zeros_like(Hz_fbank)]).reshape(Hz_fbank.shape[0], -1)[:, :-1] + Hz_fbank = np.dstack([Hz_fbank, np.zeros_like(Hz_fbank)]).reshape( + Hz_fbank.shape[0], -1)[:, :-1] Hz_fbank = scipy.signal.convolve(Hz_fbank, [Hz_lo2]) - Hz_fbank[i, (Hz_fbank.shape[1] - Hz_hi2.size) // 2 : (Hz_fbank.shape[1] + Hz_hi2.size) // 2] += Hz_hi2 - self.register_buffer('Hz_fbank', torch.as_tensor(Hz_fbank, dtype=torch.float32)) + Hz_fbank[i, (Hz_fbank.shape[1] - Hz_hi2.size) // + 2: (Hz_fbank.shape[1] + Hz_hi2.size) // 2] += Hz_hi2 + self.register_buffer('Hz_fbank', torch.as_tensor( + Hz_fbank, dtype=torch.float32)) def forward(self, images, debug_percentile=None): assert isinstance(images, torch.Tensor) and images.ndim == 4 batch_size, num_channels, height, width = images.shape device = images.device if debug_percentile is not None: - debug_percentile = torch.as_tensor(debug_percentile, dtype=torch.float32, device=device) + debug_percentile = torch.as_tensor( + debug_percentile, dtype=torch.float32, device=device) # ------------------------------------- # Select parameters for pixel blitting. @@ -201,7 +251,8 @@ class AugmentPipe(torch.nn.Module): # Apply x-flip with probability (xflip * strength). if self.xflip > 0: i = torch.floor(torch.rand([batch_size], device=device) * 2) - i = torch.where(torch.rand([batch_size], device=device) < self.xflip * self.p, i, torch.zeros_like(i)) + i = torch.where(torch.rand( + [batch_size], device=device) < self.xflip * self.p, i, torch.zeros_like(i)) if debug_percentile is not None: i = torch.full_like(i, torch.floor(debug_percentile * 2)) G_inv = G_inv @ scale2d_inv(1 - 2 * i, 1) @@ -209,18 +260,23 @@ class AugmentPipe(torch.nn.Module): # Apply 90 degree rotations with probability (rotate90 * strength). if self.rotate90 > 0: i = torch.floor(torch.rand([batch_size], device=device) * 4) - i = torch.where(torch.rand([batch_size], device=device) < self.rotate90 * self.p, i, torch.zeros_like(i)) + i = torch.where(torch.rand( + [batch_size], device=device) < self.rotate90 * self.p, i, torch.zeros_like(i)) if debug_percentile is not None: i = torch.full_like(i, torch.floor(debug_percentile * 4)) G_inv = G_inv @ rotate2d_inv(-np.pi / 2 * i) # Apply integer translation with probability (xint * strength). if self.xint > 0: - t = (torch.rand([batch_size, 2], device=device) * 2 - 1) * self.xint_max - t = torch.where(torch.rand([batch_size, 1], device=device) < self.xint * self.p, t, torch.zeros_like(t)) + t = (torch.rand([batch_size, 2], device=device) + * 2 - 1) * self.xint_max + t = torch.where(torch.rand( + [batch_size, 1], device=device) < self.xint * self.p, t, torch.zeros_like(t)) if debug_percentile is not None: - t = torch.full_like(t, (debug_percentile * 2 - 1) * self.xint_max) - G_inv = G_inv @ translate2d_inv(torch.round(t[:,0] * width), torch.round(t[:,1] * height)) + t = torch.full_like( + t, (debug_percentile * 2 - 1) * self.xint_max) + G_inv = G_inv @ translate2d_inv(torch.round( + t[:, 0] * width), torch.round(t[:, 1] * height)) # -------------------------------------------------------- # Select parameters for general geometric transformations. @@ -228,44 +284,58 @@ class AugmentPipe(torch.nn.Module): # Apply isotropic scaling with probability (scale * strength). if self.scale > 0: - s = torch.exp2(torch.randn([batch_size], device=device) * self.scale_std) - s = torch.where(torch.rand([batch_size], device=device) < self.scale * self.p, s, torch.ones_like(s)) + s = torch.exp2(torch.randn( + [batch_size], device=device) * self.scale_std) + s = torch.where(torch.rand( + [batch_size], device=device) < self.scale * self.p, s, torch.ones_like(s)) if debug_percentile is not None: - s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.scale_std)) + s = torch.full_like(s, torch.exp2(torch.erfinv( + debug_percentile * 2 - 1) * self.scale_std)) G_inv = G_inv @ scale2d_inv(s, s) # Apply pre-rotation with probability p_rot. - p_rot = 1 - torch.sqrt((1 - self.rotate * self.p).clamp(0, 1)) # P(pre OR post) = p + # P(pre OR post) = p + p_rot = 1 - torch.sqrt((1 - self.rotate * self.p).clamp(0, 1)) if self.rotate > 0: - theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max - theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta)) + theta = (torch.rand([batch_size], device=device) + * 2 - 1) * np.pi * self.rotate_max + theta = torch.where(torch.rand( + [batch_size], device=device) < p_rot, theta, torch.zeros_like(theta)) if debug_percentile is not None: - theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.rotate_max) - G_inv = G_inv @ rotate2d_inv(-theta) # Before anisotropic scaling. + theta = torch.full_like( + theta, (debug_percentile * 2 - 1) * np.pi * self.rotate_max) + G_inv = G_inv @ rotate2d_inv(-theta) # Before anisotropic scaling. # Apply anisotropic scaling with probability (aniso * strength). if self.aniso > 0: - s = torch.exp2(torch.randn([batch_size], device=device) * self.aniso_std) - s = torch.where(torch.rand([batch_size], device=device) < self.aniso * self.p, s, torch.ones_like(s)) + s = torch.exp2(torch.randn( + [batch_size], device=device) * self.aniso_std) + s = torch.where(torch.rand( + [batch_size], device=device) < self.aniso * self.p, s, torch.ones_like(s)) if debug_percentile is not None: - s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.aniso_std)) + s = torch.full_like(s, torch.exp2(torch.erfinv( + debug_percentile * 2 - 1) * self.aniso_std)) G_inv = G_inv @ scale2d_inv(s, 1 / s) # Apply post-rotation with probability p_rot. if self.rotate > 0: - theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max - theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta)) + theta = (torch.rand([batch_size], device=device) + * 2 - 1) * np.pi * self.rotate_max + theta = torch.where(torch.rand( + [batch_size], device=device) < p_rot, theta, torch.zeros_like(theta)) if debug_percentile is not None: theta = torch.zeros_like(theta) - G_inv = G_inv @ rotate2d_inv(-theta) # After anisotropic scaling. + G_inv = G_inv @ rotate2d_inv(-theta) # After anisotropic scaling. # Apply fractional translation with probability (xfrac * strength). if self.xfrac > 0: t = torch.randn([batch_size, 2], device=device) * self.xfrac_std - t = torch.where(torch.rand([batch_size, 1], device=device) < self.xfrac * self.p, t, torch.zeros_like(t)) + t = torch.where(torch.rand( + [batch_size, 1], device=device) < self.xfrac * self.p, t, torch.zeros_like(t)) if debug_percentile is not None: - t = torch.full_like(t, torch.erfinv(debug_percentile * 2 - 1) * self.xfrac_std) - G_inv = G_inv @ translate2d_inv(t[:,0] * width, t[:,1] * height) + t = torch.full_like(t, torch.erfinv( + debug_percentile * 2 - 1) * self.xfrac_std) + G_inv = G_inv @ translate2d_inv(t[:, 0] * width, t[:, 1] * height) # ---------------------------------- # Execute geometric transformations. @@ -277,33 +347,46 @@ class AugmentPipe(torch.nn.Module): # Calculate padding. cx = (width - 1) / 2 cy = (height - 1) / 2 - cp = matrix([-cx, -cy, 1], [cx, -cy, 1], [cx, cy, 1], [-cx, cy, 1], device=device) # [idx, xyz] - cp = G_inv @ cp.t() # [batch, xyz, idx] + cp = matrix([-cx, -cy, 1], [cx, -cy, 1], [cx, cy, 1], + [-cx, cy, 1], device=device) # [idx, xyz] + cp = G_inv @ cp.t() # [batch, xyz, idx] Hz_pad = self.Hz_geom.shape[0] // 4 - margin = cp[:, :2, :].permute(1, 0, 2).flatten(1) # [xy, batch * idx] - margin = torch.cat([-margin, margin]).max(dim=1).values # [x0, y0, x1, y1] - margin = margin + misc.constant([Hz_pad * 2 - cx, Hz_pad * 2 - cy] * 2, device=device) + margin = cp[:, :2, :].permute( + 1, 0, 2).flatten(1) # [xy, batch * idx] + # [x0, y0, x1, y1] + margin = torch.cat([-margin, margin]).max(dim=1).values + margin = margin + \ + misc.constant([Hz_pad * 2 - cx, Hz_pad * 2 - cy] + * 2, device=device) margin = margin.max(misc.constant([0, 0] * 2, device=device)) - margin = margin.min(misc.constant([width-1, height-1] * 2, device=device)) + margin = margin.min(misc.constant( + [width-1, height-1] * 2, device=device)) mx0, my0, mx1, my1 = margin.ceil().to(torch.int32) # Pad image and adjust origin. - images = torch.nn.functional.pad(input=images, pad=[mx0,mx1,my0,my1], mode='reflect') + images = torch.nn.functional.pad( + input=images, pad=[mx0, mx1, my0, my1], mode='reflect') G_inv = translate2d((mx0 - mx1) / 2, (my0 - my1) / 2) @ G_inv # Upsample. images = upfirdn2d.upsample2d(x=images, f=self.Hz_geom, up=2) - G_inv = scale2d(2, 2, device=device) @ G_inv @ scale2d_inv(2, 2, device=device) - G_inv = translate2d(-0.5, -0.5, device=device) @ G_inv @ translate2d_inv(-0.5, -0.5, device=device) + G_inv = scale2d( + 2, 2, device=device) @ G_inv @ scale2d_inv(2, 2, device=device) + G_inv = translate2d(-0.5, -0.5, + device=device) @ G_inv @ translate2d_inv(-0.5, -0.5, device=device) # Execute transformation. - shape = [batch_size, num_channels, (height + Hz_pad * 2) * 2, (width + Hz_pad * 2) * 2] - G_inv = scale2d(2 / images.shape[3], 2 / images.shape[2], device=device) @ G_inv @ scale2d_inv(2 / shape[3], 2 / shape[2], device=device) - grid = torch.nn.functional.affine_grid(theta=G_inv[:,:2,:], size=shape, align_corners=False) + shape = [batch_size, num_channels, + (height + Hz_pad * 2) * 2, (width + Hz_pad * 2) * 2] + G_inv = scale2d(2 / images.shape[3], 2 / images.shape[2], device=device) @ G_inv @ scale2d_inv( + 2 / shape[3], 2 / shape[2], device=device) + grid = torch.nn.functional.affine_grid( + theta=G_inv[:, :2, :], size=shape, align_corners=False) images = grid_sample_gradfix.grid_sample(images, grid) # Downsample and crop. - images = upfirdn2d.downsample2d(x=images, f=self.Hz_geom, down=2, padding=-Hz_pad*2, flip_filter=True) + images = upfirdn2d.downsample2d( + x=images, f=self.Hz_geom, down=2, padding=-Hz_pad*2, flip_filter=True) # -------------------------------------------- # Select parameters for color transformations. @@ -316,42 +399,55 @@ class AugmentPipe(torch.nn.Module): # Apply brightness with probability (brightness * strength). if self.brightness > 0: b = torch.randn([batch_size], device=device) * self.brightness_std - b = torch.where(torch.rand([batch_size], device=device) < self.brightness * self.p, b, torch.zeros_like(b)) + b = torch.where(torch.rand( + [batch_size], device=device) < self.brightness * self.p, b, torch.zeros_like(b)) if debug_percentile is not None: - b = torch.full_like(b, torch.erfinv(debug_percentile * 2 - 1) * self.brightness_std) + b = torch.full_like(b, torch.erfinv( + debug_percentile * 2 - 1) * self.brightness_std) C = translate3d(b, b, b) @ C # Apply contrast with probability (contrast * strength). if self.contrast > 0: - c = torch.exp2(torch.randn([batch_size], device=device) * self.contrast_std) - c = torch.where(torch.rand([batch_size], device=device) < self.contrast * self.p, c, torch.ones_like(c)) + c = torch.exp2(torch.randn( + [batch_size], device=device) * self.contrast_std) + c = torch.where(torch.rand( + [batch_size], device=device) < self.contrast * self.p, c, torch.ones_like(c)) if debug_percentile is not None: - c = torch.full_like(c, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.contrast_std)) + c = torch.full_like(c, torch.exp2(torch.erfinv( + debug_percentile * 2 - 1) * self.contrast_std)) C = scale3d(c, c, c) @ C # Apply luma flip with probability (lumaflip * strength). - v = misc.constant(np.asarray([1, 1, 1, 0]) / np.sqrt(3), device=device) # Luma axis. + # Luma axis. + v = misc.constant(np.asarray([1, 1, 1, 0]) / np.sqrt(3), device=device) if self.lumaflip > 0: i = torch.floor(torch.rand([batch_size, 1, 1], device=device) * 2) - i = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.lumaflip * self.p, i, torch.zeros_like(i)) + i = torch.where(torch.rand( + [batch_size, 1, 1], device=device) < self.lumaflip * self.p, i, torch.zeros_like(i)) if debug_percentile is not None: i = torch.full_like(i, torch.floor(debug_percentile * 2)) - C = (I_4 - 2 * v.ger(v) * i) @ C # Householder reflection. + C = (I_4 - 2 * v.ger(v) * i) @ C # Householder reflection. # Apply hue rotation with probability (hue * strength). if self.hue > 0 and num_channels > 1: - theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.hue_max - theta = torch.where(torch.rand([batch_size], device=device) < self.hue * self.p, theta, torch.zeros_like(theta)) + theta = (torch.rand([batch_size], device=device) + * 2 - 1) * np.pi * self.hue_max + theta = torch.where(torch.rand( + [batch_size], device=device) < self.hue * self.p, theta, torch.zeros_like(theta)) if debug_percentile is not None: - theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.hue_max) - C = rotate3d(v, theta) @ C # Rotate around v. + theta = torch.full_like( + theta, (debug_percentile * 2 - 1) * np.pi * self.hue_max) + C = rotate3d(v, theta) @ C # Rotate around v. # Apply saturation with probability (saturation * strength). if self.saturation > 0 and num_channels > 1: - s = torch.exp2(torch.randn([batch_size, 1, 1], device=device) * self.saturation_std) - s = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.saturation * self.p, s, torch.ones_like(s)) + s = torch.exp2(torch.randn( + [batch_size, 1, 1], device=device) * self.saturation_std) + s = torch.where(torch.rand( + [batch_size, 1, 1], device=device) < self.saturation * self.p, s, torch.ones_like(s)) if debug_percentile is not None: - s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.saturation_std)) + s = torch.full_like(s, torch.exp2(torch.erfinv( + debug_percentile * 2 - 1) * self.saturation_std)) C = (v.ger(v) + (I_4 - v.ger(v)) * s) @ C # ------------------------------ @@ -365,9 +461,11 @@ class AugmentPipe(torch.nn.Module): images = C[:, :3, :3] @ images + C[:, :3, 3:] elif num_channels == 1: C = C[:, :3, :].mean(dim=1, keepdims=True) - images = images * C[:, :, :3].sum(dim=2, keepdims=True) + C[:, :, 3:] + images = images * \ + C[:, :, :3].sum(dim=2, keepdims=True) + C[:, :, 3:] else: - raise ValueError('Image must be RGB (3 channels) or L (1 channel)') + raise ValueError( + 'Image must be RGB (3 channels) or L (1 channel)') images = images.reshape([batch_size, num_channels, height, width]) # ---------------------- @@ -377,31 +475,49 @@ class AugmentPipe(torch.nn.Module): if self.imgfilter > 0: num_bands = self.Hz_fbank.shape[0] assert len(self.imgfilter_bands) == num_bands - expected_power = misc.constant(np.array([10, 1, 1, 1]) / 13, device=device) # Expected power spectrum (1/f). + # Expected power spectrum (1/f). + expected_power = misc.constant( + np.array([10, 1, 1, 1]) / 13, device=device) # Apply amplification for each band with probability (imgfilter * strength * band_strength). - g = torch.ones([batch_size, num_bands], device=device) # Global gain vector (identity). + # Global gain vector (identity). + g = torch.ones([batch_size, num_bands], device=device) for i, band_strength in enumerate(self.imgfilter_bands): - t_i = torch.exp2(torch.randn([batch_size], device=device) * self.imgfilter_std) - t_i = torch.where(torch.rand([batch_size], device=device) < self.imgfilter * self.p * band_strength, t_i, torch.ones_like(t_i)) + t_i = torch.exp2(torch.randn( + [batch_size], device=device) * self.imgfilter_std) + t_i = torch.where(torch.rand( + [batch_size], device=device) < self.imgfilter * self.p * band_strength, t_i, torch.ones_like(t_i)) if debug_percentile is not None: - t_i = torch.full_like(t_i, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.imgfilter_std)) if band_strength > 0 else torch.ones_like(t_i) - t = torch.ones([batch_size, num_bands], device=device) # Temporary gain vector. - t[:, i] = t_i # Replace i'th element. - t = t / (expected_power * t.square()).sum(dim=-1, keepdims=True).sqrt() # Normalize power. - g = g * t # Accumulate into global gain. + t_i = torch.full_like(t_i, torch.exp2(torch.erfinv( + debug_percentile * 2 - 1) * self.imgfilter_std)) if band_strength > 0 else torch.ones_like(t_i) + # Temporary gain vector. + t = torch.ones([batch_size, num_bands], device=device) + # Replace i'th element. + t[:, i] = t_i + # Normalize power. + t = t / (expected_power * t.square() + ).sum(dim=-1, keepdims=True).sqrt() + # Accumulate into global gain. + g = g * t # Construct combined amplification filter. - Hz_prime = g @ self.Hz_fbank # [batch, tap] - Hz_prime = Hz_prime.unsqueeze(1).repeat([1, num_channels, 1]) # [batch, channels, tap] - Hz_prime = Hz_prime.reshape([batch_size * num_channels, 1, -1]) # [batch * channels, 1, tap] + # [batch, tap] + Hz_prime = g @ self.Hz_fbank + Hz_prime = Hz_prime.unsqueeze(1).repeat( + [1, num_channels, 1]) # [batch, channels, tap] + # [batch * channels, 1, tap] + Hz_prime = Hz_prime.reshape([batch_size * num_channels, 1, -1]) # Apply filter. p = self.Hz_fbank.shape[1] // 2 - images = images.reshape([1, batch_size * num_channels, height, width]) - images = torch.nn.functional.pad(input=images, pad=[p,p,p,p], mode='reflect') - images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(2), groups=batch_size*num_channels) - images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(3), groups=batch_size*num_channels) + images = images.reshape( + [1, batch_size * num_channels, height, width]) + images = torch.nn.functional.pad( + input=images, pad=[p, p, p, p], mode='reflect') + images = conv2d_gradfix.conv2d( + input=images, weight=Hz_prime.unsqueeze(2), groups=batch_size*num_channels) + images = conv2d_gradfix.conv2d( + input=images, weight=Hz_prime.unsqueeze(3), groups=batch_size*num_channels) images = images.reshape([batch_size, num_channels, height, width]) # ------------------------ @@ -410,27 +526,37 @@ class AugmentPipe(torch.nn.Module): # Apply additive RGB noise with probability (noise * strength). if self.noise > 0: - sigma = torch.randn([batch_size, 1, 1, 1], device=device).abs() * self.noise_std - sigma = torch.where(torch.rand([batch_size, 1, 1, 1], device=device) < self.noise * self.p, sigma, torch.zeros_like(sigma)) + sigma = torch.randn([batch_size, 1, 1, 1], + device=device).abs() * self.noise_std + sigma = torch.where(torch.rand( + [batch_size, 1, 1, 1], device=device) < self.noise * self.p, sigma, torch.zeros_like(sigma)) if debug_percentile is not None: - sigma = torch.full_like(sigma, torch.erfinv(debug_percentile) * self.noise_std) - images = images + torch.randn([batch_size, num_channels, height, width], device=device) * sigma + sigma = torch.full_like(sigma, torch.erfinv( + debug_percentile) * self.noise_std) + images = images + \ + torch.randn([batch_size, num_channels, height, + width], device=device) * sigma # Apply cutout with probability (cutout * strength). if self.cutout > 0: - size = torch.full([batch_size, 2, 1, 1, 1], self.cutout_size, device=device) - size = torch.where(torch.rand([batch_size, 1, 1, 1, 1], device=device) < self.cutout * self.p, size, torch.zeros_like(size)) + size = torch.full([batch_size, 2, 1, 1, 1], + self.cutout_size, device=device) + size = torch.where(torch.rand( + [batch_size, 1, 1, 1, 1], device=device) < self.cutout * self.p, size, torch.zeros_like(size)) center = torch.rand([batch_size, 2, 1, 1, 1], device=device) if debug_percentile is not None: size = torch.full_like(size, self.cutout_size) center = torch.full_like(center, debug_percentile) coord_x = torch.arange(width, device=device).reshape([1, 1, 1, -1]) - coord_y = torch.arange(height, device=device).reshape([1, 1, -1, 1]) - mask_x = (((coord_x + 0.5) / width - center[:, 0]).abs() >= size[:, 0] / 2) - mask_y = (((coord_y + 0.5) / height - center[:, 1]).abs() >= size[:, 1] / 2) + coord_y = torch.arange( + height, device=device).reshape([1, 1, -1, 1]) + mask_x = (((coord_x + 0.5) / width - + center[:, 0]).abs() >= size[:, 0] / 2) + mask_y = (((coord_y + 0.5) / height - + center[:, 1]).abs() >= size[:, 1] / 2) mask = torch.logical_or(mask_x, mask_y).to(torch.float32) images = images * mask return images -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/training/dataset.py b/training/dataset.py index 68c356e3b89b63211e0b4bdde88babcffd26d59e..f04842155f754b0aac49b91b1de1de6db017a776 100644 --- a/training/dataset.py +++ b/training/dataset.py @@ -21,17 +21,22 @@ try: except ImportError: pyspng = None -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class Dataset(torch.utils.data.Dataset): def __init__(self, - name, # Name of the dataset. - raw_shape, # Shape of the raw image data (NCHW). - max_size = None, # Artificially limit the size of the dataset. None = no limit. Applied before xflip. - use_labels = False, # Enable conditioning labels? False = label dimension is zero. - xflip = False, # Artificially double the size of the dataset via x-flips. Applied after max_size. - random_seed = 0, # Random seed to use when applying max_size. - ): + name, # Name of the dataset. + raw_shape, # Shape of the raw image data (NCHW). + # Artificially limit the size of the dataset. None = no limit. Applied before xflip. + max_size=None, + # Enable conditioning labels? False = label dimension is zero. + use_labels=False, + # Artificially double the size of the dataset via x-flips. Applied after max_size. + xflip=False, + # Random seed to use when applying max_size. + random_seed=0, + ): self._name = name self._raw_shape = list(raw_shape) self._use_labels = use_labels @@ -48,13 +53,15 @@ class Dataset(torch.utils.data.Dataset): self._xflip = np.zeros(self._raw_idx.size, dtype=np.uint8) if xflip: self._raw_idx = np.tile(self._raw_idx, 2) - self._xflip = np.concatenate([self._xflip, np.ones_like(self._xflip)]) + self._xflip = np.concatenate( + [self._xflip, np.ones_like(self._xflip)]) def _get_raw_labels(self): if self._raw_labels is None: self._raw_labels = self._load_raw_labels() if self._use_labels else None if self._raw_labels is None: - self._raw_labels = np.zeros([self._raw_shape[0], 0], dtype=np.float32) + self._raw_labels = np.zeros( + [self._raw_shape[0], 0], dtype=np.float32) assert isinstance(self._raw_labels, np.ndarray) assert self._raw_labels.shape[0] == self._raw_shape[0] assert self._raw_labels.dtype in [np.float32, np.int64] @@ -63,13 +70,13 @@ class Dataset(torch.utils.data.Dataset): assert np.all(self._raw_labels >= 0) return self._raw_labels - def close(self): # to be overridden by subclass + def close(self): # to be overridden by subclass pass - def _load_raw_image(self, raw_idx): # to be overridden by subclass + def _load_raw_image(self, raw_idx): # to be overridden by subclass raise NotImplementedError - def _load_raw_labels(self): # to be overridden by subclass + def _load_raw_labels(self): # to be overridden by subclass raise NotImplementedError def __getstate__(self): @@ -90,7 +97,7 @@ class Dataset(torch.utils.data.Dataset): assert list(image.shape) == self.image_shape assert image.dtype == np.uint8 if self._xflip[idx]: - assert image.ndim == 3 # CHW + assert image.ndim == 3 # CHW image = image[:, :, ::-1] return image.copy(), self.get_label(idx) @@ -119,12 +126,12 @@ class Dataset(torch.utils.data.Dataset): @property def num_channels(self): - assert len(self.image_shape) == 3 # CHW + assert len(self.image_shape) == 3 # CHW return self.image_shape[0] @property def resolution(self): - assert len(self.image_shape) == 3 # CHW + assert len(self.image_shape) == 3 # CHW assert self.image_shape[1] == self.image_shape[2] return self.image_shape[1] @@ -151,20 +158,24 @@ class Dataset(torch.utils.data.Dataset): def has_onehot_labels(self): return self._get_raw_labels().dtype == np.int64 -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class ImageFolderDataset(Dataset): def __init__(self, - path, # Path to directory or zip. - resolution = None, # Ensure specific resolution, None = highest available. - **super_kwargs, # Additional arguments for the Dataset base class. - ): + path, # Path to directory or zip. + # Ensure specific resolution, None = highest available. + resolution=None, + # Additional arguments for the Dataset base class. + **super_kwargs, + ): self._path = path self._zipfile = None if os.path.isdir(self._path): self._type = 'dir' - self._all_fnames = {os.path.relpath(os.path.join(root, fname), start=self._path) for root, _dirs, files in os.walk(self._path) for fname in files} + self._all_fnames = {os.path.relpath(os.path.join( + root, fname), start=self._path) for root, _dirs, files in os.walk(self._path) for fname in files} elif self._file_ext(self._path) == '.zip': self._type = 'zip' self._all_fnames = set(self._get_zipfile().namelist()) @@ -172,12 +183,14 @@ class ImageFolderDataset(Dataset): raise IOError('Path must point to a directory or zip') PIL.Image.init() - self._image_fnames = sorted(fname for fname in self._all_fnames if self._file_ext(fname) in PIL.Image.EXTENSION) + self._image_fnames = sorted( + fname for fname in self._all_fnames if self._file_ext(fname) in PIL.Image.EXTENSION) if len(self._image_fnames) == 0: raise IOError('No image files found in the specified path') name = os.path.splitext(os.path.basename(self._path))[0] - raw_shape = [len(self._image_fnames)] + list(self._load_raw_image(0).shape) + raw_shape = [len(self._image_fnames)] + \ + list(self._load_raw_image(0).shape) if resolution is not None and (raw_shape[2] != resolution or raw_shape[3] != resolution): raise IOError('Image files do not match the specified resolution') super().__init__(name=name, raw_shape=raw_shape, **super_kwargs) @@ -217,8 +230,8 @@ class ImageFolderDataset(Dataset): else: image = np.array(PIL.Image.open(f)) if image.ndim == 2: - image = image[:, :, np.newaxis] # HW => HWC - image = image.transpose(2, 0, 1) # HWC => CHW + image = image[:, :, np.newaxis] # HW => HWC + image = image.transpose(2, 0, 1) # HWC => CHW return image def _load_raw_labels(self): @@ -230,9 +243,10 @@ class ImageFolderDataset(Dataset): if labels is None: return None labels = dict(labels) - labels = [labels[fname.replace('\\', '/')] for fname in self._image_fnames] + labels = [labels[fname.replace('\\', '/')] + for fname in self._image_fnames] labels = np.array(labels) labels = labels.astype({1: np.int64, 2: np.float32}[labels.ndim]) return labels -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/training/loss.py b/training/loss.py index 56748095c1fb409fedbf87b2375075440440f0b4..3b6d0833ca639bb3b08f216419dfa25f1e657da2 100644 --- a/training/loss.py +++ b/training/loss.py @@ -14,38 +14,44 @@ from torch_utils import training_stats from torch_utils.ops import conv2d_gradfix from torch_utils.ops import upfirdn2d -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class Loss: - def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg): # to be overridden by subclass + # to be overridden by subclass + def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg): raise NotImplementedError() -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class StyleGAN2Loss(Loss): def __init__(self, device, G, D, augment_pipe=None, r1_gamma=10, style_mixing_prob=0, pl_weight=0, pl_batch_shrink=2, pl_decay=0.01, pl_no_weight_grad=False, blur_init_sigma=0, blur_fade_kimg=0): super().__init__() - self.device = device - self.G = G - self.D = D - self.augment_pipe = augment_pipe - self.r1_gamma = r1_gamma - self.style_mixing_prob = style_mixing_prob - self.pl_weight = pl_weight - self.pl_batch_shrink = pl_batch_shrink - self.pl_decay = pl_decay - self.pl_no_weight_grad = pl_no_weight_grad - self.pl_mean = torch.zeros([], device=device) - self.blur_init_sigma = blur_init_sigma - self.blur_fade_kimg = blur_fade_kimg + self.device = device + self.G = G + self.D = D + self.augment_pipe = augment_pipe + self.r1_gamma = r1_gamma + self.style_mixing_prob = style_mixing_prob + self.pl_weight = pl_weight + self.pl_batch_shrink = pl_batch_shrink + self.pl_decay = pl_decay + self.pl_no_weight_grad = pl_no_weight_grad + self.pl_mean = torch.zeros([], device=device) + self.blur_init_sigma = blur_init_sigma + self.blur_fade_kimg = blur_fade_kimg def run_G(self, z, c, update_emas=False): ws = self.G.mapping(z, c, update_emas=update_emas) if self.style_mixing_prob > 0: with torch.autograd.profiler.record_function('style_mixing'): - cutoff = torch.empty([], dtype=torch.int64, device=ws.device).random_(1, ws.shape[1]) - cutoff = torch.where(torch.rand([], device=ws.device) < self.style_mixing_prob, cutoff, torch.full_like(cutoff, ws.shape[1])) - ws[:, cutoff:] = self.G.mapping(torch.randn_like(z), c, update_emas=False)[:, cutoff:] + cutoff = torch.empty([], dtype=torch.int64, + device=ws.device).random_(1, ws.shape[1]) + cutoff = torch.where(torch.rand( + [], device=ws.device) < self.style_mixing_prob, cutoff, torch.full_like(cutoff, ws.shape[1])) + ws[:, cutoff:] = self.G.mapping( + torch.randn_like(z), c, update_emas=False)[:, cutoff:] img = self.G.synthesis(ws, update_emas=update_emas) return img, ws @@ -53,7 +59,8 @@ class StyleGAN2Loss(Loss): blur_size = np.floor(blur_sigma * 3) if blur_size > 0: with torch.autograd.profiler.record_function('blur'): - f = torch.arange(-blur_size, blur_size + 1, device=img.device).div(blur_sigma).square().neg().exp2() + f = torch.arange(-blur_size, blur_size + 1, + device=img.device).div(blur_sigma).square().neg().exp2() img = upfirdn2d.filter2d(img, f / f.sum()) if self.augment_pipe is not None: img = self.augment_pipe(img) @@ -66,7 +73,8 @@ class StyleGAN2Loss(Loss): phase = {'Greg': 'none', 'Gboth': 'Gmain'}.get(phase, phase) if self.r1_gamma == 0: phase = {'Dreg': 'none', 'Dboth': 'Dmain'}.get(phase, phase) - blur_sigma = max(1 - cur_nimg / (self.blur_fade_kimg * 1e3), 0) * self.blur_init_sigma if self.blur_fade_kimg > 0 else 0 + blur_sigma = max(1 - cur_nimg / (self.blur_fade_kimg * 1e3), 0) * \ + self.blur_init_sigma if self.blur_fade_kimg > 0 else 0 # Gmain: Maximize logits for generated images. if phase in ['Gmain', 'Gboth']: @@ -75,7 +83,8 @@ class StyleGAN2Loss(Loss): gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma) training_stats.report('Loss/scores/fake', gen_logits) training_stats.report('Loss/signs/fake', gen_logits.sign()) - loss_Gmain = torch.nn.functional.softplus(-gen_logits) # -log(sigmoid(gen_logits)) + # -log(sigmoid(gen_logits)) + loss_Gmain = torch.nn.functional.softplus(-gen_logits) training_stats.report('Loss/G/loss', loss_Gmain) with torch.autograd.profiler.record_function('Gmain_backward'): loss_Gmain.mean().mul(gain).backward() @@ -84,10 +93,13 @@ class StyleGAN2Loss(Loss): if phase in ['Greg', 'Gboth']: with torch.autograd.profiler.record_function('Gpl_forward'): batch_size = gen_z.shape[0] // self.pl_batch_shrink - gen_img, gen_ws = self.run_G(gen_z[:batch_size], gen_c[:batch_size]) - pl_noise = torch.randn_like(gen_img) / np.sqrt(gen_img.shape[2] * gen_img.shape[3]) + gen_img, gen_ws = self.run_G( + gen_z[:batch_size], gen_c[:batch_size]) + pl_noise = torch.randn_like( + gen_img) / np.sqrt(gen_img.shape[2] * gen_img.shape[3]) with torch.autograd.profiler.record_function('pl_grads'), conv2d_gradfix.no_weight_gradients(self.pl_no_weight_grad): - pl_grads = torch.autograd.grad(outputs=[(gen_img * pl_noise).sum()], inputs=[gen_ws], create_graph=True, only_inputs=True)[0] + pl_grads = torch.autograd.grad(outputs=[( + gen_img * pl_noise).sum()], inputs=[gen_ws], create_graph=True, only_inputs=True)[0] pl_lengths = pl_grads.square().sum(2).mean(1).sqrt() pl_mean = self.pl_mean.lerp(pl_lengths.mean(), self.pl_decay) self.pl_mean.copy_(pl_mean.detach()) @@ -103,10 +115,12 @@ class StyleGAN2Loss(Loss): if phase in ['Dmain', 'Dboth']: with torch.autograd.profiler.record_function('Dgen_forward'): gen_img, _gen_ws = self.run_G(gen_z, gen_c, update_emas=True) - gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma, update_emas=True) + gen_logits = self.run_D( + gen_img, gen_c, blur_sigma=blur_sigma, update_emas=True) training_stats.report('Loss/scores/fake', gen_logits) training_stats.report('Loss/signs/fake', gen_logits.sign()) - loss_Dgen = torch.nn.functional.softplus(gen_logits) # -log(1 - sigmoid(gen_logits)) + loss_Dgen = torch.nn.functional.softplus( + gen_logits) # -log(1 - sigmoid(gen_logits)) with torch.autograd.profiler.record_function('Dgen_backward'): loss_Dgen.mean().mul(gain).backward() @@ -115,21 +129,26 @@ class StyleGAN2Loss(Loss): if phase in ['Dmain', 'Dreg', 'Dboth']: name = 'Dreal' if phase == 'Dmain' else 'Dr1' if phase == 'Dreg' else 'Dreal_Dr1' with torch.autograd.profiler.record_function(name + '_forward'): - real_img_tmp = real_img.detach().requires_grad_(phase in ['Dreg', 'Dboth']) - real_logits = self.run_D(real_img_tmp, real_c, blur_sigma=blur_sigma) + real_img_tmp = real_img.detach().requires_grad_( + phase in ['Dreg', 'Dboth']) + real_logits = self.run_D( + real_img_tmp, real_c, blur_sigma=blur_sigma) training_stats.report('Loss/scores/real', real_logits) training_stats.report('Loss/signs/real', real_logits.sign()) loss_Dreal = 0 if phase in ['Dmain', 'Dboth']: - loss_Dreal = torch.nn.functional.softplus(-real_logits) # -log(sigmoid(real_logits)) - training_stats.report('Loss/D/loss', loss_Dgen + loss_Dreal) + # -log(sigmoid(real_logits)) + loss_Dreal = torch.nn.functional.softplus(-real_logits) + training_stats.report( + 'Loss/D/loss', loss_Dgen + loss_Dreal) loss_Dr1 = 0 if phase in ['Dreg', 'Dboth']: with torch.autograd.profiler.record_function('r1_grads'), conv2d_gradfix.no_weight_gradients(): - r1_grads = torch.autograd.grad(outputs=[real_logits.sum()], inputs=[real_img_tmp], create_graph=True, only_inputs=True)[0] - r1_penalty = r1_grads.square().sum([1,2,3]) + r1_grads = torch.autograd.grad(outputs=[real_logits.sum()], inputs=[ + real_img_tmp], create_graph=True, only_inputs=True)[0] + r1_penalty = r1_grads.square().sum([1, 2, 3]) loss_Dr1 = r1_penalty * (self.r1_gamma / 2) training_stats.report('Loss/r1_penalty', r1_penalty) training_stats.report('Loss/D/reg', loss_Dr1) @@ -137,4 +156,4 @@ class StyleGAN2Loss(Loss): with torch.autograd.profiler.record_function(name + '_backward'): (loss_Dreal + loss_Dr1).mean().mul(gain).backward() -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/training/networks_stylegan2.py b/training/networks_stylegan2.py index 8de6d96f428804b0287c9d808a5931195601e41f..6f570aad058ae63aaaa6733504d0d5ed4ba190a1 100644 --- a/training/networks_stylegan2.py +++ b/training/networks_stylegan2.py @@ -21,56 +21,68 @@ from torch_utils.ops import upfirdn2d from torch_utils.ops import bias_act from torch_utils.ops import fma -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def normalize_2nd_moment(x, dim=1, eps=1e-8): return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt() -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def modulated_conv2d( - x, # Input tensor of shape [batch_size, in_channels, in_height, in_width]. - weight, # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width]. - styles, # Modulation coefficients of shape [batch_size, in_channels]. - noise = None, # Optional noise tensor to add to the output activations. - up = 1, # Integer upsampling factor. - down = 1, # Integer downsampling factor. - padding = 0, # Padding with respect to the upsampled image. - resample_filter = None, # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter(). - demodulate = True, # Apply weight demodulation? - flip_weight = True, # False = convolution, True = correlation (matches torch.nn.functional.conv2d). - fused_modconv = True, # Perform modulation, convolution, and demodulation as a single fused operation? + # Input tensor of shape [batch_size, in_channels, in_height, in_width]. + x, + # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width]. + weight, + # Modulation coefficients of shape [batch_size, in_channels]. + styles, + noise=None, # Optional noise tensor to add to the output activations. + up=1, # Integer upsampling factor. + down=1, # Integer downsampling factor. + padding=0, # Padding with respect to the upsampled image. + # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter(). + resample_filter=None, + demodulate=True, # Apply weight demodulation? + # False = convolution, True = correlation (matches torch.nn.functional.conv2d). + flip_weight=True, + # Perform modulation, convolution, and demodulation as a single fused operation? + fused_modconv=True, ): batch_size = x.shape[0] out_channels, in_channels, kh, kw = weight.shape - misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk] - misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] - misc.assert_shape(styles, [batch_size, in_channels]) # [NI] + misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk] + misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] + misc.assert_shape(styles, [batch_size, in_channels]) # [NI] # Pre-normalize inputs to avoid FP16 overflow. if x.dtype == torch.float16 and demodulate: - weight = weight * (1 / np.sqrt(in_channels * kh * kw) / weight.norm(float('inf'), dim=[1,2,3], keepdim=True)) # max_Ikk - styles = styles / styles.norm(float('inf'), dim=1, keepdim=True) # max_I + weight = weight * (1 / np.sqrt(in_channels * kh * kw) / + weight.norm(float('inf'), dim=[1, 2, 3], keepdim=True)) # max_Ikk + styles = styles / \ + styles.norm(float('inf'), dim=1, keepdim=True) # max_I # Calculate per-sample weights and demodulation coefficients. w = None dcoefs = None if demodulate or fused_modconv: - w = weight.unsqueeze(0) # [NOIkk] - w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk] + w = weight.unsqueeze(0) # [NOIkk] + w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk] if demodulate: - dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO] + dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt() # [NO] if demodulate and fused_modconv: - w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk] + w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk] # Execute by scaling the activations before and after the convolution. if not fused_modconv: x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1) - x = conv2d_resample.conv2d_resample(x=x, w=weight.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight) + x = conv2d_resample.conv2d_resample(x=x, w=weight.to( + x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight) if demodulate and noise is not None: - x = fma.fma(x, dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1), noise.to(x.dtype)) + x = fma.fma(x, dcoefs.to(x.dtype).reshape( + batch_size, -1, 1, 1), noise.to(x.dtype)) elif demodulate: x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1) elif noise is not None: @@ -78,35 +90,40 @@ def modulated_conv2d( return x # Execute as one fused op using grouped convolution. - with misc.suppress_tracer_warnings(): # this value will be treated as a constant + with misc.suppress_tracer_warnings(): # this value will be treated as a constant batch_size = int(batch_size) misc.assert_shape(x, [batch_size, in_channels, None, None]) x = x.reshape(1, -1, *x.shape[2:]) w = w.reshape(-1, in_channels, kh, kw) - x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight) + x = conv2d_resample.conv2d_resample(x=x, w=w.to( + x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight) x = x.reshape(batch_size, -1, *x.shape[2:]) if noise is not None: x = x.add_(noise) return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class FullyConnectedLayer(torch.nn.Module): def __init__(self, - in_features, # Number of input features. - out_features, # Number of output features. - bias = True, # Apply additive bias before the activation function? - activation = 'linear', # Activation function: 'relu', 'lrelu', etc. - lr_multiplier = 1, # Learning rate multiplier. - bias_init = 0, # Initial value for the additive bias. - ): + in_features, # Number of input features. + out_features, # Number of output features. + bias=True, # Apply additive bias before the activation function? + # Activation function: 'relu', 'lrelu', etc. + activation='linear', + lr_multiplier=1, # Learning rate multiplier. + bias_init=0, # Initial value for the additive bias. + ): super().__init__() self.in_features = in_features self.out_features = out_features self.activation = activation - self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) / lr_multiplier) - self.bias = torch.nn.Parameter(torch.full([out_features], np.float32(bias_init))) if bias else None + self.weight = torch.nn.Parameter(torch.randn( + [out_features, in_features]) / lr_multiplier) + self.bias = torch.nn.Parameter(torch.full( + [out_features], np.float32(bias_init))) if bias else None self.weight_gain = lr_multiplier / np.sqrt(in_features) self.bias_gain = lr_multiplier @@ -128,23 +145,28 @@ class FullyConnectedLayer(torch.nn.Module): def extra_repr(self): return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class Conv2dLayer(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels. - out_channels, # Number of output channels. - kernel_size, # Width and height of the convolution kernel. - bias = True, # Apply additive bias before the activation function? - activation = 'linear', # Activation function: 'relu', 'lrelu', etc. - up = 1, # Integer upsampling factor. - down = 1, # Integer downsampling factor. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = None, # Clamp the output to +-X, None = disable clamping. - channels_last = False, # Expect the input to have memory_format=channels_last? - trainable = True, # Update the weights of this layer during training? - ): + in_channels, # Number of input channels. + out_channels, # Number of output channels. + # Width and height of the convolution kernel. + kernel_size, + bias=True, # Apply additive bias before the activation function? + # Activation function: 'relu', 'lrelu', etc. + activation='linear', + up=1, # Integer upsampling factor. + down=1, # Integer downsampling factor. + # Low-pass filter to apply when resampling activations. + resample_filter=[1, 3, 3, 1], + # Clamp the output to +-X, None = disable clamping. + conv_clamp=None, + channels_last=False, # Expect the input to have memory_format=channels_last? + trainable=True, # Update the weights of this layer during training? + ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels @@ -152,13 +174,15 @@ class Conv2dLayer(torch.nn.Module): self.up = up self.down = down self.conv_clamp = conv_clamp - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.register_buffer( + 'resample_filter', upfirdn2d.setup_filter(resample_filter)) self.padding = kernel_size // 2 self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) self.act_gain = bias_act.activation_funcs[activation].def_gain memory_format = torch.channels_last if channels_last else torch.contiguous_format - weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format) + weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to( + memory_format=memory_format) bias = torch.zeros([out_channels]) if bias else None if trainable: self.weight = torch.nn.Parameter(weight) @@ -173,12 +197,14 @@ class Conv2dLayer(torch.nn.Module): def forward(self, x, gain=1): w = self.weight * self.weight_gain b = self.bias.to(x.dtype) if self.bias is not None else None - flip_weight = (self.up == 1) # slightly faster - x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight) + flip_weight = (self.up == 1) # slightly faster + x = conv2d_resample.conv2d_resample(x=x, w=w.to( + x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight) act_gain = self.act_gain * gain act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None - x = bias_act.bias_act(x, b, act=self.activation, gain=act_gain, clamp=act_clamp) + x = bias_act.bias_act(x, b, act=self.activation, + gain=act_gain, clamp=act_clamp) return x def extra_repr(self): @@ -186,22 +212,32 @@ class Conv2dLayer(torch.nn.Module): f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, activation={self.activation:s},', f'up={self.up}, down={self.down}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class MappingNetwork(torch.nn.Module): def __init__(self, - z_dim, # Input latent (Z) dimensionality, 0 = no latent. - c_dim, # Conditioning label (C) dimensionality, 0 = no label. - w_dim, # Intermediate latent (W) dimensionality. - num_ws, # Number of intermediate latents to output, None = do not broadcast. - num_layers = 8, # Number of mapping layers. - embed_features = None, # Label embedding dimensionality, None = same as w_dim. - layer_features = None, # Number of intermediate features in the mapping layers, None = same as w_dim. - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers. - w_avg_beta = 0.998, # Decay for tracking the moving average of W during training, None = do not track. - ): + # Input latent (Z) dimensionality, 0 = no latent. + z_dim, + # Conditioning label (C) dimensionality, 0 = no label. + c_dim, + # Intermediate latent (W) dimensionality. + w_dim, + # Number of intermediate latents to output, None = do not broadcast. + num_ws, + num_layers=8, # Number of mapping layers. + # Label embedding dimensionality, None = same as w_dim. + embed_features=None, + # Number of intermediate features in the mapping layers, None = same as w_dim. + layer_features=None, + # Activation function: 'relu', 'lrelu', etc. + activation='lrelu', + # Learning rate multiplier for the mapping layers. + lr_multiplier=0.01, + # Decay for tracking the moving average of W during training, None = do not track. + w_avg_beta=0.998, + ): super().__init__() self.z_dim = z_dim self.c_dim = c_dim @@ -216,14 +252,16 @@ class MappingNetwork(torch.nn.Module): embed_features = 0 if layer_features is None: layer_features = w_dim - features_list = [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim] + features_list = [z_dim + embed_features] + \ + [layer_features] * (num_layers - 1) + [w_dim] if c_dim > 0: self.embed = FullyConnectedLayer(c_dim, embed_features) for idx in range(num_layers): in_features = features_list[idx] out_features = features_list[idx + 1] - layer = FullyConnectedLayer(in_features, out_features, activation=activation, lr_multiplier=lr_multiplier) + layer = FullyConnectedLayer( + in_features, out_features, activation=activation, lr_multiplier=lr_multiplier) setattr(self, f'fc{idx}', layer) if num_ws is not None and w_avg_beta is not None: @@ -249,7 +287,8 @@ class MappingNetwork(torch.nn.Module): # Update moving average of W. if update_emas and self.w_avg_beta is not None: with torch.autograd.profiler.record_function('update_w_avg'): - self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta)) + self.w_avg.copy_(x.detach().mean( + dim=0).lerp(self.w_avg, self.w_avg_beta)) # Broadcast. if self.num_ws is not None: @@ -263,29 +302,35 @@ class MappingNetwork(torch.nn.Module): if self.num_ws is None or truncation_cutoff is None: x = self.w_avg.lerp(x, truncation_psi) else: - x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi) + x[:, :truncation_cutoff] = self.w_avg.lerp( + x[:, :truncation_cutoff], truncation_psi) return x def extra_repr(self): return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisLayer(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels. - out_channels, # Number of output channels. - w_dim, # Intermediate latent (W) dimensionality. - resolution, # Resolution of this layer. - kernel_size = 3, # Convolution kernel size. - up = 1, # Integer upsampling factor. - use_noise = True, # Enable noise input? - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. - channels_last = False, # Use channels_last format for the weights? - ): + in_channels, # Number of input channels. + out_channels, # Number of output channels. + # Intermediate latent (W) dimensionality. + w_dim, + resolution, # Resolution of this layer. + kernel_size=3, # Convolution kernel size. + up=1, # Integer upsampling factor. + use_noise=True, # Enable noise input? + # Activation function: 'relu', 'lrelu', etc. + activation='lrelu', + # Low-pass filter to apply when resampling activations. + resample_filter=[1, 3, 3, 1], + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=None, + channels_last=False, # Use channels_last format for the weights? + ): super().__init__() self.in_channels = in_channels self.out_channels = out_channels @@ -295,37 +340,43 @@ class SynthesisLayer(torch.nn.Module): self.use_noise = use_noise self.activation = activation self.conv_clamp = conv_clamp - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.register_buffer( + 'resample_filter', upfirdn2d.setup_filter(resample_filter)) self.padding = kernel_size // 2 self.act_gain = bias_act.activation_funcs[activation].def_gain self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) memory_format = torch.channels_last if channels_last else torch.contiguous_format - self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) + self.weight = torch.nn.Parameter(torch.randn( + [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) if use_noise: - self.register_buffer('noise_const', torch.randn([resolution, resolution])) + self.register_buffer( + 'noise_const', torch.randn([resolution, resolution])) self.noise_strength = torch.nn.Parameter(torch.zeros([])) self.bias = torch.nn.Parameter(torch.zeros([out_channels])) def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1): assert noise_mode in ['random', 'const', 'none'] in_resolution = self.resolution // self.up - misc.assert_shape(x, [None, self.in_channels, in_resolution, in_resolution]) + misc.assert_shape(x, [None, self.in_channels, + in_resolution, in_resolution]) styles = self.affine(w) noise = None if self.use_noise and noise_mode == 'random': - noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength + noise = torch.randn([x.shape[0], 1, self.resolution, + self.resolution], device=x.device) * self.noise_strength if self.use_noise and noise_mode == 'const': noise = self.noise_const * self.noise_strength - flip_weight = (self.up == 1) # slightly faster + flip_weight = (self.up == 1) # slightly faster x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up, - padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv) + padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv) act_gain = self.act_gain * gain act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None - x = bias_act.bias_act(x, self.bias.to(x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp) + x = bias_act.bias_act(x, self.bias.to( + x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp) return x def extra_repr(self): @@ -333,7 +384,8 @@ class SynthesisLayer(torch.nn.Module): f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d},', f'resolution={self.resolution:d}, up={self.up}, activation={self.activation:s}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class ToRGBLayer(torch.nn.Module): @@ -345,38 +397,51 @@ class ToRGBLayer(torch.nn.Module): self.conv_clamp = conv_clamp self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1) memory_format = torch.channels_last if channels_last else torch.contiguous_format - self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) + self.weight = torch.nn.Parameter(torch.randn( + [out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)) self.bias = torch.nn.Parameter(torch.zeros([out_channels])) self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2)) def forward(self, x, w, fused_modconv=True): styles = self.affine(w) * self.weight_gain - x = modulated_conv2d(x=x, weight=self.weight, styles=styles, demodulate=False, fused_modconv=fused_modconv) + x = modulated_conv2d(x=x, weight=self.weight, styles=styles, + demodulate=False, fused_modconv=fused_modconv) x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp) return x def extra_repr(self): return f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisBlock(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels, 0 = first block. - out_channels, # Number of output channels. - w_dim, # Intermediate latent (W) dimensionality. - resolution, # Resolution of this block. - img_channels, # Number of output color channels. - is_last, # Is this the last block? - architecture = 'skip', # Architecture: 'orig', 'skip', 'resnet'. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = 256, # Clamp the output of convolution layers to +-X, None = disable clamping. - use_fp16 = False, # Use FP16 for this block? - fp16_channels_last = False, # Use channels-last memory format with FP16? - fused_modconv_default = True, # Default value of fused_modconv. 'inference_only' = True for inference, False for training. - **layer_kwargs, # Arguments for SynthesisLayer. - ): + # Number of input channels, 0 = first block. + in_channels, + # Number of output channels. + out_channels, + # Intermediate latent (W) dimensionality. + w_dim, + # Resolution of this block. + resolution, + # Number of output color channels. + img_channels, + is_last, # Is this the last block? + # Architecture: 'orig', 'skip', 'resnet'. + architecture='skip', + # Low-pass filter to apply when resampling activations. + resample_filter=[1, 3, 3, 1], + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=256, + use_fp16=False, # Use FP16 for this block? + fp16_channels_last=False, # Use channels-last memory format with FP16? + # Default value of fused_modconv. 'inference_only' = True for inference, False for training. + fused_modconv_default=True, + # Arguments for SynthesisLayer. + **layer_kwargs, + ): assert architecture in ['orig', 'skip', 'resnet'] super().__init__() self.in_channels = in_channels @@ -388,34 +453,37 @@ class SynthesisBlock(torch.nn.Module): self.use_fp16 = use_fp16 self.channels_last = (use_fp16 and fp16_channels_last) self.fused_modconv_default = fused_modconv_default - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.register_buffer( + 'resample_filter', upfirdn2d.setup_filter(resample_filter)) self.num_conv = 0 self.num_torgb = 0 if in_channels == 0: - self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution])) + self.const = torch.nn.Parameter(torch.randn( + [out_channels, resolution, resolution])) if in_channels != 0: self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2, - resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs) + resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs) self.num_conv += 1 self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution, - conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs) + conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs) self.num_conv += 1 if is_last or architecture == 'skip': self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim, - conv_clamp=conv_clamp, channels_last=self.channels_last) + conv_clamp=conv_clamp, channels_last=self.channels_last) self.num_torgb += 1 if in_channels != 0 and architecture == 'resnet': self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2, - resample_filter=resample_filter, channels_last=self.channels_last) + resample_filter=resample_filter, channels_last=self.channels_last) def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, update_emas=False, **layer_kwargs): - _ = update_emas # unused - misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim]) + _ = update_emas # unused + misc.assert_shape( + ws, [None, self.num_conv + self.num_torgb, self.w_dim]) w_iter = iter(ws.unbind(dim=1)) if ws.device.type != 'cuda': force_fp32 = True @@ -431,28 +499,36 @@ class SynthesisBlock(torch.nn.Module): x = self.const.to(dtype=dtype, memory_format=memory_format) x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1]) else: - misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2]) + misc.assert_shape(x, [None, self.in_channels, + self.resolution // 2, self.resolution // 2]) x = x.to(dtype=dtype, memory_format=memory_format) # Main layers. if self.in_channels == 0: - x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv1(x, next(w_iter), + fused_modconv=fused_modconv, **layer_kwargs) elif self.architecture == 'resnet': y = self.skip(x, gain=np.sqrt(0.5)) - x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) - x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs) + x = self.conv0(x, next(w_iter), + fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, + gain=np.sqrt(0.5), **layer_kwargs) x = y.add_(x) else: - x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) - x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv0(x, next(w_iter), + fused_modconv=fused_modconv, **layer_kwargs) + x = self.conv1(x, next(w_iter), + fused_modconv=fused_modconv, **layer_kwargs) # ToRGB. if img is not None: - misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2]) + misc.assert_shape( + img, [None, self.img_channels, self.resolution // 2, self.resolution // 2]) img = upfirdn2d.upsample2d(img, self.resample_filter) if self.is_last or self.architecture == 'skip': y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv) - y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format) + y = y.to(dtype=torch.float32, + memory_format=torch.contiguous_format) img = img.add_(y) if img is not None else y assert x.dtype == dtype @@ -462,29 +538,38 @@ class SynthesisBlock(torch.nn.Module): def extra_repr(self): return f'resolution={self.resolution:d}, architecture={self.architecture:s}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisNetwork(torch.nn.Module): def __init__(self, - w_dim, # Intermediate latent (W) dimensionality. - img_resolution, # Output image resolution. - img_channels, # Number of color channels. - channel_base = 32768, # Overall multiplier for the number of channels. - channel_max = 512, # Maximum number of channels in any layer. - num_fp16_res = 4, # Use FP16 for the N highest resolutions. - **block_kwargs, # Arguments for SynthesisBlock. - ): - assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0 + # Intermediate latent (W) dimensionality. + w_dim, + img_resolution, # Output image resolution. + img_channels, # Number of color channels. + # Overall multiplier for the number of channels. + channel_base=32768, + # Maximum number of channels in any layer. + channel_max=512, + # Use FP16 for the N highest resolutions. + num_fp16_res=4, + **block_kwargs, # Arguments for SynthesisBlock. + ): + assert img_resolution >= 4 and img_resolution & ( + img_resolution - 1) == 0 super().__init__() self.w_dim = w_dim self.img_resolution = img_resolution self.img_resolution_log2 = int(np.log2(img_resolution)) self.img_channels = img_channels self.num_fp16_res = num_fp16_res - self.block_resolutions = [2 ** i for i in range(2, self.img_resolution_log2 + 1)] - channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions} - fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) + self.block_resolutions = [ + 2 ** i for i in range(2, self.img_resolution_log2 + 1)] + channels_dict = {res: min(channel_base // res, channel_max) + for res in self.block_resolutions} + fp16_resolution = max( + 2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) self.num_ws = 0 for res in self.block_resolutions: @@ -493,7 +578,7 @@ class SynthesisNetwork(torch.nn.Module): use_fp16 = (res >= fp16_resolution) is_last = (res == self.img_resolution) block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res, - img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, **block_kwargs) + img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, **block_kwargs) self.num_ws += block.num_conv if is_last: self.num_ws += block.num_torgb @@ -508,7 +593,8 @@ class SynthesisNetwork(torch.nn.Module): w_idx = 0 for res in self.block_resolutions: block = getattr(self, f'b{res}') - block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb)) + block_ws.append( + ws.narrow(1, w_idx, block.num_conv + block.num_torgb)) w_idx += block.num_conv x = img = None @@ -527,21 +613,24 @@ class SynthesisNetwork(torch.nn.Module): f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},', f'num_fp16_res={self.num_fp16_res:d}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class Generator(torch.nn.Module): def __init__(self, - z_dim, # Input latent (Z) dimensionality. - c_dim, # Conditioning label (C) dimensionality. - w_dim, # Intermediate latent (W) dimensionality. - img_resolution, # Output resolution. - img_channels, # Number of output color channels. - mapping_kwargs = {}, # Arguments for MappingNetwork. - synthesis_kwargs = {}, # Arguments for SynthesisNetwork. - resize=None, - **synthesis_kwargs2, # Arguments for SynthesisNetwork. - ): + z_dim, # Input latent (Z) dimensionality. + # Conditioning label (C) dimensionality. + c_dim, + # Intermediate latent (W) dimensionality. + w_dim, + img_resolution, # Output resolution. + img_channels, # Number of output color channels. + mapping_kwargs={}, # Arguments for MappingNetwork. + synthesis_kwargs={}, # Arguments for SynthesisNetwork. + resize=None, + **synthesis_kwargs2, # Arguments for SynthesisNetwork. + ): super().__init__() self.z_dim = z_dim self.c_dim = c_dim @@ -550,9 +639,11 @@ class Generator(torch.nn.Module): self.img_channels = img_channels if len(synthesis_kwargs) == 0: synthesis_kwargs = synthesis_kwargs2 - self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs) + self.synthesis = SynthesisNetwork( + w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs) self.num_ws = self.synthesis.num_ws - self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) + self.mapping = MappingNetwork( + z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) self.resize = resize def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, input_is_w=False, return_feature=False, **synthesis_kwargs): @@ -561,8 +652,10 @@ class Generator(torch.nn.Module): if ws.dim() == 2: ws = ws.unsqueeze(1).repeat([1, self.mapping.num_ws, 1]) else: - ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas) - img = self.synthesis(ws, update_emas=update_emas, return_feature=return_feature, **synthesis_kwargs) + ws = self.mapping(z, c, truncation_psi=truncation_psi, + truncation_cutoff=truncation_cutoff, update_emas=update_emas) + img = self.synthesis(ws, update_emas=update_emas, + return_feature=return_feature, **synthesis_kwargs) if return_feature: img, feature = img if self.resize is not None: @@ -586,25 +679,37 @@ def imresize(image, size): image = image.squeeze(1) return image -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class DiscriminatorBlock(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels, 0 = first block. - tmp_channels, # Number of intermediate channels. - out_channels, # Number of output channels. - resolution, # Resolution of this block. - img_channels, # Number of input color channels. - first_layer_idx, # Index of the first layer. - architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations. - conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. - use_fp16 = False, # Use FP16 for this block? - fp16_channels_last = False, # Use channels-last memory format with FP16? - freeze_layers = 0, # Freeze-D: Number of layers to freeze. - ): + # Number of input channels, 0 = first block. + in_channels, + # Number of intermediate channels. + tmp_channels, + # Number of output channels. + out_channels, + # Resolution of this block. + resolution, + # Number of input color channels. + img_channels, + # Index of the first layer. + first_layer_idx, + # Architecture: 'orig', 'skip', 'resnet'. + architecture='resnet', + # Activation function: 'relu', 'lrelu', etc. + activation='lrelu', + # Low-pass filter to apply when resampling activations. + resample_filter=[1, 3, 3, 1], + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=None, + use_fp16=False, # Use FP16 for this block? + fp16_channels_last=False, # Use channels-last memory format with FP16? + # Freeze-D: Number of layers to freeze. + freeze_layers=0, + ): assert in_channels in [0, tmp_channels] assert architecture in ['orig', 'skip', 'resnet'] super().__init__() @@ -615,9 +720,11 @@ class DiscriminatorBlock(torch.nn.Module): self.architecture = architecture self.use_fp16 = use_fp16 self.channels_last = (use_fp16 and fp16_channels_last) - self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) + self.register_buffer( + 'resample_filter', upfirdn2d.setup_filter(resample_filter)) self.num_layers = 0 + def trainable_gen(): while True: layer_idx = self.first_layer_idx + self.num_layers @@ -628,17 +735,17 @@ class DiscriminatorBlock(torch.nn.Module): if in_channels == 0 or architecture == 'skip': self.fromrgb = Conv2dLayer(img_channels, tmp_channels, kernel_size=1, activation=activation, - trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) + trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) self.conv0 = Conv2dLayer(tmp_channels, tmp_channels, kernel_size=3, activation=activation, - trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) + trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last) self.conv1 = Conv2dLayer(tmp_channels, out_channels, kernel_size=3, activation=activation, down=2, - trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last) + trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last) if architecture == 'resnet': self.skip = Conv2dLayer(tmp_channels, out_channels, kernel_size=1, bias=False, down=2, - trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last) + trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last) def forward(self, x, img, force_fp32=False): if (x if x is not None else img).device.type != 'cuda': @@ -648,16 +755,19 @@ class DiscriminatorBlock(torch.nn.Module): # Input. if x is not None: - misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) + misc.assert_shape(x, [None, self.in_channels, + self.resolution, self.resolution]) x = x.to(dtype=dtype, memory_format=memory_format) # FromRGB. if self.in_channels == 0 or self.architecture == 'skip': - misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution]) + misc.assert_shape( + img, [None, self.img_channels, self.resolution, self.resolution]) img = img.to(dtype=dtype, memory_format=memory_format) y = self.fromrgb(img) x = x + y if x is not None else y - img = upfirdn2d.downsample2d(img, self.resample_filter) if self.architecture == 'skip' else None + img = upfirdn2d.downsample2d( + img, self.resample_filter) if self.architecture == 'skip' else None # Main layers. if self.architecture == 'resnet': @@ -675,7 +785,8 @@ class DiscriminatorBlock(torch.nn.Module): def extra_repr(self): return f'resolution={self.resolution:d}, architecture={self.architecture:s}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class MinibatchStdLayer(torch.nn.Module): @@ -686,39 +797,54 @@ class MinibatchStdLayer(torch.nn.Module): def forward(self, x): N, C, H, W = x.shape - with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants - G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor(N)) if self.group_size is not None else N + with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants + G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor( + N)) if self.group_size is not None else N F = self.num_channels c = C // F - y = x.reshape(G, -1, F, c, H, W) # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c. - y = y - y.mean(dim=0) # [GnFcHW] Subtract mean over group. - y = y.square().mean(dim=0) # [nFcHW] Calc variance over group. + # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c. + y = x.reshape(G, -1, F, c, H, W) + # [GnFcHW] Subtract mean over group. + y = y - y.mean(dim=0) + # [nFcHW] Calc variance over group. + y = y.square().mean(dim=0) y = (y + 1e-8).sqrt() # [nFcHW] Calc stddev over group. - y = y.mean(dim=[2,3,4]) # [nF] Take average over channels and pixels. + # [nF] Take average over channels and pixels. + y = y.mean(dim=[2, 3, 4]) y = y.reshape(-1, F, 1, 1) # [nF11] Add missing dimensions. - y = y.repeat(G, 1, H, W) # [NFHW] Replicate over group and pixels. - x = torch.cat([x, y], dim=1) # [NCHW] Append to input as new channels. + # [NFHW] Replicate over group and pixels. + y = y.repeat(G, 1, H, W) + # [NCHW] Append to input as new channels. + x = torch.cat([x, y], dim=1) return x def extra_repr(self): return f'group_size={self.group_size}, num_channels={self.num_channels:d}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class DiscriminatorEpilogue(torch.nn.Module): def __init__(self, - in_channels, # Number of input channels. - cmap_dim, # Dimensionality of mapped conditioning label, 0 = no label. - resolution, # Resolution of this block. - img_channels, # Number of input color channels. - architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. - mbstd_group_size = 4, # Group size for the minibatch standard deviation layer, None = entire minibatch. - mbstd_num_channels = 1, # Number of features for the minibatch standard deviation layer, 0 = disable. - activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc. - conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. - ): + in_channels, # Number of input channels. + # Dimensionality of mapped conditioning label, 0 = no label. + cmap_dim, + resolution, # Resolution of this block. + # Number of input color channels. + img_channels, + # Architecture: 'orig', 'skip', 'resnet'. + architecture='resnet', + # Group size for the minibatch standard deviation layer, None = entire minibatch. + mbstd_group_size=4, + # Number of features for the minibatch standard deviation layer, 0 = disable. + mbstd_num_channels=1, + # Activation function: 'relu', 'lrelu', etc. + activation='lrelu', + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=None, + ): assert architecture in ['orig', 'skip', 'resnet'] super().__init__() self.in_channels = in_channels @@ -728,22 +854,29 @@ class DiscriminatorEpilogue(torch.nn.Module): self.architecture = architecture if architecture == 'skip': - self.fromrgb = Conv2dLayer(img_channels, in_channels, kernel_size=1, activation=activation) - self.mbstd = MinibatchStdLayer(group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None - self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels, kernel_size=3, activation=activation, conv_clamp=conv_clamp) - self.fc = FullyConnectedLayer(in_channels * (resolution ** 2), in_channels, activation=activation) - self.out = FullyConnectedLayer(in_channels, 1 if cmap_dim == 0 else cmap_dim) + self.fromrgb = Conv2dLayer( + img_channels, in_channels, kernel_size=1, activation=activation) + self.mbstd = MinibatchStdLayer( + group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None + self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels, + kernel_size=3, activation=activation, conv_clamp=conv_clamp) + self.fc = FullyConnectedLayer( + in_channels * (resolution ** 2), in_channels, activation=activation) + self.out = FullyConnectedLayer( + in_channels, 1 if cmap_dim == 0 else cmap_dim) def forward(self, x, img, cmap, force_fp32=False): - misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) # [NCHW] - _ = force_fp32 # unused + misc.assert_shape(x, [None, self.in_channels, + self.resolution, self.resolution]) # [NCHW] + _ = force_fp32 # unused dtype = torch.float32 memory_format = torch.contiguous_format # FromRGB. x = x.to(dtype=dtype, memory_format=memory_format) if self.architecture == 'skip': - misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution]) + misc.assert_shape( + img, [None, self.img_channels, self.resolution, self.resolution]) img = img.to(dtype=dtype, memory_format=memory_format) x = x + self.fromrgb(img) @@ -757,7 +890,8 @@ class DiscriminatorEpilogue(torch.nn.Module): # Conditioning. if self.cmap_dim > 0: misc.assert_shape(cmap, [None, self.cmap_dim]) - x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim)) + x = (x * cmap).sum(dim=1, keepdim=True) * \ + (1 / np.sqrt(self.cmap_dim)) assert x.dtype == dtype return x @@ -765,39 +899,53 @@ class DiscriminatorEpilogue(torch.nn.Module): def extra_repr(self): return f'resolution={self.resolution:d}, architecture={self.architecture:s}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class Discriminator(torch.nn.Module): def __init__(self, - c_dim, # Conditioning label (C) dimensionality. - img_resolution, # Input resolution. - img_channels, # Number of input color channels. - architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'. - channel_base = 32768, # Overall multiplier for the number of channels. - channel_max = 512, # Maximum number of channels in any layer. - num_fp16_res = 4, # Use FP16 for the N highest resolutions. - conv_clamp = 256, # Clamp the output of convolution layers to +-X, None = disable clamping. - cmap_dim = None, # Dimensionality of mapped conditioning label, None = default. - block_kwargs = {}, # Arguments for DiscriminatorBlock. - mapping_kwargs = {}, # Arguments for MappingNetwork. - epilogue_kwargs = {}, # Arguments for DiscriminatorEpilogue. - ): + # Conditioning label (C) dimensionality. + c_dim, + img_resolution, # Input resolution. + # Number of input color channels. + img_channels, + # Architecture: 'orig', 'skip', 'resnet'. + architecture='resnet', + # Overall multiplier for the number of channels. + channel_base=32768, + # Maximum number of channels in any layer. + channel_max=512, + # Use FP16 for the N highest resolutions. + num_fp16_res=4, + # Clamp the output of convolution layers to +-X, None = disable clamping. + conv_clamp=256, + # Dimensionality of mapped conditioning label, None = default. + cmap_dim=None, + block_kwargs={}, # Arguments for DiscriminatorBlock. + mapping_kwargs={}, # Arguments for MappingNetwork. + # Arguments for DiscriminatorEpilogue. + epilogue_kwargs={}, + ): super().__init__() self.c_dim = c_dim self.img_resolution = img_resolution self.img_resolution_log2 = int(np.log2(img_resolution)) self.img_channels = img_channels - self.block_resolutions = [2 ** i for i in range(self.img_resolution_log2, 2, -1)] - channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions + [4]} - fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) + self.block_resolutions = [ + 2 ** i for i in range(self.img_resolution_log2, 2, -1)] + channels_dict = {res: min(channel_base // res, channel_max) + for res in self.block_resolutions + [4]} + fp16_resolution = max( + 2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8) if cmap_dim is None: cmap_dim = channels_dict[4] if c_dim == 0: cmap_dim = 0 - common_kwargs = dict(img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp) + common_kwargs = dict(img_channels=img_channels, + architecture=architecture, conv_clamp=conv_clamp) cur_layer_idx = 0 for res in self.block_resolutions: in_channels = channels_dict[res] if res < img_resolution else 0 @@ -805,15 +953,17 @@ class Discriminator(torch.nn.Module): out_channels = channels_dict[res // 2] use_fp16 = (res >= fp16_resolution) block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res, - first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs) + first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs) setattr(self, f'b{res}', block) cur_layer_idx += block.num_layers if c_dim > 0: - self.mapping = MappingNetwork(z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs) - self.b4 = DiscriminatorEpilogue(channels_dict[4], cmap_dim=cmap_dim, resolution=4, **epilogue_kwargs, **common_kwargs) + self.mapping = MappingNetwork( + z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs) + self.b4 = DiscriminatorEpilogue( + channels_dict[4], cmap_dim=cmap_dim, resolution=4, **epilogue_kwargs, **common_kwargs) def forward(self, img, c, update_emas=False, **block_kwargs): - _ = update_emas # unused + _ = update_emas # unused x = None for res in self.block_resolutions: block = getattr(self, f'b{res}') @@ -828,4 +978,4 @@ class Discriminator(torch.nn.Module): def extra_repr(self): return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/training/networks_stylegan3.py b/training/networks_stylegan3.py index e34bf87ee23a4e5612094062dd67d0a7f6de5e39..338fd287110a02d76c0b7b03fbf041c340f5adb9 100644 --- a/training/networks_stylegan3.py +++ b/training/networks_stylegan3.py @@ -20,70 +20,80 @@ from torch_utils.ops import conv2d_gradfix from torch_utils.ops import filtered_lrelu from torch_utils.ops import bias_act -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @misc.profiled_function def modulated_conv2d( - x, # Input tensor: [batch_size, in_channels, in_height, in_width] - w, # Weight tensor: [out_channels, in_channels, kernel_height, kernel_width] + # Input tensor: [batch_size, in_channels, in_height, in_width] + x, + # Weight tensor: [out_channels, in_channels, kernel_height, kernel_width] + w, s, # Style tensor: [batch_size, in_channels] - demodulate = True, # Apply weight demodulation? - padding = 0, # Padding: int or [padH, padW] - input_gain = None, # Optional scale factors for the input channels: [], [in_channels], or [batch_size, in_channels] + demodulate=True, # Apply weight demodulation? + padding=0, # Padding: int or [padH, padW] + input_gain=None, # Optional scale factors for the input channels: [], [in_channels], or [batch_size, in_channels] ): - with misc.suppress_tracer_warnings(): # this value will be treated as a constant + with misc.suppress_tracer_warnings(): # this value will be treated as a constant batch_size = int(x.shape[0]) out_channels, in_channels, kh, kw = w.shape - misc.assert_shape(w, [out_channels, in_channels, kh, kw]) # [OIkk] - misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] - misc.assert_shape(s, [batch_size, in_channels]) # [NI] + misc.assert_shape(w, [out_channels, in_channels, kh, kw]) # [OIkk] + misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] + misc.assert_shape(s, [batch_size, in_channels]) # [NI] # Pre-normalize inputs. if demodulate: - w = w * w.square().mean([1,2,3], keepdim=True).rsqrt() + w = w * w.square().mean([1, 2, 3], keepdim=True).rsqrt() s = s * s.square().mean().rsqrt() # Modulate weights. - w = w.unsqueeze(0) # [NOIkk] - w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk] + w = w.unsqueeze(0) # [NOIkk] + w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk] # Demodulate weights. if demodulate: - dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO] - w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4) # [NOIkk] + dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt() # [NO] + w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4) # [NOIkk] # Apply input scaling. if input_gain is not None: - input_gain = input_gain.expand(batch_size, in_channels) # [NI] - w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk] + input_gain = input_gain.expand(batch_size, in_channels) # [NI] + w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk] # Execute as one fused op using grouped convolution. x = x.reshape(1, -1, *x.shape[2:]) w = w.reshape(-1, in_channels, kh, kw) - x = conv2d_gradfix.conv2d(input=x, weight=w.to(x.dtype), padding=padding, groups=batch_size) + x = conv2d_gradfix.conv2d(input=x, weight=w.to( + x.dtype), padding=padding, groups=batch_size) x = x.reshape(batch_size, -1, *x.shape[2:]) return x -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class FullyConnectedLayer(torch.nn.Module): def __init__(self, - in_features, # Number of input features. - out_features, # Number of output features. - activation = 'linear', # Activation function: 'relu', 'lrelu', etc. - bias = True, # Apply additive bias before the activation function? - lr_multiplier = 1, # Learning rate multiplier. - weight_init = 1, # Initial standard deviation of the weight tensor. - bias_init = 0, # Initial value of the additive bias. - ): + in_features, # Number of input features. + out_features, # Number of output features. + # Activation function: 'relu', 'lrelu', etc. + activation='linear', + bias=True, # Apply additive bias before the activation function? + lr_multiplier=1, # Learning rate multiplier. + # Initial standard deviation of the weight tensor. + weight_init=1, + bias_init=0, # Initial value of the additive bias. + ): super().__init__() self.in_features = in_features self.out_features = out_features self.activation = activation - self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) * (weight_init / lr_multiplier)) - bias_init = np.broadcast_to(np.asarray(bias_init, dtype=np.float32), [out_features]) - self.bias = torch.nn.Parameter(torch.from_numpy(bias_init / lr_multiplier)) if bias else None + self.weight = torch.nn.Parameter(torch.randn( + [out_features, in_features]) * (weight_init / lr_multiplier)) + bias_init = np.broadcast_to(np.asarray( + bias_init, dtype=np.float32), [out_features]) + self.bias = torch.nn.Parameter(torch.from_numpy( + bias_init / lr_multiplier)) if bias else None self.weight_gain = lr_multiplier / np.sqrt(in_features) self.bias_gain = lr_multiplier @@ -104,19 +114,25 @@ class FullyConnectedLayer(torch.nn.Module): def extra_repr(self): return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class MappingNetwork(torch.nn.Module): def __init__(self, - z_dim, # Input latent (Z) dimensionality. - c_dim, # Conditioning label (C) dimensionality, 0 = no labels. - w_dim, # Intermediate latent (W) dimensionality. - num_ws, # Number of intermediate latents to output. - num_layers = 2, # Number of mapping layers. - lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers. - w_avg_beta = 0.998, # Decay for tracking the moving average of W during training. - ): + z_dim, # Input latent (Z) dimensionality. + # Conditioning label (C) dimensionality, 0 = no labels. + c_dim, + # Intermediate latent (W) dimensionality. + w_dim, + # Number of intermediate latents to output. + num_ws, + num_layers=2, # Number of mapping layers. + # Learning rate multiplier for the mapping layers. + lr_multiplier=0.01, + # Decay for tracking the moving average of W during training. + w_avg_beta=0.998, + ): super().__init__() self.z_dim = z_dim self.c_dim = c_dim @@ -126,10 +142,13 @@ class MappingNetwork(torch.nn.Module): self.w_avg_beta = w_avg_beta # Construct layers. - self.embed = FullyConnectedLayer(self.c_dim, self.w_dim) if self.c_dim > 0 else None - features = [self.z_dim + (self.w_dim if self.c_dim > 0 else 0)] + [self.w_dim] * self.num_layers + self.embed = FullyConnectedLayer( + self.c_dim, self.w_dim) if self.c_dim > 0 else None + features = [self.z_dim + (self.w_dim if self.c_dim > + 0 else 0)] + [self.w_dim] * self.num_layers for idx, in_features, out_features in zip(range(num_layers), features[:-1], features[1:]): - layer = FullyConnectedLayer(in_features, out_features, activation='lrelu', lr_multiplier=lr_multiplier) + layer = FullyConnectedLayer( + in_features, out_features, activation='lrelu', lr_multiplier=lr_multiplier) setattr(self, f'fc{idx}', layer) self.register_buffer('w_avg', torch.zeros([w_dim])) @@ -153,28 +172,31 @@ class MappingNetwork(torch.nn.Module): # Update moving average of W. if update_emas: - self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta)) + self.w_avg.copy_(x.detach().mean( + dim=0).lerp(self.w_avg, self.w_avg_beta)) # Broadcast and apply truncation. x = x.unsqueeze(1).repeat([1, self.num_ws, 1]) if truncation_psi != 1: - x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi) + x[:, :truncation_cutoff] = self.w_avg.lerp( + x[:, :truncation_cutoff], truncation_psi) return x def extra_repr(self): return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}' -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisInput(torch.nn.Module): def __init__(self, - w_dim, # Intermediate latent (W) dimensionality. - channels, # Number of output channels. - size, # Output spatial size: int or [width, height]. - sampling_rate, # Output sampling rate. - bandwidth, # Output bandwidth. - ): + w_dim, # Intermediate latent (W) dimensionality. + channels, # Number of output channels. + size, # Output spatial size: int or [width, height]. + sampling_rate, # Output sampling rate. + bandwidth, # Output bandwidth. + ): super().__init__() self.w_dim = w_dim self.channels = channels @@ -190,46 +212,58 @@ class SynthesisInput(torch.nn.Module): phases = torch.rand([self.channels]) - 0.5 # Setup parameters and buffers. - self.weight = torch.nn.Parameter(torch.randn([self.channels, self.channels])) - self.affine = FullyConnectedLayer(w_dim, 4, weight_init=0, bias_init=[1,0,0,0]) - self.register_buffer('transform', torch.eye(3, 3)) # User-specified inverse transform wrt. resulting image. + self.weight = torch.nn.Parameter( + torch.randn([self.channels, self.channels])) + self.affine = FullyConnectedLayer( + w_dim, 4, weight_init=0, bias_init=[1, 0, 0, 0]) + # User-specified inverse transform wrt. resulting image. + self.register_buffer('transform', torch.eye(3, 3)) self.register_buffer('freqs', freqs) self.register_buffer('phases', phases) def forward(self, w): # Introduce batch dimension. - transforms = self.transform.unsqueeze(0) # [batch, row, col] - freqs = self.freqs.unsqueeze(0) # [batch, channel, xy] - phases = self.phases.unsqueeze(0) # [batch, channel] + transforms = self.transform.unsqueeze(0) # [batch, row, col] + freqs = self.freqs.unsqueeze(0) # [batch, channel, xy] + phases = self.phases.unsqueeze(0) # [batch, channel] # Apply learned transformation. - t = self.affine(w) # t = (r_c, r_s, t_x, t_y) - t = t / t[:, :2].norm(dim=1, keepdim=True) # t' = (r'_c, r'_s, t'_x, t'_y) - m_r = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse rotation wrt. resulting image. + t = self.affine(w) # t = (r_c, r_s, t_x, t_y) + # t' = (r'_c, r'_s, t'_x, t'_y) + t = t / t[:, :2].norm(dim=1, keepdim=True) + # Inverse rotation wrt. resulting image. + m_r = torch.eye(3, device=w.device).unsqueeze( + 0).repeat([w.shape[0], 1, 1]) m_r[:, 0, 0] = t[:, 0] # r'_c - m_r[:, 0, 1] = -t[:, 1] # r'_s + m_r[:, 0, 1] = -t[:, 1] # r'_s m_r[:, 1, 0] = t[:, 1] # r'_s m_r[:, 1, 1] = t[:, 0] # r'_c - m_t = torch.eye(3, device=w.device).unsqueeze(0).repeat([w.shape[0], 1, 1]) # Inverse translation wrt. resulting image. - m_t[:, 0, 2] = -t[:, 2] # t'_x - m_t[:, 1, 2] = -t[:, 3] # t'_y - transforms = m_r @ m_t @ transforms # First rotate resulting image, then translate, and finally apply user-specified transform. + # Inverse translation wrt. resulting image. + m_t = torch.eye(3, device=w.device).unsqueeze( + 0).repeat([w.shape[0], 1, 1]) + m_t[:, 0, 2] = -t[:, 2] # t'_x + m_t[:, 1, 2] = -t[:, 3] # t'_y + # First rotate resulting image, then translate, and finally apply user-specified transform. + transforms = m_r @ m_t @ transforms # Transform frequencies. phases = phases + (freqs @ transforms[:, :2, 2:]).squeeze(2) freqs = freqs @ transforms[:, :2, :2] # Dampen out-of-band frequencies that may occur due to the user-specified transform. - amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) / (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1) + amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) / + (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1) # Construct sampling grid. theta = torch.eye(2, 3, device=w.device) theta[0, 0] = 0.5 * self.size[0] / self.sampling_rate theta[1, 1] = 0.5 * self.size[1] / self.sampling_rate - grids = torch.nn.functional.affine_grid(theta.unsqueeze(0), [1, 1, self.size[1], self.size[0]], align_corners=False) + grids = torch.nn.functional.affine_grid(theta.unsqueeze( + 0), [1, 1, self.size[1], self.size[0]], align_corners=False) # Compute Fourier features. - x = (grids.unsqueeze(3) @ freqs.permute(0, 2, 1).unsqueeze(1).unsqueeze(2)).squeeze(3) # [batch, height, width, channel] + x = (grids.unsqueeze(3) @ freqs.permute(0, 2, 1).unsqueeze(1).unsqueeze(2) + ).squeeze(3) # [batch, height, width, channel] x = x + phases.unsqueeze(1).unsqueeze(2) x = torch.sin(x * (np.pi * 2)) x = x * amplitudes.unsqueeze(1).unsqueeze(2) @@ -239,8 +273,9 @@ class SynthesisInput(torch.nn.Module): x = x @ weight.t() # Ensure correct shape. - x = x.permute(0, 3, 1, 2) # [batch, channel, height, width] - misc.assert_shape(x, [w.shape[0], self.channels, int(self.size[1]), int(self.size[0])]) + x = x.permute(0, 3, 1, 2) # [batch, channel, height, width] + misc.assert_shape(x, [w.shape[0], self.channels, + int(self.size[1]), int(self.size[0])]) return x def extra_repr(self): @@ -248,36 +283,50 @@ class SynthesisInput(torch.nn.Module): f'w_dim={self.w_dim:d}, channels={self.channels:d}, size={list(self.size)},', f'sampling_rate={self.sampling_rate:g}, bandwidth={self.bandwidth:g}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisLayer(torch.nn.Module): def __init__(self, - w_dim, # Intermediate latent (W) dimensionality. - is_torgb, # Is this the final ToRGB layer? - is_critically_sampled, # Does this layer use critical sampling? - use_fp16, # Does this layer use FP16? - - # Input & output specifications. - in_channels, # Number of input channels. - out_channels, # Number of output channels. - in_size, # Input spatial size: int or [width, height]. - out_size, # Output spatial size: int or [width, height]. - in_sampling_rate, # Input sampling rate (s). - out_sampling_rate, # Output sampling rate (s). - in_cutoff, # Input cutoff frequency (f_c). - out_cutoff, # Output cutoff frequency (f_c). - in_half_width, # Input transition band half-width (f_h). - out_half_width, # Output Transition band half-width (f_h). - - # Hyperparameters. - conv_kernel = 3, # Convolution kernel size. Ignored for final the ToRGB layer. - filter_size = 6, # Low-pass filter size relative to the lower resolution when up/downsampling. - lrelu_upsampling = 2, # Relative sampling rate for leaky ReLU. Ignored for final the ToRGB layer. - use_radial_filters = False, # Use radially symmetric downsampling filter? Ignored for critically sampled layers. - conv_clamp = 256, # Clamp the output to [-X, +X], None = disable clamping. - magnitude_ema_beta = 0.999, # Decay rate for the moving average of input magnitudes. - ): + # Intermediate latent (W) dimensionality. + w_dim, + is_torgb, # Is this the final ToRGB layer? + is_critically_sampled, # Does this layer use critical sampling? + use_fp16, # Does this layer use FP16? + + # Input & output specifications. + in_channels, # Number of input channels. + out_channels, # Number of output channels. + # Input spatial size: int or [width, height]. + in_size, + # Output spatial size: int or [width, height]. + out_size, + in_sampling_rate, # Input sampling rate (s). + out_sampling_rate, # Output sampling rate (s). + # Input cutoff frequency (f_c). + in_cutoff, + # Output cutoff frequency (f_c). + out_cutoff, + # Input transition band half-width (f_h). + in_half_width, + # Output Transition band half-width (f_h). + out_half_width, + + # Hyperparameters. + # Convolution kernel size. Ignored for final the ToRGB layer. + conv_kernel=3, + # Low-pass filter size relative to the lower resolution when up/downsampling. + filter_size=6, + # Relative sampling rate for leaky ReLU. Ignored for final the ToRGB layer. + lrelu_upsampling=2, + # Use radially symmetric downsampling filter? Ignored for critically sampled layers. + use_radial_filters=False, + # Clamp the output to [-X, +X], None = disable clamping. + conv_clamp=256, + # Decay rate for the moving average of input magnitudes. + magnitude_ema_beta=0.999, + ): super().__init__() self.w_dim = w_dim self.is_torgb = is_torgb @@ -289,7 +338,8 @@ class SynthesisLayer(torch.nn.Module): self.out_size = np.broadcast_to(np.asarray(out_size), [2]) self.in_sampling_rate = in_sampling_rate self.out_sampling_rate = out_sampling_rate - self.tmp_sampling_rate = max(in_sampling_rate, out_sampling_rate) * (1 if is_torgb else lrelu_upsampling) + self.tmp_sampling_rate = max( + in_sampling_rate, out_sampling_rate) * (1 if is_torgb else lrelu_upsampling) self.in_cutoff = in_cutoff self.out_cutoff = out_cutoff self.in_half_width = in_half_width @@ -299,65 +349,81 @@ class SynthesisLayer(torch.nn.Module): self.magnitude_ema_beta = magnitude_ema_beta # Setup parameters and buffers. - self.affine = FullyConnectedLayer(self.w_dim, self.in_channels, bias_init=1) - self.weight = torch.nn.Parameter(torch.randn([self.out_channels, self.in_channels, self.conv_kernel, self.conv_kernel])) + self.affine = FullyConnectedLayer( + self.w_dim, self.in_channels, bias_init=1) + self.weight = torch.nn.Parameter(torch.randn( + [self.out_channels, self.in_channels, self.conv_kernel, self.conv_kernel])) self.bias = torch.nn.Parameter(torch.zeros([self.out_channels])) self.register_buffer('magnitude_ema', torch.ones([])) # Design upsampling filter. - self.up_factor = int(np.rint(self.tmp_sampling_rate / self.in_sampling_rate)) + self.up_factor = int( + np.rint(self.tmp_sampling_rate / self.in_sampling_rate)) assert self.in_sampling_rate * self.up_factor == self.tmp_sampling_rate - self.up_taps = filter_size * self.up_factor if self.up_factor > 1 and not self.is_torgb else 1 + self.up_taps = filter_size * \ + self.up_factor if self.up_factor > 1 and not self.is_torgb else 1 self.register_buffer('up_filter', self.design_lowpass_filter( numtaps=self.up_taps, cutoff=self.in_cutoff, width=self.in_half_width*2, fs=self.tmp_sampling_rate)) # Design downsampling filter. - self.down_factor = int(np.rint(self.tmp_sampling_rate / self.out_sampling_rate)) + self.down_factor = int( + np.rint(self.tmp_sampling_rate / self.out_sampling_rate)) assert self.out_sampling_rate * self.down_factor == self.tmp_sampling_rate - self.down_taps = filter_size * self.down_factor if self.down_factor > 1 and not self.is_torgb else 1 + self.down_taps = filter_size * \ + self.down_factor if self.down_factor > 1 and not self.is_torgb else 1 self.down_radial = use_radial_filters and not self.is_critically_sampled self.register_buffer('down_filter', self.design_lowpass_filter( numtaps=self.down_taps, cutoff=self.out_cutoff, width=self.out_half_width*2, fs=self.tmp_sampling_rate, radial=self.down_radial)) # Compute padding. - pad_total = (self.out_size - 1) * self.down_factor + 1 # Desired output size before downsampling. - pad_total -= (self.in_size + self.conv_kernel - 1) * self.up_factor # Input size after upsampling. - pad_total += self.up_taps + self.down_taps - 2 # Size reduction caused by the filters. - pad_lo = (pad_total + self.up_factor) // 2 # Shift sample locations according to the symmetric interpretation (Appendix C.3). + # Desired output size before downsampling. + pad_total = (self.out_size - 1) * self.down_factor + 1 + # Input size after upsampling. + pad_total -= (self.in_size + self.conv_kernel - 1) * self.up_factor + # Size reduction caused by the filters. + pad_total += self.up_taps + self.down_taps - 2 + # Shift sample locations according to the symmetric interpretation (Appendix C.3). + pad_lo = (pad_total + self.up_factor) // 2 pad_hi = pad_total - pad_lo - self.padding = [int(pad_lo[0]), int(pad_hi[0]), int(pad_lo[1]), int(pad_hi[1])] + self.padding = [int(pad_lo[0]), int(pad_hi[0]), + int(pad_lo[1]), int(pad_hi[1])] def forward(self, x, w, noise_mode='random', force_fp32=False, update_emas=False): - assert noise_mode in ['random', 'const', 'none'] # unused - misc.assert_shape(x, [None, self.in_channels, int(self.in_size[1]), int(self.in_size[0])]) + assert noise_mode in ['random', 'const', 'none'] # unused + misc.assert_shape(x, [None, self.in_channels, int( + self.in_size[1]), int(self.in_size[0])]) misc.assert_shape(w, [x.shape[0], self.w_dim]) # Track input magnitude. if update_emas: with torch.autograd.profiler.record_function('update_magnitude_ema'): magnitude_cur = x.detach().to(torch.float32).square().mean() - self.magnitude_ema.copy_(magnitude_cur.lerp(self.magnitude_ema, self.magnitude_ema_beta)) + self.magnitude_ema.copy_(magnitude_cur.lerp( + self.magnitude_ema, self.magnitude_ema_beta)) input_gain = self.magnitude_ema.rsqrt() # Execute affine layer. styles = self.affine(w) if self.is_torgb: - weight_gain = 1 / np.sqrt(self.in_channels * (self.conv_kernel ** 2)) + weight_gain = 1 / \ + np.sqrt(self.in_channels * (self.conv_kernel ** 2)) styles = styles * weight_gain # Execute modulated conv2d. - dtype = torch.float16 if (self.use_fp16 and not force_fp32 and x.device.type == 'cuda') else torch.float32 + dtype = torch.float16 if ( + self.use_fp16 and not force_fp32 and x.device.type == 'cuda') else torch.float32 x = modulated_conv2d(x=x.to(dtype), w=self.weight, s=styles, - padding=self.conv_kernel-1, demodulate=(not self.is_torgb), input_gain=input_gain) + padding=self.conv_kernel-1, demodulate=(not self.is_torgb), input_gain=input_gain) # Execute bias, filtered leaky ReLU, and clamping. gain = 1 if self.is_torgb else np.sqrt(2) slope = 1 if self.is_torgb else 0.2 x = filtered_lrelu.filtered_lrelu(x=x, fu=self.up_filter, fd=self.down_filter, b=self.bias.to(x.dtype), - up=self.up_factor, down=self.down_factor, padding=self.padding, gain=gain, slope=slope, clamp=self.conv_clamp) + up=self.up_factor, down=self.down_factor, padding=self.padding, gain=gain, slope=slope, clamp=self.conv_clamp) # Ensure correct shape and dtype. - misc.assert_shape(x, [None, self.out_channels, int(self.out_size[1]), int(self.out_size[0])]) + misc.assert_shape(x, [None, self.out_channels, int( + self.out_size[1]), int(self.out_size[0])]) assert x.dtype == dtype return x @@ -371,14 +437,16 @@ class SynthesisLayer(torch.nn.Module): # Separable Kaiser low-pass filter. if not radial: - f = scipy.signal.firwin(numtaps=numtaps, cutoff=cutoff, width=width, fs=fs) + f = scipy.signal.firwin( + numtaps=numtaps, cutoff=cutoff, width=width, fs=fs) return torch.as_tensor(f, dtype=torch.float32) # Radially symmetric jinc-based filter. x = (np.arange(numtaps) - (numtaps - 1) / 2) / fs r = np.hypot(*np.meshgrid(x, x)) f = scipy.special.j1(2 * cutoff * (np.pi * r)) / (np.pi * r) - beta = scipy.signal.kaiser_beta(scipy.signal.kaiser_atten(numtaps, width / (fs / 2))) + beta = scipy.signal.kaiser_beta( + scipy.signal.kaiser_atten(numtaps, width / (fs / 2))) w = np.kaiser(numtaps, beta) f *= np.outer(w, w) f /= np.sum(f) @@ -394,26 +462,38 @@ class SynthesisLayer(torch.nn.Module): f'in_size={list(self.in_size)}, out_size={list(self.out_size)},', f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class SynthesisNetwork(torch.nn.Module): def __init__(self, - w_dim, # Intermediate latent (W) dimensionality. - img_resolution, # Output image resolution. - img_channels, # Number of color channels. - channel_base = 32768, # Overall multiplier for the number of channels. - channel_max = 512, # Maximum number of channels in any layer. - num_layers = 14, # Total number of layers, excluding Fourier features and ToRGB. - num_critical = 2, # Number of critically sampled layers at the end. - first_cutoff = 2, # Cutoff frequency of the first layer (f_{c,0}). - first_stopband = 2**2.1, # Minimum stopband of the first layer (f_{t,0}). - last_stopband_rel = 2**0.3, # Minimum stopband of the last layer, expressed relative to the cutoff. - margin_size = 10, # Number of additional pixels outside the image. - output_scale = 0.25, # Scale factor for the output image. - num_fp16_res = 4, # Use FP16 for the N highest resolutions. - **layer_kwargs, # Arguments for SynthesisLayer. - ): + # Intermediate latent (W) dimensionality. + w_dim, + img_resolution, # Output image resolution. + img_channels, # Number of color channels. + # Overall multiplier for the number of channels. + channel_base=32768, + # Maximum number of channels in any layer. + channel_max=512, + # Total number of layers, excluding Fourier features and ToRGB. + num_layers=14, + # Number of critically sampled layers at the end. + num_critical=2, + # Cutoff frequency of the first layer (f_{c,0}). + first_cutoff=2, + # Minimum stopband of the first layer (f_{t,0}). + first_stopband=2**2.1, + # Minimum stopband of the last layer, expressed relative to the cutoff. + last_stopband_rel=2**0.3, + # Number of additional pixels outside the image. + margin_size=10, + output_scale=0.25, # Scale factor for the output image. + # Use FP16 for the N highest resolutions. + num_fp16_res=4, + # Arguments for SynthesisLayer. + **layer_kwargs, + ): super().__init__() self.w_dim = w_dim self.num_ws = num_layers + 2 @@ -426,18 +506,24 @@ class SynthesisNetwork(torch.nn.Module): self.num_fp16_res = num_fp16_res # Geometric progression of layer cutoffs and min. stopbands. - last_cutoff = self.img_resolution / 2 # f_{c,N} - last_stopband = last_cutoff * last_stopband_rel # f_{t,N} - exponents = np.minimum(np.arange(self.num_layers + 1) / (self.num_layers - self.num_critical), 1) - cutoffs = first_cutoff * (last_cutoff / first_cutoff) ** exponents # f_c[i] - stopbands = first_stopband * (last_stopband / first_stopband) ** exponents # f_t[i] + last_cutoff = self.img_resolution / 2 # f_{c,N} + last_stopband = last_cutoff * last_stopband_rel # f_{t,N} + exponents = np.minimum( + np.arange(self.num_layers + 1) / (self.num_layers - self.num_critical), 1) + cutoffs = first_cutoff * \ + (last_cutoff / first_cutoff) ** exponents # f_c[i] + stopbands = first_stopband * \ + (last_stopband / first_stopband) ** exponents # f_t[i] # Compute remaining layer parameters. - sampling_rates = np.exp2(np.ceil(np.log2(np.minimum(stopbands * 2, self.img_resolution)))) # s[i] - half_widths = np.maximum(stopbands, sampling_rates / 2) - cutoffs # f_h[i] + sampling_rates = np.exp2( + np.ceil(np.log2(np.minimum(stopbands * 2, self.img_resolution)))) # s[i] + half_widths = np.maximum( + stopbands, sampling_rates / 2) - cutoffs # f_h[i] sizes = sampling_rates + self.margin_size * 2 sizes[-2:] = self.img_resolution - channels = np.rint(np.minimum((channel_base / 2) / cutoffs, channel_max)) + channels = np.rint(np.minimum( + (channel_base / 2) / cutoffs, channel_max)) channels[-1] = self.img_channels # Construct layers. @@ -448,11 +534,13 @@ class SynthesisNetwork(torch.nn.Module): for idx in range(self.num_layers + 1): prev = max(idx - 1, 0) is_torgb = (idx == self.num_layers) - is_critically_sampled = (idx >= self.num_layers - self.num_critical) - use_fp16 = (sampling_rates[idx] * (2 ** self.num_fp16_res) > self.img_resolution) + is_critically_sampled = ( + idx >= self.num_layers - self.num_critical) + use_fp16 = (sampling_rates[idx] * (2 ** + self.num_fp16_res) > self.img_resolution) layer = SynthesisLayer( w_dim=self.w_dim, is_torgb=is_torgb, is_critically_sampled=is_critically_sampled, use_fp16=use_fp16, - in_channels=int(channels[prev]), out_channels= int(channels[idx]), + in_channels=int(channels[prev]), out_channels=int(channels[idx]), in_size=int(sizes[prev]), out_size=int(sizes[idx]), in_sampling_rate=int(sampling_rates[prev]), out_sampling_rate=int(sampling_rates[idx]), in_cutoff=cutoffs[prev], out_cutoff=cutoffs[idx], @@ -476,7 +564,8 @@ class SynthesisNetwork(torch.nn.Module): x = x * self.output_scale # Ensure correct shape and dtype. - misc.assert_shape(x, [None, self.img_channels, self.img_resolution, self.img_resolution]) + misc.assert_shape(x, [None, self.img_channels, + self.img_resolution, self.img_resolution]) x = x.to(torch.float32) if return_feature: return x, features @@ -490,29 +579,34 @@ class SynthesisNetwork(torch.nn.Module): f'num_layers={self.num_layers:d}, num_critical={self.num_critical:d},', f'margin_size={self.margin_size:d}, num_fp16_res={self.num_fp16_res:d}']) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @persistence.persistent_class class Generator(torch.nn.Module): def __init__(self, - z_dim, # Input latent (Z) dimensionality. - c_dim, # Conditioning label (C) dimensionality. - w_dim, # Intermediate latent (W) dimensionality. - img_resolution, # Output resolution. - img_channels, # Number of output color channels. - mapping_kwargs = {}, # Arguments for MappingNetwork. - resize=None, - **synthesis_kwargs, # Arguments for SynthesisNetwork. - ): + z_dim, # Input latent (Z) dimensionality. + # Conditioning label (C) dimensionality. + c_dim, + # Intermediate latent (W) dimensionality. + w_dim, + img_resolution, # Output resolution. + img_channels, # Number of output color channels. + mapping_kwargs={}, # Arguments for MappingNetwork. + resize=None, + **synthesis_kwargs, # Arguments for SynthesisNetwork. + ): super().__init__() self.z_dim = z_dim self.c_dim = c_dim self.w_dim = w_dim self.img_resolution = img_resolution self.img_channels = img_channels - self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs) + self.synthesis = SynthesisNetwork( + w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs) self.num_ws = self.synthesis.num_ws - self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) + self.mapping = MappingNetwork( + z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) self.resize = resize def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, input_is_w=False, return_feature=False, **synthesis_kwargs): @@ -521,8 +615,10 @@ class Generator(torch.nn.Module): if ws.dim() == 2: ws = ws.unsqueeze(1).repeat([1, self.mapping.num_ws, 1]) else: - ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas) - img = self.synthesis(ws, update_emas=update_emas, return_feature=return_feature, **synthesis_kwargs) + ws = self.mapping(z, c, truncation_psi=truncation_psi, + truncation_cutoff=truncation_cutoff, update_emas=update_emas) + img = self.synthesis(ws, update_emas=update_emas, + return_feature=return_feature, **synthesis_kwargs) if return_feature: img, feature = img if self.resize is not None: @@ -532,7 +628,8 @@ class Generator(torch.nn.Module): else: return img -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def imresize(image, size): dim = image.dim() diff --git a/training/training_loop.py b/training/training_loop.py index ddd0c15e226b0436048fee4469341e3fb653c71b..b1643b2d96a597d236af29053878191859a74cb7 100644 --- a/training/training_loop.py +++ b/training/training_loop.py @@ -26,7 +26,8 @@ from torch_utils.ops import grid_sample_gradfix import legacy from metrics import metric_main -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def setup_snapshot_image_grid(training_set, random_seed=0): rnd = np.random.RandomState(random_seed) @@ -37,11 +38,12 @@ def setup_snapshot_image_grid(training_set, random_seed=0): if not training_set.has_labels: all_indices = list(range(len(training_set))) rnd.shuffle(all_indices) - grid_indices = [all_indices[i % len(all_indices)] for i in range(gw * gh)] + grid_indices = [all_indices[i % + len(all_indices)] for i in range(gw * gh)] else: # Group training samples by label. - label_groups = dict() # label => [idx, ...] + label_groups = dict() # label => [idx, ...] for idx in range(len(training_set)): label = tuple(training_set.get_details(idx).raw_label.flat[::-1]) if label not in label_groups: @@ -59,13 +61,15 @@ def setup_snapshot_image_grid(training_set, random_seed=0): label = label_order[y % len(label_order)] indices = label_groups[label] grid_indices += [indices[x % len(indices)] for x in range(gw)] - label_groups[label] = [indices[(i + gw) % len(indices)] for i in range(len(indices))] + label_groups[label] = [ + indices[(i + gw) % len(indices)] for i in range(len(indices))] # Load data. images, labels = zip(*[training_set[i] for i in grid_indices]) return (gw, gh), np.stack(images), np.stack(labels) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def save_image_grid(img, fname, drange, grid_size): lo, hi = drange @@ -85,59 +89,79 @@ def save_image_grid(img, fname, drange, grid_size): if C == 3: PIL.Image.fromarray(img, 'RGB').save(fname) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def training_loop( - run_dir = '.', # Output directory. - training_set_kwargs = {}, # Options for training set. - data_loader_kwargs = {}, # Options for torch.utils.data.DataLoader. - G_kwargs = {}, # Options for generator network. - D_kwargs = {}, # Options for discriminator network. - G_opt_kwargs = {}, # Options for generator optimizer. - D_opt_kwargs = {}, # Options for discriminator optimizer. - augment_kwargs = None, # Options for augmentation pipeline. None = disable. - loss_kwargs = {}, # Options for loss function. - metrics = [], # Metrics to evaluate during training. - random_seed = 0, # Global random seed. - num_gpus = 1, # Number of GPUs participating in the training. - rank = 0, # Rank of the current process in [0, num_gpus[. - batch_size = 4, # Total batch size for one training iteration. Can be larger than batch_gpu * num_gpus. - batch_gpu = 4, # Number of samples processed at a time by one GPU. - ema_kimg = 10, # Half-life of the exponential moving average (EMA) of generator weights. - ema_rampup = 0.05, # EMA ramp-up coefficient. None = no rampup. - G_reg_interval = None, # How often to perform regularization for G? None = disable lazy regularization. - D_reg_interval = 16, # How often to perform regularization for D? None = disable lazy regularization. - augment_p = 0, # Initial value of augmentation probability. - ada_target = None, # ADA target value. None = fixed p. - ada_interval = 4, # How often to perform ADA adjustment? - ada_kimg = 500, # ADA adjustment speed, measured in how many kimg it takes for p to increase/decrease by one unit. - total_kimg = 25000, # Total length of the training, measured in thousands of real images. - kimg_per_tick = 4, # Progress snapshot interval. - image_snapshot_ticks = 50, # How often to save image snapshots? None = disable. - network_snapshot_ticks = 50, # How often to save network snapshots? None = disable. - resume_pkl = None, # Network pickle to resume training from. - resume_kimg = 0, # First kimg to report when resuming training. - cudnn_benchmark = True, # Enable torch.backends.cudnn.benchmark? - abort_fn = None, # Callback function for determining whether to abort training. Must return consistent results across ranks. - progress_fn = None, # Callback function for updating training progress. Called for all ranks. + run_dir='.', # Output directory. + training_set_kwargs={}, # Options for training set. + data_loader_kwargs={}, # Options for torch.utils.data.DataLoader. + G_kwargs={}, # Options for generator network. + D_kwargs={}, # Options for discriminator network. + G_opt_kwargs={}, # Options for generator optimizer. + D_opt_kwargs={}, # Options for discriminator optimizer. + # Options for augmentation pipeline. None = disable. + augment_kwargs=None, + loss_kwargs={}, # Options for loss function. + metrics=[], # Metrics to evaluate during training. + random_seed=0, # Global random seed. + num_gpus=1, # Number of GPUs participating in the training. + rank=0, # Rank of the current process in [0, num_gpus[. + # Total batch size for one training iteration. Can be larger than batch_gpu * num_gpus. + batch_size=4, + batch_gpu=4, # Number of samples processed at a time by one GPU. + # Half-life of the exponential moving average (EMA) of generator weights. + ema_kimg=10, + ema_rampup=0.05, # EMA ramp-up coefficient. None = no rampup. + # How often to perform regularization for G? None = disable lazy regularization. + G_reg_interval=None, + # How often to perform regularization for D? None = disable lazy regularization. + D_reg_interval=16, + augment_p=0, # Initial value of augmentation probability. + ada_target=None, # ADA target value. None = fixed p. + ada_interval=4, # How often to perform ADA adjustment? + # ADA adjustment speed, measured in how many kimg it takes for p to increase/decrease by one unit. + ada_kimg=500, + # Total length of the training, measured in thousands of real images. + total_kimg=25000, + kimg_per_tick=4, # Progress snapshot interval. + # How often to save image snapshots? None = disable. + image_snapshot_ticks=50, + # How often to save network snapshots? None = disable. + network_snapshot_ticks=50, + resume_pkl=None, # Network pickle to resume training from. + resume_kimg=0, # First kimg to report when resuming training. + cudnn_benchmark=True, # Enable torch.backends.cudnn.benchmark? + # Callback function for determining whether to abort training. Must return consistent results across ranks. + abort_fn=None, + # Callback function for updating training progress. Called for all ranks. + progress_fn=None, ): # Initialize. start_time = time.time() device = torch.device('cuda', rank) np.random.seed(random_seed * num_gpus + rank) torch.manual_seed(random_seed * num_gpus + rank) - torch.backends.cudnn.benchmark = cudnn_benchmark # Improves training speed. - torch.backends.cuda.matmul.allow_tf32 = False # Improves numerical accuracy. - torch.backends.cudnn.allow_tf32 = False # Improves numerical accuracy. - conv2d_gradfix.enabled = True # Improves training speed. - grid_sample_gradfix.enabled = True # Avoids errors with the augmentation pipe. + # Improves training speed. + torch.backends.cudnn.benchmark = cudnn_benchmark + # Improves numerical accuracy. + torch.backends.cuda.matmul.allow_tf32 = False + # Improves numerical accuracy. + torch.backends.cudnn.allow_tf32 = False + # Improves training speed. + conv2d_gradfix.enabled = True + # Avoids errors with the augmentation pipe. + grid_sample_gradfix.enabled = True # Load training set. if rank == 0: print('Loading training set...') - training_set = dnnlib.util.construct_class_by_name(**training_set_kwargs) # subclass of training.dataset.Dataset - training_set_sampler = misc.InfiniteSampler(dataset=training_set, rank=rank, num_replicas=num_gpus, seed=random_seed) - training_set_iterator = iter(torch.utils.data.DataLoader(dataset=training_set, sampler=training_set_sampler, batch_size=batch_size//num_gpus, **data_loader_kwargs)) + training_set = dnnlib.util.construct_class_by_name( + **training_set_kwargs) # subclass of training.dataset.Dataset + training_set_sampler = misc.InfiniteSampler( + dataset=training_set, rank=rank, num_replicas=num_gpus, seed=random_seed) + training_set_iterator = iter(torch.utils.data.DataLoader( + dataset=training_set, sampler=training_set_sampler, batch_size=batch_size//num_gpus, **data_loader_kwargs)) if rank == 0: print() print('Num images: ', len(training_set)) @@ -148,9 +172,12 @@ def training_loop( # Construct networks. if rank == 0: print('Constructing networks...') - common_kwargs = dict(c_dim=training_set.label_dim, img_resolution=training_set.resolution, img_channels=training_set.num_channels) - G = dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module - D = dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module + common_kwargs = dict(c_dim=training_set.label_dim, + img_resolution=training_set.resolution, img_channels=training_set.num_channels) + G = dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs).train( + ).requires_grad_(False).to(device) # subclass of torch.nn.Module + D = dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs).train( + ).requires_grad_(False).to(device) # subclass of torch.nn.Module G_ema = copy.deepcopy(G).eval() # Resume from existing pickle. @@ -159,7 +186,8 @@ def training_loop( with dnnlib.util.open_url(resume_pkl) as f: resume_data = legacy.load_network_pkl(f) for name, module in [('G', G), ('D', D), ('G_ema', G_ema)]: - misc.copy_params_and_buffers(resume_data[name], module, require_all=False) + misc.copy_params_and_buffers( + resume_data[name], module, require_all=False) # Print network summary tables. if rank == 0: @@ -174,7 +202,8 @@ def training_loop( augment_pipe = None ada_stats = None if (augment_kwargs is not None) and (augment_p > 0 or ada_target is not None): - augment_pipe = dnnlib.util.construct_class_by_name(**augment_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module + augment_pipe = dnnlib.util.construct_class_by_name( + **augment_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module augment_pipe.p.copy_(torch.as_tensor(augment_p)) if ada_target is not None: ada_stats = training_stats.Collector(regex='Loss/signs/real') @@ -190,20 +219,26 @@ def training_loop( # Setup training phases. if rank == 0: print('Setting up training phases...') - loss = dnnlib.util.construct_class_by_name(device=device, G=G, D=D, augment_pipe=augment_pipe, **loss_kwargs) # subclass of training.loss.Loss + loss = dnnlib.util.construct_class_by_name( + device=device, G=G, D=D, augment_pipe=augment_pipe, **loss_kwargs) # subclass of training.loss.Loss phases = [] for name, module, opt_kwargs, reg_interval in [('G', G, G_opt_kwargs, G_reg_interval), ('D', D, D_opt_kwargs, D_reg_interval)]: if reg_interval is None: - opt = dnnlib.util.construct_class_by_name(params=module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer - phases += [dnnlib.EasyDict(name=name+'both', module=module, opt=opt, interval=1)] - else: # Lazy regularization. + opt = dnnlib.util.construct_class_by_name( + params=module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer + phases += [dnnlib.EasyDict(name=name+'both', + module=module, opt=opt, interval=1)] + else: # Lazy regularization. mb_ratio = reg_interval / (reg_interval + 1) opt_kwargs = dnnlib.EasyDict(opt_kwargs) opt_kwargs.lr = opt_kwargs.lr * mb_ratio opt_kwargs.betas = [beta ** mb_ratio for beta in opt_kwargs.betas] - opt = dnnlib.util.construct_class_by_name(module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer - phases += [dnnlib.EasyDict(name=name+'main', module=module, opt=opt, interval=1)] - phases += [dnnlib.EasyDict(name=name+'reg', module=module, opt=opt, interval=reg_interval)] + opt = dnnlib.util.construct_class_by_name( + module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer + phases += [dnnlib.EasyDict(name=name+'main', + module=module, opt=opt, interval=1)] + phases += [dnnlib.EasyDict(name=name+'reg', + module=module, opt=opt, interval=reg_interval)] for phase in phases: phase.start_event = None phase.end_event = None @@ -217,12 +252,17 @@ def training_loop( grid_c = None if rank == 0: print('Exporting sample images...') - grid_size, images, labels = setup_snapshot_image_grid(training_set=training_set) - save_image_grid(images, os.path.join(run_dir, 'reals.png'), drange=[0,255], grid_size=grid_size) - grid_z = torch.randn([labels.shape[0], G.z_dim], device=device).split(batch_gpu) + grid_size, images, labels = setup_snapshot_image_grid( + training_set=training_set) + save_image_grid(images, os.path.join(run_dir, 'reals.png'), + drange=[0, 255], grid_size=grid_size) + grid_z = torch.randn([labels.shape[0], G.z_dim], + device=device).split(batch_gpu) grid_c = torch.from_numpy(labels).to(device).split(batch_gpu) - images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy() - save_image_grid(images, os.path.join(run_dir, 'fakes_init.png'), drange=[-1,1], grid_size=grid_size) + images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() + for z, c in zip(grid_z, grid_c)]).numpy() + save_image_grid(images, os.path.join( + run_dir, 'fakes_init.png'), drange=[-1, 1], grid_size=grid_size) # Initialize logs. if rank == 0: @@ -256,13 +296,19 @@ def training_loop( # Fetch training data. with torch.autograd.profiler.record_function('data_fetch'): phase_real_img, phase_real_c = next(training_set_iterator) - phase_real_img = (phase_real_img.to(device).to(torch.float32) / 127.5 - 1).split(batch_gpu) + phase_real_img = (phase_real_img.to(device).to( + torch.float32) / 127.5 - 1).split(batch_gpu) phase_real_c = phase_real_c.to(device).split(batch_gpu) - all_gen_z = torch.randn([len(phases) * batch_size, G.z_dim], device=device) - all_gen_z = [phase_gen_z.split(batch_gpu) for phase_gen_z in all_gen_z.split(batch_size)] - all_gen_c = [training_set.get_label(np.random.randint(len(training_set))) for _ in range(len(phases) * batch_size)] - all_gen_c = torch.from_numpy(np.stack(all_gen_c)).pin_memory().to(device) - all_gen_c = [phase_gen_c.split(batch_gpu) for phase_gen_c in all_gen_c.split(batch_size)] + all_gen_z = torch.randn( + [len(phases) * batch_size, G.z_dim], device=device) + all_gen_z = [phase_gen_z.split( + batch_gpu) for phase_gen_z in all_gen_z.split(batch_size)] + all_gen_c = [training_set.get_label(np.random.randint( + len(training_set))) for _ in range(len(phases) * batch_size)] + all_gen_c = torch.from_numpy( + np.stack(all_gen_c)).pin_memory().to(device) + all_gen_c = [phase_gen_c.split( + batch_gpu) for phase_gen_c in all_gen_c.split(batch_size)] # Execute training phases. for phase, phase_gen_z, phase_gen_c in zip(phases, all_gen_z, all_gen_c): @@ -275,18 +321,22 @@ def training_loop( phase.opt.zero_grad(set_to_none=True) phase.module.requires_grad_(True) for real_img, real_c, gen_z, gen_c in zip(phase_real_img, phase_real_c, phase_gen_z, phase_gen_c): - loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c, gen_z=gen_z, gen_c=gen_c, gain=phase.interval, cur_nimg=cur_nimg) + loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c, + gen_z=gen_z, gen_c=gen_c, gain=phase.interval, cur_nimg=cur_nimg) phase.module.requires_grad_(False) # Update weights. with torch.autograd.profiler.record_function(phase.name + '_opt'): - params = [param for param in phase.module.parameters() if param.grad is not None] + params = [param for param in phase.module.parameters() + if param.grad is not None] if len(params) > 0: - flat = torch.cat([param.grad.flatten() for param in params]) + flat = torch.cat([param.grad.flatten() + for param in params]) if num_gpus > 1: torch.distributed.all_reduce(flat) flat /= num_gpus - misc.nan_to_num(flat, nan=0, posinf=1e5, neginf=-1e5, out=flat) + misc.nan_to_num(flat, nan=0, posinf=1e5, + neginf=-1e5, out=flat) grads = flat.split([param.numel() for param in params]) for param, grad in zip(params, grads): param.grad = grad.reshape(param.shape) @@ -314,8 +364,10 @@ def training_loop( # Execute ADA heuristic. if (ada_stats is not None) and (batch_idx % ada_interval == 0): ada_stats.update() - adjust = np.sign(ada_stats['Loss/signs/real'] - ada_target) * (batch_size * ada_interval) / (ada_kimg * 1000) - augment_pipe.p.copy_((augment_pipe.p + adjust).max(misc.constant(0, device=device))) + adjust = np.sign(ada_stats['Loss/signs/real'] - ada_target) * \ + (batch_size * ada_interval) / (ada_kimg * 1000) + augment_pipe.p.copy_( + (augment_pipe.p + adjust).max(misc.constant(0, device=device))) # Perform maintenance tasks once per tick. done = (cur_nimg >= total_kimg * 1000) @@ -325,19 +377,31 @@ def training_loop( # Print status line, accumulating the same information in training_stats. tick_end_time = time.time() fields = [] - fields += [f"tick {training_stats.report0('Progress/tick', cur_tick):<5d}"] - fields += [f"kimg {training_stats.report0('Progress/kimg', cur_nimg / 1e3):<8.1f}"] - fields += [f"time {dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}"] - fields += [f"sec/tick {training_stats.report0('Timing/sec_per_tick', tick_end_time - tick_start_time):<7.1f}"] - fields += [f"sec/kimg {training_stats.report0('Timing/sec_per_kimg', (tick_end_time - tick_start_time) / (cur_nimg - tick_start_nimg) * 1e3):<7.2f}"] - fields += [f"maintenance {training_stats.report0('Timing/maintenance_sec', maintenance_time):<6.1f}"] - fields += [f"cpumem {training_stats.report0('Resources/cpu_mem_gb', psutil.Process(os.getpid()).memory_info().rss / 2**30):<6.2f}"] - fields += [f"gpumem {training_stats.report0('Resources/peak_gpu_mem_gb', torch.cuda.max_memory_allocated(device) / 2**30):<6.2f}"] - fields += [f"reserved {training_stats.report0('Resources/peak_gpu_mem_reserved_gb', torch.cuda.max_memory_reserved(device) / 2**30):<6.2f}"] + fields += [ + f"tick {training_stats.report0('Progress/tick', cur_tick):<5d}"] + fields += [ + f"kimg {training_stats.report0('Progress/kimg', cur_nimg / 1e3):<8.1f}"] + fields += [ + f"time {dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}"] + fields += [ + f"sec/tick {training_stats.report0('Timing/sec_per_tick', tick_end_time - tick_start_time):<7.1f}"] + fields += [ + f"sec/kimg {training_stats.report0('Timing/sec_per_kimg', (tick_end_time - tick_start_time) / (cur_nimg - tick_start_nimg) * 1e3):<7.2f}"] + fields += [ + f"maintenance {training_stats.report0('Timing/maintenance_sec', maintenance_time):<6.1f}"] + fields += [ + f"cpumem {training_stats.report0('Resources/cpu_mem_gb', psutil.Process(os.getpid()).memory_info().rss / 2**30):<6.2f}"] + fields += [ + f"gpumem {training_stats.report0('Resources/peak_gpu_mem_gb', torch.cuda.max_memory_allocated(device) / 2**30):<6.2f}"] + fields += [ + f"reserved {training_stats.report0('Resources/peak_gpu_mem_reserved_gb', torch.cuda.max_memory_reserved(device) / 2**30):<6.2f}"] torch.cuda.reset_peak_memory_stats() - fields += [f"augment {training_stats.report0('Progress/augment', float(augment_pipe.p.cpu()) if augment_pipe is not None else 0):.3f}"] - training_stats.report0('Timing/total_hours', (tick_end_time - start_time) / (60 * 60)) - training_stats.report0('Timing/total_days', (tick_end_time - start_time) / (24 * 60 * 60)) + fields += [ + f"augment {training_stats.report0('Progress/augment', float(augment_pipe.p.cpu()) if augment_pipe is not None else 0):.3f}"] + training_stats.report0('Timing/total_hours', + (tick_end_time - start_time) / (60 * 60)) + training_stats.report0('Timing/total_days', + (tick_end_time - start_time) / (24 * 60 * 60)) if rank == 0: print(' '.join(fields)) @@ -350,24 +414,29 @@ def training_loop( # Save image snapshot. if (rank == 0) and (image_snapshot_ticks is not None) and (done or cur_tick % image_snapshot_ticks == 0): - images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy() - save_image_grid(images, os.path.join(run_dir, f'fakes{cur_nimg//1000:06d}.png'), drange=[-1,1], grid_size=grid_size) + images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() + for z, c in zip(grid_z, grid_c)]).numpy() + save_image_grid(images, os.path.join( + run_dir, f'fakes{cur_nimg//1000:06d}.png'), drange=[-1, 1], grid_size=grid_size) # Save network snapshot. snapshot_pkl = None snapshot_data = None if (network_snapshot_ticks is not None) and (done or cur_tick % network_snapshot_ticks == 0): - snapshot_data = dict(G=G, D=D, G_ema=G_ema, augment_pipe=augment_pipe, training_set_kwargs=dict(training_set_kwargs)) + snapshot_data = dict(G=G, D=D, G_ema=G_ema, augment_pipe=augment_pipe, + training_set_kwargs=dict(training_set_kwargs)) for key, value in snapshot_data.items(): if isinstance(value, torch.nn.Module): value = copy.deepcopy(value).eval().requires_grad_(False) if num_gpus > 1: - misc.check_ddp_consistency(value, ignore_regex=r'.*\.[^.]+_(avg|ema)') + misc.check_ddp_consistency( + value, ignore_regex=r'.*\.[^.]+_(avg|ema)') for param in misc.params_and_buffers(value): torch.distributed.broadcast(param, src=0) snapshot_data[key] = value.cpu() - del value # conserve memory - snapshot_pkl = os.path.join(run_dir, f'network-snapshot-{cur_nimg//1000:06d}.pkl') + del value # conserve memory + snapshot_pkl = os.path.join( + run_dir, f'network-snapshot-{cur_nimg//1000:06d}.pkl') if rank == 0: with open(snapshot_pkl, 'wb') as f: pickle.dump(snapshot_data, f) @@ -378,11 +447,12 @@ def training_loop( print('Evaluating metrics...') for metric in metrics: result_dict = metric_main.calc_metric(metric=metric, G=snapshot_data['G_ema'], - dataset_kwargs=training_set_kwargs, num_gpus=num_gpus, rank=rank, device=device) + dataset_kwargs=training_set_kwargs, num_gpus=num_gpus, rank=rank, device=device) if rank == 0: - metric_main.report_metric(result_dict, run_dir=run_dir, snapshot_pkl=snapshot_pkl) + metric_main.report_metric( + result_dict, run_dir=run_dir, snapshot_pkl=snapshot_pkl) stats_metrics.update(result_dict.results) - del snapshot_data # conserve memory + del snapshot_data # conserve memory # Collect statistics. for phase in phases: @@ -404,9 +474,11 @@ def training_loop( global_step = int(cur_nimg / 1e3) walltime = timestamp - start_time for name, value in stats_dict.items(): - stats_tfevents.add_scalar(name, value.mean, global_step=global_step, walltime=walltime) + stats_tfevents.add_scalar( + name, value.mean, global_step=global_step, walltime=walltime) for name, value in stats_metrics.items(): - stats_tfevents.add_scalar(f'Metrics/{name}', value, global_step=global_step, walltime=walltime) + stats_tfevents.add_scalar( + f'Metrics/{name}', value, global_step=global_step, walltime=walltime) stats_tfevents.flush() if progress_fn is not None: progress_fn(cur_nimg // 1000, total_kimg) @@ -424,4 +496,4 @@ def training_loop( print() print('Exiting...') -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/visualizer_drag.py b/visualizer_drag.py index 9120906ebf87f5c16a3a1cff6a2e1b721a502688..033cf03a57c17f95107d204ad5a8c817d9a8614f 100644 --- a/visualizer_drag.py +++ b/visualizer_drag.py @@ -24,36 +24,37 @@ from viz import latent_widget from viz import drag_widget from viz import capture_widget -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class Visualizer(imgui_window.ImguiWindow): def __init__(self, capture_dir=None): super().__init__(title='DragGAN', window_width=3840, window_height=2160) # Internals. - self._last_error_print = None - self._async_renderer = AsyncRenderer() - self._defer_rendering = 0 - self._tex_img = None - self._tex_obj = None - self._mask_obj = None - self._image_area = None - self._status = dnnlib.EasyDict() + self._last_error_print = None + self._async_renderer = AsyncRenderer() + self._defer_rendering = 0 + self._tex_img = None + self._tex_obj = None + self._mask_obj = None + self._image_area = None + self._status = dnnlib.EasyDict() # Widget interface. - self.args = dnnlib.EasyDict() - self.result = dnnlib.EasyDict() - self.pane_w = 0 - self.label_w = 0 - self.button_w = 0 - self.image_w = 0 - self.image_h = 0 + self.args = dnnlib.EasyDict() + self.result = dnnlib.EasyDict() + self.pane_w = 0 + self.label_w = 0 + self.button_w = 0 + self.image_w = 0 + self.image_h = 0 # Widgets. - self.pickle_widget = pickle_widget.PickleWidget(self) - self.latent_widget = latent_widget.LatentWidget(self) - self.drag_widget = drag_widget.DragWidget(self) - self.capture_widget = capture_widget.CaptureWidget(self) + self.pickle_widget = pickle_widget.PickleWidget(self) + self.latent_widget = latent_widget.LatentWidget(self) + self.drag_widget = drag_widget.DragWidget(self) + self.capture_widget = capture_widget.CaptureWidget(self) if capture_dir is not None: self.capture_widget.path = capture_dir @@ -61,7 +62,7 @@ class Visualizer(imgui_window.ImguiWindow): # Initialize window. self.set_position(0, 0) self._adjust_font_size() - self.skip_frame() # Layout may change after first frame. + self.skip_frame() # Layout may change after first frame. def close(self): super().close() @@ -97,9 +98,10 @@ class Visualizer(imgui_window.ImguiWindow): def _adjust_font_size(self): old = self.font_size - self.set_font_size(min(self.content_width / 120, self.content_height / 60)) + self.set_font_size( + min(self.content_width / 120, self.content_height / 60)) if self.font_size != old: - self.skip_frame() # Layout changed. + self.skip_frame() # Layout changed. def check_update_mask(self, **args): update_mask = False @@ -148,15 +150,19 @@ class Visualizer(imgui_window.ImguiWindow): # Begin control pane. imgui.set_next_window_position(0, 0) imgui.set_next_window_size(self.pane_w, self.content_height) - imgui.begin('##control_pane', closable=False, flags=(imgui.WINDOW_NO_TITLE_BAR | imgui.WINDOW_NO_RESIZE | imgui.WINDOW_NO_MOVE)) + imgui.begin('##control_pane', closable=False, flags=( + imgui.WINDOW_NO_TITLE_BAR | imgui.WINDOW_NO_RESIZE | imgui.WINDOW_NO_MOVE)) # Widgets. - expanded, _visible = imgui_utils.collapsing_header('Network & latent', default=True) + expanded, _visible = imgui_utils.collapsing_header( + 'Network & latent', default=True) self.pickle_widget(expanded) self.latent_widget(expanded) - expanded, _visible = imgui_utils.collapsing_header('Drag', default=True) + expanded, _visible = imgui_utils.collapsing_header( + 'Drag', default=True) self.drag_widget(expanded) - expanded, _visible = imgui_utils.collapsing_header('Capture', default=True) + expanded, _visible = imgui_utils.collapsing_header( + 'Capture', default=True) self.capture_widget(expanded) # Render. @@ -168,7 +174,7 @@ class Visualizer(imgui_window.ImguiWindow): self._async_renderer.set_args(**self.args) result = self._async_renderer.get_result() if result is not None: - self.result = result + self.result = result if 'stop' in self.result and self.result.stop: self.drag_widget.stop_drag() if 'points' in self.result: @@ -189,57 +195,72 @@ class Visualizer(imgui_window.ImguiWindow): if self._tex_img is not self.result.image: self._tex_img = self.result.image if self._tex_obj is None or not self._tex_obj.is_compatible(image=self._tex_img): - self._tex_obj = gl_utils.Texture(image=self._tex_img, bilinear=False, mipmap=False) + self._tex_obj = gl_utils.Texture( + image=self._tex_img, bilinear=False, mipmap=False) else: self._tex_obj.update(self._tex_img) self.image_h, self.image_w = self._tex_obj.height, self._tex_obj.width - zoom = min(max_w / self._tex_obj.width, max_h / self._tex_obj.height) + zoom = min(max_w / self._tex_obj.width, + max_h / self._tex_obj.height) zoom = np.floor(zoom) if zoom >= 1 else zoom self._tex_obj.draw(pos=pos, zoom=zoom, align=0.5, rint=True) if self.drag_widget.show_mask and hasattr(self.drag_widget, 'mask'): - mask = ((1-self.drag_widget.mask.unsqueeze(-1)) * 255).to(torch.uint8) + mask = ((1-self.drag_widget.mask.unsqueeze(-1)) + * 255).to(torch.uint8) if self._mask_obj is None or not self._mask_obj.is_compatible(image=self._tex_img): - self._mask_obj = gl_utils.Texture(image=mask, bilinear=False, mipmap=False) + self._mask_obj = gl_utils.Texture( + image=mask, bilinear=False, mipmap=False) else: self._mask_obj.update(mask) - self._mask_obj.draw(pos=pos, zoom=zoom, align=0.5, rint=True, alpha=0.15) + self._mask_obj.draw(pos=pos, zoom=zoom, + align=0.5, rint=True, alpha=0.15) if self.drag_widget.mode in ['flexible', 'fixed']: posx, posy = imgui.get_mouse_pos() if posx >= self.pane_w: pos_c = np.array([posx, posy]) - gl_utils.draw_circle(center=pos_c, radius=self.drag_widget.r_mask * zoom, alpha=0.5) - + gl_utils.draw_circle( + center=pos_c, radius=self.drag_widget.r_mask * zoom, alpha=0.5) + rescale = self._tex_obj.width / 512 * zoom - + for point in self.drag_widget.targets: - pos_x = self.pane_w + max_w / 2 + (point[1] - self.image_w//2) * zoom + pos_x = self.pane_w + max_w / 2 + \ + (point[1] - self.image_w//2) * zoom pos_y = max_h / 2 + (point[0] - self.image_h//2) * zoom - gl_utils.draw_circle(center=np.array([pos_x, pos_y]), color=[0,0,1], radius=9 * rescale) - + gl_utils.draw_circle(center=np.array([pos_x, pos_y]), color=[ + 0, 0, 1], radius=9 * rescale) + for point in self.drag_widget.points: - pos_x = self.pane_w + max_w / 2 + (point[1] - self.image_w//2) * zoom + pos_x = self.pane_w + max_w / 2 + \ + (point[1] - self.image_w//2) * zoom pos_y = max_h / 2 + (point[0] - self.image_h//2) * zoom - gl_utils.draw_circle(center=np.array([pos_x, pos_y]), color=[1,0,0], radius=9 * rescale) + gl_utils.draw_circle(center=np.array([pos_x, pos_y]), color=[ + 1, 0, 0], radius=9 * rescale) for point, target in zip(self.drag_widget.points, self.drag_widget.targets): - t_x = self.pane_w + max_w / 2 + (target[1] - self.image_w//2) * zoom + t_x = self.pane_w + max_w / 2 + \ + (target[1] - self.image_w//2) * zoom t_y = max_h / 2 + (target[0] - self.image_h//2) * zoom - p_x = self.pane_w + max_w / 2 + (point[1] - self.image_w//2) * zoom + p_x = self.pane_w + max_w / 2 + \ + (point[1] - self.image_w//2) * zoom p_y = max_h / 2 + (point[0] - self.image_h//2) * zoom - gl_utils.draw_arrow(p_x, p_y, t_x, t_y, l=8 * rescale, width = 3 * rescale) + gl_utils.draw_arrow(p_x, p_y, t_x, t_y, + l=8 * rescale, width=3 * rescale) imshow_w = int(self._tex_obj.width * zoom) imshow_h = int(self._tex_obj.height * zoom) - self._image_area = [int(self.pane_w + max_w / 2 - imshow_w / 2), int(max_h / 2 - imshow_h / 2), imshow_w, imshow_h] + self._image_area = [int(self.pane_w + max_w / 2 - imshow_w / 2), + int(max_h / 2 - imshow_h / 2), imshow_w, imshow_h] if 'error' in self.result: self.print_error(self.result.error) if 'message' not in self.result: self.result.message = str(self.result.error) if 'message' in self.result: - tex = text_utils.get_texture(self.result.message, size=self.font_size, max_width=max_w, max_height=max_h, outline=2) + tex = text_utils.get_texture( + self.result.message, size=self.font_size, max_width=max_w, max_height=max_h, outline=2) tex.draw(pos=pos, align=0.5, rint=True, color=1) # End frame. @@ -247,19 +268,20 @@ class Visualizer(imgui_window.ImguiWindow): imgui.end() self.end_frame() -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class AsyncRenderer: def __init__(self): - self._closed = False - self._is_async = False - self._cur_args = None - self._cur_result = None - self._cur_stamp = 0 - self._renderer_obj = None - self._args_queue = None - self._result_queue = None - self._process = None + self._closed = False + self._is_async = False + self._cur_args = None + self._cur_result = None + self._cur_stamp = 0 + self._renderer_obj = None + self._args_queue = None + self._result_queue = None + self._process = None def close(self): self._closed = True @@ -302,7 +324,8 @@ class AsyncRenderer: multiprocessing.set_start_method('spawn') except RuntimeError: pass - self._process = multiprocessing.Process(target=self._process_fn, args=(self._args_queue, self._result_queue), daemon=True) + self._process = multiprocessing.Process(target=self._process_fn, args=( + self._args_queue, self._result_queue), daemon=True) self._process.start() self._args_queue.put([args, self._cur_stamp]) @@ -343,7 +366,8 @@ class AsyncRenderer: cur_args = args cur_stamp = stamp -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + @click.command() @click.argument('pkls', metavar='PATH', nargs=-1) @@ -396,9 +420,10 @@ def main( viz.draw_frame() viz.close() -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + if __name__ == "__main__": main() -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/visualizer_drag_gradio.py b/visualizer_drag_gradio.py index d1cdca9ad2840c89e7b7261795c6057467e465f1..a4e14e9b81e21325a38e99064a755b24f15afac4 100644 --- a/visualizer_drag_gradio.py +++ b/visualizer_drag_gradio.py @@ -1,7 +1,13 @@ +# https://huggingface.co/DragGan/DragGan-Models +# https://arxiv.org/abs/2305.10973 import os import os.path as osp from argparse import ArgumentParser from functools import partial +from pathlib import Path +import time + +import psutil import gradio as gr import numpy as np @@ -14,14 +20,19 @@ from gradio_utils import (ImageMask, draw_mask_on_image, draw_points_on_image, on_change_single_global_state) from viz.renderer import Renderer, add_watermark_np -parser = ArgumentParser() -parser.add_argument('--share', action='store_true') -parser.add_argument('--cache-dir', type=str, default='./checkpoints') -args = parser.parse_args() -cache_dir = args.cache_dir +# download models from Hugging Face hub +from huggingface_hub import snapshot_download + +model_dir = Path('./checkpoints') +snapshot_download('DragGan/DragGan-Models', + repo_type='model', local_dir=model_dir) + +cache_dir = model_dir device = 'cuda' +IS_SPACE = "DragGan/DragGan" in os.environ.get('SPACE_ID', '') +TIMEOUT = 80 def reverse_point_pairs(points): @@ -146,19 +157,54 @@ def preprocess_mask_info(global_state, image): return global_state +def print_memory_usage(): + # Print system memory usage + print(f"System memory usage: {psutil.virtual_memory().percent}%") + + # Print GPU memory usage + if torch.cuda.is_available(): + device = torch.device("cuda") + print(f"GPU memory usage: {torch.cuda.memory_allocated() / 1e9} GB") + print( + f"Max GPU memory usage: {torch.cuda.max_memory_allocated() / 1e9} GB") + device_properties = torch.cuda.get_device_properties(device) + available_memory = device_properties.total_memory - \ + torch.cuda.max_memory_allocated() + print(f"Available GPU memory: {available_memory / 1e9} GB") + else: + print("No GPU available") + + +# filter large models running on SPACES +allowed_checkpoints = [] # all checkpoints +if IS_SPACE: + allowed_checkpoints = ["stylegan_human_v2_512.pkl", + "stylegan2_dogs_1024_pytorch.pkl"] + valid_checkpoints_dict = { - f.split('/')[-1].split('.')[0]: osp.join(cache_dir, f) - for f in os.listdir(cache_dir) - if (f.endswith('pkl') and osp.exists(osp.join(cache_dir, f))) + f.name.split('.')[0]: str(f) + for f in Path(cache_dir).glob('*.pkl') + if f.name in allowed_checkpoints or not IS_SPACE } -print(f'File under cache_dir ({cache_dir}):') -print(os.listdir(cache_dir)) print('Valid checkpoint file:') print(valid_checkpoints_dict) init_pkl = 'stylegan_human_v2_512' with gr.Blocks() as app: + gr.Markdown(""" +# DragGAN - Drag Your GAN +## Interactive Point-based Manipulation on the Generative Image Manifold +### Unofficial Gradio Demo + +**Due to high demand, only one model can be run at a time, or you can duplicate the space and run your own copy.** + + +Duplicate Space for no queue on your own hardware.

+ +* Official Repo: [XingangPan](https://github.com/XingangPan/DragGAN) +* Gradio Demo by: [LeoXing1996](https://github.com/LeoXing1996) © [OpenMMLab MMagic](https://github.com/open-mmlab/mmagic) +""") # renderer = Renderer() global_state = gr.State({ @@ -176,7 +222,7 @@ with gr.Blocks() as app: 'show_mask': True, # add button "generator_params": dnnlib.EasyDict(), "params": { - "seed": 0, + "seed": int(np.random.randint(0, 2**32 - 1)), "motion_lambda": 20, "r1_in_pixels": 3, "r2_in_pixels": 12, @@ -198,7 +244,6 @@ with gr.Blocks() as app: # init image global_state = init_images(global_state) - with gr.Row(): with gr.Row(): @@ -225,9 +270,13 @@ with gr.Blocks() as app: gr.Markdown(value='Latent', show_label=False) with gr.Column(scale=4, min_width=10): - form_seed_number = gr.Number( + form_seed_number = gr.Slider( + mininium=0, + maximum=2**32-1, + step=1, value=global_state.value['params']['seed'], interactive=True, + # randomize=True, label="Seed", ) form_lr_number = gr.Number( @@ -323,6 +372,8 @@ with gr.Blocks() as app: mask (this has the same effect as `Reset Image` button). 3. Click `Edit Flexible Area` to create a mask and constrain the unmasked region to remain unchanged. + + """) gr.HTML(""" +
+ Gradio demo supported by + + OpenMMLab MMagic +
+ """ + ) + # Network & latents tab listeners + + def on_click_reset_image(global_state): + """Reset image to the original one and clear all states + 1. Re-init images + 2. Clear all states + """ + + init_images(global_state) + clear_state(global_state) + + return global_state, global_state["images"]["image_show"] + + def on_click_reset_custom_image(global_state): + """Reset image to the original one and clear all states + 1. Re-init images + 2. Clear all states + """ + Path(global_state["pretrained_weight"]).unlink(missing_ok=True) + global_state["w_pivot"] = None + global_state["pretrained_weight"] = str( + model_dir / "stylegan2-ffhq1024x1024.pkl" + ) + + init_images(global_state) + clear_state(global_state) + + return global_state, global_state["images"]["image_show"] + + def on_image_change( + custom_image, global_state, progress=gr.Progress(track_tqdm=True) + ): + new_img = Image.open(custom_image) + new_img = new_img.convert("RGB") + from PTI.configs import paths_config + + paths_config.stylegan2_ada_ffhq = global_state["pretrained_weight"] + paths_config.dlib = (model_dir / "align.dat").as_posix() + run_name = str(uuid.uuid4()) + new_G, w_pivot = run_PTI(new_img, run_name) + + out_path = Path(f"checkpoints/stylegan2-{run_name}.pkl") + print(f"Exporting to {out_path}") + export_updated_pickle(new_G, out_path, run_name) + global_state["w_pivot"] = w_pivot + global_state["pretrained_weight"] = str(out_path) + init_images(global_state) + clear_state(global_state) + + return ( + global_state, + global_state["images"]["image_show"], + gr.Image.update(interactive=True), + ) + + form_custom_image.upload( + on_image_change, + [form_custom_image, global_state], + [global_state, form_image, form_reset_custom_image], + ) + + form_reset_custom_image.click( + on_click_reset_custom_image, [global_state], [global_state, form_image] + ) + + form_reset_image.click( + on_click_reset_image, + inputs=[global_state], + outputs=[global_state, form_image], + queue=False, + show_progress=False, + ) + + # Update parameters + def on_change_update_image_seed(seed, global_state): + """Function to handle generation seed change. + 1. Set seed to global_state + 2. Re-init images and clear all states + """ + + global_state["params"]["seed"] = int(seed) + init_images(global_state) + clear_state(global_state) + + return global_state, global_state["images"]["image_show"] + + form_seed_number.change( + on_change_update_image_seed, + inputs=[form_seed_number, global_state], + outputs=[global_state, form_image], + ) + + def on_click_latent_space(latent_space, global_state): + """Function to reset latent space to optimize. + NOTE: this function we reset the image and all controls + 1. Set latent-space to global_state + 2. Re-init images and clear all state + """ + + global_state["params"]["latent_space"] = latent_space + init_images(global_state) + clear_state(global_state) + + return global_state, global_state["images"]["image_show"] + + form_latent_space.change( + on_click_latent_space, + inputs=[form_latent_space, global_state], + outputs=[global_state, form_image], + ) + + # ==== Params + form_lambda_number.change( + partial(on_change_single_global_state, ["params", "motion_lambda"]), + inputs=[form_lambda_number, global_state], + outputs=[global_state], + ) + + def on_change_lr(lr, global_state): + if lr == 0: + print("lr is 0, do nothing.") + return global_state + else: + global_state["params"]["lr"] = lr + renderer = global_state["renderer"] + renderer.update_lr(lr) + print("New optimizer: ") + print(renderer.w_optim) + return global_state + + form_lr_number.change( + on_change_lr, + inputs=[form_lr_number, global_state], + outputs=[global_state], + queue=False, + show_progress=False, + ) + + def on_click_start(global_state, image): + p_in_pixels = [] + t_in_pixels = [] + valid_points = [] + + # handle of start drag in mask editing mode + global_state = preprocess_mask_info(global_state, image) + + # Prepare the points for the inference + if len(global_state["points"]) == 0: + # yield on_click_start_wo_points(global_state, image) + image_raw = global_state["images"]["image_raw"] + update_image_draw( + image_raw, + global_state["points"], + global_state["mask"], + global_state["show_mask"], + global_state, + ) + + yield ( + global_state, # global_state + 0, # form_steps_number, + global_state["images"]["image_show"], # form image + gr.Button.update(interactive=True), # form_reset_image + gr.Button.update(interactive=True), # add points button + gr.Button.update(interactive=True), # enable mask button + gr.Button.update(interactive=True), # undo points button + gr.Button.update(interactive=True), # reset mask button + gr.Radio.update(interactive=True), # latent space + gr.Button.update(interactive=True), # start button + gr.Button.update(interactive=False), # stop button + gr.Number.update(interactive=True), # form_seed_number + gr.Number.update(interactive=True), # form_lr_number + gr.Checkbox.update(interactive=True), # show_mask + gr.Number.update(interactive=True), # form_lambda_number + gr.Button.update(interactive=True), # form_reset_custom_image + ) + else: + # Transform the points into torch tensors + for key_point, point in global_state["points"].items(): + try: + p_start = point.get("start_temp", point["start"]) + p_end = point["target"] + + if p_start is None or p_end is None: + continue + + except KeyError: + continue + + p_in_pixels.append(p_start) + t_in_pixels.append(p_end) + valid_points.append(key_point) + + mask = torch.tensor(global_state["mask"]).float() + drag_mask = 1 - mask + + renderer: Renderer = global_state["renderer"] + global_state["temporal_params"]["stop"] = False + global_state["editing_state"] = "running" + + # reverse points order + p_to_opt = reverse_point_pairs(p_in_pixels) + t_to_opt = reverse_point_pairs(t_in_pixels) + print("Running with:") + print(f" Source: {p_in_pixels}") + print(f" Target: {t_in_pixels}") + step_idx = 0 + last_time = time.time() + while True: + print_memory_usage() + # add a TIMEOUT break + print(f"Running time: {time.time() - last_time}") + if IS_SPACE and time.time() - last_time > TIMEOUT: + print("Timeout break!") + break + if ( + global_state["temporal_params"]["stop"] + or global_state["generator_params"]["stop"] + ): + break + + # do drage here! + renderer._render_drag_impl( + global_state["generator_params"], + p_to_opt, # point + t_to_opt, # target + drag_mask, # mask, + global_state["params"]["motion_lambda"], # lambda_mask + reg=0, + feature_idx=5, # NOTE: do not support change for now + r1=global_state["params"]["r1_in_pixels"], # r1 + r2=global_state["params"]["r2_in_pixels"], # r2 + # random_seed = 0, + # noise_mode = 'const', + trunc_psi=global_state["params"]["trunc_psi"], + # force_fp32 = False, + # layer_name = None, + # sel_channels = 3, + # base_channel = 0, + # img_scale_db = 0, + # img_normalize = False, + # untransform = False, + is_drag=True, + to_pil=True, + ) + + if step_idx % global_state["draw_interval"] == 0: + print("Current Source:") + for key_point, p_i, t_i in zip(valid_points, p_to_opt, t_to_opt): + global_state["points"][key_point]["start_temp"] = [ + p_i[1], + p_i[0], + ] + global_state["points"][key_point]["target"] = [ + t_i[1], + t_i[0], + ] + start_temp = global_state["points"][key_point]["start_temp"] + print(f" {start_temp}") + + image_result = global_state["generator_params"]["image"] + image_draw = update_image_draw( + image_result, + global_state["points"], + global_state["mask"], + global_state["show_mask"], + global_state, + ) + global_state["images"]["image_raw"] = image_result + + yield ( + global_state, # global_state + step_idx, # form_steps_number, + global_state["images"]["image_show"], # form image + # gr.File.update(visible=False), + gr.Button.update(interactive=False), # form_reset_image + gr.Button.update(interactive=False), # add points button + gr.Button.update(interactive=False), # enable mask button + gr.Button.update(interactive=False), # undo points button + gr.Button.update(interactive=False), # reset mask button + # latent space + gr.Radio.update(interactive=False), # latent space + gr.Button.update(interactive=False), # start button + # enable stop button in loop + gr.Button.update(interactive=True), # stop button + # update other comps + gr.Number.update(interactive=False), # form_seed_number + gr.Number.update(interactive=False), # form_lr_number + gr.Checkbox.update(interactive=False), # show_mask + gr.Number.update(interactive=False), # form_lambda_number + gr.Button.update(interactive=False), # form_reset_custom_image + ) + + # increate step + step_idx += 1 + + image_result = global_state["generator_params"]["image"] + global_state["images"]["image_raw"] = image_result + image_draw = update_image_draw( + image_result, + global_state["points"], + global_state["mask"], + global_state["show_mask"], + global_state, + ) + + # fp = NamedTemporaryFile(suffix=".png", delete=False) + # image_result.save(fp, "PNG") + + global_state["editing_state"] = "add_points" + + yield ( + global_state, # global_state + 0, # reset step to 0 after stop. # form_steps_number, + global_state["images"]["image_show"], # form image + gr.Button.update(interactive=True), # form_reset_image + gr.Button.update(interactive=True), # add points button + gr.Button.update(interactive=True), # enable mask button + gr.Button.update(interactive=True), # undo points button + gr.Button.update(interactive=True), # reset mask button + gr.Radio.update(interactive=True), # latent space + gr.Button.update(interactive=True), # start button + gr.Button.update(interactive=False), # stop button + gr.Number.update(interactive=True), # form_seed_number + gr.Number.update(interactive=True), # form_lr_number + gr.Checkbox.update(interactive=True), # show_mask + gr.Number.update(interactive=True), # form_lambda_number + gr.Button.update(interactive=True), # form_reset_custom_image + ) + + form_start_btn.click( + on_click_start, + inputs=[global_state, form_image], + outputs=[ + global_state, + form_steps_number, + form_image, + # form_download_result_file, + # >>> buttons + form_reset_image, + enable_add_points, + enable_add_mask, + undo_points, + form_reset_mask_btn, + form_latent_space, + form_start_btn, + form_stop_btn, + # <<< buttonm + # >>> inputs comps + form_seed_number, + form_lr_number, + show_mask, + form_lambda_number, + form_reset_custom_image, + ], + ) + + def on_click_stop(global_state): + """Function to handle stop button is clicked. + 1. send a stop signal by set global_state["temporal_params"]["stop"] as True + 2. Disable Stop button + """ + global_state["temporal_params"]["stop"] = True + + return global_state, gr.Button.update(interactive=False) + + form_stop_btn.click( + on_click_stop, + inputs=[global_state], + outputs=[global_state, form_stop_btn], + queue=False, + show_progress=False, + ) + + form_draw_interval_number.change( + partial( + on_change_single_global_state, + "draw_interval", + map_transform=lambda x: int(x), + ), + inputs=[form_draw_interval_number, global_state], + outputs=[global_state], + queue=False, + show_progress=False, + ) + + def on_click_remove_point(global_state): + choice = global_state["curr_point"] + del global_state["points"][choice] + + choices = list(global_state["points"].keys()) + + if len(choices) > 0: + global_state["curr_point"] = choices[0] + + return ( + gr.Dropdown.update(choices=choices, value=choices[0]), + global_state, + ) + + # Mask + def on_click_reset_mask(global_state): + global_state["mask"] = np.ones( + ( + global_state["images"]["image_raw"].size[1], + global_state["images"]["image_raw"].size[0], + ), + dtype=np.uint8, + ) + image_draw = update_image_draw( + global_state["images"]["image_raw"], + global_state["points"], + global_state["mask"], + global_state["show_mask"], + global_state, + ) + return global_state, gr.Image.update(value=image_draw, interactive=False) + + form_reset_mask_btn.click( + on_click_reset_mask, + inputs=[global_state], + outputs=[global_state, form_image], + ) + + # Image + def on_click_enable_draw(global_state, image): + """Function to start add mask mode. + 1. Preprocess mask info from last state + 2. Change editing state to add_mask + 3. Set curr image with points and mask + """ + global_state = preprocess_mask_info(global_state, image) + global_state["editing_state"] = "add_mask" + image_raw = global_state["images"]["image_raw"] + image_draw = update_image_draw( + image_raw, global_state["points"], global_state["mask"], True, global_state + ) + return ( + global_state, + gr.Image.update(value=image_draw, interactive=True), + ) + + def on_click_remove_draw(global_state, image): + """Function to start remove mask mode. + 1. Preprocess mask info from last state + 2. Change editing state to remove_mask + 3. Set curr image with points and mask + """ + global_state = preprocess_mask_info(global_state, image) + global_state["edinting_state"] = "remove_mask" + image_raw = global_state["images"]["image_raw"] + image_draw = update_image_draw( + image_raw, global_state["points"], global_state["mask"], True, global_state + ) + return ( + global_state, + gr.Image.update(value=image_draw, interactive=True), + ) + + enable_add_mask.click( + on_click_enable_draw, + inputs=[global_state, form_image], + outputs=[ + global_state, + form_image, + ], + queue=False, + show_progress=False, + ) + + def on_click_add_point(global_state, image: dict): + """Function switch from add mask mode to add points mode. + 1. Updaste mask buffer if need + 2. Change global_state['editing_state'] to 'add_points' + 3. Set current image with mask + """ + + global_state = preprocess_mask_info(global_state, image) + global_state["editing_state"] = "add_points" + mask = global_state["mask"] + image_raw = global_state["images"]["image_raw"] + image_draw = update_image_draw( + image_raw, + global_state["points"], + mask, + global_state["show_mask"], + global_state, + ) + + return ( + global_state, + gr.Image.update(value=image_draw, interactive=False), + ) + + enable_add_points.click( + on_click_add_point, + inputs=[global_state, form_image], + outputs=[global_state, form_image], + queue=False, + show_progress=False, + ) + + def on_click_image(global_state, evt: gr.SelectData): + """This function only support click for point selection""" + xy = evt.index + if global_state["editing_state"] != "add_points": + print(f'In {global_state["editing_state"]} state. ' "Do not add points.") + + return global_state, global_state["images"]["image_show"] + + points = global_state["points"] + + point_idx = get_latest_points_pair(points) + if point_idx is None: + points[0] = {"start": xy, "target": None} + print(f"Click Image - Start - {xy}") + elif points[point_idx].get("target", None) is None: + points[point_idx]["target"] = xy + print(f"Click Image - Target - {xy}") + else: + points[point_idx + 1] = {"start": xy, "target": None} + print(f"Click Image - Start - {xy}") + + image_raw = global_state["images"]["image_raw"] + image_draw = update_image_draw( + image_raw, + global_state["points"], + global_state["mask"], + global_state["show_mask"], + global_state, + ) + + return global_state, image_draw + + form_image.select( + on_click_image, + inputs=[global_state], + outputs=[global_state, form_image], + queue=False, + show_progress=False, + ) + + def on_click_clear_points(global_state): + """Function to handle clear all control points + 1. clear global_state['points'] (clear_state) + 2. re-init network + 2. re-draw image + """ + clear_state(global_state, target="point") + + renderer: Renderer = global_state["renderer"] + renderer.feat_refs = None + + image_raw = global_state["images"]["image_raw"] + image_draw = update_image_draw( + image_raw, {}, global_state["mask"], global_state["show_mask"], global_state + ) + return global_state, image_draw + + undo_points.click( + on_click_clear_points, + inputs=[global_state], + outputs=[global_state, form_image], + queue=False, + show_progress=False, + ) + + def on_click_show_mask(global_state, show_mask): + """Function to control whether show mask on image.""" + global_state["show_mask"] = show_mask + + image_raw = global_state["images"]["image_raw"] + image_draw = update_image_draw( + image_raw, + global_state["points"], + global_state["mask"], + global_state["show_mask"], + global_state, + ) + return global_state, image_draw + + show_mask.change( + on_click_show_mask, + inputs=[global_state, show_mask], + outputs=[global_state, form_image], + queue=False, + show_progress=False, + ) + +# print("SHAReD: Start app", parser.parse_args()) +gr.close_all() +app.queue(concurrency_count=1, max_size=200, api_open=False) +app.launch(show_api=False) diff --git a/viz/capture_widget.py b/viz/capture_widget.py index 48e1373a32fde13c7811aa33bf21851e5cf5bf3c..79cc4f80c5bba2cf1e67593e85fb85cd7963ed89 100644 --- a/viz/capture_widget.py +++ b/viz/capture_widget.py @@ -16,22 +16,24 @@ from . import renderer import torch import torchvision -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class CaptureWidget: def __init__(self, viz): - self.viz = viz - self.path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '_screenshots')) - self.dump_image = False - self.dump_gui = False - self.defer_frames = 0 - self.disabled_time = 0 + self.viz = viz + self.path = os.path.abspath(os.path.join( + os.path.dirname(__file__), '..', '_screenshots')) + self.dump_image = False + self.dump_gui = False + self.defer_frames = 0 + self.disabled_time = 0 def dump_png(self, image): viz = self.viz try: _height, _width, channels = image.shape - assert channels in [1, 3] + print(viz.result) assert image.dtype == np.uint8 os.makedirs(self.path, exist_ok=True) file_id = 0 @@ -43,8 +45,10 @@ class CaptureWidget: if channels == 1: pil_image = PIL.Image.fromarray(image[:, :, 0], 'L') else: - pil_image = PIL.Image.fromarray(image, 'RGB') + pil_image = PIL.Image.fromarray(image[:, :, :3], 'RGB') pil_image.save(os.path.join(self.path, f'{file_id:05d}.png')) + np.save(os.path.join( + self.path, f'{file_id:05d}.npy'), viz.result.w) except: viz.result.error = renderer.CapturedException() @@ -57,9 +61,10 @@ class CaptureWidget: imgui.same_line(viz.label_w) _changed, self.path = imgui_utils.input_text('##path', self.path, 1024, - flags=(imgui.INPUT_TEXT_AUTO_SELECT_ALL | imgui.INPUT_TEXT_ENTER_RETURNS_TRUE), - width=(-1), - help_text='PATH') + flags=( + imgui.INPUT_TEXT_AUTO_SELECT_ALL | imgui.INPUT_TEXT_ENTER_RETURNS_TRUE), + width=(-1), + help_text='PATH') if imgui.is_item_hovered() and not imgui.is_item_active() and self.path != '': imgui.set_tooltip(self.path) imgui.text(' ') @@ -88,4 +93,4 @@ class CaptureWidget: if captured_frame is not None: self.dump_png(captured_frame) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/viz/drag_widget.py b/viz/drag_widget.py index 912b3786cf97d190ac2b306455262c9e8918a32f..348ab36de2daff2eff97589204b00d391b3a1e7e 100644 --- a/viz/drag_widget.py +++ b/viz/drag_widget.py @@ -5,29 +5,31 @@ import imgui import dnnlib from gui_utils import imgui_utils -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class DragWidget: def __init__(self, viz): - self.viz = viz - self.point = [-1, -1] - self.points = [] - self.targets = [] - self.is_point = True - self.last_click = False - self.is_drag = False - self.iteration = 0 - self.mode = 'point' - self.r_mask = 50 - self.show_mask = False - self.mask = torch.ones(256, 256) - self.lambda_mask = 20 - self.feature_idx = 5 - self.r1 = 3 - self.r2 = 12 - self.path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '_screenshots')) - self.defer_frames = 0 - self.disabled_time = 0 + self.viz = viz + self.point = [-1, -1] + self.points = [] + self.targets = [] + self.is_point = True + self.last_click = False + self.is_drag = False + self.iteration = 0 + self.mode = 'point' + self.r_mask = 50 + self.show_mask = False + self.mask = torch.ones(256, 256) + self.lambda_mask = 20 + self.feature_idx = 5 + self.r1 = 3 + self.r2 = 12 + self.path = os.path.abspath(os.path.join( + os.path.dirname(__file__), '..', '_screenshots')) + self.defer_frames = 0 + self.disabled_time = 0 def action(self, click, down, x, y): if self.mode == 'point': @@ -90,6 +92,7 @@ class DragWidget: @imgui_utils.scoped_by_object_id def __call__(self, show=True): viz = self.viz + reset = False if show: with imgui_utils.grayed_out(self.disabled_time != 0): imgui.text('Drag') @@ -118,34 +121,37 @@ class DragWidget: imgui.text(' ') imgui.same_line(viz.label_w) imgui.text(f'Steps: {self.iteration}') - + imgui.text('Mask') imgui.same_line(viz.label_w) if imgui_utils.button('Flexible area', width=viz.button_w, enabled='image' in viz.result): self.mode = 'flexible' self.show_mask = True - + imgui.same_line() if imgui_utils.button('Fixed area', width=viz.button_w, enabled='image' in viz.result): self.mode = 'fixed' self.show_mask = True - + imgui.text(' ') imgui.same_line(viz.label_w) if imgui_utils.button('Reset mask', width=viz.button_w, enabled='image' in viz.result): self.mask = torch.ones(self.height, self.width) imgui.same_line() - _clicked, self.show_mask = imgui.checkbox('Show mask', self.show_mask) + _clicked, self.show_mask = imgui.checkbox( + 'Show mask', self.show_mask) imgui.text(' ') imgui.same_line(viz.label_w) with imgui_utils.item_width(viz.font_size * 6): - changed, self.r_mask = imgui.input_int('Radius', self.r_mask) + changed, self.r_mask = imgui.input_int( + 'Radius', self.r_mask) imgui.text(' ') imgui.same_line(viz.label_w) with imgui_utils.item_width(viz.font_size * 6): - changed, self.lambda_mask = imgui.input_int('Lambda', self.lambda_mask) + changed, self.lambda_mask = imgui.input_int( + 'Lambda', self.lambda_mask) self.disabled_time = max(self.disabled_time - viz.frame_delta, 0) if self.defer_frames > 0: @@ -164,4 +170,4 @@ class DragWidget: viz.args.reset = reset -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/viz/latent_widget.py b/viz/latent_widget.py index eb8fd50c1b461fbab39cfda4b229bafdb05be511..f19cb8cb5ed7de1ba0035d744d62fa3ee9724f80 100644 --- a/viz/latent_widget.py +++ b/viz/latent_widget.py @@ -13,19 +13,20 @@ import dnnlib import torch from gui_utils import imgui_utils -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class LatentWidget: def __init__(self, viz): - self.viz = viz - self.seed = 0 - self.w_plus = True - self.reg = 0 - self.lr = 0.001 - self.w_path = '' - self.w_load = None - self.defer_frames = 0 - self.disabled_time = 0 + self.viz = viz + self.seed = 0 + self.w_plus = True + self.reg = 0 + self.lr = 0.001 + self.w_path = '' + self.w_load = None + self.defer_frames = 0 + self.disabled_time = 0 @imgui_utils.scoped_by_object_id def __call__(self, show=True): @@ -45,20 +46,22 @@ class LatentWidget: imgui.text(' ') imgui.same_line(viz.label_w) _changed, self.w_path = imgui_utils.input_text('##path', self.w_path, 1024, - flags=(imgui.INPUT_TEXT_AUTO_SELECT_ALL | imgui.INPUT_TEXT_ENTER_RETURNS_TRUE), - width=(-1), - help_text='Path to latent code') + flags=( + imgui.INPUT_TEXT_AUTO_SELECT_ALL | imgui.INPUT_TEXT_ENTER_RETURNS_TRUE), + width=(-1), + help_text='Path to latent code') if imgui.is_item_hovered() and not imgui.is_item_active() and self.w_path != '': imgui.set_tooltip(self.w_path) imgui.text(' ') imgui.same_line(viz.label_w) if imgui_utils.button('Load latent', width=viz.button_w, enabled=(self.disabled_time == 0 and 'image' in viz.result)): - assert os.path.isfile(self.w_path), f"{self.w_path} does not exist!" + assert os.path.isfile( + self.w_path), f"{self.w_path} does not exist!" self.w_load = torch.load(self.w_path) self.defer_frames = 2 self.disabled_time = 0.5 - + imgui.text(' ') imgui.same_line(viz.label_w) with imgui_utils.item_width(viz.button_w): @@ -75,7 +78,8 @@ class LatentWidget: imgui.text(' ') imgui.same_line(viz.label_w) - reset_w = imgui_utils.button('Reset', width=viz.button_w, enabled='image' in viz.result) + reset_w = imgui_utils.button( + 'Reset', width=viz.button_w, enabled='image' in viz.result) imgui.same_line() _clicked, w = imgui.checkbox('w', not self.w_plus) if w: @@ -93,4 +97,4 @@ class LatentWidget: viz.args.reset_w = reset_w viz.args.lr = lr -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/viz/pickle_widget.py b/viz/pickle_widget.py index 1482b7fe481594a9e74ac78274c8003b8fd4df89..6d92f291032749e53758b61fb62753474a9f1ad4 100644 --- a/viz/pickle_widget.py +++ b/viz/pickle_widget.py @@ -17,21 +17,24 @@ from gui_utils import imgui_utils from . import renderer -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + def _locate_results(pattern): return pattern -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class PickleWidget: def __init__(self, viz): - self.viz = viz - self.search_dirs = [] - self.cur_pkl = None - self.user_pkl = '' - self.recent_pkls = [] - self.browse_cache = dict() # {tuple(path, ...): [dnnlib.EasyDict(), ...], ...} + self.viz = viz + self.search_dirs = [] + self.cur_pkl = None + self.user_pkl = '' + self.recent_pkls = [] + # {tuple(path, ...): [dnnlib.EasyDict(), ...], ...} + self.browse_cache = dict() self.browse_refocus = False self.load('', ignore_errors=True) @@ -47,7 +50,7 @@ class PickleWidget: def load(self, pkl, ignore_errors=False): viz = self.viz viz.clear_result() - viz.skip_frame() # The input field will change on next frame. + viz.skip_frame() # The input field will change on next frame. try: resolved = self.resolve_pkl(pkl) name = resolved.replace('\\', '/').split('/')[-1] @@ -62,9 +65,11 @@ class PickleWidget: self.cur_pkl = None self.user_pkl = pkl if pkl == '': - viz.result = dnnlib.EasyDict(message='No network pickle loaded') + viz.result = dnnlib.EasyDict( + message='No network pickle loaded') else: - viz.result = dnnlib.EasyDict(error=renderer.CapturedException()) + viz.result = dnnlib.EasyDict( + error=renderer.CapturedException()) if not ignore_errors: raise @@ -77,9 +82,10 @@ class PickleWidget: imgui.same_line(viz.label_w) idx = self.user_pkl.rfind('/') changed, self.user_pkl = imgui_utils.input_text('##pkl', self.user_pkl[idx+1:], 1024, - flags=(imgui.INPUT_TEXT_AUTO_SELECT_ALL | imgui.INPUT_TEXT_ENTER_RETURNS_TRUE), - width=(-1), - help_text=' | | | | /.pkl') + flags=( + imgui.INPUT_TEXT_AUTO_SELECT_ALL | imgui.INPUT_TEXT_ENTER_RETURNS_TRUE), + width=(-1), + help_text=' | | | | /.pkl') if changed: self.load(self.user_pkl, ignore_errors=True) if imgui.is_item_hovered() and not imgui.is_item_active() and self.user_pkl != '': @@ -123,7 +129,7 @@ class PickleWidget: recurse(self.search_dirs) if self.browse_refocus: imgui.set_scroll_here() - viz.skip_frame() # Focus will change on next frame. + viz.skip_frame() # Focus will change on next frame. self.browse_refocus = False imgui.end_popup() @@ -141,11 +147,14 @@ class PickleWidget: if os.path.isdir(parent): for entry in os.scandir(parent): if entry.is_dir() and run_regex.fullmatch(entry.name): - items.append(dnnlib.EasyDict(type='run', name=entry.name, path=os.path.join(parent, entry.name))) + items.append(dnnlib.EasyDict( + type='run', name=entry.name, path=os.path.join(parent, entry.name))) if entry.is_file() and pkl_regex.fullmatch(entry.name): - items.append(dnnlib.EasyDict(type='pkl', name=entry.name, path=os.path.join(parent, entry.name))) + items.append(dnnlib.EasyDict( + type='pkl', name=entry.name, path=os.path.join(parent, entry.name))) - items = sorted(items, key=lambda item: (item.name.replace('_', ' '), item.path)) + items = sorted(items, key=lambda item: ( + item.name.replace('_', ' '), item.path)) return items def resolve_pkl(self, pattern): @@ -161,7 +170,8 @@ class PickleWidget: # Run dir => pick the last saved snapshot. if os.path.isdir(path): - pkl_files = sorted(glob.glob(os.path.join(path, 'network-snapshot-*.pkl'))) + pkl_files = sorted( + glob.glob(os.path.join(path, 'network-snapshot-*.pkl'))) if len(pkl_files) == 0: raise IOError(f'No network pickle found in "{path}"') path = pkl_files[-1] @@ -170,4 +180,4 @@ class PickleWidget: path = os.path.abspath(path) return path -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/viz/renderer.py b/viz/renderer.py index 3a7d2280f76a01135afe0ed2337f1c72b48a76b7..4c6fc27c50046e56af68166e017276d97a173580 100644 --- a/viz/renderer.py +++ b/viz/renderer.py @@ -20,9 +20,10 @@ import torch.nn.functional as F import matplotlib.cm import dnnlib from torch_utils.ops import upfirdn2d -import legacy # pylint: disable=import-error +import legacy # pylint: disable=import-error + +# ---------------------------------------------------------------------------- -#---------------------------------------------------------------------------- class CapturedException(Exception): def __init__(self, msg=None): @@ -36,16 +37,18 @@ class CapturedException(Exception): assert isinstance(msg, str) super().__init__(msg) -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class CaptureSuccess(Exception): def __init__(self, out): super().__init__() self.out = out -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + -def add_watermark_np(input_image_array, watermark_text="Watermark"): +def add_watermark_np(input_image_array, watermark_text="AI Generated"): image = Image.fromarray(np.uint8(input_image_array)).convert("RGBA") # Initialize text image @@ -54,8 +57,10 @@ def add_watermark_np(input_image_array, watermark_text="Watermark"): d = ImageDraw.Draw(txt) text_width, text_height = font.getsize(watermark_text) - text_position = (image.size[0] - text_width - 10, image.size[1] - text_height - 10) - text_color = (255, 255, 255, 128) # white color with the alpha channel set to semi-transparent + text_position = (image.size[0] - text_width - + 10, image.size[1] - text_height - 10) + # white color with the alpha channel set to semi-transparent + text_color = (255, 255, 255, 128) # Draw the text onto the text canvas d.text(text_position, watermark_text, font=font, fill=text_color) @@ -65,21 +70,24 @@ def add_watermark_np(input_image_array, watermark_text="Watermark"): watermarked_array = np.array(watermarked) return watermarked_array -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- + class Renderer: def __init__(self, disable_timing=False): - self._device = torch.device('cuda') - self._pkl_data = dict() # {pkl: dict | CapturedException, ...} - self._networks = dict() # {cache_key: torch.nn.Module, ...} - self._pinned_bufs = dict() # {(shape, dtype): torch.Tensor, ...} - self._cmaps = dict() # {name: torch.Tensor, ...} - self._is_timing = False + self._device = torch.device('cuda' if torch.cuda.is_available( + ) else 'mps' if torch.backends.mps.is_available() else 'cpu') + self._dtype = torch.float32 if self._device.type == 'mps' else torch.float64 + self._pkl_data = dict() # {pkl: dict | CapturedException, ...} + self._networks = dict() # {cache_key: torch.nn.Module, ...} + self._pinned_bufs = dict() # {(shape, dtype): torch.Tensor, ...} + self._cmaps = dict() # {name: torch.Tensor, ...} + self._is_timing = False if not disable_timing: - self._start_event = torch.cuda.Event(enable_timing=True) - self._end_event = torch.cuda.Event(enable_timing=True) + self._start_event = torch.cuda.Event(enable_timing=True) + self._end_event = torch.cuda.Event(enable_timing=True) self._disable_timing = disable_timing - self._net_layers = dict() # {cache_key: [dnnlib.EasyDict, ...], ...} + self._net_layers = dict() # {cache_key: [dnnlib.EasyDict, ...], ...} def render(self, **args): if self._disable_timing: @@ -95,6 +103,9 @@ class Renderer: if hasattr(self, 'pkl'): if self.pkl != args['pkl']: init_net = True + if hasattr(self, 'w_load'): + if self.w_load is not args['w_load']: + init_net = True if hasattr(self, 'w0_seed'): if self.w0_seed != args['w0_seed']: init_net = True @@ -122,7 +133,8 @@ class Renderer: if self._is_timing and not self._disable_timing: self._end_event.synchronize() - res.render_time = self._start_event.elapsed_time(self._end_event) * 1e-3 + res.render_time = self._start_event.elapsed_time( + self._end_event) * 1e-3 self._is_timing = False return res @@ -143,7 +155,8 @@ class Renderer: raise data orig_net = data[key] - cache_key = (orig_net, self._device, tuple(sorted(tweak_kwargs.items()))) + cache_key = (orig_net, self._device, tuple( + sorted(tweak_kwargs.items()))) net = self._networks.get(cache_key, None) if net is None: try: @@ -159,9 +172,11 @@ class Renderer: print(data[key].init_args) print(data[key].init_kwargs) if 'stylegan_human' in pkl: - net = Generator(*data[key].init_args, **data[key].init_kwargs, square=False, padding=True) + net = Generator( + *data[key].init_args, **data[key].init_kwargs, square=False, padding=True) else: - net = Generator(*data[key].init_args, **data[key].init_kwargs) + net = Generator(*data[key].init_args, + **data[key].init_kwargs) net.load_state_dict(data[key].state_dict()) net.to(self._device) except: @@ -202,26 +217,29 @@ class Renderer: return x def init_network(self, res, - pkl = None, - w0_seed = 0, - w_load = None, - w_plus = True, - noise_mode = 'const', - trunc_psi = 0.7, - trunc_cutoff = None, - input_transform = None, - lr = 0.001, - **kwargs - ): + pkl=None, + w0_seed=0, + w_load=None, + w_plus=True, + noise_mode='const', + trunc_psi=0.7, + trunc_cutoff=None, + input_transform=None, + lr=0.001, + **kwargs + ): # Dig up network details. self.pkl = pkl G = self.get_network(pkl, 'G_ema') self.G = G res.img_resolution = G.img_resolution res.num_ws = G.num_ws - res.has_noise = any('noise_const' in name for name, _buf in G.synthesis.named_buffers()) - res.has_input_transform = (hasattr(G.synthesis, 'input') and hasattr(G.synthesis.input, 'transform')) - + res.has_noise = any('noise_const' in name for name, + _buf in G.synthesis.named_buffers()) + res.has_input_transform = ( + hasattr(G.synthesis, 'input') and hasattr(G.synthesis.input, 'transform')) + res.stop = False + self.lr = lr # Set input transform. if res.has_input_transform: m = np.eye(3) @@ -238,11 +256,13 @@ class Renderer: if self.w_load is None: # Generate random latents. - z = torch.from_numpy(np.random.RandomState(w0_seed).randn(1, 512)).to(self._device).float() + z = torch.from_numpy(np.random.RandomState(w0_seed).randn( + 1, 512)).to(self._device, dtype=self._dtype) # Run mapping network. label = torch.zeros([1, G.c_dim], device=self._device) - w = G.mapping(z, label, truncation_psi=trunc_psi, truncation_cutoff=trunc_cutoff) + w = G.mapping(z, label, truncation_psi=trunc_psi, + truncation_cutoff=trunc_cutoff) else: w = self.w_load.clone().to(self._device) @@ -258,6 +278,20 @@ class Renderer: self.feat_refs = None self.points0_pt = None + def set_latent(self, w, trunc_psi, trunc_cutoff): + # label = torch.zeros([1, self.G.c_dim], device=self._device) + # w = self.G.mapping(z, label, truncation_psi=trunc_psi, truncation_cutoff=trunc_cutoff) + self.w0 = w.detach().clone() + if self.w_plus: + self.w = w.detach() + else: + self.w = w[:, 0, :].detach() + self.w.requires_grad = True + self.w_optim = torch.optim.Adam([self.w], lr=self.lr) + + self.feat_refs = None + self.points0_pt = None + def update_lr(self, lr): del self.w_optim @@ -266,118 +300,143 @@ class Renderer: print(' Remain feat_refs and points0_pt') def _render_drag_impl(self, res, - points = [], - targets = [], - mask = None, - lambda_mask = 10, - reg = 0, - feature_idx = 5, - r1 = 3, - r2 = 12, - random_seed = 0, - noise_mode = 'const', - trunc_psi = 0.7, - force_fp32 = False, - layer_name = None, - sel_channels = 3, - base_channel = 0, - img_scale_db = 0, - img_normalize = False, - untransform = False, - is_drag = False, - reset = False, - to_pil = False, - **kwargs - ): - G = self.G - ws = self.w - if ws.dim() == 2: - ws = ws.unsqueeze(1).repeat(1,6,1) - ws = torch.cat([ws[:,:6,:], self.w0[:,6:,:]], dim=1) - if hasattr(self, 'points'): - if len(points) != len(self.points): - reset = True - if reset: - self.feat_refs = None - self.points0_pt = None - self.points = points - - # Run synthesis network. - label = torch.zeros([1, G.c_dim], device=self._device) - img, feat = G(ws, label, truncation_psi=trunc_psi, noise_mode=noise_mode, input_is_w=True, return_feature=True) - - h, w = G.img_resolution, G.img_resolution - - if is_drag: - X = torch.linspace(0, h, h) - Y = torch.linspace(0, w, w) - xx, yy = torch.meshgrid(X, Y) - feat_resize = F.interpolate(feat[feature_idx], [h, w], mode='bilinear') - if self.feat_refs is None: - self.feat0_resize = F.interpolate(feat[feature_idx].detach(), [h, w], mode='bilinear') - self.feat_refs = [] - for point in points: - py, px = round(point[0]), round(point[1]) - self.feat_refs.append(self.feat0_resize[:,:,py,px]) - self.points0_pt = torch.Tensor(points).unsqueeze(0).to(self._device) # 1, N, 2 - - # Point tracking with feature matching - with torch.no_grad(): + points=[], + targets=[], + mask=None, + lambda_mask=10, + reg=0, + feature_idx=5, + r1=3, + r2=12, + random_seed=0, + noise_mode='const', + trunc_psi=0.7, + force_fp32=False, + layer_name=None, + sel_channels=3, + base_channel=0, + img_scale_db=0, + img_normalize=False, + untransform=False, + is_drag=False, + reset=False, + to_pil=False, + **kwargs + ): + try: + G = self.G + ws = self.w + if ws.dim() == 2: + ws = ws.unsqueeze(1).repeat(1, 6, 1) + ws = torch.cat([ws[:, :6, :], self.w0[:, 6:, :]], dim=1) + if hasattr(self, 'points'): + if len(points) != len(self.points): + reset = True + if reset: + self.feat_refs = None + self.points0_pt = None + self.points = points + + # Run synthesis network. + label = torch.zeros([1, G.c_dim], device=self._device) + img, feat = G(ws, label, truncation_psi=trunc_psi, + noise_mode=noise_mode, input_is_w=True, return_feature=True) + + h, w = G.img_resolution, G.img_resolution + + if is_drag: + X = torch.linspace(0, h, h) + Y = torch.linspace(0, w, w) + xx, yy = torch.meshgrid(X, Y) + feat_resize = F.interpolate( + feat[feature_idx], [h, w], mode='bilinear') + if self.feat_refs is None: + self.feat0_resize = F.interpolate( + feat[feature_idx].detach(), [h, w], mode='bilinear') + self.feat_refs = [] + for point in points: + py, px = round(point[0]), round(point[1]) + self.feat_refs.append(self.feat0_resize[:, :, py, px]) + self.points0_pt = torch.Tensor(points).unsqueeze( + 0).to(self._device) # 1, N, 2 + + # Point tracking with feature matching + with torch.no_grad(): + for j, point in enumerate(points): + r = round(r2 / 512 * h) + up = max(point[0] - r, 0) + down = min(point[0] + r + 1, h) + left = max(point[1] - r, 0) + right = min(point[1] + r + 1, w) + feat_patch = feat_resize[:, :, up:down, left:right] + L2 = torch.linalg.norm( + feat_patch - self.feat_refs[j].reshape(1, -1, 1, 1), dim=1) + _, idx = torch.min(L2.view(1, -1), -1) + width = right - left + point = [idx.item() // width + up, idx.item() % + width + left] + points[j] = point + + res.points = [[point[0], point[1]] for point in points] + + # Motion supervision + loss_motion = 0 + res.stop = True for j, point in enumerate(points): - r = round(r2 / 512 * h) - up = max(point[0] - r, 0) - down = min(point[0] + r + 1, h) - left = max(point[1] - r, 0) - right = min(point[1] + r + 1, w) - feat_patch = feat_resize[:,:,up:down,left:right] - L2 = torch.linalg.norm(feat_patch - self.feat_refs[j].reshape(1,-1,1,1), dim=1) - _, idx = torch.min(L2.view(1,-1), -1) - width = right - left - point = [idx.item() // width + up, idx.item() % width + left] - points[j] = point - - res.points = [[point[0], point[1]] for point in points] - - # Motion supervision - loss_motion = 0 - res.stop = True - for j, point in enumerate(points): - direction = torch.Tensor([targets[j][1] - point[1], targets[j][0] - point[0]]) - if torch.linalg.norm(direction) > max(2 / 512 * h, 2): - res.stop = False - if torch.linalg.norm(direction) > 1: - distance = ((xx.to(self._device) - point[0])**2 + (yy.to(self._device) - point[1])**2)**0.5 - relis, reljs = torch.where(distance < round(r1 / 512 * h)) - direction = direction / (torch.linalg.norm(direction) + 1e-7) - gridh = (relis-direction[1]) / (h-1) * 2 - 1 - gridw = (reljs-direction[0]) / (w-1) * 2 - 1 - grid = torch.stack([gridw,gridh], dim=-1).unsqueeze(0).unsqueeze(0) - target = F.grid_sample(feat_resize.float(), grid, align_corners=True).squeeze(2) - loss_motion += F.l1_loss(feat_resize[:,:,relis,reljs], target.detach()) - - loss = loss_motion - if mask is not None: - if mask.min() == 0 and mask.max() == 1: - mask_usq = mask.to(self._device).unsqueeze(0).unsqueeze(0) - loss_fix = F.l1_loss(feat_resize * mask_usq, self.feat0_resize * mask_usq) - loss += lambda_mask * loss_fix - - loss += reg * F.l1_loss(ws, self.w0) # latent code regularization - if not res.stop: - self.w_optim.zero_grad() - loss.backward() - self.w_optim.step() - - # Scale and convert to uint8. - img = img[0] - if img_normalize: - img = img / img.norm(float('inf'), dim=[1,2], keepdim=True).clip(1e-8, 1e8) - img = img * (10 ** (img_scale_db / 20)) - img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8).permute(1, 2, 0) - if to_pil: - from PIL import Image - img = img.cpu().numpy() - img = Image.fromarray(img) - res.image = img - -#---------------------------------------------------------------------------- + direction = torch.Tensor( + [targets[j][1] - point[1], targets[j][0] - point[0]]) + if torch.linalg.norm(direction) > max(2 / 512 * h, 2): + res.stop = False + if torch.linalg.norm(direction) > 1: + distance = ( + (xx.to(self._device) - point[0])**2 + (yy.to(self._device) - point[1])**2)**0.5 + relis, reljs = torch.where( + distance < round(r1 / 512 * h)) + direction = direction / \ + (torch.linalg.norm(direction) + 1e-7) + gridh = (relis-direction[1]) / (h-1) * 2 - 1 + gridw = (reljs-direction[0]) / (w-1) * 2 - 1 + grid = torch.stack( + [gridw, gridh], dim=-1).unsqueeze(0).unsqueeze(0) + target = F.grid_sample( + feat_resize.float(), grid, align_corners=True).squeeze(2) + loss_motion += F.l1_loss( + feat_resize[:, :, relis, reljs], target.detach()) + + loss = loss_motion + if mask is not None: + if mask.min() == 0 and mask.max() == 1: + mask_usq = mask.to( + self._device).unsqueeze(0).unsqueeze(0) + loss_fix = F.l1_loss( + feat_resize * mask_usq, self.feat0_resize * mask_usq) + loss += lambda_mask * loss_fix + + # latent code regularization + loss += reg * F.l1_loss(ws, self.w0) + if not res.stop: + self.w_optim.zero_grad() + loss.backward() + self.w_optim.step() + + # Scale and convert to uint8. + img = img[0] + if img_normalize: + img = img / img.norm(float('inf'), + dim=[1, 2], keepdim=True).clip(1e-8, 1e8) + img = img * (10 ** (img_scale_db / 20)) + img = (img * 127.5 + 128).clamp(0, + 255).to(torch.uint8).permute(1, 2, 0) + if to_pil: + from PIL import Image + img = img.cpu().numpy() + img = Image.fromarray(img) + res.image = img + res.w = ws.detach().cpu().numpy() + except Exception as e: + import os + print(f'Renderer error: {e}') + print("Out of memory error occurred. Restarting the app...") + os.execv(sys.executable, ['python'] + sys.argv) + +# ----------------------------------------------------------------------------