back

app.py

@@ -1,286 +1,154 @@
 import spaces
-import os
-import time
-from os import path
-from huggingface_hub import hf_hub_download
-import numpy as np
+import gradio as gr
 import torch
-import rembg
 from PIL import Image
-from torchvision.transforms import v2
-from einops import rearrange
-from pytorch_lightning import seed_everything
-from omegaconf import OmegaConf
-from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
-import gradio as gr
-import shutil
-import tempfile
-from src.utils.train_util import instantiate_from_config
-from src.utils.camera_util import (
-    FOV_to_intrinsics, 
-    get_zero123plus_input_cameras,
-    get_circular_camera_poses,
-)
-from src.utils.mesh_util import save_obj, save_glb
-from src.utils.infer_util import remove_background, resize_foreground, images_to_video
+from transformers import AutoProcessor, AutoModelForCausalLM, pipeline
+from diffusers import DiffusionPipeline
 import random
-import requests
-import io
+import numpy as np
+import os
+import subprocess
+subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
 
-# Set up cache path
-cache_path = path.join(path.dirname(path.abspath(__file__)), "models")
-os.environ["TRANSFORMERS_CACHE"] = cache_path
-os.environ["HF_HUB_CACHE"] = cache_path
-os.environ["HF_HOME"] = cache_path
+# Initialize models
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.bfloat16
 
 huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
 
-if not path.exists(cache_path):
-    os.makedirs(cache_path, exist_ok=True)
-
-torch.backends.cuda.matmul.allow_tf32 = True
-
-class timer:
-    def __init__(self, method_name="timed process"):
-        self.method = method_name
-    def __enter__(self):
-        self.start = time.time()
-        print(f"{self.method} starts")
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        end = time.time()
-        print(f"{self.method} took {str(round(end - self.start, 2))}s")
-
-def find_cuda():
-    cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH')
-    if cuda_home and os.path.exists(cuda_home):
-        return cuda_home
-    nvcc_path = shutil.which('nvcc')
-    if nvcc_path:
-        cuda_path = os.path.dirname(os.path.dirname(nvcc_path))
-        return cuda_path
-    return None
-
-cuda_path = find_cuda()
-if cuda_path:
-    print(f"CUDA installation found at: {cuda_path}")
-else:
-    print("CUDA installation not found")
-
-
-API_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
-headers = {"Authorization": f"Bearer {API_TOKEN}"}
-timeout = 100
-
-device = 'cuda'
-
-# Load 3D generation models
-config_path = 'configs/instant-mesh-large.yaml'
-config = OmegaConf.load(config_path)
-config_name = os.path.basename(config_path).replace('.yaml', '')
-model_config = config.model_config
-infer_config = config.infer_config
-
-IS_FLEXICUBES = True if config_name.startswith('instant-mesh') else False
-
-# Load diffusion model for 3D generation
-print('Loading diffusion model ...')
-pipeline = DiffusionPipeline.from_pretrained(
-    "sudo-ai/zero123plus-v1.2", 
-    custom_pipeline="zero123plus",
-    torch_dtype=torch.float16,
-)
-pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
-    pipeline.scheduler.config, timestep_spacing='trailing'
-)
-
-# Load custom white-background UNet
-unet_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="diffusion_pytorch_model.bin", repo_type="model")
-state_dict = torch.load(unet_ckpt_path, map_location='cpu')
-pipeline.unet.load_state_dict(state_dict, strict=True)
-
-pipeline = pipeline.to(device)
-
-# Load reconstruction model
-print('Loading reconstruction model ...')
-model_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="instant_mesh_large.ckpt", repo_type="model")
-model = instantiate_from_config(model_config)
-state_dict = torch.load(model_ckpt_path, map_location='cpu')['state_dict']
-state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.') and 'source_camera' not in k}
-model.load_state_dict(state_dict, strict=True)
-
-model = model.to(device)
-
-print('Loading Finished!')
-
-def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False):
-    c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation)
-    if is_flexicubes:
-        cameras = torch.linalg.inv(c2ws)
-        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1, 1)
-    else:
-        extrinsics = c2ws.flatten(-2)
-        intrinsics = FOV_to_intrinsics(50.0).unsqueeze(0).repeat(M, 1, 1).float().flatten(-2)
-        cameras = torch.cat([extrinsics, intrinsics], dim=-1)
-        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1)
-    return cameras
-
-def preprocess(input_image, do_remove_background):
-    rembg_session = rembg.new_session() if do_remove_background else None
-    if do_remove_background:
-        input_image = remove_background(input_image, rembg_session)
-        input_image = resize_foreground(input_image, 0.85)
-    return input_image
+# FLUX.1-dev model
+pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, token = huggingface_token).to(device)
 
+# Initialize Florence model
+florence_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval()
+florence_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True)
 
+# Prompt Enhancer
+enhancer_long = pipeline("summarization", model="gokaygokay/Lamini-Prompt-Enchance-Long", device=device)
 
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
 
+# Florence caption function
 @spaces.GPU
-def generate_mvs(input_image, sample_steps, sample_seed):
-    seed_everything(sample_seed)
-    z123_image = pipeline(
-        input_image, 
-        num_inference_steps=sample_steps
-    ).images[0]
-    show_image = np.asarray(z123_image, dtype=np.uint8)
-    show_image = torch.from_numpy(show_image)
-    show_image = rearrange(show_image, '(n h) (m w) c -> (n m) h w c', n=3, m=2)
-    show_image = rearrange(show_image, '(n m) h w c -> (n h) (m w) c', n=2, m=3)
-    show_image = Image.fromarray(show_image.numpy())
-    return z123_image, show_image
-
-@spaces.GPU
-def make3d(images):
-    global model
-    if IS_FLEXICUBES:
-        model.init_flexicubes_geometry(device, use_renderer=False)
-    model = model.eval()
-
-    images = np.asarray(images, dtype=np.float32) / 255.0
-    images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float()
-    images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2)
-
-    input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=4.0).to(device)
-    render_cameras = get_render_cameras(batch_size=1, radius=2.5, is_flexicubes=IS_FLEXICUBES).to(device)
-
-    images = images.unsqueeze(0).to(device)
-    images = v2.functional.resize(images, (320, 320), interpolation=3, antialias=True).clamp(0, 1)
-
-    mesh_fpath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name
-    mesh_basename = os.path.basename(mesh_fpath).split('.')[0]
-    mesh_dirname = os.path.dirname(mesh_fpath)
-    mesh_glb_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb")
-
-    with torch.no_grad():
-        planes = model.forward_planes(images, input_cameras)
-        mesh_out = model.extract_mesh(
-            planes,
-            use_texture_map=False,
-            **infer_config,
-        )
-        vertices, faces, vertex_colors = mesh_out
-        vertices = vertices[:, [1, 2, 0]]
-        save_glb(vertices, faces, vertex_colors, mesh_glb_fpath)
-        save_obj(vertices, faces, vertex_colors, mesh_fpath)
+def florence_caption(image):
+    # Convert image to PIL if it's not already
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(image)
     
-    return mesh_fpath, mesh_glb_fpath
-
-# Remove the FluxPipeline setup and replace with the query function
-def query(prompt, steps=28, cfg_scale=3.5, randomize_seed=True, seed=-1, width=1024, height=1024):
-    if not prompt:
-        return None
-
-    lora_id = "gokaygokay/Flux-Game-Assets-LoRA-v2"
-    API_URL = f"https://api-inference.huggingface.co/models/{lora_id}"
+    inputs = florence_processor(text="<MORE_DETAILED_CAPTION>", images=image, return_tensors="pt").to(device)
+    generated_ids = florence_model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=1024,
+        early_stopping=False,
+        do_sample=False,
+        num_beams=3,
+    )
+    generated_text = florence_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    parsed_answer = florence_processor.post_process_generation(
+        generated_text,
+        task="<MORE_DETAILED_CAPTION>",
+        image_size=(image.width, image.height)
+    )
+    return parsed_answer["<MORE_DETAILED_CAPTION>"]
+
+# Prompt Enhancer function
+def enhance_prompt(input_prompt):
+    result = enhancer_long("Enhance the description: " + input_prompt)
+    enhanced_text = result[0]['summary_text']
+    return enhanced_text
+
+@spaces.GPU(duration=190)
+def process_workflow(image, text_prompt, use_enhancer, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+    if image is not None:
+        # Convert image to PIL if it's not already
+        if not isinstance(image, Image.Image):
+            image = Image.fromarray(image)
+        
+        prompt = florence_caption(image)
+        print(prompt)
+    else:
+        prompt = text_prompt
+    
+    if use_enhancer:
+        prompt = enhance_prompt(prompt)
     
     if randomize_seed:
-        seed = random.randint(1, 4294967296)
+        seed = random.randint(0, MAX_SEED)
     
-    payload = {
-        "inputs": prompt,
-        "steps": steps,
-        "cfg_scale": cfg_scale,
-        "seed": seed,
-        "parameters": {
-            "width": width,
-            "height": height
-        }
-    }
-
-    response = requests.post(API_URL, headers=headers, json=payload, timeout=100)
-    if response.status_code != 200:
-        if response.status_code == 503:
-            raise gr.Error("The model is being loaded")
-        raise gr.Error(f"Error {response.status_code}")
+    generator = torch.Generator(device=device).manual_seed(seed)
+    
+    image = pipe(
+        prompt=prompt,
+        generator=generator,
+        num_inference_steps=num_inference_steps,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale
+    ).images[0]
+    
+    return image, prompt, seed
+
+custom_css = """
+.input-group, .output-group {
+    border: 1px solid #e0e0e0;
+    border-radius: 10px;
+    padding: 20px;
+    margin-bottom: 20px;
+    background-color: #f9f9f9;
+}
+.submit-btn {
+    background-color: #2980b9 !important;
+    color: white !important;
+}
+.submit-btn:hover {
+    background-color: #3498db !important;
+}
+"""
+
+title = """<h1 align="center">FLUX.1-dev with Florence-2 Captioner and Prompt Enhancer</h1>
+<p><center>
+<a href="https://huggingface.co/black-forest-labs/FLUX.1-dev" target="_blank">[FLUX.1-dev Model]</a>
+<a href="https://huggingface.co/microsoft/Florence-2-base" target="_blank">[Florence-2 Model]</a>
+<a href="https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance-Long" target="_blank">[Prompt Enhancer Long]</a>
+<p align="center">Create long prompts from images or enhance your short prompts with prompt enhancer</p>
+</center></p>
+"""
+
+with gr.Blocks(css=custom_css, theme=gr.themes.Soft(primary_hue="blue", secondary_hue="gray")) as demo:
+    gr.HTML(title)
     
-    try:
-        image_bytes = response.content
-        image = Image.open(io.BytesIO(image_bytes))
-        return image
-    except Exception as e:
-        print(f"Error when trying to open the image: {e}")
-        return None
-
-# Update the Gradio interface
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-        """
-        <div style="text-align: center; max-width: 650px; margin: 0 auto;">
-            <h1 style="font-size: 2.5rem; font-weight: 700; margin-bottom: 1rem;">Flux Image to 3D Model Generator</h1>
-        </div>
-        """
-    )
-
     with gr.Row():
-        with gr.Column(scale=3):
-            prompt = gr.Textbox(
-                label="Your Image Description",
-                placeholder="E.g., A serene landscape with mountains and a lake at sunset",
-                lines=3
-            )
+        with gr.Column(scale=1):
+            with gr.Group(elem_classes="input-group"):
+                input_image = gr.Image(label="Input Image (Florence-2 Captioner)")
             
             with gr.Accordion("Advanced Settings", open=False):
-                with gr.Group():
-                    with gr.Row():
-                        height = gr.Slider(label="Height", minimum=256, maximum=1152, step=64, value=1024)
-                        width = gr.Slider(label="Width", minimum=256, maximum=1152, step=64, value=1024)
-                    
-                    with gr.Row():
-                        steps = gr.Slider(label="Inference Steps", minimum=10, maximum=50, step=1, value=28)
-                        scales = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=5.0, step=0.1, value=3.5)
-                    
-                    seed = gr.Number(label="Seed", value=-1, precision=0)
-                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+                text_prompt = gr.Textbox(label="Text Prompt (optional, used if no image is uploaded)")
+                use_enhancer = gr.Checkbox(label="Use Prompt Enhancer", value=False)
+                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
+                width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
+                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
+                guidance_scale = gr.Slider(label="Guidance Scale", minimum=1, maximum=15, step=0.1, value=3.5)
+                num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=28)
             
-            generate_btn = gr.Button("Generate 3D Model", variant="primary")
-
-        with gr.Column(scale=4):
-            flux_output = gr.Image(label="Generated Flux Image")
-            mv_show_images = gr.Image(label="Generated Multi-views")
-            with gr.Row():
-                with gr.Tab("OBJ"):
-                    output_model_obj = gr.Model3D(label="Output Model (OBJ Format)")
-                with gr.Tab("GLB"):
-                    output_model_glb = gr.Model3D(label="Output Model (GLB Format)")
-
-    mv_images = gr.State()
-
-    def process_pipeline(prompt, height, width, steps, scales, seed, randomize_seed):
-        # Generate Flux image using the API
-        prompt_real = f"wbgmsst, {prompt}, white background"
-        flux_image = query(prompt_real, steps, scales, randomize_seed, seed, width, height)
-        if flux_image is None:
-            raise gr.Error("Failed to generate image")
-            
-        processed_image = preprocess(flux_image, do_remove_background=True)
-        mv_images, show_image = generate_mvs(processed_image, steps, seed)
-        obj_path, glb_path = make3d(mv_images)
-        return flux_image, show_image, obj_path, glb_path
-
+            generate_btn = gr.Button("Generate Image", elem_classes="submit-btn")
+        
+        with gr.Column(scale=1):
+            with gr.Group(elem_classes="output-group"):
+                output_image = gr.Image(label="Result", elem_id="gallery", show_label=False)
+                final_prompt = gr.Textbox(label="Final Prompt Used")
+                used_seed = gr.Number(label="Seed Used")
+    
     generate_btn.click(
-        fn=process_pipeline,
-        inputs=[prompt, height, width, steps, scales, seed, randomize_seed],
-        outputs=[flux_output, mv_show_images, output_model_obj, output_model_glb]
+        fn=process_workflow,
+        inputs=[
+            input_image, text_prompt, use_enhancer, seed, randomize_seed,
+            width, height, guidance_scale, num_inference_steps
+        ],
+        outputs=[output_image, final_prompt, used_seed]
     )
 
-if __name__ == "__main__":
-    demo.queue().launch()
+demo.launch(debug=True)

configs/instant-mesh-base.yaml

@@ -1,22 +0,0 @@
-model_config:
-  target: src.models.lrm_mesh.InstantMesh
-  params:
-    encoder_feat_dim: 768
-    encoder_freeze: false
-    encoder_model_name: facebook/dino-vitb16
-    transformer_dim: 1024
-    transformer_layers: 12
-    transformer_heads: 16
-    triplane_low_res: 32
-    triplane_high_res: 64
-    triplane_dim: 40
-    rendering_samples_per_ray: 96
-    grid_res: 128
-    grid_scale: 2.1
-
-
-infer_config:
-  unet_path: ckpts/diffusion_pytorch_model.bin
-  model_path: ckpts/instant_mesh_base.ckpt
-  texture_resolution: 1024
-  render_resolution: 512

configs/instant-mesh-large.yaml

@@ -1,22 +0,0 @@
-model_config:
-  target: src.models.lrm_mesh.InstantMesh
-  params:
-    encoder_feat_dim: 768
-    encoder_freeze: false
-    encoder_model_name: facebook/dino-vitb16
-    transformer_dim: 1024
-    transformer_layers: 16
-    transformer_heads: 16
-    triplane_low_res: 32
-    triplane_high_res: 64
-    triplane_dim: 80
-    rendering_samples_per_ray: 128
-    grid_res: 128
-    grid_scale: 2.1
-
-
-infer_config:
-  unet_path: ckpts/diffusion_pytorch_model.bin
-  model_path: ckpts/instant_mesh_large.ckpt
-  texture_resolution: 1024
-  render_resolution: 512

configs/instant-nerf-base.yaml

@@ -1,21 +0,0 @@
-model_config:
-  target: src.models.lrm.InstantNeRF
-  params:
-    encoder_feat_dim: 768
-    encoder_freeze: false
-    encoder_model_name: facebook/dino-vitb16
-    transformer_dim: 1024
-    transformer_layers: 12
-    transformer_heads: 16
-    triplane_low_res: 32
-    triplane_high_res: 64
-    triplane_dim: 40
-    rendering_samples_per_ray: 96
-
-
-infer_config:
-  unet_path: ckpts/diffusion_pytorch_model.bin
-  model_path: ckpts/instant_nerf_base.ckpt
-  mesh_threshold: 10.0
-  mesh_resolution: 256
-  render_resolution: 384

configs/instant-nerf-large.yaml

@@ -1,21 +0,0 @@
-model_config:
-  target: src.models.lrm.InstantNeRF
-  params:
-    encoder_feat_dim: 768
-    encoder_freeze: false
-    encoder_model_name: facebook/dino-vitb16
-    transformer_dim: 1024
-    transformer_layers: 16
-    transformer_heads: 16
-    triplane_low_res: 32
-    triplane_high_res: 64
-    triplane_dim: 80
-    rendering_samples_per_ray: 128
-
-
-infer_config:
-  unet_path: ckpts/diffusion_pytorch_model.bin
-  model_path: ckpts/instant_nerf_large.ckpt
-  mesh_threshold: 10.0
-  mesh_resolution: 256
-  render_resolution: 384

requirements.txt

@@ -1,27 +1,11 @@
+spaces
+huggingface_hub
+accelerate
+git+https://github.com/huggingface/diffusers.git
 torch==2.4.0
 torchvision==0.19.0
-torchaudio==2.4.0
-pytorch-lightning==2.1.2
-einops
-omegaconf
-deepspeed
-torchmetrics
-webdataset
+transformers==4.42.4
+xformers
 sentencepiece
-accelerate
-tensorboard
-PyMCubes
-trimesh
-rembg
-peft
-transformers==4.44.0
-diffusers==0.31.0
-bitsandbytes
-imageio[ffmpeg]
-xatlas
-plyfile
-xformers==0.0.27.post2
-git+https://github.com/NVlabs/nvdiffrast/
-huggingface-hub==0.25.2
-optimum-quanto==0.2.5
-k-diffusion
+timm
+einops

src/data/objaverse.py

@@ -1,329 +0,0 @@
-import os, sys
-import math
-import json
-import importlib
-from pathlib import Path
-
-import cv2
-import random
-import numpy as np
-from PIL import Image
-import webdataset as wds
-import pytorch_lightning as pl
-
-import torch
-import torch.nn.functional as F
-from torch.utils.data import Dataset
-from torch.utils.data import DataLoader
-from torch.utils.data.distributed import DistributedSampler
-from torchvision import transforms
-
-from src.utils.train_util import instantiate_from_config
-from src.utils.camera_util import (
-    FOV_to_intrinsics, 
-    center_looking_at_camera_pose, 
-    get_surrounding_views,
-)
-
-
-class DataModuleFromConfig(pl.LightningDataModule):
-    def __init__(
-        self, 
-        batch_size=8, 
-        num_workers=4, 
-        train=None, 
-        validation=None, 
-        test=None, 
-        **kwargs,
-    ):
-        super().__init__()
-
-        self.batch_size = batch_size
-        self.num_workers = num_workers
-
-        self.dataset_configs = dict()
-        if train is not None:
-            self.dataset_configs['train'] = train
-        if validation is not None:
-            self.dataset_configs['validation'] = validation
-        if test is not None:
-            self.dataset_configs['test'] = test
-    
-    def setup(self, stage):
-
-        if stage in ['fit']:
-            self.datasets = dict((k, instantiate_from_config(self.dataset_configs[k])) for k in self.dataset_configs)
-        else:
-            raise NotImplementedError
-
-    def train_dataloader(self):
-
-        sampler = DistributedSampler(self.datasets['train'])
-        return wds.WebLoader(self.datasets['train'], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False, sampler=sampler)
-
-    def val_dataloader(self):
-
-        sampler = DistributedSampler(self.datasets['validation'])
-        return wds.WebLoader(self.datasets['validation'], batch_size=1, num_workers=self.num_workers, shuffle=False, sampler=sampler)
-
-    def test_dataloader(self):
-
-        return wds.WebLoader(self.datasets['test'], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False)
-
-
-class ObjaverseData(Dataset):
-    def __init__(self,
-        root_dir='objaverse/',
-        meta_fname='valid_paths.json',
-        input_image_dir='rendering_random_32views',
-        target_image_dir='rendering_random_32views',
-        input_view_num=6,
-        target_view_num=2,
-        total_view_n=32,
-        fov=50,
-        camera_rotation=True,
-        validation=False,
-    ):
-        self.root_dir = Path(root_dir)
-        self.input_image_dir = input_image_dir
-        self.target_image_dir = target_image_dir
-
-        self.input_view_num = input_view_num
-        self.target_view_num = target_view_num
-        self.total_view_n = total_view_n
-        self.fov = fov
-        self.camera_rotation = camera_rotation
-
-        with open(os.path.join(root_dir, meta_fname)) as f:
-            filtered_dict = json.load(f)
-        paths = filtered_dict['good_objs']
-        self.paths = paths
-        
-        self.depth_scale = 4.0
-            
-        total_objects = len(self.paths)
-        print('============= length of dataset %d =============' % len(self.paths))
-
-    def __len__(self):
-        return len(self.paths)
-
-    def load_im(self, path, color):
-        '''
-        replace background pixel with random color in rendering
-        '''
-        pil_img = Image.open(path)
-
-        image = np.asarray(pil_img, dtype=np.float32) / 255.
-        alpha = image[:, :, 3:]
-        image = image[:, :, :3] * alpha + color * (1 - alpha)
-
-        image = torch.from_numpy(image).permute(2, 0, 1).contiguous().float()
-        alpha = torch.from_numpy(alpha).permute(2, 0, 1).contiguous().float()
-        return image, alpha
-    
-    def __getitem__(self, index):
-        # load data
-        while True:
-            input_image_path = os.path.join(self.root_dir, self.input_image_dir, self.paths[index])
-            target_image_path = os.path.join(self.root_dir, self.target_image_dir, self.paths[index])
-
-            indices = np.random.choice(range(self.total_view_n), self.input_view_num + self.target_view_num, replace=False)
-            input_indices = indices[:self.input_view_num]
-            target_indices = indices[self.input_view_num:]
-
-            '''background color, default: white'''
-            bg_white = [1., 1., 1.]
-            bg_black = [0., 0., 0.]
-
-            image_list = []
-            alpha_list = []
-            depth_list = []
-            normal_list = []
-            pose_list = []
-
-            try:
-                input_cameras = np.load(os.path.join(input_image_path, 'cameras.npz'))['cam_poses']
-                for idx in input_indices:
-                    image, alpha = self.load_im(os.path.join(input_image_path, '%03d.png' % idx), bg_white)
-                    normal, _ = self.load_im(os.path.join(input_image_path, '%03d_normal.png' % idx), bg_black)
-                    depth = cv2.imread(os.path.join(input_image_path, '%03d_depth.png' % idx), cv2.IMREAD_UNCHANGED) / 255.0 * self.depth_scale
-                    depth = torch.from_numpy(depth).unsqueeze(0)
-                    pose = input_cameras[idx]
-                    pose = np.concatenate([pose, np.array([[0, 0, 0, 1]])], axis=0)
-
-                    image_list.append(image)
-                    alpha_list.append(alpha)
-                    depth_list.append(depth)
-                    normal_list.append(normal)
-                    pose_list.append(pose)
-
-                target_cameras = np.load(os.path.join(target_image_path, 'cameras.npz'))['cam_poses']
-                for idx in target_indices:
-                    image, alpha = self.load_im(os.path.join(target_image_path, '%03d.png' % idx), bg_white)
-                    normal, _ = self.load_im(os.path.join(target_image_path, '%03d_normal.png' % idx), bg_black)
-                    depth = cv2.imread(os.path.join(target_image_path, '%03d_depth.png' % idx), cv2.IMREAD_UNCHANGED) / 255.0 * self.depth_scale
-                    depth = torch.from_numpy(depth).unsqueeze(0)
-                    pose = target_cameras[idx]
-                    pose = np.concatenate([pose, np.array([[0, 0, 0, 1]])], axis=0)
-
-                    image_list.append(image)
-                    alpha_list.append(alpha)
-                    depth_list.append(depth)
-                    normal_list.append(normal)
-                    pose_list.append(pose)
-
-            except Exception as e:
-                print(e)
-                index = np.random.randint(0, len(self.paths))
-                continue
-
-            break
-        
-        images = torch.stack(image_list, dim=0).float()                 # (6+V, 3, H, W)
-        alphas = torch.stack(alpha_list, dim=0).float()                 # (6+V, 1, H, W)
-        depths = torch.stack(depth_list, dim=0).float()                 # (6+V, 1, H, W)
-        normals = torch.stack(normal_list, dim=0).float()               # (6+V, 3, H, W)
-        w2cs = torch.from_numpy(np.stack(pose_list, axis=0)).float()    # (6+V, 4, 4)
-        c2ws = torch.linalg.inv(w2cs).float()
-
-        normals = normals * 2.0 - 1.0
-        normals = F.normalize(normals, dim=1)
-        normals = (normals + 1.0) / 2.0
-        normals = torch.lerp(torch.zeros_like(normals), normals, alphas)
-
-        # random rotation along z axis
-        if self.camera_rotation:
-            degree = np.random.uniform(0, math.pi * 2)
-            rot = torch.tensor([
-                [np.cos(degree), -np.sin(degree), 0, 0],
-                [np.sin(degree), np.cos(degree), 0, 0],
-                [0, 0, 1, 0],
-                [0, 0, 0, 1],
-            ]).unsqueeze(0).float()
-            c2ws = torch.matmul(rot, c2ws)
-
-            # rotate normals
-            N, _, H, W = normals.shape
-            normals = normals * 2.0 - 1.0
-            normals = torch.matmul(rot[:, :3, :3], normals.view(N, 3, -1)).view(N, 3, H, W)
-            normals = F.normalize(normals, dim=1)
-            normals = (normals + 1.0) / 2.0
-            normals = torch.lerp(torch.zeros_like(normals), normals, alphas)
-
-        # random scaling
-        if np.random.rand() < 0.5:
-            scale = np.random.uniform(0.8, 1.0)
-            c2ws[:, :3, 3] *= scale
-            depths *= scale
-
-        # instrinsics of perspective cameras
-        K = FOV_to_intrinsics(self.fov)
-        Ks = K.unsqueeze(0).repeat(self.input_view_num + self.target_view_num, 1, 1).float()
-
-        data = {
-            'input_images': images[:self.input_view_num],     # (6, 3, H, W)
-            'input_alphas': alphas[:self.input_view_num],           # (6, 1, H, W) 
-            'input_depths': depths[:self.input_view_num],           # (6, 1, H, W)
-            'input_normals': normals[:self.input_view_num],         # (6, 3, H, W)
-            'input_c2ws': c2ws_input[:self.input_view_num],         # (6, 4, 4)
-            'input_Ks': Ks[:self.input_view_num],                   # (6, 3, 3)
-
-            # lrm generator input and supervision
-            'target_images': images[self.input_view_num:],          # (V, 3, H, W)
-            'target_alphas': alphas[self.input_view_num:],          # (V, 1, H, W)
-            'target_depths': depths[self.input_view_num:],          # (V, 1, H, W)
-            'target_normals': normals[self.input_view_num:],        # (V, 3, H, W)
-            'target_c2ws': c2ws[self.input_view_num:],              # (V, 4, 4)
-            'target_Ks': Ks[self.input_view_num:],                  # (V, 3, 3)
-
-            'depth_available': 1,
-        }
-        return data
-
-
-class ValidationData(Dataset):
-    def __init__(self,
-        root_dir='objaverse/',
-        input_view_num=6,
-        input_image_size=256,
-        fov=50,
-    ):
-        self.root_dir = Path(root_dir)
-        self.input_view_num = input_view_num
-        self.input_image_size = input_image_size
-        self.fov = fov
-
-        self.paths = sorted(os.listdir(self.root_dir))
-        print('============= length of dataset %d =============' % len(self.paths))
-
-        cam_distance = 2.5
-        azimuths = np.array([30, 90, 150, 210, 270, 330])
-        elevations = np.array([30, -20, 30, -20, 30, -20])
-        azimuths = np.deg2rad(azimuths)
-        elevations = np.deg2rad(elevations)
-
-        x = cam_distance * np.cos(elevations) * np.cos(azimuths)
-        y = cam_distance * np.cos(elevations) * np.sin(azimuths)
-        z = cam_distance * np.sin(elevations)
-
-        cam_locations = np.stack([x, y, z], axis=-1)
-        cam_locations = torch.from_numpy(cam_locations).float()
-        c2ws = center_looking_at_camera_pose(cam_locations)
-        self.c2ws = c2ws.float()
-        self.Ks = FOV_to_intrinsics(self.fov).unsqueeze(0).repeat(6, 1, 1).float()
-
-        render_c2ws = get_surrounding_views(M=8, radius=cam_distance)
-        render_Ks = FOV_to_intrinsics(self.fov).unsqueeze(0).repeat(render_c2ws.shape[0], 1, 1)
-        self.render_c2ws = render_c2ws.float()
-        self.render_Ks = render_Ks.float()
-
-    def __len__(self):
-        return len(self.paths)
-
-    def load_im(self, path, color):
-        '''
-        replace background pixel with random color in rendering
-        '''
-        pil_img = Image.open(path)
-        pil_img = pil_img.resize((self.input_image_size, self.input_image_size), resample=Image.BICUBIC)
-
-        image = np.asarray(pil_img, dtype=np.float32) / 255.
-        if image.shape[-1] == 4:
-            alpha = image[:, :, 3:]
-            image = image[:, :, :3] * alpha + color * (1 - alpha)
-        else:
-            alpha = np.ones_like(image[:, :, :1])
-
-        image = torch.from_numpy(image).permute(2, 0, 1).contiguous().float()
-        alpha = torch.from_numpy(alpha).permute(2, 0, 1).contiguous().float()
-        return image, alpha
-    
-    def __getitem__(self, index):
-        # load data
-        input_image_path = os.path.join(self.root_dir, self.paths[index])
-
-        '''background color, default: white'''
-        # color = np.random.uniform(0.48, 0.52)
-        bkg_color = [1.0, 1.0, 1.0]
-
-        image_list = []
-        alpha_list = []
-
-        for idx in range(self.input_view_num):
-            image, alpha = self.load_im(os.path.join(input_image_path, f'{idx:03d}.png'), bkg_color)
-            image_list.append(image)
-            alpha_list.append(alpha)
-        
-        images = torch.stack(image_list, dim=0).float()                     # (6+V, 3, H, W)
-        alphas = torch.stack(alpha_list, dim=0).float()                 # (6+V, 1, H, W)
-
-        data = {
-            'input_images': images,                 # (6, 3, H, W)
-            'input_alphas': alphas,             # (6, 1, H, W)
-            'input_c2ws': self.c2ws,            # (6, 4, 4)
-            'input_Ks': self.Ks,                # (6, 3, 3)
-
-            'render_c2ws': self.render_c2ws,
-            'render_Ks': self.render_Ks,
-        }
-        return data

src/model.py

@@ -1,310 +0,0 @@
-import os
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torchvision.transforms import v2
-from torchvision.utils import make_grid, save_image
-from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
-import pytorch_lightning as pl
-from einops import rearrange, repeat
-
-from src.utils.train_util import instantiate_from_config
-
-
-class MVRecon(pl.LightningModule):
-    def __init__(
-        self,
-        lrm_generator_config,
-        lrm_path=None,
-        input_size=256,
-        render_size=192,
-    ):
-        super(MVRecon, self).__init__()
-
-        self.input_size = input_size
-        self.render_size = render_size
-
-        # init modules
-        self.lrm_generator = instantiate_from_config(lrm_generator_config)
-        if lrm_path is not None:
-            lrm_ckpt = torch.load(lrm_path)
-            self.lrm_generator.load_state_dict(lrm_ckpt['weights'], strict=False)
-
-        self.lpips = LearnedPerceptualImagePatchSimilarity(net_type='vgg')
-        
-        self.validation_step_outputs = []
-    
-    def on_fit_start(self):
-        if self.global_rank == 0:
-            os.makedirs(os.path.join(self.logdir, 'images'), exist_ok=True)
-            os.makedirs(os.path.join(self.logdir, 'images_val'), exist_ok=True)
-    
-    def prepare_batch_data(self, batch):
-        lrm_generator_input = {}
-        render_gt = {}   # for supervision
-
-        # input images
-        images = batch['input_images']
-        images = v2.functional.resize(
-            images, self.input_size, interpolation=3, antialias=True).clamp(0, 1)
-
-        lrm_generator_input['images'] = images.to(self.device)
-
-        # input cameras and render cameras
-        input_c2ws = batch['input_c2ws'].flatten(-2)
-        input_Ks = batch['input_Ks'].flatten(-2)
-        target_c2ws = batch['target_c2ws'].flatten(-2)
-        target_Ks = batch['target_Ks'].flatten(-2)
-        render_cameras_input = torch.cat([input_c2ws, input_Ks], dim=-1)
-        render_cameras_target = torch.cat([target_c2ws, target_Ks], dim=-1)
-        render_cameras = torch.cat([render_cameras_input, render_cameras_target], dim=1)
-
-        input_extrinsics = input_c2ws[:, :, :12]
-        input_intrinsics = torch.stack([
-            input_Ks[:, :, 0], input_Ks[:, :, 4], 
-            input_Ks[:, :, 2], input_Ks[:, :, 5],
-        ], dim=-1)
-        cameras = torch.cat([input_extrinsics, input_intrinsics], dim=-1)
-
-        # add noise to input cameras
-        cameras = cameras + torch.rand_like(cameras) * 0.04 - 0.02
-
-        lrm_generator_input['cameras'] = cameras.to(self.device)
-        lrm_generator_input['render_cameras'] = render_cameras.to(self.device)
-
-        # target images
-        target_images = torch.cat([batch['input_images'], batch['target_images']], dim=1)
-        target_depths = torch.cat([batch['input_depths'], batch['target_depths']], dim=1)
-        target_alphas = torch.cat([batch['input_alphas'], batch['target_alphas']], dim=1)
-
-        # random crop
-        render_size = np.random.randint(self.render_size, 513)
-        target_images = v2.functional.resize(
-            target_images, render_size, interpolation=3, antialias=True).clamp(0, 1)
-        target_depths = v2.functional.resize(
-            target_depths, render_size, interpolation=0, antialias=True)
-        target_alphas = v2.functional.resize(
-            target_alphas, render_size, interpolation=0, antialias=True)
-
-        crop_params = v2.RandomCrop.get_params(
-            target_images, output_size=(self.render_size, self.render_size))
-        target_images = v2.functional.crop(target_images, *crop_params)
-        target_depths = v2.functional.crop(target_depths, *crop_params)[:, :, 0:1]
-        target_alphas = v2.functional.crop(target_alphas, *crop_params)[:, :, 0:1]
-
-        lrm_generator_input['render_size'] = render_size
-        lrm_generator_input['crop_params'] = crop_params
-
-        render_gt['target_images'] = target_images.to(self.device)
-        render_gt['target_depths'] = target_depths.to(self.device)
-        render_gt['target_alphas'] = target_alphas.to(self.device)
-
-        return lrm_generator_input, render_gt
-    
-    def prepare_validation_batch_data(self, batch):
-        lrm_generator_input = {}
-
-        # input images
-        images = batch['input_images']
-        images = v2.functional.resize(
-            images, self.input_size, interpolation=3, antialias=True).clamp(0, 1)
-
-        lrm_generator_input['images'] = images.to(self.device)
-
-        input_c2ws = batch['input_c2ws'].flatten(-2)
-        input_Ks = batch['input_Ks'].flatten(-2)
-
-        input_extrinsics = input_c2ws[:, :, :12]
-        input_intrinsics = torch.stack([
-            input_Ks[:, :, 0], input_Ks[:, :, 4], 
-            input_Ks[:, :, 2], input_Ks[:, :, 5],
-        ], dim=-1)
-        cameras = torch.cat([input_extrinsics, input_intrinsics], dim=-1)
-
-        lrm_generator_input['cameras'] = cameras.to(self.device)
-
-        render_c2ws = batch['render_c2ws'].flatten(-2)
-        render_Ks = batch['render_Ks'].flatten(-2)
-        render_cameras = torch.cat([render_c2ws, render_Ks], dim=-1)
-
-        lrm_generator_input['render_cameras'] = render_cameras.to(self.device)
-        lrm_generator_input['render_size'] = 384
-        lrm_generator_input['crop_params'] = None
-
-        return lrm_generator_input
-    
-    def forward_lrm_generator(
-        self, 
-        images, 
-        cameras, 
-        render_cameras, 
-        render_size=192, 
-        crop_params=None, 
-        chunk_size=1,
-    ):
-        planes = torch.utils.checkpoint.checkpoint(
-            self.lrm_generator.forward_planes, 
-            images, 
-            cameras, 
-            use_reentrant=False,
-        )
-        frames = []
-        for i in range(0, render_cameras.shape[1], chunk_size):
-            frames.append(
-                torch.utils.checkpoint.checkpoint(
-                    self.lrm_generator.synthesizer,
-                    planes,
-                    cameras=render_cameras[:, i:i+chunk_size],
-                    render_size=render_size, 
-                    crop_params=crop_params,
-                    use_reentrant=False
-                )
-            )
-        frames = {
-            k: torch.cat([r[k] for r in frames], dim=1)
-            for k in frames[0].keys()
-        }
-        return frames
-    
-    def forward(self, lrm_generator_input):
-        images = lrm_generator_input['images']
-        cameras = lrm_generator_input['cameras']
-        render_cameras = lrm_generator_input['render_cameras']
-        render_size = lrm_generator_input['render_size']
-        crop_params = lrm_generator_input['crop_params']
-
-        out = self.forward_lrm_generator(
-            images, 
-            cameras, 
-            render_cameras, 
-            render_size=render_size, 
-            crop_params=crop_params, 
-            chunk_size=1,
-        )
-        render_images = torch.clamp(out['images_rgb'], 0.0, 1.0)
-        render_depths = out['images_depth']
-        render_alphas = torch.clamp(out['images_weight'], 0.0, 1.0)
-
-        out = {
-            'render_images': render_images,
-            'render_depths': render_depths,
-            'render_alphas': render_alphas,
-        }
-        return out
-
-    def training_step(self, batch, batch_idx):
-        lrm_generator_input, render_gt = self.prepare_batch_data(batch)
-
-        render_out = self.forward(lrm_generator_input)
-
-        loss, loss_dict = self.compute_loss(render_out, render_gt)
-
-        self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-
-        if self.global_step % 1000 == 0 and self.global_rank == 0:
-            B, N, C, H, W = render_gt['target_images'].shape
-            N_in = lrm_generator_input['images'].shape[1]
-
-            input_images = v2.functional.resize(
-                lrm_generator_input['images'], (H, W), interpolation=3, antialias=True).clamp(0, 1)
-            input_images = torch.cat(
-                [input_images, torch.ones(B, N-N_in, C, H, W).to(input_images)], dim=1)
-
-            input_images = rearrange(
-                input_images, 'b n c h w -> b c h (n w)')
-            target_images = rearrange(
-                render_gt['target_images'], 'b n c h w -> b c h (n w)')
-            render_images = rearrange(
-                render_out['render_images'], 'b n c h w -> b c h (n w)')
-            target_alphas = rearrange(
-                repeat(render_gt['target_alphas'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)')
-            render_alphas = rearrange(
-                repeat(render_out['render_alphas'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)')
-            target_depths = rearrange(
-                repeat(render_gt['target_depths'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)')
-            render_depths = rearrange(
-                repeat(render_out['render_depths'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)')
-            MAX_DEPTH = torch.max(target_depths)
-            target_depths = target_depths / MAX_DEPTH * target_alphas
-            render_depths = render_depths / MAX_DEPTH
-
-            grid = torch.cat([
-                input_images, 
-                target_images, render_images, 
-                target_alphas, render_alphas, 
-                target_depths, render_depths,
-            ], dim=-2)
-            grid = make_grid(grid, nrow=target_images.shape[0], normalize=True, value_range=(0, 1))
-
-            save_image(grid, os.path.join(self.logdir, 'images', f'train_{self.global_step:07d}.png'))
-
-        return loss
-    
-    def compute_loss(self, render_out, render_gt):
-        # NOTE: the rgb value range of OpenLRM is [0, 1]
-        render_images = render_out['render_images']
-        target_images = render_gt['target_images'].to(render_images)
-        render_images = rearrange(render_images, 'b n ... -> (b n) ...') * 2.0 - 1.0
-        target_images = rearrange(target_images, 'b n ... -> (b n) ...') * 2.0 - 1.0
-
-        loss_mse = F.mse_loss(render_images, target_images)
-        loss_lpips = 2.0 * self.lpips(render_images, target_images)
-
-        render_alphas = render_out['render_alphas']
-        target_alphas = render_gt['target_alphas']
-        loss_mask = F.mse_loss(render_alphas, target_alphas)
-
-        loss = loss_mse + loss_lpips + loss_mask
-
-        prefix = 'train'
-        loss_dict = {}
-        loss_dict.update({f'{prefix}/loss_mse': loss_mse})
-        loss_dict.update({f'{prefix}/loss_lpips': loss_lpips})
-        loss_dict.update({f'{prefix}/loss_mask': loss_mask})
-        loss_dict.update({f'{prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    @torch.no_grad()
-    def validation_step(self, batch, batch_idx):
-        lrm_generator_input = self.prepare_validation_batch_data(batch)
-
-        render_out = self.forward(lrm_generator_input)
-        render_images = render_out['render_images']
-        render_images = rearrange(render_images, 'b n c h w -> b c h (n w)')
-
-        self.validation_step_outputs.append(render_images)
-    
-    def on_validation_epoch_end(self):
-        images = torch.cat(self.validation_step_outputs, dim=-1)
-
-        all_images = self.all_gather(images)
-        all_images = rearrange(all_images, 'r b c h w -> (r b) c h w')
-
-        if self.global_rank == 0:
-            image_path = os.path.join(self.logdir, 'images_val', f'val_{self.global_step:07d}.png')
-
-            grid = make_grid(all_images, nrow=1, normalize=True, value_range=(0, 1))
-            save_image(grid, image_path)
-            print(f"Saved image to {image_path}")
-
-        self.validation_step_outputs.clear()
-
-    def configure_optimizers(self):
-        lr = self.learning_rate
-
-        params = []
-
-        lrm_params_fast, lrm_params_slow = [], []
-        for n, p in self.lrm_generator.named_parameters():
-            if 'adaLN_modulation' in n or 'camera_embedder' in n:
-                lrm_params_fast.append(p)
-            else:
-                lrm_params_slow.append(p)
-        params.append({"params": lrm_params_fast, "lr": lr, "weight_decay": 0.01 })
-        params.append({"params": lrm_params_slow, "lr": lr / 10.0, "weight_decay": 0.01 })
-
-        optimizer = torch.optim.AdamW(params, lr=lr, betas=(0.90, 0.95))
-        scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, 3000, eta_min=lr/4)
-
-        return {'optimizer': optimizer, 'lr_scheduler': scheduler}

src/model_mesh.py

@@ -1,325 +0,0 @@
-import os
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torchvision.transforms import v2
-from torchvision.utils import make_grid, save_image
-from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
-import pytorch_lightning as pl
-from einops import rearrange, repeat
-
-from src.utils.train_util import instantiate_from_config
-
-
-# Regulrarization loss for FlexiCubes
-def sdf_reg_loss_batch(sdf, all_edges):
-    sdf_f1x6x2 = sdf[:, all_edges.reshape(-1)].reshape(sdf.shape[0], -1, 2)
-    mask = torch.sign(sdf_f1x6x2[..., 0]) != torch.sign(sdf_f1x6x2[..., 1])
-    sdf_f1x6x2 = sdf_f1x6x2[mask]
-    sdf_diff = F.binary_cross_entropy_with_logits(
-        sdf_f1x6x2[..., 0], (sdf_f1x6x2[..., 1] > 0).float()) + \
-               F.binary_cross_entropy_with_logits(
-                   sdf_f1x6x2[..., 1], (sdf_f1x6x2[..., 0] > 0).float())
-    return sdf_diff
-
-
-class MVRecon(pl.LightningModule):
-    def __init__(
-        self,
-        lrm_generator_config,
-        input_size=256,
-        render_size=512,
-        init_ckpt=None,
-    ):
-        super(MVRecon, self).__init__()
-
-        self.input_size = input_size
-        self.render_size = render_size
-
-        # init modules
-        self.lrm_generator = instantiate_from_config(lrm_generator_config)
-
-        self.lpips = LearnedPerceptualImagePatchSimilarity(net_type='vgg')
-
-        # Load weights from pretrained MVRecon model, and use the mlp 
-        # weights to initialize the weights of sdf and rgb mlps.
-        if init_ckpt is not None:
-            sd = torch.load(init_ckpt, map_location='cpu')['state_dict']
-            sd = {k: v for k, v in sd.items() if k.startswith('lrm_generator')}
-            sd_fc = {}
-            for k, v in sd.items():
-                if k.startswith('lrm_generator.synthesizer.decoder.net.'):
-                    if k.startswith('lrm_generator.synthesizer.decoder.net.6.'):    # last layer
-                        # Here we assume the density filed's isosurface threshold is t, 
-                        # we reverse the sign of density filed to initialize SDF field.  
-                        # -(w*x + b - t) = (-w)*x + (t - b)
-                        if 'weight' in k:
-                            sd_fc[k.replace('net.', 'net_sdf.')] = -v[0:1]
-                        else:
-                            sd_fc[k.replace('net.', 'net_sdf.')] = 3.0 - v[0:1]
-                        sd_fc[k.replace('net.', 'net_rgb.')] = v[1:4]
-                    else:
-                        sd_fc[k.replace('net.', 'net_sdf.')] = v
-                        sd_fc[k.replace('net.', 'net_rgb.')] = v
-                else:
-                    sd_fc[k] = v
-            sd_fc = {k.replace('lrm_generator.', ''): v for k, v in sd_fc.items()}
-            # missing `net_deformation` and `net_weight` parameters
-            self.lrm_generator.load_state_dict(sd_fc, strict=False)
-            print(f'Loaded weights from {init_ckpt}')
-        
-        self.validation_step_outputs = []
-    
-    def on_fit_start(self):
-        device = torch.device(f'cuda:{self.global_rank}')
-        self.lrm_generator.init_flexicubes_geometry(device)
-        if self.global_rank == 0:
-            os.makedirs(os.path.join(self.logdir, 'images'), exist_ok=True)
-            os.makedirs(os.path.join(self.logdir, 'images_val'), exist_ok=True)
-    
-    def prepare_batch_data(self, batch):
-        lrm_generator_input = {}
-        render_gt = {}
-
-        # input images
-        images = batch['input_images']
-        images = v2.functional.resize(
-            images, self.input_size, interpolation=3, antialias=True).clamp(0, 1)
-
-        lrm_generator_input['images'] = images.to(self.device)
-
-        # input cameras and render cameras
-        input_c2ws = batch['input_c2ws']
-        input_Ks = batch['input_Ks']
-        target_c2ws = batch['target_c2ws']
-
-        render_c2ws = torch.cat([input_c2ws, target_c2ws], dim=1)
-        render_w2cs = torch.linalg.inv(render_c2ws)
-
-        input_extrinsics = input_c2ws.flatten(-2)
-        input_extrinsics = input_extrinsics[:, :, :12]
-        input_intrinsics = input_Ks.flatten(-2)
-        input_intrinsics = torch.stack([
-            input_intrinsics[:, :, 0], input_intrinsics[:, :, 4], 
-            input_intrinsics[:, :, 2], input_intrinsics[:, :, 5],
-        ], dim=-1)
-        cameras = torch.cat([input_extrinsics, input_intrinsics], dim=-1)
-
-        # add noise to input_cameras
-        cameras = cameras + torch.rand_like(cameras) * 0.04 - 0.02
-
-        lrm_generator_input['cameras'] = cameras.to(self.device)
-        lrm_generator_input['render_cameras'] = render_w2cs.to(self.device)
-
-        # target images
-        target_images = torch.cat([batch['input_images'], batch['target_images']], dim=1)
-        target_depths = torch.cat([batch['input_depths'], batch['target_depths']], dim=1)
-        target_alphas = torch.cat([batch['input_alphas'], batch['target_alphas']], dim=1)
-        target_normals = torch.cat([batch['input_normals'], batch['target_normals']], dim=1)
-
-        render_size = self.render_size
-        target_images = v2.functional.resize(
-            target_images, render_size, interpolation=3, antialias=True).clamp(0, 1)
-        target_depths = v2.functional.resize(
-            target_depths, render_size, interpolation=0, antialias=True)
-        target_alphas = v2.functional.resize(
-            target_alphas, render_size, interpolation=0, antialias=True)
-        target_normals = v2.functional.resize(
-            target_normals, render_size, interpolation=3, antialias=True)
-
-        lrm_generator_input['render_size'] = render_size
-
-        render_gt['target_images'] = target_images.to(self.device)
-        render_gt['target_depths'] = target_depths.to(self.device)
-        render_gt['target_alphas'] = target_alphas.to(self.device)
-        render_gt['target_normals'] = target_normals.to(self.device)
-
-        return lrm_generator_input, render_gt
-    
-    def prepare_validation_batch_data(self, batch):
-        lrm_generator_input = {}
-
-        # input images
-        images = batch['input_images']
-        images = v2.functional.resize(
-            images, self.input_size, interpolation=3, antialias=True).clamp(0, 1)
-
-        lrm_generator_input['images'] = images.to(self.device)
-
-        # input cameras
-        input_c2ws = batch['input_c2ws'].flatten(-2)
-        input_Ks = batch['input_Ks'].flatten(-2)
-
-        input_extrinsics = input_c2ws[:, :, :12]
-        input_intrinsics = torch.stack([
-            input_Ks[:, :, 0], input_Ks[:, :, 4], 
-            input_Ks[:, :, 2], input_Ks[:, :, 5],
-        ], dim=-1)
-        cameras = torch.cat([input_extrinsics, input_intrinsics], dim=-1)
-
-        lrm_generator_input['cameras'] = cameras.to(self.device)
-
-        # render cameras
-        render_c2ws = batch['render_c2ws']
-        render_w2cs = torch.linalg.inv(render_c2ws)
-
-        lrm_generator_input['render_cameras'] = render_w2cs.to(self.device)
-        lrm_generator_input['render_size'] = 384
-
-        return lrm_generator_input
-    
-    def forward_lrm_generator(self, images, cameras, render_cameras, render_size=512):
-        planes = torch.utils.checkpoint.checkpoint(
-            self.lrm_generator.forward_planes, 
-            images, 
-            cameras, 
-            use_reentrant=False,
-        )
-        out = self.lrm_generator.forward_geometry(
-            planes, 
-            render_cameras, 
-            render_size,
-        )
-        return out
-    
-    def forward(self, lrm_generator_input):
-        images = lrm_generator_input['images']
-        cameras = lrm_generator_input['cameras']
-        render_cameras = lrm_generator_input['render_cameras']
-        render_size = lrm_generator_input['render_size']
-
-        out = self.forward_lrm_generator(
-            images, cameras, render_cameras, render_size=render_size)
-
-        return out
-
-    def training_step(self, batch, batch_idx):
-        lrm_generator_input, render_gt = self.prepare_batch_data(batch)
-
-        render_out = self.forward(lrm_generator_input)
-
-        loss, loss_dict = self.compute_loss(render_out, render_gt)
-
-        self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True)
-
-        if self.global_step % 1000 == 0 and self.global_rank == 0:
-            B, N, C, H, W = render_gt['target_images'].shape
-            N_in = lrm_generator_input['images'].shape[1]
-
-            target_images = rearrange(
-                render_gt['target_images'], 'b n c h w -> b c h (n w)')
-            render_images = rearrange(
-                render_out['img'], 'b n c h w -> b c h (n w)')
-            target_alphas = rearrange(
-                repeat(render_gt['target_alphas'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)')
-            render_alphas = rearrange(
-                repeat(render_out['mask'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)')
-            target_depths = rearrange(
-                repeat(render_gt['target_depths'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)')
-            render_depths = rearrange(
-                repeat(render_out['depth'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)')
-            target_normals = rearrange(
-                render_gt['target_normals'], 'b n c h w -> b c h (n w)')
-            render_normals = rearrange(
-                render_out['normal'], 'b n c h w -> b c h (n w)')
-            MAX_DEPTH = torch.max(target_depths)
-            target_depths = target_depths / MAX_DEPTH * target_alphas
-            render_depths = render_depths / MAX_DEPTH
-
-            grid = torch.cat([
-                target_images, render_images, 
-                target_alphas, render_alphas, 
-                target_depths, render_depths, 
-                target_normals, render_normals,
-            ], dim=-2)
-            grid = make_grid(grid, nrow=target_images.shape[0], normalize=True, value_range=(0, 1))
-
-            image_path = os.path.join(self.logdir, 'images', f'train_{self.global_step:07d}.png')
-            save_image(grid, image_path)
-            print(f"Saved image to {image_path}")
-
-        return loss
-    
-    def compute_loss(self, render_out, render_gt):
-        # NOTE: the rgb value range of OpenLRM is [0, 1]
-        render_images = render_out['img']
-        target_images = render_gt['target_images'].to(render_images)
-        render_images = rearrange(render_images, 'b n ... -> (b n) ...') * 2.0 - 1.0
-        target_images = rearrange(target_images, 'b n ... -> (b n) ...') * 2.0 - 1.0
-        loss_mse = F.mse_loss(render_images, target_images)
-        loss_lpips = 2.0 * self.lpips(render_images, target_images)
-
-        render_alphas = render_out['mask']
-        target_alphas = render_gt['target_alphas']
-        loss_mask = F.mse_loss(render_alphas, target_alphas)
-
-        render_depths = render_out['depth']
-        target_depths = render_gt['target_depths']
-        loss_depth = 0.5 * F.l1_loss(render_depths[target_alphas>0], target_depths[target_alphas>0])
-
-        render_normals = render_out['normal'] * 2.0 - 1.0
-        target_normals = render_gt['target_normals'] * 2.0 - 1.0
-        similarity = (render_normals * target_normals).sum(dim=-3).abs()
-        normal_mask = target_alphas.squeeze(-3)
-        loss_normal = 1 - similarity[normal_mask>0].mean()
-        loss_normal = 0.2 * loss_normal
-
-        # flexicubes regularization loss
-        sdf = render_out['sdf']
-        sdf_reg_loss = render_out['sdf_reg_loss']
-        sdf_reg_loss_entropy = sdf_reg_loss_batch(sdf, self.lrm_generator.geometry.all_edges).mean() * 0.01
-        _, flexicubes_surface_reg, flexicubes_weights_reg = sdf_reg_loss
-        flexicubes_surface_reg = flexicubes_surface_reg.mean() * 0.5
-        flexicubes_weights_reg = flexicubes_weights_reg.mean() * 0.1
-
-        loss_reg = sdf_reg_loss_entropy + flexicubes_surface_reg + flexicubes_weights_reg
-
-        loss = loss_mse + loss_lpips + loss_mask + loss_normal + loss_reg
-
-        prefix = 'train'
-        loss_dict = {}
-        loss_dict.update({f'{prefix}/loss_mse': loss_mse})
-        loss_dict.update({f'{prefix}/loss_lpips': loss_lpips})
-        loss_dict.update({f'{prefix}/loss_mask': loss_mask})
-        loss_dict.update({f'{prefix}/loss_normal': loss_normal})
-        loss_dict.update({f'{prefix}/loss_depth': loss_depth})
-        loss_dict.update({f'{prefix}/loss_reg_sdf': sdf_reg_loss_entropy})
-        loss_dict.update({f'{prefix}/loss_reg_surface': flexicubes_surface_reg})
-        loss_dict.update({f'{prefix}/loss_reg_weights': flexicubes_weights_reg})
-        loss_dict.update({f'{prefix}/loss': loss})
-
-        return loss, loss_dict
-
-    @torch.no_grad()
-    def validation_step(self, batch, batch_idx):
-        lrm_generator_input = self.prepare_validation_batch_data(batch)
-
-        render_out = self.forward(lrm_generator_input)
-        render_images = render_out['img']
-        render_images = rearrange(render_images, 'b n c h w -> b c h (n w)')
-
-        self.validation_step_outputs.append(render_images)
-    
-    def on_validation_epoch_end(self):
-        images = torch.cat(self.validation_step_outputs, dim=-1)
-
-        all_images = self.all_gather(images)
-        all_images = rearrange(all_images, 'r b c h w -> (r b) c h w')
-
-        if self.global_rank == 0:
-            image_path = os.path.join(self.logdir, 'images_val', f'val_{self.global_step:07d}.png')
-
-            grid = make_grid(all_images, nrow=1, normalize=True, value_range=(0, 1))
-            save_image(grid, image_path)
-            print(f"Saved image to {image_path}")
-
-        self.validation_step_outputs.clear()
-    
-    def configure_optimizers(self):
-        lr = self.learning_rate
-
-        optimizer = torch.optim.AdamW(
-            self.lrm_generator.parameters(), lr=lr, betas=(0.90, 0.95), weight_decay=0.01)
-        scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, 100000, eta_min=0)
-
-        return {'optimizer': optimizer, 'lr_scheduler': scheduler}

src/models/decoder/transformer.py

@@ -1,123 +0,0 @@
-# Copyright (c) 2023, Zexin He
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import torch
-import torch.nn as nn
-
-
-class BasicTransformerBlock(nn.Module):
-    """
-    Transformer block that takes in a cross-attention condition and another modulation vector applied to sub-blocks.
-    """
-    # use attention from torch.nn.MultiHeadAttention
-    # Block contains a cross-attention layer, a self-attention layer, and a MLP
-    def __init__(
-        self, 
-        inner_dim: int, 
-        cond_dim: int, 
-        num_heads: int, 
-        eps: float,
-        attn_drop: float = 0., 
-        attn_bias: bool = False,
-        mlp_ratio: float = 4., 
-        mlp_drop: float = 0.,
-    ):
-        super().__init__()
-
-        self.norm1 = nn.LayerNorm(inner_dim)
-        self.cross_attn = nn.MultiheadAttention(
-            embed_dim=inner_dim, num_heads=num_heads, kdim=cond_dim, vdim=cond_dim,
-            dropout=attn_drop, bias=attn_bias, batch_first=True)
-        self.norm2 = nn.LayerNorm(inner_dim)
-        self.self_attn = nn.MultiheadAttention(
-            embed_dim=inner_dim, num_heads=num_heads,
-            dropout=attn_drop, bias=attn_bias, batch_first=True)
-        self.norm3 = nn.LayerNorm(inner_dim)
-        self.mlp = nn.Sequential(
-            nn.Linear(inner_dim, int(inner_dim * mlp_ratio)),
-            nn.GELU(),
-            nn.Dropout(mlp_drop),
-            nn.Linear(int(inner_dim * mlp_ratio), inner_dim),
-            nn.Dropout(mlp_drop),
-        )
-
-    def forward(self, x, cond):
-        # x: [N, L, D]
-        # cond: [N, L_cond, D_cond]
-        x = x + self.cross_attn(self.norm1(x), cond, cond)[0]
-        before_sa = self.norm2(x)
-        x = x + self.self_attn(before_sa, before_sa, before_sa)[0]
-        x = x + self.mlp(self.norm3(x))
-        return x
-
-
-class TriplaneTransformer(nn.Module):
-    """
-    Transformer with condition that generates a triplane representation.
-    
-    Reference:
-    Timm: https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py#L486
-    """
-    def __init__(
-        self, 
-        inner_dim: int, 
-        image_feat_dim: int,
-        triplane_low_res: int, 
-        triplane_high_res: int, 
-        triplane_dim: int,
-        num_layers: int, 
-        num_heads: int,
-        eps: float = 1e-6,
-    ):
-        super().__init__()
-
-        # attributes
-        self.triplane_low_res = triplane_low_res
-        self.triplane_high_res = triplane_high_res
-        self.triplane_dim = triplane_dim
-
-        # modules
-        # initialize pos_embed with 1/sqrt(dim) * N(0, 1)
-        self.pos_embed = nn.Parameter(torch.randn(1, 3*triplane_low_res**2, inner_dim) * (1. / inner_dim) ** 0.5)
-        self.layers = nn.ModuleList([
-            BasicTransformerBlock(
-                inner_dim=inner_dim, cond_dim=image_feat_dim, num_heads=num_heads, eps=eps)
-            for _ in range(num_layers)
-        ])
-        self.norm = nn.LayerNorm(inner_dim, eps=eps)
-        self.deconv = nn.ConvTranspose2d(inner_dim, triplane_dim, kernel_size=2, stride=2, padding=0)
-
-    def forward(self, image_feats):
-        # image_feats: [N, L_cond, D_cond]
-
-        N = image_feats.shape[0]
-        H = W = self.triplane_low_res
-        L = 3 * H * W
-
-        x = self.pos_embed.repeat(N, 1, 1)  # [N, L, D]
-        for layer in self.layers:
-            x = layer(x, image_feats)
-        x = self.norm(x)
-
-        # separate each plane and apply deconv
-        x = x.view(N, 3, H, W, -1)
-        x = torch.einsum('nihwd->indhw', x)  # [3, N, D, H, W]
-        x = x.contiguous().view(3*N, -1, H, W)  # [3*N, D, H, W]
-        x = self.deconv(x)  # [3*N, D', H', W']
-        x = x.view(3, N, *x.shape[-3:])  # [3, N, D', H', W']
-        x = torch.einsum('indhw->nidhw', x)  # [N, 3, D', H', W']
-        x = x.contiguous()
-
-        return x

src/models/encoder/dino.py

@@ -1,550 +0,0 @@
-# coding=utf-8
-# Copyright 2021 Google AI, Ross Wightman, The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" PyTorch ViT model."""
-
-
-import collections.abc
-import math
-from typing import Dict, List, Optional, Set, Tuple, Union
-
-import torch
-from torch import nn
-
-from transformers.activations import ACT2FN
-from transformers.modeling_outputs import (
-    BaseModelOutput,
-    BaseModelOutputWithPooling,
-)
-from transformers import PreTrainedModel, ViTConfig
-from transformers.pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
-
-
-class ViTEmbeddings(nn.Module):
-    """
-    Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
-    """
-
-    def __init__(self, config: ViTConfig, use_mask_token: bool = False) -> None:
-        super().__init__()
-
-        self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size))
-        self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) if use_mask_token else None
-        self.patch_embeddings = ViTPatchEmbeddings(config)
-        num_patches = self.patch_embeddings.num_patches
-        self.position_embeddings = nn.Parameter(torch.randn(1, num_patches + 1, config.hidden_size))
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        self.config = config
-
-    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
-        """
-        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
-        resolution images.
-
-        Source:
-        https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
-        """
-
-        num_patches = embeddings.shape[1] - 1
-        num_positions = self.position_embeddings.shape[1] - 1
-        if num_patches == num_positions and height == width:
-            return self.position_embeddings
-        class_pos_embed = self.position_embeddings[:, 0]
-        patch_pos_embed = self.position_embeddings[:, 1:]
-        dim = embeddings.shape[-1]
-        h0 = height // self.config.patch_size
-        w0 = width // self.config.patch_size
-        # we add a small number to avoid floating point error in the interpolation
-        # see discussion at https://github.com/facebookresearch/dino/issues/8
-        h0, w0 = h0 + 0.1, w0 + 0.1
-        patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
-        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
-        patch_pos_embed = nn.functional.interpolate(
-            patch_pos_embed,
-            scale_factor=(h0 / math.sqrt(num_positions), w0 / math.sqrt(num_positions)),
-            mode="bicubic",
-            align_corners=False,
-        )
-        assert int(h0) == patch_pos_embed.shape[-2] and int(w0) == patch_pos_embed.shape[-1]
-        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
-        return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
-
-    def forward(
-        self,
-        pixel_values: torch.Tensor,
-        bool_masked_pos: Optional[torch.BoolTensor] = None,
-        interpolate_pos_encoding: bool = False,
-    ) -> torch.Tensor:
-        batch_size, num_channels, height, width = pixel_values.shape
-        embeddings = self.patch_embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
-
-        if bool_masked_pos is not None:
-            seq_length = embeddings.shape[1]
-            mask_tokens = self.mask_token.expand(batch_size, seq_length, -1)
-            # replace the masked visual tokens by mask_tokens
-            mask = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
-            embeddings = embeddings * (1.0 - mask) + mask_tokens * mask
-
-        # add the [CLS] token to the embedded patch tokens
-        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
-        embeddings = torch.cat((cls_tokens, embeddings), dim=1)
-
-        # add positional encoding to each token
-        if interpolate_pos_encoding:
-            embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
-        else:
-            embeddings = embeddings + self.position_embeddings
-
-        embeddings = self.dropout(embeddings)
-
-        return embeddings
-
-
-class ViTPatchEmbeddings(nn.Module):
-    """
-    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
-    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
-    Transformer.
-    """
-
-    def __init__(self, config):
-        super().__init__()
-        image_size, patch_size = config.image_size, config.patch_size
-        num_channels, hidden_size = config.num_channels, config.hidden_size
-
-        image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
-        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
-        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
-        self.image_size = image_size
-        self.patch_size = patch_size
-        self.num_channels = num_channels
-        self.num_patches = num_patches
-
-        self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
-
-    def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
-        batch_size, num_channels, height, width = pixel_values.shape
-        if num_channels != self.num_channels:
-            raise ValueError(
-                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
-                f" Expected {self.num_channels} but got {num_channels}."
-            )
-        if not interpolate_pos_encoding:
-            if height != self.image_size[0] or width != self.image_size[1]:
-                raise ValueError(
-                    f"Input image size ({height}*{width}) doesn't match model"
-                    f" ({self.image_size[0]}*{self.image_size[1]})."
-                )
-        embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
-        return embeddings
-
-
-class ViTSelfAttention(nn.Module):
-    def __init__(self, config: ViTConfig) -> None:
-        super().__init__()
-        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
-            raise ValueError(
-                f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
-                f"heads {config.num_attention_heads}."
-            )
-
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
-        self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
-        self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-
-    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        x = x.view(new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(
-        self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
-    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
-        mixed_query_layer = self.query(hidden_states)
-
-        key_layer = self.transpose_for_scores(self.key(hidden_states))
-        value_layer = self.transpose_for_scores(self.value(hidden_states))
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs = self.dropout(attention_probs)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attention_probs = attention_probs * head_mask
-
-        context_layer = torch.matmul(attention_probs, value_layer)
-
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(new_context_layer_shape)
-
-        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
-
-        return outputs
-
-
-class ViTSelfOutput(nn.Module):
-    """
-    The residual connection is defined in ViTLayer instead of here (as is the case with other models), due to the
-    layernorm applied before each block.
-    """
-
-    def __init__(self, config: ViTConfig) -> None:
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-
-        return hidden_states
-
-
-class ViTAttention(nn.Module):
-    def __init__(self, config: ViTConfig) -> None:
-        super().__init__()
-        self.attention = ViTSelfAttention(config)
-        self.output = ViTSelfOutput(config)
-        self.pruned_heads = set()
-
-    def prune_heads(self, heads: Set[int]) -> None:
-        if len(heads) == 0:
-            return
-        heads, index = find_pruneable_heads_and_indices(
-            heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
-        )
-
-        # Prune linear layers
-        self.attention.query = prune_linear_layer(self.attention.query, index)
-        self.attention.key = prune_linear_layer(self.attention.key, index)
-        self.attention.value = prune_linear_layer(self.attention.value, index)
-        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
-
-        # Update hyper params and store pruned heads
-        self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
-        self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
-        self.pruned_heads = self.pruned_heads.union(heads)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        head_mask: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
-        self_outputs = self.attention(hidden_states, head_mask, output_attentions)
-
-        attention_output = self.output(self_outputs[0], hidden_states)
-
-        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
-        return outputs
-
-
-class ViTIntermediate(nn.Module):
-    def __init__(self, config: ViTConfig) -> None:
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.intermediate_act_fn = config.hidden_act
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-
-        return hidden_states
-
-
-class ViTOutput(nn.Module):
-    def __init__(self, config: ViTConfig) -> None:
-        super().__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-
-        hidden_states = hidden_states + input_tensor
-
-        return hidden_states
-
-
-def modulate(x, shift, scale):
-    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
-
-
-class ViTLayer(nn.Module):
-    """This corresponds to the Block class in the timm implementation."""
-
-    def __init__(self, config: ViTConfig) -> None:
-        super().__init__()
-        self.chunk_size_feed_forward = config.chunk_size_feed_forward
-        self.seq_len_dim = 1
-        self.attention = ViTAttention(config)
-        self.intermediate = ViTIntermediate(config)
-        self.output = ViTOutput(config)
-        self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-
-        self.adaLN_modulation = nn.Sequential(
-            nn.SiLU(),
-            nn.Linear(config.hidden_size, 4 * config.hidden_size, bias=True)
-        )
-        nn.init.constant_(self.adaLN_modulation[-1].weight, 0)
-        nn.init.constant_(self.adaLN_modulation[-1].bias, 0)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        adaln_input: torch.Tensor = None,
-        head_mask: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-    ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
-        shift_msa, scale_msa, shift_mlp, scale_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1)
-
-        self_attention_outputs = self.attention(
-            modulate(self.layernorm_before(hidden_states), shift_msa, scale_msa),  # in ViT, layernorm is applied before self-attention
-            head_mask,
-            output_attentions=output_attentions,
-        )
-        attention_output = self_attention_outputs[0]
-        outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
-
-        # first residual connection
-        hidden_states = attention_output + hidden_states
-
-        # in ViT, layernorm is also applied after self-attention
-        layer_output = modulate(self.layernorm_after(hidden_states), shift_mlp, scale_mlp)
-        layer_output = self.intermediate(layer_output)
-
-        # second residual connection is done here
-        layer_output = self.output(layer_output, hidden_states)
-
-        outputs = (layer_output,) + outputs
-
-        return outputs
-
-
-class ViTEncoder(nn.Module):
-    def __init__(self, config: ViTConfig) -> None:
-        super().__init__()
-        self.config = config
-        self.layer = nn.ModuleList([ViTLayer(config) for _ in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        adaln_input: torch.Tensor = None,
-        head_mask: Optional[torch.Tensor] = None,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = True,
-    ) -> Union[tuple, BaseModelOutput]:
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attentions = () if output_attentions else None
-
-        for i, layer_module in enumerate(self.layer):
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            layer_head_mask = head_mask[i] if head_mask is not None else None
-
-            if self.gradient_checkpointing and self.training:
-                layer_outputs = self._gradient_checkpointing_func(
-                    layer_module.__call__,
-                    hidden_states,
-                    adaln_input,
-                    layer_head_mask,
-                    output_attentions,
-                )
-            else:
-                layer_outputs = layer_module(hidden_states, adaln_input, layer_head_mask, output_attentions)
-
-            hidden_states = layer_outputs[0]
-
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (layer_outputs[1],)
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=hidden_states,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-        )
-
-
-class ViTPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = ViTConfig
-    base_model_prefix = "vit"
-    main_input_name = "pixel_values"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["ViTEmbeddings", "ViTLayer"]
-
-    def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
-        """Initialize the weights"""
-        if isinstance(module, (nn.Linear, nn.Conv2d)):
-            # Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
-            # `trunc_normal_cpu` not implemented in `half` issues
-            module.weight.data = nn.init.trunc_normal_(
-                module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range
-            ).to(module.weight.dtype)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-        elif isinstance(module, ViTEmbeddings):
-            module.position_embeddings.data = nn.init.trunc_normal_(
-                module.position_embeddings.data.to(torch.float32),
-                mean=0.0,
-                std=self.config.initializer_range,
-            ).to(module.position_embeddings.dtype)
-
-            module.cls_token.data = nn.init.trunc_normal_(
-                module.cls_token.data.to(torch.float32),
-                mean=0.0,
-                std=self.config.initializer_range,
-            ).to(module.cls_token.dtype)
-
-
-class ViTModel(ViTPreTrainedModel):
-    def __init__(self, config: ViTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False):
-        super().__init__(config)
-        self.config = config
-
-        self.embeddings = ViTEmbeddings(config, use_mask_token=use_mask_token)
-        self.encoder = ViTEncoder(config)
-
-        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.pooler = ViTPooler(config) if add_pooling_layer else None
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self) -> ViTPatchEmbeddings:
-        return self.embeddings.patch_embeddings
-
-    def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
-        """
-        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
-        class PreTrainedModel
-        """
-        for layer, heads in heads_to_prune.items():
-            self.encoder.layer[layer].attention.prune_heads(heads)
-
-    def forward(
-        self,
-        pixel_values: Optional[torch.Tensor] = None,
-        adaln_input: Optional[torch.Tensor] = None,
-        bool_masked_pos: Optional[torch.BoolTensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        interpolate_pos_encoding: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPooling]:
-        r"""
-        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
-            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
-        """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if pixel_values is None:
-            raise ValueError("You have to specify pixel_values")
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-
-        # TODO: maybe have a cleaner way to cast the input (from `ImageProcessor` side?)
-        expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype
-        if pixel_values.dtype != expected_dtype:
-            pixel_values = pixel_values.to(expected_dtype)
-
-        embedding_output = self.embeddings(
-            pixel_values, bool_masked_pos=bool_masked_pos, interpolate_pos_encoding=interpolate_pos_encoding
-        )
-
-        encoder_outputs = self.encoder(
-            embedding_output,
-            adaln_input=adaln_input,
-            head_mask=head_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        sequence_output = encoder_outputs[0]
-        sequence_output = self.layernorm(sequence_output)
-        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
-
-        if not return_dict:
-            head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,)
-            return head_outputs + encoder_outputs[1:]
-
-        return BaseModelOutputWithPooling(
-            last_hidden_state=sequence_output,
-            pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-        )
-
-
-class ViTPooler(nn.Module):
-    def __init__(self, config: ViTConfig):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.activation = nn.Tanh()
-
-    def forward(self, hidden_states):
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-        first_token_tensor = hidden_states[:, 0]
-        pooled_output = self.dense(first_token_tensor)
-        pooled_output = self.activation(pooled_output)
-        return pooled_output

src/models/encoder/dino_wrapper.py

@@ -1,80 +0,0 @@
-# Copyright (c) 2023, Zexin He
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import torch.nn as nn
-from transformers import ViTImageProcessor
-from einops import rearrange, repeat
-from .dino import ViTModel
-
-
-class DinoWrapper(nn.Module):
-    """
-    Dino v1 wrapper using huggingface transformer implementation.
-    """
-    def __init__(self, model_name: str, freeze: bool = True):
-        super().__init__()
-        self.model, self.processor = self._build_dino(model_name)
-        self.camera_embedder = nn.Sequential(
-            nn.Linear(16, self.model.config.hidden_size, bias=True),
-            nn.SiLU(),
-            nn.Linear(self.model.config.hidden_size, self.model.config.hidden_size, bias=True)
-        )
-        if freeze:
-            self._freeze()
-
-    def forward(self, image, camera):
-        # image: [B, N, C, H, W]
-        # camera: [B, N, D]
-        # RGB image with [0,1] scale and properly sized
-        if image.ndim == 5:
-            image = rearrange(image, 'b n c h w -> (b n) c h w')
-        dtype = image.dtype
-        inputs = self.processor(
-            images=image.float(), 
-            return_tensors="pt", 
-            do_rescale=False, 
-            do_resize=False,
-        ).to(self.model.device).to(dtype)
-        # embed camera
-        N = camera.shape[1]
-        camera_embeddings = self.camera_embedder(camera)
-        camera_embeddings = rearrange(camera_embeddings, 'b n d -> (b n) d')
-        embeddings = camera_embeddings
-        # This resampling of positional embedding uses bicubic interpolation
-        outputs = self.model(**inputs, adaln_input=embeddings, interpolate_pos_encoding=True)
-        last_hidden_states = outputs.last_hidden_state
-        return last_hidden_states
-
-    def _freeze(self):
-        print(f"======== Freezing DinoWrapper ========")
-        self.model.eval()
-        for name, param in self.model.named_parameters():
-            param.requires_grad = False
-
-    @staticmethod
-    def _build_dino(model_name: str, proxy_error_retries: int = 3, proxy_error_cooldown: int = 5):
-        import requests
-        try:
-            model = ViTModel.from_pretrained(model_name, add_pooling_layer=False)
-            processor = ViTImageProcessor.from_pretrained(model_name)
-            return model, processor
-        except requests.exceptions.ProxyError as err:
-            if proxy_error_retries > 0:
-                print(f"Huggingface ProxyError: Retrying in {proxy_error_cooldown} seconds...")
-                import time
-                time.sleep(proxy_error_cooldown)
-                return DinoWrapper._build_dino(model_name, proxy_error_retries - 1, proxy_error_cooldown)
-            else:
-                raise err

src/models/geometry/__init__.py

@@ -1,7 +0,0 @@
-# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION & AFFILIATES 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 & AFFILIATES is strictly prohibited.

src/models/geometry/camera/__init__.py

@@ -1,16 +0,0 @@
-# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION & AFFILIATES 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 & AFFILIATES is strictly prohibited.
-
-import torch
-from torch import nn
-
-
-class Camera(nn.Module):
-    def __init__(self):
-        super(Camera, self).__init__()
-        pass

src/models/geometry/camera/perspective_camera.py

@@ -1,35 +0,0 @@
-# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION & AFFILIATES 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 & AFFILIATES is strictly prohibited.
-
-import torch
-from . import Camera
-import numpy as np
-
-
-def projection(x=0.1, n=1.0, f=50.0, near_plane=None):
-    if near_plane is None:
-        near_plane = n
-    return np.array(
-        [[n / x, 0, 0, 0],
-         [0, n / -x, 0, 0],
-         [0, 0, -(f + near_plane) / (f - near_plane), -(2 * f * near_plane) / (f - near_plane)],
-         [0, 0, -1, 0]]).astype(np.float32)
-
-
-class PerspectiveCamera(Camera):
-    def __init__(self, fovy=49.0, device='cuda'):
-        super(PerspectiveCamera, self).__init__()
-        self.device = device
-        focal = np.tan(fovy / 180.0 * np.pi * 0.5)
-        self.proj_mtx = torch.from_numpy(projection(x=focal, f=1000.0, n=1.0, near_plane=0.1)).to(self.device).unsqueeze(dim=0)
-
-    def project(self, points_bxnx4):
-        out = torch.matmul(
-            points_bxnx4,
-            torch.transpose(self.proj_mtx, 1, 2))
-        return out

src/models/geometry/render/__init__.py

@@ -1,8 +0,0 @@
-import torch
-
-class Renderer():
-    def __init__(self):
-        pass
-
-    def forward(self):
-        pass

src/models/geometry/render/neural_render.py

@@ -1,121 +0,0 @@
-# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION & AFFILIATES 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 & AFFILIATES is strictly prohibited.
-
-import torch
-import torch.nn.functional as F
-import nvdiffrast.torch as dr
-from . import Renderer
-
-_FG_LUT = None
-
-
-def interpolate(attr, rast, attr_idx, rast_db=None):
-    return dr.interpolate(
-        attr.contiguous(), rast, attr_idx, rast_db=rast_db,
-        diff_attrs=None if rast_db is None else 'all')
-
-
-def xfm_points(points, matrix, use_python=True):
-    '''Transform points.
-    Args:
-        points: Tensor containing 3D points with shape [minibatch_size, num_vertices, 3] or [1, num_vertices, 3]
-        matrix: A 4x4 transform matrix with shape [minibatch_size, 4, 4]
-        use_python: Use PyTorch's torch.matmul (for validation)
-    Returns:
-        Transformed points in homogeneous 4D with shape [minibatch_size, num_vertices, 4].
-    '''
-    out = torch.matmul(torch.nn.functional.pad(points, pad=(0, 1), mode='constant', value=1.0), torch.transpose(matrix, 1, 2))
-    if torch.is_anomaly_enabled():
-        assert torch.all(torch.isfinite(out)), "Output of xfm_points contains inf or NaN"
-    return out
-
-
-def dot(x, y):
-    return torch.sum(x * y, -1, keepdim=True)
-
-
-def compute_vertex_normal(v_pos, t_pos_idx):
-    i0 = t_pos_idx[:, 0]
-    i1 = t_pos_idx[:, 1]
-    i2 = t_pos_idx[:, 2]
-
-    v0 = v_pos[i0, :]
-    v1 = v_pos[i1, :]
-    v2 = v_pos[i2, :]
-
-    face_normals = torch.cross(v1 - v0, v2 - v0)
-
-    # Splat face normals to vertices
-    v_nrm = torch.zeros_like(v_pos)
-    v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)
-    v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)
-    v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)
-
-    # Normalize, replace zero (degenerated) normals with some default value
-    v_nrm = torch.where(
-        dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm)
-    )
-    v_nrm = F.normalize(v_nrm, dim=1)
-    assert torch.all(torch.isfinite(v_nrm))
-
-    return v_nrm
-
-
-class NeuralRender(Renderer):
-    def __init__(self, device='cuda', camera_model=None):
-        super(NeuralRender, self).__init__()
-        self.device = device
-        self.ctx = dr.RasterizeCudaContext(device=device)
-        self.projection_mtx = None
-        self.camera = camera_model
-
-    def render_mesh(
-            self,
-            mesh_v_pos_bxnx3,
-            mesh_t_pos_idx_fx3,
-            camera_mv_bx4x4,
-            mesh_v_feat_bxnxd,
-            resolution=256,
-            spp=1,
-            device='cuda',
-            hierarchical_mask=False
-    ):
-        assert not hierarchical_mask
-        
-        mtx_in = torch.tensor(camera_mv_bx4x4, dtype=torch.float32, device=device) if not torch.is_tensor(camera_mv_bx4x4) else camera_mv_bx4x4
-        v_pos = xfm_points(mesh_v_pos_bxnx3, mtx_in)  # Rotate it to camera coordinates
-        v_pos_clip = self.camera.project(v_pos)  # Projection in the camera
-
-        v_nrm = compute_vertex_normal(mesh_v_pos_bxnx3[0], mesh_t_pos_idx_fx3.long())  # vertex normals in world coordinates
-
-        # Render the image,
-        # Here we only return the feature (3D location) at each pixel, which will be used as the input for neural render
-        num_layers = 1
-        mask_pyramid = None
-        assert mesh_t_pos_idx_fx3.shape[0] > 0  # Make sure we have shapes
-        mesh_v_feat_bxnxd = torch.cat([mesh_v_feat_bxnxd.repeat(v_pos.shape[0], 1, 1), v_pos], dim=-1)  # Concatenate the pos
-
-        with dr.DepthPeeler(self.ctx, v_pos_clip, mesh_t_pos_idx_fx3, [resolution * spp, resolution * spp]) as peeler:
-            for _ in range(num_layers):
-                rast, db = peeler.rasterize_next_layer()
-                gb_feat, _ = interpolate(mesh_v_feat_bxnxd, rast, mesh_t_pos_idx_fx3)
-
-        hard_mask = torch.clamp(rast[..., -1:], 0, 1)
-        antialias_mask = dr.antialias(
-            hard_mask.clone().contiguous(), rast, v_pos_clip,
-            mesh_t_pos_idx_fx3)
-
-        depth = gb_feat[..., -2:-1]
-        ori_mesh_feature = gb_feat[..., :-4]
-
-        normal, _ = interpolate(v_nrm[None, ...], rast, mesh_t_pos_idx_fx3)
-        normal = dr.antialias(normal.clone().contiguous(), rast, v_pos_clip, mesh_t_pos_idx_fx3)
-        normal = F.normalize(normal, dim=-1)
-        normal = torch.lerp(torch.zeros_like(normal), (normal + 1.0) / 2.0, hard_mask.float())      # black background
-
-        return ori_mesh_feature, antialias_mask, hard_mask, rast, v_pos_clip, mask_pyramid, depth, normal

src/models/geometry/rep_3d/__init__.py

@@ -1,18 +0,0 @@
-# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION & AFFILIATES 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 & AFFILIATES is strictly prohibited.
-
-import torch
-import numpy as np
-
-
-class Geometry():
-    def __init__(self):
-        pass
-
-    def forward(self):
-        pass

src/models/geometry/rep_3d/dmtet.py

@@ -1,504 +0,0 @@
-# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION & AFFILIATES 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 & AFFILIATES is strictly prohibited.
-
-import torch
-import numpy as np
-import os
-from . import Geometry
-from .dmtet_utils import get_center_boundary_index
-import torch.nn.functional as F
-
-
-###############################################################################
-# DMTet utility functions
-###############################################################################
-def create_mt_variable(device):
-    triangle_table = torch.tensor(
-        [
-            [-1, -1, -1, -1, -1, -1],
-            [1, 0, 2, -1, -1, -1],
-            [4, 0, 3, -1, -1, -1],
-            [1, 4, 2, 1, 3, 4],
-            [3, 1, 5, -1, -1, -1],
-            [2, 3, 0, 2, 5, 3],
-            [1, 4, 0, 1, 5, 4],
-            [4, 2, 5, -1, -1, -1],
-            [4, 5, 2, -1, -1, -1],
-            [4, 1, 0, 4, 5, 1],
-            [3, 2, 0, 3, 5, 2],
-            [1, 3, 5, -1, -1, -1],
-            [4, 1, 2, 4, 3, 1],
-            [3, 0, 4, -1, -1, -1],
-            [2, 0, 1, -1, -1, -1],
-            [-1, -1, -1, -1, -1, -1]
-        ], dtype=torch.long, device=device)
-
-    num_triangles_table = torch.tensor([0, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 1, 0], dtype=torch.long, device=device)
-    base_tet_edges = torch.tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long, device=device)
-    v_id = torch.pow(2, torch.arange(4, dtype=torch.long, device=device))
-    return triangle_table, num_triangles_table, base_tet_edges, v_id
-
-
-def sort_edges(edges_ex2):
-    with torch.no_grad():
-        order = (edges_ex2[:, 0] > edges_ex2[:, 1]).long()
-        order = order.unsqueeze(dim=1)
-        a = torch.gather(input=edges_ex2, index=order, dim=1)
-        b = torch.gather(input=edges_ex2, index=1 - order, dim=1)
-    return torch.stack([a, b], -1)
-
-
-###############################################################################
-# marching tetrahedrons (differentiable)
-###############################################################################
-
-def marching_tets(pos_nx3, sdf_n, tet_fx4, triangle_table, num_triangles_table, base_tet_edges, v_id):
-    with torch.no_grad():
-        occ_n = sdf_n > 0
-        occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)
-        occ_sum = torch.sum(occ_fx4, -1)
-        valid_tets = (occ_sum > 0) & (occ_sum < 4)
-        occ_sum = occ_sum[valid_tets]
-
-        # find all vertices
-        all_edges = tet_fx4[valid_tets][:, base_tet_edges].reshape(-1, 2)
-        all_edges = sort_edges(all_edges)
-        unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)
-
-        unique_edges = unique_edges.long()
-        mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1
-        mapping = torch.ones((unique_edges.shape[0]), dtype=torch.long, device=sdf_n.device) * -1
-        mapping[mask_edges] = torch.arange(mask_edges.sum(), dtype=torch.long, device=sdf_n.device)
-        idx_map = mapping[idx_map]  # map edges to verts
-
-        interp_v = unique_edges[mask_edges]  # .long()
-    edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3)
-    edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1)
-    edges_to_interp_sdf[:, -1] *= -1
-
-    denominator = edges_to_interp_sdf.sum(1, keepdim=True)
-
-    edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator
-    verts = (edges_to_interp * edges_to_interp_sdf).sum(1)
-
-    idx_map = idx_map.reshape(-1, 6)
-
-    tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1)
-    num_triangles = num_triangles_table[tetindex]
-
-    # Generate triangle indices
-    faces = torch.cat(
-        (
-            torch.gather(
-                input=idx_map[num_triangles == 1], dim=1,
-                index=triangle_table[tetindex[num_triangles == 1]][:, :3]).reshape(-1, 3),
-            torch.gather(
-                input=idx_map[num_triangles == 2], dim=1,
-                index=triangle_table[tetindex[num_triangles == 2]][:, :6]).reshape(-1, 3),
-        ), dim=0)
-    return verts, faces
-
-
-def create_tetmesh_variables(device='cuda'):
-    tet_table = torch.tensor(
-        [[-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],
-         [0, 4, 5, 6, -1, -1, -1, -1, -1, -1, -1, -1],
-         [1, 4, 7, 8, -1, -1, -1, -1, -1, -1, -1, -1],
-         [1, 0, 8, 7, 0, 5, 8, 7, 0, 5, 6, 8],
-         [2, 5, 7, 9, -1, -1, -1, -1, -1, -1, -1, -1],
-         [2, 0, 9, 7, 0, 4, 9, 7, 0, 4, 6, 9],
-         [2, 1, 9, 5, 1, 4, 9, 5, 1, 4, 8, 9],
-         [6, 0, 1, 2, 6, 1, 2, 8, 6, 8, 2, 9],
-         [3, 6, 8, 9, -1, -1, -1, -1, -1, -1, -1, -1],
-         [3, 0, 9, 8, 0, 4, 9, 8, 0, 4, 5, 9],
-         [3, 1, 9, 6, 1, 4, 9, 6, 1, 4, 7, 9],
-         [5, 0, 1, 3, 5, 1, 3, 7, 5, 7, 3, 9],
-         [3, 2, 8, 6, 2, 5, 8, 6, 2, 5, 7, 8],
-         [4, 0, 2, 3, 4, 2, 3, 7, 4, 7, 3, 8],
-         [4, 1, 2, 3, 4, 2, 3, 5, 4, 5, 3, 6],
-         [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]], dtype=torch.long, device=device)
-    num_tets_table = torch.tensor([0, 1, 1, 3, 1, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 0], dtype=torch.long, device=device)
-    return tet_table, num_tets_table
-
-
-def marching_tets_tetmesh(
-        pos_nx3, sdf_n, tet_fx4, triangle_table, num_triangles_table, base_tet_edges, v_id,
-        return_tet_mesh=False, ori_v=None, num_tets_table=None, tet_table=None):
-    with torch.no_grad():
-        occ_n = sdf_n > 0
-        occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)
-        occ_sum = torch.sum(occ_fx4, -1)
-        valid_tets = (occ_sum > 0) & (occ_sum < 4)
-        occ_sum = occ_sum[valid_tets]
-
-        # find all vertices
-        all_edges = tet_fx4[valid_tets][:, base_tet_edges].reshape(-1, 2)
-        all_edges = sort_edges(all_edges)
-        unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)
-
-        unique_edges = unique_edges.long()
-        mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1
-        mapping = torch.ones((unique_edges.shape[0]), dtype=torch.long, device=sdf_n.device) * -1
-        mapping[mask_edges] = torch.arange(mask_edges.sum(), dtype=torch.long, device=sdf_n.device)
-        idx_map = mapping[idx_map]  # map edges to verts
-
-        interp_v = unique_edges[mask_edges]  # .long()
-    edges_to_interp = pos_nx3[interp_v.reshape(-1)].reshape(-1, 2, 3)
-    edges_to_interp_sdf = sdf_n[interp_v.reshape(-1)].reshape(-1, 2, 1)
-    edges_to_interp_sdf[:, -1] *= -1
-
-    denominator = edges_to_interp_sdf.sum(1, keepdim=True)
-
-    edges_to_interp_sdf = torch.flip(edges_to_interp_sdf, [1]) / denominator
-    verts = (edges_to_interp * edges_to_interp_sdf).sum(1)
-
-    idx_map = idx_map.reshape(-1, 6)
-
-    tetindex = (occ_fx4[valid_tets] * v_id.unsqueeze(0)).sum(-1)
-    num_triangles = num_triangles_table[tetindex]
-
-    # Generate triangle indices
-    faces = torch.cat(
-        (
-            torch.gather(
-                input=idx_map[num_triangles == 1], dim=1,
-                index=triangle_table[tetindex[num_triangles == 1]][:, :3]).reshape(-1, 3),
-            torch.gather(
-                input=idx_map[num_triangles == 2], dim=1,
-                index=triangle_table[tetindex[num_triangles == 2]][:, :6]).reshape(-1, 3),
-        ), dim=0)
-    if not return_tet_mesh:
-        return verts, faces
-    occupied_verts = ori_v[occ_n]
-    mapping = torch.ones((pos_nx3.shape[0]), dtype=torch.long, device="cuda") * -1
-    mapping[occ_n] = torch.arange(occupied_verts.shape[0], device="cuda")
-    tet_fx4 = mapping[tet_fx4.reshape(-1)].reshape((-1, 4))
-
-    idx_map = torch.cat([tet_fx4[valid_tets] + verts.shape[0], idx_map], -1)  # t x 10
-    tet_verts = torch.cat([verts, occupied_verts], 0)
-    num_tets = num_tets_table[tetindex]
-
-    tets = torch.cat(
-        (
-            torch.gather(input=idx_map[num_tets == 1], dim=1, index=tet_table[tetindex[num_tets == 1]][:, :4]).reshape(
-                -1,
-                4),
-            torch.gather(input=idx_map[num_tets == 3], dim=1, index=tet_table[tetindex[num_tets == 3]][:, :12]).reshape(
-                -1,
-                4),
-        ), dim=0)
-    # add fully occupied tets
-    fully_occupied = occ_fx4.sum(-1) == 4
-    tet_fully_occupied = tet_fx4[fully_occupied] + verts.shape[0]
-    tets = torch.cat([tets, tet_fully_occupied])
-
-    return verts, faces, tet_verts, tets
-
-
-###############################################################################
-# Compact tet grid
-###############################################################################
-
-def compact_tets(pos_nx3, sdf_n, tet_fx4):
-    with torch.no_grad():
-        # Find surface tets
-        occ_n = sdf_n > 0
-        occ_fx4 = occ_n[tet_fx4.reshape(-1)].reshape(-1, 4)
-        occ_sum = torch.sum(occ_fx4, -1)
-        valid_tets = (occ_sum > 0) & (occ_sum < 4)  # one value per tet, these are the surface tets
-
-        valid_vtx = tet_fx4[valid_tets].reshape(-1)
-        unique_vtx, idx_map = torch.unique(valid_vtx, dim=0, return_inverse=True)
-        new_pos = pos_nx3[unique_vtx]
-        new_sdf = sdf_n[unique_vtx]
-        new_tets = idx_map.reshape(-1, 4)
-        return new_pos, new_sdf, new_tets
-
-
-###############################################################################
-# Subdivide volume
-###############################################################################
-
-def batch_subdivide_volume(tet_pos_bxnx3, tet_bxfx4, grid_sdf):
-    device = tet_pos_bxnx3.device
-    # get new verts
-    tet_fx4 = tet_bxfx4[0]
-    edges = [0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3]
-    all_edges = tet_fx4[:, edges].reshape(-1, 2)
-    all_edges = sort_edges(all_edges)
-    unique_edges, idx_map = torch.unique(all_edges, dim=0, return_inverse=True)
-    idx_map = idx_map + tet_pos_bxnx3.shape[1]
-    all_values = torch.cat([tet_pos_bxnx3, grid_sdf], -1)
-    mid_points_pos = all_values[:, unique_edges.reshape(-1)].reshape(
-        all_values.shape[0], -1, 2,
-        all_values.shape[-1]).mean(2)
-    new_v = torch.cat([all_values, mid_points_pos], 1)
-    new_v, new_sdf = new_v[..., :3], new_v[..., 3]
-
-    # get new tets
-
-    idx_a, idx_b, idx_c, idx_d = tet_fx4[:, 0], tet_fx4[:, 1], tet_fx4[:, 2], tet_fx4[:, 3]
-    idx_ab = idx_map[0::6]
-    idx_ac = idx_map[1::6]
-    idx_ad = idx_map[2::6]
-    idx_bc = idx_map[3::6]
-    idx_bd = idx_map[4::6]
-    idx_cd = idx_map[5::6]
-
-    tet_1 = torch.stack([idx_a, idx_ab, idx_ac, idx_ad], dim=1)
-    tet_2 = torch.stack([idx_b, idx_bc, idx_ab, idx_bd], dim=1)
-    tet_3 = torch.stack([idx_c, idx_ac, idx_bc, idx_cd], dim=1)
-    tet_4 = torch.stack([idx_d, idx_ad, idx_cd, idx_bd], dim=1)
-    tet_5 = torch.stack([idx_ab, idx_ac, idx_ad, idx_bd], dim=1)
-    tet_6 = torch.stack([idx_ab, idx_ac, idx_bd, idx_bc], dim=1)
-    tet_7 = torch.stack([idx_cd, idx_ac, idx_bd, idx_ad], dim=1)
-    tet_8 = torch.stack([idx_cd, idx_ac, idx_bc, idx_bd], dim=1)
-
-    tet_np = torch.cat([tet_1, tet_2, tet_3, tet_4, tet_5, tet_6, tet_7, tet_8], dim=0)
-    tet_np = tet_np.reshape(1, -1, 4).expand(tet_pos_bxnx3.shape[0], -1, -1)
-    tet = tet_np.long().to(device)
-
-    return new_v, tet, new_sdf
-
-
-###############################################################################
-# Adjacency
-###############################################################################
-def tet_to_tet_adj_sparse(tet_tx4):
-    # include self connection!!!!!!!!!!!!!!!!!!!
-    with torch.no_grad():
-        t = tet_tx4.shape[0]
-        device = tet_tx4.device
-        idx_array = torch.LongTensor(
-            [0, 1, 2,
-             1, 0, 3,
-             2, 3, 0,
-             3, 2, 1]).to(device).reshape(4, 3).unsqueeze(0).expand(t, -1, -1)  # (t, 4, 3)
-
-        # get all faces
-        all_faces = torch.gather(input=tet_tx4.unsqueeze(1).expand(-1, 4, -1), index=idx_array, dim=-1).reshape(
-            -1,
-            3)  # (tx4, 3)
-        all_faces_tet_idx = torch.arange(t, device=device).unsqueeze(-1).expand(-1, 4).reshape(-1)
-        # sort and group
-        all_faces_sorted, _ = torch.sort(all_faces, dim=1)
-
-        all_faces_unique, inverse_indices, counts = torch.unique(
-            all_faces_sorted, dim=0, return_counts=True,
-            return_inverse=True)
-        tet_face_fx3 = all_faces_unique[counts == 2]
-        counts = counts[inverse_indices]  # tx4
-        valid = (counts == 2)
-
-        group = inverse_indices[valid]
-        # print (inverse_indices.shape, group.shape, all_faces_tet_idx.shape)
-        _, indices = torch.sort(group)
-        all_faces_tet_idx_grouped = all_faces_tet_idx[valid][indices]
-        tet_face_tetidx_fx2 = torch.stack([all_faces_tet_idx_grouped[::2], all_faces_tet_idx_grouped[1::2]], dim=-1)
-
-        tet_adj_idx = torch.cat([tet_face_tetidx_fx2, torch.flip(tet_face_tetidx_fx2, [1])])
-        adj_self = torch.arange(t, device=tet_tx4.device)
-        adj_self = torch.stack([adj_self, adj_self], -1)
-        tet_adj_idx = torch.cat([tet_adj_idx, adj_self])
-
-        tet_adj_idx = torch.unique(tet_adj_idx, dim=0)
-        values = torch.ones(
-            tet_adj_idx.shape[0], device=tet_tx4.device).float()
-        adj_sparse = torch.sparse.FloatTensor(
-            tet_adj_idx.t(), values, torch.Size([t, t]))
-
-        # normalization
-        neighbor_num = 1.0 / torch.sparse.sum(
-            adj_sparse, dim=1).to_dense()
-        values = torch.index_select(neighbor_num, 0, tet_adj_idx[:, 0])
-        adj_sparse = torch.sparse.FloatTensor(
-            tet_adj_idx.t(), values, torch.Size([t, t]))
-    return adj_sparse
-
-
-###############################################################################
-# Compact grid
-###############################################################################
-
-def get_tet_bxfx4x3(bxnxz, bxfx4):
-    n_batch, z = bxnxz.shape[0], bxnxz.shape[2]
-    gather_input = bxnxz.unsqueeze(2).expand(
-        n_batch, bxnxz.shape[1], 4, z)
-    gather_index = bxfx4.unsqueeze(-1).expand(
-        n_batch, bxfx4.shape[1], 4, z).long()
-    tet_bxfx4xz = torch.gather(
-        input=gather_input, dim=1, index=gather_index)
-
-    return tet_bxfx4xz
-
-
-def shrink_grid(tet_pos_bxnx3, tet_bxfx4, grid_sdf):
-    with torch.no_grad():
-        assert tet_pos_bxnx3.shape[0] == 1
-
-        occ = grid_sdf[0] > 0
-        occ_sum = get_tet_bxfx4x3(occ.unsqueeze(0).unsqueeze(-1), tet_bxfx4).reshape(-1, 4).sum(-1)
-        mask = (occ_sum > 0) & (occ_sum < 4)
-
-        # build connectivity graph
-        adj_matrix = tet_to_tet_adj_sparse(tet_bxfx4[0])
-        mask = mask.float().unsqueeze(-1)
-
-        # Include a one ring of neighbors
-        for i in range(1):
-            mask = torch.sparse.mm(adj_matrix, mask)
-        mask = mask.squeeze(-1) > 0
-
-        mapping = torch.zeros((tet_pos_bxnx3.shape[1]), device=tet_pos_bxnx3.device, dtype=torch.long)
-        new_tet_bxfx4 = tet_bxfx4[:, mask].long()
-        selected_verts_idx = torch.unique(new_tet_bxfx4)
-        new_tet_pos_bxnx3 = tet_pos_bxnx3[:, selected_verts_idx]
-        mapping[selected_verts_idx] = torch.arange(selected_verts_idx.shape[0], device=tet_pos_bxnx3.device)
-        new_tet_bxfx4 = mapping[new_tet_bxfx4.reshape(-1)].reshape(new_tet_bxfx4.shape)
-        new_grid_sdf = grid_sdf[:, selected_verts_idx]
-        return new_tet_pos_bxnx3, new_tet_bxfx4, new_grid_sdf
-
-
-###############################################################################
-# Regularizer
-###############################################################################
-
-def sdf_reg_loss(sdf, all_edges):
-    sdf_f1x6x2 = sdf[all_edges.reshape(-1)].reshape(-1, 2)
-    mask = torch.sign(sdf_f1x6x2[..., 0]) != torch.sign(sdf_f1x6x2[..., 1])
-    sdf_f1x6x2 = sdf_f1x6x2[mask]
-    sdf_diff = torch.nn.functional.binary_cross_entropy_with_logits(
-        sdf_f1x6x2[..., 0],
-        (sdf_f1x6x2[..., 1] > 0).float()) + \
-               torch.nn.functional.binary_cross_entropy_with_logits(
-                   sdf_f1x6x2[..., 1],
-                   (sdf_f1x6x2[..., 0] > 0).float())
-    return sdf_diff
-
-
-def sdf_reg_loss_batch(sdf, all_edges):
-    sdf_f1x6x2 = sdf[:, all_edges.reshape(-1)].reshape(sdf.shape[0], -1, 2)
-    mask = torch.sign(sdf_f1x6x2[..., 0]) != torch.sign(sdf_f1x6x2[..., 1])
-    sdf_f1x6x2 = sdf_f1x6x2[mask]
-    sdf_diff = torch.nn.functional.binary_cross_entropy_with_logits(sdf_f1x6x2[..., 0], (sdf_f1x6x2[..., 1] > 0).float()) + \
-               torch.nn.functional.binary_cross_entropy_with_logits(sdf_f1x6x2[..., 1], (sdf_f1x6x2[..., 0] > 0).float())
-    return sdf_diff
-
-
-###############################################################################
-#  Geometry interface
-###############################################################################
-class DMTetGeometry(Geometry):
-    def __init__(
-            self, grid_res=64, scale=2.0, device='cuda', renderer=None,
-            render_type='neural_render', args=None):
-        super(DMTetGeometry, self).__init__()
-        self.grid_res = grid_res
-        self.device = device
-        self.args = args
-        tets = np.load('data/tets/%d_compress.npz' % (grid_res))
-        self.verts = torch.from_numpy(tets['vertices']).float().to(self.device)
-        # Make sure the tet is zero-centered and length is equal to 1
-        length = self.verts.max(dim=0)[0] - self.verts.min(dim=0)[0]
-        length = length.max()
-        mid = (self.verts.max(dim=0)[0] + self.verts.min(dim=0)[0]) / 2.0
-        self.verts = (self.verts - mid.unsqueeze(dim=0)) / length
-        if isinstance(scale, list):
-            self.verts[:, 0] = self.verts[:, 0] * scale[0]
-            self.verts[:, 1] = self.verts[:, 1] * scale[1]
-            self.verts[:, 2] = self.verts[:, 2] * scale[1]
-        else:
-            self.verts = self.verts * scale
-        self.indices = torch.from_numpy(tets['tets']).long().to(self.device)
-        self.triangle_table, self.num_triangles_table, self.base_tet_edges, self.v_id = create_mt_variable(self.device)
-        self.tet_table, self.num_tets_table = create_tetmesh_variables(self.device)
-        # Parameters for regularization computation
-        edges = torch.tensor([0, 1, 0, 2, 0, 3, 1, 2, 1, 3, 2, 3], dtype=torch.long, device=self.device)
-        all_edges = self.indices[:, edges].reshape(-1, 2)
-        all_edges_sorted = torch.sort(all_edges, dim=1)[0]
-        self.all_edges = torch.unique(all_edges_sorted, dim=0)
-
-        # Parameters used for fix boundary sdf
-        self.center_indices, self.boundary_indices = get_center_boundary_index(self.verts)
-        self.renderer = renderer
-        self.render_type = render_type
-
-    def getAABB(self):
-        return torch.min(self.verts, dim=0).values, torch.max(self.verts, dim=0).values
-
-    def get_mesh(self, v_deformed_nx3, sdf_n, with_uv=False, indices=None):
-        if indices is None:
-            indices = self.indices
-        verts, faces = marching_tets(
-            v_deformed_nx3, sdf_n, indices, self.triangle_table,
-            self.num_triangles_table, self.base_tet_edges, self.v_id)
-        faces = torch.cat(
-            [faces[:, 0:1],
-             faces[:, 2:3],
-             faces[:, 1:2], ], dim=-1)
-        return verts, faces
-
-    def get_tet_mesh(self, v_deformed_nx3, sdf_n, with_uv=False, indices=None):
-        if indices is None:
-            indices = self.indices
-        verts, faces, tet_verts, tets = marching_tets_tetmesh(
-            v_deformed_nx3, sdf_n, indices, self.triangle_table,
-            self.num_triangles_table, self.base_tet_edges, self.v_id, return_tet_mesh=True,
-            num_tets_table=self.num_tets_table, tet_table=self.tet_table, ori_v=v_deformed_nx3)
-        faces = torch.cat(
-            [faces[:, 0:1],
-             faces[:, 2:3],
-             faces[:, 1:2], ], dim=-1)
-        return verts, faces, tet_verts, tets
-
-    def render_mesh(self, mesh_v_nx3, mesh_f_fx3, camera_mv_bx4x4, resolution=256, hierarchical_mask=False):
-        return_value = dict()
-        if self.render_type == 'neural_render':
-            tex_pos, mask, hard_mask, rast, v_pos_clip, mask_pyramid, depth = self.renderer.render_mesh(
-                mesh_v_nx3.unsqueeze(dim=0),
-                mesh_f_fx3.int(),
-                camera_mv_bx4x4,
-                mesh_v_nx3.unsqueeze(dim=0),
-                resolution=resolution,
-                device=self.device,
-                hierarchical_mask=hierarchical_mask
-            )
-
-            return_value['tex_pos'] = tex_pos
-            return_value['mask'] = mask
-            return_value['hard_mask'] = hard_mask
-            return_value['rast'] = rast
-            return_value['v_pos_clip'] = v_pos_clip
-            return_value['mask_pyramid'] = mask_pyramid
-            return_value['depth'] = depth
-        else:
-            raise NotImplementedError
-
-        return return_value
-
-    def render(self, v_deformed_bxnx3=None, sdf_bxn=None, camera_mv_bxnviewx4x4=None, resolution=256):
-        # Here I assume a batch of meshes (can be different mesh and geometry), for the other shapes, the batch is 1
-        v_list = []
-        f_list = []
-        n_batch = v_deformed_bxnx3.shape[0]
-        all_render_output = []
-        for i_batch in range(n_batch):
-            verts_nx3, faces_fx3 = self.get_mesh(v_deformed_bxnx3[i_batch], sdf_bxn[i_batch])
-            v_list.append(verts_nx3)
-            f_list.append(faces_fx3)
-            render_output = self.render_mesh(verts_nx3, faces_fx3, camera_mv_bxnviewx4x4[i_batch], resolution)
-            all_render_output.append(render_output)
-
-        # Concatenate all render output
-        return_keys = all_render_output[0].keys()
-        return_value = dict()
-        for k in return_keys:
-            value = [v[k] for v in all_render_output]
-            return_value[k] = value
-            # We can do concatenation outside of the render
-        return return_value

src/models/geometry/rep_3d/dmtet_utils.py

@@ -1,20 +0,0 @@
-# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION & AFFILIATES 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 & AFFILIATES is strictly prohibited.
-
-import torch
-
-
-def get_center_boundary_index(verts):
-    length_ = torch.sum(verts ** 2, dim=-1)
-    center_idx = torch.argmin(length_)
-    boundary_neg = verts == verts.max()
-    boundary_pos = verts == verts.min()
-    boundary = torch.bitwise_or(boundary_pos, boundary_neg)
-    boundary = torch.sum(boundary.float(), dim=-1)
-    boundary_idx = torch.nonzero(boundary)
-    return center_idx, boundary_idx.squeeze(dim=-1)

src/models/geometry/rep_3d/extract_texture_map.py

@@ -1,40 +0,0 @@
-# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION & AFFILIATES 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 & AFFILIATES is strictly prohibited.
-
-import torch
-import xatlas
-import numpy as np
-import nvdiffrast.torch as dr
-
-
-# ==============================================================================================
-def interpolate(attr, rast, attr_idx, rast_db=None):
-    return dr.interpolate(attr.contiguous(), rast, attr_idx, rast_db=rast_db, diff_attrs=None if rast_db is None else 'all')
-
-
-def xatlas_uvmap(ctx, mesh_v, mesh_pos_idx, resolution):
-    vmapping, indices, uvs = xatlas.parametrize(mesh_v.detach().cpu().numpy(), mesh_pos_idx.detach().cpu().numpy())
-
-    # Convert to tensors
-    indices_int64 = indices.astype(np.uint64, casting='same_kind').view(np.int64)
-
-    uvs = torch.tensor(uvs, dtype=torch.float32, device=mesh_v.device)
-    mesh_tex_idx = torch.tensor(indices_int64, dtype=torch.int64, device=mesh_v.device)
-    # mesh_v_tex. ture
-    uv_clip = uvs[None, ...] * 2.0 - 1.0
-
-    # pad to four component coordinate
-    uv_clip4 = torch.cat((uv_clip, torch.zeros_like(uv_clip[..., 0:1]), torch.ones_like(uv_clip[..., 0:1])), dim=-1)
-
-    # rasterize
-    rast, _ = dr.rasterize(ctx, uv_clip4, mesh_tex_idx.int(), (resolution, resolution))
-
-    # Interpolate world space position
-    gb_pos, _ = interpolate(mesh_v[None, ...], rast, mesh_pos_idx.int())
-    mask = rast[..., 3:4] > 0
-    return uvs, mesh_tex_idx, gb_pos, mask

src/models/geometry/rep_3d/flexicubes.py

@@ -1,579 +0,0 @@
-# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION & AFFILIATES 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 & AFFILIATES is strictly prohibited.
-import torch
-from .tables import *
-
-__all__ = [
-    'FlexiCubes'
-]
-
-
-class FlexiCubes:
-    """
-    This class implements the FlexiCubes method for extracting meshes from scalar fields. 
-    It maintains a series of lookup tables and indices to support the mesh extraction process. 
-    FlexiCubes, a differentiable variant of the Dual Marching Cubes (DMC) scheme, enhances 
-    the geometric fidelity and mesh quality of reconstructed meshes by dynamically adjusting 
-    the surface representation through gradient-based optimization.
-
-    During instantiation, the class loads DMC tables from a file and transforms them into 
-    PyTorch tensors on the specified device.
-
-    Attributes:
-        device (str): Specifies the computational device (default is "cuda").
-        dmc_table (torch.Tensor): Dual Marching Cubes (DMC) table that encodes the edges 
-            associated with each dual vertex in 256 Marching Cubes (MC) configurations.
-        num_vd_table (torch.Tensor): Table holding the number of dual vertices in each of 
-            the 256 MC configurations.
-        check_table (torch.Tensor): Table resolving ambiguity in cases C16 and C19 
-            of the DMC configurations.
-        tet_table (torch.Tensor): Lookup table used in tetrahedralizing the isosurface.
-        quad_split_1 (torch.Tensor): Indices for splitting a quad into two triangles 
-            along one diagonal.
-        quad_split_2 (torch.Tensor): Alternative indices for splitting a quad into 
-            two triangles along the other diagonal.
-        quad_split_train (torch.Tensor): Indices for splitting a quad into four triangles 
-            during training by connecting all edges to their midpoints.
-        cube_corners (torch.Tensor): Defines the positions of a standard unit cube's 
-            eight corners in 3D space, ordered starting from the origin (0,0,0), 
-            moving along the x-axis, then y-axis, and finally z-axis. 
-            Used as a blueprint for generating a voxel grid.
-        cube_corners_idx (torch.Tensor): Cube corners indexed as powers of 2, used 
-            to retrieve the case id.
-        cube_edges (torch.Tensor): Edge connections in a cube, listed in pairs. 
-            Used to retrieve edge vertices in DMC.
-        edge_dir_table (torch.Tensor): A mapping tensor that associates edge indices with 
-            their corresponding axis. For instance, edge_dir_table[0] = 0 indicates that the 
-            first edge is oriented along the x-axis. 
-        dir_faces_table (torch.Tensor): A tensor that maps the corresponding axis of shared edges 
-            across four adjacent cubes to the shared faces of these cubes. For instance, 
-            dir_faces_table[0] = [5, 4] implies that for four cubes sharing an edge along 
-            the x-axis, the first and second cubes share faces indexed as 5 and 4, respectively. 
-            This tensor is only utilized during isosurface tetrahedralization.
-        adj_pairs (torch.Tensor): 
-            A tensor containing index pairs that correspond to neighboring cubes that share the same edge.
-        qef_reg_scale (float):
-            The scaling factor applied to the regularization loss to prevent issues with singularity 
-            when solving the QEF. This parameter is only used when a 'grad_func' is specified.
-        weight_scale (float):
-            The scale of weights in FlexiCubes. Should be between 0 and 1.
-    """
-
-    def __init__(self, device="cuda", qef_reg_scale=1e-3, weight_scale=0.99):
-
-        self.device = device
-        self.dmc_table = torch.tensor(dmc_table, dtype=torch.long, device=device, requires_grad=False)
-        self.num_vd_table = torch.tensor(num_vd_table,
-                                         dtype=torch.long, device=device, requires_grad=False)
-        self.check_table = torch.tensor(
-            check_table,
-            dtype=torch.long, device=device, requires_grad=False)
-
-        self.tet_table = torch.tensor(tet_table, dtype=torch.long, device=device, requires_grad=False)
-        self.quad_split_1 = torch.tensor([0, 1, 2, 0, 2, 3], dtype=torch.long, device=device, requires_grad=False)
-        self.quad_split_2 = torch.tensor([0, 1, 3, 3, 1, 2], dtype=torch.long, device=device, requires_grad=False)
-        self.quad_split_train = torch.tensor(
-            [0, 1, 1, 2, 2, 3, 3, 0], dtype=torch.long, device=device, requires_grad=False)
-
-        self.cube_corners = torch.tensor([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 1], [
-                                         1, 0, 1], [0, 1, 1], [1, 1, 1]], dtype=torch.float, device=device)
-        self.cube_corners_idx = torch.pow(2, torch.arange(8, requires_grad=False))
-        self.cube_edges = torch.tensor([0, 1, 1, 5, 4, 5, 0, 4, 2, 3, 3, 7, 6, 7, 2, 6,
-                                       2, 0, 3, 1, 7, 5, 6, 4], dtype=torch.long, device=device, requires_grad=False)
-
-        self.edge_dir_table = torch.tensor([0, 2, 0, 2, 0, 2, 0, 2, 1, 1, 1, 1],
-                                           dtype=torch.long, device=device)
-        self.dir_faces_table = torch.tensor([
-            [[5, 4], [3, 2], [4, 5], [2, 3]],
-            [[5, 4], [1, 0], [4, 5], [0, 1]],
-            [[3, 2], [1, 0], [2, 3], [0, 1]]
-        ], dtype=torch.long, device=device)
-        self.adj_pairs = torch.tensor([0, 1, 1, 3, 3, 2, 2, 0], dtype=torch.long, device=device)
-        self.qef_reg_scale = qef_reg_scale
-        self.weight_scale = weight_scale
-
-    def construct_voxel_grid(self, res):
-        """
-        Generates a voxel grid based on the specified resolution.
-
-        Args:
-            res (int or list[int]): The resolution of the voxel grid. If an integer
-                is provided, it is used for all three dimensions. If a list or tuple 
-                of 3 integers is provided, they define the resolution for the x, 
-                y, and z dimensions respectively.
-
-        Returns:
-            (torch.Tensor, torch.Tensor): Returns the vertices and the indices of the 
-                cube corners (index into vertices) of the constructed voxel grid. 
-                The vertices are centered at the origin, with the length of each 
-                dimension in the grid being one.
-        """
-        base_cube_f = torch.arange(8).to(self.device)
-        if isinstance(res, int):
-            res = (res, res, res)
-        voxel_grid_template = torch.ones(res, device=self.device)
-
-        res = torch.tensor([res], dtype=torch.float, device=self.device)
-        coords = torch.nonzero(voxel_grid_template).float() / res  # N, 3
-        verts = (self.cube_corners.unsqueeze(0) / res + coords.unsqueeze(1)).reshape(-1, 3)
-        cubes = (base_cube_f.unsqueeze(0) +
-                 torch.arange(coords.shape[0], device=self.device).unsqueeze(1) * 8).reshape(-1)
-
-        verts_rounded = torch.round(verts * 10**5) / (10**5)
-        verts_unique, inverse_indices = torch.unique(verts_rounded, dim=0, return_inverse=True)
-        cubes = inverse_indices[cubes.reshape(-1)].reshape(-1, 8)
-
-        return verts_unique - 0.5, cubes
-
-    def __call__(self, x_nx3, s_n, cube_fx8, res, beta_fx12=None, alpha_fx8=None,
-                 gamma_f=None, training=False, output_tetmesh=False, grad_func=None):
-        r"""
-        Main function for mesh extraction from scalar field using FlexiCubes. This function converts 
-        discrete signed distance fields, encoded on voxel grids and additional per-cube parameters, 
-        to triangle or tetrahedral meshes using a differentiable operation as described in 
-        `Flexible Isosurface Extraction for Gradient-Based Mesh Optimization`_. FlexiCubes enhances 
-        mesh quality and geometric fidelity by adjusting the surface representation based on gradient 
-        optimization. The output surface is differentiable with respect to the input vertex positions, 
-        scalar field values, and weight parameters.
-
-        If you intend to extract a surface mesh from a fixed Signed Distance Field without the 
-        optimization of parameters, it is suggested to provide the "grad_func" which should 
-        return the surface gradient at any given 3D position. When grad_func is provided, the process 
-        to determine the dual vertex position adapts to solve a Quadratic Error Function (QEF), as 
-        described in the `Manifold Dual Contouring`_ paper, and employs an smart splitting strategy. 
-        Please note, this approach is non-differentiable.
-
-        For more details and example usage in optimization, refer to the 
-        `Flexible Isosurface Extraction for Gradient-Based Mesh Optimization`_ SIGGRAPH 2023 paper.
-
-        Args:
-            x_nx3 (torch.Tensor): Coordinates of the voxel grid vertices, can be deformed.
-            s_n (torch.Tensor): Scalar field values at each vertex of the voxel grid. Negative values 
-                denote that the corresponding vertex resides inside the isosurface. This affects 
-                the directions of the extracted triangle faces and volume to be tetrahedralized.
-            cube_fx8 (torch.Tensor): Indices of 8 vertices for each cube in the voxel grid.
-            res (int or list[int]): The resolution of the voxel grid. If an integer is provided, it 
-                is used for all three dimensions. If a list or tuple of 3 integers is provided, they 
-                specify the resolution for the x, y, and z dimensions respectively.
-            beta_fx12 (torch.Tensor, optional): Weight parameters for the cube edges to adjust dual 
-                vertices positioning. Defaults to uniform value for all edges.
-            alpha_fx8 (torch.Tensor, optional): Weight parameters for the cube corners to adjust dual 
-                vertices positioning. Defaults to uniform value for all vertices.
-            gamma_f (torch.Tensor, optional): Weight parameters to control the splitting of 
-                quadrilaterals into triangles. Defaults to uniform value for all cubes.
-            training (bool, optional): If set to True, applies differentiable quad splitting for 
-                training. Defaults to False.
-            output_tetmesh (bool, optional): If set to True, outputs a tetrahedral mesh, otherwise, 
-                outputs a triangular mesh. Defaults to False.
-            grad_func (callable, optional): A function to compute the surface gradient at specified 
-                3D positions (input: Nx3 positions). The function should return gradients as an Nx3 
-                tensor. If None, the original FlexiCubes algorithm is utilized. Defaults to None.
-
-        Returns:
-            (torch.Tensor, torch.LongTensor, torch.Tensor): Tuple containing:
-                - Vertices for the extracted triangular/tetrahedral mesh.
-                - Faces for the extracted triangular/tetrahedral mesh.
-                - Regularizer L_dev, computed per dual vertex.
-
-        .. _Flexible Isosurface Extraction for Gradient-Based Mesh Optimization:
-            https://research.nvidia.com/labs/toronto-ai/flexicubes/
-        .. _Manifold Dual Contouring:
-            https://people.engr.tamu.edu/schaefer/research/dualsimp_tvcg.pdf
-        """
-
-        surf_cubes, occ_fx8 = self._identify_surf_cubes(s_n, cube_fx8)
-        if surf_cubes.sum() == 0:
-            return torch.zeros(
-                (0, 3),
-                device=self.device), torch.zeros(
-                (0, 4),
-                dtype=torch.long, device=self.device) if output_tetmesh else torch.zeros(
-                (0, 3),
-                dtype=torch.long, device=self.device), torch.zeros(
-                (0),
-                device=self.device)
-        beta_fx12, alpha_fx8, gamma_f = self._normalize_weights(beta_fx12, alpha_fx8, gamma_f, surf_cubes)
-
-        case_ids = self._get_case_id(occ_fx8, surf_cubes, res)
-
-        surf_edges, idx_map, edge_counts, surf_edges_mask = self._identify_surf_edges(s_n, cube_fx8, surf_cubes)
-
-        vd, L_dev, vd_gamma, vd_idx_map = self._compute_vd(
-            x_nx3, cube_fx8[surf_cubes], surf_edges, s_n, case_ids, beta_fx12, alpha_fx8, gamma_f, idx_map, grad_func)
-        vertices, faces, s_edges, edge_indices = self._triangulate(
-            s_n, surf_edges, vd, vd_gamma, edge_counts, idx_map, vd_idx_map, surf_edges_mask, training, grad_func)
-        if not output_tetmesh:
-            return vertices, faces, L_dev
-        else:
-            vertices, tets = self._tetrahedralize(
-                x_nx3, s_n, cube_fx8, vertices, faces, surf_edges, s_edges, vd_idx_map, case_ids, edge_indices,
-                surf_cubes, training)
-            return vertices, tets, L_dev
-
-    def _compute_reg_loss(self, vd, ue, edge_group_to_vd, vd_num_edges):
-        """
-        Regularizer L_dev as in Equation 8
-        """
-        dist = torch.norm(ue - torch.index_select(input=vd, index=edge_group_to_vd, dim=0), dim=-1)
-        mean_l2 = torch.zeros_like(vd[:, 0])
-        mean_l2 = (mean_l2).index_add_(0, edge_group_to_vd, dist) / vd_num_edges.squeeze(1).float()
-        mad = (dist - torch.index_select(input=mean_l2, index=edge_group_to_vd, dim=0)).abs()
-        return mad
-
-    def _normalize_weights(self, beta_fx12, alpha_fx8, gamma_f, surf_cubes):
-        """
-        Normalizes the given weights to be non-negative. If input weights are None, it creates and returns a set of weights of ones.
-        """
-        n_cubes = surf_cubes.shape[0]
-
-        if beta_fx12 is not None:
-            beta_fx12 = (torch.tanh(beta_fx12) * self.weight_scale + 1)
-        else:
-            beta_fx12 = torch.ones((n_cubes, 12), dtype=torch.float, device=self.device)
-
-        if alpha_fx8 is not None:
-            alpha_fx8 = (torch.tanh(alpha_fx8) * self.weight_scale + 1)
-        else:
-            alpha_fx8 = torch.ones((n_cubes, 8), dtype=torch.float, device=self.device)
-
-        if gamma_f is not None:
-            gamma_f = torch.sigmoid(gamma_f) * self.weight_scale + (1 - self.weight_scale)/2
-        else:
-            gamma_f = torch.ones((n_cubes), dtype=torch.float, device=self.device)
-
-        return beta_fx12[surf_cubes], alpha_fx8[surf_cubes], gamma_f[surf_cubes]
-
-    @torch.no_grad()
-    def _get_case_id(self, occ_fx8, surf_cubes, res):
-        """
-        Obtains the ID of topology cases based on cell corner occupancy. This function resolves the 
-        ambiguity in the Dual Marching Cubes (DMC) configurations as described in Section 1.3 of the 
-        supplementary material. It should be noted that this function assumes a regular grid.
-        """
-        case_ids = (occ_fx8[surf_cubes] * self.cube_corners_idx.to(self.device).unsqueeze(0)).sum(-1)
-
-        problem_config = self.check_table.to(self.device)[case_ids]
-        to_check = problem_config[..., 0] == 1
-        problem_config = problem_config[to_check]
-        if not isinstance(res, (list, tuple)):
-            res = [res, res, res]
-
-        # The 'problematic_configs' only contain configurations for surface cubes. Next, we construct a 3D array,
-        # 'problem_config_full', to store configurations for all cubes (with default config for non-surface cubes).
-        # This allows efficient checking on adjacent cubes.
-        problem_config_full = torch.zeros(list(res) + [5], device=self.device, dtype=torch.long)
-        vol_idx = torch.nonzero(problem_config_full[..., 0] == 0)  # N, 3
-        vol_idx_problem = vol_idx[surf_cubes][to_check]
-        problem_config_full[vol_idx_problem[..., 0], vol_idx_problem[..., 1], vol_idx_problem[..., 2]] = problem_config
-        vol_idx_problem_adj = vol_idx_problem + problem_config[..., 1:4]
-
-        within_range = (
-            vol_idx_problem_adj[..., 0] >= 0) & (
-            vol_idx_problem_adj[..., 0] < res[0]) & (
-            vol_idx_problem_adj[..., 1] >= 0) & (
-            vol_idx_problem_adj[..., 1] < res[1]) & (
-            vol_idx_problem_adj[..., 2] >= 0) & (
-            vol_idx_problem_adj[..., 2] < res[2])
-
-        vol_idx_problem = vol_idx_problem[within_range]
-        vol_idx_problem_adj = vol_idx_problem_adj[within_range]
-        problem_config = problem_config[within_range]
-        problem_config_adj = problem_config_full[vol_idx_problem_adj[..., 0],
-                                                 vol_idx_problem_adj[..., 1], vol_idx_problem_adj[..., 2]]
-        # If two cubes with cases C16 and C19 share an ambiguous face, both cases are inverted.
-        to_invert = (problem_config_adj[..., 0] == 1)
-        idx = torch.arange(case_ids.shape[0], device=self.device)[to_check][within_range][to_invert]
-        case_ids.index_put_((idx,), problem_config[to_invert][..., -1])
-        return case_ids
-
-    @torch.no_grad()
-    def _identify_surf_edges(self, s_n, cube_fx8, surf_cubes):
-        """
-        Identifies grid edges that intersect with the underlying surface by checking for opposite signs. As each edge 
-        can be shared by multiple cubes, this function also assigns a unique index to each surface-intersecting edge 
-        and marks the cube edges with this index.
-        """
-        occ_n = s_n < 0
-        all_edges = cube_fx8[surf_cubes][:, self.cube_edges].reshape(-1, 2)
-        unique_edges, _idx_map, counts = torch.unique(all_edges, dim=0, return_inverse=True, return_counts=True)
-
-        unique_edges = unique_edges.long()
-        mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 1
-
-        surf_edges_mask = mask_edges[_idx_map]
-        counts = counts[_idx_map]
-
-        mapping = torch.ones((unique_edges.shape[0]), dtype=torch.long, device=cube_fx8.device) * -1
-        mapping[mask_edges] = torch.arange(mask_edges.sum(), device=cube_fx8.device)
-        # Shaped as [number of cubes x 12 edges per cube]. This is later used to map a cube edge to the unique index
-        # for a surface-intersecting edge. Non-surface-intersecting edges are marked with -1.
-        idx_map = mapping[_idx_map]
-        surf_edges = unique_edges[mask_edges]
-        return surf_edges, idx_map, counts, surf_edges_mask
-
-    @torch.no_grad()
-    def _identify_surf_cubes(self, s_n, cube_fx8):
-        """
-        Identifies grid cubes that intersect with the underlying surface by checking if the signs at 
-        all corners are not identical.
-        """
-        occ_n = s_n < 0
-        occ_fx8 = occ_n[cube_fx8.reshape(-1)].reshape(-1, 8)
-        _occ_sum = torch.sum(occ_fx8, -1)
-        surf_cubes = (_occ_sum > 0) & (_occ_sum < 8)
-        return surf_cubes, occ_fx8
-
-    def _linear_interp(self, edges_weight, edges_x):
-        """
-        Computes the location of zero-crossings on 'edges_x' using linear interpolation with 'edges_weight'.
-        """
-        edge_dim = edges_weight.dim() - 2
-        assert edges_weight.shape[edge_dim] == 2
-        edges_weight = torch.cat([torch.index_select(input=edges_weight, index=torch.tensor(1, device=self.device), dim=edge_dim), -
-                                 torch.index_select(input=edges_weight, index=torch.tensor(0, device=self.device), dim=edge_dim)], edge_dim)
-        denominator = edges_weight.sum(edge_dim)
-        ue = (edges_x * edges_weight).sum(edge_dim) / denominator
-        return ue
-
-    def _solve_vd_QEF(self, p_bxnx3, norm_bxnx3, c_bx3=None):
-        p_bxnx3 = p_bxnx3.reshape(-1, 7, 3)
-        norm_bxnx3 = norm_bxnx3.reshape(-1, 7, 3)
-        c_bx3 = c_bx3.reshape(-1, 3)
-        A = norm_bxnx3
-        B = ((p_bxnx3) * norm_bxnx3).sum(-1, keepdims=True)
-
-        A_reg = (torch.eye(3, device=p_bxnx3.device) * self.qef_reg_scale).unsqueeze(0).repeat(p_bxnx3.shape[0], 1, 1)
-        B_reg = (self.qef_reg_scale * c_bx3).unsqueeze(-1)
-        A = torch.cat([A, A_reg], 1)
-        B = torch.cat([B, B_reg], 1)
-        dual_verts = torch.linalg.lstsq(A, B).solution.squeeze(-1)
-        return dual_verts
-
-    def _compute_vd(self, x_nx3, surf_cubes_fx8, surf_edges, s_n, case_ids, beta_fx12, alpha_fx8, gamma_f, idx_map, grad_func):
-        """
-        Computes the location of dual vertices as described in Section 4.2
-        """
-        alpha_nx12x2 = torch.index_select(input=alpha_fx8, index=self.cube_edges, dim=1).reshape(-1, 12, 2)
-        surf_edges_x = torch.index_select(input=x_nx3, index=surf_edges.reshape(-1), dim=0).reshape(-1, 2, 3)
-        surf_edges_s = torch.index_select(input=s_n, index=surf_edges.reshape(-1), dim=0).reshape(-1, 2, 1)
-        zero_crossing = self._linear_interp(surf_edges_s, surf_edges_x)
-
-        idx_map = idx_map.reshape(-1, 12)
-        num_vd = torch.index_select(input=self.num_vd_table, index=case_ids, dim=0)
-        edge_group, edge_group_to_vd, edge_group_to_cube, vd_num_edges, vd_gamma = [], [], [], [], []
-
-        total_num_vd = 0
-        vd_idx_map = torch.zeros((case_ids.shape[0], 12), dtype=torch.long, device=self.device, requires_grad=False)
-        if grad_func is not None:
-            normals = torch.nn.functional.normalize(grad_func(zero_crossing), dim=-1)
-            vd = []
-        for num in torch.unique(num_vd):
-            cur_cubes = (num_vd == num)  # consider cubes with the same numbers of vd emitted (for batching)
-            curr_num_vd = cur_cubes.sum() * num
-            curr_edge_group = self.dmc_table[case_ids[cur_cubes], :num].reshape(-1, num * 7)
-            curr_edge_group_to_vd = torch.arange(
-                curr_num_vd, device=self.device).unsqueeze(-1).repeat(1, 7) + total_num_vd
-            total_num_vd += curr_num_vd
-            curr_edge_group_to_cube = torch.arange(idx_map.shape[0], device=self.device)[
-                cur_cubes].unsqueeze(-1).repeat(1, num * 7).reshape_as(curr_edge_group)
-
-            curr_mask = (curr_edge_group != -1)
-            edge_group.append(torch.masked_select(curr_edge_group, curr_mask))
-            edge_group_to_vd.append(torch.masked_select(curr_edge_group_to_vd.reshape_as(curr_edge_group), curr_mask))
-            edge_group_to_cube.append(torch.masked_select(curr_edge_group_to_cube, curr_mask))
-            vd_num_edges.append(curr_mask.reshape(-1, 7).sum(-1, keepdims=True))
-            vd_gamma.append(torch.masked_select(gamma_f, cur_cubes).unsqueeze(-1).repeat(1, num).reshape(-1))
-
-            if grad_func is not None:
-                with torch.no_grad():
-                    cube_e_verts_idx = idx_map[cur_cubes]
-                    curr_edge_group[~curr_mask] = 0
-
-                    verts_group_idx = torch.gather(input=cube_e_verts_idx, dim=1, index=curr_edge_group)
-                    verts_group_idx[verts_group_idx == -1] = 0
-                    verts_group_pos = torch.index_select(
-                        input=zero_crossing, index=verts_group_idx.reshape(-1), dim=0).reshape(-1, num.item(), 7, 3)
-                    v0 = x_nx3[surf_cubes_fx8[cur_cubes][:, 0]].reshape(-1, 1, 1, 3).repeat(1, num.item(), 1, 1)
-                    curr_mask = curr_mask.reshape(-1, num.item(), 7, 1)
-                    verts_centroid = (verts_group_pos * curr_mask).sum(2) / (curr_mask.sum(2))
-
-                    normals_bx7x3 = torch.index_select(input=normals, index=verts_group_idx.reshape(-1), dim=0).reshape(
-                        -1, num.item(), 7,
-                        3)
-                    curr_mask = curr_mask.squeeze(2)
-                    vd.append(self._solve_vd_QEF((verts_group_pos - v0) * curr_mask, normals_bx7x3 * curr_mask,
-                                                 verts_centroid - v0.squeeze(2)) + v0.reshape(-1, 3))
-        edge_group = torch.cat(edge_group)
-        edge_group_to_vd = torch.cat(edge_group_to_vd)
-        edge_group_to_cube = torch.cat(edge_group_to_cube)
-        vd_num_edges = torch.cat(vd_num_edges)
-        vd_gamma = torch.cat(vd_gamma)
-
-        if grad_func is not None:
-            vd = torch.cat(vd)
-            L_dev = torch.zeros([1], device=self.device)
-        else:
-            vd = torch.zeros((total_num_vd, 3), device=self.device)
-            beta_sum = torch.zeros((total_num_vd, 1), device=self.device)
-
-            idx_group = torch.gather(input=idx_map.reshape(-1), dim=0, index=edge_group_to_cube * 12 + edge_group)
-
-            x_group = torch.index_select(input=surf_edges_x, index=idx_group.reshape(-1), dim=0).reshape(-1, 2, 3)
-            s_group = torch.index_select(input=surf_edges_s, index=idx_group.reshape(-1), dim=0).reshape(-1, 2, 1)
-
-            zero_crossing_group = torch.index_select(
-                input=zero_crossing, index=idx_group.reshape(-1), dim=0).reshape(-1, 3)
-
-            alpha_group = torch.index_select(input=alpha_nx12x2.reshape(-1, 2), dim=0,
-                                             index=edge_group_to_cube * 12 + edge_group).reshape(-1, 2, 1)
-            ue_group = self._linear_interp(s_group * alpha_group, x_group)
-
-            beta_group = torch.gather(input=beta_fx12.reshape(-1), dim=0,
-                                      index=edge_group_to_cube * 12 + edge_group).reshape(-1, 1)
-            beta_sum = beta_sum.index_add_(0, index=edge_group_to_vd, source=beta_group)
-            vd = vd.index_add_(0, index=edge_group_to_vd, source=ue_group * beta_group) / beta_sum
-            L_dev = self._compute_reg_loss(vd, zero_crossing_group, edge_group_to_vd, vd_num_edges)
-
-        v_idx = torch.arange(vd.shape[0], device=self.device)  # + total_num_vd
-
-        vd_idx_map = (vd_idx_map.reshape(-1)).scatter(dim=0, index=edge_group_to_cube *
-                                                      12 + edge_group, src=v_idx[edge_group_to_vd])
-
-        return vd, L_dev, vd_gamma, vd_idx_map
-
-    def _triangulate(self, s_n, surf_edges, vd, vd_gamma, edge_counts, idx_map, vd_idx_map, surf_edges_mask, training, grad_func):
-        """
-        Connects four neighboring dual vertices to form a quadrilateral. The quadrilaterals are then split into 
-        triangles based on the gamma parameter, as described in Section 4.3.
-        """
-        with torch.no_grad():
-            group_mask = (edge_counts == 4) & surf_edges_mask  # surface edges shared by 4 cubes.
-            group = idx_map.reshape(-1)[group_mask]
-            vd_idx = vd_idx_map[group_mask]
-            edge_indices, indices = torch.sort(group, stable=True)
-            quad_vd_idx = vd_idx[indices].reshape(-1, 4)
-
-            # Ensure all face directions point towards the positive SDF to maintain consistent winding.
-            s_edges = s_n[surf_edges[edge_indices.reshape(-1, 4)[:, 0]].reshape(-1)].reshape(-1, 2)
-            flip_mask = s_edges[:, 0] > 0
-            quad_vd_idx = torch.cat((quad_vd_idx[flip_mask][:, [0, 1, 3, 2]],
-                                     quad_vd_idx[~flip_mask][:, [2, 3, 1, 0]]))
-        if grad_func is not None:
-            # when grad_func is given, split quadrilaterals along the diagonals with more consistent gradients.
-            with torch.no_grad():
-                vd_gamma = torch.nn.functional.normalize(grad_func(vd), dim=-1)
-                quad_gamma = torch.index_select(input=vd_gamma, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4, 3)
-                gamma_02 = (quad_gamma[:, 0] * quad_gamma[:, 2]).sum(-1, keepdims=True)
-                gamma_13 = (quad_gamma[:, 1] * quad_gamma[:, 3]).sum(-1, keepdims=True)
-        else:
-            quad_gamma = torch.index_select(input=vd_gamma, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4)
-            gamma_02 = torch.index_select(input=quad_gamma, index=torch.tensor(
-                0, device=self.device), dim=1) * torch.index_select(input=quad_gamma, index=torch.tensor(2, device=self.device), dim=1)
-            gamma_13 = torch.index_select(input=quad_gamma, index=torch.tensor(
-                1, device=self.device), dim=1) * torch.index_select(input=quad_gamma, index=torch.tensor(3, device=self.device), dim=1)
-        if not training:
-            mask = (gamma_02 > gamma_13).squeeze(1)
-            faces = torch.zeros((quad_gamma.shape[0], 6), dtype=torch.long, device=quad_vd_idx.device)
-            faces[mask] = quad_vd_idx[mask][:, self.quad_split_1]
-            faces[~mask] = quad_vd_idx[~mask][:, self.quad_split_2]
-            faces = faces.reshape(-1, 3)
-        else:
-            vd_quad = torch.index_select(input=vd, index=quad_vd_idx.reshape(-1), dim=0).reshape(-1, 4, 3)
-            vd_02 = (torch.index_select(input=vd_quad, index=torch.tensor(0, device=self.device), dim=1) +
-                     torch.index_select(input=vd_quad, index=torch.tensor(2, device=self.device), dim=1)) / 2
-            vd_13 = (torch.index_select(input=vd_quad, index=torch.tensor(1, device=self.device), dim=1) +
-                     torch.index_select(input=vd_quad, index=torch.tensor(3, device=self.device), dim=1)) / 2
-            weight_sum = (gamma_02 + gamma_13) + 1e-8
-            vd_center = ((vd_02 * gamma_02.unsqueeze(-1) + vd_13 * gamma_13.unsqueeze(-1)) /
-                         weight_sum.unsqueeze(-1)).squeeze(1)
-            vd_center_idx = torch.arange(vd_center.shape[0], device=self.device) + vd.shape[0]
-            vd = torch.cat([vd, vd_center])
-            faces = quad_vd_idx[:, self.quad_split_train].reshape(-1, 4, 2)
-            faces = torch.cat([faces, vd_center_idx.reshape(-1, 1, 1).repeat(1, 4, 1)], -1).reshape(-1, 3)
-        return vd, faces, s_edges, edge_indices
-
-    def _tetrahedralize(
-            self, x_nx3, s_n, cube_fx8, vertices, faces, surf_edges, s_edges, vd_idx_map, case_ids, edge_indices,
-            surf_cubes, training):
-        """
-        Tetrahedralizes the interior volume to produce a tetrahedral mesh, as described in Section 4.5.
-        """
-        occ_n = s_n < 0
-        occ_fx8 = occ_n[cube_fx8.reshape(-1)].reshape(-1, 8)
-        occ_sum = torch.sum(occ_fx8, -1)
-
-        inside_verts = x_nx3[occ_n]
-        mapping_inside_verts = torch.ones((occ_n.shape[0]), dtype=torch.long, device=self.device) * -1
-        mapping_inside_verts[occ_n] = torch.arange(occ_n.sum(), device=self.device) + vertices.shape[0]
-        """ 
-        For each grid edge connecting two grid vertices with different
-        signs, we first form a four-sided pyramid by connecting one
-        of the grid vertices with four mesh vertices that correspond
-        to the grid edge and then subdivide the pyramid into two tetrahedra
-        """
-        inside_verts_idx = mapping_inside_verts[surf_edges[edge_indices.reshape(-1, 4)[:, 0]].reshape(-1, 2)[
-            s_edges < 0]]
-        if not training:
-            inside_verts_idx = inside_verts_idx.unsqueeze(1).expand(-1, 2).reshape(-1)
-        else:
-            inside_verts_idx = inside_verts_idx.unsqueeze(1).expand(-1, 4).reshape(-1)
-
-        tets_surface = torch.cat([faces, inside_verts_idx.unsqueeze(-1)], -1)
-        """ 
-        For each grid edge connecting two grid vertices with the
-        same sign, the tetrahedron is formed by the two grid vertices
-        and two vertices in consecutive adjacent cells
-        """
-        inside_cubes = (occ_sum == 8)
-        inside_cubes_center = x_nx3[cube_fx8[inside_cubes].reshape(-1)].reshape(-1, 8, 3).mean(1)
-        inside_cubes_center_idx = torch.arange(
-            inside_cubes_center.shape[0], device=inside_cubes.device) + vertices.shape[0] + inside_verts.shape[0]
-
-        surface_n_inside_cubes = surf_cubes | inside_cubes
-        edge_center_vertex_idx = torch.ones(((surface_n_inside_cubes).sum(), 13),
-                                            dtype=torch.long, device=x_nx3.device) * -1
-        surf_cubes = surf_cubes[surface_n_inside_cubes]
-        inside_cubes = inside_cubes[surface_n_inside_cubes]
-        edge_center_vertex_idx[surf_cubes, :12] = vd_idx_map.reshape(-1, 12)
-        edge_center_vertex_idx[inside_cubes, 12] = inside_cubes_center_idx
-
-        all_edges = cube_fx8[surface_n_inside_cubes][:, self.cube_edges].reshape(-1, 2)
-        unique_edges, _idx_map, counts = torch.unique(all_edges, dim=0, return_inverse=True, return_counts=True)
-        unique_edges = unique_edges.long()
-        mask_edges = occ_n[unique_edges.reshape(-1)].reshape(-1, 2).sum(-1) == 2
-        mask = mask_edges[_idx_map]
-        counts = counts[_idx_map]
-        mapping = torch.ones((unique_edges.shape[0]), dtype=torch.long, device=self.device) * -1
-        mapping[mask_edges] = torch.arange(mask_edges.sum(), device=self.device)
-        idx_map = mapping[_idx_map]
-
-        group_mask = (counts == 4) & mask
-        group = idx_map.reshape(-1)[group_mask]
-        edge_indices, indices = torch.sort(group)
-        cube_idx = torch.arange((_idx_map.shape[0] // 12), dtype=torch.long,
-                                device=self.device).unsqueeze(1).expand(-1, 12).reshape(-1)[group_mask]
-        edge_idx = torch.arange((12), dtype=torch.long, device=self.device).unsqueeze(
-            0).expand(_idx_map.shape[0] // 12, -1).reshape(-1)[group_mask]
-        # Identify the face shared by the adjacent cells.
-        cube_idx_4 = cube_idx[indices].reshape(-1, 4)
-        edge_dir = self.edge_dir_table[edge_idx[indices]].reshape(-1, 4)[..., 0]
-        shared_faces_4x2 = self.dir_faces_table[edge_dir].reshape(-1)
-        cube_idx_4x2 = cube_idx_4[:, self.adj_pairs].reshape(-1)
-        # Identify an edge of the face with different signs and
-        # select the mesh vertex corresponding to the identified edge.
-        case_ids_expand = torch.ones((surface_n_inside_cubes).sum(), dtype=torch.long, device=x_nx3.device) * 255
-        case_ids_expand[surf_cubes] = case_ids
-        cases = case_ids_expand[cube_idx_4x2]
-        quad_edge = edge_center_vertex_idx[cube_idx_4x2, self.tet_table[cases, shared_faces_4x2]].reshape(-1, 2)
-        mask = (quad_edge == -1).sum(-1) == 0
-        inside_edge = mapping_inside_verts[unique_edges[mask_edges][edge_indices].reshape(-1)].reshape(-1, 2)
-        tets_inside = torch.cat([quad_edge, inside_edge], -1)[mask]
-
-        tets = torch.cat([tets_surface, tets_inside])
-        vertices = torch.cat([vertices, inside_verts, inside_cubes_center])
-        return vertices, tets

src/models/geometry/rep_3d/flexicubes_geometry.py

@@ -1,120 +0,0 @@
-# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION & AFFILIATES 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 & AFFILIATES is strictly prohibited.
-
-import torch
-import numpy as np
-import os
-from . import Geometry
-from .flexicubes import FlexiCubes # replace later
-from .dmtet import sdf_reg_loss_batch
-import torch.nn.functional as F
-
-def get_center_boundary_index(grid_res, device):
-    v = torch.zeros((grid_res + 1, grid_res + 1, grid_res + 1), dtype=torch.bool, device=device)
-    v[grid_res // 2 + 1, grid_res // 2 + 1, grid_res // 2 + 1] = True
-    center_indices = torch.nonzero(v.reshape(-1))
-
-    v[grid_res // 2 + 1, grid_res // 2 + 1, grid_res // 2 + 1] = False
-    v[:2, ...] = True
-    v[-2:, ...] = True
-    v[:, :2, ...] = True
-    v[:, -2:, ...] = True
-    v[:, :, :2] = True
-    v[:, :, -2:] = True
-    boundary_indices = torch.nonzero(v.reshape(-1))
-    return center_indices, boundary_indices
-
-###############################################################################
-#  Geometry interface
-###############################################################################
-class FlexiCubesGeometry(Geometry):
-    def __init__(
-            self, grid_res=64, scale=2.0, device='cuda', renderer=None,
-            render_type='neural_render', args=None):
-        super(FlexiCubesGeometry, self).__init__()
-        self.grid_res = grid_res
-        self.device = device
-        self.args = args
-        self.fc = FlexiCubes(device, weight_scale=0.5)
-        self.verts, self.indices = self.fc.construct_voxel_grid(grid_res)
-        if isinstance(scale, list):
-            self.verts[:, 0] = self.verts[:, 0] * scale[0]
-            self.verts[:, 1] = self.verts[:, 1] * scale[1]
-            self.verts[:, 2] = self.verts[:, 2] * scale[1]
-        else:
-            self.verts = self.verts * scale
-            
-        all_edges = self.indices[:, self.fc.cube_edges].reshape(-1, 2)
-        self.all_edges = torch.unique(all_edges, dim=0)
-
-        # Parameters used for fix boundary sdf
-        self.center_indices, self.boundary_indices = get_center_boundary_index(self.grid_res, device)
-        self.renderer = renderer
-        self.render_type = render_type
-
-    def getAABB(self):
-        return torch.min(self.verts, dim=0).values, torch.max(self.verts, dim=0).values
-
-    def get_mesh(self, v_deformed_nx3, sdf_n, weight_n=None, with_uv=False, indices=None, is_training=False):
-        if indices is None:
-            indices = self.indices
-
-        verts, faces, v_reg_loss = self.fc(v_deformed_nx3, sdf_n, indices, self.grid_res,
-                                            beta_fx12=weight_n[:, :12], alpha_fx8=weight_n[:, 12:20],
-                                            gamma_f=weight_n[:, 20], training=is_training
-                                            )
-        return verts, faces, v_reg_loss
-
-
-    def render_mesh(self, mesh_v_nx3, mesh_f_fx3, camera_mv_bx4x4, resolution=256, hierarchical_mask=False):
-        return_value = dict()
-        if self.render_type == 'neural_render':
-            tex_pos, mask, hard_mask, rast, v_pos_clip, mask_pyramid, depth, normal = self.renderer.render_mesh(
-                mesh_v_nx3.unsqueeze(dim=0),
-                mesh_f_fx3.int(),
-                camera_mv_bx4x4,
-                mesh_v_nx3.unsqueeze(dim=0),
-                resolution=resolution,
-                device=self.device,
-                hierarchical_mask=hierarchical_mask
-            )
-
-            return_value['tex_pos'] = tex_pos
-            return_value['mask'] = mask
-            return_value['hard_mask'] = hard_mask
-            return_value['rast'] = rast
-            return_value['v_pos_clip'] = v_pos_clip
-            return_value['mask_pyramid'] = mask_pyramid
-            return_value['depth'] = depth
-            return_value['normal'] = normal
-        else:
-            raise NotImplementedError
-
-        return return_value
-
-    def render(self, v_deformed_bxnx3=None, sdf_bxn=None, camera_mv_bxnviewx4x4=None, resolution=256):
-        # Here I assume a batch of meshes (can be different mesh and geometry), for the other shapes, the batch is 1
-        v_list = []
-        f_list = []
-        n_batch = v_deformed_bxnx3.shape[0]
-        all_render_output = []
-        for i_batch in range(n_batch):
-            verts_nx3, faces_fx3 = self.get_mesh(v_deformed_bxnx3[i_batch], sdf_bxn[i_batch])
-            v_list.append(verts_nx3)
-            f_list.append(faces_fx3)
-            render_output = self.render_mesh(verts_nx3, faces_fx3, camera_mv_bxnviewx4x4[i_batch], resolution)
-            all_render_output.append(render_output)
-
-        # Concatenate all render output
-        return_keys = all_render_output[0].keys()
-        return_value = dict()
-        for k in return_keys:
-            value = [v[k] for v in all_render_output]
-            return_value[k] = value
-            # We can do concatenation outside of the render
-        return return_value

src/models/geometry/rep_3d/tables.py

@@ -1,791 +0,0 @@
-# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION & AFFILIATES 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 & AFFILIATES is strictly prohibited.
-dmc_table = [
-[[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 3, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 1, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[1, 3, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 3, 4, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 1, 9, -1, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[1, 3, 4, 7, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 3, 8, -1, -1, -1, -1], [4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 1, 4, 5, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[1, 3, 4, 5, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[5, 7, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 3, 5, 7, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 1, 5, 7, 8, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[1, 3, 5, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 2, 8, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 1, 9, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[1, 2, 8, 9, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[4, 7, 8, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 2, 4, 7, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 1, 9, -1, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[1, 2, 4, 7, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[4, 5, 9, -1, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 2, 8, 11, -1, -1, -1], [4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 1, 4, 5, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[1, 2, 4, 5, 8, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[5, 7, 8, 9, -1, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 2, 5, 7, 9, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 1, 5, 7, 8, -1, -1], [2, 3, 11, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[1, 2, 5, 7, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 3, 8, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 2, 9, 10, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[2, 3, 8, 9, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[4, 7, 8, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 3, 4, 7, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 2, 9, 10, -1, -1, -1], [4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[2, 3, 4, 7, 9, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[4, 5, 9, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 3, 8, -1, -1, -1, -1], [4, 5, 9, -1, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 2, 4, 5, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[2, 3, 4, 5, 8, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[5, 7, 8, 9, -1, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 3, 5, 7, 9, -1, -1], [1, 2, 10, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 2, 5, 7, 8, 10, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[2, 3, 5, 7, 10, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[1, 3, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 1, 8, 10, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 3, 9, 10, 11, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[8, 9, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[4, 7, 8, -1, -1, -1, -1], [1, 3, 10, 11, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 1, 4, 7, 10, 11, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 3, 9, 10, 11, -1, -1], [4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
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-[[4, 5, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[1, 3, 4, 7, 9, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 1, 4, 7, 8, 9, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 3, 4, 7, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[4, 7, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[1, 3, 8, 9, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 1, 9, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[0, 3, 8, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
-[[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]]
-]
-num_vd_table = [0, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 3, 1, 2, 2,
-2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 2, 1, 2, 3, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2,
-1, 2, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 2, 3, 2, 2, 1, 1, 1, 1,
-1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 2, 3, 2, 2, 2, 2, 2, 1, 3, 4, 2,
-2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2,
-3, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 3, 2, 3, 2, 4, 2, 2, 2, 2, 1, 2, 1, 2, 1, 1,
-2, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1,
-1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2,
-1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,
-1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]
-check_table = [
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, 1, 0, 0, 194],
-[1, -1, 0, 0, 193],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, 0, 1, 0, 164],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, 0, -1, 0, 161],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, 0, 0, 1, 152],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, 0, 0, 1, 145],
-[1, 0, 0, 1, 144],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, 0, 0, -1, 137],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, 0, 1, 0, 133],
-[1, 0, 1, 0, 132],
-[1, 1, 0, 0, 131],
-[1, 1, 0, 0, 130],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, 0, 0, 1, 100],
-[0, 0, 0, 0, 0],
-[1, 0, 0, 1, 98],
-[0, 0, 0, 0, 0],
-[1, 0, 0, 1, 96],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, 0, 1, 0, 88],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, 0, -1, 0, 82],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, 0, 1, 0, 74],
-[0, 0, 0, 0, 0],
-[1, 0, 1, 0, 72],
-[0, 0, 0, 0, 0],
-[1, 0, 0, -1, 70],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, -1, 0, 0, 67],
-[0, 0, 0, 0, 0],
-[1, -1, 0, 0, 65],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, 1, 0, 0, 56],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, -1, 0, 0, 52],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, 1, 0, 0, 44],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, 1, 0, 0, 40],
-[0, 0, 0, 0, 0],
-[1, 0, 0, -1, 38],
-[1, 0, -1, 0, 37],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, 0, -1, 0, 33],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, -1, 0, 0, 28],
-[0, 0, 0, 0, 0],
-[1, 0, -1, 0, 26],
-[1, 0, 0, -1, 25],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, -1, 0, 0, 20],
-[0, 0, 0, 0, 0],
-[1, 0, -1, 0, 18],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, 0, 0, -1, 9],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[1, 0, 0, -1, 6],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0]
-]
-tet_table = [
-[-1, -1, -1, -1, -1, -1],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[1, 1, 1, 1, 1, 1],
-[4, 4, 4, 4, 4, 4],
-[0, 0, 0, 0, 0, 0],
-[4, 0, 0, 4, 4, -1],
-[1, 1, 1, 1, 1, 1],
-[4, 4, 4, 4, 4, 4],
-[0, 4, 0, 4, 4, -1],
-[0, 0, 0, 0, 0, 0],
-[1, 1, 1, 1, 1, 1],
-[5, 5, 5, 5, 5, 5],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[1, 1, 1, 1, 1, 1],
-[2, 2, 2, 2, 2, 2],
-[0, 0, 0, 0, 0, 0],
-[2, 0, 2, -1, 0, 2],
-[1, 1, 1, 1, 1, 1],
-[2, -1, 2, 4, 4, 2],
-[0, 0, 0, 0, 0, 0],
-[2, 0, 2, 4, 4, 2],
-[1, 1, 1, 1, 1, 1],
-[2, 4, 2, 4, 4, 2],
-[0, 4, 0, 4, 4, 0],
-[2, 0, 2, 0, 0, 2],
-[1, 1, 1, 1, 1, 1],
-[2, 5, 2, 5, 5, 2],
-[0, 0, 0, 0, 0, 0],
-[2, 0, 2, 0, 0, 2],
-[1, 1, 1, 1, 1, 1],
-[1, 1, 1, 1, 1, 1],
-[0, 1, 1, -1, 0, 1],
-[0, 0, 0, 0, 0, 0],
-[2, 2, 2, 2, 2, 2],
-[4, 1, 1, 4, 4, 1],
-[0, 1, 1, 0, 0, 1],
-[4, 0, 0, 4, 4, 0],
-[2, 2, 2, 2, 2, 2],
-[-1, 1, 1, 4, 4, 1],
-[0, 1, 1, 4, 4, 1],
-[0, 0, 0, 0, 0, 0],
-[2, 2, 2, 2, 2, 2],
-[5, 1, 1, 5, 5, 1],
-[0, 1, 1, 0, 0, 1],
-[0, 0, 0, 0, 0, 0],
-[2, 2, 2, 2, 2, 2],
-[1, 1, 1, 1, 1, 1],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[8, 8, 8, 8, 8, 8],
-[1, 1, 1, 4, 4, 1],
-[0, 0, 0, 0, 0, 0],
-[4, 0, 0, 4, 4, 0],
-[4, 4, 4, 4, 4, 4],
-[1, 1, 1, 4, 4, 1],
-[0, 4, 0, 4, 4, 0],
-[0, 0, 0, 0, 0, 0],
-[4, 4, 4, 4, 4, 4],
-[1, 1, 1, 5, 5, 1],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[5, 5, 5, 5, 5, 5],
-[6, 6, 6, 6, 6, 6],
-[6, -1, 0, 6, 0, 6],
-[6, 0, 0, 6, 0, 6],
-[6, 1, 1, 6, 1, 6],
-[4, 4, 4, 4, 4, 4],
-[0, 0, 0, 0, 0, 0],
-[4, 0, 0, 4, 4, 4],
-[1, 1, 1, 1, 1, 1],
-[6, 4, -1, 6, 4, 6],
-[6, 4, 0, 6, 4, 6],
-[6, 0, 0, 6, 0, 6],
-[6, 1, 1, 6, 1, 6],
-[5, 5, 5, 5, 5, 5],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[1, 1, 1, 1, 1, 1],
-[2, 2, 2, 2, 2, 2],
-[0, 0, 0, 0, 0, 0],
-[2, 0, 2, 2, 0, 2],
-[1, 1, 1, 1, 1, 1],
-[2, 2, 2, 2, 2, 2],
-[0, 0, 0, 0, 0, 0],
-[2, 0, 2, 2, 2, 2],
-[1, 1, 1, 1, 1, 1],
-[2, 4, 2, 2, 4, 2],
-[0, 4, 0, 4, 4, 0],
-[2, 0, 2, 2, 0, 2],
-[1, 1, 1, 1, 1, 1],
-[2, 2, 2, 2, 2, 2],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[1, 1, 1, 1, 1, 1],
-[6, 1, 1, 6, -1, 6],
-[6, 1, 1, 6, 0, 6],
-[6, 0, 0, 6, 0, 6],
-[6, 2, 2, 6, 2, 6],
-[4, 1, 1, 4, 4, 1],
-[0, 1, 1, 0, 0, 1],
-[4, 0, 0, 4, 4, 4],
-[2, 2, 2, 2, 2, 2],
-[6, 1, 1, 6, 4, 6],
-[6, 1, 1, 6, 4, 6],
-[6, 0, 0, 6, 0, 6],
-[6, 2, 2, 6, 2, 6],
-[5, 1, 1, 5, 5, 1],
-[0, 1, 1, 0, 0, 1],
-[0, 0, 0, 0, 0, 0],
-[2, 2, 2, 2, 2, 2],
-[1, 1, 1, 1, 1, 1],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[6, 6, 6, 6, 6, 6],
-[1, 1, 1, 1, 1, 1],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[4, 4, 4, 4, 4, 4],
-[1, 1, 1, 1, 4, 1],
-[0, 4, 0, 4, 4, 0],
-[0, 0, 0, 0, 0, 0],
-[4, 4, 4, 4, 4, 4],
-[1, 1, 1, 1, 1, 1],
-[0, 0, 0, 0, 0, 0],
-[0, 5, 0, 5, 0, 5],
-[5, 5, 5, 5, 5, 5],
-[5, 5, 5, 5, 5, 5],
-[0, 5, 0, 5, 0, 5],
-[-1, 5, 0, 5, 0, 5],
-[1, 5, 1, 5, 1, 5],
-[4, 5, -1, 5, 4, 5],
-[0, 5, 0, 5, 0, 5],
-[4, 5, 0, 5, 4, 5],
-[1, 5, 1, 5, 1, 5],
-[4, 4, 4, 4, 4, 4],
-[0, 4, 0, 4, 4, 4],
-[0, 0, 0, 0, 0, 0],
-[1, 1, 1, 1, 1, 1],
-[6, 6, 6, 6, 6, 6],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[1, 1, 1, 1, 1, 1],
-[2, 5, 2, 5, -1, 5],
-[0, 5, 0, 5, 0, 5],
-[2, 5, 2, 5, 0, 5],
-[1, 5, 1, 5, 1, 5],
-[2, 5, 2, 5, 4, 5],
-[0, 5, 0, 5, 0, 5],
-[2, 5, 2, 5, 4, 5],
-[1, 5, 1, 5, 1, 5],
-[2, 4, 2, 4, 4, 2],
-[0, 4, 0, 4, 4, 4],
-[2, 0, 2, 0, 0, 2],
-[1, 1, 1, 1, 1, 1],
-[2, 6, 2, 6, 6, 2],
-[0, 0, 0, 0, 0, 0],
-[2, 0, 2, 0, 0, 2],
-[1, 1, 1, 1, 1, 1],
-[1, 1, 1, 1, 1, 1],
-[0, 1, 1, 1, 0, 1],
-[0, 0, 0, 0, 0, 0],
-[2, 2, 2, 2, 2, 2],
-[4, 1, 1, 1, 4, 1],
-[0, 1, 1, 1, 0, 1],
-[4, 0, 0, 4, 4, 0],
-[2, 2, 2, 2, 2, 2],
-[1, 1, 1, 1, 1, 1],
-[0, 1, 1, 1, 1, 1],
-[0, 0, 0, 0, 0, 0],
-[2, 2, 2, 2, 2, 2],
-[1, 1, 1, 1, 1, 1],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[2, 2, 2, 2, 2, 2],
-[1, 1, 1, 1, 1, 1],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[5, 5, 5, 5, 5, 5],
-[1, 1, 1, 1, 4, 1],
-[0, 0, 0, 0, 0, 0],
-[4, 0, 0, 4, 4, 0],
-[4, 4, 4, 4, 4, 4],
-[1, 1, 1, 1, 1, 1],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[4, 4, 4, 4, 4, 4],
-[1, 1, 1, 1, 1, 1],
-[6, 0, 0, 6, 0, 6],
-[0, 0, 0, 0, 0, 0],
-[6, 6, 6, 6, 6, 6],
-[5, 5, 5, 5, 5, 5],
-[5, 5, 0, 5, 0, 5],
-[5, 5, 0, 5, 0, 5],
-[5, 5, 1, 5, 1, 5],
-[4, 4, 4, 4, 4, 4],
-[0, 0, 0, 0, 0, 0],
-[4, 4, 0, 4, 4, 4],
-[1, 1, 1, 1, 1, 1],
-[4, 4, 4, 4, 4, 4],
-[4, 4, 0, 4, 4, 4],
-[0, 0, 0, 0, 0, 0],
-[1, 1, 1, 1, 1, 1],
-[8, 8, 8, 8, 8, 8],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[1, 1, 1, 1, 1, 1],
-[2, 2, 2, 2, 2, 2],
-[0, 0, 0, 0, 0, 0],
-[2, 2, 2, 2, 0, 2],
-[1, 1, 1, 1, 1, 1],
-[2, 2, 2, 2, 2, 2],
-[0, 0, 0, 0, 0, 0],
-[2, 2, 2, 2, 2, 2],
-[1, 1, 1, 1, 1, 1],
-[2, 2, 2, 2, 2, 2],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[4, 1, 1, 4, 4, 1],
-[2, 2, 2, 2, 2, 2],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[1, 1, 1, 1, 1, 1],
-[1, 1, 1, 1, 1, 1],
-[1, 1, 1, 1, 0, 1],
-[0, 0, 0, 0, 0, 0],
-[2, 2, 2, 2, 2, 2],
-[1, 1, 1, 1, 1, 1],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[2, 4, 2, 4, 4, 2],
-[1, 1, 1, 1, 1, 1],
-[1, 1, 1, 1, 1, 1],
-[0, 0, 0, 0, 0, 0],
-[2, 2, 2, 2, 2, 2],
-[1, 1, 1, 1, 1, 1],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[2, 2, 2, 2, 2, 2],
-[1, 1, 1, 1, 1, 1],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[5, 5, 5, 5, 5, 5],
-[1, 1, 1, 1, 1, 1],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[4, 4, 4, 4, 4, 4],
-[1, 1, 1, 1, 1, 1],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[4, 4, 4, 4, 4, 4],
-[1, 1, 1, 1, 1, 1],
-[0, 0, 0, 0, 0, 0],
-[0, 0, 0, 0, 0, 0],
-[12, 12, 12, 12, 12, 12]
-]

src/models/lrm.py

@@ -1,196 +0,0 @@
-# Copyright (c) 2023, Zexin He
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-import torch
-import torch.nn as nn
-import mcubes
-import nvdiffrast.torch as dr
-from einops import rearrange, repeat
-
-from .encoder.dino_wrapper import DinoWrapper
-from .decoder.transformer import TriplaneTransformer
-from .renderer.synthesizer import TriplaneSynthesizer
-from ..utils.mesh_util import xatlas_uvmap
-
-
-class InstantNeRF(nn.Module):
-    """
-    Full model of the large reconstruction model.
-    """
-    def __init__(
-        self, 
-        encoder_freeze: bool = False, 
-        encoder_model_name: str = 'facebook/dino-vitb16', 
-        encoder_feat_dim: int = 768,
-        transformer_dim: int = 1024, 
-        transformer_layers: int = 16, 
-        transformer_heads: int = 16,
-        triplane_low_res: int = 32, 
-        triplane_high_res: int = 64, 
-        triplane_dim: int = 80,
-        rendering_samples_per_ray: int = 128,
-    ):
-        super().__init__()
-        
-        # modules
-        self.encoder = DinoWrapper(
-            model_name=encoder_model_name,
-            freeze=encoder_freeze,
-        )
-
-        self.transformer = TriplaneTransformer(
-            inner_dim=transformer_dim, 
-            num_layers=transformer_layers, 
-            num_heads=transformer_heads,
-            image_feat_dim=encoder_feat_dim,
-            triplane_low_res=triplane_low_res, 
-            triplane_high_res=triplane_high_res, 
-            triplane_dim=triplane_dim,
-        )
-
-        self.synthesizer = TriplaneSynthesizer(
-            triplane_dim=triplane_dim, 
-            samples_per_ray=rendering_samples_per_ray,
-        )
-
-    def forward_planes(self, images, cameras):
-        # images: [B, V, C_img, H_img, W_img]
-        # cameras: [B, V, 16]
-        B = images.shape[0]
-
-        # encode images
-        image_feats = self.encoder(images, cameras)
-        image_feats = rearrange(image_feats, '(b v) l d -> b (v l) d', b=B)
-        
-        # transformer generating planes
-        planes = self.transformer(image_feats)
-
-        return planes
-
-    def forward(self, images, cameras, render_cameras, render_size: int):
-        # images: [B, V, C_img, H_img, W_img]
-        # cameras: [B, V, 16]
-        # render_cameras: [B, M, D_cam_render]
-        # render_size: int
-        B, M = render_cameras.shape[:2]
-
-        planes = self.forward_planes(images, cameras)
-
-        # render target views
-        render_results = self.synthesizer(planes, render_cameras, render_size)
-
-        return {
-            'planes': planes,
-            **render_results,
-        }
-    
-    def get_texture_prediction(self, planes, tex_pos, hard_mask=None):
-        '''
-        Predict Texture given triplanes
-        :param planes: the triplane feature map
-        :param tex_pos: Position we want to query the texture field
-        :param hard_mask: 2D silhoueete of the rendered image
-        '''
-        tex_pos = torch.cat(tex_pos, dim=0)
-        if not hard_mask is None:
-            tex_pos = tex_pos * hard_mask.float()
-        batch_size = tex_pos.shape[0]
-        tex_pos = tex_pos.reshape(batch_size, -1, 3)
-        ###################
-        # We use mask to get the texture location (to save the memory)
-        if hard_mask is not None:
-            n_point_list = torch.sum(hard_mask.long().reshape(hard_mask.shape[0], -1), dim=-1)
-            sample_tex_pose_list = []
-            max_point = n_point_list.max()
-            expanded_hard_mask = hard_mask.reshape(batch_size, -1, 1).expand(-1, -1, 3) > 0.5
-            for i in range(tex_pos.shape[0]):
-                tex_pos_one_shape = tex_pos[i][expanded_hard_mask[i]].reshape(1, -1, 3)
-                if tex_pos_one_shape.shape[1] < max_point:
-                    tex_pos_one_shape = torch.cat(
-                        [tex_pos_one_shape, torch.zeros(
-                            1, max_point - tex_pos_one_shape.shape[1], 3,
-                            device=tex_pos_one_shape.device, dtype=torch.float32)], dim=1)
-                sample_tex_pose_list.append(tex_pos_one_shape)
-            tex_pos = torch.cat(sample_tex_pose_list, dim=0)
-
-        tex_feat = self.synthesizer.forward_points(planes, tex_pos)['rgb']
-
-        if hard_mask is not None:
-            final_tex_feat = torch.zeros(
-                planes.shape[0], hard_mask.shape[1] * hard_mask.shape[2], tex_feat.shape[-1], device=tex_feat.device)
-            expanded_hard_mask = hard_mask.reshape(hard_mask.shape[0], -1, 1).expand(-1, -1, final_tex_feat.shape[-1]) > 0.5
-            for i in range(planes.shape[0]):
-                final_tex_feat[i][expanded_hard_mask[i]] = tex_feat[i][:n_point_list[i]].reshape(-1)
-            tex_feat = final_tex_feat
-
-        return tex_feat.reshape(planes.shape[0], hard_mask.shape[1], hard_mask.shape[2], tex_feat.shape[-1])
-
-    def extract_mesh(
-        self, 
-        planes: torch.Tensor, 
-        mesh_resolution: int = 256, 
-        mesh_threshold: int = 10.0, 
-        use_texture_map: bool = False, 
-        texture_resolution: int = 1024,
-        **kwargs,
-    ):
-        '''
-        Extract a 3D mesh from triplane nerf. Only support batch_size 1.
-        :param planes: triplane features
-        :param mesh_resolution: marching cubes resolution
-        :param mesh_threshold: iso-surface threshold
-        :param use_texture_map: use texture map or vertex color
-        :param texture_resolution: the resolution of texture map
-        '''
-        assert planes.shape[0] == 1
-        device = planes.device
-
-        grid_out = self.synthesizer.forward_grid(
-            planes=planes,
-            grid_size=mesh_resolution,
-        )
-        
-        vertices, faces = mcubes.marching_cubes(
-            grid_out['sigma'].squeeze(0).squeeze(-1).cpu().numpy(), 
-            mesh_threshold,
-        )
-        vertices = vertices / (mesh_resolution - 1) * 2 - 1
-
-        if not use_texture_map:
-            # query vertex colors
-            vertices_tensor = torch.tensor(vertices, dtype=torch.float32, device=device).unsqueeze(0)
-            vertices_colors = self.synthesizer.forward_points(
-                planes, vertices_tensor)['rgb'].squeeze(0).cpu().numpy()
-            vertices_colors = (vertices_colors * 255).astype(np.uint8)
-
-            return vertices, faces, vertices_colors
-        
-        # use x-atlas to get uv mapping for the mesh
-        vertices = torch.tensor(vertices, dtype=torch.float32, device=device)
-        faces = torch.tensor(faces.astype(int), dtype=torch.long, device=device)
-
-        ctx = dr.RasterizeCudaContext(device=device)
-        uvs, mesh_tex_idx, gb_pos, tex_hard_mask = xatlas_uvmap(
-            ctx, vertices, faces, resolution=texture_resolution)
-        tex_hard_mask = tex_hard_mask.float()
-
-        # query the texture field to get the RGB color for texture map
-        tex_feat = self.get_texture_prediction(
-            planes, [gb_pos], tex_hard_mask)
-        background_feature = torch.zeros_like(tex_feat)
-        img_feat = torch.lerp(background_feature, tex_feat, tex_hard_mask)
-        texture_map = img_feat.permute(0, 3, 1, 2).squeeze(0)
-
-        return vertices, faces, uvs, mesh_tex_idx, texture_map

src/models/lrm_mesh.py

@@ -1,385 +0,0 @@
-# Copyright (c) 2023, Tencent Inc
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-import torch
-import torch.nn as nn
-import nvdiffrast.torch as dr
-from einops import rearrange, repeat
-
-from .encoder.dino_wrapper import DinoWrapper
-from .decoder.transformer import TriplaneTransformer
-from .renderer.synthesizer_mesh import TriplaneSynthesizer
-from .geometry.camera.perspective_camera import PerspectiveCamera
-from .geometry.render.neural_render import NeuralRender
-from .geometry.rep_3d.flexicubes_geometry import FlexiCubesGeometry
-from ..utils.mesh_util import xatlas_uvmap
-
-
-class InstantMesh(nn.Module):
-    """
-    Full model of the large reconstruction model.
-    """
-    def __init__(
-        self, 
-        encoder_freeze: bool = False, 
-        encoder_model_name: str = 'facebook/dino-vitb16', 
-        encoder_feat_dim: int = 768,
-        transformer_dim: int = 1024, 
-        transformer_layers: int = 16, 
-        transformer_heads: int = 16,
-        triplane_low_res: int = 32, 
-        triplane_high_res: int = 64, 
-        triplane_dim: int = 80,
-        rendering_samples_per_ray: int = 128,
-        grid_res: int = 128, 
-        grid_scale: float = 2.0,
-    ):
-        super().__init__()
-        
-        # attributes
-        self.grid_res = grid_res
-        self.grid_scale = grid_scale
-        self.deformation_multiplier = 4.0
-
-        # modules
-        self.encoder = DinoWrapper(
-            model_name=encoder_model_name,
-            freeze=encoder_freeze,
-        )
-
-        self.transformer = TriplaneTransformer(
-            inner_dim=transformer_dim, 
-            num_layers=transformer_layers, 
-            num_heads=transformer_heads,
-            image_feat_dim=encoder_feat_dim,
-            triplane_low_res=triplane_low_res, 
-            triplane_high_res=triplane_high_res, 
-            triplane_dim=triplane_dim,
-        )
-        
-        self.synthesizer = TriplaneSynthesizer(
-            triplane_dim=triplane_dim, 
-            samples_per_ray=rendering_samples_per_ray,
-        )
-
-    def init_flexicubes_geometry(self, device, fovy=50.0, use_renderer=True):
-        camera = PerspectiveCamera(fovy=fovy, device=device)
-        if use_renderer:
-            renderer = NeuralRender(device, camera_model=camera)
-        else:
-            renderer = None
-        self.geometry = FlexiCubesGeometry(
-            grid_res=self.grid_res, 
-            scale=self.grid_scale, 
-            renderer=renderer, 
-            render_type='neural_render',
-            device=device,
-        )
-
-    def forward_planes(self, images, cameras):
-        # images: [B, V, C_img, H_img, W_img]
-        # cameras: [B, V, 16]
-        B = images.shape[0]
-
-        # encode images
-        image_feats = self.encoder(images, cameras)
-        image_feats = rearrange(image_feats, '(b v) l d -> b (v l) d', b=B)
-        
-        # decode triplanes
-        planes = self.transformer(image_feats)
-
-        return planes
-    
-    def get_sdf_deformation_prediction(self, planes):
-        '''
-        Predict SDF and deformation for tetrahedron vertices
-        :param planes: triplane feature map for the geometry
-        '''
-        init_position = self.geometry.verts.unsqueeze(0).expand(planes.shape[0], -1, -1)
-        
-        # Step 1: predict the SDF and deformation
-        sdf, deformation, weight = torch.utils.checkpoint.checkpoint(
-            self.synthesizer.get_geometry_prediction,
-            planes, 
-            init_position, 
-            self.geometry.indices,
-            use_reentrant=False,
-        )
-
-        # Step 2: Normalize the deformation to avoid the flipped triangles.
-        deformation = 1.0 / (self.grid_res * self.deformation_multiplier) * torch.tanh(deformation)
-        sdf_reg_loss = torch.zeros(sdf.shape[0], device=sdf.device, dtype=torch.float32)
-
-        ####
-        # Step 3: Fix some sdf if we observe empty shape (full positive or full negative)
-        sdf_bxnxnxn = sdf.reshape((sdf.shape[0], self.grid_res + 1, self.grid_res + 1, self.grid_res + 1))
-        sdf_less_boundary = sdf_bxnxnxn[:, 1:-1, 1:-1, 1:-1].reshape(sdf.shape[0], -1)
-        pos_shape = torch.sum((sdf_less_boundary > 0).int(), dim=-1)
-        neg_shape = torch.sum((sdf_less_boundary < 0).int(), dim=-1)
-        zero_surface = torch.bitwise_or(pos_shape == 0, neg_shape == 0)
-        if torch.sum(zero_surface).item() > 0:
-            update_sdf = torch.zeros_like(sdf[0:1])
-            max_sdf = sdf.max()
-            min_sdf = sdf.min()
-            update_sdf[:, self.geometry.center_indices] += (1.0 - min_sdf)  # greater than zero
-            update_sdf[:, self.geometry.boundary_indices] += (-1 - max_sdf)  # smaller than zero
-            new_sdf = torch.zeros_like(sdf)
-            for i_batch in range(zero_surface.shape[0]):
-                if zero_surface[i_batch]:
-                    new_sdf[i_batch:i_batch + 1] += update_sdf
-            update_mask = (new_sdf == 0).float()
-            # Regulraization here is used to push the sdf to be a different sign (make it not fully positive or fully negative)
-            sdf_reg_loss = torch.abs(sdf).mean(dim=-1).mean(dim=-1)
-            sdf_reg_loss = sdf_reg_loss * zero_surface.float()
-            sdf = sdf * update_mask + new_sdf * (1 - update_mask)
-
-        # Step 4: Here we remove the gradient for the bad sdf (full positive or full negative)
-        final_sdf = []
-        final_def = []
-        for i_batch in range(zero_surface.shape[0]):
-            if zero_surface[i_batch]:
-                final_sdf.append(sdf[i_batch: i_batch + 1].detach())
-                final_def.append(deformation[i_batch: i_batch + 1].detach())
-            else:
-                final_sdf.append(sdf[i_batch: i_batch + 1])
-                final_def.append(deformation[i_batch: i_batch + 1])
-        sdf = torch.cat(final_sdf, dim=0)
-        deformation = torch.cat(final_def, dim=0)
-        return sdf, deformation, sdf_reg_loss, weight
-    
-    def get_geometry_prediction(self, planes=None):
-        '''
-        Function to generate mesh with give triplanes
-        :param planes: triplane features
-        '''
-        # Step 1: first get the sdf and deformation value for each vertices in the tetrahedon grid.
-        sdf, deformation, sdf_reg_loss, weight = self.get_sdf_deformation_prediction(planes)
-        v_deformed = self.geometry.verts.unsqueeze(dim=0).expand(sdf.shape[0], -1, -1) + deformation
-        tets = self.geometry.indices
-        n_batch = planes.shape[0]
-        v_list = []
-        f_list = []
-        flexicubes_surface_reg_list = []
-        
-        # Step 2: Using marching tet to obtain the mesh
-        for i_batch in range(n_batch):
-            verts, faces, flexicubes_surface_reg = self.geometry.get_mesh(
-                v_deformed[i_batch], 
-                sdf[i_batch].squeeze(dim=-1),
-                with_uv=False, 
-                indices=tets, 
-                weight_n=weight[i_batch].squeeze(dim=-1),
-                is_training=self.training,
-            )
-            flexicubes_surface_reg_list.append(flexicubes_surface_reg)
-            v_list.append(verts)
-            f_list.append(faces)
-        
-        flexicubes_surface_reg = torch.cat(flexicubes_surface_reg_list).mean()
-        flexicubes_weight_reg = (weight ** 2).mean()
-        
-        return v_list, f_list, sdf, deformation, v_deformed, (sdf_reg_loss, flexicubes_surface_reg, flexicubes_weight_reg)
-    
-    def get_texture_prediction(self, planes, tex_pos, hard_mask=None):
-        '''
-        Predict Texture given triplanes
-        :param planes: the triplane feature map
-        :param tex_pos: Position we want to query the texture field
-        :param hard_mask: 2D silhoueete of the rendered image
-        '''
-        tex_pos = torch.cat(tex_pos, dim=0)
-        if not hard_mask is None:
-            tex_pos = tex_pos * hard_mask.float()
-        batch_size = tex_pos.shape[0]
-        tex_pos = tex_pos.reshape(batch_size, -1, 3)
-        ###################
-        # We use mask to get the texture location (to save the memory)
-        if hard_mask is not None:
-            n_point_list = torch.sum(hard_mask.long().reshape(hard_mask.shape[0], -1), dim=-1)
-            sample_tex_pose_list = []
-            max_point = n_point_list.max()
-            expanded_hard_mask = hard_mask.reshape(batch_size, -1, 1).expand(-1, -1, 3) > 0.5
-            for i in range(tex_pos.shape[0]):
-                tex_pos_one_shape = tex_pos[i][expanded_hard_mask[i]].reshape(1, -1, 3)
-                if tex_pos_one_shape.shape[1] < max_point:
-                    tex_pos_one_shape = torch.cat(
-                        [tex_pos_one_shape, torch.zeros(
-                            1, max_point - tex_pos_one_shape.shape[1], 3,
-                            device=tex_pos_one_shape.device, dtype=torch.float32)], dim=1)
-                sample_tex_pose_list.append(tex_pos_one_shape)
-            tex_pos = torch.cat(sample_tex_pose_list, dim=0)
-
-        tex_feat = torch.utils.checkpoint.checkpoint(
-            self.synthesizer.get_texture_prediction,
-            planes, 
-            tex_pos,
-            use_reentrant=False,
-        )
-
-        if hard_mask is not None:
-            final_tex_feat = torch.zeros(
-                planes.shape[0], hard_mask.shape[1] * hard_mask.shape[2], tex_feat.shape[-1], device=tex_feat.device)
-            expanded_hard_mask = hard_mask.reshape(hard_mask.shape[0], -1, 1).expand(-1, -1, final_tex_feat.shape[-1]) > 0.5
-            for i in range(planes.shape[0]):
-                final_tex_feat[i][expanded_hard_mask[i]] = tex_feat[i][:n_point_list[i]].reshape(-1)
-            tex_feat = final_tex_feat
-
-        return tex_feat.reshape(planes.shape[0], hard_mask.shape[1], hard_mask.shape[2], tex_feat.shape[-1])
-    
-    def render_mesh(self, mesh_v, mesh_f, cam_mv, render_size=256):
-        '''
-        Function to render a generated mesh with nvdiffrast
-        :param mesh_v: List of vertices for the mesh
-        :param mesh_f: List of faces for the mesh
-        :param cam_mv:  4x4 rotation matrix
-        :return:
-        '''
-        return_value_list = []
-        for i_mesh in range(len(mesh_v)):
-            return_value = self.geometry.render_mesh(
-                mesh_v[i_mesh],
-                mesh_f[i_mesh].int(),
-                cam_mv[i_mesh],
-                resolution=render_size,
-                hierarchical_mask=False
-            )
-            return_value_list.append(return_value)
-
-        return_keys = return_value_list[0].keys()
-        return_value = dict()
-        for k in return_keys:
-            value = [v[k] for v in return_value_list]
-            return_value[k] = value
-
-        mask = torch.cat(return_value['mask'], dim=0)
-        hard_mask = torch.cat(return_value['hard_mask'], dim=0)
-        tex_pos = return_value['tex_pos']
-        depth = torch.cat(return_value['depth'], dim=0)
-        normal = torch.cat(return_value['normal'], dim=0)
-        return mask, hard_mask, tex_pos, depth, normal
-    
-    def forward_geometry(self, planes, render_cameras, render_size=256):
-        '''
-        Main function of our Generator. It first generate 3D mesh, then render it into 2D image
-        with given `render_cameras`.
-        :param planes: triplane features
-        :param render_cameras: cameras to render generated 3D shape
-        '''
-        B, NV = render_cameras.shape[:2]
-
-        # Generate 3D mesh first
-        mesh_v, mesh_f, sdf, deformation, v_deformed, sdf_reg_loss = self.get_geometry_prediction(planes)
-
-        # Render the mesh into 2D image (get 3d position of each image plane)
-        cam_mv = render_cameras
-        run_n_view = cam_mv.shape[1]
-        antilias_mask, hard_mask, tex_pos, depth, normal = self.render_mesh(mesh_v, mesh_f, cam_mv, render_size=render_size)
-
-        tex_hard_mask = hard_mask
-        tex_pos = [torch.cat([pos[i_view:i_view + 1] for i_view in range(run_n_view)], dim=2) for pos in tex_pos]
-        tex_hard_mask = torch.cat(
-            [torch.cat(
-                [tex_hard_mask[i * run_n_view + i_view: i * run_n_view + i_view + 1]
-                 for i_view in range(run_n_view)], dim=2)
-                for i in range(planes.shape[0])], dim=0)
-
-        # Querying the texture field to predict the texture feature for each pixel on the image
-        tex_feat = self.get_texture_prediction(planes, tex_pos, tex_hard_mask)
-        background_feature = torch.ones_like(tex_feat)      # white background
-
-        # Merge them together
-        img_feat = tex_feat * tex_hard_mask + background_feature * (1 - tex_hard_mask)
-
-        # We should split it back to the original image shape
-        img_feat = torch.cat(
-            [torch.cat(
-                [img_feat[i:i + 1, :, render_size * i_view: render_size * (i_view + 1)]
-                 for i_view in range(run_n_view)], dim=0) for i in range(len(tex_pos))], dim=0)
-
-        img = img_feat.clamp(0, 1).permute(0, 3, 1, 2).unflatten(0, (B, NV))
-        antilias_mask = antilias_mask.permute(0, 3, 1, 2).unflatten(0, (B, NV))
-        depth = -depth.permute(0, 3, 1, 2).unflatten(0, (B, NV))        # transform negative depth to positive
-        normal = normal.permute(0, 3, 1, 2).unflatten(0, (B, NV))
-
-        out = {
-            'img': img,
-            'mask': antilias_mask,
-            'depth': depth,
-            'normal': normal,
-            'sdf': sdf,
-            'mesh_v': mesh_v,
-            'mesh_f': mesh_f,
-            'sdf_reg_loss': sdf_reg_loss,
-        }
-        return out
-
-    def forward(self, images, cameras, render_cameras, render_size: int):
-        # images: [B, V, C_img, H_img, W_img]
-        # cameras: [B, V, 16]
-        # render_cameras: [B, M, D_cam_render]
-        # render_size: int
-        B, M = render_cameras.shape[:2]
-
-        planes = self.forward_planes(images, cameras)
-        out = self.forward_geometry(planes, render_cameras, render_size=render_size)
-
-        return {
-            'planes': planes,
-            **out
-        }
-
-    def extract_mesh(
-        self, 
-        planes: torch.Tensor, 
-        use_texture_map: bool = False,
-        texture_resolution: int = 1024,
-        **kwargs,
-    ):
-        '''
-        Extract a 3D mesh from FlexiCubes. Only support batch_size 1.
-        :param planes: triplane features
-        :param use_texture_map: use texture map or vertex color
-        :param texture_resolution: the resolution of texure map
-        '''
-        assert planes.shape[0] == 1
-        device = planes.device
-
-        # predict geometry first
-        mesh_v, mesh_f, sdf, deformation, v_deformed, sdf_reg_loss = self.get_geometry_prediction(planes)
-        vertices, faces = mesh_v[0], mesh_f[0]
-
-        if not use_texture_map:
-            # query vertex colors
-            vertices_tensor = vertices.unsqueeze(0)
-            vertices_colors = self.synthesizer.get_texture_prediction(
-                planes, vertices_tensor).clamp(0, 1).squeeze(0).cpu().numpy()
-            vertices_colors = (vertices_colors * 255).astype(np.uint8)
-
-            return vertices.cpu().numpy(), faces.cpu().numpy(), vertices_colors
-
-        # use x-atlas to get uv mapping for the mesh
-        ctx = dr.RasterizeCudaContext(device=device)
-        uvs, mesh_tex_idx, gb_pos, tex_hard_mask = xatlas_uvmap(
-            self.geometry.renderer.ctx, vertices, faces, resolution=texture_resolution)
-        tex_hard_mask = tex_hard_mask.float()
-
-        # query the texture field to get the RGB color for texture map
-        tex_feat = self.get_texture_prediction(
-            planes, [gb_pos], tex_hard_mask)
-        background_feature = torch.zeros_like(tex_feat)
-        img_feat = torch.lerp(background_feature, tex_feat, tex_hard_mask)
-        texture_map = img_feat.permute(0, 3, 1, 2).squeeze(0)
-
-        return vertices, faces, uvs, mesh_tex_idx, texture_map

src/models/renderer/__init__.py

@@ -1,9 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
-#
-# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
-# property and proprietary rights in and to this material, related
-# documentation and any modifications thereto. Any use, reproduction,
-# disclosure or distribution of this material and related documentation
-# without an express license agreement from NVIDIA CORPORATION or
-# its affiliates is strictly prohibited.

src/models/renderer/synthesizer.py

@@ -1,203 +0,0 @@
-# ORIGINAL LICENSE
-# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
-#
-# Modified by Jiale Xu
-# The modifications are subject to the same license as the original.
-
-
-import itertools
-import torch
-import torch.nn as nn
-
-from .utils.renderer import ImportanceRenderer
-from .utils.ray_sampler import RaySampler
-
-
-class OSGDecoder(nn.Module):
-    """
-    Triplane decoder that gives RGB and sigma values from sampled features.
-    Using ReLU here instead of Softplus in the original implementation.
-    
-    Reference:
-    EG3D: https://github.com/NVlabs/eg3d/blob/main/eg3d/training/triplane.py#L112
-    """
-    def __init__(self, n_features: int,
-                 hidden_dim: int = 64, num_layers: int = 4, activation: nn.Module = nn.ReLU):
-        super().__init__()
-        self.net = nn.Sequential(
-            nn.Linear(3 * n_features, hidden_dim),
-            activation(),
-            *itertools.chain(*[[
-                nn.Linear(hidden_dim, hidden_dim),
-                activation(),
-            ] for _ in range(num_layers - 2)]),
-            nn.Linear(hidden_dim, 1 + 3),
-        )
-        # init all bias to zero
-        for m in self.modules():
-            if isinstance(m, nn.Linear):
-                nn.init.zeros_(m.bias)
-
-    def forward(self, sampled_features, ray_directions):
-        # Aggregate features by mean
-        # sampled_features = sampled_features.mean(1)
-        # Aggregate features by concatenation
-        _N, n_planes, _M, _C = sampled_features.shape
-        sampled_features = sampled_features.permute(0, 2, 1, 3).reshape(_N, _M, n_planes*_C)
-        x = sampled_features
-
-        N, M, C = x.shape
-        x = x.contiguous().view(N*M, C)
-
-        x = self.net(x)
-        x = x.view(N, M, -1)
-        rgb = torch.sigmoid(x[..., 1:])*(1 + 2*0.001) - 0.001  # Uses sigmoid clamping from MipNeRF
-        sigma = x[..., 0:1]
-
-        return {'rgb': rgb, 'sigma': sigma}
-
-
-class TriplaneSynthesizer(nn.Module):
-    """
-    Synthesizer that renders a triplane volume with planes and a camera.
-    
-    Reference:
-    EG3D: https://github.com/NVlabs/eg3d/blob/main/eg3d/training/triplane.py#L19
-    """
-
-    DEFAULT_RENDERING_KWARGS = {
-        'ray_start': 'auto',
-        'ray_end': 'auto',
-        'box_warp': 2.,
-        'white_back': True,
-        'disparity_space_sampling': False,
-        'clamp_mode': 'softplus',
-        'sampler_bbox_min': -1.,
-        'sampler_bbox_max': 1.,
-    }
-
-    def __init__(self, triplane_dim: int, samples_per_ray: int):
-        super().__init__()
-
-        # attributes
-        self.triplane_dim = triplane_dim
-        self.rendering_kwargs = {
-            **self.DEFAULT_RENDERING_KWARGS,
-            'depth_resolution': samples_per_ray // 2,
-            'depth_resolution_importance': samples_per_ray // 2,
-        }
-
-        # renderings
-        self.renderer = ImportanceRenderer()
-        self.ray_sampler = RaySampler()
-
-        # modules
-        self.decoder = OSGDecoder(n_features=triplane_dim)
-
-    def forward(self, planes, cameras, render_size=128, crop_params=None):
-        # planes: (N, 3, D', H', W')
-        # cameras: (N, M, D_cam)
-        # render_size: int
-        assert planes.shape[0] == cameras.shape[0], "Batch size mismatch for planes and cameras"
-        N, M = cameras.shape[:2]
-        
-        cam2world_matrix = cameras[..., :16].view(N, M, 4, 4)
-        intrinsics = cameras[..., 16:25].view(N, M, 3, 3)
-
-        # Create a batch of rays for volume rendering
-        ray_origins, ray_directions = self.ray_sampler(
-            cam2world_matrix=cam2world_matrix.reshape(-1, 4, 4),
-            intrinsics=intrinsics.reshape(-1, 3, 3),
-            render_size=render_size,
-        )
-        assert N*M == ray_origins.shape[0], "Batch size mismatch for ray_origins"
-        assert ray_origins.dim() == 3, "ray_origins should be 3-dimensional"
-
-        # Crop rays if crop_params is available
-        if crop_params is not None:
-            ray_origins = ray_origins.reshape(N*M, render_size, render_size, 3)
-            ray_directions = ray_directions.reshape(N*M, render_size, render_size, 3)
-            i, j, h, w = crop_params
-            ray_origins = ray_origins[:, i:i+h, j:j+w, :].reshape(N*M, -1, 3)
-            ray_directions = ray_directions[:, i:i+h, j:j+w, :].reshape(N*M, -1, 3)
-
-        # Perform volume rendering
-        rgb_samples, depth_samples, weights_samples = self.renderer(
-            planes.repeat_interleave(M, dim=0), self.decoder, ray_origins, ray_directions, self.rendering_kwargs,
-        )
-
-        # Reshape into 'raw' neural-rendered image
-        if crop_params is not None:
-            Himg, Wimg = crop_params[2:]
-        else:
-            Himg = Wimg = render_size
-        rgb_images = rgb_samples.permute(0, 2, 1).reshape(N, M, rgb_samples.shape[-1], Himg, Wimg).contiguous()
-        depth_images = depth_samples.permute(0, 2, 1).reshape(N, M, 1, Himg, Wimg)
-        weight_images = weights_samples.permute(0, 2, 1).reshape(N, M, 1, Himg, Wimg)
-
-        out = {
-            'images_rgb': rgb_images,
-            'images_depth': depth_images,
-            'images_weight': weight_images,
-        }
-        return out
-
-    def forward_grid(self, planes, grid_size: int, aabb: torch.Tensor = None):
-        # planes: (N, 3, D', H', W')
-        # grid_size: int
-        # aabb: (N, 2, 3)
-        if aabb is None:
-            aabb = torch.tensor([
-                [self.rendering_kwargs['sampler_bbox_min']] * 3,
-                [self.rendering_kwargs['sampler_bbox_max']] * 3,
-            ], device=planes.device, dtype=planes.dtype).unsqueeze(0).repeat(planes.shape[0], 1, 1)
-        assert planes.shape[0] == aabb.shape[0], "Batch size mismatch for planes and aabb"
-        N = planes.shape[0]
-
-        # create grid points for triplane query
-        grid_points = []
-        for i in range(N):
-            grid_points.append(torch.stack(torch.meshgrid(
-                torch.linspace(aabb[i, 0, 0], aabb[i, 1, 0], grid_size, device=planes.device),
-                torch.linspace(aabb[i, 0, 1], aabb[i, 1, 1], grid_size, device=planes.device),
-                torch.linspace(aabb[i, 0, 2], aabb[i, 1, 2], grid_size, device=planes.device),
-                indexing='ij',
-            ), dim=-1).reshape(-1, 3))
-        cube_grid = torch.stack(grid_points, dim=0).to(planes.device)
-
-        features = self.forward_points(planes, cube_grid)
-
-        # reshape into grid
-        features = {
-            k: v.reshape(N, grid_size, grid_size, grid_size, -1)
-            for k, v in features.items()
-        }
-        return features
-
-    def forward_points(self, planes, points: torch.Tensor, chunk_size: int = 2**20):
-        # planes: (N, 3, D', H', W')
-        # points: (N, P, 3)
-        N, P = points.shape[:2]
-
-        # query triplane in chunks
-        outs = []
-        for i in range(0, points.shape[1], chunk_size):
-            chunk_points = points[:, i:i+chunk_size]
-
-            # query triplane
-            chunk_out = self.renderer.run_model_activated(
-                planes=planes,
-                decoder=self.decoder,
-                sample_coordinates=chunk_points,
-                sample_directions=torch.zeros_like(chunk_points),
-                options=self.rendering_kwargs,
-            )
-            outs.append(chunk_out)
-
-        # concatenate the outputs
-        point_features = {
-            k: torch.cat([out[k] for out in outs], dim=1)
-            for k in outs[0].keys()
-        }
-        return point_features

src/models/renderer/synthesizer_mesh.py

@@ -1,141 +0,0 @@
-# ORIGINAL LICENSE
-# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
-#
-# Modified by Jiale Xu
-# The modifications are subject to the same license as the original.
-
-import itertools
-import torch
-import torch.nn as nn
-
-from .utils.renderer import generate_planes, project_onto_planes, sample_from_planes
-
-
-class OSGDecoder(nn.Module):
-    """
-    Triplane decoder that gives RGB and sigma values from sampled features.
-    Using ReLU here instead of Softplus in the original implementation.
-    
-    Reference:
-    EG3D: https://github.com/NVlabs/eg3d/blob/main/eg3d/training/triplane.py#L112
-    """
-    def __init__(self, n_features: int,
-                 hidden_dim: int = 64, num_layers: int = 4, activation: nn.Module = nn.ReLU):
-        super().__init__()
-
-        self.net_sdf = nn.Sequential(
-            nn.Linear(3 * n_features, hidden_dim),
-            activation(),
-            *itertools.chain(*[[
-                nn.Linear(hidden_dim, hidden_dim),
-                activation(),
-            ] for _ in range(num_layers - 2)]),
-            nn.Linear(hidden_dim, 1),
-        )
-        self.net_rgb = nn.Sequential(
-            nn.Linear(3 * n_features, hidden_dim),
-            activation(),
-            *itertools.chain(*[[
-                nn.Linear(hidden_dim, hidden_dim),
-                activation(),
-            ] for _ in range(num_layers - 2)]),
-            nn.Linear(hidden_dim, 3),
-        )
-        self.net_deformation = nn.Sequential(
-            nn.Linear(3 * n_features, hidden_dim),
-            activation(),
-            *itertools.chain(*[[
-                nn.Linear(hidden_dim, hidden_dim),
-                activation(),
-            ] for _ in range(num_layers - 2)]),
-            nn.Linear(hidden_dim, 3),
-        )
-        self.net_weight = nn.Sequential(
-            nn.Linear(8 * 3 * n_features, hidden_dim),
-            activation(),
-            *itertools.chain(*[[
-                nn.Linear(hidden_dim, hidden_dim),
-                activation(),
-            ] for _ in range(num_layers - 2)]),
-            nn.Linear(hidden_dim, 21),
-        )
-
-        # init all bias to zero
-        for m in self.modules():
-            if isinstance(m, nn.Linear):
-                nn.init.zeros_(m.bias)
-
-    def get_geometry_prediction(self, sampled_features, flexicubes_indices):
-        _N, n_planes, _M, _C = sampled_features.shape
-        sampled_features = sampled_features.permute(0, 2, 1, 3).reshape(_N, _M, n_planes*_C)
-
-        sdf = self.net_sdf(sampled_features)
-        deformation = self.net_deformation(sampled_features)
-
-        grid_features = torch.index_select(input=sampled_features, index=flexicubes_indices.reshape(-1), dim=1)
-        grid_features = grid_features.reshape(
-            sampled_features.shape[0], flexicubes_indices.shape[0], flexicubes_indices.shape[1] * sampled_features.shape[-1])
-        weight = self.net_weight(grid_features) * 0.1
-
-        return sdf, deformation, weight
-    
-    def get_texture_prediction(self, sampled_features):
-        _N, n_planes, _M, _C = sampled_features.shape
-        sampled_features = sampled_features.permute(0, 2, 1, 3).reshape(_N, _M, n_planes*_C)
-
-        rgb = self.net_rgb(sampled_features)
-        rgb = torch.sigmoid(rgb)*(1 + 2*0.001) - 0.001  # Uses sigmoid clamping from MipNeRF
-
-        return rgb
-
-
-class TriplaneSynthesizer(nn.Module):
-    """
-    Synthesizer that renders a triplane volume with planes and a camera.
-    
-    Reference:
-    EG3D: https://github.com/NVlabs/eg3d/blob/main/eg3d/training/triplane.py#L19
-    """
-
-    DEFAULT_RENDERING_KWARGS = {
-        'ray_start': 'auto',
-        'ray_end': 'auto',
-        'box_warp': 2.,
-        'white_back': True,
-        'disparity_space_sampling': False,
-        'clamp_mode': 'softplus',
-        'sampler_bbox_min': -1.,
-        'sampler_bbox_max': 1.,
-    }
-
-    def __init__(self, triplane_dim: int, samples_per_ray: int):
-        super().__init__()
-
-        # attributes
-        self.triplane_dim = triplane_dim
-        self.rendering_kwargs = {
-            **self.DEFAULT_RENDERING_KWARGS,
-            'depth_resolution': samples_per_ray // 2,
-            'depth_resolution_importance': samples_per_ray // 2,
-        }
-
-        # modules
-        self.plane_axes = generate_planes()
-        self.decoder = OSGDecoder(n_features=triplane_dim)
-
-    def get_geometry_prediction(self, planes, sample_coordinates, flexicubes_indices):
-        plane_axes = self.plane_axes.to(planes.device)
-        sampled_features = sample_from_planes(
-            plane_axes, planes, sample_coordinates, padding_mode='zeros', box_warp=self.rendering_kwargs['box_warp'])
-
-        sdf, deformation, weight = self.decoder.get_geometry_prediction(sampled_features, flexicubes_indices)
-        return sdf, deformation, weight
-    
-    def get_texture_prediction(self, planes, sample_coordinates):
-        plane_axes = self.plane_axes.to(planes.device)
-        sampled_features = sample_from_planes(
-            plane_axes, planes, sample_coordinates, padding_mode='zeros', box_warp=self.rendering_kwargs['box_warp'])
-
-        rgb = self.decoder.get_texture_prediction(sampled_features)
-        return rgb

src/models/renderer/utils/__init__.py

@@ -1,9 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
-#
-# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
-# property and proprietary rights in and to this material, related
-# documentation and any modifications thereto. Any use, reproduction,
-# disclosure or distribution of this material and related documentation
-# without an express license agreement from NVIDIA CORPORATION or
-# its affiliates is strictly prohibited.

src/models/renderer/utils/math_utils.py

@@ -1,118 +0,0 @@
-# MIT License
-
-# Copyright (c) 2022 Petr Kellnhofer
-
-# 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.
-
-import torch
-
-def transform_vectors(matrix: torch.Tensor, vectors4: torch.Tensor) -> torch.Tensor:
-    """
-    Left-multiplies MxM @ NxM. Returns NxM.
-    """
-    res = torch.matmul(vectors4, matrix.T)
-    return res
-
-
-def normalize_vecs(vectors: torch.Tensor) -> torch.Tensor:
-    """
-    Normalize vector lengths.
-    """
-    return vectors / (torch.norm(vectors, dim=-1, keepdim=True))
-
-def torch_dot(x: torch.Tensor, y: torch.Tensor):
-    """
-    Dot product of two tensors.
-    """
-    return (x * y).sum(-1)
-
-
-def get_ray_limits_box(rays_o: torch.Tensor, rays_d: torch.Tensor, box_side_length):
-    """
-    Author: Petr Kellnhofer
-    Intersects rays with the [-1, 1] NDC volume.
-    Returns min and max distance of entry.
-    Returns -1 for no intersection.
-    https://www.scratchapixel.com/lessons/3d-basic-rendering/minimal-ray-tracer-rendering-simple-shapes/ray-box-intersection
-    """
-    o_shape = rays_o.shape
-    rays_o = rays_o.detach().reshape(-1, 3)
-    rays_d = rays_d.detach().reshape(-1, 3)
-
-
-    bb_min = [-1*(box_side_length/2), -1*(box_side_length/2), -1*(box_side_length/2)]
-    bb_max = [1*(box_side_length/2), 1*(box_side_length/2), 1*(box_side_length/2)]
-    bounds = torch.tensor([bb_min, bb_max], dtype=rays_o.dtype, device=rays_o.device)
-    is_valid = torch.ones(rays_o.shape[:-1], dtype=bool, device=rays_o.device)
-
-    # Precompute inverse for stability.
-    invdir = 1 / rays_d
-    sign = (invdir < 0).long()
-
-    # Intersect with YZ plane.
-    tmin = (bounds.index_select(0, sign[..., 0])[..., 0] - rays_o[..., 0]) * invdir[..., 0]
-    tmax = (bounds.index_select(0, 1 - sign[..., 0])[..., 0] - rays_o[..., 0]) * invdir[..., 0]
-
-    # Intersect with XZ plane.
-    tymin = (bounds.index_select(0, sign[..., 1])[..., 1] - rays_o[..., 1]) * invdir[..., 1]
-    tymax = (bounds.index_select(0, 1 - sign[..., 1])[..., 1] - rays_o[..., 1]) * invdir[..., 1]
-
-    # Resolve parallel rays.
-    is_valid[torch.logical_or(tmin > tymax, tymin > tmax)] = False
-
-    # Use the shortest intersection.
-    tmin = torch.max(tmin, tymin)
-    tmax = torch.min(tmax, tymax)
-
-    # Intersect with XY plane.
-    tzmin = (bounds.index_select(0, sign[..., 2])[..., 2] - rays_o[..., 2]) * invdir[..., 2]
-    tzmax = (bounds.index_select(0, 1 - sign[..., 2])[..., 2] - rays_o[..., 2]) * invdir[..., 2]
-
-    # Resolve parallel rays.
-    is_valid[torch.logical_or(tmin > tzmax, tzmin > tmax)] = False
-
-    # Use the shortest intersection.
-    tmin = torch.max(tmin, tzmin)
-    tmax = torch.min(tmax, tzmax)
-
-    # Mark invalid.
-    tmin[torch.logical_not(is_valid)] = -1
-    tmax[torch.logical_not(is_valid)] = -2
-
-    return tmin.reshape(*o_shape[:-1], 1), tmax.reshape(*o_shape[:-1], 1)
-
-
-def linspace(start: torch.Tensor, stop: torch.Tensor, num: int):
-    """
-    Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive.
-    Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch.
-    """
-    # create a tensor of 'num' steps from 0 to 1
-    steps = torch.arange(num, dtype=torch.float32, device=start.device) / (num - 1)
-
-    # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings
-    # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript
-    #   "cannot statically infer the expected size of a list in this contex", hence the code below
-    for i in range(start.ndim):
-        steps = steps.unsqueeze(-1)
-
-    # the output starts at 'start' and increments until 'stop' in each dimension
-    out = start[None] + steps * (stop - start)[None]
-
-    return out

src/models/renderer/utils/ray_marcher.py

@@ -1,72 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
-#
-# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
-# property and proprietary rights in and to this material, related
-# documentation and any modifications thereto. Any use, reproduction,
-# disclosure or distribution of this material and related documentation
-# without an express license agreement from NVIDIA CORPORATION or
-# its affiliates is strictly prohibited.
-#
-# Modified by Jiale Xu
-# The modifications are subject to the same license as the original.
-
-
-"""
-The ray marcher takes the raw output of the implicit representation and uses the volume rendering equation to produce composited colors and depths.
-Based off of the implementation in MipNeRF (this one doesn't do any cone tracing though!)
-"""
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class MipRayMarcher2(nn.Module):
-    def __init__(self, activation_factory):
-        super().__init__()
-        self.activation_factory = activation_factory
-
-    def run_forward(self, colors, densities, depths, rendering_options, normals=None):
-        dtype = colors.dtype
-        deltas = depths[:, :, 1:] - depths[:, :, :-1]
-        colors_mid = (colors[:, :, :-1] + colors[:, :, 1:]) / 2
-        densities_mid = (densities[:, :, :-1] + densities[:, :, 1:]) / 2
-        depths_mid = (depths[:, :, :-1] + depths[:, :, 1:]) / 2
-
-        # using factory mode for better usability
-        densities_mid = self.activation_factory(rendering_options)(densities_mid).to(dtype)
-
-        density_delta = densities_mid * deltas
-
-        alpha = 1 - torch.exp(-density_delta).to(dtype)
-
-        alpha_shifted = torch.cat([torch.ones_like(alpha[:, :, :1]), 1-alpha + 1e-10], -2)
-        weights = alpha * torch.cumprod(alpha_shifted, -2)[:, :, :-1]
-        weights = weights.to(dtype)
-
-        composite_rgb = torch.sum(weights * colors_mid, -2)
-        weight_total = weights.sum(2)
-        # composite_depth = torch.sum(weights * depths_mid, -2) / weight_total
-        composite_depth = torch.sum(weights * depths_mid, -2)
-
-        # clip the composite to min/max range of depths
-        composite_depth = torch.nan_to_num(composite_depth, float('inf')).to(dtype)
-        composite_depth = torch.clamp(composite_depth, torch.min(depths), torch.max(depths))
-
-        if rendering_options.get('white_back', False):
-            composite_rgb = composite_rgb + 1 - weight_total
-
-        # rendered value scale is 0-1, comment out original mipnerf scaling
-        # composite_rgb = composite_rgb * 2 - 1 # Scale to (-1, 1)
-
-        return composite_rgb, composite_depth, weights
-
-
-    def forward(self, colors, densities, depths, rendering_options, normals=None):
-        if normals is not None:
-            composite_rgb, composite_depth, composite_normals, weights = self.run_forward(colors, densities, depths, rendering_options, normals)
-            return composite_rgb, composite_depth, composite_normals, weights
-
-        composite_rgb, composite_depth, weights = self.run_forward(colors, densities, depths, rendering_options)
-        return composite_rgb, composite_depth, weights

src/models/renderer/utils/ray_sampler.py

@@ -1,141 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
-#
-# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
-# property and proprietary rights in and to this material, related
-# documentation and any modifications thereto. Any use, reproduction,
-# disclosure or distribution of this material and related documentation
-# without an express license agreement from NVIDIA CORPORATION or
-# its affiliates is strictly prohibited.
-#
-# Modified by Jiale Xu
-# The modifications are subject to the same license as the original.
-
-
-"""
-The ray sampler is a module that takes in camera matrices and resolution and batches of rays.
-Expects cam2world matrices that use the OpenCV camera coordinate system conventions.
-"""
-
-import torch
-
-class RaySampler(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.ray_origins_h, self.ray_directions, self.depths, self.image_coords, self.rendering_options = None, None, None, None, None
-
-
-    def forward(self, cam2world_matrix, intrinsics, render_size):
-        """
-        Create batches of rays and return origins and directions.
-
-        cam2world_matrix: (N, 4, 4)
-        intrinsics: (N, 3, 3)
-        render_size: int
-
-        ray_origins: (N, M, 3)
-        ray_dirs: (N, M, 2)
-        """
-
-        dtype = cam2world_matrix.dtype
-        device = cam2world_matrix.device
-        N, M = cam2world_matrix.shape[0], render_size**2
-        cam_locs_world = cam2world_matrix[:, :3, 3]
-        fx = intrinsics[:, 0, 0]
-        fy = intrinsics[:, 1, 1]
-        cx = intrinsics[:, 0, 2]
-        cy = intrinsics[:, 1, 2]
-        sk = intrinsics[:, 0, 1]
-
-        uv = torch.stack(torch.meshgrid(
-            torch.arange(render_size, dtype=dtype, device=device),
-            torch.arange(render_size, dtype=dtype, device=device),
-            indexing='ij',
-        ))
-        uv = uv.flip(0).reshape(2, -1).transpose(1, 0)
-        uv = uv.unsqueeze(0).repeat(cam2world_matrix.shape[0], 1, 1)
-
-        x_cam = uv[:, :, 0].view(N, -1) * (1./render_size) + (0.5/render_size)
-        y_cam = uv[:, :, 1].view(N, -1) * (1./render_size) + (0.5/render_size)
-        z_cam = torch.ones((N, M), dtype=dtype, device=device)
-
-        x_lift = (x_cam - cx.unsqueeze(-1) + cy.unsqueeze(-1)*sk.unsqueeze(-1)/fy.unsqueeze(-1) - sk.unsqueeze(-1)*y_cam/fy.unsqueeze(-1)) / fx.unsqueeze(-1) * z_cam
-        y_lift = (y_cam - cy.unsqueeze(-1)) / fy.unsqueeze(-1) * z_cam
-
-        cam_rel_points = torch.stack((x_lift, y_lift, z_cam, torch.ones_like(z_cam)), dim=-1).to(dtype)
-
-        _opencv2blender = torch.tensor([
-            [1, 0, 0, 0],
-            [0, -1, 0, 0],
-            [0, 0, -1, 0],
-            [0, 0, 0, 1],
-        ], dtype=dtype, device=device).unsqueeze(0).repeat(N, 1, 1)
-
-        cam2world_matrix = torch.bmm(cam2world_matrix, _opencv2blender)
-
-        world_rel_points = torch.bmm(cam2world_matrix, cam_rel_points.permute(0, 2, 1)).permute(0, 2, 1)[:, :, :3]
-
-        ray_dirs = world_rel_points - cam_locs_world[:, None, :]
-        ray_dirs = torch.nn.functional.normalize(ray_dirs, dim=2).to(dtype)
-
-        ray_origins = cam_locs_world.unsqueeze(1).repeat(1, ray_dirs.shape[1], 1)
-
-        return ray_origins, ray_dirs
-
-
-class OrthoRaySampler(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.ray_origins_h, self.ray_directions, self.depths, self.image_coords, self.rendering_options = None, None, None, None, None
-
-
-    def forward(self, cam2world_matrix, ortho_scale, render_size):
-        """
-        Create batches of rays and return origins and directions.
-
-        cam2world_matrix: (N, 4, 4)
-        ortho_scale: float
-        render_size: int
-
-        ray_origins: (N, M, 3)
-        ray_dirs: (N, M, 3)
-        """
-
-        N, M = cam2world_matrix.shape[0], render_size**2
-
-        uv = torch.stack(torch.meshgrid(
-            torch.arange(render_size, dtype=torch.float32, device=cam2world_matrix.device),
-            torch.arange(render_size, dtype=torch.float32, device=cam2world_matrix.device),
-            indexing='ij',
-        ))
-        uv = uv.flip(0).reshape(2, -1).transpose(1, 0)
-        uv = uv.unsqueeze(0).repeat(cam2world_matrix.shape[0], 1, 1)
-
-        x_cam = uv[:, :, 0].view(N, -1) * (1./render_size) + (0.5/render_size)
-        y_cam = uv[:, :, 1].view(N, -1) * (1./render_size) + (0.5/render_size)
-        z_cam = torch.zeros((N, M), device=cam2world_matrix.device)
-
-        x_lift = (x_cam - 0.5) * ortho_scale
-        y_lift = (y_cam - 0.5) * ortho_scale
-
-        cam_rel_points = torch.stack((x_lift, y_lift, z_cam, torch.ones_like(z_cam)), dim=-1)
-
-        _opencv2blender = torch.tensor([
-            [1, 0, 0, 0],
-            [0, -1, 0, 0],
-            [0, 0, -1, 0],
-            [0, 0, 0, 1],
-        ], dtype=torch.float32, device=cam2world_matrix.device).unsqueeze(0).repeat(N, 1, 1)
-
-        cam2world_matrix = torch.bmm(cam2world_matrix, _opencv2blender)
-
-        ray_origins = torch.bmm(cam2world_matrix, cam_rel_points.permute(0, 2, 1)).permute(0, 2, 1)[:, :, :3]
-
-        ray_dirs_cam = torch.stack([
-            torch.zeros((N, M), device=cam2world_matrix.device),
-            torch.zeros((N, M), device=cam2world_matrix.device),
-            torch.ones((N, M), device=cam2world_matrix.device),
-        ], dim=-1)
-        ray_dirs = torch.bmm(cam2world_matrix[:, :3, :3], ray_dirs_cam.permute(0, 2, 1)).permute(0, 2, 1)
-
-        return ray_origins, ray_dirs

src/models/renderer/utils/renderer.py

@@ -1,323 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
-#
-# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
-# property and proprietary rights in and to this material, related
-# documentation and any modifications thereto. Any use, reproduction,
-# disclosure or distribution of this material and related documentation
-# without an express license agreement from NVIDIA CORPORATION or
-# its affiliates is strictly prohibited.
-#
-# Modified by Jiale Xu
-# The modifications are subject to the same license as the original.
-
-
-"""
-The renderer is a module that takes in rays, decides where to sample along each
-ray, and computes pixel colors using the volume rendering equation.
-"""
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .ray_marcher import MipRayMarcher2
-from . import math_utils
-
-
-def generate_planes():
-    """
-    Defines planes by the three vectors that form the "axes" of the
-    plane. Should work with arbitrary number of planes and planes of
-    arbitrary orientation.
-
-    Bugfix reference: https://github.com/NVlabs/eg3d/issues/67
-    """
-    return torch.tensor([[[1, 0, 0],
-                            [0, 1, 0],
-                            [0, 0, 1]],
-                            [[1, 0, 0],
-                            [0, 0, 1],
-                            [0, 1, 0]],
-                            [[0, 0, 1],
-                            [0, 1, 0],
-                            [1, 0, 0]]], dtype=torch.float32)
-
-def project_onto_planes(planes, coordinates):
-    """
-    Does a projection of a 3D point onto a batch of 2D planes,
-    returning 2D plane coordinates.
-
-    Takes plane axes of shape n_planes, 3, 3
-    # Takes coordinates of shape N, M, 3
-    # returns projections of shape N*n_planes, M, 2
-    """
-    N, M, C = coordinates.shape
-    n_planes, _, _ = planes.shape
-    coordinates = coordinates.unsqueeze(1).expand(-1, n_planes, -1, -1).reshape(N*n_planes, M, 3)
-    inv_planes = torch.linalg.inv(planes).unsqueeze(0).expand(N, -1, -1, -1).reshape(N*n_planes, 3, 3)
-    projections = torch.bmm(coordinates, inv_planes)
-    return projections[..., :2]
-
-def sample_from_planes(plane_axes, plane_features, coordinates, mode='bilinear', padding_mode='zeros', box_warp=None):
-    assert padding_mode == 'zeros'
-    N, n_planes, C, H, W = plane_features.shape
-    _, M, _ = coordinates.shape
-    plane_features = plane_features.view(N*n_planes, C, H, W)
-    dtype = plane_features.dtype
-
-    coordinates = (2/box_warp) * coordinates # add specific box bounds
-
-    projected_coordinates = project_onto_planes(plane_axes, coordinates).unsqueeze(1)
-    output_features = torch.nn.functional.grid_sample(
-        plane_features, 
-        projected_coordinates.to(dtype), 
-        mode=mode, 
-        padding_mode=padding_mode, 
-        align_corners=False,
-    ).permute(0, 3, 2, 1).reshape(N, n_planes, M, C)
-    return output_features
-
-def sample_from_3dgrid(grid, coordinates):
-    """
-    Expects coordinates in shape (batch_size, num_points_per_batch, 3)
-    Expects grid in shape (1, channels, H, W, D)
-    (Also works if grid has batch size)
-    Returns sampled features of shape (batch_size, num_points_per_batch, feature_channels)
-    """
-    batch_size, n_coords, n_dims = coordinates.shape
-    sampled_features = torch.nn.functional.grid_sample(
-        grid.expand(batch_size, -1, -1, -1, -1),
-        coordinates.reshape(batch_size, 1, 1, -1, n_dims),
-        mode='bilinear', 
-        padding_mode='zeros', 
-        align_corners=False,
-    )
-    N, C, H, W, D = sampled_features.shape
-    sampled_features = sampled_features.permute(0, 4, 3, 2, 1).reshape(N, H*W*D, C)
-    return sampled_features
-
-class ImportanceRenderer(torch.nn.Module):
-    """
-    Modified original version to filter out-of-box samples as TensoRF does.
-    
-    Reference:
-    TensoRF: https://github.com/apchenstu/TensoRF/blob/main/models/tensorBase.py#L277
-    """
-    def __init__(self):
-        super().__init__()
-        self.activation_factory = self._build_activation_factory()
-        self.ray_marcher = MipRayMarcher2(self.activation_factory)
-        self.plane_axes = generate_planes()
-
-    def _build_activation_factory(self):
-        def activation_factory(options: dict):
-            if options['clamp_mode'] == 'softplus':
-                return lambda x: F.softplus(x - 1)  # activation bias of -1 makes things initialize better
-            else:
-                assert False, "Renderer only supports `clamp_mode`=`softplus`!"
-        return activation_factory
-
-    def _forward_pass(self, depths: torch.Tensor, ray_directions: torch.Tensor, ray_origins: torch.Tensor,
-                        planes: torch.Tensor, decoder: nn.Module, rendering_options: dict):
-        """
-        Additional filtering is applied to filter out-of-box samples.
-        Modifications made by Zexin He.
-        """
-
-        # context related variables
-        batch_size, num_rays, samples_per_ray, _ = depths.shape
-        device = depths.device
-
-        # define sample points with depths
-        sample_directions = ray_directions.unsqueeze(-2).expand(-1, -1, samples_per_ray, -1).reshape(batch_size, -1, 3)
-        sample_coordinates = (ray_origins.unsqueeze(-2) + depths * ray_directions.unsqueeze(-2)).reshape(batch_size, -1, 3)
-
-        # filter out-of-box samples
-        mask_inbox = \
-            (rendering_options['sampler_bbox_min'] <= sample_coordinates) & \
-                (sample_coordinates <= rendering_options['sampler_bbox_max'])
-        mask_inbox = mask_inbox.all(-1)
-
-        # forward model according to all samples
-        _out = self.run_model(planes, decoder, sample_coordinates, sample_directions, rendering_options)
-
-        # set out-of-box samples to zeros(rgb) & -inf(sigma)
-        SAFE_GUARD = 3
-        DATA_TYPE = _out['sigma'].dtype
-        colors_pass = torch.zeros(batch_size, num_rays * samples_per_ray, 3, device=device, dtype=DATA_TYPE)
-        densities_pass = torch.nan_to_num(torch.full((batch_size, num_rays * samples_per_ray, 1), -float('inf'), device=device, dtype=DATA_TYPE)) / SAFE_GUARD
-        colors_pass[mask_inbox], densities_pass[mask_inbox] = _out['rgb'][mask_inbox], _out['sigma'][mask_inbox]
-
-        # reshape back
-        colors_pass = colors_pass.reshape(batch_size, num_rays, samples_per_ray, colors_pass.shape[-1])
-        densities_pass = densities_pass.reshape(batch_size, num_rays, samples_per_ray, densities_pass.shape[-1])
-
-        return colors_pass, densities_pass
-
-    def forward(self, planes, decoder, ray_origins, ray_directions, rendering_options):
-        # self.plane_axes = self.plane_axes.to(ray_origins.device)
-
-        if rendering_options['ray_start'] == rendering_options['ray_end'] == 'auto':
-            ray_start, ray_end = math_utils.get_ray_limits_box(ray_origins, ray_directions, box_side_length=rendering_options['box_warp'])
-            is_ray_valid = ray_end > ray_start
-            if torch.any(is_ray_valid).item():
-                ray_start[~is_ray_valid] = ray_start[is_ray_valid].min()
-                ray_end[~is_ray_valid] = ray_start[is_ray_valid].max()
-            depths_coarse = self.sample_stratified(ray_origins, ray_start, ray_end, rendering_options['depth_resolution'], rendering_options['disparity_space_sampling'])
-        else:
-            # Create stratified depth samples
-            depths_coarse = self.sample_stratified(ray_origins, rendering_options['ray_start'], rendering_options['ray_end'], rendering_options['depth_resolution'], rendering_options['disparity_space_sampling'])
-
-        # Coarse Pass
-        colors_coarse, densities_coarse = self._forward_pass(
-            depths=depths_coarse, ray_directions=ray_directions, ray_origins=ray_origins,
-            planes=planes, decoder=decoder, rendering_options=rendering_options)
-
-        # Fine Pass
-        N_importance = rendering_options['depth_resolution_importance']
-        if N_importance > 0:
-            _, _, weights = self.ray_marcher(colors_coarse, densities_coarse, depths_coarse, rendering_options)
-
-            depths_fine = self.sample_importance(depths_coarse, weights, N_importance)
-
-            colors_fine, densities_fine = self._forward_pass(
-                depths=depths_fine, ray_directions=ray_directions, ray_origins=ray_origins,
-                planes=planes, decoder=decoder, rendering_options=rendering_options)
-
-            all_depths, all_colors, all_densities = self.unify_samples(depths_coarse, colors_coarse, densities_coarse,
-                                                                depths_fine, colors_fine, densities_fine)
-
-            rgb_final, depth_final, weights = self.ray_marcher(all_colors, all_densities, all_depths, rendering_options)
-        else:
-            rgb_final, depth_final, weights = self.ray_marcher(colors_coarse, densities_coarse, depths_coarse, rendering_options)
-
-        return rgb_final, depth_final, weights.sum(2)
-    
-    def run_model(self, planes, decoder, sample_coordinates, sample_directions, options):
-        plane_axes = self.plane_axes.to(planes.device)
-        sampled_features = sample_from_planes(plane_axes, planes, sample_coordinates, padding_mode='zeros', box_warp=options['box_warp'])
-
-        out = decoder(sampled_features, sample_directions)
-        if options.get('density_noise', 0) > 0:
-            out['sigma'] += torch.randn_like(out['sigma']) * options['density_noise']
-        return out
-
-    def run_model_activated(self, planes, decoder, sample_coordinates, sample_directions, options):
-        out = self.run_model(planes, decoder, sample_coordinates, sample_directions, options)
-        out['sigma'] = self.activation_factory(options)(out['sigma'])
-        return out
-
-    def sort_samples(self, all_depths, all_colors, all_densities):
-        _, indices = torch.sort(all_depths, dim=-2)
-        all_depths = torch.gather(all_depths, -2, indices)
-        all_colors = torch.gather(all_colors, -2, indices.expand(-1, -1, -1, all_colors.shape[-1]))
-        all_densities = torch.gather(all_densities, -2, indices.expand(-1, -1, -1, 1))
-        return all_depths, all_colors, all_densities
-
-    def unify_samples(self, depths1, colors1, densities1, depths2, colors2, densities2, normals1=None, normals2=None):
-        all_depths = torch.cat([depths1, depths2], dim = -2)
-        all_colors = torch.cat([colors1, colors2], dim = -2)
-        all_densities = torch.cat([densities1, densities2], dim = -2)
-
-        if normals1 is not None and normals2 is not None:
-            all_normals = torch.cat([normals1, normals2], dim = -2)
-        else:
-            all_normals = None
-
-        _, indices = torch.sort(all_depths, dim=-2)
-        all_depths = torch.gather(all_depths, -2, indices)
-        all_colors = torch.gather(all_colors, -2, indices.expand(-1, -1, -1, all_colors.shape[-1]))
-        all_densities = torch.gather(all_densities, -2, indices.expand(-1, -1, -1, 1))
-
-        if all_normals is not None: 
-            all_normals = torch.gather(all_normals, -2, indices.expand(-1, -1, -1, all_normals.shape[-1]))
-            return all_depths, all_colors, all_normals, all_densities
-
-        return all_depths, all_colors, all_densities
-
-    def sample_stratified(self, ray_origins, ray_start, ray_end, depth_resolution, disparity_space_sampling=False):
-        """
-        Return depths of approximately uniformly spaced samples along rays.
-        """
-        N, M, _ = ray_origins.shape
-        if disparity_space_sampling:
-            depths_coarse = torch.linspace(0,
-                                    1,
-                                    depth_resolution,
-                                    device=ray_origins.device).reshape(1, 1, depth_resolution, 1).repeat(N, M, 1, 1)
-            depth_delta = 1/(depth_resolution - 1)
-            depths_coarse += torch.rand_like(depths_coarse) * depth_delta
-            depths_coarse = 1./(1./ray_start * (1. - depths_coarse) + 1./ray_end * depths_coarse)
-        else:
-            if type(ray_start) == torch.Tensor:
-                depths_coarse = math_utils.linspace(ray_start, ray_end, depth_resolution).permute(1,2,0,3)
-                depth_delta = (ray_end - ray_start) / (depth_resolution - 1)
-                depths_coarse += torch.rand_like(depths_coarse) * depth_delta[..., None]
-            else:
-                depths_coarse = torch.linspace(ray_start, ray_end, depth_resolution, device=ray_origins.device).reshape(1, 1, depth_resolution, 1).repeat(N, M, 1, 1)
-                depth_delta = (ray_end - ray_start)/(depth_resolution - 1)
-                depths_coarse += torch.rand_like(depths_coarse) * depth_delta
-
-        return depths_coarse
-
-    def sample_importance(self, z_vals, weights, N_importance):
-        """
-        Return depths of importance sampled points along rays. See NeRF importance sampling for more.
-        """
-        with torch.no_grad():
-            batch_size, num_rays, samples_per_ray, _ = z_vals.shape
-
-            z_vals = z_vals.reshape(batch_size * num_rays, samples_per_ray)
-            weights = weights.reshape(batch_size * num_rays, -1) # -1 to account for loss of 1 sample in MipRayMarcher
-
-            # smooth weights
-            weights = torch.nn.functional.max_pool1d(weights.unsqueeze(1), 2, 1, padding=1)
-            weights = torch.nn.functional.avg_pool1d(weights, 2, 1).squeeze()
-            weights = weights + 0.01
-
-            z_vals_mid = 0.5 * (z_vals[: ,:-1] + z_vals[: ,1:])
-            importance_z_vals = self.sample_pdf(z_vals_mid, weights[:, 1:-1],
-                                             N_importance).detach().reshape(batch_size, num_rays, N_importance, 1)
-        return importance_z_vals
-
-    def sample_pdf(self, bins, weights, N_importance, det=False, eps=1e-5):
-        """
-        Sample @N_importance samples from @bins with distribution defined by @weights.
-        Inputs:
-            bins: (N_rays, N_samples_+1) where N_samples_ is "the number of coarse samples per ray - 2"
-            weights: (N_rays, N_samples_)
-            N_importance: the number of samples to draw from the distribution
-            det: deterministic or not
-            eps: a small number to prevent division by zero
-        Outputs:
-            samples: the sampled samples
-        """
-        N_rays, N_samples_ = weights.shape
-        weights = weights + eps # prevent division by zero (don't do inplace op!)
-        pdf = weights / torch.sum(weights, -1, keepdim=True) # (N_rays, N_samples_)
-        cdf = torch.cumsum(pdf, -1) # (N_rays, N_samples), cumulative distribution function
-        cdf = torch.cat([torch.zeros_like(cdf[: ,:1]), cdf], -1)  # (N_rays, N_samples_+1)
-                                                                   # padded to 0~1 inclusive
-
-        if det:
-            u = torch.linspace(0, 1, N_importance, device=bins.device)
-            u = u.expand(N_rays, N_importance)
-        else:
-            u = torch.rand(N_rays, N_importance, device=bins.device)
-        u = u.contiguous()
-
-        inds = torch.searchsorted(cdf, u, right=True)
-        below = torch.clamp_min(inds-1, 0)
-        above = torch.clamp_max(inds, N_samples_)
-
-        inds_sampled = torch.stack([below, above], -1).view(N_rays, 2*N_importance)
-        cdf_g = torch.gather(cdf, 1, inds_sampled).view(N_rays, N_importance, 2)
-        bins_g = torch.gather(bins, 1, inds_sampled).view(N_rays, N_importance, 2)
-
-        denom = cdf_g[...,1]-cdf_g[...,0]
-        denom[denom<eps] = 1 # denom equals 0 means a bin has weight 0, in which case it will not be sampled
-                             # anyway, therefore any value for it is fine (set to 1 here)
-
-        samples = bins_g[...,0] + (u-cdf_g[...,0])/denom * (bins_g[...,1]-bins_g[...,0])
-        return samples

src/utils/camera_util.py

@@ -1,111 +0,0 @@
-import torch
-import torch.nn.functional as F
-import numpy as np
-
-
-def pad_camera_extrinsics_4x4(extrinsics):
-    if extrinsics.shape[-2] == 4:
-        return extrinsics
-    padding = torch.tensor([[0, 0, 0, 1]]).to(extrinsics)
-    if extrinsics.ndim == 3:
-        padding = padding.unsqueeze(0).repeat(extrinsics.shape[0], 1, 1)
-    extrinsics = torch.cat([extrinsics, padding], dim=-2)
-    return extrinsics
-
-
-def center_looking_at_camera_pose(camera_position: torch.Tensor, look_at: torch.Tensor = None, up_world: torch.Tensor = None):
-    """
-    Create OpenGL camera extrinsics from camera locations and look-at position.
-
-    camera_position: (M, 3) or (3,)
-    look_at: (3)
-    up_world: (3)
-    return: (M, 3, 4) or (3, 4)
-    """
-    # by default, looking at the origin and world up is z-axis
-    if look_at is None:
-        look_at = torch.tensor([0, 0, 0], dtype=torch.float32)
-    if up_world is None:
-        up_world = torch.tensor([0, 0, 1], dtype=torch.float32)
-    if camera_position.ndim == 2:
-        look_at = look_at.unsqueeze(0).repeat(camera_position.shape[0], 1)
-        up_world = up_world.unsqueeze(0).repeat(camera_position.shape[0], 1)
-
-    # OpenGL camera: z-backward, x-right, y-up
-    z_axis = camera_position - look_at
-    z_axis = F.normalize(z_axis, dim=-1).float()
-    x_axis = torch.linalg.cross(up_world, z_axis, dim=-1)
-    x_axis = F.normalize(x_axis, dim=-1).float()
-    y_axis = torch.linalg.cross(z_axis, x_axis, dim=-1)
-    y_axis = F.normalize(y_axis, dim=-1).float()
-
-    extrinsics = torch.stack([x_axis, y_axis, z_axis, camera_position], dim=-1)
-    extrinsics = pad_camera_extrinsics_4x4(extrinsics)
-    return extrinsics
-
-
-def spherical_camera_pose(azimuths: np.ndarray, elevations: np.ndarray, radius=2.5):
-    azimuths = np.deg2rad(azimuths)
-    elevations = np.deg2rad(elevations)
-
-    xs = radius * np.cos(elevations) * np.cos(azimuths)
-    ys = radius * np.cos(elevations) * np.sin(azimuths)
-    zs = radius * np.sin(elevations)
-
-    cam_locations = np.stack([xs, ys, zs], axis=-1)
-    cam_locations = torch.from_numpy(cam_locations).float()
-
-    c2ws = center_looking_at_camera_pose(cam_locations)
-    return c2ws
-
-
-def get_circular_camera_poses(M=120, radius=2.5, elevation=30.0):
-    # M: number of circular views
-    # radius: camera dist to center
-    # elevation: elevation degrees of the camera
-    # return: (M, 4, 4)
-    assert M > 0 and radius > 0
-
-    elevation = np.deg2rad(elevation)
-
-    camera_positions = []
-    for i in range(M):
-        azimuth = 2 * np.pi * i / M
-        x = radius * np.cos(elevation) * np.cos(azimuth)
-        y = radius * np.cos(elevation) * np.sin(azimuth)
-        z = radius * np.sin(elevation)
-        camera_positions.append([x, y, z])
-    camera_positions = np.array(camera_positions)
-    camera_positions = torch.from_numpy(camera_positions).float()
-    extrinsics = center_looking_at_camera_pose(camera_positions)
-    return extrinsics
-
-
-def FOV_to_intrinsics(fov, device='cpu'):
-    """
-    Creates a 3x3 camera intrinsics matrix from the camera field of view, specified in degrees.
-    Note the intrinsics are returned as normalized by image size, rather than in pixel units.
-    Assumes principal point is at image center.
-    """
-    focal_length = 0.5 / np.tan(np.deg2rad(fov) * 0.5)
-    intrinsics = torch.tensor([[focal_length, 0, 0.5], [0, focal_length, 0.5], [0, 0, 1]], device=device)
-    return intrinsics
-
-
-def get_zero123plus_input_cameras(batch_size=1, radius=4.0, fov=30.0):
-    """
-    Get the input camera parameters.
-    """
-    azimuths = np.array([30, 90, 150, 210, 270, 330]).astype(float)
-    elevations = np.array([20, -10, 20, -10, 20, -10]).astype(float)
-    
-    c2ws = spherical_camera_pose(azimuths, elevations, radius)
-    c2ws = c2ws.float().flatten(-2)
-
-    Ks = FOV_to_intrinsics(fov).unsqueeze(0).repeat(6, 1, 1).float().flatten(-2)
-
-    extrinsics = c2ws[:, :12]
-    intrinsics = torch.stack([Ks[:, 0], Ks[:, 4], Ks[:, 2], Ks[:, 5]], dim=-1)
-    cameras = torch.cat([extrinsics, intrinsics], dim=-1)
-
-    return cameras.unsqueeze(0).repeat(batch_size, 1, 1)

src/utils/infer_util.py

@@ -1,84 +0,0 @@
-import os
-import imageio
-import rembg
-import torch
-import numpy as np
-import PIL.Image
-from PIL import Image
-from typing import Any
-
-
-def remove_background(image: PIL.Image.Image,
-    rembg_session: Any = None,
-    force: bool = False,
-    **rembg_kwargs,
-) -> PIL.Image.Image:
-    do_remove = True
-    if image.mode == "RGBA" and image.getextrema()[3][0] < 255:
-        do_remove = False
-    do_remove = do_remove or force
-    if do_remove:
-        image = rembg.remove(image, session=rembg_session, **rembg_kwargs)
-    return image
-
-
-def resize_foreground(
-    image: PIL.Image.Image,
-    ratio: float,
-) -> PIL.Image.Image:
-    image = np.array(image)
-    assert image.shape[-1] == 4
-    alpha = np.where(image[..., 3] > 0)
-    y1, y2, x1, x2 = (
-        alpha[0].min(),
-        alpha[0].max(),
-        alpha[1].min(),
-        alpha[1].max(),
-    )
-    # crop the foreground
-    fg = image[y1:y2, x1:x2]
-    # pad to square
-    size = max(fg.shape[0], fg.shape[1])
-    ph0, pw0 = (size - fg.shape[0]) // 2, (size - fg.shape[1]) // 2
-    ph1, pw1 = size - fg.shape[0] - ph0, size - fg.shape[1] - pw0
-    new_image = np.pad(
-        fg,
-        ((ph0, ph1), (pw0, pw1), (0, 0)),
-        mode="constant",
-        constant_values=((0, 0), (0, 0), (0, 0)),
-    )
-
-    # compute padding according to the ratio
-    new_size = int(new_image.shape[0] / ratio)
-    # pad to size, double side
-    ph0, pw0 = (new_size - size) // 2, (new_size - size) // 2
-    ph1, pw1 = new_size - size - ph0, new_size - size - pw0
-    new_image = np.pad(
-        new_image,
-        ((ph0, ph1), (pw0, pw1), (0, 0)),
-        mode="constant",
-        constant_values=((0, 0), (0, 0), (0, 0)),
-    )
-    new_image = PIL.Image.fromarray(new_image)
-    return new_image
-
-
-def images_to_video(
-    images: torch.Tensor, 
-    output_path: str, 
-    fps: int = 30,
-) -> None:
-    # images: (N, C, H, W)
-    video_dir = os.path.dirname(output_path)
-    video_name = os.path.basename(output_path)
-    os.makedirs(video_dir, exist_ok=True)
-
-    frames = []
-    for i in range(len(images)):
-        frame = (images[i].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)
-        assert frame.shape[0] == images.shape[2] and frame.shape[1] == images.shape[3], \
-            f"Frame shape mismatch: {frame.shape} vs {images.shape}"
-        assert frame.min() >= 0 and frame.max() <= 255, \
-            f"Frame value out of range: {frame.min()} ~ {frame.max()}"
-        frames.append(frame)
-    imageio.mimwrite(output_path, np.stack(frames), fps=fps, quality=10)

src/utils/mesh_util.py

@@ -1,181 +0,0 @@
-# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION & AFFILIATES 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 & AFFILIATES is strictly prohibited.
-
-import torch
-import xatlas
-import trimesh
-import cv2
-import numpy as np
-import nvdiffrast.torch as dr
-from PIL import Image
-
-
-def save_obj(pointnp_px3, facenp_fx3, colornp_px3, fpath):
-
-    pointnp_px3 = pointnp_px3 @ np.array([[1, 0, 0], [0, 1, 0], [0, 0, -1]])
-    facenp_fx3 = facenp_fx3[:, [2, 1, 0]]
-
-    mesh = trimesh.Trimesh(
-        vertices=pointnp_px3, 
-        faces=facenp_fx3, 
-        vertex_colors=colornp_px3,
-    )
-    mesh.export(fpath, 'obj')
-
-
-def save_glb(pointnp_px3, facenp_fx3, colornp_px3, fpath):
-
-    pointnp_px3 = pointnp_px3 @ np.array([[-1, 0, 0], [0, 1, 0], [0, 0, -1]])
-
-    mesh = trimesh.Trimesh(
-        vertices=pointnp_px3, 
-        faces=facenp_fx3, 
-        vertex_colors=colornp_px3,
-    )
-    mesh.export(fpath, 'glb')
-
-
-def save_obj_with_mtl(pointnp_px3, tcoords_px2, facenp_fx3, facetex_fx3, texmap_hxwx3, fname):
-    import os
-    fol, na = os.path.split(fname)
-    na, _ = os.path.splitext(na)
-
-    matname = '%s/%s.mtl' % (fol, na)
-    fid = open(matname, 'w')
-    fid.write('newmtl material_0\n')
-    fid.write('Kd 1 1 1\n')
-    fid.write('Ka 0 0 0\n')
-    fid.write('Ks 0.4 0.4 0.4\n')
-    fid.write('Ns 10\n')
-    fid.write('illum 2\n')
-    fid.write('map_Kd %s.png\n' % na)
-    fid.close()
-    ####
-
-    fid = open(fname, 'w')
-    fid.write('mtllib %s.mtl\n' % na)
-
-    for pidx, p in enumerate(pointnp_px3):
-        pp = p
-        fid.write('v %f %f %f\n' % (pp[0], pp[1], pp[2]))
-
-    for pidx, p in enumerate(tcoords_px2):
-        pp = p
-        fid.write('vt %f %f\n' % (pp[0], pp[1]))
-
-    fid.write('usemtl material_0\n')
-    for i, f in enumerate(facenp_fx3):
-        f1 = f + 1
-        f2 = facetex_fx3[i] + 1
-        fid.write('f %d/%d %d/%d %d/%d\n' % (f1[0], f2[0], f1[1], f2[1], f1[2], f2[2]))
-    fid.close()
-
-    # save texture map
-    lo, hi = 0, 1
-    img = np.asarray(texmap_hxwx3, dtype=np.float32)
-    img = (img - lo) * (255 / (hi - lo))
-    img = img.clip(0, 255)
-    mask = np.sum(img.astype(np.float32), axis=-1, keepdims=True)
-    mask = (mask <= 3.0).astype(np.float32)
-    kernel = np.ones((3, 3), 'uint8')
-    dilate_img = cv2.dilate(img, kernel, iterations=1)
-    img = img * (1 - mask) + dilate_img * mask
-    img = img.clip(0, 255).astype(np.uint8)
-    Image.fromarray(np.ascontiguousarray(img[::-1, :, :]), 'RGB').save(f'{fol}/{na}.png')
-
-
-def loadobj(meshfile):
-    v = []
-    f = []
-    meshfp = open(meshfile, 'r')
-    for line in meshfp.readlines():
-        data = line.strip().split(' ')
-        data = [da for da in data if len(da) > 0]
-        if len(data) != 4:
-            continue
-        if data[0] == 'v':
-            v.append([float(d) for d in data[1:]])
-        if data[0] == 'f':
-            data = [da.split('/')[0] for da in data]
-            f.append([int(d) for d in data[1:]])
-    meshfp.close()
-
-    # torch need int64
-    facenp_fx3 = np.array(f, dtype=np.int64) - 1
-    pointnp_px3 = np.array(v, dtype=np.float32)
-    return pointnp_px3, facenp_fx3
-
-
-def loadobjtex(meshfile):
-    v = []
-    vt = []
-    f = []
-    ft = []
-    meshfp = open(meshfile, 'r')
-    for line in meshfp.readlines():
-        data = line.strip().split(' ')
-        data = [da for da in data if len(da) > 0]
-        if not ((len(data) == 3) or (len(data) == 4) or (len(data) == 5)):
-            continue
-        if data[0] == 'v':
-            assert len(data) == 4
-
-            v.append([float(d) for d in data[1:]])
-        if data[0] == 'vt':
-            if len(data) == 3 or len(data) == 4:
-                vt.append([float(d) for d in data[1:3]])
-        if data[0] == 'f':
-            data = [da.split('/') for da in data]
-            if len(data) == 4:
-                f.append([int(d[0]) for d in data[1:]])
-                ft.append([int(d[1]) for d in data[1:]])
-            elif len(data) == 5:
-                idx1 = [1, 2, 3]
-                data1 = [data[i] for i in idx1]
-                f.append([int(d[0]) for d in data1])
-                ft.append([int(d[1]) for d in data1])
-                idx2 = [1, 3, 4]
-                data2 = [data[i] for i in idx2]
-                f.append([int(d[0]) for d in data2])
-                ft.append([int(d[1]) for d in data2])
-    meshfp.close()
-
-    # torch need int64
-    facenp_fx3 = np.array(f, dtype=np.int64) - 1
-    ftnp_fx3 = np.array(ft, dtype=np.int64) - 1
-    pointnp_px3 = np.array(v, dtype=np.float32)
-    uvs = np.array(vt, dtype=np.float32)
-    return pointnp_px3, facenp_fx3, uvs, ftnp_fx3
-
-
-# ==============================================================================================
-def interpolate(attr, rast, attr_idx, rast_db=None):
-    return dr.interpolate(attr.contiguous(), rast, attr_idx, rast_db=rast_db, diff_attrs=None if rast_db is None else 'all')
-
-
-def xatlas_uvmap(ctx, mesh_v, mesh_pos_idx, resolution):
-    vmapping, indices, uvs = xatlas.parametrize(mesh_v.detach().cpu().numpy(), mesh_pos_idx.detach().cpu().numpy())
-
-    # Convert to tensors
-    indices_int64 = indices.astype(np.uint64, casting='same_kind').view(np.int64)
-
-    uvs = torch.tensor(uvs, dtype=torch.float32, device=mesh_v.device)
-    mesh_tex_idx = torch.tensor(indices_int64, dtype=torch.int64, device=mesh_v.device)
-    # mesh_v_tex. ture
-    uv_clip = uvs[None, ...] * 2.0 - 1.0
-
-    # pad to four component coordinate
-    uv_clip4 = torch.cat((uv_clip, torch.zeros_like(uv_clip[..., 0:1]), torch.ones_like(uv_clip[..., 0:1])), dim=-1)
-
-    # rasterize
-    rast, _ = dr.rasterize(ctx, uv_clip4, mesh_tex_idx.int(), (resolution, resolution))
-
-    # Interpolate world space position
-    gb_pos, _ = interpolate(mesh_v[None, ...], rast, mesh_pos_idx.int())
-    mask = rast[..., 3:4] > 0
-    return uvs, mesh_tex_idx, gb_pos, mask

src/utils/train_util.py

@@ -1,26 +0,0 @@
-import importlib
-
-
-def count_params(model, verbose=False):
-    total_params = sum(p.numel() for p in model.parameters())
-    if verbose:
-        print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
-    return total_params
-
-
-def instantiate_from_config(config):
-    if not "target" in config:
-        if config == '__is_first_stage__':
-            return None
-        elif config == "__is_unconditional__":
-            return None
-        raise KeyError("Expected key `target` to instantiate.")
-    return get_obj_from_str(config["target"])(**config.get("params", dict()))
-
-
-def get_obj_from_str(string, reload=False):
-    module, cls = string.rsplit(".", 1)
-    if reload:
-        module_imp = importlib.import_module(module)
-        importlib.reload(module_imp)
-    return getattr(importlib.import_module(module, package=None), cls)

zero123plus/pipeline.py

@@ -1,406 +0,0 @@
-from typing import Any, Dict, Optional
-from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.schedulers import KarrasDiffusionSchedulers
-
-import numpy
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint
-import torch.distributed
-import transformers
-from collections import OrderedDict
-from PIL import Image
-from torchvision import transforms
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
-
-import diffusers
-from diffusers import (
-    AutoencoderKL,
-    DDPMScheduler,
-    DiffusionPipeline,
-    EulerAncestralDiscreteScheduler,
-    UNet2DConditionModel,
-    ImagePipelineOutput
-)
-from diffusers.image_processor import VaeImageProcessor
-from diffusers.models.attention_processor import Attention, AttnProcessor, XFormersAttnProcessor, AttnProcessor2_0
-from diffusers.utils.import_utils import is_xformers_available
-
-
-def to_rgb_image(maybe_rgba: Image.Image):
-    if maybe_rgba.mode == 'RGB':
-        return maybe_rgba
-    elif maybe_rgba.mode == 'RGBA':
-        rgba = maybe_rgba
-        img = numpy.random.randint(255, 256, size=[rgba.size[1], rgba.size[0], 3], dtype=numpy.uint8)
-        img = Image.fromarray(img, 'RGB')
-        img.paste(rgba, mask=rgba.getchannel('A'))
-        return img
-    else:
-        raise ValueError("Unsupported image type.", maybe_rgba.mode)
-
-
-class ReferenceOnlyAttnProc(torch.nn.Module):
-    def __init__(
-        self,
-        chained_proc,
-        enabled=False,
-        name=None
-    ) -> None:
-        super().__init__()
-        self.enabled = enabled
-        self.chained_proc = chained_proc
-        self.name = name
-
-    def __call__(
-        self, attn: Attention, hidden_states, encoder_hidden_states=None, attention_mask=None,
-        mode="w", ref_dict: dict = None, is_cfg_guidance = False
-    ) -> Any:
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        if self.enabled and is_cfg_guidance:
-            res0 = self.chained_proc(attn, hidden_states[:1], encoder_hidden_states[:1], attention_mask)
-            hidden_states = hidden_states[1:]
-            encoder_hidden_states = encoder_hidden_states[1:]
-        if self.enabled:
-            if mode == 'w':
-                ref_dict[self.name] = encoder_hidden_states
-            elif mode == 'r':
-                encoder_hidden_states = torch.cat([encoder_hidden_states, ref_dict.pop(self.name)], dim=1)
-            elif mode == 'm':
-                encoder_hidden_states = torch.cat([encoder_hidden_states, ref_dict[self.name]], dim=1)
-            else:
-                assert False, mode
-        res = self.chained_proc(attn, hidden_states, encoder_hidden_states, attention_mask)
-        if self.enabled and is_cfg_guidance:
-            res = torch.cat([res0, res])
-        return res
-
-
-class RefOnlyNoisedUNet(torch.nn.Module):
-    def __init__(self, unet: UNet2DConditionModel, train_sched: DDPMScheduler, val_sched: EulerAncestralDiscreteScheduler) -> None:
-        super().__init__()
-        self.unet = unet
-        self.train_sched = train_sched
-        self.val_sched = val_sched
-
-        unet_lora_attn_procs = dict()
-        for name, _ in unet.attn_processors.items():
-            if torch.__version__ >= '2.0':
-                default_attn_proc = AttnProcessor2_0()
-            elif is_xformers_available():
-                default_attn_proc = XFormersAttnProcessor()
-            else:
-                default_attn_proc = AttnProcessor()
-            unet_lora_attn_procs[name] = ReferenceOnlyAttnProc(
-                default_attn_proc, enabled=name.endswith("attn1.processor"), name=name
-            )
-        unet.set_attn_processor(unet_lora_attn_procs)
-
-    def __getattr__(self, name: str):
-        try:
-            return super().__getattr__(name)
-        except AttributeError:
-            return getattr(self.unet, name)
-
-    def forward_cond(self, noisy_cond_lat, timestep, encoder_hidden_states, class_labels, ref_dict, is_cfg_guidance, **kwargs):
-        if is_cfg_guidance:
-            encoder_hidden_states = encoder_hidden_states[1:]
-            class_labels = class_labels[1:]
-        self.unet(
-            noisy_cond_lat, timestep,
-            encoder_hidden_states=encoder_hidden_states,
-            class_labels=class_labels,
-            cross_attention_kwargs=dict(mode="w", ref_dict=ref_dict),
-            **kwargs
-        )
-
-    def forward(
-        self, sample, timestep, encoder_hidden_states, class_labels=None,
-        *args, cross_attention_kwargs,
-        down_block_res_samples=None, mid_block_res_sample=None,
-        **kwargs
-    ):
-        cond_lat = cross_attention_kwargs['cond_lat']
-        is_cfg_guidance = cross_attention_kwargs.get('is_cfg_guidance', False)
-        noise = torch.randn_like(cond_lat)
-        if self.training:
-            noisy_cond_lat = self.train_sched.add_noise(cond_lat, noise, timestep)
-            noisy_cond_lat = self.train_sched.scale_model_input(noisy_cond_lat, timestep)
-        else:
-            noisy_cond_lat = self.val_sched.add_noise(cond_lat, noise, timestep.reshape(-1))
-            noisy_cond_lat = self.val_sched.scale_model_input(noisy_cond_lat, timestep.reshape(-1))
-        ref_dict = {}
-        self.forward_cond(
-            noisy_cond_lat, timestep,
-            encoder_hidden_states, class_labels,
-            ref_dict, is_cfg_guidance, **kwargs
-        )
-        weight_dtype = self.unet.dtype
-        return self.unet(
-            sample, timestep,
-            encoder_hidden_states, *args,
-            class_labels=class_labels,
-            cross_attention_kwargs=dict(mode="r", ref_dict=ref_dict, is_cfg_guidance=is_cfg_guidance),
-            down_block_additional_residuals=[
-                sample.to(dtype=weight_dtype) for sample in down_block_res_samples
-            ] if down_block_res_samples is not None else None,
-            mid_block_additional_residual=(
-                mid_block_res_sample.to(dtype=weight_dtype)
-                if mid_block_res_sample is not None else None
-            ),
-            **kwargs
-        )
-
-
-def scale_latents(latents):
-    latents = (latents - 0.22) * 0.75
-    return latents
-
-
-def unscale_latents(latents):
-    latents = latents / 0.75 + 0.22
-    return latents
-
-
-def scale_image(image):
-    image = image * 0.5 / 0.8
-    return image
-
-
-def unscale_image(image):
-    image = image / 0.5 * 0.8
-    return image
-
-
-class DepthControlUNet(torch.nn.Module):
-    def __init__(self, unet: RefOnlyNoisedUNet, controlnet: Optional[diffusers.ControlNetModel] = None, conditioning_scale=1.0) -> None:
-        super().__init__()
-        self.unet = unet
-        if controlnet is None:
-            self.controlnet = diffusers.ControlNetModel.from_unet(unet.unet)
-        else:
-            self.controlnet = controlnet
-        DefaultAttnProc = AttnProcessor2_0
-        if is_xformers_available():
-            DefaultAttnProc = XFormersAttnProcessor
-        self.controlnet.set_attn_processor(DefaultAttnProc())
-        self.conditioning_scale = conditioning_scale
-
-    def __getattr__(self, name: str):
-        try:
-            return super().__getattr__(name)
-        except AttributeError:
-            return getattr(self.unet, name)
-
-    def forward(self, sample, timestep, encoder_hidden_states, class_labels=None, *args, cross_attention_kwargs: dict, **kwargs):
-        cross_attention_kwargs = dict(cross_attention_kwargs)
-        control_depth = cross_attention_kwargs.pop('control_depth')
-        down_block_res_samples, mid_block_res_sample = self.controlnet(
-            sample,
-            timestep,
-            encoder_hidden_states=encoder_hidden_states,
-            controlnet_cond=control_depth,
-            conditioning_scale=self.conditioning_scale,
-            return_dict=False,
-        )
-        return self.unet(
-            sample,
-            timestep,
-            encoder_hidden_states=encoder_hidden_states,
-            down_block_res_samples=down_block_res_samples,
-            mid_block_res_sample=mid_block_res_sample,
-            cross_attention_kwargs=cross_attention_kwargs
-        )
-
-
-class ModuleListDict(torch.nn.Module):
-    def __init__(self, procs: dict) -> None:
-        super().__init__()
-        self.keys = sorted(procs.keys())
-        self.values = torch.nn.ModuleList(procs[k] for k in self.keys)
-
-    def __getitem__(self, key):
-        return self.values[self.keys.index(key)]
-
-
-class SuperNet(torch.nn.Module):
-    def __init__(self, state_dict: Dict[str, torch.Tensor]):
-        super().__init__()
-        state_dict = OrderedDict((k, state_dict[k]) for k in sorted(state_dict.keys()))
-        self.layers = torch.nn.ModuleList(state_dict.values())
-        self.mapping = dict(enumerate(state_dict.keys()))
-        self.rev_mapping = {v: k for k, v in enumerate(state_dict.keys())}
-
-        # .processor for unet, .self_attn for text encoder
-        self.split_keys = [".processor", ".self_attn"]
-
-        # we add a hook to state_dict() and load_state_dict() so that the
-        # naming fits with `unet.attn_processors`
-        def map_to(module, state_dict, *args, **kwargs):
-            new_state_dict = {}
-            for key, value in state_dict.items():
-                num = int(key.split(".")[1])  # 0 is always "layers"
-                new_key = key.replace(f"layers.{num}", module.mapping[num])
-                new_state_dict[new_key] = value
-
-            return new_state_dict
-
-        def remap_key(key, state_dict):
-            for k in self.split_keys:
-                if k in key:
-                    return key.split(k)[0] + k
-            return key.split('.')[0]
-
-        def map_from(module, state_dict, *args, **kwargs):
-            all_keys = list(state_dict.keys())
-            for key in all_keys:
-                replace_key = remap_key(key, state_dict)
-                new_key = key.replace(replace_key, f"layers.{module.rev_mapping[replace_key]}")
-                state_dict[new_key] = state_dict[key]
-                del state_dict[key]
-
-        self._register_state_dict_hook(map_to)
-        self._register_load_state_dict_pre_hook(map_from, with_module=True)
-
-
-class Zero123PlusPipeline(diffusers.StableDiffusionPipeline):
-    tokenizer: transformers.CLIPTokenizer
-    text_encoder: transformers.CLIPTextModel
-    vision_encoder: transformers.CLIPVisionModelWithProjection
-
-    feature_extractor_clip: transformers.CLIPImageProcessor
-    unet: UNet2DConditionModel
-    scheduler: diffusers.schedulers.KarrasDiffusionSchedulers
-
-    vae: AutoencoderKL
-    ramping: nn.Linear
-
-    feature_extractor_vae: transformers.CLIPImageProcessor
-
-    depth_transforms_multi = transforms.Compose([
-        transforms.ToTensor(),
-        transforms.Normalize([0.5], [0.5])
-    ])
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: KarrasDiffusionSchedulers,
-        vision_encoder: transformers.CLIPVisionModelWithProjection,
-        feature_extractor_clip: CLIPImageProcessor, 
-        feature_extractor_vae: CLIPImageProcessor,
-        ramping_coefficients: Optional[list] = None,
-        safety_checker=None,
-    ):
-        DiffusionPipeline.__init__(self)
-
-        self.register_modules(
-            vae=vae, text_encoder=text_encoder, tokenizer=tokenizer,
-            unet=unet, scheduler=scheduler, safety_checker=None,
-            vision_encoder=vision_encoder,
-            feature_extractor_clip=feature_extractor_clip,
-            feature_extractor_vae=feature_extractor_vae
-        )
-        self.register_to_config(ramping_coefficients=ramping_coefficients)
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-
-    def prepare(self):
-        train_sched = DDPMScheduler.from_config(self.scheduler.config)
-        if isinstance(self.unet, UNet2DConditionModel):
-            self.unet = RefOnlyNoisedUNet(self.unet, train_sched, self.scheduler).eval()
-
-    def add_controlnet(self, controlnet: Optional[diffusers.ControlNetModel] = None, conditioning_scale=1.0):
-        self.prepare()
-        self.unet = DepthControlUNet(self.unet, controlnet, conditioning_scale)
-        return SuperNet(OrderedDict([('controlnet', self.unet.controlnet)]))
-
-    def encode_condition_image(self, image: torch.Tensor):
-        image = self.vae.encode(image).latent_dist.sample()
-        return image
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        image: Image.Image = None,
-        prompt = "",
-        *args,
-        num_images_per_prompt: Optional[int] = 1,
-        guidance_scale=4.0,
-        depth_image: Image.Image = None,
-        output_type: Optional[str] = "pil",
-        width=640,
-        height=960,
-        num_inference_steps=28,
-        return_dict=True,
-        **kwargs
-    ):
-        self.prepare()
-        if image is None:
-            raise ValueError("Inputting embeddings not supported for this pipeline. Please pass an image.")
-        assert not isinstance(image, torch.Tensor)
-        image = to_rgb_image(image)
-        image_1 = self.feature_extractor_vae(images=image, return_tensors="pt").pixel_values
-        image_2 = self.feature_extractor_clip(images=image, return_tensors="pt").pixel_values
-        if depth_image is not None and hasattr(self.unet, "controlnet"):
-            depth_image = to_rgb_image(depth_image)
-            depth_image = self.depth_transforms_multi(depth_image).to(
-                device=self.unet.controlnet.device, dtype=self.unet.controlnet.dtype
-            )
-        image = image_1.to(device=self.vae.device, dtype=self.vae.dtype)
-        image_2 = image_2.to(device=self.vae.device, dtype=self.vae.dtype)
-        cond_lat = self.encode_condition_image(image)
-        if guidance_scale > 1:
-            negative_lat = self.encode_condition_image(torch.zeros_like(image))
-            cond_lat = torch.cat([negative_lat, cond_lat])
-        encoded = self.vision_encoder(image_2, output_hidden_states=False)
-        global_embeds = encoded.image_embeds
-        global_embeds = global_embeds.unsqueeze(-2)
-        
-        if hasattr(self, "encode_prompt"):
-            encoder_hidden_states = self.encode_prompt(
-                prompt,
-                self.device,
-                num_images_per_prompt,
-                False
-            )[0]
-        else:
-            encoder_hidden_states = self._encode_prompt(
-                prompt,
-                self.device,
-                num_images_per_prompt,
-                False
-            )
-        ramp = global_embeds.new_tensor(self.config.ramping_coefficients).unsqueeze(-1)
-        encoder_hidden_states = encoder_hidden_states + global_embeds * ramp
-        cak = dict(cond_lat=cond_lat)
-        if hasattr(self.unet, "controlnet"):
-            cak['control_depth'] = depth_image
-        latents: torch.Tensor = super().__call__(
-            None,
-            *args,
-            cross_attention_kwargs=cak,
-            guidance_scale=guidance_scale,
-            num_images_per_prompt=num_images_per_prompt,
-            prompt_embeds=encoder_hidden_states,
-            num_inference_steps=num_inference_steps,
-            output_type='latent',
-            width=width,
-            height=height,
-            **kwargs
-        ).images
-        latents = unscale_latents(latents)
-        if not output_type == "latent":
-            image = unscale_image(self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0])
-        else:
-            image = latents
-
-        image = self.image_processor.postprocess(image, output_type=output_type)
-        if not return_dict:
-            return (image,)
-
-        return ImagePipelineOutput(images=image)