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#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact [email protected]
#
import os
import numpy as np
import torch
from random import randint
from utils.loss_utils import l1_loss, ssim
from gaussian_renderer import render, network_gui, render_confidence
import sys
from scene import Scene, GaussianModel
from utils.general_utils import safe_state
from tqdm import tqdm
from utils.image_utils import psnr
from argparse import ArgumentParser, Namespace
from arguments import ModelParams, PipelineParams, OptimizationParams
from scene.cameras import Camera
from utils.pose_utils import get_camera_from_tensor
import torchvision
import dearpygui.dearpygui as dpg
from scipy.spatial.transform import Rotation
import random
def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from, args):
first_iter = 0
gaussians = GaussianModel(dataset.sh_degree)
scene = Scene(dataset, gaussians, opt=args, shuffle=False)
gaussians.training_setup(opt)
if checkpoint:
(model_params, first_iter) = torch.load(checkpoint)
gaussians.restore(model_params, opt)
bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
iter_start = torch.cuda.Event(enable_timing = True)
iter_end = torch.cuda.Event(enable_timing = True)
viewpoint_stack = None
ema_loss_for_log = 0.0
progress_bar = tqdm(range(first_iter, opt.iterations), desc="Training progress")
first_iter += 1
for iteration in range(first_iter, opt.iterations + 1):
iter_start.record()
gaussians.update_learning_rate(iteration)
if args.optim_pose==False:
gaussians.get_P().requires_grad_(False)
# Every 1000 its we increase the levels of SH up to a maximum degree
if iteration % 3000 == 0:
gaussians.oneupSHdegree()
# Pick a random Camera
if not viewpoint_stack:
viewpoint_stack = scene.getTrainCameras().copy()
# Render
if (iteration - 1) == debug_from:
pipe.debug = True
bg = torch.rand((3), device="cuda") if opt.random_background else background
viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack)-1))
pose = gaussians.get_RT(viewpoint_cam.uid)
render_pkg = render(viewpoint_cam, gaussians, pipe, bg, camera_pose=pose)
image, viewspace_point_tensor, visibility_filter, radii = render_pkg["render"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"]
# Loss
gt_image = viewpoint_cam.original_image.cuda()
static = gaussians._conf_static[viewpoint_cam.uid]
image = image * static
gt_image = gt_image * static
Ll1 = l1_loss(image, gt_image, reduce=False)
Lssim = ssim(image, gt_image, size_average=False)
psnr_frame = psnr(image, gt_image).mean()
loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - Lssim)
loss = (loss).mean()
loss.backward(retain_graph=True)
with torch.no_grad():
gaussians.optimizer.step()
gaussians.optimizer.zero_grad(set_to_none = True)
if psnr_frame > args.psnr_threshold:
gaussians.optimizer_cam.step()
gaussians.optimizer_cam.zero_grad(set_to_none = True)
if not viewpoint_stack:
viewpoint_stack = scene.getTestCameras().copy()
# Render
if (iteration - 1) == debug_from:
pipe.debug = True
bg = torch.rand((3), device="cuda") if opt.random_background else background
while len(viewpoint_stack) > 0:
viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack)-1))
# print(f"getting test cam pose frame {viewpoint_cam.colmap_id}")
pose = gaussians.get_RT_test(viewpoint_cam.uid)
render_pkg = render(viewpoint_cam, gaussians, pipe, bg, camera_pose=pose)
image, viewspace_point_tensor, visibility_filter, radii = render_pkg["render"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"]
# Loss
gt_image = viewpoint_cam.original_image.cuda()
gt_static_mask = 1 - viewpoint_cam.gt_dynamic_mask.to("cuda")
image = image * gt_static_mask
gt_image = gt_image * gt_static_mask
Ll1 = l1_loss(image, gt_image, reduce=False)
Lssim = ssim(image, gt_image, size_average=False)
psnr_frame = psnr(image, gt_image).mean()
loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - Lssim)
loss = (loss).mean()
loss.backward(retain_graph=True)
with torch.no_grad():
# gaussians.optimizer.step()
gaussians.optimizer.zero_grad(set_to_none = True)
if psnr_frame > args.psnr_threshold:
gaussians.optimizer_cam.step()
gaussians.optimizer_cam.zero_grad(set_to_none = True)
iter_end.record()
with torch.no_grad():
# Progress bar
ema_loss_for_log = 0.4 * loss.item() + 0.6 * ema_loss_for_log
if iteration % 10 == 0:
progress_bar.set_postfix({"Loss": f"{ema_loss_for_log:.{7}f}"})
progress_bar.update(10)
if iteration == opt.iterations:
progress_bar.close()
# Log and save
training_report(iteration, Ll1, loss, l1_loss, iter_start.elapsed_time(iter_end), testing_iterations, scene, render, (pipe, background))
if (iteration in saving_iterations):
print("\n[ITER {}] Saving Gaussians".format(iteration))
scene.save(iteration)
# Densification
# if iteration < opt.densify_until_iter:
# Keep track of max radii in image-space for pruning
# gaussians.max_radii2D[visibility_filter] = torch.max(gaussians.max_radii2D[visibility_filter], radii[visibility_filter])
# gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter)
# if iteration > opt.densify_from_iter and iteration % opt.densification_interval == 0:
# size_threshold = 20 if iteration > opt.opacity_reset_interval else None
# gaussians.densify_and_prune(opt.densify_grad_threshold, 0.005, scene.cameras_extent, size_threshold)
# if iteration % opt.opacity_reset_interval == 0 or (dataset.white_background and iteration == opt.densify_from_iter):
# gaussians.reset_opacity()
# Optimizer step
# if iteration < opt.iterations:
if (iteration in checkpoint_iterations):
print("\n[ITER {}] Saving Checkpoint".format(iteration))
torch.save((gaussians.capture(), iteration), scene.model_path + "/chkpnt" + str(iteration) + ".pth")
def save_pose(path, quat_pose, train_cams, llffhold=2):
output_poses=[]
index_colmap = [cam.colmap_id for cam in train_cams]
for quat_t in quat_pose:
w2c = get_camera_from_tensor(quat_t)
output_poses.append(w2c)
return index_colmap, output_poses
def convert_colmap_to_quat(colmap_poses):
quat_pose = []
for pose in colmap_poses:
rotation = Rotation.from_matrix(pose[:3, :3])
quat = rotation.as_quat()
translation = pose[:3, 3]
quat_pose.append(np.concatenate([quat, translation]))
return quat_pose
def c2w_to_tumpose(c2w):
"""
Convert a camera-to-world matrix to a tuple of translation and rotation
input: c2w: 4x4 matrix
output: tuple of translation and rotation (x y z qw qx qy qz)
"""
# convert input to numpy
c2w = c2w
c2w = np.linalg.inv(c2w)
xyz = c2w[:3, -1]
rot = Rotation.from_matrix(c2w[:3, :3])
qx, qy, qz, qw = rot.as_quat()
tum_pose = np.concatenate([xyz, [qw, qx, qy, qz]])
return tum_pose
def tumpose_to_c2w(tum_pose):
"""
Convert a tuple of translation and rotation to a camera-to-world matrix
input: tum_pose: tuple of translation and rotation (x y z qw qx qy qz)
output: c2w: 4x4 matrix
"""
xyz = tum_pose[:3]
qw, qx, qy, qz = tum_pose[3:]
rot = Rotation.from_quat([qx, qy, qz, qw])
c2w = np.eye(4)
c2w[:3, :3] = rot.as_matrix()
c2w[:3, -1] = xyz
c2w = np.linalg.inv(c2w)
return c2w
def training_report(iteration, Ll1, loss, l1_loss, elapsed, testing_iterations, scene : Scene, renderFunc, renderArgs):
# Report test and samples of training set
if iteration in testing_iterations:
torch.cuda.empty_cache()
validation_configs = ({'name': 'test', 'cameras' : scene.getTestCameras()},)
for config in validation_configs:
if config['cameras'] and len(config['cameras']) > 0:
l1_test = 0.0
psnr_test = 0.0
lens = 0
for idx, viewpoint in enumerate(config['cameras']):
if config['name']=="train":
pose = scene.gaussians.get_RT(viewpoint.uid)
else:
pose = scene.gaussians.get_RT_test(viewpoint.uid)
image = torch.clamp(renderFunc(viewpoint, scene.gaussians, *renderArgs, camera_pose=pose)["render"], 0.0, 1.0)
torchvision.utils.save_image(
image, os.path.join(scene.model_path, "{0:05d}".format(viewpoint.colmap_id) + ".png")
)
gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0)
if hasattr(viewpoint, 'gt_dynamic_mask'):
gt_static_mask = 1 - viewpoint.gt_dynamic_mask.to("cuda")
np.save(os.path.join(scene.model_path, f"{viewpoint.colmap_id}_image.npy"), image.cpu().numpy())
np.save(os.path.join(scene.model_path, f"{viewpoint.colmap_id}_gt_image.npy"), gt_image.cpu().numpy())
np.save(os.path.join(scene.model_path, f"{viewpoint.colmap_id}_gt_static_mask.npy"), gt_static_mask.cpu().numpy())
image = image * gt_static_mask
gt_image = gt_image * gt_static_mask
l1_test += l1_loss(image, gt_image).mean().double()
psnr_test += psnr(image, gt_image).mean().double()
# import matplotlib.pyplot as plt
# plt.figure(figsize=(10, 5))
# plt.subplot(1, 2, 1)
# plt.title('Predicted Image')
# plt.imshow(image.cpu().permute(1,2,0))
# plt.axis('off')
# plt.subplot(1, 2, 2)
# plt.title('Ground Truth Image')
# plt.imshow(gt_image.cpu().permute(1,2,0))
# plt.axis('off')
# plt.show()
lens += 1
psnr_test /= lens
l1_test /= lens
print("\n[ITER {}] Evaluating {}: L1 {} PSNR {}".format(iteration, config['name'], l1_test, psnr_test))
with open(os.path.join(scene.model_path, f"{config['name']}_log.txt"), 'a') as log_file:
log_file.write(f"[ITER {iteration}] Evaluating {config['name']}: L1 {l1_test} PSNR {psnr_test}\n")
torch.cuda.empty_cache()
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Training script parameters")
lp = ModelParams(parser)
op = OptimizationParams(parser)
pp = PipelineParams(parser)
parser.add_argument('--ip', type=str, default="127.0.0.1")
parser.add_argument('--port', type=int, default=6009)
parser.add_argument('--debug_from', type=int, default=-1)
parser.add_argument('--detect_anomaly', action='store_true', default=False)
parser.add_argument("--test_iterations", nargs="+", type=int, default=[500, 800, 1000, 1500, 2000, 3000, 4000, 5000, 6000, 7_000, 30_000])
parser.add_argument("--save_iterations", nargs="+", type=int, default=[])
parser.add_argument("--quiet", action="store_true")
parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[])
parser.add_argument("--start_checkpoint", type=str, default = None)
parser.add_argument("--scene", type=str, default=None)
parser.add_argument("--n_views", type=int, default=None)
parser.add_argument("--get_video", action="store_true")
parser.add_argument("--optim_pose", action="store_true")
parser.add_argument("--gui", action="store_true")
parser.add_argument("--eval_pose", action="store_true")
parser.add_argument('--pose_eval_interval', type=int, default=100)
parser.add_argument('--psnr_threshold', type=float, default=26)
parser.add_argument('--gt_dynamic_mask', type=str, default='data/sintel/training/dynamic_label_perfect')
parser.add_argument('--dataset', type=str, default='sintel')
args = parser.parse_args(sys.argv[1:])
args.save_iterations.append(args.iterations)
lp.eval = True
args.eval = True
os.makedirs(args.model_path, exist_ok=True)
print("Optimizing " + args.model_path)
torch.autograd.set_detect_anomaly(args.detect_anomaly)
training(lp.extract(args), op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations, args.checkpoint_iterations, args.start_checkpoint, args.debug_from, args)
# All done
print("\nTraining complete.")
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