# # 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 george.drettakis@inria.fr # import os import random import torch from random import randint from utils.loss_utils import l1_loss, l2_loss, patchify, ssim from gaussian_renderer import render, render_motion, render_motion_mouth import sys from scene import Scene, GaussianModel, MotionNetwork, MouthMotionNetwork from utils.general_utils import safe_state import lpips import uuid from tqdm import tqdm from utils.image_utils import psnr from argparse import ArgumentParser, Namespace from arguments import ModelParams, PipelineParams, OptimizationParams try: from torch.utils.tensorboard import SummaryWriter TENSORBOARD_FOUND = True except ImportError: TENSORBOARD_FOUND = False def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from): opt.iterations = 10000 opt.densify_until_iter = 0 testing_iterations = [i for i in range(0, opt.iterations + 1, 2000)] checkpoint_iterations = [opt.iterations] # vars bg_iter = opt.densify_until_iter lpips_start_iter = 5000 first_iter = 0 tb_writer = prepare_output_and_logger(dataset) gaussians = GaussianModel(dataset.sh_degree) scene = Scene(dataset, gaussians) gaussians_mouth = GaussianModel(dataset.sh_degree) with torch.no_grad(): motion_net_mouth = MouthMotionNetwork(args=dataset).cuda() motion_net = MotionNetwork(args=dataset).cuda() gaussians.training_setup(opt) gaussians_mouth.training_setup(opt) (model_params, motion_params, _, _) = torch.load(os.path.join(scene.model_path, "chkpnt_face_latest.pth")) gaussians.restore(model_params, opt) motion_net.load_state_dict(motion_params) (model_params, motion_params, _, _) = torch.load(os.path.join(scene.model_path, "chkpnt_mouth_latest.pth")) gaussians_mouth.restore(model_params, opt) motion_net_mouth.load_state_dict(motion_params) lpips_criterion = lpips.LPIPS(net='alex').eval().cuda() bg_color = [0, 1, 0] # [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), ascii=True, dynamic_ncols=True, desc="Training progress") first_iter += 1 for iteration in range(first_iter, opt.iterations + 1): iter_start.record() # Pick a random Camera if not viewpoint_stack: viewpoint_stack = scene.getTrainCameras().copy() viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack)-1)) gaussians.update_learning_rate(iteration) face_mask = torch.as_tensor(viewpoint_cam.talking_dict["face_mask"]).cuda() hair_mask = torch.as_tensor(viewpoint_cam.talking_dict["hair_mask"]).cuda() mouth_mask = torch.as_tensor(viewpoint_cam.talking_dict["mouth_mask"]).cuda() head_mask = face_mask + hair_mask + mouth_mask # Render if (iteration - 1) == debug_from: pipe.debug = True render_pkg = render_motion(viewpoint_cam, gaussians, motion_net, pipe, background) render_pkg_mouth = render_motion_mouth(viewpoint_cam, gaussians_mouth, motion_net_mouth, pipe, background) viewspace_point_tensor, visibility_filter = render_pkg["viewspace_points"], render_pkg["visibility_filter"] viewspace_point_tensor_mouth, visibility_filter_mouth = render_pkg_mouth["viewspace_points"], render_pkg_mouth["visibility_filter"] alpha_mouth = render_pkg_mouth["alpha"] alpha = render_pkg["alpha"] mouth_image = render_pkg_mouth["render"] - background[:, None, None] * (1.0 - alpha_mouth) + viewpoint_cam.background.cuda() / 255.0 * (1.0 - alpha_mouth) image = render_pkg["render"] - background[:, None, None] * (1.0 - alpha) + mouth_image * (1.0 - alpha) gt_image = viewpoint_cam.original_image.cuda() / 255.0 gt_image_white = gt_image * head_mask + background[:, None, None] * ~head_mask if iteration > bg_iter: for param in motion_net.parameters(): param.requires_grad = False for param in motion_net_mouth.parameters(): param.requires_grad = False gaussians._xyz.requires_grad = False # gaussians._opacity.requires_grad = False gaussians._scaling.requires_grad = False gaussians._rotation.requires_grad = False gaussians_mouth._xyz.requires_grad = False gaussians_mouth._opacity.requires_grad = False gaussians_mouth._scaling.requires_grad = False gaussians_mouth._rotation.requires_grad = False # Loss if iteration < bg_iter: image[:, ~head_mask] = background[:, None] # gt_image_white[:, ~head_mask] = background[:, None] Ll1 = l1_loss(image, gt_image_white) loss = Ll1 + opt.lambda_dssim * (1.0 - ssim(image, gt_image_white)) loss += 1e-3 * (((1-alpha) * head_mask).mean() + (alpha * ~head_mask).mean()) image_t = image.clone() gt_image_t = gt_image_white.clone() else: Ll1 = l1_loss(image, gt_image) loss = Ll1 + opt.lambda_dssim * (1.0 - ssim(image, gt_image)) image_t = image.clone() gt_image_t = gt_image.clone() if iteration > lpips_start_iter: # mask mouth # [xmin, xmax, ymin, ymax] = viewpoint_cam.talking_dict['lips_rect'] # image_t[:, xmin:xmax, ymin:ymax] = 1 # gt_image_t[:, xmin:xmax, ymin:ymax] = 1 patch_size = random.randint(16, 21) * 2 loss += 0.5 * lpips_criterion(patchify(image_t[None, ...] * 2 - 1, patch_size), patchify(gt_image_t[None, ...] * 2 - 1, patch_size)).mean() loss.backward() 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:.{5}f}"}) progress_bar.update(10) if iteration == opt.iterations: progress_bar.close() # Log and save training_report(tb_writer, iteration, testing_iterations, image, gt_image) if (iteration in saving_iterations): print("\n[ITER {}] Saving Gaussians".format(iteration)) scene.save(iteration) if (iteration in checkpoint_iterations): print("\n[ITER {}] Saving Checkpoint".format(iteration)) ckpt = (gaussians.capture(), motion_net.state_dict(), gaussians_mouth.capture(), motion_net_mouth.state_dict()) torch.save(ckpt, scene.model_path + "/chkpnt_fuse_" + str(iteration) + ".pth") torch.save(ckpt, scene.model_path + "/chkpnt_fuse_latest" + ".pth") # Densification if iteration < opt.densify_until_iter: # Keep track of max radii in image-space for pruning gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter) gaussians_mouth.add_densification_stats(viewspace_point_tensor_mouth, visibility_filter_mouth) if iteration % opt.densification_interval == 0: size_threshold = 20 if iteration > opt.opacity_reset_interval else None gaussians.densify_and_prune(opt.densify_grad_threshold, 0.3, scene.cameras_extent, size_threshold) gaussians_mouth.densify_and_prune(opt.densify_grad_threshold, 0.3, scene.cameras_extent, size_threshold) # Optimizer step if iteration < opt.iterations: gaussians.optimizer.step() gaussians_mouth.optimizer.step() gaussians.optimizer.zero_grad(set_to_none = True) gaussians_mouth.optimizer.zero_grad(set_to_none = True) def prepare_output_and_logger(args): if not args.model_path: if os.getenv('OAR_JOB_ID'): unique_str=os.getenv('OAR_JOB_ID') else: unique_str = str(uuid.uuid4()) args.model_path = os.path.join("./output/", unique_str[0:10]) # Set up output folder print("Output folder: {}".format(args.model_path)) os.makedirs(args.model_path, exist_ok = True) with open(os.path.join(args.model_path, "cfg_args"), 'w') as cfg_log_f: cfg_log_f.write(str(Namespace(**vars(args)))) # Create Tensorboard writer tb_writer = None if TENSORBOARD_FOUND: tb_writer = SummaryWriter(args.model_path) else: print("Tensorboard not available: not logging progress") return tb_writer def training_report(tb_writer, iteration, testing_iterations, image, gt_image): # Report test and samples of training set if iteration in testing_iterations: tb_writer.add_images("fuse/render", image[None], global_step=iteration) tb_writer.add_images("fuse/ground_truth", gt_image[None], global_step=iteration) 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('--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=[]) 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) args = parser.parse_args(sys.argv[1:]) args.save_iterations.append(args.iterations) print("Optimizing " + args.model_path) # Initialize system state (RNG) safe_state(args.quiet) # Start GUI server, configure and run training 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) # All done print("\nTraining complete.")