# # 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 import sys from scene import Scene, GaussianModel, MotionNetwork 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): testing_iterations = [i for i in range(0, opt.iterations + 1, 2000)] checkpoint_iterations = saving_iterations = [i for i in range(0, opt.iterations + 1, 10000)] + [opt.iterations] # vars warm_step = 3000 opt.densify_until_iter = opt.iterations - 1000 bg_iter = opt.iterations # opt.densify_until_iter lpips_start_iter = opt.densify_until_iter - 2000 motion_stop_iter = bg_iter mouth_select_iter = bg_iter - 10000 mouth_step = 1 / mouth_select_iter hair_mask_interval = 7 select_interval = 15 first_iter = 0 tb_writer = prepare_output_and_logger(dataset) gaussians = GaussianModel(dataset.sh_degree) scene = Scene(dataset, gaussians) motion_net = MotionNetwork(args=dataset).cuda() motion_optimizer = torch.optim.AdamW(motion_net.get_params(5e-3, 5e-4), betas=(0.9, 0.99), eps=1e-8) scheduler = torch.optim.lr_scheduler.LambdaLR(motion_optimizer, lambda iter: (0.5 ** (iter / mouth_select_iter)) if iter < mouth_select_iter else 0.1 ** (iter / bg_iter)) lpips_criterion = lpips.LPIPS(net='alex').eval().cuda() gaussians.training_setup(opt) if checkpoint: (model_params, motion_params, motion_optimizer_params, first_iter) = torch.load(checkpoint) gaussians.restore(model_params, opt) motion_net.load_state_dict(motion_params) motion_optimizer.load_state_dict(motion_optimizer_params) 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() gaussians.update_learning_rate(iteration) # Every 1000 its we increase the levels of SH up to a maximum degree if iteration % 1000 == 0: gaussians.oneupSHdegree() # Pick a random Camera if not viewpoint_stack: viewpoint_stack = scene.getTrainCameras().copy() viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack)-1)) # find a big mouth mouth_global_lb = viewpoint_cam.talking_dict['mouth_bound'][0] mouth_global_ub = viewpoint_cam.talking_dict['mouth_bound'][1] mouth_global_lb += (mouth_global_ub - mouth_global_lb) * 0.2 mouth_window = (mouth_global_ub - mouth_global_lb) * 0.2 mouth_lb = mouth_global_lb + mouth_step * iteration * (mouth_global_ub - mouth_global_lb) mouth_ub = mouth_lb + mouth_window mouth_lb = mouth_lb - mouth_window au_global_lb = 0 au_global_ub = 1 au_window = 0.3 au_lb = au_global_lb + mouth_step * iteration * (au_global_ub - au_global_lb) au_ub = au_lb + au_window au_lb = au_lb - au_window * 0.5 if iteration < warm_step: if iteration % select_interval == 0: while viewpoint_cam.talking_dict['mouth_bound'][2] < mouth_lb or viewpoint_cam.talking_dict['mouth_bound'][2] > mouth_ub: if not viewpoint_stack: viewpoint_stack = scene.getTrainCameras().copy() viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack)-1)) if warm_step < iteration < mouth_select_iter: if iteration % select_interval == 0: while viewpoint_cam.talking_dict['blink'] < au_lb or viewpoint_cam.talking_dict['blink'] > au_ub: if not viewpoint_stack: viewpoint_stack = scene.getTrainCameras().copy() viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack)-1)) # Render if (iteration - 1) == debug_from: pipe.debug = True 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 if iteration > lpips_start_iter: max_pool = torch.nn.MaxPool2d(kernel_size=3, stride=1, padding=1) mouth_mask = (-max_pool(-max_pool(mouth_mask[None].float())))[0].bool() hair_mask_iter = (warm_step < iteration < lpips_start_iter - 1000) and iteration % hair_mask_interval != 0 if iteration < warm_step: render_pkg = render(viewpoint_cam, gaussians, pipe, background) else: render_pkg = render_motion(viewpoint_cam, gaussians, motion_net, pipe, background, return_attn=True) image_white, alpha, viewspace_point_tensor, visibility_filter, radii = render_pkg["render"], render_pkg["alpha"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"] gt_image = viewpoint_cam.original_image.cuda() / 255.0 gt_image_white = gt_image * head_mask + background[:, None, None] * ~head_mask if iteration > motion_stop_iter: for param in motion_net.parameters(): param.requires_grad = False if iteration > bg_iter: gaussians._xyz.requires_grad = False gaussians._opacity.requires_grad = False # gaussians._features_dc.requires_grad = False # gaussians._features_rest.requires_grad = False gaussians._scaling.requires_grad = False gaussians._rotation.requires_grad = False # Loss if iteration < bg_iter: if hair_mask_iter: image_white[:, hair_mask] = background[:, None] gt_image_white[:, hair_mask] = background[:, None] # image_white[:, mouth_mask] = 1 gt_image_white[:, mouth_mask] = background[:, None] Ll1 = l1_loss(image_white, gt_image_white) loss = Ll1 + opt.lambda_dssim * (1.0 - ssim(image_white, gt_image_white)) # mouth_alpha_loss = 1e-2 * (alpha[:,mouth_mask]).mean() # if not torch.isnan(mouth_alpha_loss): # loss += mouth_alpha_loss # print(alpha[:,mouth_mask], mouth_mask.sum()) if iteration > warm_step: loss += 1e-5 * (render_pkg['motion']['d_xyz'].abs()).mean() loss += 1e-5 * (render_pkg['motion']['d_rot'].abs()).mean() loss += 1e-5 * (render_pkg['motion']['d_opa'].abs()).mean() loss += 1e-5 * (render_pkg['motion']['d_scale'].abs()).mean() loss += 1e-3 * (((1-alpha) * head_mask).mean() + (alpha * ~head_mask).mean()) [xmin, xmax, ymin, ymax] = viewpoint_cam.talking_dict['lips_rect'] loss += 1e-4 * (render_pkg["attn"][1, xmin:xmax, ymin:ymax]).mean() if not hair_mask_iter: loss += 1e-4 * (render_pkg["attn"][1][hair_mask]).mean() loss += 1e-4 * (render_pkg["attn"][0][hair_mask]).mean() # loss += l2_loss(image_white[:, xmin:xmax, ymin:ymax], image_white[:, xmin:xmax, ymin:ymax]) image_t = image_white.clone() gt_image_t = gt_image_white.clone() else: # with real bg image = image_white - background[:, None, None] * (1.0 - alpha) + viewpoint_cam.background.cuda() / 255.0 * (1.0 - alpha) 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'] loss += 0.01 * lpips_criterion(image_t.clone()[:, xmin:xmax, ymin:ymax] * 2 - 1, gt_image_t.clone()[:, xmin:xmax, ymin:ymax] * 2 - 1).mean() image_t[:, xmin:xmax, ymin:ymax] = background[:, None, None] gt_image_t[:, xmin:xmax, ymin:ymax] = background[:, None, None] patch_size = random.randint(32, 48) * 2 loss += 0.2 * lpips_criterion(patchify(image_t[None, ...] * 2 - 1, patch_size), patchify(gt_image_t[None, ...] * 2 - 1, patch_size)).mean() # loss += 0.5 * lpips_criterion(image_t[None, ...] * 2 - 1, gt_image_t[None, ...] * 2 - 1).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}", "Mouth": f"{mouth_lb:.{1}f}-{mouth_ub:.{1}f}"}) # , "AU25": f"{au_lb:.{1}f}-{au_ub:.{1}f}" progress_bar.update(10) if iteration == opt.iterations: progress_bar.close() # Log and save training_report(tb_writer, iteration, Ll1, loss, l1_loss, iter_start.elapsed_time(iter_end), testing_iterations, scene, motion_net, render if iteration < warm_step else render_motion, (pipe, background)) if (iteration in saving_iterations): print("\n[ITER {}] Saving Gaussians".format(iteration)) scene.save(str(iteration)+'_face') if (iteration in checkpoint_iterations): print("\n[ITER {}] Saving Checkpoint".format(iteration)) ckpt = (gaussians.capture(), motion_net.state_dict(), motion_optimizer.state_dict(), iteration) torch.save(ckpt, scene.model_path + "/chkpnt_face_" + str(iteration) + ".pth") torch.save(ckpt, scene.model_path + "/chkpnt_face_latest" + ".pth") # 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, 0.05 + 0.25 * iteration / opt.densify_until_iter, scene.cameras_extent, size_threshold) # bg prune if iteration > opt.densify_from_iter and iteration % opt.densification_interval == 0: from utils.sh_utils import eval_sh shs_view = gaussians.get_features.transpose(1, 2).view(-1, 3, (gaussians.max_sh_degree+1)**2) dir_pp = (gaussians.get_xyz - viewpoint_cam.camera_center.repeat(gaussians.get_features.shape[0], 1)) dir_pp_normalized = dir_pp/dir_pp.norm(dim=1, keepdim=True) sh2rgb = eval_sh(gaussians.active_sh_degree, shs_view, dir_pp_normalized) colors_precomp = torch.clamp_min(sh2rgb + 0.5, 0.0) bg_color_mask = (colors_precomp[..., 0] < 30/255) * (colors_precomp[..., 1] > 225/255) * (colors_precomp[..., 2] < 30/255) gaussians.prune_points(bg_color_mask.squeeze()) # Optimizer step if iteration < opt.iterations: motion_optimizer.step() gaussians.optimizer.step() motion_optimizer.zero_grad() gaussians.optimizer.zero_grad(set_to_none = True) scheduler.step() 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, Ll1, loss, l1_loss, elapsed, testing_iterations, scene : Scene, motion_net, renderFunc, renderArgs): if tb_writer: tb_writer.add_scalar('train_loss_patches/l1_loss', Ll1.item(), iteration) tb_writer.add_scalar('train_loss_patches/total_loss', loss.item(), iteration) tb_writer.add_scalar('iter_time', elapsed, iteration) # Report test and samples of training set if iteration in testing_iterations: torch.cuda.empty_cache() validation_configs = ({'name': 'test', 'cameras' : [scene.getTestCameras()[idx % len(scene.getTestCameras())] for idx in range(5, 100, 5)]}, {'name': 'train', 'cameras' : [scene.getTrainCameras()[idx % len(scene.getTrainCameras())] for idx in range(5, 30, 5)]}) for config in validation_configs: if config['cameras'] and len(config['cameras']) > 0: l1_test = 0.0 psnr_test = 0.0 for idx, viewpoint in enumerate(config['cameras']): if renderFunc is render: render_pkg = renderFunc(viewpoint, scene.gaussians, *renderArgs) else: render_pkg = renderFunc(viewpoint, scene.gaussians, motion_net, return_attn=True, frame_idx=0, *renderArgs) image = torch.clamp(render_pkg["render"], 0.0, 1.0) alpha = render_pkg["alpha"] image = image - renderArgs[1][:, None, None] * (1.0 - alpha) + viewpoint.background.cuda() / 255.0 * (1.0 - alpha) gt_image = torch.clamp(viewpoint.original_image.to("cuda") / 255.0, 0.0, 1.0) mouth_mask = torch.as_tensor(viewpoint.talking_dict["mouth_mask"]).cuda() max_pool = torch.nn.MaxPool2d(kernel_size=3, stride=1, padding=1) mouth_mask_post = (-max_pool(-max_pool(mouth_mask[None].float())))[0].bool() if tb_writer and (idx < 5): tb_writer.add_images(config['name'] + "_view_{}/render".format(viewpoint.image_name), image[None], global_step=iteration) tb_writer.add_images(config['name'] + "_view_{}/ground_truth".format(viewpoint.image_name), gt_image[None], global_step=iteration) # tb_writer.add_images(config['name'] + "_view_{}/depth".format(viewpoint.image_name), (render_pkg["depth"] / render_pkg["depth"].max())[None], global_step=iteration) tb_writer.add_images(config['name'] + "_view_{}/mouth_mask_post".format(viewpoint.image_name), (~mouth_mask_post * gt_image)[None], global_step=iteration) tb_writer.add_images(config['name'] + "_view_{}/mouth_mask".format(viewpoint.image_name), (~mouth_mask[None] * gt_image)[None], global_step=iteration) if renderFunc is not render: tb_writer.add_images(config['name'] + "_view_{}/attn_a".format(viewpoint.image_name), (render_pkg["attn"][0] / render_pkg["attn"][0].max())[None, None], global_step=iteration) tb_writer.add_images(config['name'] + "_view_{}/attn_e".format(viewpoint.image_name), (render_pkg["attn"][1] / render_pkg["attn"][1].max())[None, None], global_step=iteration) l1_test += l1_loss(image, gt_image).mean().double() psnr_test += psnr(image, gt_image).mean().double() psnr_test /= len(config['cameras']) l1_test /= len(config['cameras']) print("\n[ITER {}] Evaluating {}: L1 {} PSNR {}".format(iteration, config['name'], l1_test, psnr_test)) if tb_writer: tb_writer.add_scalar(config['name'] + '/loss_viewpoint - l1_loss', l1_test, iteration) tb_writer.add_scalar(config['name'] + '/loss_viewpoint - psnr', psnr_test, iteration) if tb_writer: tb_writer.add_histogram("scene/opacity_histogram", scene.gaussians.get_opacity, iteration) tb_writer.add_scalar('total_points', scene.gaussians.get_xyz.shape[0], iteration) 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=[]) 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.")