# # 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 torch from random import randint from utils.loss_utils import l1_loss, ssim, kl_divergence from gaussian_renderer import render, network_gui import sys from scene import Scene, GaussianModel, DeformModel from utils.general_utils import safe_state, get_linear_noise_func 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 # batch_timeformer = 4 # n_layer = 1 # weight_reg = 0 # weight_t = 0.8 from TimeFormer import TimeFormer # timeFormer = TimeFormer(input_dims=4, multires=4, nhead=4, hidden_dims=36, num_layer=n_layer).to('cuda') def trans(xyz, t): temp = torch.zeros((xyz.shape[0], 1)).to('cuda') temp[:, :] = 2 * t - 1 temp = torch.cat([xyz, temp], dim=1) return temp def training(dataset, opt, pipe, testing_iterations, saving_iterations): tb_writer = prepare_output_and_logger(dataset) gaussians = GaussianModel(dataset.sh_degree) deform = DeformModel(dataset.is_blender, dataset.is_6dof) deform.train_setting(opt) n_layer = dataset.n_layer weight_reg = dataset.weight_reg weight_t = dataset.weight_t batch_timeformer = dataset.batch timeFormer = TimeFormer(input_dims=4, multires=4, nhead=4, hidden_dims=36, num_layer=n_layer).to('cuda') timeFormer.train_setting(opt) scene = Scene(dataset, gaussians) gaussians.training_setup(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 best_psnr = 0.0 best_iteration = 0 progress_bar = tqdm(range(opt.iterations), desc="Training progress") smooth_term = get_linear_noise_func(lr_init=0.1, lr_final=1e-15, lr_delay_mult=0.01, max_steps=20000) for iteration in range(1, opt.iterations + 1): if network_gui.conn == None: network_gui.try_connect() while network_gui.conn != None: try: net_image_bytes = None custom_cam, do_training, pipe.do_shs_python, pipe.do_cov_python, keep_alive, scaling_modifer = network_gui.receive() if custom_cam != None: net_image = render(custom_cam, gaussians, pipe, background, scaling_modifer)["render"] net_image_bytes = memoryview((torch.clamp(net_image, min=0, max=1.0) * 255).byte().permute(1, 2, 0).contiguous().cpu().numpy()) network_gui.send(net_image_bytes, dataset.source_path) if do_training and ((iteration < int(opt.iterations)) or not keep_alive): break except Exception as e: network_gui.conn = None iter_start.record() # 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 or len(viewpoint_stack) < batch_timeformer: viewpoint_stack = scene.getTrainCameras().copy() total_frame = len(viewpoint_stack) time_interval = 1 / total_frame viewpoint_cams = [] for _ in range(batch_timeformer) : viewpoint_cam = viewpoint_stack.pop(randint(0,len(viewpoint_stack)-1)) viewpoint_cams.append(viewpoint_cam) # breakpoint() time_ori = torch.cat([trans(gaussians.get_xyz, viewpoint_cam.fid).unsqueeze(0) for viewpoint_cam in viewpoint_cams], dim=0) time_offset = timeFormer(time_ori) images_gt = [] images = [] images_t = [] for i in range(len(viewpoint_cams)): viewpoint_cam = viewpoint_cams[i] # viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack) - 1)) if dataset.load2gpu_on_the_fly: viewpoint_cam.load2device() fid = viewpoint_cam.fid # w/ time offset if iteration < opt.warm_up: d_xyz, d_rotation, d_scaling = 0.0, 0.0, 0.0 else: N = gaussians.get_xyz.shape[0] time_input = fid.unsqueeze(0).expand(N, -1) ast_noise = 0 if dataset.is_blender else torch.randn(1, 1, device='cuda').expand(N, -1) * time_interval * smooth_term(iteration) d_xyz, d_rotation, d_scaling = deform.step(gaussians.get_xyz.detach()+time_offset[i, :, :], time_input + ast_noise) render_pkg_re_t = render(viewpoint_cam, gaussians, pipe, background, d_xyz, d_rotation, d_scaling, dataset.is_6dof) image_t, _, _, _ = render_pkg_re_t["render"], render_pkg_re_t[ "viewspace_points"], render_pkg_re_t["visibility_filter"], render_pkg_re_t["radii"] # w/o time offset if iteration < opt.warm_up: d_xyz, d_rotation, d_scaling = 0.0, 0.0, 0.0 else: N = gaussians.get_xyz.shape[0] time_input = fid.unsqueeze(0).expand(N, -1) ast_noise = 0 if dataset.is_blender else torch.randn(1, 1, device='cuda').expand(N, -1) * time_interval * smooth_term(iteration) d_xyz, d_rotation, d_scaling = deform.step(gaussians.get_xyz.detach(), time_input + ast_noise) render_pkg_re = render(viewpoint_cam, gaussians, pipe, background, d_xyz, d_rotation, d_scaling, dataset.is_6dof) image, viewspace_point_tensor, visibility_filter, radii = render_pkg_re["render"], render_pkg_re[ "viewspace_points"], render_pkg_re["visibility_filter"], render_pkg_re["radii"] # Loss gt_image = viewpoint_cam.original_image.cuda() images.append(image.unsqueeze(0)) images_t.append(image_t.unsqueeze(0)) images_gt.append(gt_image.unsqueeze(0)) image_tensor = torch.cat(images, 0) image_tensor_t = torch.cat(images_t, 0) gt_image_tensor = torch.cat(images_gt, 0) # breakpoint() Ll1 = l1_loss(image_tensor, gt_image_tensor[:,:3,:,:]) Ll1_t = l1_loss(image_tensor_t, gt_image_tensor[:, :3, :, :]) l_reg = l1_loss(time_offset, 0) loss = Ll1 + weight_t * Ll1_t + weight_reg * (l_reg if iteration >= opt.warm_up else 0.0) # loss += (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim(image, gt_image)) loss.backward() iter_end.record() if dataset.load2gpu_on_the_fly: viewpoint_cam.load2device('cpu') 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() # 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]) # Log and save if iteration % 10 == 0: cur_psnr = training_report(tb_writer, iteration, Ll1, loss, l1_loss, iter_start.elapsed_time(iter_end), testing_iterations, scene, render, (pipe, background), deform, dataset.load2gpu_on_the_fly, dataset.is_6dof, Ll1_t, l_reg) if iteration in testing_iterations: if cur_psnr.item() > best_psnr: best_psnr = cur_psnr.item() best_iteration = iteration if iteration in saving_iterations: print("\n[ITER {}] Saving Gaussians".format(iteration)) scene.save(iteration) deform.save_weights(args.model_path, iteration) # Densification if iteration < opt.densify_until_iter: viewspace_point_tensor_densify = render_pkg_re["viewspace_points_densify"] gaussians.add_densification_stats(viewspace_point_tensor_densify, 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() pass # Optimizer step if iteration < opt.iterations: gaussians.optimizer.step() gaussians.update_learning_rate(iteration) deform.optimizer.step() gaussians.optimizer.zero_grad(set_to_none=True) deform.optimizer.zero_grad() deform.update_learning_rate(iteration) timeFormer.optimizer.step() timeFormer.optimizer.zero_grad() timeFormer.update_learning_rate(iteration) # def print_model_param_values(model): # for name, param in model.named_parameters(): # print(name, param.data) # print_model_param_values(timeFormer.output_layer) print("Best PSNR = {} in Iteration {}".format(best_psnr, best_iteration)) 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, renderFunc, renderArgs, deform, load2gpu_on_the_fly, is_6dof=False, loss_t=0.0, loss_reg=0.0): 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('train_loss_patches/iter_time', elapsed, iteration) tb_writer.add_scalar(f'train_loss_patches/l1_timeformer_loss', loss_t.item(), iteration) tb_writer.add_scalar(f'train_loss_patches/reg_loss', loss_reg.item(), iteration) if tb_writer: # tb_writer.add_histogram("scene/opacity_histogram", scene.gaussians.get_opacity, iteration) tb_writer.add_scalar('train_loss_patches/total_points', scene.gaussians.get_xyz.shape[0], iteration) torch.cuda.empty_cache() test_psnr = 0.0 # Report test and samples of training set if iteration in testing_iterations: torch.cuda.empty_cache() validation_configs = ({'name': 'test', 'cameras': scene.getTestCameras()}, {'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: images = torch.tensor([], device="cuda") gts = torch.tensor([], device="cuda") for idx, viewpoint in enumerate(config['cameras']): if load2gpu_on_the_fly: viewpoint.load2device() fid = viewpoint.fid xyz = scene.gaussians.get_xyz time_input = fid.unsqueeze(0).expand(xyz.shape[0], -1) d_xyz, d_rotation, d_scaling = deform.step(xyz.detach(), time_input) image = torch.clamp( renderFunc(viewpoint, scene.gaussians, *renderArgs, d_xyz, d_rotation, d_scaling, is_6dof)["render"], 0.0, 1.0) gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0) images = torch.cat((images, image.unsqueeze(0)), dim=0) gts = torch.cat((gts, gt_image.unsqueeze(0)), dim=0) if load2gpu_on_the_fly: viewpoint.load2device('cpu') if tb_writer and (idx < 5): tb_writer.add_images(config['name'] + "_view_{}/render".format(viewpoint.image_name), image[None], global_step=iteration) if iteration == testing_iterations[0]: tb_writer.add_images(config['name'] + "_view_{}/ground_truth".format(viewpoint.image_name), gt_image[None], global_step=iteration) l1_test = l1_loss(images, gts) psnr_test = psnr(images, gts).mean() if config['name'] == 'test' or len(validation_configs[0]['cameras']) == 0: test_psnr = psnr_test 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) return test_psnr 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('--n_layer', type=int, default=4) parser.add_argument('--batch', type=int, default=4) parser.add_argument('--weight_reg', type=float, default=0.0001) parser.add_argument('--weight_t', type=float, default=0.8) parser.add_argument('--detect_anomaly', action='store_true', default=False) parser.add_argument("--test_iterations", nargs="+", type=int, default=[2_000, 3_000, 4_000, 5000, 6000, 7_000, 8_000, 9_000] + list(range(10000, 40001, 1000))) parser.add_argument("--save_iterations", nargs="+", type=int, default=[5_000, 10_000, 15_000, 20_000, 40000]+ list(range(3000, 20001, 1000))) parser.add_argument("--quiet", action="store_true") args = parser.parse_args(sys.argv[1:]) args.save_iterations.append(args.iterations) weight_t_str = str(args.weight_t) weight_reg_str = str(args.weight_reg) args.model_path += f'_batch_{args.batch}_trans_{weight_t_str}_reg_{weight_reg_str}_layer_{args.n_layer}_random' print("Optimizing " + args.model_path) # Initialize system state (RNG) safe_state(args.quiet) # Start GUI server, configure and run training # network_gui.init(args.ip, args.port) torch.autograd.set_detect_anomaly(args.detect_anomaly) training(lp.extract(args), op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations) # All done print("\nTraining complete.")