# # 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 from gaussian_renderer import render, network_gui import sys from scene import Scene, GaussianModel from utils.general_utils import safe_state import uuid from tqdm import tqdm from utils.image_utils import psnr from utils.graphics_utils import point_double_to_normal, depth_double_to_normal 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 from scene.cameras import Camera import matplotlib.pyplot as plt from utils.vis_utils import apply_depth_colormap # function L1_loss_appearance is fork from GOF https://github.com/autonomousvision/gaussian-opacity-fields/blob/main/train.py def L1_loss_appearance(image, gt_image, gaussians, view_idx, return_transformed_image=False): appearance_embedding = gaussians.get_apperance_embedding(view_idx) # center crop the image origH, origW = image.shape[1:] H = origH // 32 * 32 W = origW // 32 * 32 left = origW // 2 - W // 2 top = origH // 2 - H // 2 crop_image = image[:, top:top+H, left:left+W] crop_gt_image = gt_image[:, top:top+H, left:left+W] # down sample the image crop_image_down = torch.nn.functional.interpolate(crop_image[None], size=(H//32, W//32), mode="bilinear", align_corners=True)[0] crop_image_down = torch.cat([crop_image_down, appearance_embedding[None].repeat(H//32, W//32, 1).permute(2, 0, 1)], dim=0)[None] mapping_image = gaussians.appearance_network(crop_image_down) transformed_image = mapping_image * crop_image if not return_transformed_image: return l1_loss(transformed_image, crop_gt_image) else: transformed_image = torch.nn.functional.interpolate(transformed_image, size=(origH, origW), mode="bilinear", align_corners=True)[0] return transformed_image def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from): first_iter = 0 tb_writer = prepare_output_and_logger(dataset) gaussians = GaussianModel(dataset.sh_degree) scene = Scene(dataset, gaussians) 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") kernel_size = dataset.kernel_size iter_start = torch.cuda.Event(enable_timing = True) iter_end = torch.cuda.Event(enable_timing = True) trainCameras = scene.getTrainCameras().copy() if dataset.disable_filter3D: gaussians.reset_3D_filter() else: gaussians.compute_3D_filter(cameras=trainCameras) viewpoint_stack = None ema_loss_for_log, ema_depth_loss_for_log, ema_mask_loss_for_log, ema_normal_loss_for_log = 0.0, 0.0, 0.0, 0.0 require_depth = not dataset.use_coord_map require_coord = dataset.use_coord_map progress_bar = tqdm(range(first_iter, opt.iterations), desc="Training progress") first_iter += 1 for iteration in range(first_iter, 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.convert_SHs_python, pipe.compute_cov3D_python, keep_alive, scaling_modifer = network_gui.receive() if custom_cam != None: net_image = render(custom_cam, gaussians, pipe, background, kernel_size, 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() 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: Camera = viewpoint_stack.pop(randint(0, len(viewpoint_stack)-1)) # Render if (iteration - 1) == debug_from: pipe.debug = True reg_kick_on = iteration >= opt.regularization_from_iter render_pkg = render(viewpoint_cam, gaussians, pipe, background, kernel_size, require_coord = require_coord and reg_kick_on, require_depth = require_depth and reg_kick_on) rendered_image: torch.Tensor rendered_image, viewspace_point_tensor, visibility_filter, radii = ( render_pkg["render"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"]) gt_image = viewpoint_cam.original_image.cuda() if dataset.use_decoupled_appearance: Ll1_render = L1_loss_appearance(rendered_image, gt_image, gaussians, viewpoint_cam.uid) else: Ll1_render = l1_loss(rendered_image, gt_image) if reg_kick_on: lambda_depth_normal = opt.lambda_depth_normal if require_depth: rendered_expected_depth: torch.Tensor = render_pkg["expected_depth"] rendered_median_depth: torch.Tensor = render_pkg["median_depth"] rendered_normal: torch.Tensor = render_pkg["normal"] depth_middepth_normal = depth_double_to_normal(viewpoint_cam, rendered_expected_depth, rendered_median_depth) else: rendered_expected_coord: torch.Tensor = render_pkg["expected_coord"] rendered_median_coord: torch.Tensor = render_pkg["median_coord"] rendered_normal: torch.Tensor = render_pkg["normal"] depth_middepth_normal = point_double_to_normal(viewpoint_cam, rendered_expected_coord, rendered_median_coord) depth_ratio = 0.6 normal_error_map = (1 - (rendered_normal.unsqueeze(0) * depth_middepth_normal).sum(dim=1)) depth_normal_loss = (1-depth_ratio) * normal_error_map[0].mean() + depth_ratio * normal_error_map[1].mean() else: lambda_depth_normal = 0 depth_normal_loss = torch.tensor([0],dtype=torch.float32,device="cuda") rgb_loss = (1.0 - opt.lambda_dssim) * Ll1_render + opt.lambda_dssim * (1.0 - ssim(rendered_image, gt_image.unsqueeze(0))) loss = rgb_loss + depth_normal_loss * lambda_depth_normal 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 ema_normal_loss_for_log = 0.4 * depth_normal_loss.item() + 0.6 * ema_normal_loss_for_log if iteration % 10 == 0: progress_bar.set_postfix({"Loss": f"{ema_loss_for_log:.{4}f}", "loss_normal": f"{ema_normal_loss_for_log:.{4}f}"}) progress_bar.update(10) if iteration == opt.iterations: progress_bar.close() # Log and save training_report(tb_writer, iteration, Ll1_render, loss, depth_normal_loss, l1_loss, iter_start.elapsed_time(iter_end), testing_iterations, scene, render, (pipe, background, kernel_size)) 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, 0.05, scene.cameras_extent, size_threshold) if dataset.disable_filter3D: gaussians.reset_3D_filter() else: gaussians.compute_3D_filter(cameras=trainCameras) if iteration % opt.opacity_reset_interval == 0 or (dataset.white_background and iteration == opt.densify_from_iter): gaussians.reset_opacity() if iteration % 100 == 0 and iteration > opt.densify_until_iter and not dataset.disable_filter3D: if iteration < opt.iterations - 100: # don't update in the end of training gaussians.compute_3D_filter(cameras=trainCameras) # Optimizer step if iteration < opt.iterations: gaussians.optimizer.step() gaussians.optimizer.zero_grad(set_to_none = True) 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 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, normal_loss, l1_loss, elapsed, testing_iterations, scene : Scene, renderFunc, renderArgs): if tb_writer: tb_writer.add_scalar('train_loss_patches/l1_loss', Ll1.item(), iteration) tb_writer.add_scalar('train_loss_patches/normal_loss', normal_loss.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()}, {'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']): render_result = renderFunc(viewpoint, scene.gaussians, *renderArgs) image = torch.clamp(render_result["render"], 0.0, 1.0) gt_image = torch.clamp(viewpoint.original_image.cuda(), 0.0, 1.0) 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(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 config["name"] == "test": with open(scene.model_path + "/chkpnt" + str(iteration) + ".txt", "w") as file_object: print("\n[ITER {}] Evaluating {}: L1 {} PSNR {}".format(iteration, config['name'], l1_test, psnr_test), file=file_object) 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=[7_000, 30_000]) parser.add_argument("--save_iterations", nargs="+", type=int, default=[7_000, 30_000]) parser.add_argument("--quiet", action="store_true") parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[15000]) 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 # network_gui.init(args.ip, args.port) # torch.autograd.set_detect_anomaly(args.detect_anomaly) training(dataset=lp.extract(args), opt=op.extract(args), pipe=pp.extract(args), testing_iterations=args.test_iterations, saving_iterations=args.save_iterations, checkpoint_iterations=args.checkpoint_iterations, checkpoint=args.start_checkpoint, debug_from=args.debug_from) # All done print("\nTraining complete.")