# # 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 numpy as np import torch from random import randint from utils.loss_utils import l1_loss, ssim from gaussian_renderer import render, render_gsplat import sys from scene import Scene, Feat2GaussianModel from argparse import ArgumentParser from arguments import ModelParams, PipelineParams, OptimizationParams from utils.pose_utils import get_camera_from_tensor from tqdm import tqdm from time import perf_counter 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) colmap_poses = [] for i in range(len(index_colmap)): ind = index_colmap.index(i+1) bb=output_poses[ind] bb = bb#.inverse() colmap_poses.append(bb) colmap_poses = torch.stack(colmap_poses).detach().cpu().numpy() np.save(path, colmap_poses) def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from, args): first_iter = 0 # tb_writer = prepare_output_and_logger(dataset, opt.iterations) feat_type = '-'.join(args.feat_type) feat_dim = args.feat_dim if feat_type not in ['iuv', 'iuvrgb'] else dataset.feat_default_dim[feat_type] gs_params_group = dataset.gs_params_group[args.model] gaussians = Feat2GaussianModel(dataset.sh_degree, feat_dim, gs_params_group) scene = Scene(dataset, gaussians, opt=args, shuffle=True) gaussians.training_setup(opt) # if checkpoint: # (model_params, first_iter) = torch.load(checkpoint) # gaussians.restore(model_params, opt) train_cams_init = scene.getTrainCameras().copy() os.makedirs(scene.model_path + 'pose', exist_ok=True) # save_pose(scene.model_path + 'pose' + "/pose_org.npy", gaussians.P, train_cams_init) 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 warm_iter = 1000 start = perf_counter() 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, 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() if iteration > warm_iter: if iteration == warm_iter+1: gaussians.pc_feat.requires_grad_(False) gaussians.setup_rendering_learning_rate() gaussians.update_learning_rate(iteration - warm_iter) else: gaussians.update_warm_start_learning_rate(iteration) if args.optim_pose==False: gaussians.P.requires_grad_(False) # (DISABLED) 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)) pose = gaussians.get_RT(viewpoint_cam.uid) # Render if (iteration - 1) == debug_from: pipe.debug = True bg = torch.rand((3), device="cuda") if opt.random_background else background gaussians.inference() pretrained_loss_dict = { 'xyz': l1_loss(gaussians._xyz, gaussians.param_init['xyz']), # 'f_dc': l1_loss(gaussians._features_dc, gaussians.param_init['f_dc']), # 'f_rest': l1_loss(gaussians._features_rest, gaussians.param_init['f_rest']), 'opacity': l1_loss(gaussians._opacity, gaussians.param_init['opacity']), 'scaling': l1_loss(gaussians._scaling, gaussians.param_init['scaling']), 'rotation': l1_loss(gaussians._rotation, gaussians.param_init['rotation']), # 'pose': l1_loss(gaussians.P, gaussians.param_init['pose']), # 'focal': l1_loss(gaussians._focal_params, gaussians.param_init['focal']), # 'pc_feat':l1_loss(gaussians.pc_feat, gaussians.param_init['pc_feat']), } if iteration <= warm_iter: loss = sum(loss for key, loss in pretrained_loss_dict.items() if key in gs_params_group['head']) Ll1 = torch.tensor(0) if iteration > warm_iter: 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() Ll1 = l1_loss(image, gt_image) loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim(image, gt_image)) # if feat_type in ['iuv', 'iuvrgb']: # # Add scaling regularization for 'iuv' and 'iuvrgb' features # # Prevents their gaussians scale from becoming too large to cause CUDA out of memory # loss += l1_loss(gaussians._scaling, gaussians.param_init['scaling']) * 0.1 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:.{7}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, render_gsplat, (pipe, background), pretrained_loss_dict) if (iteration in saving_iterations): print("\n[ITER {}] Saving Gaussians".format(iteration)) scene.save(iteration) save_pose(scene.model_path + 'pose' + f"/pose_{iteration}.npy", gaussians.P, train_cams_init) # (DISABLED) 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: 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") end = perf_counter() train_time = end - start # We commented out log&save operations, and then calculate train time. # train_time = np.array(train_time) # print("total_test_time_epoch: ", 1) # print("train_time_mean: ", train_time.mean()) # print("train_time_median: ", np.median(train_time)) 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, \ 8_000, 9_000, 10_000, 11_000, 12_000, 13_000, 14_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("--feat_type", type=str, nargs='*', default=None, help="Feature type(s). Multiple types can be specified for combination.") parser.add_argument("--method", type=str, default='dust3r', help="Method of Initialization, e.g., 'dust3r' or 'mast3r'") parser.add_argument("--feat_dim", type=int, default=None, help="Feture dimension after PCA . If None, PCA is not applied.") parser.add_argument("--model", type=str, default='G', help="Model of Feat2gs, 'G'='geometry'/'T'='texture'/'A'='all'") args = parser.parse_args(sys.argv[1:]) args.save_iterations.append(args.iterations) os.makedirs(args.model_path, exist_ok=True) 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, args.checkpoint_iterations, args.start_checkpoint, args.debug_from, args) # All done print("\nTraining complete.")