From 1404b5e894f6782b413883f0ef98c30fa4558727 Mon Sep 17 00:00:00 2001 From: Xun2001 Date: Fri, 6 Jun 2025 10:40:25 +0800 Subject: [PATCH] sky init without densify --- scene/gaussian_model.py | 2 +- train-densify.py | 285 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 286 insertions(+), 1 deletion(-) create mode 100644 train-densify.py diff --git a/scene/gaussian_model.py b/scene/gaussian_model.py index 271d778..e06a4a9 100644 --- a/scene/gaussian_model.py +++ b/scene/gaussian_model.py @@ -229,7 +229,7 @@ class GaussianModel: xyz = torch.concat((groundbox_xyz.cuda(), xyz), dim=0) fused_color = torch.concat((groundbox_color.cuda(), fused_color), dim=0) - debug_xy = True + debug_xy = False if debug_xy: folder = "/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_debug/mini3/outputs_debug" sky_ply_path = os.path.join(folder, "skybox_scene_init.ply") diff --git a/train-densify.py b/train-densify.py new file mode 100644 index 0000000..8206903 --- /dev/null +++ b/train-densify.py @@ -0,0 +1,285 @@ +# +# 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, get_expon_lr_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 + +try: + from fused_ssim import fused_ssim + FUSED_SSIM_AVAILABLE = True +except: + FUSED_SSIM_AVAILABLE = False + +try: + from diff_gaussian_rasterization import SparseGaussianAdam + SPARSE_ADAM_AVAILABLE = True +except: + SPARSE_ADAM_AVAILABLE = False + +def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from): + + if not SPARSE_ADAM_AVAILABLE and opt.optimizer_type == "sparse_adam": + sys.exit(f"Trying to use sparse adam but it is not installed, please install the correct rasterizer using pip install [3dgs_accel].") + + first_iter = 0 + tb_writer = prepare_output_and_logger(dataset) + gaussians = GaussianModel(dataset.sh_degree, opt.optimizer_type) + 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") + + iter_start = torch.cuda.Event(enable_timing = True) + iter_end = torch.cuda.Event(enable_timing = True) + + use_sparse_adam = opt.optimizer_type == "sparse_adam" and SPARSE_ADAM_AVAILABLE + depth_l1_weight = get_expon_lr_func(opt.depth_l1_weight_init, opt.depth_l1_weight_final, max_steps=opt.iterations) + + viewpoint_stack = scene.getTrainCameras().copy() + viewpoint_indices = list(range(len(viewpoint_stack))) + ema_loss_for_log = 0.0 + ema_Ll1depth_for_log = 0.0 + + 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, scaling_modifier=scaling_modifer, use_trained_exp=dataset.train_test_exp, separate_sh=SPARSE_ADAM_AVAILABLE)["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_indices = list(range(len(viewpoint_stack))) + rand_idx = randint(0, len(viewpoint_indices) - 1) + viewpoint_cam = viewpoint_stack.pop(rand_idx) + vind = viewpoint_indices.pop(rand_idx) + + # Render + if (iteration - 1) == debug_from: + pipe.debug = True + + bg = torch.rand((3), device="cuda") if opt.random_background else background + + render_pkg = render(viewpoint_cam, gaussians, pipe, bg, use_trained_exp=dataset.train_test_exp, separate_sh=SPARSE_ADAM_AVAILABLE) + image, viewspace_point_tensor, visibility_filter, radii = render_pkg["render"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"] + + if viewpoint_cam.alpha_mask is not None: + alpha_mask = viewpoint_cam.alpha_mask.cuda() + image *= alpha_mask + + # Loss + gt_image = viewpoint_cam.original_image.cuda() + Ll1 = l1_loss(image, gt_image) + if FUSED_SSIM_AVAILABLE: + ssim_value = fused_ssim(image.unsqueeze(0), gt_image.unsqueeze(0)) + else: + ssim_value = ssim(image, gt_image) + + loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim_value) + + # Depth regularization + Ll1depth_pure = 0.0 + if depth_l1_weight(iteration) > 0 and viewpoint_cam.depth_reliable: + invDepth = render_pkg["depth"] + mono_invdepth = viewpoint_cam.invdepthmap.cuda() + depth_mask = viewpoint_cam.depth_mask.cuda() + + Ll1depth_pure = torch.abs((invDepth - mono_invdepth) * depth_mask).mean() + Ll1depth = depth_l1_weight(iteration) * Ll1depth_pure + loss += Ll1depth + Ll1depth = Ll1depth.item() + else: + Ll1depth = 0 + + 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_Ll1depth_for_log = 0.4 * Ll1depth + 0.6 * ema_Ll1depth_for_log + + if iteration % 10 == 0: + progress_bar.set_postfix({"Loss": f"{ema_loss_for_log:.{7}f}", "Depth Loss": f"{ema_Ll1depth_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, (pipe, background, 1., SPARSE_ADAM_AVAILABLE, None, dataset.train_test_exp), dataset.train_test_exp) + 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, 0.005, scene.cameras_extent, size_threshold, radii) + + 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.exposure_optimizer.step() + gaussians.exposure_optimizer.zero_grad(set_to_none = True) + if use_sparse_adam: + visible = radii > 0 + gaussians.optimizer.step(visible, radii.shape[0]) + gaussians.optimizer.zero_grad(set_to_none = True) + else: + 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, l1_loss, elapsed, testing_iterations, scene : Scene, renderFunc, renderArgs, train_test_exp): + 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()}, + {'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']): + image = torch.clamp(renderFunc(viewpoint, scene.gaussians, *renderArgs)["render"], 0.0, 1.0) + gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0) + if train_test_exp: + image = image[..., image.shape[-1] // 2:] + gt_image = gt_image[..., gt_image.shape[-1] // 2:] + 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 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('--disable_viewer', action='store_true', default=False) + 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 + if not args.disable_viewer: + 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) + + # All done + print("\nTraining complete.")