# # 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, network_gui, render_confidence import sys from scene import Scene, GaussianModel from utils.general_utils import safe_state from tqdm import tqdm from utils.image_utils import psnr from argparse import ArgumentParser, Namespace from arguments import ModelParams, PipelineParams, OptimizationParams from scene.cameras import Camera from utils.pose_utils import get_camera_from_tensor import torchvision import dearpygui.dearpygui as dpg from scipy.spatial.transform import Rotation import random def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from, args): first_iter = 0 gaussians = GaussianModel(dataset.sh_degree) scene = Scene(dataset, gaussians, opt=args, shuffle=False) 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) viewpoint_stack = None ema_loss_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): iter_start.record() gaussians.update_learning_rate(iteration) if args.optim_pose==False: gaussians.get_P().requires_grad_(False) # Every 1000 its we increase the levels of SH up to a maximum degree if iteration % 3000 == 0: gaussians.oneupSHdegree() # Pick a random Camera if not viewpoint_stack: viewpoint_stack = scene.getTrainCameras().copy() # Render if (iteration - 1) == debug_from: pipe.debug = True bg = torch.rand((3), device="cuda") if opt.random_background else background viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack)-1)) pose = gaussians.get_RT(viewpoint_cam.uid) 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() static = gaussians._conf_static[viewpoint_cam.uid] image = image * static gt_image = gt_image * static Ll1 = l1_loss(image, gt_image, reduce=False) Lssim = ssim(image, gt_image, size_average=False) psnr_frame = psnr(image, gt_image).mean() loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - Lssim) loss = (loss).mean() loss.backward(retain_graph=True) with torch.no_grad(): gaussians.optimizer.step() gaussians.optimizer.zero_grad(set_to_none = True) if psnr_frame > args.psnr_threshold: gaussians.optimizer_cam.step() gaussians.optimizer_cam.zero_grad(set_to_none = True) if not viewpoint_stack: viewpoint_stack = scene.getTestCameras().copy() # Render if (iteration - 1) == debug_from: pipe.debug = True bg = torch.rand((3), device="cuda") if opt.random_background else background while len(viewpoint_stack) > 0: viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack)-1)) # print(f"getting test cam pose frame {viewpoint_cam.colmap_id}") pose = gaussians.get_RT_test(viewpoint_cam.uid) 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() gt_static_mask = 1 - viewpoint_cam.gt_dynamic_mask.to("cuda") image = image * gt_static_mask gt_image = gt_image * gt_static_mask Ll1 = l1_loss(image, gt_image, reduce=False) Lssim = ssim(image, gt_image, size_average=False) psnr_frame = psnr(image, gt_image).mean() loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - Lssim) loss = (loss).mean() loss.backward(retain_graph=True) with torch.no_grad(): # gaussians.optimizer.step() gaussians.optimizer.zero_grad(set_to_none = True) if psnr_frame > args.psnr_threshold: gaussians.optimizer_cam.step() gaussians.optimizer_cam.zero_grad(set_to_none = True) 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(iteration, Ll1, loss, l1_loss, iter_start.elapsed_time(iter_end), testing_iterations, scene, render, (pipe, background)) 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) # 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: 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 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) return index_colmap, output_poses def convert_colmap_to_quat(colmap_poses): quat_pose = [] for pose in colmap_poses: rotation = Rotation.from_matrix(pose[:3, :3]) quat = rotation.as_quat() translation = pose[:3, 3] quat_pose.append(np.concatenate([quat, translation])) return quat_pose def c2w_to_tumpose(c2w): """ Convert a camera-to-world matrix to a tuple of translation and rotation input: c2w: 4x4 matrix output: tuple of translation and rotation (x y z qw qx qy qz) """ # convert input to numpy c2w = c2w c2w = np.linalg.inv(c2w) xyz = c2w[:3, -1] rot = Rotation.from_matrix(c2w[:3, :3]) qx, qy, qz, qw = rot.as_quat() tum_pose = np.concatenate([xyz, [qw, qx, qy, qz]]) return tum_pose def tumpose_to_c2w(tum_pose): """ Convert a tuple of translation and rotation to a camera-to-world matrix input: tum_pose: tuple of translation and rotation (x y z qw qx qy qz) output: c2w: 4x4 matrix """ xyz = tum_pose[:3] qw, qx, qy, qz = tum_pose[3:] rot = Rotation.from_quat([qx, qy, qz, qw]) c2w = np.eye(4) c2w[:3, :3] = rot.as_matrix() c2w[:3, -1] = xyz c2w = np.linalg.inv(c2w) return c2w def training_report(iteration, Ll1, loss, l1_loss, elapsed, testing_iterations, scene : Scene, renderFunc, renderArgs): # Report test and samples of training set if iteration in testing_iterations: torch.cuda.empty_cache() validation_configs = ({'name': 'test', 'cameras' : scene.getTestCameras()},) for config in validation_configs: if config['cameras'] and len(config['cameras']) > 0: l1_test = 0.0 psnr_test = 0.0 lens = 0 for idx, viewpoint in enumerate(config['cameras']): if config['name']=="train": pose = scene.gaussians.get_RT(viewpoint.uid) else: pose = scene.gaussians.get_RT_test(viewpoint.uid) image = torch.clamp(renderFunc(viewpoint, scene.gaussians, *renderArgs, camera_pose=pose)["render"], 0.0, 1.0) torchvision.utils.save_image( image, os.path.join(scene.model_path, "{0:05d}".format(viewpoint.colmap_id) + ".png") ) gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0) if hasattr(viewpoint, 'gt_dynamic_mask'): gt_static_mask = 1 - viewpoint.gt_dynamic_mask.to("cuda") np.save(os.path.join(scene.model_path, f"{viewpoint.colmap_id}_image.npy"), image.cpu().numpy()) np.save(os.path.join(scene.model_path, f"{viewpoint.colmap_id}_gt_image.npy"), gt_image.cpu().numpy()) np.save(os.path.join(scene.model_path, f"{viewpoint.colmap_id}_gt_static_mask.npy"), gt_static_mask.cpu().numpy()) image = image * gt_static_mask gt_image = gt_image * gt_static_mask l1_test += l1_loss(image, gt_image).mean().double() psnr_test += psnr(image, gt_image).mean().double() # import matplotlib.pyplot as plt # plt.figure(figsize=(10, 5)) # plt.subplot(1, 2, 1) # plt.title('Predicted Image') # plt.imshow(image.cpu().permute(1,2,0)) # plt.axis('off') # plt.subplot(1, 2, 2) # plt.title('Ground Truth Image') # plt.imshow(gt_image.cpu().permute(1,2,0)) # plt.axis('off') # plt.show() lens += 1 psnr_test /= lens l1_test /= lens print("\n[ITER {}] Evaluating {}: L1 {} PSNR {}".format(iteration, config['name'], l1_test, psnr_test)) with open(os.path.join(scene.model_path, f"{config['name']}_log.txt"), 'a') as log_file: log_file.write(f"[ITER {iteration}] Evaluating {config['name']}: L1 {l1_test} PSNR {psnr_test}\n") 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=[500, 800, 1000, 1500, 2000, 3000, 4000, 5000, 6000, 7_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("--gui", action="store_true") parser.add_argument("--eval_pose", action="store_true") parser.add_argument('--pose_eval_interval', type=int, default=100) parser.add_argument('--psnr_threshold', type=float, default=26) parser.add_argument('--gt_dynamic_mask', type=str, default='data/sintel/training/dynamic_label_perfect') parser.add_argument('--dataset', type=str, default='sintel') args = parser.parse_args(sys.argv[1:]) args.save_iterations.append(args.iterations) lp.eval = True args.eval = True os.makedirs(args.model_path, exist_ok=True) print("Optimizing " + args.model_path) 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.")