# # 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 gc import random import torch from random import randint from utils.loss_utils import l1_loss, ssim, l2_loss, compute_depth 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 argparse import ArgumentParser, Namespace from arguments import ModelParams, PipelineParams, OptimizationParams, ModelHiddenParams from torch.utils.data import DataLoader from utils.timer import Timer # import lpips from utils.scene_utils import render_training_image from time import time import copy import numpy as np import time import json from utils.video_utils import render_pixels, save_videos from utils.visualization_tools import compute_optical_flow_and_save from scene.gaussian_model import merge_models to8b = lambda x : (255*np.clip(x.cpu().numpy(),0,1)).astype(np.uint8) # try: # from torch.utils.tensorboard import SummaryWriter # TENSORBOARD_FOUND = True # except ImportError: TENSORBOARD_FOUND = False current_time = time.strftime("%Y-%m-%d_%H-%M-%S", time.localtime()) render_keys = [ "gt_rgbs", "rgbs", "depths", "dynamic_rgbs", "static_rgbs", # "forward_flows", # "backward_flows", ] @torch.no_grad() def do_evaluation( viewpoint_stack_full, viewpoint_stack_test, viewpoint_stack_train, gaussians, bg, pipe, eval_dir, render_full, step: int = 0, args = None, ): if len(viewpoint_stack_test) != 0: print("Evaluating Test Set Pixels...") render_results = render_pixels( viewpoint_stack_test, gaussians, bg, pipe, compute_metrics=True, return_decomposition=True, debug=args.debug_test ) eval_dict = {} for k, v in render_results.items(): if k in [ "psnr", "ssim", "lpips", # "feat_psnr", "masked_psnr", "masked_ssim", # "masked_feat_psnr", ]: eval_dict[f"pixel_metrics/test/{k}"] = v os.makedirs(f"{eval_dir}/metrics", exist_ok=True) os.makedirs(f"{eval_dir}/test_videos", exist_ok=True) test_metrics_file = f"{eval_dir}/metrics/{step}_images_test_{current_time}.json" with open(test_metrics_file, "w") as f: json.dump(eval_dict, f) print(f"Image evaluation metrics saved to {test_metrics_file}") video_output_pth = f"{eval_dir}/test_videos/{step}.mp4" vis_frame_dict = save_videos( render_results, video_output_pth, num_timestamps=int(len(viewpoint_stack_test)//3), keys=render_keys, num_cams=3, save_seperate_video=True, save_images=args.eval_save_images, fps=24, verbose=True, ) del render_results, vis_frame_dict torch.cuda.empty_cache() if len(viewpoint_stack_train) != 0 and len(viewpoint_stack_test) != 0: print("Evaluating train Set Pixels...") render_results = render_pixels( viewpoint_stack_train, gaussians, bg, pipe, compute_metrics=True, return_decomposition=False, debug=args.debug_test ) eval_dict = {} for k, v in render_results.items(): if k in [ "psnr", "ssim", "lpips", # "feat_psnr", "masked_psnr", "masked_ssim", # "masked_feat_psnr", ]: eval_dict[f"pixel_metrics/train/{k}"] = v os.makedirs(f"{eval_dir}/metrics", exist_ok=True) os.makedirs(f"{eval_dir}/train_videos", exist_ok=True) train_metrics_file = f"{eval_dir}/metrics/{step}_images_train.json" with open(train_metrics_file, "w") as f: json.dump(eval_dict, f) print(f"Image evaluation metrics saved to {train_metrics_file}") video_output_pth = f"{eval_dir}/train_videos/{step}.mp4" vis_frame_dict = save_videos( render_results, video_output_pth, num_timestamps=int(len(viewpoint_stack_train)//3), keys=render_keys, num_cams=3, save_seperate_video=True, save_images=args.eval_save_images, fps=24, verbose=True, ) del render_results torch.cuda.empty_cache() if render_full: print("Evaluating Full Set...") render_results = render_pixels( viewpoint_stack_full, gaussians, bg, pipe, compute_metrics=True, return_decomposition=True, debug=args.debug_test ) eval_dict = {} for k, v in render_results.items(): if k in [ "psnr", "ssim", "lpips", # "feat_psnr", "masked_psnr", "masked_ssim", # "masked_feat_psnr", ]: eval_dict[f"pixel_metrics/full/{k}"] = v os.makedirs(f"{eval_dir}/metrics", exist_ok=True) os.makedirs(f"{eval_dir}/full_videos", exist_ok=True) test_metrics_file = f"{eval_dir}/metrics/{step}_images_full_{current_time}.json" with open(test_metrics_file, "w") as f: json.dump(eval_dict, f) print(f"Image evaluation metrics saved to {test_metrics_file}") # if render_video_postfix is None: video_output_pth = f"{eval_dir}/full_videos/{step}.mp4" vis_frame_dict = save_videos( render_results, video_output_pth, num_timestamps=int(len(viewpoint_stack_full)//3), keys=render_keys, num_cams=3, save_seperate_video=True, save_images=args.eval_save_images, fps=24, verbose=True, ) del render_results, vis_frame_dict torch.cuda.empty_cache() def scene_reconstruction(dataset, opt, hyper, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from, gaussians, scene, stage, tb_writer, train_iter,timer): first_iter = 0 gaussians.training_setup(opt) if checkpoint: # breakpoint() if stage == "coarse" and stage not in checkpoint: print("start from fine stage, skip coarse stage.") # process is in the coarse stage, but start from fine stage return if stage in 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") if args.eval_only: torch.save(gaussians._deformation.state_dict(),os.path.join(args.model_path, "deformation.pth")) eval_dir = os.path.join(args.model_path,"eval") os.makedirs(eval_dir,exist_ok=True) viewpoint_stack_full = scene.getFullCameras().copy() viewpoint_stack_test = scene.getTestCameras().copy() viewpoint_stack_train = scene.getTrainCameras().copy() # TODO：可视化光流 and 静动态点云分离 do_evaluation( viewpoint_stack_full, viewpoint_stack_test, viewpoint_stack_train, gaussians, background, pipe, eval_dir, render_full=True, step=first_iter, args=args ) # save 静动态点云分离 # pcd_dir = os.path.join(eval_dir, "split_pcd") # os.makedirs(eval_dir,exist_ok=True) # gaussians.save_ply_split(pcd_dir) exit() 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 ema_psnr_for_log = 0.0 final_iter = train_iter progress_bar = tqdm(range(first_iter, final_iter), desc="Training progress") first_iter += 1 # lpips_model = lpips.LPIPS(net="alex").cuda() # video_cams = scene.getVideoCameras() test_cams = scene.getTestCameras() train_cams = scene.getTrainCameras() if not viewpoint_stack: viewpoint_stack = [i for i in train_cams] temp_list = copy.deepcopy(viewpoint_stack) batch_size = opt.batch_size print("data loading done") count = 0 psnr_dict = {} for iteration in range(first_iter, final_iter+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: # count +=1 # viewpoint_index = (count ) % len(video_cams) # if (count //(len(video_cams))) % 2 == 0: # viewpoint_index = viewpoint_index # else: # viewpoint_index = len(video_cams) - viewpoint_index - 1 # # print(viewpoint_index) # viewpoint = video_cams[viewpoint_index] # custom_cam.time = viewpoint.time # # print(custom_cam.time, viewpoint_index, count) # net_image = render(custom_cam, gaussians, pipe, background, scaling_modifer, stage=stage)["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: # print(e) # network_gui.conn = None iter_start.record() position_lr = 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() # batch size idx = 0 viewpoint_cams = [] while idx < batch_size : viewpoint_cam = viewpoint_stack.pop(randint(0,len(viewpoint_stack)-1)) if not viewpoint_stack : viewpoint_stack = temp_list.copy() # print("find the worst viewpoint") # 对 PSNR 字典按值进行排序，找出最低的 PSNR 值对应的 UID, 最后一个psnr 没办法得到，所以实际上比较 n*3 -1 个psnr # with torch.no_grad(): # if 'fine' in stage: # psnr_dict = sorted(psnr_dict.items(), key=lambda x: x[1]) # # 将最低 PSNR 值对应的 UID 添加到列表中，直到列表的长度达到 args.end_time / 5 # lowest_psnr_uids = [] # for uid, _ in psnr_dict[:(args.end_time+1)]: # lowest_psnr_uids.append(uid) # psnr_dict = {} # # 将 lowest_psnr_uids 中 UID 对应的 Camera 对象加到 viewpoint_stack 的末尾 # for uid in lowest_psnr_uids: # for cam in viewpoint_stack: # if cam.uid == int(uid): # viewpoint_stack.append(cam) # break viewpoint_cams.append(viewpoint_cam) idx +=1 if len(viewpoint_cams) == 0: continue # print(len(viewpoint_cams)) # breakpoint() # Render if (iteration - 1) == debug_from: pipe.debug = True images = [] gt_images = [] depth_preds = [] gt_depths = [] radii_list = [] visibility_filter_list = [] viewspace_point_tensor_list = [] for viewpoint_cam in viewpoint_cams: render_pkg = render(viewpoint_cam, gaussians, pipe, background, stage=stage,return_dx=True,render_feat = True if ('fine' in stage and args.feat_head) else False) image, viewspace_point_tensor, visibility_filter, radii = render_pkg["render"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"] depth_pred = render_pkg["depth"] depth_preds.append(depth_pred.unsqueeze(0)) images.append(image.unsqueeze(0)) gt_image = viewpoint_cam.original_image.cuda() gt_depth = viewpoint_cam.depth_map.cuda() gt_images.append(gt_image.unsqueeze(0)) gt_depths.append(gt_depth.unsqueeze(0)) radii_list.append(radii.unsqueeze(0)) visibility_filter_list.append(visibility_filter.unsqueeze(0)) viewspace_point_tensor_list.append(viewspace_point_tensor) radii = torch.cat(radii_list,0).max(dim=0).values visibility_filter = torch.cat(visibility_filter_list).any(dim=0) image_tensor = torch.cat(images,0) depth_pred_tensor = torch.cat(depth_preds,0) gt_image_tensor = torch.cat(gt_images,0) gt_depth_tensor = torch.cat(gt_depths,0).float() # Loss # breakpoint() Ll1 = l1_loss(image_tensor, gt_image_tensor[:,:3,:,:]) psnr_ = psnr(image_tensor, gt_image_tensor).mean().double() # if 'fine' in stage: # psnr_dict.update({f"{viewpoint_cam.uid}": psnr_}) # norm loss = Ll1 # dx loss if 'fine' in stage and not args.no_dx and opt.lambda_dx !=0: dx_abs = torch.abs(render_pkg['dx']) dx_loss = torch.mean(dx_abs) * opt.lambda_dx loss += dx_loss if 'fine' in stage and not args.no_dshs and opt.lambda_dshs != 0: dshs_abs = torch.abs(render_pkg['dshs']) dshs_loss = torch.mean(dshs_abs) * opt.lambda_dshs loss += dshs_loss if opt.lambda_depth != 0: depth_loss = compute_depth("l2", depth_pred_tensor, gt_depth_tensor) * opt.lambda_depth loss += depth_loss if stage == "fine" and hyper.time_smoothness_weight != 0: # tv_loss = 0 tv_loss = gaussians.compute_regulation(hyper.time_smoothness_weight, hyper.l1_time_planes, hyper.plane_tv_weight) loss += tv_loss if opt.lambda_dssim != 0: ssim_loss = ssim(image_tensor,gt_image_tensor) loss += opt.lambda_dssim * (1.0-ssim_loss) if stage == 'fine' and args.feat_head: feat = render_pkg['feat'].to('cuda') # [3,640,960] gt_feat = viewpoint_cam.feat_map.permute(2,0,1).to('cuda') loss_feat = l2_loss(feat, gt_feat) * opt.lambda_feat loss += loss_feat # if opt.lambda_lpips !=0: # lpipsloss = lpips_loss(image_tensor,gt_image_tensor,lpips_model) # loss += opt.lambda_lpips * lpipsloss loss.backward() if torch.isnan(loss).any(): print("loss is nan,end training, reexecv program now.") os.execv(sys.executable, [sys.executable] + sys.argv) viewspace_point_tensor_grad = torch.zeros_like(viewspace_point_tensor) for idx in range(0, len(viewspace_point_tensor_list)): viewspace_point_tensor_grad = viewspace_point_tensor_grad + viewspace_point_tensor_list[idx].grad iter_end.record() with torch.no_grad(): # Progress bar ema_loss_for_log = 0.4 * loss.item() + 0.6 * ema_loss_for_log ema_psnr_for_log = 0.4 * psnr_ + 0.6 * ema_psnr_for_log total_point = gaussians._xyz.shape[0] if iteration % 100 == 0: dynamic_points = 0 if 'fine' in stage and not args.no_dx: dx_abs = torch.abs(render_pkg['dx']) # [N,3] max_values = torch.max(dx_abs, dim=1)[0] # [N] thre = torch.mean(max_values) mask = (max_values > thre) dynamic_points = torch.sum(mask).item() print_dict = { "step": f"{iteration}", "Loss": f"{ema_loss_for_log:.{7}f}", "psnr": f"{psnr_:.{2}f}", "dynamic point": f"{dynamic_points}", "point":f"{total_point}", } progress_bar.set_postfix(print_dict) metrics_file = f"{scene.model_path}/logger.json" with open(metrics_file, "a") as f: json.dump(print_dict, f) f.write('\n') progress_bar.update(100) if iteration == final_iter: progress_bar.close() # Log and save timer.pause() # training_report(tb_writer, iteration, Ll1, loss, l1_loss, iter_start.elapsed_time(iter_end), testing_iterations, scene, render, [pipe, background], stage) # if (iteration in saving_iterations): # print("\n[ITER {}] Saving Gaussians".format(iteration)) # scene.save(iteration, stage) if dataset.render_process: if (iteration < 10000 and iteration % 1000 == 999) \ or (iteration < 30000 and iteration % 2000 == 1999) \ or (iteration < 60000 and iteration % 3000 == 2999) : # breakpoint() if len(test_cams) != 0: render_training_image(scene, gaussians, [test_cams[iteration%len(test_cams)]], render, pipe, background, stage+"test", iteration,timer.get_elapsed_time()) render_training_image(scene, gaussians, [train_cams[iteration%len(train_cams)]], render, pipe, background, stage+"train", iteration,timer.get_elapsed_time()) # total_images.append(to8b(temp_image).transpose(1,2,0)) timer.start() # 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_grad, visibility_filter) if stage == "coarse": opacity_threshold = opt.opacity_threshold_coarse densify_threshold = opt.densify_grad_threshold_coarse else: opacity_threshold = opt.opacity_threshold_fine_init - iteration*(opt.opacity_threshold_fine_init - opt.opacity_threshold_fine_after)/(opt.densify_until_iter) densify_threshold = opt.densify_grad_threshold_fine_init - iteration*(opt.densify_grad_threshold_fine_init - opt.densify_grad_threshold_after)/(opt.densify_until_iter ) if iteration > opt.densify_from_iter and iteration % opt.densification_interval == 0 and gaussians.get_xyz.shape[0]<2000000: size_threshold = 20 if iteration > opt.opacity_reset_interval else None gaussians.densify(densify_threshold, opacity_threshold, scene.cameras_extent, size_threshold, 5, 5, scene.model_path, iteration, stage) if iteration > opt.pruning_from_iter and iteration % opt.pruning_interval == 0 : # and gaussians.get_xyz.shape[0]>200000 size_threshold = 20 if iteration > opt.opacity_reset_interval else None gaussians.prune(densify_threshold, opacity_threshold, scene.cameras_extent, size_threshold) # if iteration > opt.densify_from_iter and iteration % opt.densification_interval == 0 : # if iteration % opt.densification_interval == 0 and gaussians.get_xyz.shape[0]<360000 and opt.add_point: # gaussians.grow(5,5,scene.model_path,iteration,stage) # torch.cuda.empty_cache() if iteration % opt.opacity_reset_interval == 0: print("reset opacity") gaussians.reset_opacity() # Optimizer step if iteration < final_iter+1: gaussians.optimizer.step() gaussians.optimizer.zero_grad(set_to_none = True) if (iteration in checkpoint_iterations): save_path= "chkpnt" +f"_{stage}_" + str(30000) + ".pth" for file in os.listdir(scene.model_path): if file.endswith(".pth") and file != save_path: os.remove(os.path.join(scene.model_path, file)) print("\n[ITER {}] Saving Checkpoint".format(iteration)) torch.save((gaussians.capture(), iteration), scene.model_path + "/chkpnt" +f"_{stage}_" + str(iteration) + ".pth") if (iteration == 30000): eval_dir = os.path.join(args.model_path,"eval") os.makedirs(eval_dir,exist_ok=True) viewpoint_stack_full = scene.getFullCameras().copy() viewpoint_stack_test = scene.getTestCameras().copy() viewpoint_stack_train = scene.getTrainCameras().copy() do_evaluation( viewpoint_stack_full, viewpoint_stack_test, viewpoint_stack_train, gaussians, background, pipe, eval_dir, render_full=True, step=iteration, args=args ) def training(dataset, hyper, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from, expname): # first_iter = 0 tb_writer = prepare_output_and_logger(expname) gaussians = GaussianModel(dataset.sh_degree, hyper) dataset.model_path = args.model_path timer = Timer() scene = Scene(dataset, gaussians, load_coarse=None) timer.start() # eval eval_dir = os.path.join(args.model_path,"eval") os.makedirs(eval_dir,exist_ok=True) viewpoint_stack_full = scene.getFullCameras().copy() viewpoint_stack_test = scene.getTestCameras().copy() viewpoint_stack_train = scene.getTrainCameras().copy() bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0] background = torch.tensor(bg_color, dtype=torch.float32, device="cuda") # if args.merge and args.prior_checkpoint and args.prior_checkpoint2: # # 这个是最新的，deformation的网络要用这个 # gaussians_new = GaussianModel(dataset.sh_degree, hyper) # (model_params, first_iter) = torch.load(args.prior_checkpoint2) # gaussians_new.restore(model_params, opt) # deformation_net = gaussians_new._deformation # del gaussians_new # gc.collect() # torch.cuda.empty_cache() # # 这个是上一个 # gaussians_prev = GaussianModel(dataset.sh_degree, hyper) # (model_params, first_iter) = torch.load(args.prior_checkpoint) # gaussians_prev.restore(model_params, opt) # gaussians_prev._deformation = deformation_net.to('cuda') # do_evaluation( # viewpoint_stack_full, # viewpoint_stack_test, # gaussians_prev, # background, # pipe, # eval_dir, # render_full=True, # step=99999, # args=args # ) # merge # gaussians = merge_models(gaussians_new, gaussians_prev, hyper, gaussians) scene_reconstruction(dataset, opt, hyper, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from, gaussians, scene, "coarse", tb_writer, opt.coarse_iterations,timer) if args.prior_checkpoint: assert 'fine' in args.prior_checkpoint gaussians_prev = GaussianModel(dataset.sh_degree, hyper) (model_params, first_iter) = torch.load(args.prior_checkpoint) gaussians_prev.restore(model_params, opt) deformation_net = gaussians_prev._deformation del gaussians_prev gc.collect() torch.cuda.empty_cache() gaussians._deformation = deformation_net.to('cuda') scene_reconstruction(dataset, opt, hyper, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from, gaussians, scene, "fine", tb_writer, opt.iterations,timer) do_evaluation( viewpoint_stack_full, viewpoint_stack_test, viewpoint_stack_train, gaussians, background, pipe, eval_dir, render_full=True, step=opt.iterations, args=args ) def prepare_output_and_logger(expname): if not args.model_path: # if os.getenv('OAR_JOB_ID'): # unique_str=os.getenv('OAR_JOB_ID') # else: # unique_str = str(uuid.uuid4()) unique_str = expname args.model_path = os.path.join("./output/", unique_str) # 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 = None # 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, stage): if tb_writer: tb_writer.add_scalar(f'{stage}/train_loss_patches/l1_loss', Ll1.item(), iteration) tb_writer.add_scalar(f'{stage}/train_loss_patchestotal_loss', loss.item(), iteration) tb_writer.add_scalar(f'{stage}/iter_time', elapsed, iteration) # Report test and samples of training set if iteration in testing_iterations: torch.cuda.empty_cache() if len(scene.getTestCameras()) != 0: validation_configs = ({'name': 'test', 'cameras' : [scene.getTestCameras()[idx % len(scene.getTestCameras())] for idx in range(10, 5000, 299)]}, {'name': 'train', 'cameras' : [scene.getTrainCameras()[idx % len(scene.getTrainCameras())] for idx in range(10, 5000, 299)]}) else: validation_configs = ({'name': 'train', 'cameras' : [scene.getTrainCameras()[idx % len(scene.getTrainCameras())] for idx in range(10, 5000, 299)]}) 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,stage=stage, *renderArgs)["render"], 0.0, 1.0) gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0) try: if tb_writer and (idx < 5): tb_writer.add_images(stage + "/"+config['name'] + "_view_{}/render".format(viewpoint.image_name), image[None], global_step=iteration) if iteration == testing_iterations[0]: tb_writer.add_images(stage + "/"+config['name'] + "_view_{}/ground_truth".format(viewpoint.image_name), gt_image[None], global_step=iteration) except: pass l1_test += l1_loss(image, gt_image).mean().double() # mask=viewpoint.mask 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)) # print("sh feature",scene.gaussians.get_features.shape) if tb_writer: tb_writer.add_scalar(stage + "/"+config['name'] + '/loss_viewpoint - l1_loss', l1_test, iteration) tb_writer.add_scalar(stage+"/"+config['name'] + '/loss_viewpoint - psnr', psnr_test, iteration) if tb_writer: tb_writer.add_histogram(f"{stage}/scene/opacity_histogram", scene.gaussians.get_opacity, iteration) tb_writer.add_scalar(f'{stage}/total_points', scene.gaussians.get_xyz.shape[0], iteration) tb_writer.add_scalar(f'{stage}/deformation_rate', scene.gaussians._deformation_table.sum()/scene.gaussians.get_xyz.shape[0], iteration) tb_writer.add_histogram(f"{stage}/scene/motion_histogram", scene.gaussians._deformation_accum.mean(dim=-1)/100, iteration,max_bins=500) torch.cuda.empty_cache() def setup_seed(seed): torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) random.seed(seed) torch.backends.cudnn.deterministic = True if __name__ == "__main__": # Set up command line argument parser # torch.set_default_tensor_type('torch.FloatTensor') torch.cuda.empty_cache() parser = ArgumentParser(description="Training script parameters") setup_seed(6666) lp = ModelParams(parser) op = OptimizationParams(parser) pp = PipelineParams(parser) hp = ModelHiddenParams(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=[3000,7000,14000]) parser.add_argument("--save_iterations", nargs="+", type=int, default=[ 14000, 20000, 30_000, 45000, 60000]) parser.add_argument("--quiet", action="store_true") parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[10_000,20_000,30_000,40_000,50_000]) parser.add_argument("--start_checkpoint", type=str, default = None) parser.add_argument("--expname", type=str, default = "waymo") parser.add_argument("--configs", type=str, default = "") parser.add_argument("--eval_only", action="store_true", help="perform evaluation only") parser.add_argument("--eval_save_images", action="store_true", help="also save rendered eval frames as PNG images") parser.add_argument("--prior_checkpoint", type=str, default = None) parser.add_argument("--merge", action="store_true", help="merge gaussians") parser.add_argument("--prior_checkpoint2", type=str, default = None) args = parser.parse_args(sys.argv[1:]) args.save_iterations.append(args.iterations) if args.configs: import mmcv from utils.params_utils import merge_hparams config = mmcv.Config.fromfile(args.configs) args = merge_hparams(args, config) 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), hp.extract(args), op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations, args.checkpoint_iterations, args.start_checkpoint, args.debug_from, args.expname) # All done print("\nTraining complete.")