# # 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 PIL import Image from mediapy import read_video from utils.loss_utils import l1_loss, ssim, lpips 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 from scripts.sampling.simple_mv_latent_sample import sample_one try: from torch.utils.tensorboard import SummaryWriter TENSORBOARD_FOUND = True except ImportError: TENSORBOARD_FOUND = False 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") 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): 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() 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 = viewpoint_stack.pop(randint(0, len(viewpoint_stack) - 1)) # 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) 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 opt.lambda_lpips > 0: loss += opt.lambda_lpips * lpips(image, gt_image) loss += torch.mean(gaussians.get_opacity) * 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, (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: 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, ): 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 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("--seed", type=int, default=42) parser.add_argument("--video", type=str, default="") 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=[]) parser.add_argument("--start_checkpoint", type=str, default=None) parser.add_argument("--border_ratio", type=float, default=0.3) parser.add_argument("--min_guidance_scale", type=float, default=1.0) parser.add_argument("--max_guidance_scale", type=float, default=2.5) parser.add_argument("--sigma_max", type=float, 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) print("=====Start generating MV Images=====") # images, _ = sample_one( # args.image, # args.ckpt_path, # seed=args.seed, # border_ratio=args.border_ratio, # min_guidance_scale=args.min_guidance_scale, # max_guidance_scale=args.max_guidance_scale, # sigma_max=args.sigma_max, # ) images = [] frames = read_video(args.video) for frame in frames: images.append(Image.fromarray(frame)) print("=====Finish generating MV Images=====") lp = lp.extract(args) lp.images = images training( lp, 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.")