344 lines
17 KiB
Python
344 lines
17 KiB
Python
#
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# Copyright (C) 2023, Inria
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# GRAPHDECO research group, https://team.inria.fr/graphdeco
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# All rights reserved.
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#
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# This software is free for non-commercial, research and evaluation use
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# under the terms of the LICENSE.md file.
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#
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# For inquiries contact george.drettakis@inria.fr
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#
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import numpy as np
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import random
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import os
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import torch
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from random import randint
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from utils.loss_utils import l1_loss, ssim, l2_loss, lpips_loss
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from gaussian_renderer import render, network_gui
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import sys
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from scene import Scene, GaussianModel
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from utils.general_utils import safe_state
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import uuid
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from tqdm import tqdm
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from utils.image_utils import psnr
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from argparse import ArgumentParser, Namespace
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from arguments import ModelParams, PipelineParams, OptimizationParams, ModelHiddenParams
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from torch.utils.data import DataLoader
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from utils.timer import Timer
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import lpips
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from utils.scene_utils import render_training_image
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from time import time
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to8b = lambda x : (255*np.clip(x.cpu().numpy(),0,1)).astype(np.uint8)
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try:
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from torch.utils.tensorboard import SummaryWriter
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TENSORBOARD_FOUND = True
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except ImportError:
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TENSORBOARD_FOUND = False
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def scene_reconstruction(dataset, opt, hyper, pipe, testing_iterations, saving_iterations,
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checkpoint_iterations, checkpoint, debug_from,
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gaussians, scene, stage, tb_writer, train_iter,timer):
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first_iter = 0
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gaussians.training_setup(opt)
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if checkpoint:
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(model_params, first_iter) = torch.load(checkpoint)
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gaussians.restore(model_params, opt)
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bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
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background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
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iter_start = torch.cuda.Event(enable_timing = True)
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iter_end = torch.cuda.Event(enable_timing = True)
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viewpoint_stack = None
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ema_loss_for_log = 0.0
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ema_psnr_for_log = 0.0
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final_iter = train_iter
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progress_bar = tqdm(range(first_iter, final_iter), desc="Training progress")
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first_iter += 1
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lpips_model = lpips.LPIPS(net="alex").cuda()
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video_cams = scene.getVideoCameras()
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for iteration in range(first_iter, final_iter+1):
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if network_gui.conn == None:
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network_gui.try_connect()
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while network_gui.conn != None:
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try:
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net_image_bytes = None
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custom_cam, do_training, pipe.convert_SHs_python, pipe.compute_cov3D_python, keep_alive, scaling_modifer, ts = network_gui.receive()
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if custom_cam != None:
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net_image = render(custom_cam, gaussians, pipe, background, scaling_modifer, stage="stage")["render"]
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net_image_bytes = memoryview((torch.clamp(net_image, min=0, max=1.0) * 255).byte().permute(1, 2, 0).contiguous().cpu().numpy())
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network_gui.send(net_image_bytes, dataset.source_path)
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if do_training and ((iteration < int(opt.iterations)) or not keep_alive):
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break
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except Exception as e:
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network_gui.conn = None
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iter_start.record()
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gaussians.update_learning_rate(iteration)
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# Every 1000 its we increase the levels of SH up to a maximum degree
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if iteration % 1000 == 0:
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gaussians.oneupSHdegree()
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# Pick a random Camera
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if not viewpoint_stack:
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viewpoint_stack = scene.getTrainCameras()
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batch_size = 1
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viewpoint_stack_loader = DataLoader(viewpoint_stack, batch_size=batch_size,shuffle=True,num_workers=32,collate_fn=list)
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loader = iter(viewpoint_stack_loader)
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if opt.dataloader:
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try:
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viewpoint_cams = next(loader)
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except StopIteration:
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print("reset dataloader")
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batch_size = 1
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loader = iter(viewpoint_stack_loader)
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else:
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idx = randint(0, len(viewpoint_stack)-1)
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viewpoint_cams = [viewpoint_stack[idx]]
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# Render
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if (iteration - 1) == debug_from:
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pipe.debug = True
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images = []
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gt_images = []
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radii_list = []
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visibility_filter_list = []
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viewspace_point_tensor_list = []
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for viewpoint_cam in viewpoint_cams:
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render_pkg = render(viewpoint_cam, gaussians, pipe, background, stage=stage)
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image, viewspace_point_tensor, visibility_filter, radii = render_pkg["render"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"]
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images.append(image.unsqueeze(0))
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gt_image = viewpoint_cam.original_image.cuda()
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gt_images.append(gt_image.unsqueeze(0))
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radii_list.append(radii.unsqueeze(0))
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visibility_filter_list.append(visibility_filter.unsqueeze(0))
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viewspace_point_tensor_list.append(viewspace_point_tensor)
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radii = torch.cat(radii_list,0).max(dim=0).values
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visibility_filter = torch.cat(visibility_filter_list).any(dim=0)
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image_tensor = torch.cat(images,0)
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gt_image_tensor = torch.cat(gt_images,0)
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# Loss
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Ll1 = l1_loss(image_tensor, gt_image_tensor)
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# Ll1 = l2_loss(image, gt_image)
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psnr_ = psnr(image_tensor, gt_image_tensor).mean().double()
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# norm
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loss = Ll1
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if stage == "fine" and hyper.time_smoothness_weight != 0:
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# tv_loss = 0
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tv_loss = gaussians.compute_regulation(hyper.time_smoothness_weight, hyper.plane_tv_weight, hyper.l1_time_planes)
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loss += tv_loss
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if opt.lambda_dssim != 0:
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ssim_loss = ssim(image_tensor,gt_image_tensor)
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loss += opt.lambda_dssim * (1.0-ssim_loss)
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if opt.lambda_lpips !=0:
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lpipsloss = lpips_loss(image_tensor,gt_image_tensor,lpips_model)
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loss += opt.lambda_lpips * lpipsloss
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loss.backward()
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viewspace_point_tensor_grad = torch.zeros_like(viewspace_point_tensor)
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for idx in range(0, len(viewspace_point_tensor_list)):
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viewspace_point_tensor_grad = viewspace_point_tensor_grad + viewspace_point_tensor_list[idx].grad
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iter_end.record()
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with torch.no_grad():
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# Progress bar
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ema_loss_for_log = 0.4 * loss.item() + 0.6 * ema_loss_for_log
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ema_psnr_for_log = 0.4 * psnr_ + 0.6 * ema_psnr_for_log
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total_point = gaussians._xyz.shape[0]
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if iteration % 10 == 0:
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progress_bar.set_postfix({"Loss": f"{ema_loss_for_log:.{7}f}",
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"psnr": f"{psnr_:.{2}f}",
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"point":f"{total_point}"})
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progress_bar.update(10)
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if iteration == opt.iterations:
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progress_bar.close()
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# Log and save
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timer.pause()
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training_report(tb_writer, iteration, Ll1, loss, l1_loss, iter_start.elapsed_time(iter_end), testing_iterations, scene, render, [pipe, background], stage)
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if (iteration in saving_iterations):
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print("\n[ITER {}] Saving Gaussians".format(iteration))
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scene.save(iteration, stage)
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if dataset.render_process:
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if (iteration < 1000 and iteration % 10 == 1) \
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or (iteration < 3000 and iteration % 50 == 1) \
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or (iteration < 10000 and iteration % 100 == 1) \
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or (iteration < 60000 and iteration % 100 ==1):
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render_training_image(scene, gaussians, video_cams, render, pipe, background, stage, iteration-1,timer.get_elapsed_time())
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# total_images.append(to8b(temp_image).transpose(1,2,0))
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timer.start()
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# Densification
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if iteration < opt.densify_until_iter :
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# Keep track of max radii in image-space for pruning
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gaussians.max_radii2D[visibility_filter] = torch.max(gaussians.max_radii2D[visibility_filter], radii[visibility_filter])
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gaussians.add_densification_stats(viewspace_point_tensor_grad, visibility_filter)
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if stage == "coarse":
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opacity_threshold = opt.opacity_threshold_coarse
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densify_threshold = opt.densify_grad_threshold_coarse
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else:
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opacity_threshold = opt.opacity_threshold_fine_init - iteration*(opt.opacity_threshold_fine_init - opt.opacity_threshold_fine_after)/(opt.densify_until_iter)
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densify_threshold = opt.densify_grad_threshold_fine_init - iteration*(opt.densify_grad_threshold_fine_init - opt.densify_grad_threshold_after)/(opt.densify_until_iter )
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if iteration > opt.densify_from_iter and iteration % opt.densification_interval == 0 :
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size_threshold = 20 if iteration > opt.opacity_reset_interval else None
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gaussians.densify(densify_threshold, opacity_threshold, scene.cameras_extent, size_threshold)
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if iteration > opt.pruning_from_iter and iteration % opt.pruning_interval == 0:
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size_threshold = 20 if iteration > opt.opacity_reset_interval else None
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gaussians.prune(densify_threshold, opacity_threshold, scene.cameras_extent, size_threshold)
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if iteration % opt.opacity_reset_interval == 0 or (dataset.white_background and iteration == opt.densify_from_iter):
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print("reset opacity")
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gaussians.reset_opacity()
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# Optimizer step
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if iteration < opt.iterations:
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gaussians.optimizer.step()
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gaussians.optimizer.zero_grad(set_to_none = True)
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if (iteration in checkpoint_iterations):
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print("\n[ITER {}] Saving Checkpoint".format(iteration))
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torch.save((gaussians.capture(), iteration), scene.model_path + "/chkpnt" + str(iteration) + ".pth")
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def training(dataset, hyper, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from, expname):
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# first_iter = 0
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tb_writer = prepare_output_and_logger(expname)
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gaussians = GaussianModel(dataset.sh_degree, hyper)
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dataset.model_path = args.model_path
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timer = Timer()
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scene = Scene(dataset, gaussians, load_coarse=None)
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timer.start()
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scene_reconstruction(dataset, opt, hyper, pipe, testing_iterations, saving_iterations,
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checkpoint_iterations, checkpoint, debug_from,
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gaussians, scene, "coarse", tb_writer, opt.coarse_iterations,timer)
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scene_reconstruction(dataset, opt, hyper, pipe, testing_iterations, saving_iterations,
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checkpoint_iterations, checkpoint, debug_from,
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gaussians, scene, "fine", tb_writer, opt.iterations,timer)
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def prepare_output_and_logger(expname):
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if not args.model_path:
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# if os.getenv('OAR_JOB_ID'):
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# unique_str=os.getenv('OAR_JOB_ID')
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# else:
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# unique_str = str(uuid.uuid4())
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unique_str = expname
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args.model_path = os.path.join("./output/", unique_str)
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# Set up output folder
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print("Output folder: {}".format(args.model_path))
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os.makedirs(args.model_path, exist_ok = True)
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with open(os.path.join(args.model_path, "cfg_args"), 'w') as cfg_log_f:
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cfg_log_f.write(str(Namespace(**vars(args))))
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# Create Tensorboard writer
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tb_writer = None
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if TENSORBOARD_FOUND:
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tb_writer = SummaryWriter(args.model_path)
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else:
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print("Tensorboard not available: not logging progress")
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return tb_writer
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def training_report(tb_writer, iteration, Ll1, loss, l1_loss, elapsed, testing_iterations, scene : Scene, renderFunc, renderArgs, stage):
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if tb_writer:
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tb_writer.add_scalar(f'{stage}/train_loss_patches/l1_loss', Ll1.item(), iteration)
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tb_writer.add_scalar(f'{stage}/train_loss_patchestotal_loss', loss.item(), iteration)
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tb_writer.add_scalar(f'{stage}/iter_time', elapsed, iteration)
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# Report test and samples of training set
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if iteration in testing_iterations:
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torch.cuda.empty_cache()
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validation_configs = ({'name': 'test', 'cameras' : [scene.getTestCameras()[idx % len(scene.getTestCameras())] for idx in range(10, 5000, 299)]},
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{'name': 'train', 'cameras' : [scene.getTrainCameras()[idx % len(scene.getTrainCameras())] for idx in range(10, 5000, 299)]})
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for config in validation_configs:
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if config['cameras'] and len(config['cameras']) > 0:
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l1_test = 0.0
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psnr_test = 0.0
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for idx, viewpoint in enumerate(config['cameras']):
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image = torch.clamp(renderFunc(viewpoint, scene.gaussians,stage=stage, *renderArgs)["render"], 0.0, 1.0)
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gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0)
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if tb_writer and (idx < 5):
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tb_writer.add_images(stage + "/"+config['name'] + "_view_{}/render".format(viewpoint.image_name), image[None], global_step=iteration)
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if iteration == testing_iterations[0]:
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tb_writer.add_images(stage + "/"+config['name'] + "_view_{}/ground_truth".format(viewpoint.image_name), gt_image[None], global_step=iteration)
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l1_test += l1_loss(image, gt_image).mean().double()
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psnr_test += psnr(image, gt_image).mean().double()
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psnr_test /= len(config['cameras'])
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l1_test /= len(config['cameras'])
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print("\n[ITER {}] Evaluating {}: L1 {} PSNR {}".format(iteration, config['name'], l1_test, psnr_test))
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if tb_writer:
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tb_writer.add_scalar(stage + "/"+config['name'] + '/loss_viewpoint - l1_loss', l1_test, iteration)
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tb_writer.add_scalar(stage+"/"+config['name'] + '/loss_viewpoint - psnr', psnr_test, iteration)
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if tb_writer:
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tb_writer.add_histogram(f"{stage}/scene/opacity_histogram", scene.gaussians.get_opacity, iteration)
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tb_writer.add_scalar(f'{stage}/total_points', scene.gaussians.get_xyz.shape[0], iteration)
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tb_writer.add_scalar(f'{stage}/deformation_rate', scene.gaussians._deformation_table.sum()/scene.gaussians.get_xyz.shape[0], iteration)
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tb_writer.add_histogram(f"{stage}/scene/motion_histogram", scene.gaussians._deformation_accum.mean(dim=-1)/100, iteration,max_bins=500)
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torch.cuda.empty_cache()
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def setup_seed(seed):
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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np.random.seed(seed)
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random.seed(seed)
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torch.backends.cudnn.deterministic = True
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if __name__ == "__main__":
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# Set up command line argument parser
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# torch.set_default_tensor_type('torch.FloatTensor')
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torch.cuda.empty_cache()
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parser = ArgumentParser(description="Training script parameters")
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setup_seed(6666)
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lp = ModelParams(parser)
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op = OptimizationParams(parser)
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pp = PipelineParams(parser)
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hp = ModelHiddenParams(parser)
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parser.add_argument('--ip', type=str, default="127.0.0.1")
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parser.add_argument('--port', type=int, default=6009)
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parser.add_argument('--debug_from', type=int, default=-1)
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parser.add_argument('--detect_anomaly', action='store_true', default=False)
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parser.add_argument("--test_iterations", nargs="+", type=int, default=[i*500 for i in range(0,120)])
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parser.add_argument("--save_iterations", nargs="+", type=int, default=[2000, 3000, 7_000, 8000, 9000, 14000, 20000, 30_000,45000,60000])
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parser.add_argument("--quiet", action="store_true")
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parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[])
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parser.add_argument("--start_checkpoint", type=str, default = None)
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parser.add_argument("--expname", type=str, default = "")
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parser.add_argument("--configs", type=str, default = "")
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args = parser.parse_args(sys.argv[1:])
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args.save_iterations.append(args.iterations)
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if args.configs:
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import mmcv
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from utils.params_utils import merge_hparams
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config = mmcv.Config.fromfile(args.configs)
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args = merge_hparams(args, config)
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print("Optimizing " + args.model_path)
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# Initialize system state (RNG)
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safe_state(args.quiet)
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# Start GUI server, configure and run training
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network_gui.init(args.ip, args.port)
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torch.autograd.set_detect_anomaly(args.detect_anomaly)
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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)
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# All done
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print("\nTraining complete.")
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