mirror of
https://github.com/graphdeco-inria/gaussian-splatting
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256 lines
13 KiB
Python
256 lines
13 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 os
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import numpy as np
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import torch
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from random import randint
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from PIL import Image
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from utils.loss_utils import l1_loss, ssim
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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
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# 尝试导入 PyTorch 提供的 TensorBoard 记录器 SummaryWriter 类
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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 training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from):
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'''
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dataset: 只存储与Moedl相关参数的args
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opt: 优化相关参数
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pipe: 管道相关参数
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checkpoint: 已训练模型的路径
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debug_from: 从哪一个迭代开始debug
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'''
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first_iter = 0
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# 创建保存结果的文件夹，并保存模型相关的参数到cfg_args文件；尝试创建tensorboard_writer，记录训练过程
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tb_writer = prepare_output_and_logger(dataset)
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gaussians = GaussianModel(dataset.sh_degree) # 创建初始化高斯模型，用于表示场景中的每个点的3D高斯分布
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scene = Scene(dataset, gaussians) # 创建初始3D场景对象，加载数据集和对应的相机参数
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gaussians.training_setup(opt) # 为高斯模型参数设置优化器和学习率调度器
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# 如果提供了checkpoint，则从checkpoint加载模型参数并恢复训练进度
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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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# 设置背景颜色，白色或黑色取决于数据集要求
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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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# 创建CUDA事件用于计时
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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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# 使用tqdm库创建进度条，追踪训练进度
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progress_bar = tqdm(range(first_iter, opt.iterations), desc="Training progress")
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first_iter += 1
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for iteration in range(first_iter, opt.iterations + 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 = 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)["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() # 记录迭代开始时间
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gaussians.update_learning_rate(iteration) # 根据当前迭代次数更新学习率
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# 每1000次迭代，提升球谐函数的次数以改进模型复杂度，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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# 随机选择一个训练用的相机视角，Pick a random Camera
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if not viewpoint_stack:
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viewpoint_stack = scene.getTrainCameras().copy()
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viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack)-1))
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# 如果达到调试起始点，启用调试模式
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if (iteration - 1) == debug_from:
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pipe.debug = True
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# Render，根据相机参数使用可微光栅化器渲染图像
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# 根据设置决定是否使用随机背景颜色
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bg = torch.rand((3), device="cuda") if opt.random_background else background
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# 渲染当前视角的图像
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render_pkg = render(viewpoint_cam, gaussians, pipe, bg)
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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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# Loss
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gt_image = viewpoint_cam.original_image.cuda()
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Ll1 = l1_loss(image, gt_image)
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loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim(image, gt_image))
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loss.backward()
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iter_end.record() # 记录迭代结束时间
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with torch.no_grad():
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# 更新进度条和损失显示，Progress bar
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ema_loss_for_log = 0.4 * loss.item() + 0.6 * ema_loss_for_log
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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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progress_bar.update(10)
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if iteration == opt.iterations:
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progress_bar.close()
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# 定期记录训练数据并保存模型，Log and save
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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))
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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)
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# 在指定迭代区间内，对3D高斯模型进行增密和修剪，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, visibility_filter)
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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_and_prune(opt.densify_grad_threshold, 0.005, 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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gaussians.reset_opacity()
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# 执行优化器的一步，并准备下一次迭代，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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# 定期保存checkpoint
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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 prepare_output_and_logger(args):
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# 没有预设模型输出路径，则随机生成一个文件名，存储输出结果
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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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args.model_path = os.path.join("./output/", unique_str[0:10])
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# 创建输出文件夹
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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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# 保存模型的配置参数
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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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# 创建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) # 创建一个 SummaryWriter 对象,向 TensorBoard 记录训练过程中的各种指标
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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):
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if tb_writer:
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tb_writer.add_scalar('train_loss_patches/l1_loss', Ll1.item(), iteration)
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tb_writer.add_scalar('train_loss_patches/total_loss', loss.item(), iteration)
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tb_writer.add_scalar('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()},
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{'name': 'train', 'cameras' : [scene.getTrainCameras()[idx % len(scene.getTrainCameras())] for idx in range(5, 30, 5)]})
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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, *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(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(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(config['name'] + '/loss_viewpoint - l1_loss', l1_test, iteration)
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tb_writer.add_scalar(config['name'] + '/loss_viewpoint - psnr', psnr_test, iteration)
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if tb_writer:
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tb_writer.add_histogram("scene/opacity_histogram", scene.gaussians.get_opacity, iteration)
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tb_writer.add_scalar('total_points', scene.gaussians.get_xyz.shape[0], iteration)
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torch.cuda.empty_cache()
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if __name__ == "__main__":
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# Set up command line argument parser
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parser = ArgumentParser(description="Training script parameters")
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model_prams = ModelParams(parser) # 定义存储 模型 相关参数的arg对象
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optim_prams = OptimizationParams(parser) # 定义存储 优化 相关参数的arg对象
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pipeline_prams = PipelineParams(parser) # 定义存储 渲染 相关参数的arg对象
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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) # 指定从哪一迭代（>= 0）开始debug
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parser.add_argument('--detect_anomaly', action='store_true', default=False) # action='store_true' 如果命令行中包含了这个参数,它的值将被设置为 True
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parser.add_argument("--test_iterations", nargs="+", type=int, default=[7_000, 30_000])
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parser.add_argument("--save_iterations", nargs="+", type=int, default=[7_000, 30_000])
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parser.add_argument("--quiet", action="store_true")
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parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[30_000])
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parser.add_argument("--start_checkpoint", type=str, default = None)
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# 从命令行参数中解析出所有的参数值,并与上面设置的参数一起存储到 Namespace 对象中，即args
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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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print("Optimizing " + args.model_path)
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# 初始化系统的随机状态,以确保实验结果可复现 (RNG)
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safe_state(args.quiet)
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# 启动GUI 服务器, 监听指定的 IP 地址和端口，观察训练进度和调试问题
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network_gui.init(args.ip, args.port)
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torch.autograd.set_detect_anomaly(args.detect_anomaly) # 设置pytorch是否检测梯度异常
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# model_prams.extract(args)：将args中的属性，即命令行和预设的参数中 与 ModelParams类中定义的参数相匹配的值，并将它们封装到一个新的 GroupParams 对象中
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training(model_prams.extract(args), optim_prams.extract(args), pipeline_prams.extract(args), args.test_iterations, args.save_iterations, args.checkpoint_iterations, args.start_checkpoint, args.debug_from)
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# All done
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print("\nTraining complete.")
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