train_test.py

import os
import torch
import random 
from utils.loss_utils import l1_loss, ssim
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 utils.camera_utils import update_pose
from itertools import cycle

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):
    opt.iterations = 600
    first_iter = 0
    tb_writer = prepare_output_and_logger(dataset) # 准备 Tensorboard 的输出和日志记录
    gaussians = GaussianModel(dataset.sh_degree) # 创建高斯模型
    scene = Scene(dataset, gaussians) # 初始化场景
    gaussians.training_setup(opt) # 设置训练参数

    if checkpoint: # 如果有检查点，从检查点恢复模型
        (model_params, first_iter) = torch.load(checkpoint)
        gaussians.restore(model_params, opt)

    # 设置背景颜色
    bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
    background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")

    # torch.cuda.Event精确记录gpu的运行时间
    iter_start = torch.cuda.Event(enable_timing=True) # 记录迭代开始时间
    iter_end = torch.cuda.Event(enable_timing=True) # 记录迭代结束时间

    viewpoint_stack = None # 视点栈
    ema_loss_for_log = 0.0

    # 创建了一个从 first_iter 到 opt.iterations 的整数序列的循环，并使用 tqdm 函数将其转换为一个可视化的进度条，用于显示训练过程中的进度。
    progress_bar = tqdm(range(first_iter, opt.iterations), desc="Training progress")
    first_iter += 1
    
    train_cameras = scene.getTrainCameras()
    camera_cycle = cycle(train_cameras)
    
    '''其目的是不断尝试建立与某个 GUI 界面的连接，然后接收和发送数据。'''
    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
                # 从 GUI 界面接收数据，这些数据包括自定义相机参数、训练指示、转换参数等。
                custom_cam, do_training, pipe.convert_SHs_python, pipe.compute_cov3D_python, keep_alive, scaling_modifer = network_gui.receive()
                if custom_cam != None:
                    # 使用接收到的自定义相机参数进行渲染，得到渲染图像。
                    net_image = render(custom_cam, gaussians, pipe, background, scaling_modifer)["render"] # core code 1
                    # 将渲染图像转换为字节流的形式。
                    net_image_bytes = memoryview((torch.clamp(net_image, min=0, max=1.0) * 255).byte().permute(1, 2, 0).contiguous().cpu().numpy())
                # 将渲染图像的字节流发送回 GUI 界面。
                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() # 这一行代码是用来记录迭代开始的时间点。

        gaussians.update_learning_rate(iteration) #  这是调用了一个方法或函数来更新学习率。

        #  we increase the levels of SH up to a maximum degree
        if iteration % 10 == 0:
            gaussians.oneupSHdegree()

        # Pick a random Camera
        if iteration % len(train_cameras) == 0:
            random.shuffle(train_cameras)
            camera_cycle = cycle(train_cameras)

        viewpoint_cam = next(camera_cycle)
        # Render
        if (iteration - 1) == debug_from:
            pipe.debug = True # 开启调试模式，以便对算法进行调试和跟踪。
            
        opt_params = [
                {"params": [viewpoint_cam.cam_rot_delta], "lr": 0.003, "name": "rot_{}".format(viewpoint_cam.uid)},
                {"params": [viewpoint_cam.cam_trans_delta], "lr": 0.001, "name": "trans_{}".format(viewpoint_cam.uid)},
                {"params": [viewpoint_cam.exposure_a], "lr": 0.01, "name": "exposure_a_{}".format(viewpoint_cam.uid)},
                {"params": [viewpoint_cam.exposure_b], "lr": 0.01, "name": "exposure_b_{}".format(viewpoint_cam.uid)},
            ]
        camera_optimizer = torch.optim.Adam(opt_params)
        
        for it in range(50):
            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"]
            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))
            loss.backward()
            
            iter_end.record()

            with torch.no_grad():
                ema_loss_for_log = 0.4 * loss.item() + 0.6 * ema_loss_for_log
                if it % 100 == 0:
                    progress_bar.set_postfix({"Loss": f"{ema_loss_for_log:.{7}f}"})
                    progress_bar.update(1)
                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)

                if iteration < opt.densify_until_iter:
                    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()

                if iteration < opt.iterations:
                    gaussians.optimizer.step()
                    gaussians.optimizer.zero_grad(set_to_none=True)
                    
                    camera_optimizer.step()
                    # Update camera pose
                    converged = update_pose(viewpoint_cam)
                    camera_optimizer.zero_grad()
                    
                if (iteration in checkpoint_iterations):
                    print("\n[ITER {}] Saving Checkpoint".format(iteration))
                    torch.save((gaussians.capture(), iteration), scene.model_path + "/chkpnt" + str(iteration) + ".pth")
    
                if converged:
                    break
    
    # opt_params = []
    # for viewpoint_cam in train_cameras:
    #     opt_params.append(
    #         {
    #             "params": [viewpoint_cam.cam_rot_delta],
    #             "lr": 0.003,
    #             "name": "rot_{}".format(viewpoint_cam.uid),
    #         }
    #     )
    #     opt_params.append(
    #         {
    #             "params": [viewpoint_cam.cam_trans_delta],
    #             "lr": 0.001,
    #             "name": "trans_{}".format(viewpoint_cam.uid),
    #         }
    #     )
    #     opt_params.append(
    #         {
    #             "params": [viewpoint_cam.exposure_a],
    #             "lr": 0.01,
    #             "name": "exposure_a_{}".format(viewpoint_cam.uid),
    #         }
    #     )
    #     opt_params.append(
    #         {
    #             "params": [viewpoint_cam.exposure_b],
    #             "lr": 0.01,
    #             "name": "exposure_b_{}".format(viewpoint_cam.uid),
    #         }
    #     )
    # global_optimizer = torch.optim.Adam(opt_params + list(gaussians.parameters()), lr=opt.lr)
    
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)

    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('--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=[140, 600])
    parser.add_argument("--save_iterations", nargs="+", type=int, default=[140, 600])  
    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)

    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
    
    # 调用GUI转gaussian_renderer/network_gui.py，GUI初始化
    network_gui.init(args.ip, args.port)
    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)

    # All done
    print("\nTraining complete.")