mytrain.py

#
# 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
import logging
import shutil
from random import randint
from utils.loss_utils import l1_loss, ssim
from gaussian_renderer import render, network_gui
import sys
from scene import Scene, GaussianModel
#from scene.gaussian_model import add_ply
from scene.gaussian_model import 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
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, output_path):
    first_iter = 0
    tb_writer = prepare_output_and_logger(dataset, output_path)
    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
        render_pkg = render(viewpoint_cam, gaussians, pipe, background)
        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))
        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, output_path):
    if not args.model_path:
        args.model_path = output_path
        
    # 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()


def colmap(args):
    colmap_command = '"{}"'.format(args.colmap_executable) if len(args.colmap_executable) > 0 else "colmap"
    magick_command = '"{}"'.format(args.magick_executable) if len(args.magick_executable) > 0 else "magick"
    use_gpu = 1 if not args.no_gpu else 0

    if not args.skip_matching:
        os.makedirs(args.source_path + "/input/distorted/sparse", exist_ok=True)

        ## Feature extraction
        feat_extracton_cmd = colmap_command + " feature_extractor " \
                                              "--database_path " + args.source_path + "/input/distorted/database.db \
            --image_path " + args.source_path + "/input \
            --ImageReader.single_camera 1 \
            --ImageReader.camera_model " + args.camera + " \
            --SiftExtraction.use_gpu " + str(use_gpu)
        exit_code = os.system(feat_extracton_cmd)
        if exit_code != 0:
            logging.error(f"Feature extraction failed with code {exit_code}. Exiting.")
            exit(exit_code)

        ## Feature matching
        feat_matching_cmd = colmap_command + " exhaustive_matcher \
            --database_path " + args.source_path + "/input/distorted/database.db \
            --SiftMatching.use_gpu " + str(use_gpu)
        exit_code = os.system(feat_matching_cmd)
        if exit_code != 0:
            logging.error(f"Feature matching failed with code {exit_code}. Exiting.")
            exit(exit_code)

        ### Bundle adjustment
        # The default Mapper tolerance is unnecessarily large,
        # decreasing it speeds up bundle adjustment steps.
        mapper_cmd = (colmap_command + " mapper \
            --database_path " + args.source_path + "/input/distorted/database.db \
            --image_path " + args.source_path + "/input \
            --output_path " + args.source_path + "/input/distorted/sparse \
            --Mapper.ba_global_function_tolerance=0.000001")
        exit_code = os.system(mapper_cmd)
        if exit_code != 0:
            logging.error(f"Mapper failed with code {exit_code}. Exiting.")
            exit(exit_code)

    ### Image undistortion
    ## We need to undistort our images into ideal pinhole intrinsics.
    img_undist_cmd = (colmap_command + " image_undistorter \
        --image_path " + args.source_path + "/input \
        --input_path " + args.source_path + "/input/distorted/sparse/0 \
        --output_path " + args.source_path + "\
        --output_type COLMAP")
    exit_code = os.system(img_undist_cmd)
    if exit_code != 0:
        logging.error(f"Mapper failed with code {exit_code}. Exiting.")
        exit(exit_code)

    files = os.listdir(args.source_path + "/sparse")
    os.makedirs(args.source_path + "/sparse/0", exist_ok=True)
    # Copy each file from the source directory to the destination directory
    for file in files:
        if file == '0':
            continue
        source_file = os.path.join(args.source_path, "sparse", file)
        destination_file = os.path.join(args.source_path, "sparse", "0", file)
        shutil.move(source_file, destination_file)
    files = os.listdir(args.source_path + "/images/images")
    for file in files:
        if file == '0':
            continue
        source_file = os.path.join(args.source_path, "images/images", file)
        destination_file = os.path.join(args.source_path, "images", file)
        shutil.move(source_file, destination_file)
    shutil.rmtree(os.path.join(args.source_path, "images/images"))
    shutil.rmtree(os.path.join(args.source_path, "input"))
    shutil.rmtree(os.path.join(args.source_path, "stereo"))
    shutil.os.remove(os.path.join(args.source_path, "run-colmap-geometric.sh"))
    shutil.os.remove(os.path.join(args.source_path, "run-colmap-photometric.sh"))

    if (args.resize):
        print("Copying and resizing...")

        # Resize images.
        os.makedirs(args.source_path + "/images_2", exist_ok=True)
        os.makedirs(args.source_path + "/images_4", exist_ok=True)
        os.makedirs(args.source_path + "/images_8", exist_ok=True)
        # Get the list of files in the source directory
        files = os.listdir(args.source_path + "/images")
        # Copy each file from the source directory to the destination directory
        for file in files:
            source_file = os.path.join(args.source_path, "images", file)

            destination_file = os.path.join(args.source_path, "images_2", file)
            shutil.copy2(source_file, destination_file)
            exit_code = os.system(magick_command + " mogrify -resize 50% " + destination_file)
            if exit_code != 0:
                logging.error(f"50% resize failed with code {exit_code}. Exiting.")
                exit(exit_code)

            destination_file = os.path.join(args.source_path, "images_4", file)
            shutil.copy2(source_file, destination_file)
            exit_code = os.system(magick_command + " mogrify -resize 25% " + destination_file)
            if exit_code != 0:
                logging.error(f"25% resize failed with code {exit_code}. Exiting.")
                exit(exit_code)

            destination_file = os.path.join(args.source_path, "images_8", file)
            shutil.copy2(source_file, destination_file)
            exit_code = os.system(magick_command + " mogrify -resize 12.5% " + destination_file)
            if exit_code != 0:
                logging.error(f"12.5% resize failed with code {exit_code}. Exiting.")
                exit(exit_code)


def 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=[7_000, 30_000,40000])
    parser.add_argument("--save_iterations", nargs="+", type=int, default=[7_000, 30_000,40000])
    parser.add_argument('--output_path', type=str)
    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("--no_gpu", action='store_true')
    parser.add_argument("--skip_matching", action='store_true')
    parser.add_argument("--camera", default="OPENCV", type=str)
    parser.add_argument("--colmap_executable", default="", type=str)
    parser.add_argument("--resize", action="store_true")
    parser.add_argument("--magick_executable", default="", type=str)
    parser.add_argument("--skip_colmap", action='store_true')
    args = parser.parse_args(sys.argv[1:])
    args.save_iterations.append(args.iterations)
    if not args.skip_colmap:
        colmap(args)

    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), op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations, args.checkpoint_iterations, args.start_checkpoint, args.debug_from,  args.output_path)

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

def main2():
    # 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=[7_000,30000, 40_000])
    parser.add_argument("--save_iterations", nargs="+", type=int, default=[7_000,30000,40_000])
    parser.add_argument('--output_path', type=str)
    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("--no_gpu", action='store_true')
    parser.add_argument("--skip_matching", action='store_true')
    parser.add_argument("--camera", default="OPENCV", type=str)
    parser.add_argument("--colmap_executable", default="", type=str)
    parser.add_argument("--resize", action="store_true")
    parser.add_argument("--magick_executable", default="", type=str)
    parser.add_argument("--skip_colmap", action='store_true')
    args = parser.parse_args(sys.argv[1:])
    args.save_iterations.append(args.iterations)
    if not args.skip_colmap:
        colmap(args)

    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)
    dataset = lp.extract(args)
    gaussians = GaussianModel(dataset.sh_degree)

    # path1='/home/kanjing/3Dconstruction/gaussian-splatting/output/xiaobieshu(copy)/point_cloud/iteration_30000/cropped11.ply'
    # path2='/home/kanjing/3Dconstruction/gaussian-splatting/output/xiaobieshu(copy)/point_cloud/iteration_30000/cropped12.ply'
    # path3 = '/home/kanjing/3Dconstruction/gaussian-splatting/output/xiaobieshu(copy)/point_cloud/iteration_30000/cropped13.ply'
    # path='/home/kanjing/3Dconstruction/gaussian-splatting/output/xiaobieshu(copy)/point_cloud/iteration_30000/merge.ply'
    # #gaussianmodel1= GaussianModel
    # #gaussianmodel1.add1
    # #gaussians.add_ply(self, path, path1, path2)
    # #gaussians.add_ply(path, path1, path2)
    # gaussians.add_ply(path, path1, path3)
    # print('finish')

if __name__ == "__main__":
    main()