leave_one_out_stage2.py

import sys
import os
import uuid
from argparse import ArgumentParser, Namespace
from arguments import ModelParams, PipelineParams, OptimizationParams
from random import randint

import torch
import torch.nn.functional as F
import cv2
import numpy as np
import torchvision
from tqdm import tqdm
from torchmetrics.functional.regression import pearson_corrcoef

from utils.general_utils import safe_state
from utils.loss_utils import l1_loss, ssim, monodisp
from utils.image_utils import psnr
from gaussian_renderer import render
from scene import Scene, GaussianModel

try:
    from torch.utils.tensorboard.writer import SummaryWriter
    TENSORBOARD_FOUND = True
except ImportError:
    TENSORBOARD_FOUND = False

def leave_one_out_training(args, dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from, train_id):
    first_iter = 6000 # in this code, we just use the data from 6000 iter
    tb_writer = prepare_output_and_logger(dataset)
    gaussians = GaussianModel(dataset.sh_degree)
    scene = Scene(dataset, gaussians, shuffle=False, extra_opts=args) # make sure we load "densify_until_iter" model
    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)

    num_id, image_id = train_id
    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):

        iter_start.record() # type: ignore

        gaussians.update_learning_rate(iteration)

        # Every 1000 its we increase the levels of SH up to a maximum degree
        if iteration % 10000 == 0:
            gaussians.oneupSHdegree()

        # Pick a random Camera
        if not viewpoint_stack:
            viewpoint_stack = scene.getTrainCameras().copy()[:num_id] + scene.getTrainCameras().copy()[num_id+1:] # leave one out
        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
        loss, Ll1 = cal_loss(opt, args, image, render_pkg, viewpoint_cam, bg, mono_loss_type=args.mono_loss_type, iteration=iteration)

        loss.backward()

        iter_end.record()  # type: ignore

        with torch.no_grad():
            # Progress bar
            ema_loss_for_log = 0.4 * loss.item() + 0.6 * ema_loss_for_log
            num_gauss = len(gaussians._xyz)
            if iteration % 10 == 0:
                progress_bar.set_postfix({'Loss': f"{ema_loss_for_log:.{7}f}",  'n': f"{num_gauss}"})
                progress_bar.update(10)
            if iteration == opt.iterations:
                progress_bar.close()

            # Log
            training_report(tb_writer, iteration, Ll1, loss, l1_loss, iter_start.elapsed_time(iter_end), testing_iterations, scene, render, (pipe, background))

            # Save
            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()

                # if iteration % (opt.opacity_reset_interval // 2) == 0 or (dataset.white_background and iteration == opt.densify_from_iter):
                if iteration % opt.remove_outliers_interval == 0 or (dataset.white_background and iteration == opt.densify_from_iter):
                    gaussians.remove_outliers(opt, iteration, linear=True)

            # 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")

    # in the end, we use the cached gaussians and the final gaussians to get the \delta gaussians
    cur_status = gaussians.cache
    pre_status = torch.load(os.path.join(args.model_path, 'gaussians_cache.pth'))
    diffs = {}
    keys = ['_xyz', '_features_dc', '_features_rest', '_scaling', '_rotation', '_opacity']
    for key, pre_c, cur_c in zip(keys, pre_status, cur_status):
        diff = pre_c - cur_c
        mean_diff = torch.mean(diff, dim=0).cpu().numpy()
        std_diff = torch.std(diff, dim=0).cpu().numpy()
        diffs[key] = [mean_diff, std_diff]

    import pickle
    with open(os.path.join(args.model_path, 'diffs.pkl'), 'wb') as f:
        pickle.dump(diffs, f)
    return dataset, gaussians, scene

def prepare_output_and_logger(args):    
    if not args.model_path:
        if os.getenv('OAR_JOB_ID'):
            unique_str=os.getenv('OAR_JOB_ID')
            args.model_path = os.path.join("./output/", unique_str)
        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()

def cal_loss(opt, args, image, render_pkg, viewpoint_cam, bg, silhouette_loss_type="bce", mono_loss_type="mid", iteration=0):
    """
    Calculate the loss of the image, contains l1 loss and ssim loss.
    l1 loss: Ll1 = l1_loss(image, gt_image)
    ssim loss: Lssim = 1 - ssim(image, gt_image)
    Optional: [silhouette loss, monodepth loss]
    """
    gt_image = viewpoint_cam.original_image.to(image.dtype).cuda()
    if opt.random_background:
        gt_image = gt_image * viewpoint_cam.mask + bg[:, None, None] * (1 - viewpoint_cam.mask).squeeze()
    Ll1 = l1_loss(image, gt_image)
    loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim(image, gt_image))

    if hasattr(args, "use_mask") and args.use_mask:
        if silhouette_loss_type == "bce":
            silhouette_loss = F.binary_cross_entropy(render_pkg["rendered_alpha"], viewpoint_cam.mask)
        elif silhouette_loss_type == "mse":
            silhouette_loss = F.mse_loss(render_pkg["rendered_alpha"], viewpoint_cam.mask)
        else:
            raise NotImplementedError
        loss = loss + opt.lambda_silhouette * silhouette_loss
        
    if hasattr(viewpoint_cam, "mono_depth") and  viewpoint_cam.mono_depth is not None:
        if mono_loss_type == "mid":
            # we apply masked monocular loss
            gt_mask = torch.where(viewpoint_cam.mask > 0.5, True, False)
            render_mask = torch.where(render_pkg["rendered_alpha"] > 0.5, True, False)
            mask = torch.logical_and(gt_mask, render_mask)
            if mask.sum() < 10:
                depth_loss = 0.0
            else:
                disp_mono = 1 / viewpoint_cam.mono_depth[mask].clamp(1e-6) # shape: [N]
                disp_render = 1 / render_pkg["rendered_depth"][mask].clamp(1e-6) # shape: [N]
                depth_loss = monodisp(disp_mono, disp_render, 'l1')[-1]
        elif mono_loss_type == "pearson":
            zoe_depth = viewpoint_cam.mono_depth[viewpoint_cam.mask > 0.5].clamp(1e-6)
            rendered_depth = render_pkg["rendered_depth"][viewpoint_cam.mask > 0.5].clamp(1e-6)
            depth_loss = min(
                (1 - pearson_corrcoef( -zoe_depth, rendered_depth)),
                (1 - pearson_corrcoef(1 / (zoe_depth + 200.), rendered_depth))
                )
        elif mono_loss_type == "dust3r":
            gt_mask = torch.where(viewpoint_cam.mask > 0.5, True, False)
            render_mask = torch.where(render_pkg["rendered_alpha"] > 0.5, True, False)
            mask = torch.logical_and(gt_mask, render_mask)
            if mask.sum() < 10:
                depth_loss = 0.0
            else:
                disp_mono = 1 / viewpoint_cam.mono_depth[mask].clamp(1e-6) # shape: [N]
                disp_render = 1 / render_pkg["rendered_depth"][mask].clamp(1e-6) # shape: [N]
                depth_loss = torch.abs((disp_render - disp_mono)).mean()
            depth_loss *= (opt.iterations - iteration) / opt.iterations # linear scheduler
        else:
            raise NotImplementedError

        loss = loss + args.mono_depth_weight * depth_loss

    return loss, Ll1

def train_3dgs(args, ids):
    print("Optimizing " + args.model_path)

    # Initialize system state (RNG)
    safe_state(args.quiet)
    torch.autograd.set_detect_anomaly(args.detect_anomaly)
    dataset = lp.extract(args)
    pipeline = pp.extract(args)
    model_path_root = args.model_path

    for num_id, image_id in zip(range(args.sparse_view_num), ids): # num_id: leave one out id, image_id: the id of the image to be infered
        args.model_path = os.path.join(model_path_root, f'leave_{image_id}')
        dataset.model_path = args.model_path
        args.start_checkpoint = os.path.join(args.model_path, 'chkpnt6000.pth') # load this ckpt
        # os.makedirs(args.model_path, exist_ok=True)
        leave_one_out_training(args,
                    dataset,
                    op.extract(args),
                    pipeline,
                    args.test_iterations,
                    args.save_iterations,
                    args.checkpoint_iterations,
                    args.start_checkpoint,
                    args.debug_from,
                    train_id = (num_id, image_id))


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=[7_000])
    parser.add_argument("--save_iterations", nargs="+", type=int, default=[7_0000, 15_0000])
    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)
    ### some exp args
    parser.add_argument("--sparse_view_num", type=int, default=-1, 
                    help="Use sparse view or dense view, if sparse_view_num > 0, use sparse view, \
                    else use dense view. In sparse setting, sparse views will be used as training data, \
                    others will be used as testing data.")
    parser.add_argument("--use_mask", default=True, help="Use masked image, by default True")
    parser.add_argument('--use_dust3r', action='store_true', default=False,
                        help='use dust3r estimated poses')
    parser.add_argument('--dust3r_json', type=str, default=None)
    parser.add_argument("--init_pcd_name", default='origin', type=str, help="the init pcd name. 'random' for random, 'origin' for pcd from the whole scene")
    parser.add_argument('--mono_depth_weight', type=float, default=0.0005, help="The rate of monodepth loss")
    parser.add_argument('--mono_loss_type', type=str, default="mid")

    args = parser.parse_args(sys.argv[1:])
    args.save_iterations.append(args.iterations)

    assert args.sparse_view_num > 0, 'leave_one_out is for sparse view training'
    assert os.path.exists(os.path.join(args.source_path, f"sparse_{args.sparse_view_num}.txt")), f"sparse_{args.sparse_view_num}.txt not found!"

    ids = np.loadtxt(os.path.join(args.source_path, f"sparse_{args.sparse_view_num}.txt"), dtype=np.int32).tolist()

    train_3dgs(args, ids)

    # All done
    print("\nAll training complete.")