recon/train_from_vid.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  [email protected]
#

import os
import torch
from random import randint
from PIL import Image
from mediapy import read_video
from utils.loss_utils import l1_loss, ssim, lpips
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 scripts.sampling.simple_mv_latent_sample import sample_one

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,
):
    first_iter = 0
    tb_writer = prepare_output_and_logger(dataset)
    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

        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
        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)
        )
        if opt.lambda_lpips > 0:
            loss += opt.lambda_lpips * lpips(image, gt_image)

        loss += torch.mean(gaussians.get_opacity) * 0.1

        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):
    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)

    # 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()


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("--seed", type=int, default=42)
    parser.add_argument("--video", type=str, default="")
    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]
    )
    parser.add_argument(
        "--save_iterations", nargs="+", type=int, default=[7_000, 30_000]
    )
    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("--border_ratio", type=float, default=0.3)
    parser.add_argument("--min_guidance_scale", type=float, default=1.0)
    parser.add_argument("--max_guidance_scale", type=float, default=2.5)
    parser.add_argument("--sigma_max", type=float, 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
    network_gui.init(args.ip, args.port)
    torch.autograd.set_detect_anomaly(args.detect_anomaly)

    print("=====Start generating MV Images=====")

    # images, _ = sample_one(
    #     args.image,
    #     args.ckpt_path,
    #     seed=args.seed,
    #     border_ratio=args.border_ratio,
    #     min_guidance_scale=args.min_guidance_scale,
    #     max_guidance_scale=args.max_guidance_scale,
    #     sigma_max=args.sigma_max,
    # )
    images = []
    frames = read_video(args.video)
    for frame in frames:
        images.append(Image.fromarray(frame))

    print("=====Finish generating MV Images=====")

    lp = lp.extract(args)
    lp.images = images

    training(
        lp,
        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.")