train_swin_224.py

import os
import sys
import wandb
import argparse
import numpy as np
from tqdm import tqdm
from PIL import Image
from datetime import datetime
from zoneinfo import ZoneInfo
from time import gmtime, strftime
from collections import OrderedDict
import random

import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torchvision.transforms import CenterCrop
from torch.utils.data import ConcatDataset, DataLoader
import torchvision.transforms as torch_transforms
from torchvision.utils import make_grid

from src.losses import (
    ContextualLoss,
    ContextualLoss_forward,
    Perceptual_loss,
    consistent_loss_fn,
    discriminator_loss_fn,
    generator_loss_fn,
    l1_loss_fn,
    smoothness_loss_fn,
)
from src.models.CNN.GAN_models import Discriminator_x64_224
from src.models.CNN.ColorVidNet import GeneralColorVidNet
from src.models.CNN.FrameColor import frame_colorization
from src.models.CNN.NonlocalNet import WeightedAverage_color, NonlocalWeightedAverage, GeneralWarpNet
from src.models.vit.embed import GeneralEmbedModel
from src.data import transforms
from src.data.dataloader import VideosDataset, VideosDataset_ImageNet
from src.utils import CenterPad_threshold
from src.utils import (
    TimeHandler,
    RGB2Lab,
    ToTensor,
    Normalize,
    LossHandler,
    WarpingLayer,
    uncenter_l,
    tensor_lab2rgb,
    print_num_params,
)
from src.scheduler import PolynomialLR

parser = argparse.ArgumentParser()
parser.add_argument("--video_data_root_list", type=str, default="dataset")
parser.add_argument("--flow_data_root_list", type=str, default="flow")
parser.add_argument("--mask_data_root_list", type=str, default="mask")
parser.add_argument("--data_root_imagenet", default="imagenet", type=str)
parser.add_argument("--annotation_file_path", default="dataset/annotation.csv", type=str)
parser.add_argument("--imagenet_pairs_file", default="imagenet_pairs.txt", type=str)
parser.add_argument("--gpu_ids", type=str, default="0,1,2,3", help="separate by comma")
parser.add_argument("--workers", type=int, default=0)
parser.add_argument("--batch_size", type=int, default=2)
parser.add_argument("--image_size", type=int, default=[384, 384])
parser.add_argument("--ic", type=int, default=7)
parser.add_argument("--epoch", type=int, default=40)
parser.add_argument("--resume_epoch", type=int, default=0)
parser.add_argument("--resume", type=bool, default=False)
parser.add_argument("--load_pretrained_model", type=bool, default=False)
parser.add_argument("--lr", type=float, default=1e-4)
parser.add_argument("--beta1", type=float, default=0.5)
parser.add_argument("--lr_step", type=int, default=1)
parser.add_argument("--lr_gamma", type=float, default=0.9)
parser.add_argument("--checkpoint_dir", type=str, default="checkpoints")
parser.add_argument("--checkpoint_step", type=int, default=500)
parser.add_argument("--real_reference_probability", type=float, default=0.7)
parser.add_argument("--nonzero_placeholder_probability", type=float, default=0.0)
parser.add_argument("--domain_invariant", type=bool, default=False)
parser.add_argument("--weigth_l1", type=float, default=2.0)
parser.add_argument("--weight_contextual", type=float, default="0.5")
parser.add_argument("--weight_perceptual", type=float, default="0.02")
parser.add_argument("--weight_smoothness", type=float, default="5.0")
parser.add_argument("--weight_gan", type=float, default="0.5")
parser.add_argument("--weight_nonlocal_smoothness", type=float, default="0.0")
parser.add_argument("--weight_nonlocal_consistent", type=float, default="0.0")
parser.add_argument("--weight_consistent", type=float, default="0.05")
parser.add_argument("--luminance_noise", type=float, default="2.0")
parser.add_argument("--permute_data", type=bool, default=True)
parser.add_argument("--contextual_loss_direction", type=str, default="forward", help="forward or backward matching")
parser.add_argument("--batch_accum_size", type=int, default=10)
parser.add_argument("--epoch_train_discriminator", type=int, default=3)
parser.add_argument("--vit_version", type=str, default="vit_tiny_patch16_384")
parser.add_argument("--use_dummy", type=bool, default=False)
parser.add_argument("--use_wandb", type=bool, default=False)
parser.add_argument("--use_feature_transform", type=bool, default=False)
parser.add_argument("--head_out_idx", type=str, default="8,9,10,11")
parser.add_argument("--wandb_token", type=str, default="")
parser.add_argument("--wandb_name", type=str, default="")


def load_data():
    transforms_video = [
        CenterCrop(opt.image_size),
        RGB2Lab(),
        ToTensor(),
        Normalize(),
    ]

    train_dataset_videos = [
        VideosDataset(
            video_data_root=video_data_root,
            flow_data_root=flow_data_root,
            mask_data_root=mask_data_root,
            imagenet_folder=opt.data_root_imagenet,
            annotation_file_path=opt.annotation_file_path,
            image_size=opt.image_size,
            image_transform=transforms.Compose(transforms_video),
            real_reference_probability=opt.real_reference_probability,
            nonzero_placeholder_probability=opt.nonzero_placeholder_probability,
        )
        for video_data_root, flow_data_root, mask_data_root in zip(
            opt.video_data_root_list, opt.flow_data_root_list, opt.mask_data_root_list
        )
    ]

    transforms_imagenet = [CenterPad_threshold(opt.image_size), RGB2Lab(), ToTensor(), Normalize()]
    extra_reference_transform = [
        torch_transforms.RandomHorizontalFlip(0.5),
        torch_transforms.RandomResizedCrop(480, (0.98, 1.0), ratio=(0.8, 1.2)),
    ]

    train_dataset_imagenet = VideosDataset_ImageNet(
        imagenet_data_root=opt.data_root_imagenet,
        pairs_file=opt.imagenet_pairs_file,
        image_size=opt.image_size,
        transforms_imagenet=transforms_imagenet,
        distortion_level=4,
        brightnessjitter=5,
        nonzero_placeholder_probability=opt.nonzero_placeholder_probability,
        extra_reference_transform=extra_reference_transform,
        real_reference_probability=opt.real_reference_probability,
    )

    # video_training_length = sum([len(dataset) for dataset in train_dataset_videos])
    # imagenet_training_length = len(train_dataset_imagenet)
    # dataset_training_length = sum([dataset.real_len for dataset in train_dataset_videos]) + +train_dataset_imagenet.real_len
    dataset_combined = ConcatDataset(train_dataset_videos + [train_dataset_imagenet])
    # sampler=[]
    # seed_sampler=int.from_bytes(os.urandom(4),"big")
    # random.seed(seed_sampler)
    # for idx in range(opt.epoch):
    #     sampler = sampler + random.sample(range(dataset_training_length),dataset_training_length)
    # wandb.log({"Sampler_Seed":seed_sampler})
    # sampler = sampler+WeightedRandomSampler([1] * video_training_length + [1] * imagenet_training_length, dataset_training_length*opt.epoch)

    # video_training_length = sum([len(dataset) for dataset in train_dataset_videos])
    # dataset_training_length = sum([dataset.real_len for dataset in train_dataset_videos])
    # dataset_combined = ConcatDataset(train_dataset_videos)
    # sampler = WeightedRandomSampler([1] * video_training_length, dataset_training_length * opt.epoch)

    data_loader = DataLoader(dataset_combined, batch_size=opt.batch_size, shuffle=True, num_workers=opt.workers)
    return data_loader


def training_logger():
    if (total_iter % opt.checkpoint_step == 0) or (total_iter == len(data_loader)):
        train_loss_dict = {"train/" + str(k): v / loss_handler.count_sample for k, v in loss_handler.loss_dict.items()}
        train_loss_dict["train/opt_g_lr_1"] = step_optim_scheduler_g.get_last_lr()[0]
        train_loss_dict["train/opt_g_lr_2"] = step_optim_scheduler_g.get_last_lr()[1]
        train_loss_dict["train/opt_d_lr"] = step_optim_scheduler_d.get_last_lr()[0]

        alert_text = f"l1_loss: {l1_loss.item()}\npercep_loss: {perceptual_loss.item()}\nctx_loss: {contextual_loss_total.item()}\ncst_loss: {consistent_loss.item()}\nsm_loss: {smoothness_loss.item()}\ntotal: {total_loss.item()}"

        if opt.use_wandb:
            wandb.log(train_loss_dict)
            wandb.alert(title=f"Progress training #{total_iter}", text=alert_text)

            for idx in range(I_predict_rgb.shape[0]):
                concated_I = make_grid(
                    [(I_predict_rgb[idx] * 255), (I_reference_rgb[idx] * 255), (I_current_rgb[idx] * 255)], nrow=3
                )
                wandb_concated_I = wandb.Image(
                    concated_I,
                    caption="[LEFT] Predict, [CENTER] Reference, [RIGHT] Ground truth\n[REF] {}, [FRAME] {}".format(
                        ref_path[idx], curr_frame_path[idx]
                    ),
                )
                wandb.log({f"example_{idx}": wandb_concated_I})

        torch.save(
            nonlocal_net.state_dict(),
            os.path.join(opt.checkpoint_dir, "nonlocal_net_iter.pth"),
        )
        torch.save(
            colornet.state_dict(),
            os.path.join(opt.checkpoint_dir, "colornet_iter.pth"),
        )
        torch.save(
            discriminator.state_dict(),
            os.path.join(opt.checkpoint_dir, "discriminator_iter.pth"),
        )
        torch.save(embed_net.state_dict(), os.path.join(opt.checkpoint_dir, "embed_net_iter.pth"))

        loss_handler.reset()


def load_params(ckpt_file):
    params = torch.load(ckpt_file)
    new_params = []
    for key, value in params.items():
        new_params.append((key, value))
    return OrderedDict(new_params)


def parse(parser, save=True):
    opt = parser.parse_args()
    args = vars(opt)

    print("------------------------------ Options -------------------------------")
    for k, v in sorted(args.items()):
        print("%s: %s" % (str(k), str(v)))
    print("-------------------------------- End ---------------------------------")

    if save:
        file_name = os.path.join("opt.txt")
        with open(file_name, "wt") as opt_file:
            opt_file.write(os.path.basename(sys.argv[0]) + " " + strftime("%Y-%m-%d %H:%M:%S", gmtime()) + "\n")
            opt_file.write("------------------------------ Options -------------------------------\n")
            for k, v in sorted(args.items()):
                opt_file.write("%s: %s\n" % (str(k), str(v)))
            opt_file.write("-------------------------------- End ---------------------------------\n")
    return opt


def gpu_setup():
    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
    cudnn.benchmark = True
    torch.cuda.set_device(opt.gpu_ids[0])
    device = torch.device("cuda")
    print("running on GPU", opt.gpu_ids)
    return device


if __name__ == "__main__":
    ############################################## SETUP ###############################################
    torch.multiprocessing.set_start_method("spawn", force=True)
    # =============== GET PARSER OPTION ================
    opt = parse(parser)
    opt.video_data_root_list = opt.video_data_root_list.split(",")
    opt.flow_data_root_list = opt.flow_data_root_list.split(",")
    opt.mask_data_root_list = opt.mask_data_root_list.split(",")
    opt.gpu_ids = list(map(int, opt.gpu_ids.split(",")))
    opt.head_out_idx = list(map(int, opt.head_out_idx.split(",")))
    n_dim_output = 3 if opt.use_feature_transform else 4
    assert len(opt.head_out_idx) == 4, "Size of head_out_idx must be 4"

    os.makedirs(opt.checkpoint_dir, exist_ok=True)

    # =================== INIT WANDB ===================
    if opt.use_wandb:
        print("Save images to Wandb")
        if opt.wandb_token != "":
            try:
                wandb.login(key=opt.wandb_token)
            except:
                pass
        wandb.init(
            project="video-colorization",
            name=f"{opt.wandb_name} {datetime.now(tz=ZoneInfo('Asia/Ho_Chi_Minh')).strftime('%Y/%m/%d_%H-%M-%S')}",
        )

    # ================== SETUP DEVICE ==================
    # torch.multiprocessing.set_start_method("spawn", force=True)
    # device = gpu_setup()
    device = "cuda" if torch.cuda.is_available() else "cpu"

    ############################################ LOAD DATA #############################################
    if opt.use_dummy:
        H, W = 224, 224
        I_last_lab = torch.rand(opt.batch_size, 3, H, W)
        I_current_lab = torch.rand(opt.batch_size, 3, H, W)
        I_reference_lab = torch.rand(opt.batch_size, 3, H, W)
        flow_forward = torch.rand(opt.batch_size, 2, H, W)
        mask = torch.rand(opt.batch_size, 1, H, W)
        placeholder_lab = torch.rand(opt.batch_size, 3, H, W)
        self_ref_flag = torch.rand(opt.batch_size, 3, H, W)
        data_loader = [
            [I_last_lab, I_current_lab, I_reference_lab, flow_forward, mask, placeholder_lab, self_ref_flag, None, None, None]
            for _ in range(1)
        ]
    else:
        data_loader = load_data()

    ########################################## DEFINE NETWORK ##########################################
    colornet = GeneralColorVidNet(opt.ic).to(device)
    nonlocal_net = GeneralWarpNet(feature_channel=256).to(device)  # change to 128 in swin tiny
    discriminator = Discriminator_x64_224(ndf=64).to(device)
    weighted_layer_color = WeightedAverage_color().to(device)
    nonlocal_weighted_layer = NonlocalWeightedAverage().to(device)
    warping_layer = WarpingLayer(device=device).to(device)
    embed_net = GeneralEmbedModel(pretrained_model="swin-small", device=device).to(device)

    print("-" * 59)
    print("|    TYPE   |          Model name            | Num params |")
    print("-" * 59)
    colornet_params = print_num_params(colornet)
    nonlocal_net_params = print_num_params(nonlocal_net)
    discriminator_params = print_num_params(discriminator)
    weighted_layer_color_params = print_num_params(weighted_layer_color)
    nonlocal_weighted_layer_params = print_num_params(nonlocal_weighted_layer)
    warping_layer_params = print_num_params(warping_layer)
    embed_net_params = print_num_params(embed_net)

    print("-" * 59)
    print(
        f"|   TOTAL   |                                | {('{:,}'.format(colornet_params+nonlocal_net_params+discriminator_params+weighted_layer_color_params+nonlocal_weighted_layer_params+warping_layer_params+embed_net_params)).rjust(10)} |"
    )
    print("-" * 59)

    if opt.use_wandb:
        wandb.watch(discriminator, log="all", log_freq=opt.checkpoint_step, idx=0)
        wandb.watch(embed_net, log="all", log_freq=opt.checkpoint_step, idx=1)
        wandb.watch(colornet, log="all", log_freq=opt.checkpoint_step, idx=2)
        wandb.watch(nonlocal_net, log="all", log_freq=opt.checkpoint_step, idx=3)

    # ============= USE PRETRAINED OR NOT ==============
    if opt.load_pretrained_model:
        # pretrained_path = "/workspace/video_colorization/ckpt_folder_ver_1_vit_small_patch16_384"
        nonlocal_net.load_state_dict(load_params(os.path.join(opt.checkpoint_dir, "nonlocal_net_iter.pth")))
        colornet.load_state_dict(load_params(os.path.join(opt.checkpoint_dir, "colornet_iter.pth")))
        discriminator.load_state_dict(load_params(os.path.join(opt.checkpoint_dir, "discriminator_iter.pth")))
        embed_net_params = load_params(os.path.join(opt.checkpoint_dir, "embed_net_iter.pth"))
        embed_net.load_state_dict(embed_net_params)

    ###################################### DEFINE LOSS FUNCTIONS #######################################
    perceptual_loss_fn = Perceptual_loss(opt.domain_invariant, opt.weight_perceptual)
    contextual_loss = ContextualLoss().to(device)
    contextual_forward_loss = ContextualLoss_forward().to(device)

    ######################################## DEFINE OPTIMIZERS #########################################
    optimizer_g = optim.AdamW(
        [
            {"params": nonlocal_net.parameters(), "lr": opt.lr},
            {"params": colornet.parameters(), "lr": 2 * opt.lr},
            {"params": embed_net.parameters(), "lr": opt.lr},
        ],
        betas=(0.5, 0.999),
        eps=1e-5,
        amsgrad=True,
    )

    optimizer_d = optim.AdamW(
        filter(lambda p: p.requires_grad, discriminator.parameters()),
        lr=opt.lr,
        betas=(0.5, 0.999),
        amsgrad=True,
    )

    step_optim_scheduler_g = PolynomialLR(
        optimizer_g,
        step_size=opt.lr_step,
        iter_warmup=0,
        iter_max=len(data_loader) * opt.epoch,
        power=0.9,
        min_lr=1e-8,
    )
    step_optim_scheduler_d = PolynomialLR(
        optimizer_d,
        step_size=opt.lr_step,
        iter_warmup=0,
        iter_max=len(data_loader) * opt.epoch,
        power=0.9,
        min_lr=1e-8,
    )
    ########################################## DEFINE OTHERS ###########################################
    downsampling_by2 = nn.AvgPool2d(kernel_size=2).to(device)
    timer_handler = TimeHandler()
    loss_handler = LossHandler()  # Handle loss value
    ############################################## TRAIN ###############################################

    total_iter = 0
    for epoch_num in range(1, opt.epoch + 1):
        # if opt.use_wandb:
        #     wandb.log({"Current_trainning_epoch": epoch_num})
        with tqdm(total=len(data_loader), position=0, leave=True) as pbar:
            for iter, sample in enumerate(data_loader):
                timer_handler.compute_time("load_sample")
                total_iter += 1

                # =============== LOAD DATA SAMPLE ================
                (
                    I_last_lab,  ######## (3, H, W)
                    I_current_lab,  ##### (3, H, W)
                    I_reference_lab,  ### (3, H, W)
                    flow_forward,  ###### (2, H, W)
                    mask,  ############## (1, H, W)
                    placeholder_lab,  ### (3, H, W)
                    self_ref_flag,  ##### (3, H, W)
                    prev_frame_path,
                    curr_frame_path,
                    ref_path,
                ) = sample

                I_last_lab = I_last_lab.to(device)
                I_current_lab = I_current_lab.to(device)
                I_reference_lab = I_reference_lab.to(device)
                flow_forward = flow_forward.to(device)
                mask = mask.to(device)
                placeholder_lab = placeholder_lab.to(device)
                self_ref_flag = self_ref_flag.to(device)

                I_last_l = I_last_lab[:, 0:1, :, :]
                I_last_ab = I_last_lab[:, 1:3, :, :]
                I_current_l = I_current_lab[:, 0:1, :, :]
                I_current_ab = I_current_lab[:, 1:3, :, :]
                I_reference_l = I_reference_lab[:, 0:1, :, :]
                I_reference_ab = I_reference_lab[:, 1:3, :, :]
                I_reference_rgb = tensor_lab2rgb(torch.cat((uncenter_l(I_reference_l), I_reference_ab), dim=1))

                _load_sample_time = timer_handler.compute_time("load_sample")
                timer_handler.compute_time("forward_model")

                features_B = embed_net(I_reference_rgb)
                B_feat_0, B_feat_1, B_feat_2, B_feat_3 = features_B

                # ================== COLORIZATION ==================
                # The last frame
                I_last_ab_predict, I_last_nonlocal_lab_predict = frame_colorization(
                    IA_l=I_last_l,
                    IB_lab=I_reference_lab,
                    IA_last_lab=placeholder_lab,
                    features_B=features_B,
                    embed_net=embed_net,
                    colornet=colornet,
                    nonlocal_net=nonlocal_net,
                    luminance_noise=opt.luminance_noise,
                )
                I_last_lab_predict = torch.cat((I_last_l, I_last_ab_predict), dim=1)

                # The current frame
                I_current_ab_predict, I_current_nonlocal_lab_predict = frame_colorization(
                    IA_l=I_current_l,
                    IB_lab=I_reference_lab,
                    IA_last_lab=I_last_lab_predict,
                    features_B=features_B,
                    embed_net=embed_net,
                    colornet=colornet,
                    nonlocal_net=nonlocal_net,
                    luminance_noise=opt.luminance_noise,
                )
                I_current_lab_predict = torch.cat((I_last_l, I_current_ab_predict), dim=1)

                # ================ UPDATE GENERATOR ================
                if opt.weight_gan > 0:
                    optimizer_g.zero_grad()
                    optimizer_d.zero_grad()
                    fake_data_lab = torch.cat(
                        (
                            uncenter_l(I_current_l),
                            I_current_ab_predict,
                            uncenter_l(I_last_l),
                            I_last_ab_predict,
                        ),
                        dim=1,
                    )
                    real_data_lab = torch.cat(
                        (
                            uncenter_l(I_current_l),
                            I_current_ab,
                            uncenter_l(I_last_l),
                            I_last_ab,
                        ),
                        dim=1,
                    )

                    if opt.permute_data:
                        batch_index = torch.arange(-1, opt.batch_size - 1, dtype=torch.long)
                        real_data_lab = real_data_lab[batch_index, ...]

                    discriminator_loss = discriminator_loss_fn(real_data_lab, fake_data_lab, discriminator)
                    discriminator_loss.backward()
                    optimizer_d.step()

                optimizer_g.zero_grad()
                optimizer_d.zero_grad()

                # ================== COMPUTE LOSS ==================
                # L1 loss
                l1_loss = l1_loss_fn(I_current_ab, I_current_ab_predict) * opt.weigth_l1

                # Generator_loss. TODO: freeze this to train some first epoch
                if epoch_num > opt.epoch_train_discriminator:
                    generator_loss = generator_loss_fn(real_data_lab, fake_data_lab, discriminator, opt.weight_gan, device)

                # Perceptual Loss
                I_predict_rgb = tensor_lab2rgb(torch.cat((uncenter_l(I_current_l), I_current_ab_predict), dim=1))
                pred_feat_0, pred_feat_1, pred_feat_2, pred_feat_3 = embed_net(I_predict_rgb)

                I_current_rgb = tensor_lab2rgb(torch.cat((uncenter_l(I_current_l), I_current_ab), dim=1))
                A_feat_0, _, _, A_feat_3 = embed_net(I_current_rgb)

                perceptual_loss = perceptual_loss_fn(A_feat_3, pred_feat_3)

                # Contextual Loss
                contextual_style5_1 = torch.mean(contextual_forward_loss(pred_feat_3, B_feat_3.detach())) * 8
                contextual_style4_1 = torch.mean(contextual_forward_loss(pred_feat_2, B_feat_2.detach())) * 4
                contextual_style3_1 = torch.mean(contextual_forward_loss(pred_feat_1, B_feat_1.detach())) * 2
                contextual_style2_1 = torch.mean(contextual_forward_loss(pred_feat_0, B_feat_0.detach()))
                # if opt.use_feature_transform:
                #     contextual_style3_1 = (
                #         torch.mean(
                #             contextual_forward_loss(
                #                 downsampling_by2(pred_feat_1),
                #                 downsampling_by2(),
                #             )
                #         )
                #         * 2
                #     )
                # else:
                #     contextual_style3_1 = (
                #         torch.mean(
                #             contextual_forward_loss(
                #                 pred_feat_1,
                #                 B_feat_1.detach(),
                #             )
                #         )
                #         * 2
                #     )

                contextual_loss_total = (
                    contextual_style5_1 + contextual_style4_1 + contextual_style3_1 + contextual_style2_1
                ) * opt.weight_contextual

                # Consistent Loss
                consistent_loss = consistent_loss_fn(
                    I_current_lab_predict,
                    I_last_ab_predict,
                    I_current_nonlocal_lab_predict,
                    I_last_nonlocal_lab_predict,
                    flow_forward,
                    mask,
                    warping_layer,
                    weight_consistent=opt.weight_consistent,
                    weight_nonlocal_consistent=opt.weight_nonlocal_consistent,
                    device=device,
                )

                # Smoothness loss
                smoothness_loss = smoothness_loss_fn(
                    I_current_l,
                    I_current_lab,
                    I_current_ab_predict,
                    A_feat_0,
                    weighted_layer_color,
                    nonlocal_weighted_layer,
                    weight_smoothness=opt.weight_smoothness,
                    weight_nonlocal_smoothness=opt.weight_nonlocal_smoothness,
                    device=device,
                )

                # Total loss
                total_loss = l1_loss + perceptual_loss + contextual_loss_total + consistent_loss + smoothness_loss
                if epoch_num > opt.epoch_train_discriminator:
                    total_loss += generator_loss

                # Add loss to loss handler
                loss_handler.add_loss(key="total_loss", loss=total_loss.item())
                loss_handler.add_loss(key="l1_loss", loss=l1_loss.item())
                loss_handler.add_loss(key="perceptual_loss", loss=perceptual_loss.item())
                loss_handler.add_loss(key="contextual_loss", loss=contextual_loss_total.item())
                loss_handler.add_loss(key="consistent_loss", loss=consistent_loss.item())
                loss_handler.add_loss(key="smoothness_loss", loss=smoothness_loss.item())
                loss_handler.add_loss(key="discriminator_loss", loss=discriminator_loss.item())
                if epoch_num > opt.epoch_train_discriminator:
                    loss_handler.add_loss(key="generator_loss", loss=generator_loss.item())
                loss_handler.count_one_sample()

                total_loss.backward()

                optimizer_g.step()
                step_optim_scheduler_g.step()
                step_optim_scheduler_d.step()

                _forward_model_time = timer_handler.compute_time("forward_model")

                timer_handler.compute_time("training_logger")
                training_logger()
                _training_logger_time = timer_handler.compute_time("training_logger")

                pbar.set_description(
                    f"Epochs: {epoch_num}, Load_sample: {_load_sample_time:.3f}s, Forward: {_forward_model_time:.3f}s, log: {_training_logger_time:.3f}s"
                )
                pbar.update(1)