4_ControlModule/4_train_control_module.py

# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
import argparse
import datetime
import numpy as np
import time
import torch
import torch.backends.cudnn as cudnn
import json
from pathlib import Path
from torch.cuda.amp import GradScaler, autocast
from torchvision.utils import make_grid
import importlib
import logging
import torch.nn as nn

import sys
from typing import Iterable, Optional
import logging
import torch.distributed as dist
import os
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from PIL import Image, ImageDraw, ImageFont
from tqdm import tqdm
import matplotlib.pyplot as plt
from utils.util import show_params, visualize_features, init_distributed_mode, get_rank, get_world_size
from dataset.build_dataset import CustomCocoDataset

from omegaconf import OmegaConf
from model import ControlLDM, Diffusion
from utils.common import instantiate_from_config
from utils.sampler import SpacedSampler


def get_args_parser():
    parser = argparse.ArgumentParser('DeiT training and evaluation script', add_help=False)
    parser.add_argument('--batch-size', default=64, type=int)
    parser.add_argument('--epochs', default=300, type=int)
    parser.add_argument('--bce-loss', action='store_true')
    parser.add_argument('--unscale-lr', action='store_true')

    # Feature extractor parameters
    parser.add_argument('--model', default='deit_base_patch16_224', type=str)
    parser.add_argument('--target_model', default='deit_base_patch16_224', type=str)
    parser.add_argument('--input-size', default=224, type=int, help='images input size')

    parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
                        help='Dropout rate (default: 0.)')
    parser.add_argument('--drop-path', type=float, default=0.1, metavar='PCT',
                        help='Drop path rate (default: 0.1)')

    parser.add_argument('--model-ema', action='store_true')
    parser.add_argument('--no-model-ema', action='store_false', dest='model_ema')
    parser.set_defaults(model_ema=True)
    parser.add_argument('--model-ema-decay', type=float, default=0.99996, help='')
    parser.add_argument('--model-ema-force-cpu', action='store_true', default=False, help='')

    # CLDM parameters
    parser.add_argument("--config", type=str, required=True)
    parser.add_argument('--total_train_steps', default=300000, type=int)
    parser.add_argument('--resume_path', default='', help='resume from checkpoint for controlnet')
    parser.add_argument('--cldm_learning_rate', default=1e-4, type=float, help='learning rate for controlnet')
    parser.add_argument('--sd_locked', default=True, help='whether to lock the sd of controlnet')
    parser.add_argument('--only_mid_control', default=False, help='only control the middle layers of controlnet')
    parser.add_argument('--cldm_yaml', default='./models/cldm_v15.yaml', help='yaml file for controlnet')
    parser.add_argument('--exp_dir', default='./exp', help='experiment directory')
    parser.add_argument('--image_floder', default='./data', help='training image floder')
    parser.add_argument("--log_every", default=20, type=int, help="log every n steps")
    parser.add_argument("--ckpt_every", default=1000, type=int, help="save checkpoint every n steps")
    parser.add_argument("--image_every", default=1000, type=int, help="log image every n steps")
    # parser.add_argument('--controlnet_dir', default='', type=str, help='resume from checkpoint for controlnet')
    parser.add_argument('--global_step', default=0, type=int, help='global step')
    parser.add_argument('--ddim_steps', default=50, type=int, help='ddim steps')
    parser.add_argument('--eta', default=0, type=float, help='ddim eta')
    parser.add_argument('--unconditional_guidance_scale', default=1.25, type=float, help='unconditional guidance scale')

    # Optimizer parameters
    parser.add_argument('--opt', default='adamw', type=str, metavar='OPTIMIZER',
                        help='Optimizer (default: "adamw"')
    parser.add_argument('--opt-eps', default=1e-8, type=float, metavar='EPSILON',
                        help='Optimizer Epsilon (default: 1e-8)')
    parser.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA',
                        help='Optimizer Betas (default: None, use opt default)')
    parser.add_argument('--clip-grad', type=float, default=None, metavar='NORM',
                        help='Clip gradient norm (default: None, no clipping)')
    parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
                        help='SGD momentum (default: 0.9)')
    parser.add_argument('--weight-decay', type=float, default=0.05,
                        help='weight decay (default: 0.05)')
    # Learning rate schedule parameters
    parser.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER',
                        help='LR scheduler (default: "cosine"')
    parser.add_argument('--lr', type=float, default=4e-4, metavar='LR',
                        help='learning rate (default: 5e-4)')
    parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct',
                        help='learning rate noise on/off epoch percentages')
    parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT',
                        help='learning rate noise limit percent (default: 0.67)')
    parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
                        help='learning rate noise std-dev (default: 1.0)')
    parser.add_argument('--warmup-lr', type=float, default=1e-6, metavar='LR',
                        help='warmup learning rate (default: 1e-6)')
    parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',
                        help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')

    parser.add_argument('--decay-epochs', type=float, default=30, metavar='N',
                        help='epoch interval to decay LR')
    parser.add_argument('--warmup-epochs', type=int, default=5, metavar='N',
                        help='epochs to warmup LR, if scheduler supports')
    parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N',
                        help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
    parser.add_argument('--patience-epochs', type=int, default=10, metavar='N',
                        help='patience epochs for Plateau LR scheduler (default: 10')
    parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
                        help='LR decay rate (default: 0.1)')

    # Augmentation parameters
    parser.add_argument('--color-jitter', type=float, default=0.3, metavar='PCT',
                        help='Color jitter factor (default: 0.3)')
    parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME',
                        help='Use AutoAugment policy. "v0" or "original". " + \
                             "(default: rand-m9-mstd0.5-inc1)'),
    parser.add_argument('--smoothing', type=float, default=0.1, help='Label smoothing (default: 0.1)')
    parser.add_argument('--train-interpolation', type=str, default='bicubic',
                        help='Training interpolation (random, bilinear, bicubic default: "bicubic")')

    parser.add_argument('--repeated-aug', action='store_true')
    parser.add_argument('--no-repeated-aug', action='store_false', dest='repeated_aug')
    parser.set_defaults(repeated_aug=True)
    
    parser.add_argument('--train-mode', action='store_true')
    parser.add_argument('--no-train-mode', action='store_false', dest='train_mode')
    parser.set_defaults(train_mode=True)
    
    parser.add_argument('--ThreeAugment', action='store_true') #3augment
    
    parser.add_argument('--src', action='store_true') #simple random crop
    
    # add dataset parameters
    parser.add_argument('--global_crops_size', '--img_size', default=224, type=int,
                        help="this should be equal to image size")
    parser.add_argument('--patch_size', default=16, type=int,
                        help="patch size for vit patch embedding")
    
    # add masking parameter
    parser.add_argument('--mask_ratio', default=(0.1, 0.5), type=float, nargs='+',
                        help="mask ratio can be either a value or a range")
    parser.add_argument('--mask_probability', default=0., type=float,
                        help="how many samples with be applied with masking")
    parser.add_argument('--mask_first_n', action='store_true',
                        help="mask the first n sample to avoid shuffling. Needed for MAE-style encoder")
    parser.add_argument('--clone_batch', default=1, type=int,
                        help="how many times to clone the batch for masking (default: 1, not cloning)")
    
    # * Random Erase params
    parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT',
                        help='Random erase prob (default: 0.25)')
    parser.add_argument('--remode', type=str, default='pixel',
                        help='Random erase mode (default: "pixel")')
    parser.add_argument('--recount', type=int, default=1,
                        help='Random erase count (default: 1)')
    parser.add_argument('--resplit', action='store_true', default=False,
                        help='Do not random erase first (clean) augmentation split')

    # * Mixup params
    parser.add_argument('--mixup', type=float, default=0.8,
                        help='mixup alpha, mixup enabled if > 0. (default: 0.8)')
    parser.add_argument('--cutmix', type=float, default=1.0,
                        help='cutmix alpha, cutmix enabled if > 0. (default: 1.0)')
    parser.add_argument('--cutmix-minmax', type=float, nargs='+', default=None,
                        help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
    parser.add_argument('--mixup-prob', type=float, default=1.0,
                        help='Probability of performing mixup or cutmix when either/both is enabled')
    parser.add_argument('--mixup-switch-prob', type=float, default=0.5,
                        help='Probability of switching to cutmix when both mixup and cutmix enabled')
    parser.add_argument('--mixup-mode', type=str, default='batch',
                        help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')

    # Distillation parameters
    parser.add_argument('--teacher-model', default='base', type=str)
    parser.add_argument('--teacher-path', type=str, default='')
    parser.add_argument('--distillation-type', default='none', choices=['none', 'soft', 'hard'], type=str, help="")
    parser.add_argument('--distillation-alpha', default=0.5, type=float, help="")
    parser.add_argument('--distillation-tau', default=1.0, type=float, help="")
    parser.add_argument('--lambda_token', type=float, default=1.0)
    parser.add_argument('--lambda_fea', type=float, default=1.0)
    parser.add_argument('--lambda_patch', type=float, default=1.0)
    
    # * Cosub params
    parser.add_argument('--cosub', action='store_true') 
    
    # * Finetuning params
    parser.add_argument('--finetune', default='', help='finetune from checkpoint')
    parser.add_argument('--attn-only', action='store_true')
    parser.add_argument('--weight_inherit', default='') 
    
    # Dataset parameters
    parser.add_argument('--data-path', default='/datasets01/imagenet_full_size/061417/', type=str,
                        help='dataset path')
    parser.add_argument('--data-set', default='IMNET', choices=['CIFAR', 'IMNET', 'IMNET_ibot', 'IMNET_ibot_aug', 'IMNET_ibot_fast_aug', 'INAT', 'INAT19', 'IMNET_L', 'IMNET_L_ibot'],
                        type=str, help='Image Net dataset path')
    parser.add_argument('--inat-category', default='name',
                        choices=['kingdom', 'phylum', 'class', 'order', 'supercategory', 'family', 'genus', 'name'],
                        type=str, help='semantic granularity')

    parser.add_argument('--output_dir', default='',
                        help='path where to save, empty for no saving')
    parser.add_argument('--log_dir', default='/data1/qiyp/Proteus-pytorch/pretrain/log/DINOv2_training/log', 
                        type=str, help='saving logging info every 20 iters')
    parser.add_argument('--device', default='cuda',
                        help='device to use for training / testing')
    parser.add_argument('--seed', default=231, type=int)
    parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
                        help='start epoch')
    parser.add_argument('--eval', action='store_true', help='Perform evaluation only')
    parser.add_argument('--eval-crop-ratio', default=0.875, type=float, help="Crop ratio for evaluation")
    parser.add_argument('--dist-eval', action='store_true', default=False, help='Enabling distributed evaluation')
    parser.add_argument('--num_workers', default=8, type=int)
    parser.add_argument('--pin-mem', action='store_true',
                        help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
    parser.add_argument('--no-pin-mem', action='store_false', dest='pin_mem',
                        help='')
    parser.set_defaults(pin_mem=True)

    # distributed training parameters
    parser.add_argument('--distributed', action='store_true', default=False, help='Enabling distributed training')
    parser.add_argument('--world_size', default=1, type=int,
                        help='number of distributed processes')
    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
    return parser

def setup_logger(log_dir, rank=0):
    if rank != 0:
        return  # 只有主进程（rank 0）配置日志记录器
    log_formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s]  %(message)s")
    root_logger = logging.getLogger()
    root_logger.setLevel(logging.INFO)

    log_file_handler = logging.FileHandler(log_dir, encoding='utf-8')
    log_file_handler.setFormatter(log_formatter)
    root_logger.addHandler(log_file_handler)

    log_stream_handler = logging.StreamHandler(sys.stdout)
    log_stream_handler.setFormatter(log_formatter)
    root_logger.addHandler(log_stream_handler)

    logging.info('Logging file is %s' % log_dir)

def main(args):
    init_distributed_mode(args)

    print(args)

    device = torch.device(args.device)
     # 获取当前进程的 rank
    rank = dist.get_rank() if dist.is_initialized() else 0
    # set up logger
    os.makedirs(args.log_dir, exist_ok=True)
    setup_logger(args.log_dir + '/' + time.strftime('%Y%m%d_%H%M%S') + '.log', rank)
    logging.info('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__))))
    logging.info("{}".format(args).replace(', ', ',\n') + '\n')

    # set_seed(231)
    # fix the seed for reproducibility
    seed = args.seed + get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    # random.seed(seed)

    cudnn.benchmark = True

    # Setup an experiment folder
    exp_dir = args.exp_dir
    os.makedirs(exp_dir, exist_ok=True)
    ckpt_dir = os.path.join(exp_dir, "checkpoints")
    os.makedirs(ckpt_dir, exist_ok=True)
    logging.info(f"Experiment directory created at {exp_dir}")

    # Create Dataset
    img_folder = args.image_floder
    dataset = CustomCocoDataset(img_folder)
    logging.info(f"Loaded train dataset with {len(dataset)} samples")

    # Distributed Sampler
    if args.distributed:
        num_tasks = get_world_size()
        global_rank = get_rank()
        sampler_train = torch.utils.data.DistributedSampler(
            dataset, num_replicas=num_tasks, rank=global_rank, shuffle=True
        )
    else:
        sampler_train = torch.utils.data.RandomSampler(dataset)
    logging.info("Sampler_train = %s" % str(sampler_train))

    # Dataloader
    dataloader = DataLoader(
        dataset, sampler=sampler_train, batch_size=args.batch_size, num_workers=args.num_workers,
        pin_memory=args.pin_mem, drop_last=True)
    logging.info('Dataloader created')

    # ========== create IF model ========== #
    logging.info(f"# ===== Creating Feature Extractor: {args.model} ===== #")
    meta_arch_module = importlib.import_module(args.model) # models_proteus_dinov2
    MetaArch = meta_arch_module.MetaArch

    model = MetaArch(args)
    logging.info("Model = %s" % str(model))

    if args.finetune:
        checkpoint = torch.load(args.finetune, map_location='cpu')

        if 'state_dict' in checkpoint:
            pretrained_dict = checkpoint['state_dict']
        elif 'model' in checkpoint:
            pretrained_dict = checkpoint['model']
        else:
            pretrained_dict = checkpoint

        missing_keys, unexpected_keys = model.load_state_dict(pretrained_dict, False)
        logging.info('Finetuning from %s' % args.finetune)
        logging.info('missing_keys: %s' % str(missing_keys))
        logging.info('unexpected_keys: %s' % str(unexpected_keys))

    if args.attn_only:
        for name_p,p in model.named_parameters():
            if '.attn.' in name_p:
                p.requires_grad = True
            else:
                p.requires_grad = False
        try:
            model.head.weight.requires_grad = True
            model.head.bias.requires_grad = True
        except:
            model.fc.weight.requires_grad = True
            model.fc.bias.requires_grad = True
        try:
            model.pos_embed.requires_grad = True
        except:
            print('no position encoding')
        try:
            for p in model.patch_embed.parameters():
                p.requires_grad = False
        except:
            print('no patch embed')
            
    model.to(device)
    model.eval()

    # ========= create cldm ========= #
    cfg = OmegaConf.load(args.config)
    cldm: ControlLDM = instantiate_from_config(cfg.model.cldm)
    sd = torch.load(cfg.train.sd_path, map_location="cpu")["state_dict"]
    unused = cldm.load_pretrained_sd(sd)
    logging.info(f"strictly load pretrained SD weight from {cfg.train.sd_path}\n"
          f"unused weights: {unused}")
    
    if cfg.train.resume:
        ckpt = torch.load(cfg.train.resume, map_location="cpu")
        cldm_state_dict = ckpt['model_state_dict']
        cldm.load_controlnet_from_ckpt(cldm_state_dict)
        logging.info(f"strictly load controlnet weight from checkpoint: {cfg.train.resume}")
        if 'global_step' in ckpt:
            global_step = ckpt['global_step']
        if 'epoch' in ckpt:
            epoch = ckpt['epoch']
        logging.info(f"Resumed from global step {global_step}, epoch {epoch}")
    else:
        init_with_new_zero, init_with_scratch = cldm.load_controlnet_from_unet()
        logging.info(f"strictly load controlnet weight from pretrained SD\n"
                  f"weights initialized with newly added zeros: {init_with_new_zero}\n"
                  f"weights initialized from scratch: {init_with_scratch}")
        global_step = 0
        epoch = 0

    cldm = cldm.to(device)
    if args.distributed:
        cldm = torch.nn.parallel.DistributedDataParallel(cldm, device_ids=[args.gpu], find_unused_parameters=True)
        cldm_without_ddp = cldm.module
    else:
        cldm_without_ddp = cldm

    diffusion: Diffusion = instantiate_from_config(cfg.model.diffusion)

    # Setup optimizer:
    optimizer = torch.optim.AdamW(cldm_without_ddp.controlnet.parameters(), lr=args.cldm_learning_rate)
    if cfg.train.resume:
        checkpoint = torch.load(cfg.train.resume, map_location="cpu")
        # 恢复优化器状态
        if 'optimizer_state_dict' in checkpoint:
            optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
            logging.info(f"Optimizer state loaded from checkpoint: {cfg.train.resume}")
    
    show_params(cldm_without_ddp.controlnet)

    ddim_sampler = SpacedSampler(diffusion.betas)
    logging.info("# ========== Building model done! ========== #")
    
    # Preparation for training:
    cldm_without_ddp.train()
    diffusion.to(device)
    # 使用 GradScaler 进行混合精度训练
    scaler = GradScaler(enabled=True)
    if cfg.train.resume:
        checkpoint = torch.load(cfg.train.resume, map_location="cpu")
        # 恢复优化器状态
        if 'scaler_state_dict' in checkpoint:
            scaler.load_state_dict(checkpoint['scaler_state_dict'])  # Load GradScaler state
            logging.info(f"Loss Scaler state loaded from checkpoint: {cfg.train.resume}")

    max_steps = args.total_train_steps
    step_loss = []
    epoch_loss = []
    # Create SummaryWriter only in the main process
    if rank == 0:
        writer = SummaryWriter(exp_dir)

    logging.info(f"Training for {max_steps} steps...")

    while global_step < max_steps:
        pbar = tqdm(iterable=dataloader, unit="batch")
        for batch in dataloader:
            if args.distributed:
                sampler_train.set_epoch(epoch)

            gt = batch["jpg"].to(device)
            hint = batch["hint"].to(device)
            prompt = batch["txt"]
            _, _, h, w = hint.shape
            _, _, H, W = gt.shape
            with torch.no_grad():
                with autocast(dtype=torch.bfloat16):  # 使用 bf16 自动混合精度
                    # z_0 = cldm_without_ddp.get_latent(2 * gt - 1) # rescale to [-1, 1]
                    z_0 = cldm_without_ddp.vae_encode(2 * gt - 1)
                    features_dict = model.student.backbone(hint, is_training=True)
                    features = features_dict['x_norm_patchtokens']
                    features, _ = model.info_bottleneck(features, is_training=False)
                    features = features.view(-1, h//14, w//14, features.shape[2])  # [B, h, w, c]
                    features = features.permute(0, 3, 1, 2)
                    features = (features - features.mean()) / features.std()
                    features = torch.clamp(features, -5, 5)
                    features_not_zero = features
                    if torch.rand(1).item() < 0.1: # 10% of the time, set features to zero
                        features = torch.zeros_like(features)
                    cond = cldm_without_ddp.prepare_condition(features, prompt) # cond["c_txt"]: txt embed, cond["c_img"]: features
            
            with autocast(dtype=torch.bfloat16):  # bf16 自动混合精度
                # t = torch.randint(0, cldm_without_ddp.num_timesteps, (z_0.shape[0],), device=device).long()
                t = torch.randint(0, diffusion.num_timesteps, (z_0.shape[0],), device=device).long()
                # loss, _ = cldm_without_ddp.p_losses(z_0, cond, t)
                loss = diffusion.p_losses(cldm_without_ddp, z_0, t, cond)
            optimizer.zero_grad()
            # loss.backward()
            # optimizer.step()
            scaler.scale(loss).backward()  # 使用 GradScaler 缩放损失
            scaler.step(optimizer)
            scaler.update()

            # print("Gradient for input_hint_block:")
            # for name, param in cldm_without_ddp.controlnet.input_hint_block.named_parameters():
            #     if param.grad is not None:
            #         print(f"{name} grad: {param.grad}")
            #     else:
            #         print(f"{name} grad is None")

            global_step += 1
            step_loss.append(loss.item())
            epoch_loss.append(loss.item())
            pbar.update(1)
            pbar.set_description(f"Epoch: {epoch:04d}, Global Step: {global_step:07d}, Loss: {loss.item():.6f}")

            # Log loss values
            if rank == 0 and global_step % args.log_every == 0 and global_step > 0:
                avg_loss = np.mean(step_loss)
                step_loss.clear()
                writer.add_scalar("loss/loss_simple_step", avg_loss, global_step)

            # Save checkpoint
            if rank == 0 and global_step % args.ckpt_every == 0 and global_step > 0:
                # Create a checkpoint dictionary
                checkpoint = {
                    'model_state_dict': cldm_without_ddp.controlnet.state_dict(),
                    'optimizer_state_dict': optimizer.state_dict(),  # Save optimizer state
                    'scaler_state_dict': scaler.state_dict(),  # Save GradScaler state
                    'global_step': global_step,  # Save current iteration
                    'epoch': epoch,  # Save current epoch
                }
                ckpt_path = f"{ckpt_dir}/{global_step:07d}.pt"
                torch.save(checkpoint, ckpt_path)

            if rank == 0 and (global_step % args.image_every == 0 or global_step <= 1):
                eta = args.eta
                ddim_steps = args.ddim_steps
                cldm_without_ddp.eval()
                N = 1
                log_features = features_not_zero[:N]
                log_cond = cldm_without_ddp.prepare_condition(log_features, prompt[:N])
                log_uncond = cldm_without_ddp.prepare_condition(torch.zeros_like(log_features), prompt[:N])
                log_gt = gt[:N]
                log_hint = hint[:N]
                # shape = (4, H // 8, W // 8)

                with torch.no_grad():
                    with torch.cuda.amp.autocast(dtype=torch.bfloat16):

                        z = ddim_sampler.sample(
                            model=cldm_without_ddp, device=device, steps=50, batch_size=len(log_gt), x_size=z_0.shape[1:],
                            cond=log_cond, uncond=None, cfg_scale=1.0, x_T=None
                        )
                        x_samples = cldm_without_ddp.vae_decode(z)
                        x_samples = (x_samples + 1) / 2

                        z_cfg = ddim_sampler.sample(
                            model=cldm_without_ddp, device=device, steps=50, batch_size=len(log_gt), x_size=z_0.shape[1:],
                            cond=log_cond, uncond=log_uncond, cfg_scale=args.unconditional_guidance_scale, x_T=None
                        )
                        x_samples_cfg = cldm_without_ddp.vae_decode(z_cfg)
                        x_samples_cfg = (x_samples_cfg + 1) / 2

                vis_features = visualize_features(log_features)
                for tag, image in [
                        ("image/samples", x_samples), 
                        ("image/samples_cfg", x_samples_cfg), 
                        ("image/gt", log_gt), ("image/condition", vis_features), 
                        ("image/hint", log_hint),
                    ]:
                    # Convert BFloat16 images to float32 before logging
                    image = image.to(torch.float32)  # Alternatively, you can convert to uint8 if required
                    writer.add_image(tag, make_grid(image, nrow=1), global_step)
                cldm_without_ddp.train()
            if global_step == max_steps:
                break

        pbar.close()
        epoch += 1

        if rank == 0:
            avg_epoch_loss = np.mean(epoch_loss)
            epoch_loss.clear()
            writer.add_scalar("loss/loss_simple_epoch", avg_epoch_loss, global_step)
            logging.info(f"Epoch: {epoch:04d}, Global Step: {global_step:07d}, Loss: {avg_epoch_loss:.6f}")

    logging.info("done!")
    if rank == 0:
        writer.close()
    if args.distributed:
            dist.barrier()


if __name__ == '__main__':
    parser = argparse.ArgumentParser('DeiT training and evaluation script', parents=[get_args_parser()])
    args = parser.parse_args()
    if args.output_dir:
        Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    main(args)