main_flowar.py

import argparse
import datetime
import numpy as np
import os
import time
from pathlib import Path

import torch
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
import torch.backends.cudnn as cudnn
from torch.utils.tensorboard import SummaryWriter
import torchvision.transforms as transforms
import torchvision.datasets as datasets

from util.crop import center_crop_arr
import util.misc as misc
from util.misc import NativeScalerWithGradNormCount as NativeScaler
from util.loader import CachedFolder

from models.vae import AutoencoderKL
from models import flowar
from engine import train_one_epoch, evaluate
import copy


def get_args_parser():
    parser = argparse.ArgumentParser('FlowAR training with flow matching Loss', add_help=False)
    parser.add_argument('--batch_size', default=16, type=int,
                        help='Batch size per GPU (effective batch size is batch_size * # gpus')
    parser.add_argument('--epochs', default=400, type=int)

    # Model parameters
    parser.add_argument('--model', default='flowar_large', type=str, metavar='MODEL',
                        help='Name of model to train')

    # VAE parameters
    parser.add_argument('--img_size', default=256, type=int,
                        help='images input size')
    parser.add_argument('--vae_path', default="pretrained_models/vae/kl16.ckpt", type=str,
                        help='images input size')
    parser.add_argument('--vae_embed_dim', default=16, type=int,
                        help='vae output embedding dimension')
    parser.add_argument('--vae_stride', default=16, type=int,
                        help='tokenizer stride, default use KL16')
    parser.add_argument('--patch_size', default=1, type=int,
                        help='number of tokens to group as a patch.')

    # Generation parameters
    parser.add_argument('--num_step', default=25, type=int,
                        help='number of flow matching steps')
    parser.add_argument('--guidance', default=0.9, type=float,
                        help='guidance of flow matching')
    parser.add_argument('--num_images', default=50000, type=int,
                        help='number of images to generate')
    parser.add_argument('--cfg', default=1.0, type=float, help="classifier-free guidance")
    parser.add_argument('--cfg_schedule', default="linear", type=str)
    parser.add_argument('--label_drop_prob', default=0.1, type=float)
    parser.add_argument('--eval_freq', type=int, default=40, help='evaluation frequency')
    parser.add_argument('--save_last_freq', type=int, default=5, help='save last frequency')
    parser.add_argument('--online_eval', action='store_true')
    parser.add_argument('--evaluate', action='store_true')
    parser.add_argument('--eval_bsz', type=int, default=64, help='generation batch size')

    # Optimizer parameters
    parser.add_argument('--weight_decay', type=float, default=0.02,
                        help='weight decay (default: 0.02)')

    parser.add_argument('--lr', type=float, default=None, metavar='LR',
                        help='learning rate (absolute lr)')
    parser.add_argument('--blr', type=float, default=1e-4, metavar='LR',
                        help='base learning rate: absolute_lr = base_lr * total_batch_size / 256')
    parser.add_argument('--min_lr', type=float, default=1e-5, metavar='LR',
                        help='lower lr bound for cyclic schedulers that hit 0')
    parser.add_argument('--lr_schedule', type=str, default='cosine',
                        help='learning rate schedule')
    parser.add_argument('--warmup_epochs', type=int, default=100, metavar='N',
                        help='epochs to warmup LR')
    parser.add_argument('--ema_rate', default=0.9999, type=float)

    # params
    parser.add_argument('--grad_clip', type=float, default=3.0,
                        help='Gradient clip')
    parser.add_argument('--attn_dropout', type=float, default=0.1,
                        help='attention dropout')
    parser.add_argument('--proj_dropout', type=float, default=0.1,
                        help='projection dropout')
    parser.add_argument('--buffer_size', type=int, default=0)

    # Diffusion Loss params
    parser.add_argument('--diffloss_d', type=int, default=12)
    parser.add_argument('--diffloss_w', type=int, default=1536)
    parser.add_argument('--temperature', default=1.0, type=float, help='diffusion loss sampling temperature')

    # Dataset parameters
    parser.add_argument('--data_path', default='./data/imagenet', type=str,
                        help='dataset path')
    parser.add_argument('--class_num', default=1000, type=int)

    parser.add_argument('--output_dir', default='./output_dir',
                        help='path where to save, empty for no saving')
    parser.add_argument('--log_dir', default='./output_dir',
                        help='path where to tensorboard log')
    parser.add_argument('--device', default='cuda',
                        help='device to use for training / testing')
    parser.add_argument('--seed', default=1, type=int)
    parser.add_argument('--resume', default='',
                        help='resume from checkpoint')

    parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
                        help='start epoch')
    parser.add_argument('--num_workers', default=10, 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')
    parser.set_defaults(pin_mem=True)
    parser.add_argument('--use_checkpoint', action='store_true')
    parser.set_defaults(use_checkpoint=False)

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

    # caching latents
    parser.add_argument('--use_cached', action='store_true', dest='use_cached',
                        help='Use cached latents')
    parser.set_defaults(use_cached=False)
    parser.add_argument('--cached_path', default='', help='path to cached latents')

    return parser


def main(args):
    misc.init_distributed_mode(args)

    print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__))))
    print("{}".format(args).replace(', ', ',\n'))

    device = torch.device(args.device)

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

    cudnn.benchmark = True

    num_tasks = misc.get_world_size()
    global_rank = misc.get_rank()

    if global_rank == 0 and args.log_dir is not None:
        os.makedirs(args.log_dir, exist_ok=True)
        log_writer = SummaryWriter(log_dir=args.log_dir)
    else:
        log_writer = None

    # augmentation following DiT and ADM
    transform_train = transforms.Compose([
        transforms.Resize(int(256*1.125)),
        transforms.RandomCrop((256,256)),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
    ])
    if args.evaluate:
        pass
    else:

        dataset_train = datasets.ImageFolder(os.path.join(args.data_path, 'train'), transform=transform_train)
        print(dataset_train)

        sampler_train = torch.utils.data.DistributedSampler(
            dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
        )
        print("Sampler_train = %s" % str(sampler_train))

        data_loader_train = torch.utils.data.DataLoader(
            dataset_train, sampler=sampler_train,
            batch_size=args.batch_size,
            num_workers=args.num_workers,
            pin_memory=args.pin_mem,
            drop_last=True,
        )

    vae = AutoencoderKL(embed_dim=args.vae_embed_dim, ch_mult=(1, 1, 2, 2, 4), ckpt_path=args.vae_path).cuda().eval()
    for param in vae.parameters():
        param.requires_grad = False
        
    model = flowar.__dict__[args.model](
        img_size=args.img_size,
        vae_stride=args.vae_stride,
        patch_size=args.patch_size,
        vae_embed_dim=args.vae_embed_dim,
        label_drop_prob=args.label_drop_prob,
        class_num=args.class_num,
        attn_dropout=args.attn_dropout,
        proj_dropout=args.proj_dropout,
        buffer_size=args.buffer_size,
        diffloss_d=args.diffloss_d,
        diffloss_w=args.diffloss_w,
        use_checkpoint=args.use_checkpoint
    )
    
    print("Model = %s" % str(model))
    # following timm: set wd as 0 for bias and norm layers
    n_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
    print("Number of trainable parameters: {}M".format(n_params / 1e6))

    model.to(device)
    model_without_ddp = model

    eff_batch_size = args.batch_size * misc.get_world_size()

    if args.lr is None:  # only base_lr is specified
        args.lr = args.blr * eff_batch_size / 256

    print("base lr: %.2e" % (args.lr * 256 / eff_batch_size))
    print("actual lr: %.2e" % args.lr)
    print("effective batch size: %d" % eff_batch_size)

    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        model_without_ddp = model.module

    # no weight decay on bias, norm layers, and diffloss MLP
    param_groups = misc.add_weight_decay(model_without_ddp, args.weight_decay)
    optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95))
    print(optimizer)
    loss_scaler = NativeScaler()

    # resume training
    if args.resume and os.path.exists(os.path.join(args.resume, "checkpoint-last.pth")):
        checkpoint = torch.load(os.path.join(args.resume, "checkpoint-last.pth"), map_location='cpu')
        model_without_ddp.load_state_dict(checkpoint['model'])
        model_params = list(model_without_ddp.parameters())
        ema_state_dict = checkpoint['model_ema']
        ema_params = [ema_state_dict[name].cuda() for name, _ in model_without_ddp.named_parameters()]
        print("Resume checkpoint %s" % args.resume)

        if 'optimizer' in checkpoint and 'epoch' in checkpoint:
            optimizer.load_state_dict(checkpoint['optimizer'])
            args.start_epoch = checkpoint['epoch'] + 1
            if 'scaler' in checkpoint:
                loss_scaler.load_state_dict(checkpoint['scaler'])
            print("With optim & sched!")
        del checkpoint
    else:
        model_params = list(model_without_ddp.parameters())
        ema_params = copy.deepcopy(model_params)
        print("Training from scratch")
    # training
    print(f"Start training for {args.epochs} epochs")
    start_time = time.time()
    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            data_loader_train.sampler.set_epoch(epoch)

        train_one_epoch(
            model, vae,
            model_params, ema_params,
            data_loader_train,
            optimizer, device, epoch, loss_scaler,
            log_writer=log_writer,
            args=args
        )

        # save checkpoint
        if epoch % args.save_last_freq == 0 or epoch + 1 == args.epochs:
            misc.save_model(args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                            loss_scaler=loss_scaler, epoch=epoch, ema_params=ema_params, epoch_name="last")

        if epoch % 100 == 0 or epoch + 1 == args.epochs:
            misc.save_model(args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                            loss_scaler=loss_scaler, epoch=epoch, ema_params=ema_params, epoch_name=epoch)

        # online evaluation
        if args.online_eval and (epoch % args.eval_freq == 0 or epoch + 1 == args.epochs):
            torch.cuda.empty_cache()
            evaluate(model, model_without_ddp, ema_params, args, epoch, batch_size=args.eval_bsz, log_writer=log_writer,
                     cfg=1.0, use_ema=True)
            if not (args.cfg == 1.0 or args.cfg == 0.0):
                evaluate(model, model_without_ddp, ema_params, args, epoch, batch_size=args.eval_bsz // 2,
                         log_writer=log_writer, cfg=args.cfg, use_ema=True)
            torch.cuda.empty_cache()

        if misc.is_main_process():
            if log_writer is not None:
                log_writer.flush()

    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    print('Training time {}'.format(total_time_str))


if __name__ == '__main__':
    args = get_args_parser()
    args = args.parse_args()
    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    args.log_dir = args.output_dir
    main(args)