main_vtab.py

# --------------------------------------------------------
# References:
# DeiT: https://github.com/facebookresearch/deit
# MoCo v3: https://github.com/facebookresearch/moco-v3
# MAE: https://github.com/facebookresearch/mae
# --------------------------------------------------------

import argparse
import datetime
import json
import numpy as np
import os
import time
from pathlib import Path
from easydict import EasyDict
import torch
import torch.backends.cudnn as cudnn
from torch.utils.tensorboard import SummaryWriter

from configs import DATASETS, CHECKPOINTS
import timm
# assert timm.__version__ == "0.3.2" # version check
from timm.models.layers import trunc_normal_

import misc as misc
from util.pos_embed import interpolate_pos_embed_ori as interpolate_pos_embed
from misc import NativeScalerWithGradNormCount as NativeScaler

from datasets.image_datasets_noaug import build_image_dataset
from engine_finetune import train_one_epoch, evaluate
from models.vision_transformer_IN21K import vit_base_patch16_224_in21k


from util.logger import create_logger
from models.losses import AdaLoss

def get_args_parser():
    parser = argparse.ArgumentParser('AdaptFormer fine-tuning for action recognition for image classification', add_help=False)
    parser.add_argument('--batch_size', default=512, type=int,
                        help='Batch size per GPU (effective batch size is batch_size * accum_iter * # gpus')
    parser.add_argument('--epochs', default=100, type=int)
    parser.add_argument('--accum_iter', default=1, type=int,
                        help='Accumulate gradient iterations (for increasing the effective batch size under memory constraints)')

    # Model parameters
    parser.add_argument('--model', default='vit_base_patch16', type=str, metavar='MODEL',
                        help='Name of model to train')

    # Optimizer parameters
    parser.add_argument('--weight_decay', type=float, default=0.,
                        help='weight decay (default: 0 for linear probe following MoCo v1)')

    parser.add_argument('--lr', type=float, default=None, metavar='LR',
                        help='learning rate (absolute lr)')
    parser.add_argument('--blr', type=float, default=0.1, 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('--warmup_epochs', type=int, default=10, metavar='N',
                        help='epochs to warmup LR')

    # * Finetuning params
    parser.add_argument('--finetune', default='',
                        help='finetune from checkpoint')
    parser.add_argument('--global_pool', action='store_true')
    parser.set_defaults(global_pool=False)
    parser.add_argument('--cls_token', action='store_false', dest='global_pool',
                        help='Use class token instead of global pool for classification')

    # Dataset parameters
    # parser.add_argument('--data_path', default='/datasets01/imagenet_full_size/061417/', type=str,
    #                     help='dataset path')
    parser.add_argument('--nb_classes', default=1000, type=int,
                        help='number of the classification types')

    parser.add_argument('--output_dir', default='./output_dir',
                        help='path where to save, empty for no saving')
    parser.add_argument('--log_dir', default=None,
                        help='path where to tensorboard log')
    parser.add_argument('--device', default='cuda',
                        help='device to use for training / testing')
    parser.add_argument('--seed', default=0, 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('--eval', action='store_true',
                        help='Perform evaluation only')
    parser.add_argument('--dist_eval', action='store_true', default=False,
                        help='Enabling distributed evaluation (recommended during training for faster monitor')
    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('--save_freq', default=1, type=int)
    parser.add_argument('--auto_remove', action='store_true', help='automatically remove the last checkpoint.')
    parser.add_argument('--eval_freq', default=1, type=int)

    # 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')

    # custom configs
    parser.add_argument('--dataset', default='imagenet')
    parser.add_argument('--drop_path', type=float, default=0.0, metavar='PCT',
                        help='Drop path rate (default: 0.0)')

    parser.add_argument('--inception', default=False, action='store_true', help='whether use INCPETION mean and std'
                                                                                '(for Jx provided IN-21K pretrain')
    # AdaptFormer related parameters
    parser.add_argument('--ffn_adapt', default=False, action='store_true', help='whether activate AdaptFormer')
    parser.add_argument('--ffn_num', default=8, type=int, help='bottleneck middle dimension')
    parser.add_argument('--vpt', default=False, action='store_true', help='whether activate VPT')
    parser.add_argument('--vpt_num', default=1, type=int, help='number of VPT prompts')
    parser.add_argument('--fulltune', default=False, action='store_true', help='full finetune model')
    parser.add_argument('--token_target_ratio', type=float, default=0.5)
    return parser


def main(args):
    if args.log_dir is None:
        args.log_dir = args.output_dir
    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)


    cudnn.benchmark = True

    dataset_train, dataset_val, args.nb_classes, args.metric  = build_image_dataset(args)

    print(dataset_train.transform)
    print(dataset_val.transform)


    num_tasks = misc.get_world_size()
    global_rank = misc.get_rank()
    
    if global_rank == 0 and args.log_dir is not None and not args.eval:
        os.makedirs(args.log_dir, exist_ok=True)
        log_writer = SummaryWriter(log_dir=args.log_dir)
    else:
        log_writer = None
    
        # logger
    logger = create_logger(output_dir=args.output_dir, dist_rank=misc.get_rank(), name=f"{args.model}_{int(time.time())}")
    logger.info(f"working dir: {args.output_dir}")
    
        
    
    dataloader_train = torch.utils.data.DataLoader(
        dataset_train,
        batch_size=args.batch_size,
        sampler=torch.utils.data.DistributedSampler(dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True),
        num_workers=args.num_workers,
        pin_memory=True,
        )
    
    dataloader_val = torch.utils.data.DataLoader(
        torch.utils.data.Subset(dataset_val, range(misc.get_rank(), len(dataset_val), misc.get_world_size())),
        batch_size=args.batch_size,
        shuffle=False,
        num_workers=args.num_workers,
        pin_memory=True,
        )

    # fine-tuning configs
    tuning_config = EasyDict(
        # AdaptFormer
        ffn_adapt=args.ffn_adapt,
        ffn_option="parallel",
        ffn_adapter_layernorm_option="none",
        ffn_adapter_init_option="lora",
        ffn_adapter_scalar=args.scale,
        ffn_num=args.ffn_num,
        d_model=768,
    )
    
    select_config = EasyDict(
        open=True,
        keep_layers=0,
        layer_target_ratio=0.5,
        layer_loss_ratio=2.0,
        layer_diverse_ratio=0.0,
        layer_entropy_weight=0.0,
        layer_minimal_weight= 0.0,
        layer_minimal=0.0,
        
        token_ratio=2.,
        token_target_ratio=args.token_target_ratio,
        token_minimal=0.,
        token_minimal_weight=0.,        
        ) # 0.0


    if os.path.basename(args.finetune).startswith('VIT_BASE_IN21K'):
        model = vit_base_patch16_224_in21k(num_classes=args.nb_classes,  drop_path_rate=args.drop_path, tuning_config=tuning_config, select_config=select_config)



    if args.finetune and not args.eval:
        checkpoint = torch.load(args.pretrain_ckpts[args.finetune], map_location='cpu')

        logger.info("Load pre-trained checkpoint from: %s" % args.finetune)
        checkpoint_model = checkpoint['model'] if 'model' in checkpoint else checkpoint
        state_dict = model.state_dict()
        for k in ['head.weight', 'head.bias']:
            if k in checkpoint_model and checkpoint_model[k].shape != state_dict[k].shape:
                logger.info(f"Removing key {k} from pretrained checkpoint")
                del checkpoint_model[k]
                
        if "pre_logits.fc.weight" in checkpoint_model: # we will not use pre_logits
            for k in ['pre_logits.fc.bias', 'pre_logits.fc.weight']:
                logger.info(f"Removing key {k} from pretrained checkpoint")
                del checkpoint_model[k]

        # interpolate position embedding
        # interpolate_pos_embed(model, checkpoint_model)

        # load pre-trained model
        msg = model.load_state_dict(checkpoint_model, strict=False)
        logger.info(msg)

        # manually initialize fc layer: following MoCo v3
        trunc_normal_(model.head.weight, std=0.01)

    # freeze all but the head
    for name, p in model.named_parameters():
        if name in msg.missing_keys:
            p.requires_grad = True
        else:
            p.requires_grad = False if not args.fulltune else True
    for _, p in model.head.named_parameters():
        p.requires_grad = True

    model.to(device)

    model_without_ddp = model
    n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)

    logger.info("Model = %s" % str(model_without_ddp))
    logger.info('number of params (M): %.2f' % (n_parameters / 1.e6))

    eff_batch_size = args.batch_size * args.accum_iter * misc.get_world_size()
    
    if args.lr is None:  # only base_lr is specified
        args.lr = args.blr * eff_batch_size / 256


    logger.info("actual lr: %.2e" % args.lr)



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

    optimizer = torch.optim.AdamW([p for name, p in model.named_parameters() if p.requires_grad], lr=args.lr, weight_decay=1e-4)
    logger.info(optimizer)
    loss_scaler = NativeScaler()

    base_loss = torch.nn.CrossEntropyLoss()
    criterion = AdaLoss(base_criterion=base_loss, 
                        layer_target_ratio=select_config.layer_target_ratio,  # 0.5
                        layer_loss_ratio=select_config.layer_loss_ratio, # 2.0
                        layer_diverse_ratio=select_config.layer_diverse_ratio, # 0.0
                        layer_entropy_weight=select_config.layer_entropy_weight, # 0.0
                        layer_minimal_weight=select_config.layer_minimal_weight, # 0.0
                        layer_minimal=select_config.layer_minimal,
                        
                        
                        token_target_ratio=select_config.token_target_ratio,
                        token_loss_ratio=select_config.token_ratio,
                        token_minimal=select_config.token_minimal,
                        token_minimal_weight=select_config.token_minimal_weight
                        ) # 0.0

    logger.info("criterion = %s" % str(criterion))

    misc.load_model(args=args, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler)

    if args.eval:
        test_stats = evaluate(dataloader_val, model, device, logger)
        logger.info(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
        exit(0)

    logger.info(f"Start training for {args.epochs} epochs")
    start_time = time.time()
    max_metric = 0.0
    
    base_flops = 0.1
    flops_dict = 0.1
    
    for epoch in range(args.start_epoch, args.epochs):
        dataloader_train.sampler.set_epoch(epoch)
        train_stats = train_one_epoch(
            model, criterion, dataloader_train,
            optimizer, device, epoch, loss_scaler,
            max_norm=None,
            log_writer=log_writer,
            args=args,
            logger=logger
        )

            
        if (epoch + 1) % args.eval_freq == 0 or (epoch + 1) == args.epochs:
            test_stats = evaluate(dataloader_val, model, device, logger, base_flops=base_flops, flops_dict=flops_dict, args=args)
            # logger.info(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
            
            if args.output_dir and (test_stats["metric"] >= max_metric):
                misc.save_model(
                    args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                    loss_scaler=loss_scaler, epoch=epoch, save_force=True)
                
            
            
            max_metric = max(max_metric, test_stats["metric"])
            logger.info(f'Max metric: {max_metric:.2f}%')


    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    to_save = {'model': model_without_ddp.state_dict()}
    logger.info('Training time {}'.format(total_time_str))
    del logger


if __name__ == '__main__':
    args = get_args_parser()
    args = args.parse_args()
    if args.output_dir:
        Path(args.output_dir).mkdir(parents=True, exist_ok=True)
        
    output_dir = args.output_dir
        
    args.data_path = DATASETS
    args.pretrain_ckpts = CHECKPOINTS

    # these hyperparameters can be searched
    args.scale=1
    args.lr = 1e-3
    main(args)