run_beit3_finetuning.py

# --------------------------------------------------------
# Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks (https://arxiv.org/abs/2208.10442)
# Github source: https://github.com/microsoft/unilm/tree/master/beit3
# Copyright (c) 2023 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------'

import argparse
import datetime
import numpy as np
import time
import torch
import torch.backends.cudnn as cudnn
import json
import os

from pathlib import Path

from timm.data.mixup import Mixup
from timm.models import create_model
from timm.utils import ModelEma
from optim_factory import create_optimizer, get_parameter_groups, \
    LayerDecayValueAssigner, get_is_head_flag_for_vit

from engine_for_finetuning import train_one_epoch, get_handler, evaluate
from datasets import create_downstream_dataset, create_downstream_hwj_test_dataset
from utils import NativeScalerWithGradNormCount as NativeScaler
import utils
import modeling_finetune

def get_args():
    parser = argparse.ArgumentParser('BEiT fine-tuning and evaluation script for image classification', add_help=False)

    # Model parameters
    parser.add_argument('--model', default='beit_base_patch16_224', type=str, metavar='MODEL',
                        help='Name of model to train')
    parser.add_argument('--task', type=str, required=True, 
                        choices=['nlvr2', 'vqav2', 'flickr30k', 'coco_retrieval', 'coco_captioning', 'nocaps', 'imagenet'], 
                        help='Name of task to fine-tuning')

    parser.add_argument('--input_size', default=224, type=int,
                        help='images input size')
    parser.add_argument('--drop_path', type=float, default=0.1, metavar='PCT',
                        help='Drop path rate (default: 0.1)')

    parser.add_argument('--checkpoint_activations', action='store_true', default=None, 
                        help='Enable checkpointing to save your memory.')
    parser.add_argument('--sentencepiece_model', type=str, required=True,
                        help='Sentencepiece model path for the pretrained model.')
    parser.add_argument('--vocab_size', type=int, default=64010)
    parser.add_argument('--num_max_bpe_tokens', type=int, default=64)

    parser.add_argument('--model_ema', action='store_true', default=False)
    parser.add_argument('--model_ema_decay', type=float, default=0.9999, help='')
    parser.add_argument('--model_ema_force_cpu', action='store_true', default=False, help='')

    # 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=[0.9, 0.999], type=float, nargs='+', metavar='BETA',
                        help='Optimizer Betas (default: 0.9, 0.999, 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)')

    parser.add_argument('--lr', type=float, default=5e-4, metavar='LR',
                        help='learning rate (default: 5e-4)')
    parser.add_argument('--layer_decay', type=float, default=0.9)
    parser.add_argument('--task_head_lr_weight', type=float, default=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-6, metavar='LR',
                        help='lower lr bound for cyclic schedulers that hit 0 (1e-6)')
    parser.add_argument('--warmup_epochs', type=int, default=5, metavar='N',
                        help='epochs to warmup LR, if scheduler supports')
    parser.add_argument('--warmup_steps', type=int, default=-1, metavar='N',
                        help='num of steps to warmup LR, will overload warmup_epochs if set > 0')

    parser.add_argument('--batch_size', default=64, type=int)
    parser.add_argument('--eval_batch_size', default=None, type=int)
    parser.add_argument('--epochs', default=20, type=int)
    parser.add_argument('--update_freq', default=1, type=int)
    parser.add_argument('--save_ckpt_freq', default=5, type=int)

    # Augmentation parameters
    parser.add_argument('--randaug', action='store_true', default=False)
    parser.add_argument('--train_interpolation', type=str, default='bicubic',
                        help='Training interpolation (random, bilinear, bicubic default: "bicubic")')

    # Finetuning params
    parser.add_argument('--finetune', default='',
                        help='finetune from checkpoint')
    parser.add_argument('--model_key', default='model|module', type=str)
    parser.add_argument('--model_prefix', default='', type=str)

    # Dataset parameters
    parser.add_argument('--data_path', default='/datasets01/imagenet_full_size/061417/', type=str,
                        help='dataset path')

    parser.add_argument('--output_dir', default='',
                        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('--auto_resume', action='store_true')
    parser.add_argument('--no_auto_resume', action='store_false', dest='auto_resume')
    parser.set_defaults(auto_resume=True)

    parser.add_argument('--save_ckpt', action='store_true')
    parser.add_argument('--no_save_ckpt', action='store_false', dest='save_ckpt')
    parser.set_defaults(save_ckpt=True)

    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')
    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)

    # 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')

    # parameter for dump predictions (VQA, COCO captioning, NoCaps)
    parser.add_argument('--task_cache_path', default=None, type=str)

    # parameter for imagenet finetuning
    parser.add_argument('--nb_classes', default=1000, type=int,
                        help='number of the classification types')
    parser.add_argument('--mixup', type=float, default=0,
                        help='mixup alpha, mixup enabled if > 0.')
    parser.add_argument('--cutmix', type=float, default=0,
                        help='cutmix alpha, cutmix enabled if > 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"')
    
    # augmentation parameters for imagenet finetuning
    parser.add_argument('--color_jitter', type=float, default=0.4, metavar='PCT',
                        help='Color jitter factor (default: 0.4)')
    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)')

    # evaluation parameters for imagenet
    parser.add_argument('--crop_pct', type=float, default=None)

    # random Erase params for imagenet finetuning
    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')

    # parameter for captioning finetuning
    parser.add_argument('--captioning_mask_prob', type=float, default=0.6)
    parser.add_argument('--drop_worst_ratio', type=float, default=0.2)
    parser.add_argument('--drop_worst_after', type=int, default=12000)
    parser.add_argument('--num_beams', type=int, default=3)
    parser.add_argument('--length_penalty', type=float, default=0.6)

    # label smoothing for imagenet and captioning
    parser.add_argument('--label_smoothing', type=float, default=0.1)

    # deepspeed parameters
    parser.add_argument('--enable_deepspeed', action='store_true', default=False)
    parser.add_argument('--initial_scale_power', type=int, default=16)
    parser.add_argument('--zero_stage', default=0, type=int,
                        help='ZeRO optimizer stage (default: 0)')
    parser.add_argument('--checkpoint', type=str,
                        help='checkpoint path, load your checkpoint. Added by hwj.')
    parser.add_argument('--hwj_test', action='store_true',
                        help='evaluate the testset instead of valset. Added by hwj.')

    known_args, _ = parser.parse_known_args()

    if known_args.enable_deepspeed:
        try:
            import deepspeed
            from deepspeed import DeepSpeedConfig
            parser = deepspeed.add_config_arguments(parser)
            ds_init = deepspeed.initialize
        except:
            print("Please 'pip install deepspeed==0.4.0'")
            exit(0)
    else:
        ds_init = None

    return parser.parse_args(), ds_init


def main(args, ds_init):
    utils.init_distributed_mode(args)

    if ds_init is not None:
        utils.create_ds_config(args)

    if args.task_cache_path is None:
        args.task_cache_path = args.output_dir

    print(args)

    device = torch.device(args.device)

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

    cudnn.benchmark = True

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

    data_loader_train, data_loader_val = create_downstream_dataset(args)

    if not args.model.endswith(args.task):
        if args.task in ("flickr30k", "coco_retrieval"):
            model_config = "%s_retrieval" % args.model
        elif args.task in ("coco_captioning", "nocaps"):
            model_config = "%s_captioning" % args.model
        elif args.task in ("imagenet"):
            model_config = "%s_imageclassification" % args.model
        else:
            model_config = "%s_%s" % (args.model, args.task)
    else:
        model_config = args.model
    print("model_config = %s" % model_config)
    model = create_model(
        model_config,
        pretrained=False,
        drop_path_rate=args.drop_path,
        vocab_size=args.vocab_size,
        checkpoint_activations=args.checkpoint_activations,
    )

    if args.finetune:
        utils.load_model_and_may_interpolate(args.finetune, model, args.model_key, args.model_prefix)

    if args.checkpoint:
        model.load_state_dict(torch.load(args.checkpoint)['module'])
    
    model.to(device)

    model_ema = None
    if args.model_ema:
        # Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
        model_ema = ModelEma(
            model,
            decay=args.model_ema_decay,
            device='cpu' if args.model_ema_force_cpu else '',
            resume='')
        print("Using EMA with decay = %.8f" % args.model_ema_decay)

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

    print("Model = %s" % str(model_without_ddp))
    print('number of params:', n_parameters)

    total_batch_size = args.batch_size * args.update_freq * utils.get_world_size()
    num_training_steps_per_epoch = len(data_loader_train.dataset) // total_batch_size
    print("LR = %.8f" % args.lr)
    print("Batch size = %d" % total_batch_size)
    print("Update frequent = %d" % args.update_freq)
    print("Number of training examples = %d" % len(data_loader_train.dataset))
    print("Number of training training per epoch = %d" % num_training_steps_per_epoch)

    num_layers = model_without_ddp.get_num_layers()
    if args.layer_decay < 1.0:
        lrs = list(args.layer_decay ** (num_layers + 1 - i) for i in range(num_layers + 2))
        assigner = LayerDecayValueAssigner(lrs)
    elif args.task_head_lr_weight > 1:
        assigner = LayerDecayValueAssigner([1.0, args.task_head_lr_weight], scale_handler=get_is_head_flag_for_vit)
    else:
        assigner = None

    if assigner is not None:
        print("Assigned values = %s" % str(assigner.values))

    skip_weight_decay_list = model.no_weight_decay()

    if args.distributed:
        torch.distributed.barrier()
    if args.enable_deepspeed:
        loss_scaler = None
        optimizer_params = get_parameter_groups(
            model, args.weight_decay, skip_weight_decay_list,
            assigner.get_layer_id if assigner is not None else None,
            assigner.get_scale if assigner is not None else None)
        model, optimizer, _, _ = ds_init(
            args=args, model=model, model_parameters=optimizer_params,
            dist_init_required=not args.distributed,
        )

        print("model.gradient_accumulation_steps() = %d" % model.gradient_accumulation_steps())
        assert model.gradient_accumulation_steps() == args.update_freq
    else:
        if args.distributed:
            model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True)
            model_without_ddp = model.module

        optimizer = create_optimizer(
            args, model_without_ddp, skip_list=skip_weight_decay_list,
            get_num_layer=assigner.get_layer_id if assigner is not None else None, 
            get_layer_scale=assigner.get_scale if assigner is not None else None)
        loss_scaler = NativeScaler()

    lr_schedule_values = utils.cosine_scheduler(
        args.lr, args.min_lr, args.epochs, num_training_steps_per_epoch,
        warmup_epochs=args.warmup_epochs, warmup_steps=args.warmup_steps,
    )

    utils.auto_load_model(
        args=args, model=model, model_without_ddp=model_without_ddp,
        optimizer=optimizer, loss_scaler=loss_scaler, model_ema=model_ema)

    task_handler = get_handler(args)

    # mixup for imagenet
    mixup_fn = None
    if args.task in ["imagenet", "in1k"]:
        mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
        if mixup_active:
            print("Mixup is activated!")
            mixup_fn = Mixup(
                mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
                prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
                label_smoothing=args.label_smoothing, num_classes=args.nb_classes)

    if args.eval:
        data_loader_test = create_downstream_dataset(args, is_eval=True)
        if args.task in ["nlvr2", "flickr30k", "coco_retrieval", "imagenet"]:
            ext_test_stats, task_key = evaluate(data_loader_test, model, device, task_handler)
            print(f"Accuracy of the network on the {len(data_loader_test.dataset)} test images: {ext_test_stats[task_key]:.3f}%")
            exit(0)
        elif args.task == "vqav2":
            result, _ = evaluate(data_loader_test, model, device, task_handler)
            utils.dump_predictions(args, result, "vqav2_test")
            exit(0)
        elif args.task in ["coco_captioning", "nocaps"]:
            predictions, _ = evaluate(data_loader_test, model, device, task_handler)
            prediction_file = utils.dump_predictions(args, predictions, "{}_test".format(args.task))
            if utils.is_main_process() and args.task == "coco_captioning":
                captioning_result = utils.coco_caption_eval(args.output_dir, prediction_file, "{}_test".format(args.task))
                result_file = os.path.join(args.output_dir, f"{args.task}_result.json")
                print(json.dumps(captioning_result))
                utils.write_result_to_jsonl(captioning_result, result_file)
            exit(0)

    if args.hwj_test:
        data_loader_test = create_downstream_hwj_test_dataset(args, is_eval=True)
        if args.task in ["nlvr2", "flickr30k", "coco_retrieval", "imagenet"]:
            ext_test_stats, task_key = evaluate(data_loader_test, model, device, task_handler)
            print(f"Accuracy of the network on the {len(data_loader_test.dataset)} test images: {ext_test_stats[task_key]:.3f}%")
            exit(0)
        elif args.task == "vqav2":
            result, _ = evaluate(data_loader_test, model, device, task_handler)
            utils.dump_predictions(args, result, "vqav2_test")
            exit(0)
        elif args.task in ["coco_captioning", "nocaps"]:
            predictions, _ = evaluate(data_loader_test, model, device, task_handler)
            prediction_file = utils.dump_predictions(args, predictions, "{}_test".format(args.task))
            if utils.is_main_process() and args.task == "coco_captioning":
                captioning_result = utils.coco_caption_eval(args.output_dir, prediction_file, "{}_test".format(args.task))
                result_file = os.path.join(args.output_dir, f"{args.task}_result.json")
                print(json.dumps(captioning_result))
                utils.write_result_to_jsonl(captioning_result, result_file)
            exit(0)


    print(f"Start training for {args.epochs} epochs")
    start_time = time.time()

    max_accuracy = 0.0
    for epoch in range(args.start_epoch, args.epochs):
        if args.distributed:
            data_loader_train.sampler.set_epoch(epoch)
        if log_writer is not None:
            log_writer.set_step(epoch * num_training_steps_per_epoch * args.update_freq)
        train_stats = train_one_epoch(
            model, data_loader_train, optimizer, device, task_handler, epoch, 
            epoch * num_training_steps_per_epoch, lr_schedule_values, loss_scaler, 
            args.clip_grad, args.update_freq, model_ema, log_writer, args.task, mixup_fn,
        )
        if args.output_dir and args.save_ckpt:
            if (epoch + 1) % args.save_ckpt_freq == 0 or epoch + 1 == args.epochs:
                utils.save_model(
                    args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                    loss_scaler=loss_scaler, epoch=epoch, model_ema=model_ema)
        if data_loader_val is not None:
            if args.task not in ["coco_captioning", "nocaps"]:
                test_stats, task_key = evaluate(data_loader_val, model, device, task_handler)
            else:
                predictions, _ = evaluate(data_loader_val, model, device, task_handler)
                prediction_file = utils.dump_predictions(args, predictions, f"{args.task}_val_e{epoch}")
                result_file = os.path.join(args.output_dir, f"{args.task}_result_val_e{epoch}.json")
                task_key = "CIDEr"
                if utils.is_main_process():
                    test_stats = utils.coco_caption_eval(args.output_dir, prediction_file, "{}_val".format(args.task))
                    utils.write_result_to_jsonl(test_stats, result_file)
                torch.distributed.barrier()
                if not utils.is_main_process():
                    test_stats = utils.read_result_from_jsonl(result_file)

            print(f"Performance of the network on the {len(data_loader_val.dataset)} val images: {test_stats[task_key]:.1f}%")
            if max_accuracy < test_stats[task_key]:
                max_accuracy = test_stats[task_key]
                if args.output_dir and args.save_ckpt:
                    utils.save_model(
                        args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
                        loss_scaler=loss_scaler, epoch="best", model_ema=model_ema)

            print(f'Max performance: {max_accuracy:.2f}%')
            if log_writer is not None:
                log_writer.update(acc=test_stats[task_key], head="perf", step=epoch)
            
            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                        **{f'val_{k}': v for k, v in test_stats.items()},
                        'epoch': epoch,
                        'n_parameters': n_parameters}
        else:
            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                         # **{f'test_{k}': v for k, v in test_stats.items()},
                         'epoch': epoch,
                         'n_parameters': n_parameters}

        if args.output_dir and utils.is_main_process():
            if log_writer is not None:
                log_writer.flush()
            with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f:
                f.write(json.dumps(log_stats) + "\n")

    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__':
    opts, ds_init = get_args()
    if opts.output_dir:
        Path(opts.output_dir).mkdir(parents=True, exist_ok=True)
    main(opts, ds_init)