# -------------------------------------------------------- # 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 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)') 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) 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) 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)