# -------------------------------------------------------- # BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254) # Github source: https://github.com/microsoft/unilm/tree/master/beit # Copyright (c) 2021 Microsoft # Licensed under The MIT License [see LICENSE for details] # By Hangbo Bao # Based on timm, DINO and DeiT code bases # https://github.com/rwightman/pytorch-image-models/tree/master/timm # https://github.com/facebookresearch/deit # https://github.com/facebookresearch/dino # --------------------------------------------------------' 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.models import create_model from optim_factory import create_optimizer from datasets import build_beit_pretraining_dataset from engine_for_pretraining import train_one_epoch from utils import NativeScalerWithGradNormCount as NativeScaler import utils import modeling_pretrain def get_args(): parser = argparse.ArgumentParser('BEiT pre-training script', add_help=False) parser.add_argument('--batch_size', default=64, type=int) parser.add_argument('--epochs', default=300, type=int) parser.add_argument('--save_ckpt_freq', default=20, type=int) parser.add_argument("--discrete_vae_weight_path", type=str) parser.add_argument("--discrete_vae_type", type=str, default="dall-e") # Model parameters parser.add_argument('--model', default='beit_base_patch16_224_8k_vocab', type=str, metavar='MODEL', help='Name of model to train') parser.add_argument('--rel_pos_bias', action='store_true') parser.add_argument('--disable_rel_pos_bias', action='store_false', dest='rel_pos_bias') parser.set_defaults(rel_pos_bias=True) parser.add_argument('--abs_pos_emb', action='store_true') parser.set_defaults(abs_pos_emb=False) parser.add_argument('--layer_scale_init_value', default=0.1, type=float, help="0.1 for base, 1e-5 for large. set 0 to disable layer scale") parser.add_argument('--num_mask_patches', default=75, type=int, help='number of the visual tokens/patches need be masked') parser.add_argument('--max_mask_patches_per_block', type=int, default=None) parser.add_argument('--min_mask_patches_per_block', type=int, default=16) parser.add_argument('--input_size', default=224, type=int, help='images input size for backbone') parser.add_argument('--second_input_size', default=112, type=int, help='images input size for discrete vae') parser.add_argument('--drop_path', type=float, default=0.1, metavar='PCT', help='Drop path rate (default: 0.1)') # 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('--weight_decay_end', type=float, default=None, help="""Final value of the weight decay. We use a cosine schedule for WD. (Set the same value with args.weight_decay to keep weight decay no change)""") parser.add_argument('--lr', type=float, default=5e-4, metavar='LR', help='learning rate (default: 5e-4)') 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('--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='epochs to warmup LR, if scheduler supports') # Augmentation parameters parser.add_argument('--train_interpolation', type=str, default='bicubic', help='Training interpolation (random, bilinear, bicubic default: "bicubic")') parser.add_argument('--second_interpolation', type=str, default='lanczos', help='Interpolation for discrete vae (random, bilinear, bicubic default: "lanczos")') # Dataset parameters parser.add_argument('--data_path', default='/datasets01/imagenet_full_size/061417/', type=str, help='dataset path') parser.add_argument('--imagenet_default_mean_and_std', default=False, action='store_true') 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('--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', help='') 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') return parser.parse_args() def get_model(args): print(f"Creating model: {args.model}") model = create_model( args.model, pretrained=False, drop_path_rate=args.drop_path, drop_block_rate=None, use_shared_rel_pos_bias=args.rel_pos_bias, use_abs_pos_emb=args.abs_pos_emb, init_values=args.layer_scale_init_value, ) return model def main(args): utils.init_distributed_mode(args) 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 model = get_model(args) patch_size = model.patch_embed.patch_size print("Patch size = %s" % str(patch_size)) args.window_size = (args.input_size // patch_size[0], args.input_size // patch_size[1]) args.patch_size = patch_size # get dataset dataset_train = build_beit_pretraining_dataset(args) # prepare discrete vae d_vae = utils.create_d_vae( weight_path=args.discrete_vae_weight_path, d_vae_type=args.discrete_vae_type, device=device, image_size=args.second_input_size) if True: # args.distributed: num_tasks = utils.get_world_size() global_rank = utils.get_rank() sampler_rank = global_rank num_training_steps_per_epoch = len(dataset_train) // args.batch_size // num_tasks sampler_train = torch.utils.data.DistributedSampler( dataset_train, num_replicas=num_tasks, rank=sampler_rank, shuffle=True ) print("Sampler_train = %s" % str(sampler_train)) else: sampler_train = torch.utils.data.RandomSampler(dataset_train) if global_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 = 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, ) model.to(device) 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 * utils.get_world_size() print("LR = %.8f" % args.lr) print("Batch size = %d" % total_batch_size) print("Number of training steps = %d" % num_training_steps_per_epoch) print("Number of training examples per epoch = %d" % (total_batch_size * num_training_steps_per_epoch)) 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) loss_scaler = NativeScaler() print("Use step level LR & WD scheduler!") 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, ) if args.weight_decay_end is None: args.weight_decay_end = args.weight_decay wd_schedule_values = utils.cosine_scheduler( args.weight_decay, args.weight_decay_end, args.epochs, num_training_steps_per_epoch) print("Max WD = %.7f, Min WD = %.7f" % (max(wd_schedule_values), min(wd_schedule_values))) utils.auto_load_model( args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler) 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) if log_writer is not None: log_writer.set_step(epoch * num_training_steps_per_epoch) train_stats = train_one_epoch( model, d_vae, data_loader_train, optimizer, device, epoch, loss_scaler, args.clip_grad, log_writer=log_writer, start_steps=epoch * num_training_steps_per_epoch, lr_schedule_values=lr_schedule_values, wd_schedule_values=wd_schedule_values, ) if args.output_dir: 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) log_stats = {**{f'train_{k}': v for k, v in train_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 = get_args() if opts.output_dir: Path(opts.output_dir).mkdir(parents=True, exist_ok=True) main(opts)