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_pretraining_dataset from engine_for_pretraining import train_one_epoch from utils_mae import NativeScalerWithGradNormCount as NativeScaler import utils_mae as utils import modeling_pretrain from timm.models.vision_transformer import vit_small_patch16_224, vit_base_patch16_224, vit_large_patch16_224 from modeling_pretrain import FeatureExtractor def get_args(): parser = argparse.ArgumentParser('VideoMAE pre-training script', add_help=False) parser.add_argument('--batch_size', default=64, type=int) parser.add_argument('--epochs', default=800, type=int) parser.add_argument('--save_ckpt_freq', default=50, type=int) # Model parameters parser.add_argument('--model', default='pretrain_videomae_base_patch16_224', type=str, metavar='MODEL', help='Name of model to train') parser.add_argument('--decoder_depth', default=4, type=int, help='depth of decoder') parser.add_argument('--mask_type', default='tube', choices=['random', 'tube', 'tubelet'], type=str, help='masked strategy of video tokens/patches') parser.add_argument('--sub_mask_type', default='tube+picked_frame_visible', choices=['tube', 'tube+picked_frame_visible', 'tube+traj_mask'], type=str, help='sub masked strategy of tubelet masking') parser.add_argument('--mask_ratio', default=0.75, type=float, help='ratio of the visual tokens/patches need be masked') parser.add_argument('--input_size', default=224, type=int, help='videos input size for backbone') parser.add_argument('--drop_path', type=float, default=0.0, metavar='PCT', help='Drop path rate (default: 0.1)') parser.add_argument('--normlize_target', default=True, type=bool, help='normalized the target patch pixels') # 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=None, type=float, nargs='+', metavar='BETA', help='Optimizer Betas (default: None, 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=1.5e-4, metavar='LR', help='learning rate (default: 1.5e-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=40, 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') parser.add_argument('--use_checkpoint', action='store_true') parser.set_defaults(use_checkpoint=False) # Augmentation parameters parser.add_argument('--color_jitter', type=float, default=0.0, metavar='PCT', help='Color jitter factor (default: 0.4)') parser.add_argument('--train_interpolation', type=str, default='bicubic', help='Training interpolation (random, bilinear, bicubic default: "bicubic")') # Dataset parameters parser.add_argument('--data_path', default='/path/to/list_kinetics-400', type=str, help='dataset path') parser.add_argument('--imagenet_default_mean_and_std', default=True, action='store_true') parser.add_argument('--num_frames', type=int, default= 16) parser.add_argument('--sampling_rate', type=int, default= 4) 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') # Tubelet params parser.add_argument('--add_tubelets', action='store_true') parser.set_defaults(add_tubelets=False) parser.add_argument('--use_objects', action='store_true') parser.set_defaults(use_objects=False) parser.add_argument('--objects_path', type=str, default=None) parser.add_argument('--motion_type', type=str, default='gaussian') parser.add_argument('--scales', type=str, default='[32, 48, 56, 64, 96, 128]') parser.add_argument('--visible_frames', type=str, default=None) # not used parser.add_argument('--traj_unmask_ratio', type=float, default=0.1) #dino params parser.add_argument('--target_type', default='pixel', choices=['pixel', 'dino_v1', 'clip'], type=str, help='define target type for loss') parser.add_argument('--distillation_teacher', default="clip_b", type=str, choices=['dino_s', 'dino_b', 'clip_b'], help='distillation teacher model') # multiple sampling parser.add_argument('--multiple_sampling', action='store_true') # for 2nd stage training parser.add_argument('--first_stage_path', type=str, default=None) return parser.parse_args() def get_teacher_student_models(args): print(f"Creating model: {args.model}") if args.target_type=='pixel': dec_dim = 1536 elif 'dino' in args.target_type or 'clip' in args.target_type: if args.distillation_teacher == 'dino_s': dec_dim = 384 elif args.distillation_teacher == 'dino_b' or args.distillation_teacher == 'clip_b': dec_dim = 768 student_model = create_model( args.model, pretrained=False, drop_path_rate=args.drop_path, drop_block_rate=None, decoder_depth=args.decoder_depth, use_checkpoint=args.use_checkpoint, decoder_num_classes=dec_dim, ) if args.target_type == 'dino_v1': # load dino if args.distillation_teacher == 'dino_s': pretraining = torch.hub.load('facebookresearch/dino:main', 'dino_vits16') teacher_model = vit_small_patch16_224(pretrained=False) elif args.distillation_teacher == 'dino_b': pretraining = torch.hub.load('facebookresearch/dino:main', 'dino_vitb16') teacher_model = vit_base_patch16_224(pretrained=False) msg =teacher_model.load_state_dict(pretraining.state_dict(), strict=False) teacher_model = FeatureExtractor(teacher_model, args.input_size, 16) print(msg) teacher_model.eval() elif args.target_type == 'clip': # load clip from utils_viclip.config import Config from utils_viclip.config_utils import setup_viclip from tasks.shared_utils import setup_model from models_viclip.viclip import ViCLIP config = setup_viclip('configs/config.py') model_cls = eval(config.model.get('model_cls', 'ViCLIP')) teacher_model = setup_model( config, model_cls=model_cls, has_decoder=False, pretrain=False, find_unused_parameters=False, ) teacher_model.eval() else: teacher_model = None return student_model, teacher_model def load_first_stage(model,args): if args.first_stage_path is not None: checkpoint = torch.load(args.first_stage_path, map_location='cpu') print("loading first stage from ",args.first_stage_path) checkpoint_model = checkpoint['model'] utils.load_state_dict(model, checkpoint_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) cudnn.benchmark = True student_model, teacher_model = get_teacher_student_models(args) patch_size = student_model.encoder.patch_embed.patch_size print("Patch size = %s" % str(patch_size)) args.window_size = (args.num_frames // 2, args.input_size // patch_size[0], args.input_size // patch_size[1]) # [8, 14, 14] print(f"Window Size = {args.window_size}") args.patch_size = patch_size # Start from pretrained first stage model if args.first_stage_path is not None: load_first_stage(student_model,args) # get dataset dataset_train = build_pretraining_dataset(args) num_tasks = utils.get_world_size() global_rank = utils.get_rank() sampler_rank = global_rank total_batch_size = args.batch_size * num_tasks num_training_steps_per_epoch = len(dataset_train) // total_batch_size sampler_train = torch.utils.data.DistributedSampler( dataset_train, num_replicas=num_tasks, rank=sampler_rank, shuffle=True ) print("Sampler_train = %s" % str(sampler_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 if not args.multiple_sampling else int(args.batch_size/2), num_workers=args.num_workers, pin_memory=args.pin_mem, drop_last=True, worker_init_fn=utils.seed_worker ) student_model.to(device) if teacher_model is not None: teacher_model.to(device) model_without_ddp = student_model n_parameters = sum(p.numel() for p in student_model.parameters() if p.requires_grad) print("Model = %s" % str(model_without_ddp)) print('number of params: {} M'.format(n_parameters / 1e6)) args.lr = args.lr * total_batch_size / 256 args.min_lr = args.min_lr * total_batch_size / 256 args.warmup_lr = args.warmup_lr * total_batch_size / 256 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: student_model = torch.nn.parallel.DistributedDataParallel(student_model, device_ids=[args.gpu], find_unused_parameters=False) model_without_ddp = student_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=student_model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler) torch.cuda.empty_cache() 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( student_model, 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, patch_size=patch_size[0], normlize_target=args.normlize_target, teacher_model = teacher_model, target_type=args.target_type, multiple_sampling=args.multiple_sampling, ) if args.output_dir: if (epoch + 1) % args.save_ckpt_freq == 0 or epoch + 1 == args.epochs: utils.save_model( args=args, model=student_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") #if (epoch + 1) % 2 == 0: #exit(0) 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)