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| import argparse | |
| import datetime | |
| import numpy as np | |
| import time | |
| import torch | |
| import torch.backends.cudnn as cudnn | |
| import json | |
| import os | |
| import warnings | |
| from pathlib import Path | |
| from timm.data import Mixup | |
| from timm.models import create_model | |
| from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy | |
| from timm.scheduler import create_scheduler | |
| from timm.optim import create_optimizer | |
| from timm.utils import NativeScaler, get_state_dict, ModelEma | |
| #from datasets import build_dataset | |
| from engine import train_one_epoch, evaluate | |
| import models | |
| import my_models | |
| import torch.nn as nn | |
| import utils | |
| from video_dataset import VideoDataSet | |
| from video_dataset_aug import get_augmentor, build_dataflow | |
| from video_dataset_config import get_dataset_config, DATASET_CONFIG | |
| warnings.filterwarnings("ignore", category=UserWarning) | |
| def get_args_parser(): | |
| parser = argparse.ArgumentParser('DeiT training and evaluation script', add_help=False) | |
| parser.add_argument('--model_name',default="TALL_SWIN") | |
| parser.add_argument('--batch-size', default=2, type=int) | |
| parser.add_argument('--epochs', default=30, type=int) | |
| # Dataset parameters | |
| parser.add_argument('--data_txt_dir', type=str,default='##path_for_dataset_txt##', help='path to text of dataset') | |
| parser.add_argument('--data_dir', type=str,default="##path_for_dataset##", help='path to dataset') | |
| parser.add_argument('--dataset', default='ffpp_train', | |
| choices=list(DATASET_CONFIG.keys()), help='path to dataset file list') | |
| parser.add_argument('--duration', default=8, type=int, help='number of frames') | |
| parser.add_argument('--frames_per_group', default=1, type=int, | |
| help='[uniform sampling] number of frames per group; ' | |
| '[dense sampling]: sampling frequency') | |
| parser.add_argument('--threed_data', default=False, help='load data in the layout for 3D conv') | |
| parser.add_argument('--input_size', default=224, type=int, metavar='N', help='input image size') | |
| parser.add_argument('--disable_scaleup', action='store_true', | |
| help='do not scale up and then crop a small region, directly crop the input_size') | |
| parser.add_argument('--random_sampling', action='store_true', | |
| help='perform determinstic sampling for data loader') | |
| parser.add_argument('--dense_sampling', default=True, | |
| help='perform dense sampling for data loader') | |
| parser.add_argument('--augmentor_ver', default='v1', type=str, choices=['v1', 'v2'], | |
| help='[v1] TSN data argmentation, [v2] resize the shorter side to `scale_range`') | |
| parser.add_argument('--scale_range', default=[256, 320], type=int, nargs="+", | |
| metavar='scale_range', help='scale range for augmentor v2') | |
| parser.add_argument('--modality', default='rgb', type=str, help='rgb or flow') | |
| parser.add_argument('--use_lmdb', default=False, help='use lmdb instead of jpeg.') | |
| parser.add_argument('--use_pyav', default=False, help='use video directly.') | |
| # temporal module | |
| parser.add_argument('--pretrained', action='store_true', default=False, | |
| help='Start with pretrained version of specified network (if avail)') | |
| parser.add_argument('--temporal_module_name', default=None, type=str, metavar='TEM', choices=['ResNet3d', 'TAM', 'TTAM', 'TSM', 'TTSM', 'MSA'], | |
| help='temporal module applied. [TAM]') | |
| parser.add_argument('--temporal_attention_only', action='store_true', default=False, | |
| help='use attention only in temporal module]') | |
| parser.add_argument('--no_token_mask', action='store_true', default=False, help='do not apply token mask') | |
| parser.add_argument('--temporal_heads_scale', default=1.0, type=float, help='scale of the number of spatial heads') | |
| parser.add_argument('--temporal_mlp_scale', default=1.0, type=float, help='scale of spatial mlp') | |
| parser.add_argument('--rel_pos', action='store_true', default=False, | |
| help='use relative positioning in temporal module]') | |
| parser.add_argument('--temporal_pooling', type=str, default=None, choices=['avg', 'max', 'conv', 'depthconv'], | |
| help='perform temporal pooling]') | |
| parser.add_argument('--bottleneck', default=None, choices=['regular', 'dw'], | |
| help='use depth-wise bottleneck in temporal attention') | |
| parser.add_argument('--window_size', default=14, type=int, help='number of frames') | |
| parser.add_argument('--thumbnail_rows', default=4, type=int, help='number of frames per row') | |
| parser.add_argument('--hpe_to_token', default=False, action='store_true', | |
| help='add hub position embedding to image tokens') | |
| # Model parameters | |
| parser.add_argument('--model', default='TALL_SWIN', type=str, metavar='MODEL', | |
| help='Name of model to train') | |
| parser.add_argument('--input-size', default=224, type=int, help='images input size') | |
| parser.add_argument('--drop', type=float, default=0.0, metavar='PCT', | |
| help='Dropout rate (default: 0.)') | |
| parser.add_argument('--drop-path', type=float, default=0.1, metavar='PCT', | |
| help='Drop path rate (default: 0.1)') | |
| parser.add_argument('--drop-block', type=float, default=None, metavar='PCT', | |
| help='Drop block rate (default: None)') | |
| parser.add_argument('--model-ema', action='store_true') | |
| parser.add_argument('--no-model-ema', action='store_false', dest='model_ema') | |
| parser.set_defaults(model_ema=True) | |
| parser.add_argument('--model-ema-decay', type=float, default=0.99996, 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=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=1e-5, | |
| help='weight decay (default: 0.05)') | |
| # Learning rate schedule parameters | |
| parser.add_argument('--sched', default='cosine', type=str, metavar='SCHEDULER', | |
| help='LR scheduler (default: "cosine"') | |
| parser.add_argument('--lr', type=float, default=5e-5, metavar='LR', | |
| help='learning rate (default: 5e-4)') | |
| parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct', | |
| help='learning rate noise on/off epoch percentages') | |
| parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT', | |
| help='learning rate noise limit percent (default: 0.67)') | |
| parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV', | |
| help='learning rate noise std-dev (default: 1.0)') | |
| parser.add_argument('--warmup-lr', type=float, default=1e-8, metavar='LR', | |
| help='warmup learning rate (default: 1e-6)') | |
| parser.add_argument('--min-lr', type=float, default=1e-7, metavar='LR', | |
| help='lower lr bound for cyclic schedulers that hit 0 (1e-5)') | |
| parser.add_argument('--decay-epochs', type=float, default=10, metavar='N', | |
| help='epoch interval to decay LR') | |
| parser.add_argument('--warmup-epochs', type=int, default=10, metavar='N', | |
| help='epochs to warmup LR, if scheduler supports') | |
| parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N', | |
| help='epochs to cooldown LR at min_lr, after cyclic schedule ends') | |
| parser.add_argument('--patience-epochs', type=int, default=10, metavar='N', | |
| help='patience epochs for Plateau LR scheduler (default: 10') | |
| parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE', | |
| help='LR decay rate (default: 0.1)') | |
| # Augmentation parameters | |
| 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', 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)') | |
| parser.add_argument('--train-interpolation', type=str, default='bicubic', | |
| help='Training interpolation (random, bilinear, bicubic default: "bicubic")') | |
| parser.add_argument('--repeated-aug', action='store_true') | |
| parser.add_argument('--no-repeated-aug', action='store_false', dest='repeated_aug') | |
| parser.set_defaults(repeated_aug=False) | |
| # * Random Erase params | |
| parser.add_argument('--reprob', type=float, default=0.0, 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') | |
| # * Mixup params | |
| parser.add_argument('--cutout',default=True) | |
| parser.add_argument('--mixup', type=float, default=0, | |
| help='mixup alpha, mixup enabled if > 0. (default: 0.8)') | |
| parser.add_argument('--cutmix', type=float, default=0, | |
| help='cutmix alpha, cutmix enabled if > 0. (default: 1.0)') | |
| 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"') | |
| # Dataset parameters | |
| parser.add_argument('--output_dir', default="", | |
| help='path where to save, empty for no saving') | |
| parser.add_argument('--device', default='cuda', | |
| help='device to use for training / testing') | |
| parser.add_argument('--seed', default=42, type=int) | |
| parser.add_argument('--resume', default="", help='resume from checkpoint') | |
| parser.add_argument('--no-resume-loss-scaler', action='store_false', dest='resume_loss_scaler') | |
| parser.add_argument('--no-amp', action='store_false', dest='amp', help='disable amp') | |
| parser.add_argument('--use_checkpoint', default=False, help='use checkpoint to save memory') | |
| 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('--num_workers', default=8, 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) | |
| # for testing and validation | |
| parser.add_argument('--num_crops', default=1, type=int, choices=[1, 3, 5, 10]) | |
| parser.add_argument('--num_clips', 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", type=int) | |
| parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training') | |
| parser.add_argument('--auto-resume', default=True, help='auto resume') | |
| # exp | |
| # parser.add_argument('--simclr_w', type=float, default=0., help='weights for simclr loss') | |
| parser.add_argument('--contrastive_nomixup', action='store_true', help='do not involve mixup in contrastive learning') | |
| parser.add_argument('--finetune', default=False, help='finetune model') | |
| parser.add_argument('--initial_checkpoint', type=str, default='', help='path to the pretrained model') | |
| parser.add_argument('--hard_contrastive', action='store_true', help='use HEXA') | |
| # parser.add_argument('--selfdis_w', type=float, default=0., help='enable self distillation') | |
| return parser | |
| def main(args): | |
| utils.init_distributed_mode(args) | |
| print(args) | |
| # Patch | |
| if not hasattr(args, 'hard_contrastive'): | |
| args.hard_contrastive = False | |
| if not hasattr(args, 'selfdis_w'): | |
| args.selfdis_w = 0.0 | |
| #is_imnet21k = args.data_set == 'IMNET21K' | |
| 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 | |
| num_classes, train_list_name, val_list_name, test_list_name, filename_seperator, image_tmpl, filter_video, label_file = get_dataset_config( | |
| args.dataset, args.use_lmdb) | |
| args.num_classes = num_classes | |
| if args.modality == 'rgb': | |
| args.input_channels = 3 | |
| elif args.modality == 'flow': | |
| args.input_channels = 2 * 5 | |
| mixup_fn = None | |
| mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None | |
| if mixup_active: | |
| 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.smoothing, num_classes=args.num_classes) | |
| print(f"Creating model: {args.model}") | |
| model =create_model( | |
| args.model, | |
| pretrained=args.pretrained, | |
| duration=args.duration, | |
| hpe_to_token = args.hpe_to_token, | |
| rel_pos = args.rel_pos, | |
| window_size=args.window_size, | |
| thumbnail_rows = args.thumbnail_rows, | |
| token_mask=not args.no_token_mask, | |
| online_learning = False, | |
| num_classes=args.num_classes, | |
| drop_rate=args.drop, | |
| drop_path_rate=args.drop_path, | |
| drop_block_rate=args.drop_block, | |
| use_checkpoint=args.use_checkpoint | |
| ) | |
| # TODO: finetuning | |
| 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=args.resume) | |
| model_without_ddp = model | |
| if args.distributed: | |
| #model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=True) | |
| model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) | |
| model_without_ddp = model.module | |
| n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) | |
| print('number of params:', n_parameters) | |
| optimizer = create_optimizer(args, model) | |
| loss_scaler = NativeScaler() | |
| #print(f"Scaled learning rate (batch size: {args.batch_size * utils.get_world_size()}): {linear_scaled_lr}") | |
| lr_scheduler, _ = create_scheduler(args, optimizer) | |
| criterion = LabelSmoothingCrossEntropy() | |
| if args.mixup > 0.: | |
| # smoothing is handled with mixup label transform | |
| criterion = SoftTargetCrossEntropy() | |
| elif args.smoothing: | |
| criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing) | |
| else: | |
| criterion = torch.nn.CrossEntropyLoss() | |
| if args.distributed: | |
| mean = (0.5, 0.5, 0.5) if 'mean' not in model.module.default_cfg else model.module.default_cfg['mean'] | |
| std = (0.5, 0.5, 0.5) if 'std' not in model.module.default_cfg else model.module.default_cfg['std'] | |
| else: | |
| mean = (0.5, 0.5, 0.5) if 'mean' not in model.default_cfg else model.default_cfg['mean'] | |
| std = (0.5, 0.5, 0.5) if 'std' not in model.default_cfg else model.default_cfg['std'] | |
| # dataset_train, args.nb_classes = build_dataset(is_train=True, args=args) | |
| # create data loaders w/ augmentation pipeiine | |
| video_data_cls = VideoDataSet | |
| train_list = os.path.join(args.data_txt_dir, train_list_name) | |
| train_augmentor = get_augmentor(True, args.input_size, mean, std, threed_data=False, | |
| version=args.augmentor_ver, scale_range=args.scale_range, cut_out = args.cutout,dataset=args.dataset) | |
| dataset_train = video_data_cls(args.data_dir, train_list, args.duration, args.frames_per_group, | |
| num_clips=args.num_clips, | |
| modality=args.modality, image_tmpl=image_tmpl, | |
| dense_sampling=args.dense_sampling, | |
| transform=train_augmentor, is_train=True, test_mode=False, | |
| seperator=filename_seperator, filter_video=filter_video) | |
| num_tasks = utils.get_world_size() | |
| data_loader_train = build_dataflow(dataset_train, is_train=True, batch_size=args.batch_size, | |
| workers=args.num_workers, is_distributed=args.distributed) | |
| val_list = os.path.join(args.data_txt_dir, val_list_name) | |
| val_augmentor = get_augmentor(False, args.input_size, mean, std, args.disable_scaleup, | |
| threed_data=args.threed_data, version=args.augmentor_ver, | |
| scale_range=args.scale_range, num_clips=args.num_clips, num_crops=args.num_crops,cut_out = False, dataset=args.dataset) | |
| dataset_val = video_data_cls(args.data_dir, val_list, args.duration, args.frames_per_group, | |
| num_clips=args.num_clips, | |
| modality=args.modality, image_tmpl=image_tmpl, | |
| dense_sampling=args.dense_sampling, | |
| transform=val_augmentor, is_train=False, test_mode=False, | |
| seperator=filename_seperator, filter_video=filter_video) | |
| data_loader_val = build_dataflow(dataset_val, is_train=False, batch_size=args.batch_size, | |
| workers=args.num_workers, is_distributed=args.distributed) | |
| max_accuracy = 0.0 | |
| output_dir = Path(args.output_dir) | |
| if args.initial_checkpoint: | |
| checkpoint = torch.load(args.initial_checkpoint, map_location='cpu') | |
| utils.load_checkpoint(model, checkpoint['model']) | |
| if args.auto_resume: | |
| if args.resume == '': | |
| args.resume = str(output_dir / "checkpoint.pth") | |
| if not os.path.exists(args.resume): | |
| args.resume = '' | |
| if args.resume: | |
| if args.resume.startswith('https'): | |
| checkpoint = torch.hub.load_state_dict_from_url( | |
| args.resume, map_location='cpu', check_hash=True) | |
| else: | |
| checkpoint = torch.load(args.resume, map_location='cpu') | |
| utils.load_checkpoint(model, checkpoint['model']) | |
| if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint: | |
| optimizer.load_state_dict(checkpoint['optimizer']) | |
| lr_scheduler.load_state_dict(checkpoint['lr_scheduler']) | |
| args.start_epoch = checkpoint['epoch'] + 1 | |
| if 'scaler' in checkpoint and args.resume_loss_scaler: | |
| print("Resume with previous loss scaler state") | |
| loss_scaler.load_state_dict(checkpoint['scaler']) | |
| if args.model_ema: | |
| utils._load_checkpoint_for_ema(model_ema, checkpoint['model_ema']) | |
| max_accuracy = checkpoint['max_accuracy'] | |
| if args.eval: | |
| test_stats = evaluate(data_loader_val, model, device, num_tasks, distributed=args.distributed, amp=args.amp, num_crops=args.num_crops, num_clips=args.num_clips) | |
| print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%") | |
| return | |
| print(f"Start training, currnet max acc is {max_accuracy:.2f}") | |
| start_time = time.time() | |
| for epoch in range(args.start_epoch, args.epochs): | |
| if args.distributed: | |
| data_loader_train.sampler.set_epoch(epoch) | |
| train_stats = train_one_epoch( | |
| model, criterion, data_loader_train,args.num_clips, | |
| optimizer, device, epoch, loss_scaler, | |
| args.clip_grad, model_ema, mixup_fn, num_tasks, True, | |
| amp=args.amp, | |
| contrastive_nomixup=args.contrastive_nomixup, | |
| hard_contrastive=args.hard_contrastive, | |
| finetune=args.finetune | |
| ) | |
| lr_scheduler.step(epoch) | |
| test_stats = evaluate(data_loader_val, model, device, num_tasks, distributed=args.distributed, amp=args.amp, num_crops=args.num_crops, num_clips=args.num_clips) | |
| print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%") | |
| max_accuracy = max(max_accuracy, test_stats["acc1"]) | |
| print(f'Max accuracy: {max_accuracy:.2f}%') | |
| if args.output_dir: | |
| checkpoint_paths = [output_dir / 'checkpoint{}.pth'.format(epoch)] | |
| if test_stats["acc1"] == max_accuracy: | |
| checkpoint_paths.append(output_dir / 'model_best.pth') | |
| for checkpoint_path in checkpoint_paths: | |
| state_dict = { | |
| 'model': model_without_ddp.state_dict(), | |
| 'optimizer': optimizer.state_dict(), | |
| 'lr_scheduler': lr_scheduler.state_dict(), | |
| 'epoch': epoch, | |
| 'args': args, | |
| 'scaler': loss_scaler.state_dict(), | |
| 'max_accuracy': max_accuracy | |
| } | |
| if args.model_ema: | |
| state_dict['model_ema'] = get_state_dict(model_ema) | |
| utils.save_on_master(state_dict, checkpoint_path) | |
| 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(): | |
| with (output_dir / "log.txt").open("a") 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__': | |
| parser = argparse.ArgumentParser('DeiT training and evaluation script', parents=[get_args_parser()]) | |
| args = parser.parse_args() | |
| if args.output_dir: | |
| Path(args.output_dir).mkdir(parents=True, exist_ok=True) | |
| main(args) | |