test.py

import argparse
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import os
import warnings

from pathlib import Path

from timm.models import create_model
from timm.utils import ModelEma

#from datasets import build_dataset
import my_models
from engine import evaluate
#import simclr
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)
#torch.multiprocessing.set_start_method('spawn', force=True)

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',
                        choices=list(DATASET_CONFIG.keys()), help='path to dataset file list')
    parser.add_argument('--duration', default=1, 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=7, type=int, help='number of frames')
    parser.add_argument('--thumbnail_rows', default=3, 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-7, metavar='LR',
                        help='warmup learning rate (default: 1e-6)')
    parser.add_argument('--min-lr', type=float, default=2e-6, 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-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)')
    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('--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="./output",
                        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=3, 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


    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)


    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

    num_tasks = utils.get_world_size()

    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, 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, 
                                dense_sampling=args.dense_sampling,
                                image_tmpl=image_tmpl,
                                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)


    if args.initial_checkpoint:
                checkpoint = torch.load(args.initial_checkpoint, map_location='cpu')
                utils.load_checkpoint(model, checkpoint['model'])

                state = 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: {state['acc1']:.1f}%")

if __name__ == '__main__':
    parser = argparse.ArgumentParser('DeiT 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)