Ejemplo n.º 1
0
 def get(cls, args):
     train_file = args.train_file
     val_file = args.val_file
     train_dataset = cls(args,
                         args.data,
                         'train',
                         train_file,
                         args.cache,
                         transform=transforms.Compose([
                             videotransforms.RandomCrop(args.input_size),
                             videotransforms.RandomHorizontalFlip()
                         ]),
                         input_size=args.input_size)
     val_dataset = cls(args,
                       args.valdata,
                       'val',
                       val_file,
                       args.cache,
                       transform=transforms.Compose(
                           [videotransforms.CenterCrop(args.input_size)]),
                       input_size=args.input_size)
     valvideo_dataset = cls(args,
                            args.valdata,
                            'val_video',
                            val_file,
                            args.cache,
                            transform=transforms.Compose([
                                videotransforms.CenterCrop(args.input_size)
                            ]),
                            input_size=args.input_size)
     return train_dataset, val_dataset, valvideo_dataset
Ejemplo n.º 2
0
 def get(cls, args, splits=('train', 'val', 'val_video')):
     train_file = args.train_file
     val_file = args.val_file
     if 'train' in splits:
         train_dataset = cls(
             args, args.data, 'train', train_file, args.cache,
             transform=transforms.Compose([
                 videotransforms.RandomCrop(args.input_size),
                 videotransforms.RandomHorizontalFlip()
             ]),
             input_size=args.input_size)
     else:
         train_dataset = None
     if 'val' in splits:
         val_dataset = cls(
             args, args.data, 'val', val_file, args.cache,
             transform=transforms.Compose([
                 videotransforms.CenterCrop(args.input_size)
             ]),
             input_size=args.input_size)
     else:
         val_dataset = None
     if 'val_video' in splits:
         valvideo_dataset = cls(
             args, args.data, 'val_video', val_file, args.cache,
             transform=transforms.Compose([
                 videotransforms.CenterCrop(args.input_size)
             ]),
             input_size=args.input_size)
     else:
         valvideo_dataset = None
     return train_dataset, val_dataset, valvideo_dataset
    def get(cls, args):
        if ';' in args.train_file:
            args = copy.deepcopy(args)
            vars(args).update({
                'train_file': args.train_file.split(';')[0],
                'val_file': args.val_file.split(';')[0],
                'data': args.data.split(';')[0]
            })
        val_dataset = cls(args,
                          args.data,
                          'val',
                          args.val_file,
                          args.cache,
                          transform=transforms.Compose(
                              [videotransforms.CenterCrop(args.input_size)]),
                          input_size=args.input_size)

        val_loader = torch.utils.data.DataLoader(
            val_dataset,
            batch_size=args.video_batch_size,
            shuffle=False,
            num_workers=args.workers,
            pin_memory=True)

        return val_loader