wehub-resource-sync
93 lines
3.3 KiB
Python
93 lines
3.3 KiB
Python
# --------------------------------------------------------
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# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
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# Github source: https://github.com/microsoft/unilm/tree/master/beit
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# Copyright (c) 2021 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# By Hangbo Bao
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# Based on timm, DINO and DeiT code bases
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# https://github.com/rwightman/pytorch-image-models/tree/master/timm
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# https://github.com/facebookresearch/deit/
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# https://github.com/facebookresearch/dino
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# --------------------------------------------------------'
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from timm.data import create_transform
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from timm.data.constants import \
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IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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from timm.data.transforms import str_to_interp_mode
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from torchvision import transforms
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from dataset_folder import RvlcdipImageFolder
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def build_dataset(is_train, args):
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transform = build_transform(is_train, args)
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print("Transform = ")
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if isinstance(transform, tuple):
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for trans in transform:
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print(" - - - - - - - - - - ")
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for t in trans.transforms:
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print(t)
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else:
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for t in transform.transforms:
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print(t)
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print("---------------------------")
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if args.data_set == 'rvlcdip':
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root = args.data_path if is_train else args.eval_data_path
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split = "train" if is_train else "test"
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dataset = RvlcdipImageFolder(root, split=split, transform=transform)
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nb_classes = args.nb_classes
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assert len(dataset.class_to_idx) == nb_classes
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else:
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raise NotImplementedError()
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assert nb_classes == args.nb_classes
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print("Number of the class = %d" % args.nb_classes)
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return dataset, nb_classes
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def build_transform(is_train, args):
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resize_im = args.input_size > 32
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imagenet_default_mean_and_std = args.imagenet_default_mean_and_std
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mean = IMAGENET_INCEPTION_MEAN if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_MEAN
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std = IMAGENET_INCEPTION_STD if not imagenet_default_mean_and_std else IMAGENET_DEFAULT_STD
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if is_train:
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# this should always dispatch to transforms_imagenet_train
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transform = create_transform(
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input_size=args.input_size,
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is_training=True,
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color_jitter=args.color_jitter,
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auto_augment=args.aa,
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interpolation=args.train_interpolation,
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re_prob=args.reprob,
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re_mode=args.remode,
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re_count=args.recount,
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mean=mean,
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std=std,
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)
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if not resize_im:
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# replace RandomResizedCropAndInterpolation with
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# RandomCrop
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transform.transforms[0] = transforms.RandomCrop(
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args.input_size, padding=4)
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return transform
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t = []
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if resize_im:
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if args.crop_pct is None:
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if args.input_size < 384:
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args.crop_pct = 224 / 256
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else:
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args.crop_pct = 1.0
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size = int(args.input_size / args.crop_pct)
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t.append(
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transforms.Resize(size, interpolation=str_to_interp_mode("bicubic")),
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# to maintain same ratio w.r.t. 224 images
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)
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t.append(transforms.CenterCrop(args.input_size))
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t.append(transforms.ToTensor())
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t.append(transforms.Normalize(mean, std))
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return transforms.Compose(t)
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