зеркало из https://github.com/microsoft/SPACH.git
60 строки
1.9 KiB
Python
60 строки
1.9 KiB
Python
# Copyright (c) 2015-present, Facebook, Inc.
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# All rights reserved.
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import os
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import json
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from torchvision import datasets, transforms
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from torchvision.datasets.folder import ImageFolder, default_loader
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from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from timm.data import create_transform
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from torch.utils.data import Dataset
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def build_dataset(is_train, args):
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transform = build_transform(is_train, args)
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if args.data_set == 'IMNET':
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root = os.path.join(args.data_path, 'train' if is_train else 'val')
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dataset = datasets.ImageFolder(root, transform=transform)
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nb_classes = 1000
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else:
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raise NotImplementedError("Support ImageNet only.")
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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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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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)
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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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size = int((256 / 224) * args.input_size)
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t.append(
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transforms.Resize(size, interpolation=3), # 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(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD))
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return transforms.Compose(t)
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