70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
from torch.utils.data import Dataset
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import torchvision.transforms as T
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from torchvision import datasets
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# Wrapper for basic pytorch datasets which re-wraps them into a format usable by ExtensibleTrainer.
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from data.images.cifar import CIFAR100, CIFAR10
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from utils.util import opt_get
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class TorchDataset(Dataset):
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def __init__(self, opt):
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DATASET_MAP = {
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"mnist": datasets.MNIST,
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"fmnist": datasets.FashionMNIST,
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"cifar10": CIFAR10,
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"cifar100": CIFAR100,
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"imagenet": datasets.ImageNet,
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"imagefolder": datasets.ImageFolder
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}
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normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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if opt_get(opt, ['random_crop'], False):
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transforms = [
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T.RandomResizedCrop(opt['image_size']),
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T.RandomHorizontalFlip(),
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T.ToTensor(),
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normalize,
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]
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else:
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transforms = [
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T.Resize(opt['image_size']),
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T.CenterCrop(opt['image_size']),
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T.RandomHorizontalFlip(),
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T.ToTensor(),
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normalize,
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]
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transforms = T.Compose(transforms)
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self.dataset = DATASET_MAP[opt['dataset']](transform=transforms, **opt['kwargs'])
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self.len = opt_get(opt, ['fixed_len'], len(self.dataset))
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self.offset = opt_get(opt, ['offset'], 0)
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def __getitem__(self, item):
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item = self.dataset[item+self.offset]
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if len(item) == 2:
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underlying_item, lbl = item
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coarselbl = None
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elif len(item) == 3:
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underlying_item, lbl, coarselbl = item
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else:
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raise NotImplementedError
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return {'lq': underlying_item, 'hq': underlying_item, 'labels': lbl, 'coarse_labels': coarselbl,
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'LQ_path': str(item), 'GT_path': str(item)}
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def __len__(self):
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return self.len-self.offset
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if __name__ == '__main__':
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opt = {
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'flip': True,
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'crop_sz': None,
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'dataset': 'cifar100',
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'image_size': 32,
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'normalize': True,
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'kwargs': {
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'root': 'E:\\4k6k\\datasets\\images\\cifar100',
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'download': True
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}
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}
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set = TorchDataset(opt)
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j = set[0]
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