11155aead4
This has been a long time coming. Cleans up messy "GT" nomenclature and simplifies ExtensibleTraner.feed_data
32 lines
1.3 KiB
Python
32 lines
1.3 KiB
Python
import torch
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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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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": datasets.CIFAR10,
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}
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transforms = []
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if opt['flip']:
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transforms.append(T.RandomHorizontalFlip())
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if opt['crop_sz']:
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transforms.append(T.RandomCrop(opt['crop_sz'], padding=opt['padding'], padding_mode="reflect"))
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transforms.append(T.ToTensor())
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transforms = T.Compose(transforms)
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is_for_training = opt['test'] if 'test' in opt.keys() else True
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self.dataset = DATASET_MAP[opt['dataset']](opt['datapath'], train=is_for_training, download=True, transform=transforms)
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self.len = opt['fixed_len'] if 'fixed_len' in opt.keys() else len(self.dataset)
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def __getitem__(self, item):
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underlying_item = self.dataset[item][0]
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return {'lq': underlying_item, 'hq': underlying_item,
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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
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