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| import os | |
| import torch | |
| import torch.utils.data as data | |
| import numpy as np | |
| from torchvision.datasets import ImageNet | |
| from PIL import Image, ImageFilter | |
| import h5py | |
| from glob import glob | |
| class ImagenetSegmentation(data.Dataset): | |
| CLASSES = 2 | |
| def __init__(self, | |
| path, | |
| transform=None, | |
| target_transform=None): | |
| self.path = path | |
| self.transform = transform | |
| self.target_transform = target_transform | |
| self.h5py = None | |
| tmp = h5py.File(path, 'r') | |
| self.data_length = len(tmp['/value/img']) | |
| tmp.close() | |
| del tmp | |
| def __getitem__(self, index): | |
| if self.h5py is None: | |
| self.h5py = h5py.File(self.path, 'r') | |
| img = np.array(self.h5py[self.h5py['/value/img'][index, 0]]).transpose((2, 1, 0)) | |
| target = np.array(self.h5py[self.h5py[self.h5py['/value/gt'][index, 0]][0, 0]]).transpose((1, 0)) | |
| img = Image.fromarray(img).convert('RGB') | |
| target = Image.fromarray(target) | |
| if self.transform is not None: | |
| img = self.transform(img) | |
| if self.target_transform is not None: | |
| target = np.array(self.target_transform(target)).astype('int32') | |
| target = torch.from_numpy(target).long() | |
| return img, target | |
| def __len__(self): | |
| return self.data_length | |