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| import torch.utils.data as data | |
| import utils.utils_image as util | |
| class DatasetL(data.Dataset): | |
| ''' | |
| # ----------------------------------------- | |
| # Get L in testing. | |
| # Only "dataroot_L" is needed. | |
| # ----------------------------------------- | |
| # ----------------------------------------- | |
| ''' | |
| def __init__(self, opt): | |
| super(DatasetL, self).__init__() | |
| print('Read L in testing. Only "dataroot_L" is needed.') | |
| self.opt = opt | |
| self.n_channels = opt['n_channels'] if opt['n_channels'] else 3 | |
| # ------------------------------------ | |
| # get the path of L | |
| # ------------------------------------ | |
| self.paths_L = util.get_image_paths(opt['dataroot_L']) | |
| assert self.paths_L, 'Error: L paths are empty.' | |
| def __getitem__(self, index): | |
| L_path = None | |
| # ------------------------------------ | |
| # get L image | |
| # ------------------------------------ | |
| L_path = self.paths_L[index] | |
| img_L = util.imread_uint(L_path, self.n_channels) | |
| # ------------------------------------ | |
| # HWC to CHW, numpy to tensor | |
| # ------------------------------------ | |
| img_L = util.uint2tensor3(img_L) | |
| return {'L': img_L, 'L_path': L_path} | |
| def __len__(self): | |
| return len(self.paths_L) | |