| from torch.utils.data import Dataset | |
| from PIL import Image | |
| from utils import data_utils | |
| class InferenceDataset(Dataset): | |
| def __init__(self, root, opts, transform=None, preprocess=None): | |
| self.paths = sorted(data_utils.make_dataset(root)) | |
| self.transform = transform | |
| self.preprocess = preprocess | |
| self.opts = opts | |
| def __len__(self): | |
| return len(self.paths) | |
| def __getitem__(self, index): | |
| from_path = self.paths[index] | |
| if self.preprocess is not None: | |
| from_im = self.preprocess(from_path) | |
| else: | |
| from_im = Image.open(from_path).convert('RGB') | |
| if self.transform: | |
| from_im = self.transform(from_im) | |
| return from_im | |