Null Dataset for Performance
| 39 | from torch.utils import data |
| 40 | |
| 41 | class Loader(BaseLoader): |
| 42 | """ |
| 43 | Null Dataset for Performance |
| 44 | """ |
| 45 | num_classes = 19 |
| 46 | ignore_label = 255 |
| 47 | trainid_to_name = {} |
| 48 | color_mapping = [] |
| 49 | |
| 50 | def __init__(self, mode, quality=None, joint_transform_list=None, |
| 51 | img_transform=None, label_transform=None, eval_folder=None): |
| 52 | super(Loader, self).__init__(quality=quality, |
| 53 | mode=mode, |
| 54 | joint_transform_list=joint_transform_list, |
| 55 | img_transform=img_transform, |
| 56 | label_transform=label_transform) |
| 57 | |
| 58 | def __getitem__(self, index): |
| 59 | # return img, mask, img_name, scale_float |
| 60 | crop_size = cfg.DATASET.CROP_SIZE |
| 61 | if ',' in crop_size: |
| 62 | crop_size = [int(x) for x in crop_size.split(',')] |
| 63 | else: |
| 64 | crop_size = int(crop_size) |
| 65 | crop_size = [crop_size, crop_size] |
| 66 | |
| 67 | img = torch.FloatTensor(np.zeros([3] + crop_size)) |
| 68 | mask = torch.LongTensor(np.zeros(crop_size)) |
| 69 | img_name = f'img{index}' |
| 70 | scale_float = 0.0 |
| 71 | return img, mask, img_name, scale_float |
| 72 | |
| 73 | def __len__(self): |
| 74 | return 3000 |
nothing calls this directly
no outgoing calls
no test coverage detected