Args data: x_data targets: y_data (if not exist, None) num_classes: number of label classes transform: basic transformation of data use_strong_transform: If True, this dataset returns both weakly and strongly augmented images.
(self,
alg,
data,
targets=None,
num_classes=None,
transform=None,
is_ulb=False,
strong_transform=None,
onehot=False,
*args, **kwargs)
| 18 | """ |
| 19 | |
| 20 | def __init__(self, |
| 21 | alg, |
| 22 | data, |
| 23 | targets=None, |
| 24 | num_classes=None, |
| 25 | transform=None, |
| 26 | is_ulb=False, |
| 27 | strong_transform=None, |
| 28 | onehot=False, |
| 29 | *args, **kwargs): |
| 30 | """ |
| 31 | Args |
| 32 | data: x_data |
| 33 | targets: y_data (if not exist, None) |
| 34 | num_classes: number of label classes |
| 35 | transform: basic transformation of data |
| 36 | use_strong_transform: If True, this dataset returns both weakly and strongly augmented images. |
| 37 | strong_transform: list of transformation functions for strong augmentation |
| 38 | onehot: If True, label is converted into onehot vector. |
| 39 | """ |
| 40 | super(BasicDataset, self).__init__() |
| 41 | self.alg = alg |
| 42 | self.data = data |
| 43 | self.targets = targets |
| 44 | |
| 45 | self.num_classes = num_classes |
| 46 | self.is_ulb = is_ulb |
| 47 | self.onehot = onehot |
| 48 | |
| 49 | self.transform = transform |
| 50 | if self.is_ulb: |
| 51 | if strong_transform is None: |
| 52 | self.strong_transform = copy.deepcopy(transform) |
| 53 | self.strong_transform.transforms.insert(0, RandAugment(3, 5)) |
| 54 | else: |
| 55 | self.strong_transform = strong_transform |
| 56 | |
| 57 | def __getitem__(self, idx): |
| 58 | """ |
nothing calls this directly
no test coverage detected