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| from abc import abstractmethod | |
| import torchvision.transforms as transforms | |
| class TransformsConfig(object): | |
| def __init__(self, opts): | |
| self.opts = opts | |
| def get_transforms(self): | |
| pass | |
| class EncodeTransforms(TransformsConfig): | |
| def __init__(self, opts): | |
| super(EncodeTransforms, self).__init__(opts) | |
| def get_transforms(self): | |
| transforms_dict = { | |
| 'transform_gt_train': transforms.Compose([ | |
| transforms.Resize((256, 256)), | |
| transforms.RandomHorizontalFlip(0.5), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), | |
| 'transform_source': None, | |
| 'transform_test': transforms.Compose([ | |
| transforms.Resize((256, 256)), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), | |
| 'transform_inference': transforms.Compose([ | |
| transforms.Resize((256, 256)), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) | |
| } | |
| return transforms_dict | |
| class CarsEncodeTransforms(TransformsConfig): | |
| def __init__(self, opts): | |
| super(CarsEncodeTransforms, self).__init__(opts) | |
| def get_transforms(self): | |
| transforms_dict = { | |
| 'transform_gt_train': transforms.Compose([ | |
| transforms.Resize((192, 256)), | |
| transforms.RandomHorizontalFlip(0.5), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), | |
| 'transform_source': None, | |
| 'transform_test': transforms.Compose([ | |
| transforms.Resize((192, 256)), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), | |
| 'transform_inference': transforms.Compose([ | |
| transforms.Resize((192, 256)), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) | |
| } | |
| return transforms_dict | |