def parse_transform(self, transform_type): if transform_type=='ImageJitter': method = add_transforms.ImageJitter( self.jitter_param ) return method method = getattr(transforms, transform_type) if transform_type=='RandomSizedCrop': return method(self.image_size) elif transform_type=='CenterCrop': return method(self.image_size) elif transform_type=='Scale': return method([int(self.image_size*1.15), int(self.image_size*1.15)]) elif transform_type=='Normalize': return method(**self.normalize_param ) else: return method()
def parse_transform(self, transform_type): if transform_type == "ImageJitter": method = add_transforms.ImageJitter(self.jitter_param) return method method = getattr(transforms, transform_type) if transform_type == "RandomResizedCrop": return method(self.image_size, scale=(0.8, 1.0)) elif transform_type == "CenterCrop": return method(self.image_size) elif transform_type == "Resize": return method( [int(self.image_size * 1.15), int(self.image_size * 1.15)]) elif transform_type == "Normalize": return method(**self.normalize_param) else: return method()
def parse_transform(self, transform_type): if isinstance(transform_type, tuple) and transform_type[0] == 'Noise': _, confound_noise, confound_noise_class_weight = transform_type method = add_transforms.Noiser(confound_noise, confound_noise_class_weight) return method if transform_type == 'ImageJitter': method = add_transforms.ImageJitter(self.jitter_param) return method method = getattr(transforms, transform_type) if transform_type == 'RandomResizedCrop': return method(self.image_size) elif transform_type == 'CenterCrop': return method(self.image_size) elif transform_type == 'Resize': return method( [int(self.image_size * 1.15), int(self.image_size * 1.15)]) elif transform_type == 'Normalize': return method(**self.normalize_param) else: return method()