def __call__(self, kspace, target, attrs, fname, slice): kspace = np.array(kspace) target = np.array(target) kspace = transforms.to_tensor(kspace) target = transforms.to_tensor(target) kspace = transforms.complex_center_crop( kspace, (self.resolution, self.resolution)) target = transforms.center_crop(target, (self.resolution, self.resolution)) return kspace, target
def _get_cutout_train_transform(self): transform = torchvision.transforms.Compose([ transforms.normalize(self.mean, self.std), transforms.cutout(self.config['cutout_size'], self.config['cutout_prob'], self.config['cutout_inside']), transforms.to_tensor(), ]) return transform
def _get_cutout_train_transform(self): transform = torchvision.transforms.Compose([ torchvision.transforms.RandomCrop(28, padding=4), torchvision.transforms.RandomHorizontalFlip(), transforms.normalize(self.mean, self.std), transforms.cutout(self.config['cutout_size'], self.config['cutout_prob'], self.config['cutout_inside']), transforms.to_tensor(), ]) return transform
def _get_random_erasing_train_transform(self): transform = torchvision.transforms.Compose([ transforms.normalize(self.mean, self.std), transforms.random_erasing( self.config['random_erasing_prob'], self.config['random_erasing_area_ratio_range'], self.config['random_erasing_min_aspect_ratio'], self.config['random_erasing_max_attempt']), transforms.to_tensor(), ]) return transform
def verify_img_data(img_data, expected_output, mode): if mode is None: img = transforms.ToPILImage()(img_data) assert img.mode == 'RGB' # default should assume RGB else: img = transforms.ToPILImage(mode=mode)(img_data) assert img.mode == mode split = img.split() for i in range(3): assert np.allclose(expected_output[i].numpy(), transforms.to_tensor(split[i]).numpy())
def __call__(self, pic): """ Args: pic (PIL or numpy.ndarray): Image to be converted to tensor Returns: Tensor: Converted image. """ if isinstance(pic, np.ndarray): # This is what TorchVision 0.2.0 returns for transforms.toTensor() for np.ndarray return torch.from_numpy(pic.transpose((2, 0, 1))).float().div(255) else: return transforms.to_tensor(pic)
def _get_random_erasing_train_transform(self): transform = torchvision.transforms.Compose([ torchvision.transforms.Resize((32,32)), torchvision.transforms.ColorJitter(0.1,0.1,0.1), # torchvision.transforms.RandomRotation(15), # RandomAffine(degrees=15,scale=(0.8,1.2),shear=15), torchvision.transforms.RandomCrop(32, padding=4), torchvision.transforms.RandomHorizontalFlip(), transforms.normalize(self.mean, self.std), transforms.random_erasing( self.config['random_erasing_prob'], self.config['random_erasing_area_ratio_range'], self.config['random_erasing_min_aspect_ratio'], self.config['random_erasing_max_attempt']), transforms.to_tensor(), ]) return transform
def _get_test_transform(self): transform = torchvision.transforms.Compose([ transforms.normalize(self.mean, self.std), transforms.to_tensor(), ]) return transform
def _add_to_tensor(self): self._train_transforms.append(transforms.to_tensor())