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| """Dataset Utils.""" | |
| # Copyright (C) 2020 Intel Corporation | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, | |
| # software distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions | |
| # and limitations under the License. | |
| from typing import List, Optional, Tuple | |
| import numpy as np | |
| from torch import Tensor | |
| class Denormalize: | |
| """Denormalize Torch Tensor into np image format.""" | |
| def __init__(self, mean: Optional[List[float]] = None, std: Optional[List[float]] = None): | |
| """Denormalize Torch Tensor into np image format. | |
| Args: | |
| mean: Mean | |
| std: Standard deviation. | |
| """ | |
| # If no mean and std provided, assign ImageNet values. | |
| if mean is None: | |
| mean = [0.485, 0.456, 0.406] | |
| if std is None: | |
| std = [0.229, 0.224, 0.225] | |
| self.mean = Tensor(mean) | |
| self.std = Tensor(std) | |
| def __call__(self, tensor: Tensor) -> np.ndarray: | |
| """Denormalize the input. | |
| Args: | |
| tensor (Tensor): Input tensor image (C, H, W) | |
| Returns: | |
| Denormalized numpy array (H, W, C). | |
| """ | |
| if tensor.dim() == 4: | |
| if tensor.size(0): | |
| tensor = tensor.squeeze(0) | |
| else: | |
| raise ValueError(f"Tensor has batch size of {tensor.size(0)}. Only single batch is supported.") | |
| for tnsr, mean, std in zip(tensor, self.mean, self.std): | |
| tnsr.mul_(std).add_(mean) | |
| array = (tensor * 255).permute(1, 2, 0).cpu().numpy().astype(np.uint8) | |
| return array | |
| def __repr__(self): | |
| """Representational string.""" | |
| return self.__class__.__name__ + "()" | |
| class ToNumpy: | |
| """Convert Tensor into Numpy Array.""" | |
| def __call__(self, tensor: Tensor, dims: Optional[Tuple[int, ...]] = None) -> np.ndarray: | |
| """Convert Tensor into Numpy Array. | |
| Args: | |
| tensor (Tensor): Tensor to convert. Input tensor in range 0-1. | |
| dims (Optional[Tuple[int, ...]], optional): Convert dimensions from torch to numpy format. | |
| Tuple corresponding to axis permutation from torch tensor to numpy array. Defaults to None. | |
| Returns: | |
| Converted numpy ndarray. | |
| """ | |
| # Default support is (C, H, W) or (N, C, H, W) | |
| if dims is None: | |
| dims = (0, 2, 3, 1) if len(tensor.shape) == 4 else (1, 2, 0) | |
| array = (tensor * 255).permute(dims).cpu().numpy().astype(np.uint8) | |
| if array.shape[0] == 1: | |
| array = array.squeeze(0) | |
| if array.shape[-1] == 1: | |
| array = array.squeeze(-1) | |
| return array | |
| def __repr__(self) -> str: | |
| """Representational string.""" | |
| return self.__class__.__name__ + "()" | |