| """Tools for min-max normalization.""" | |
| # Copyright (C) 2022-2024 Intel Corporation | |
| # SPDX-License-Identifier: Apache-2.0 | |
| import numpy as np | |
| import torch | |
| def normalize( | |
| targets: np.ndarray | np.float32 | torch.Tensor, | |
| threshold: float | np.ndarray | torch.Tensor, | |
| min_val: float | np.ndarray | torch.Tensor, | |
| max_val: float | np.ndarray | torch.Tensor, | |
| ) -> np.ndarray | torch.Tensor: | |
| """Apply min-max normalization and shift the values such that the threshold value is centered at 0.5.""" | |
| normalized = ((targets - threshold) / (max_val - min_val)) + 0.5 | |
| if isinstance(targets, np.ndarray | np.float32 | np.float64): | |
| normalized = np.minimum(normalized, 1) | |
| normalized = np.maximum(normalized, 0) | |
| elif isinstance(targets, torch.Tensor): | |
| normalized = torch.minimum(normalized, torch.tensor(1)) # pylint: disable=not-callable | |
| normalized = torch.maximum(normalized, torch.tensor(0)) # pylint: disable=not-callable | |
| else: | |
| msg = f"Targets must be either Tensor or Numpy array. Received {type(targets)}" | |
| raise TypeError(msg) | |
| return normalized | |