| |
| import numpy as np |
|
|
|
|
| def quantize(arr, min_val, max_val, levels, dtype=np.int64): |
| """Quantize an array of (-inf, inf) to [0, levels-1]. |
| |
| Args: |
| arr (ndarray): Input array. |
| min_val (scalar): Minimum value to be clipped. |
| max_val (scalar): Maximum value to be clipped. |
| levels (int): Quantization levels. |
| dtype (np.type): The type of the quantized array. |
| |
| Returns: |
| tuple: Quantized array. |
| """ |
| if not (isinstance(levels, int) and levels > 1): |
| raise ValueError( |
| f'levels must be a positive integer, but got {levels}') |
| if min_val >= max_val: |
| raise ValueError( |
| f'min_val ({min_val}) must be smaller than max_val ({max_val})') |
|
|
| arr = np.clip(arr, min_val, max_val) - min_val |
| quantized_arr = np.minimum( |
| np.floor(levels * arr / (max_val - min_val)).astype(dtype), levels - 1) |
|
|
| return quantized_arr |
|
|
|
|
| def dequantize(arr, min_val, max_val, levels, dtype=np.float64): |
| """Dequantize an array. |
| |
| Args: |
| arr (ndarray): Input array. |
| min_val (scalar): Minimum value to be clipped. |
| max_val (scalar): Maximum value to be clipped. |
| levels (int): Quantization levels. |
| dtype (np.type): The type of the dequantized array. |
| |
| Returns: |
| tuple: Dequantized array. |
| """ |
| if not (isinstance(levels, int) and levels > 1): |
| raise ValueError( |
| f'levels must be a positive integer, but got {levels}') |
| if min_val >= max_val: |
| raise ValueError( |
| f'min_val ({min_val}) must be smaller than max_val ({max_val})') |
|
|
| dequantized_arr = (arr + 0.5).astype(dtype) * (max_val - |
| min_val) / levels + min_val |
|
|
| return dequantized_arr |
|
|
