| | |
| | 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 |
| |
|
