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