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# Copyright (c) OpenMMLab. All rights reserved.
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
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