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- # Copyright (c) Open-MMLab. All rights reserved.
- from __future__ import division
-
- import numpy as np
- import pytest
-
- import mmcv
-
-
- def test_quantize():
- arr = np.random.randn(10, 10)
- levels = 20
-
- qarr = mmcv.quantize(arr, -1, 1, levels)
- assert qarr.shape == arr.shape
- assert qarr.dtype == np.dtype('int64')
- for i in range(arr.shape[0]):
- for j in range(arr.shape[1]):
- ref = min(levels - 1,
- int(np.floor(10 * (1 + max(min(arr[i, j], 1), -1)))))
- assert qarr[i, j] == ref
-
- qarr = mmcv.quantize(arr, -1, 1, 20, dtype=np.uint8)
- assert qarr.shape == arr.shape
- assert qarr.dtype == np.dtype('uint8')
-
- with pytest.raises(ValueError):
- mmcv.quantize(arr, -1, 1, levels=0)
- with pytest.raises(ValueError):
- mmcv.quantize(arr, -1, 1, levels=10.0)
- with pytest.raises(ValueError):
- mmcv.quantize(arr, 2, 1, levels)
-
-
- def test_dequantize():
- levels = 20
- qarr = np.random.randint(levels, size=(10, 10))
-
- arr = mmcv.dequantize(qarr, -1, 1, levels)
- assert arr.shape == qarr.shape
- assert arr.dtype == np.dtype('float64')
- for i in range(qarr.shape[0]):
- for j in range(qarr.shape[1]):
- assert arr[i, j] == (qarr[i, j] + 0.5) / 10 - 1
-
- arr = mmcv.dequantize(qarr, -1, 1, levels, dtype=np.float32)
- assert arr.shape == qarr.shape
- assert arr.dtype == np.dtype('float32')
-
- with pytest.raises(ValueError):
- mmcv.dequantize(arr, -1, 1, levels=0)
- with pytest.raises(ValueError):
- mmcv.dequantize(arr, -1, 1, levels=10.0)
- with pytest.raises(ValueError):
- mmcv.dequantize(arr, 2, 1, levels)
-
-
- def test_joint():
- arr = np.random.randn(100, 100)
- levels = 1000
- qarr = mmcv.quantize(arr, -1, 1, levels)
- recover = mmcv.dequantize(qarr, -1, 1, levels)
- assert np.abs(recover[arr < -1] + 0.999).max() < 1e-6
- assert np.abs(recover[arr > 1] - 0.999).max() < 1e-6
- assert np.abs((recover - arr)[(arr >= -1) & (arr <= 1)]).max() <= 1e-3
-
- arr = np.clip(np.random.randn(100) / 1000, -0.01, 0.01)
- levels = 99
- qarr = mmcv.quantize(arr, -1, 1, levels)
- recover = mmcv.dequantize(qarr, -1, 1, levels)
- assert np.all(recover == 0)
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