| |
|
|
| 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) |
|
|
