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from test.unit.util import is_equal_tf |
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import numpy as np |
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import pytest |
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import tensorflow as tf |
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from deepreg.loss.kernel import ( |
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cauchy_kernel1d, |
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gaussian_kernel1d_sigma, |
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gaussian_kernel1d_size, |
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rectangular_kernel1d, |
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triangular_kernel1d, |
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) |
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@pytest.mark.parametrize("sigma", [1, 3, 2.2]) |
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def test_cauchy_kernel1d(sigma): |
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""" |
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Testing the 1-D cauchy kernel |
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:param sigma: float |
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:return: |
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""" |
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tail = int(sigma * 5) |
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expected = [1 / ((x / sigma) ** 2 + 1) for x in range(-tail, tail + 1)] |
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expected = expected / np.sum(expected) |
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got = cauchy_kernel1d(sigma) |
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assert is_equal_tf(got, expected) |
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@pytest.mark.parametrize("sigma", [1, 3, 2.2]) |
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def test_gaussian_kernel1d_sigma(sigma): |
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""" |
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Testing the 1-D gaussian kernel given sigma as input |
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:param sigma: float |
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:return: |
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""" |
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tail = int(sigma * 3) |
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expected = [np.exp(-0.5 * x ** 2 / sigma ** 2) for x in range(-tail, tail + 1)] |
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expected = expected / np.sum(expected) |
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got = gaussian_kernel1d_sigma(sigma) |
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assert is_equal_tf(got, expected) |
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@pytest.mark.parametrize("kernel_size", [3, 7, 11]) |
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def test_gaussian_kernel1d_size(kernel_size): |
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""" |
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Testing the 1-D gaussian kernel given size as input |
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:param kernel_size: int |
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:return: |
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""" |
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mean = (kernel_size - 1) / 2.0 |
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sigma = kernel_size / 3 |
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grid = tf.range(0, kernel_size, dtype=tf.float32) |
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expected = tf.exp(-tf.square(grid - mean) / (2 * sigma ** 2)) |
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got = gaussian_kernel1d_size(kernel_size) |
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assert is_equal_tf(got, expected) |
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@pytest.mark.parametrize("kernel_size", [3, 7, 11]) |
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def test_rectangular_kernel1d(kernel_size): |
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""" |
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Testing the 1-D rectangular kernel |
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:param kernel_size: int |
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:return: |
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""" |
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expected = tf.ones(shape=(kernel_size,), dtype=tf.float32) |
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got = rectangular_kernel1d(kernel_size) |
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assert is_equal_tf(got, expected) |
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@pytest.mark.parametrize("kernel_size", [3, 5, 7, 9]) |
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def test_triangular_kernel1d(kernel_size): |
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""" |
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Testing the 1-D triangular kernel |
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:param kernel_size: int (odd number) |
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:return: |
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""" |
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expected = np.zeros(shape=(kernel_size,), dtype=np.float32) |
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expected[kernel_size // 2] = kernel_size // 2 + 1 |
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for it_k in range(kernel_size // 2): |
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expected[it_k] = it_k + 1 |
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expected[-it_k - 1] = it_k + 1 |
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got = triangular_kernel1d(kernel_size) |
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assert is_equal_tf(got, expected) |
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DeepRegNet /
DeepReg
