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# coding=utf-8 |
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""" |
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Tests for deepreg/model/loss/label.py in |
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pytest style |
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""" |
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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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import deepreg.model.loss.label as label |
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@pytest.mark.parametrize("sigma", [1, 3, 2.2]) |
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def test_gaussian_kernel1d(sigma): |
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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 = label.gaussian_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_cauchy_kernel1d(sigma): |
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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 = label.cauchy_kernel1d(sigma) |
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assert is_equal_tf(got, expected) |
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def test_separable_filter(): |
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""" |
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Testing separable filter case where non |
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zero length tensor is passed to the |
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function. |
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""" |
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k = np.ones((3, 3, 3, 3, 1), dtype=np.float32) |
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array_eye = np.identity(3, dtype=np.float32) |
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tensor_pred = np.zeros((3, 3, 3, 3, 1), dtype=np.float32) |
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tensor_pred[:, :, 0, 0, 0] = array_eye |
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tensor_pred = tf.convert_to_tensor(tensor_pred, dtype=tf.float32) |
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k = tf.convert_to_tensor(k, dtype=tf.float32) |
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expect = np.ones((3, 3, 3, 3, 1), dtype=np.float32) |
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expect = tf.convert_to_tensor(expect, dtype=tf.float32) |
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get = label.separable_filter(tensor_pred, k) |
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assert is_equal_tf(get, expect) |
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DeepRegNet /
DeepReg
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