68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
import os
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import sys
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import unittest
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import torch
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PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
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MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
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sys.path.append(MODULE_ROOT)
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from pytorch_wrapper.ptn_batch_norm2d import PtnBatchNorm2d
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class TestPtnBatchNorm2d(unittest.TestCase):
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def setUp(self):
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"""Set up test instance."""
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self.model_node = PtnBatchNorm2d()
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def check_normalization(self, x, num_features):
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model = self.model_node.f(num_features,
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affine=False,
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track_running_stats=False,
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momentum=0.001)[0]
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out = model(x)
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self.assertEqual(out.shape, x.shape)
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# Compute std along normalized axes before and after
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std_before = x.std(dim=(0, 2, 3), unbiased=True)
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std_after = out.std(dim=(0, 2, 3), unbiased=True)
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mean_after = out.mean(dim=(0, 2, 3))
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# Ensure std is not one before normalization
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# Reference value: [2.2758, 2.4911, 2.2218]
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self.assertFalse(
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torch.allclose(
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std_before,
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torch.ones_like(std_after),
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atol=1e-1))
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# Ensure normalization happened (std should be 1 unless elements are very close)
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# Reference value: [1.0190, 1.0190, 1.0190]
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self.assertTrue(
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torch.allclose(
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std_after,
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torch.ones_like(std_after),
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atol=1e-1))
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# Reference value: [0.0000e+00, 0.0000e+00, 2.2076e-09]
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self.assertTrue(
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torch.allclose(
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mean_after,
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torch.zeros_like(mean_after),
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atol=1e-1))
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def test_normalization_cases(self):
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test_cases = [
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# Data, num_features on the channel axis
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(torch.randn(2, 3, 3, 3) * 2, 3), # c = 3
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(torch.randn(3, 3, 3, 3) * 2, 3), # c = 3
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]
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for x, norm_shape in test_cases:
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with self.subTest(num_features=norm_shape):
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self.check_normalization(x, norm_shape)
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if __name__ == "__main__":
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unittest.main()
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