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| | import unittest |
| | import hypothesis.strategies as st |
| | from hypothesis import given, settings |
| | import numpy as np |
| | from caffe2.python import core, workspace |
| | import caffe2.python.hypothesis_test_util as hu |
| | import caffe2.python.ideep_test_util as mu |
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| | @unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.") |
| | class LRNTest(hu.HypothesisTestCase): |
| | @given(input_channels=st.integers(1, 3), |
| | batch_size=st.integers(1, 3), |
| | im_size=st.integers(1, 10), |
| | order=st.sampled_from(["NCHW"]), |
| | **mu.gcs) |
| | @settings(deadline=10000) |
| | def test_LRN(self, input_channels, |
| | batch_size, im_size, order, |
| | gc, dc): |
| | op = core.CreateOperator( |
| | "LRN", |
| | ["X"], |
| | ["Y", "Y_scale"], |
| | size=5, |
| | alpha=0.001, |
| | beta=0.75, |
| | bias=2.0, |
| | order=order, |
| | ) |
| | X = np.random.rand( |
| | batch_size, input_channels, im_size, im_size).astype(np.float32) |
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| | self.assertDeviceChecks(dc, op, [X], [0]) |
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| | self.assertGradientChecks(gc, op, [X], 0, [0]) |
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| | if __name__ == "__main__": |
| | unittest.main() |
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