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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 ChannelShuffleTest(hu.HypothesisTestCase): |
| | @given(size=st.integers(8, 10), |
| | input_channels=st.integers(1, 3), |
| | batch_size=st.integers(1, 32), |
| | group=st.integers(2, 4), |
| | stride=st.integers(1, 3), |
| | pad=st.integers(0, 3), |
| | kernel=st.integers(3, 5), |
| | **mu.gcs) |
| | @settings(max_examples=10, deadline=None) |
| | def test_channel_shuffle(self, size, input_channels, batch_size, group, stride, pad, kernel, gc, dc): |
| | op = core.CreateOperator( |
| | "ChannelShuffle", |
| | ["X"], |
| | ["Y"], |
| | group=group, |
| | stride=stride, |
| | pad=pad, |
| | kernel=kernel, |
| | ) |
| | X = np.random.rand( |
| | batch_size, input_channels * group, size, size).astype(np.float32) - 0.5 |
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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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