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| import unittest |
| from hypothesis import given |
| import hypothesis.strategies as st |
| 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 |
| |
| |
| @unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.") |
| class DropoutTest(hu.HypothesisTestCase): |
| |
| @given(X=hu.tensor(), |
| in_place=st.booleans(), |
| ratio=st.floats(0, 0.999), |
| **mu.gcs) |
| def test_dropout_is_test(self, X, in_place, ratio, gc, dc): |
| """Test with is_test=True for a deterministic reference impl.""" |
| op = core.CreateOperator('Dropout', ['X'], |
| ['X' if in_place else 'Y'], |
| ratio=ratio, is_test=True) |
| |
| self.assertDeviceChecks(dc, op, [X], [0]) |
| # No sense in checking gradients for test phase |
| |
| def reference_dropout_test(x): |
| return x, np.ones(x.shape, dtype=bool) |
| self.assertReferenceChecks( |
| gc, op, [X], reference_dropout_test, |
| # The 'mask' output may be uninitialized |
| outputs_to_check=[0]) |
| |
| @given(X=hu.tensor(), |
| in_place=st.booleans(), |
| output_mask=st.booleans(), |
| **mu.gcs) |
| @unittest.skipIf(True, "Skip duo to different rand seed.") |
| def test_dropout_ratio0(self, X, in_place, output_mask, gc, dc): |
| """Test with ratio=0 for a deterministic reference impl.""" |
| is_test = not output_mask |
| op = core.CreateOperator('Dropout', ['X'], |
| ['X' if in_place else 'Y'] + |
| (['mask'] if output_mask else []), |
| ratio=0.0, is_test=is_test) |
| |
| self.assertDeviceChecks(dc, op, [X], [0]) |
| |
| def reference_dropout_ratio0(x): |
| return (x,) if is_test else (x, np.ones(x.shape, dtype=bool)) |
| self.assertReferenceChecks( |
| gc, op, [X], reference_dropout_ratio0, outputs_to_check=[0]) |
| |
| if __name__ == "__main__": |
| unittest.main() |