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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/49980 From ``` ./python/libcst/libcst codemod remove_unused_imports.RemoveUnusedImportsWithGlean --no-format caffe2/ ``` Test Plan: Standard sandcastle tests Reviewed By: xush6528 Differential Revision: D25727359 fbshipit-source-id: c4f60005b10546423dc093d31d46deb418352286
60 lines
2.0 KiB
Python
60 lines
2.0 KiB
Python
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import unittest
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from hypothesis import given
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import hypothesis.strategies as st
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import numpy as np
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from caffe2.python import core, workspace
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.ideep_test_util as mu
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@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
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class DropoutTest(hu.HypothesisTestCase):
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@given(X=hu.tensor(),
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in_place=st.booleans(),
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ratio=st.floats(0, 0.999),
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**mu.gcs)
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def test_dropout_is_test(self, X, in_place, ratio, gc, dc):
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"""Test with is_test=True for a deterministic reference impl."""
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op = core.CreateOperator('Dropout', ['X'],
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['X' if in_place else 'Y'],
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ratio=ratio, is_test=True)
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self.assertDeviceChecks(dc, op, [X], [0])
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# No sense in checking gradients for test phase
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def reference_dropout_test(x):
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return x, np.ones(x.shape, dtype=np.bool)
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self.assertReferenceChecks(
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gc, op, [X], reference_dropout_test,
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# The 'mask' output may be uninitialized
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outputs_to_check=[0])
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@given(X=hu.tensor(),
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in_place=st.booleans(),
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output_mask=st.booleans(),
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**mu.gcs)
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@unittest.skipIf(True, "Skip duo to different rand seed.")
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def test_dropout_ratio0(self, X, in_place, output_mask, gc, dc):
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"""Test with ratio=0 for a deterministic reference impl."""
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is_test = not output_mask
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op = core.CreateOperator('Dropout', ['X'],
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['X' if in_place else 'Y'] +
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(['mask'] if output_mask else []),
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ratio=0.0, is_test=is_test)
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self.assertDeviceChecks(dc, op, [X], [0])
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def reference_dropout_ratio0(x):
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return (x,) if is_test else (x, np.ones(x.shape, dtype=np.bool))
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self.assertReferenceChecks(
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gc, op, [X], reference_dropout_ratio0, outputs_to_check=[0])
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if __name__ == "__main__":
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unittest.main()
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