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https://github.com/saymrwulf/pytorch.git
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df47bbe9c1
Summary: Signed-off-by: Edward Z. Yang <ezyang@fb.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/12975 Differential Revision: D10513493 Pulled By: ezyang fbshipit-source-id: ac183aeb4ae7f0a5f91f1a369b595ae92c3e844d
44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import core
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.serialized_test.serialized_test_util as serial
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from hypothesis import assume, given, settings, HealthCheck
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import hypothesis.strategies as st
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import numpy as np
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import unittest
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@st.composite
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def _glu_old_input(draw):
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dims = draw(st.lists(st.integers(min_value=1, max_value=5), min_size=1, max_size=3))
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axis = draw(st.integers(min_value=0, max_value=len(dims)))
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# The axis dimension must be divisible by two
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axis_dim = 2 * draw(st.integers(min_value=1, max_value=2))
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dims.insert(axis, axis_dim)
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X = draw(hu.arrays(dims, np.float32, None))
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return (X, axis)
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class TestGlu(serial.SerializedTestCase):
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@serial.given(
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X_axis=_glu_old_input(),
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**hu.gcs
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)
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def test_glu_old(self, X_axis, gc, dc):
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X, axis = X_axis
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def glu_ref(X):
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x1, x2 = np.split(X, [X.shape[axis] // 2], axis=axis)
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Y = x1 * (1. / (1. + np.exp(-x2)))
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return [Y]
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op = core.CreateOperator("Glu", ["X"], ["Y"], dim=axis)
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self.assertReferenceChecks(gc, op, [X], glu_ref)
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if __name__ == "__main__":
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unittest.main()
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