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https://github.com/saymrwulf/pytorch.git
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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
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from caffe2.python import core
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from hypothesis import given, settings
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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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import hypothesis.strategies as st
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import numpy as np
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import unittest
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class TestSelu(serial.SerializedTestCase):
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@serial.given(X=hu.tensor(),
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engine=st.sampled_from(["", "CUDNN"]),
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**hu.gcs)
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def test_selu_1(self, X, gc, dc, engine):
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alpha = 1.0
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scale = 2.0
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op = core.CreateOperator("Selu", ["X"], ["Y"],
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alpha=alpha, scale=scale, engine=engine)
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X = TestSelu.fix0(X)
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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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self.assertReferenceChecks(
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gc, op, [X], lambda x: TestSelu.selu_ref(x, alpha=alpha, scale=scale)
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)
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@given(X=hu.tensor(),
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engine=st.sampled_from(["", "CUDNN"]),
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**hu.gcs)
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@settings(deadline=1000)
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def test_selu_2(self, X, gc, dc, engine):
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alpha = 1.6732
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scale = 1.0507
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op = core.CreateOperator("Selu", ["X"], ["Y"],
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alpha=alpha, scale=scale, engine=engine)
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X = TestSelu.fix0(X)
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self.assertDeviceChecks(dc, op, [X], [0])
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self.assertGradientChecks(gc, op, [X], 0, [0], stepsize=1e-2, threshold=1e-2)
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self.assertReferenceChecks(
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gc, op, [X], lambda x: TestSelu.selu_ref(x, alpha=alpha, scale=scale)
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)
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@given(X=hu.tensor(),
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engine=st.sampled_from(["", "CUDNN"]),
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**hu.gcs)
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@settings(deadline=1000)
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def test_selu_3(self, X, gc, dc, engine):
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alpha = 1.3
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scale = 1.1
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op = core.CreateOperator("Selu", ["X"], ["Y"],
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alpha=alpha, scale=scale, engine=engine)
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X = TestSelu.fix0(X)
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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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self.assertReferenceChecks(
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gc, op, [X], lambda x: TestSelu.selu_ref(x, alpha=alpha, scale=scale)
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)
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@given(X=hu.tensor(),
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engine=st.sampled_from(["", "CUDNN"]),
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**hu.gcs)
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def test_selu_inplace(self, X, gc, dc, engine):
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alpha = 1.3
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scale = 1.1
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op = core.CreateOperator("Selu", ["X"], ["X"],
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alpha=alpha, scale=scale, engine=engine)
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X = TestSelu.fix0(X)
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self.assertDeviceChecks(dc, op, [X], [0])
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# inplace gradient
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Y = TestSelu.selu_ref(X, alpha=alpha, scale=scale)
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dX = np.ones_like(X)
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op2 = core.CreateOperator("SeluGradient", ["Y", "dX"], ["dX"],
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alpha=alpha, scale=scale, engine=engine)
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self.assertDeviceChecks(dc, op2, [Y, dX], [0])
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@staticmethod
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def fix0(X):
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# go away from the origin point to avoid kink problems
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X += 0.02 * np.sign(X)
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X[X == 0.0] += 0.02
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return X
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@staticmethod
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def selu_ref(x, scale, alpha):
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ret = scale * ((x > 0) * x + (x <= 0) * (alpha * (np.exp(x) - 1)))
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return [ret]
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if __name__ == "__main__":
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unittest.main()
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