mirror of
https://github.com/zebrajr/pytorch.git
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8286ce1e3a
Summary: Closes https://github.com/caffe2/caffe2/pull/1260 Differential Revision: D5906739 Pulled By: Yangqing fbshipit-source-id: e482ba9ba60b5337d9165f28f7ec68d4518a0902
66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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##############################################################################
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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, dyndep
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import caffe2.python.hypothesis_test_util as hu
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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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dyndep.InitOpsLibrary('@/caffe2/caffe2/contrib/torch:th_ops')
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try:
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dyndep.InitOpsLibrary('@/caffe2/caffe2/contrib/torch:th_ops_gpu')
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HAS_GPU = True
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except Exception as e:
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print("Exception loading Torch GPU library: ", e)
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# GPU import can fail, as Torch is not using cuda-lazy
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HAS_GPU = False
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pass
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class THOpsTest(hu.HypothesisTestCase):
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@given(X=hu.tensor(),
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alpha=st.floats(min_value=0.1, max_value=2.0),
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in_place=st.booleans(),
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**(hu.gcs if HAS_GPU else hu.gcs_cpu_only))
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def test_elu(self, X, alpha, in_place, gc, dc):
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op = core.CreateOperator(
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"ELU",
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["X"],
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["X" if in_place else "Y"],
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engine="THNN",
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alpha=alpha)
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self.assertDeviceChecks(dc, op, [X], [0])
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def elu(X):
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Y = np.copy(X)
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Y[Y <= 0] = (np.exp(Y[Y <= 0]) - 1) * alpha
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return (Y,)
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self.assertReferenceChecks(gc, op, [X], elu)
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# Avoid the nonlinearity at 0 for gradient checker.
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X[X == 0] += 0.2
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X[np.abs(X) < 0.2] += np.sign(X[np.abs(X) < 0.2])
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assert len(X[np.abs(X) < 0.2]) == 0
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self.assertGradientChecks(gc, op, [X], 0, [0])
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