mirror of
https://github.com/saymrwulf/pytorch.git
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560e5c94bd
Summary: To match the semantic in ONNX, change the default value of alpha of LeakyRelu to 0.01 Reviewed By: dzhulgakov Differential Revision: D6840975 fbshipit-source-id: 08543f80fd86cbe96a0eee8d725ef137a5bf4ab8
146 lines
4.9 KiB
Python
146 lines
4.9 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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import numpy as np
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from hypothesis import given
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import hypothesis.strategies as st
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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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class TestActivations(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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inplace=st.booleans(),
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**hu.gcs)
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def test_elu(self, X, alpha, inplace, gc, dc):
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# go away from the origin point to avoid kink problems
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X += 0.04 * np.sign(X)
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X[X == 0.0] += 0.04
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def elu_ref(X):
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Y = X.copy()
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neg_indices = X <= 0
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Y[neg_indices] = alpha * (np.exp(Y[neg_indices]) - 1)
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return (Y,)
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op = core.CreateOperator(
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"Elu",
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["X"], ["Y" if not inplace else "X"],
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alpha=alpha)
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self.assertReferenceChecks(gc, op, [X], elu_ref)
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# Check over multiple devices
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self.assertDeviceChecks(dc, op, [X], [0])
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# Gradient check wrt X
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self.assertGradientChecks(gc, op, [X], 0, [0])
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@given(X=hu.tensor(min_dim=4, max_dim=4),
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alpha=st.floats(min_value=0.1, max_value=2.0),
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inplace=st.booleans(),
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shared=st.booleans(),
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order=st.sampled_from(["NCHW", "NHWC"]),
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seed=st.sampled_from([20, 100]),
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**hu.gcs)
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def test_prelu(self, X, alpha, inplace, shared, order, seed, gc, dc):
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np.random.seed(seed)
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W = np.random.randn(
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X.shape[1] if order == "NCHW" else X.shape[3]).astype(np.float32)
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if shared:
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W = np.random.randn(1).astype(np.float32)
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# go away from the origin point to avoid kink problems
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X += 0.04 * np.sign(X)
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X[X == 0.0] += 0.04
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def prelu_ref(X, W):
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Y = X.copy()
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W = W.reshape(1, -1, 1, 1) if order == "NCHW" \
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else W.reshape(1, 1, 1, -1)
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assert len(X.shape) == 4
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neg_indices = X <= 0
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assert len(neg_indices.shape) == 4
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assert X.shape == neg_indices.shape
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Y[neg_indices] = (Y * W)[neg_indices]
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return (Y,)
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op = core.CreateOperator(
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"PRelu", ["X", "W"], ["Y" if not inplace else "X"],
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alpha=alpha, order=order)
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self.assertReferenceChecks(gc, op, [X, W], prelu_ref)
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# Check over multiple devices
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self.assertDeviceChecks(dc, op, [X, W], [0])
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if not inplace:
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# Gradient check wrt X
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self.assertGradientChecks(gc, op, [X, W], 0, [0], stepsize=1e-2)
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# Gradient check wrt W
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self.assertGradientChecks(gc, op, [X, W], 1, [0], stepsize=1e-2)
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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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inplace=st.booleans(),
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**hu.gcs)
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def test_leaky_relu(self, X, alpha, inplace, gc, dc):
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# go away from the origin point to avoid kink problems
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X += 0.04 * np.sign(X)
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X[X == 0.0] += 0.04
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def leaky_relu_ref(X):
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Y = X.copy()
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neg_indices = X <= 0
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Y[neg_indices] = Y[neg_indices] * alpha
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return (Y,)
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op = core.CreateOperator(
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"LeakyRelu",
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["X"], ["Y" if not inplace else "X"],
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alpha=alpha)
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self.assertReferenceChecks(gc, op, [X], leaky_relu_ref)
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# Check over multiple devices
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self.assertDeviceChecks(dc, op, [X], [0])
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@given(X=hu.tensor(),
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inplace=st.booleans(),
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**hu.gcs)
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def test_leaky_relu_default(self, X, inplace, gc, dc):
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# go away from the origin point to avoid kink problems
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X += 0.04 * np.sign(X)
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X[X == 0.0] += 0.04
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def leaky_relu_ref(X):
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Y = X.copy()
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neg_indices = X <= 0
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Y[neg_indices] = Y[neg_indices] * 0.01
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return (Y,)
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op = core.CreateOperator(
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"LeakyRelu",
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["X"], ["Y" if not inplace else "X"])
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self.assertReferenceChecks(gc, op, [X], leaky_relu_ref)
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# Check over multiple devices
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self.assertDeviceChecks(dc, op, [X], [0])
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if __name__ == "__main__":
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import unittest
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unittest.main()
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