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133 lines
4.7 KiB
133 lines
4.7 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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class TestElementwiseOp(OpTest):
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def setUp(self):
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self.op_type = "elementwise_max"
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# If x and y have the same value, the max() is not differentiable.
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# So we generate test data by the following method
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# to avoid them being too close to each other.
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x = np.random.uniform(0.1, 1, [13, 17]).astype("float32")
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sgn = np.random.choice([-1, 1], [13, 17]).astype("float32")
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y = x + sgn * np.random.uniform(0.1, 1, [13, 17]).astype("float32")
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.005)
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def test_check_grad_ingore_x(self):
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self.check_grad(
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['Y'], 'Out', max_relative_error=0.005, no_grad_set=set("X"))
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def test_check_grad_ingore_y(self):
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self.check_grad(
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['X'], 'Out', max_relative_error=0.005, no_grad_set=set('Y'))
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class TestElementwiseMaxOp_scalar(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_max"
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x = np.random.random_integers(-5, 5, [2, 3, 4]).astype("float32")
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y = np.array([0.5]).astype("float32")
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}
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class TestElementwiseMaxOp_Vector(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_max"
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x = np.random.random((32, )).astype("float32")
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sgn = np.random.choice([-1, 1], (32, )).astype("float32")
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y = x + sgn * np.random.uniform(0.1, 1, (32, )).astype("float32")
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])}
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class TestElementwiseMaxOp_broadcast_0(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_max"
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x = np.random.uniform(0.5, 1, (2, 3, 4)).astype(np.float32)
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sgn = np.random.choice([-1, 1], (2, )).astype(np.float32)
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y = x[:, 0, 0] + sgn * \
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np.random.uniform(1, 2, (2, )).astype(np.float32)
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self.inputs = {'X': x, 'Y': y}
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self.attrs = {'axis': 0}
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self.outputs = {
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'Out':
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np.maximum(self.inputs['X'], self.inputs['Y'].reshape(2, 1, 1))
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}
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class TestElementwiseMaxOp_broadcast_1(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_max"
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x = np.random.uniform(0.5, 1, (2, 3, 4)).astype(np.float32)
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sgn = np.random.choice([-1, 1], (3, )).astype(np.float32)
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y = x[0, :, 0] + sgn * \
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np.random.uniform(1, 2, (3, )).astype(np.float32)
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self.inputs = {'X': x, 'Y': y}
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self.attrs = {'axis': 1}
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self.outputs = {
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'Out':
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np.maximum(self.inputs['X'], self.inputs['Y'].reshape(1, 3, 1))
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}
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class TestElementwiseMaxOp_broadcast_2(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_max"
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x = np.random.uniform(0.5, 1, (2, 3, 4)).astype(np.float32)
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sgn = np.random.choice([-1, 1], (4, )).astype(np.float32)
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y = x[0, 0, :] + sgn * \
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np.random.uniform(1, 2, (4, )).astype(np.float32)
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {
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'Out':
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np.maximum(self.inputs['X'], self.inputs['Y'].reshape(1, 1, 4))
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}
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class TestElementwiseMaxOp_broadcast_3(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_max"
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x = np.random.uniform(0.5, 1, (2, 3, 4, 5)).astype(np.float32)
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sgn = np.random.choice([-1, 1], (3, 4)).astype(np.float32)
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y = x[0, :, :, 0] + sgn * \
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np.random.uniform(1, 2, (3, 4)).astype(np.float32)
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self.inputs = {'X': x, 'Y': y}
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self.attrs = {'axis': 1}
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self.outputs = {
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'Out':
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np.maximum(self.inputs['X'], self.inputs['Y'].reshape(1, 3, 4, 1))
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}
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if __name__ == '__main__':
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unittest.main()
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