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120 lines
3.7 KiB
120 lines
3.7 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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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_sub"
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self.inputs = {
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'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"),
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'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32")
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}
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self.outputs = {'Out': 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 TestElementwiseSubOp_scalar(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.inputs = {
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'X': np.random.rand(2, 3, 4).astype(np.float32),
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'Y': np.random.rand(1).astype(np.float32)
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}
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self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
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class TestElementwiseSubOp_Vector(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.inputs = {
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'X': np.random.random((32, )).astype("float32"),
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'Y': np.random.random((32, )).astype("float32")
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}
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self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']}
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class TestElementwiseSubOp_broadcast_0(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.inputs = {
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'X': np.random.rand(2, 3, 4).astype(np.float32),
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'Y': np.random.rand(2).astype(np.float32)
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}
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self.attrs = {'axis': 0}
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self.outputs = {
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'Out': self.inputs['X'] - self.inputs['Y'].reshape(2, 1, 1)
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}
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class TestElementwiseSubOp_broadcast_1(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.inputs = {
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'X': np.random.rand(2, 3, 4).astype(np.float32),
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'Y': np.random.rand(3).astype(np.float32)
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}
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self.attrs = {'axis': 1}
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self.outputs = {
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'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 3, 1)
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}
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class TestElementwiseSubOp_broadcast_2(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.inputs = {
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'X': np.random.rand(2, 3, 4).astype(np.float32),
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'Y': np.random.rand(4).astype(np.float32)
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}
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self.outputs = {
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'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 1, 4)
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}
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class TestElementwiseSubOp_broadcast_3(TestElementwiseOp):
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def setUp(self):
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self.op_type = "elementwise_sub"
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self.inputs = {
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'X': np.random.rand(2, 3, 4, 5).astype(np.float32),
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'Y': np.random.rand(3, 4).astype(np.float32)
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}
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self.attrs = {'axis': 1}
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self.outputs = {
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'Out': 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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