# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import numpy as np from op_test import OpTest class TestElementwiseOp(OpTest): def setUp(self): self.op_type = "elementwise_sub" self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 3]).astype("float32"), 'Y': np.random.uniform(0.1, 1, [2, 3]).astype("float32") } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} def test_check_output(self): self.check_output() def test_check_grad_normal(self): self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.005) def test_check_grad_ingore_x(self): self.check_grad( ['Y'], 'Out', max_relative_error=0.005, no_grad_set=set("X")) def test_check_grad_ingore_y(self): self.check_grad( ['X'], 'Out', max_relative_error=0.005, no_grad_set=set('Y')) class TestElementwiseSubOp_scalar(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.inputs = { 'X': np.random.rand(2, 3, 4).astype(np.float32), 'Y': np.random.rand(1).astype(np.float32) } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} class TestElementwiseSubOp_Vector(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.inputs = { 'X': np.random.random((32, )).astype("float32"), 'Y': np.random.random((32, )).astype("float32") } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} class TestElementwiseSubOp_broadcast_0(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.inputs = { 'X': np.random.rand(2, 3, 4).astype(np.float32), 'Y': np.random.rand(2).astype(np.float32) } self.attrs = {'axis': 0} self.outputs = { 'Out': self.inputs['X'] - self.inputs['Y'].reshape(2, 1, 1) } class TestElementwiseSubOp_broadcast_1(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.inputs = { 'X': np.random.rand(2, 3, 4).astype(np.float32), 'Y': np.random.rand(3).astype(np.float32) } self.attrs = {'axis': 1} self.outputs = { 'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 3, 1) } class TestElementwiseSubOp_broadcast_2(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.inputs = { 'X': np.random.rand(2, 3, 4).astype(np.float32), 'Y': np.random.rand(4).astype(np.float32) } self.outputs = { 'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 1, 4) } class TestElementwiseSubOp_broadcast_3(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.inputs = { 'X': np.random.rand(2, 3, 4, 5).astype(np.float32), 'Y': np.random.rand(3, 4).astype(np.float32) } self.attrs = {'axis': 1} self.outputs = { 'Out': self.inputs['X'] - self.inputs['Y'].reshape(1, 3, 4, 1) } class TestElementwiseSubOp_broadcast_4(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_sub" self.inputs = { 'X': np.random.rand(2, 3, 4, 5).astype(np.float32), 'Y': np.random.rand(2, 3, 1, 5).astype(np.float32) } self.outputs = {'Out': self.inputs['X'] - self.inputs['Y']} if __name__ == '__main__': unittest.main()