# 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 ElementwiseDivOp(OpTest): def setUp(self): self.op_type = "elementwise_div" self.dtype = np.float32 self.init_dtype() """ Warning CPU gradient check error! 'X': np.random.random((32,84)).astype("float32"), 'Y': np.random.random((32,84)).astype("float32") """ self.inputs = { 'X': np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype), 'Y': np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype) } self.outputs = {'Out': np.divide(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=0.05) def test_check_grad_ingore_x(self): self.check_grad( ['Y'], 'Out', max_relative_error=0.05, no_grad_set=set("X")) def test_check_grad_ingore_y(self): self.check_grad( ['X'], 'Out', max_relative_error=0.05, no_grad_set=set('Y')) def init_dtype(self): pass class TestElementwiseDivOp_scalar(ElementwiseDivOp): def setUp(self): self.op_type = "elementwise_div" self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 3, 4]).astype(np.float32), 'Y': np.random.uniform(0.1, 1, [1]).astype(np.float32) } self.outputs = {'Out': self.inputs['X'] / self.inputs['Y']} class TestElementwiseDivOp_Vector(ElementwiseDivOp): def setUp(self): self.op_type = "elementwise_div" self.inputs = { 'X': np.random.uniform(0.1, 1, [32]).astype("float32"), 'Y': np.random.uniform(0.1, 1, [32]).astype("float32") } self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])} class TestElementwiseDivOp_broadcast_0(ElementwiseDivOp): def setUp(self): self.op_type = "elementwise_div" self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 3, 4]).astype("float32"), 'Y': np.random.uniform(0.1, 1, [2]).astype("float32") } self.attrs = {'axis': 0} self.outputs = { 'Out': np.divide(self.inputs['X'], self.inputs['Y'].reshape(2, 1, 1)) } class TestElementwiseDivOp_broadcast_1(ElementwiseDivOp): def setUp(self): self.op_type = "elementwise_div" self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 3, 4]).astype("float32"), 'Y': np.random.uniform(0.1, 1, [3]).astype("float32") } self.attrs = {'axis': 1} self.outputs = { 'Out': np.divide(self.inputs['X'], self.inputs['Y'].reshape(1, 3, 1)) } class TestElementwiseDivOp_broadcast_2(ElementwiseDivOp): def setUp(self): self.op_type = "elementwise_div" self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 3, 4]).astype("float32"), 'Y': np.random.uniform(0.1, 1, [4]).astype("float32") } self.outputs = { 'Out': np.divide(self.inputs['X'], self.inputs['Y'].reshape(1, 1, 4)) } class TestElementwiseDivOp_broadcast_3(ElementwiseDivOp): def setUp(self): self.op_type = "elementwise_div" self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype("float32"), 'Y': np.random.uniform(0.1, 1, [3, 4]).astype("float32") } self.attrs = {'axis': 1} self.outputs = { 'Out': np.divide(self.inputs['X'], self.inputs['Y'].reshape(1, 3, 4, 1)) } class TestElementwiseDivOp_broadcast_4(ElementwiseDivOp): def setUp(self): self.op_type = "elementwise_div" self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 3, 4]).astype("float32"), 'Y': np.random.uniform(0.1, 1, [2, 1, 4]).astype("float32") } self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])} class TestElementwiseDivOp_broadcast_5(ElementwiseDivOp): def setUp(self): self.op_type = "elementwise_div" self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype("float32"), 'Y': np.random.uniform(0.1, 1, [2, 3, 1, 5]).astype("float32") } self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])} class TestElementwiseDivOp_commonuse_1(ElementwiseDivOp): def setUp(self): self.op_type = "elementwise_div" self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 3, 4]).astype("float32"), 'Y': np.random.uniform(0.1, 1, [1, 1, 4]).astype("float32"), } self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])} class TestElementwiseDivOp_commonuse_2(ElementwiseDivOp): def setUp(self): self.op_type = "elementwise_div" self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 3, 1, 5]).astype("float32"), 'Y': np.random.uniform(0.1, 1, [2, 1, 4, 1]).astype("float32"), } self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])} class TestElementwiseDivOp_xsize_lessthan_ysize(ElementwiseDivOp): def setUp(self): self.op_type = "elementwise_div" self.inputs = { 'X': np.random.uniform(0.1, 1, [4, 5]).astype("float32"), 'Y': np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype("float32"), } self.attrs = {'axis': 2} self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])} class TestElementwiseDivOp_INT(OpTest): def setUp(self): self.op_type = "elementwise_div" self.dtype = np.int32 self.init_dtype() self.inputs = { 'X': np.random.randint( 1, 5, size=[2, 3]).astype(self.dtype), 'Y': np.random.randint( 1, 5, size=[2, 3]).astype(self.dtype) } self.outputs = {'Out': self.inputs['X'] // self.inputs['Y']} def test_check_output(self): self.check_output() def init_dtype(self): pass class TestElementwiseDivOpFp16(ElementwiseDivOp): def init_dtype(self): self.dtype = np.float16 def test_check_grad_normal(self): self.check_grad(['X', 'Y'], 'Out', max_relative_error=1) def test_check_grad_ingore_x(self): self.check_grad( ['Y'], 'Out', max_relative_error=1, no_grad_set=set("X")) def test_check_grad_ingore_y(self): self.check_grad( ['X'], 'Out', max_relative_error=1, no_grad_set=set('Y')) if __name__ == '__main__': unittest.main()