# 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 import paddle import paddle.fluid as fluid import paddle.fluid.core as core from op_test import OpTest, skip_check_grad_ci class ElementwiseDivOp(OpTest): def setUp(self): self.op_type = "elementwise_div" self.dtype = np.float64 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 @skip_check_grad_ci( reason="[skip shape check] Use y_shape(1) to test broadcast.") class TestElementwiseDivOp_scalar(ElementwiseDivOp): def setUp(self): self.op_type = "elementwise_div" self.inputs = { 'X': np.random.uniform(0.1, 1, [20, 3, 4]).astype(np.float64), 'Y': np.random.uniform(0.1, 1, [1]).astype(np.float64) } 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, [100]).astype("float64"), 'Y': np.random.uniform(0.1, 1, [100]).astype("float64") } 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, [100, 3, 4]).astype("float64"), 'Y': np.random.uniform(0.1, 1, [100]).astype("float64") } self.attrs = {'axis': 0} self.outputs = { 'Out': np.divide(self.inputs['X'], self.inputs['Y'].reshape(100, 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, 100, 4]).astype("float64"), 'Y': np.random.uniform(0.1, 1, [100]).astype("float64") } self.attrs = {'axis': 1} self.outputs = { 'Out': np.divide(self.inputs['X'], self.inputs['Y'].reshape(1, 100, 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, 100]).astype("float64"), 'Y': np.random.uniform(0.1, 1, [100]).astype("float64") } self.outputs = { 'Out': np.divide(self.inputs['X'], self.inputs['Y'].reshape(1, 1, 100)) } class TestElementwiseDivOp_broadcast_3(ElementwiseDivOp): def setUp(self): self.op_type = "elementwise_div" self.inputs = { 'X': np.random.uniform(0.1, 1, [2, 10, 12, 5]).astype("float64"), 'Y': np.random.uniform(0.1, 1, [10, 12]).astype("float64") } self.attrs = {'axis': 1} self.outputs = { 'Out': np.divide(self.inputs['X'], self.inputs['Y'].reshape(1, 10, 12, 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, 50]).astype("float64"), 'Y': np.random.uniform(0.1, 1, [2, 1, 50]).astype("float64") } 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, 20]).astype("float64"), 'Y': np.random.uniform(0.1, 1, [2, 3, 1, 20]).astype("float64") } 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, 100]).astype("float64"), 'Y': np.random.uniform(0.1, 1, [1, 1, 100]).astype("float64"), } 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, [30, 3, 1, 5]).astype("float64"), 'Y': np.random.uniform(0.1, 1, [30, 1, 4, 1]).astype("float64"), } 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, [10, 12]).astype("float64"), 'Y': np.random.uniform(0.1, 1, [2, 3, 10, 12]).astype("float64"), } 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=[13, 17]).astype(self.dtype), 'Y': np.random.randint( 1, 5, size=[13, 17]).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 @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") 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')) class TestElementwiseDivBroadcast(unittest.TestCase): def test_shape_with_batch_sizes(self): with fluid.program_guard(fluid.Program()): x_var = fluid.data( name='x', dtype='float32', shape=[None, 3, None, None]) one = 2. out = one / x_var exe = fluid.Executor(fluid.CPUPlace()) x = np.random.uniform(0.1, 0.6, (1, 3, 32, 32)).astype("float32") out_result, = exe.run(feed={'x': x}, fetch_list=[out]) self.assertEqual((out_result == (2 / x)).all(), True) class TestDivideOp(unittest.TestCase): def test_name(self): with fluid.program_guard(fluid.Program()): x = fluid.data(name="x", shape=[2, 3], dtype="float32") y = fluid.data(name='y', shape=[2, 3], dtype='float32') y_1 = paddle.divide(x, y, name='div_res') self.assertEqual(('div_res' in y_1.name), True) def test_dygraph(self): with fluid.dygraph.guard(): np_x = np.array([2, 3, 4]).astype('float64') np_y = np.array([1, 5, 2]).astype('float64') x = paddle.to_tensor(np_x) y = paddle.to_tensor(np_y) z = paddle.divide(x, y) np_z = z.numpy() z_expected = np.array([2., 0.6, 2.]) self.assertEqual((np_z == z_expected).all(), True) class TestComplexElementwiseDivOp(OpTest): def setUp(self): self.op_type = "elementwise_div" self.init_base_dtype() self.init_input_output() self.init_grad_input_output() self.inputs = { 'X': OpTest.np_dtype_to_fluid_dtype(self.x), 'Y': OpTest.np_dtype_to_fluid_dtype(self.y) } self.attrs = {'axis': -1, 'use_mkldnn': False} self.outputs = {'Out': self.out} def init_base_dtype(self): self.dtype = np.float64 def init_input_output(self): self.x = np.random.random( (2, 3, 4, 5)).astype(self.dtype) + 1J * np.random.random( (2, 3, 4, 5)).astype(self.dtype) self.y = np.random.random( (2, 3, 4, 5)).astype(self.dtype) + 1J * np.random.random( (2, 3, 4, 5)).astype(self.dtype) self.out = self.x / self.y def init_grad_input_output(self): self.grad_out = np.ones((2, 3, 4, 5), self.dtype) + 1J * np.ones( (2, 3, 4, 5), self.dtype) self.grad_x = self.grad_out / np.conj(self.y) self.grad_y = -self.grad_out * np.conj(self.x / self.y / self.y) def test_check_output(self): self.check_output() def test_check_grad_normal(self): self.check_grad( ['X', 'Y'], 'Out', user_defined_grads=[self.grad_x, self.grad_y], user_defined_grad_outputs=[self.grad_out]) def test_check_grad_ingore_x(self): self.check_grad( ['Y'], 'Out', no_grad_set=set("X"), user_defined_grads=[self.grad_y], user_defined_grad_outputs=[self.grad_out]) def test_check_grad_ingore_y(self): self.check_grad( ['X'], 'Out', no_grad_set=set('Y'), user_defined_grads=[self.grad_x], user_defined_grad_outputs=[self.grad_out]) class TestRealComplexElementwiseDivOp(TestComplexElementwiseDivOp): def init_input_output(self): self.x = np.random.random((2, 3, 4, 5)).astype(self.dtype) self.y = np.random.random( (2, 3, 4, 5)).astype(self.dtype) + 1J * np.random.random( (2, 3, 4, 5)).astype(self.dtype) self.out = self.x / self.y def init_grad_input_output(self): self.grad_out = np.ones((2, 3, 4, 5), self.dtype) + 1J * np.ones( (2, 3, 4, 5), self.dtype) self.grad_x = np.real(self.grad_out / np.conj(self.y)) self.grad_y = -self.grad_out * np.conj(self.x / self.y / self.y) if __name__ == '__main__': paddle.enable_static() unittest.main()