# 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, skip_check_grad_ci, convert_float_to_uint16 import os import re import paddle.fluid.core as core class TestElementwiseOp(OpTest): def setUp(self): self.op_type = "elementwise_max" # If x and y have the same value, the max() is not differentiable. # So we generate test data by the following method # to avoid them being too close to each other. x = np.random.uniform(0.1, 1, [13, 17]).astype("float64") sgn = np.random.choice([-1, 1], [13, 17]).astype("float64") y = x + sgn * np.random.uniform(0.1, 1, [13, 17]).astype("float64") self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': np.maximum(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') 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')) @unittest.skipIf( core.is_compiled_with_cuda() and core.cudnn_version() < 8100, "run test when gpu is availble and the minimum cudnn version is 8.1.0.") class TestElementwiseBF16Op(OpTest): def setUp(self): self.op_type = "elementwise_max" self.dtype = np.uint16 # If x and y have the same value, the max() is not differentiable. # So we generate test data by the following method # to avoid them being too close to each other. x = np.random.uniform(0.1, 1, [13, 17]).astype(np.float32) sgn = np.random.choice([-1, 1], [13, 17]).astype(np.float32) y = x + sgn * np.random.uniform(0.1, 1, [13, 17]).astype(np.float32) self.inputs = { 'X': convert_float_to_uint16(x), 'Y': convert_float_to_uint16(y) } self.outputs = {'Out': convert_float_to_uint16(np.maximum(x, y))} def test_check_output(self): self.check_output() def test_check_grad_normal(self): self.check_grad(['X', 'Y'], 'Out') def test_check_grad_ingore_x(self): self.check_grad(['Y'], 'Out', no_grad_set=set("X")) def test_check_grad_ingore_y(self): self.check_grad(['X'], 'Out', no_grad_set=set('Y')) @skip_check_grad_ci( reason="[skip shape check] Use y_shape(1) to test broadcast.") class TestElementwiseMaxOp_scalar(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_max" x = np.random.random_integers(-5, 5, [2, 3, 20]).astype("float64") y = np.array([0.5]).astype("float64") self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])} class TestElementwiseMaxOp_Vector(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_max" x = np.random.random((100, )).astype("float64") sgn = np.random.choice([-1, 1], (100, )).astype("float64") y = x + sgn * np.random.uniform(0.1, 1, (100, )).astype("float64") self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])} class TestElementwiseMaxOp_broadcast_0(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_max" x = np.random.uniform(0.5, 1, (100, 5, 2)).astype(np.float64) sgn = np.random.choice([-1, 1], (100, )).astype(np.float64) y = x[:, 0, 0] + sgn * \ np.random.uniform(1, 2, (100, )).astype(np.float64) self.inputs = {'X': x, 'Y': y} self.attrs = {'axis': 0} self.outputs = { 'Out': np.maximum(self.inputs['X'], self.inputs['Y'].reshape(100, 1, 1)) } class TestElementwiseMaxOp_broadcast_1(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_max" x = np.random.uniform(0.5, 1, (2, 100, 3)).astype(np.float64) sgn = np.random.choice([-1, 1], (100, )).astype(np.float64) y = x[0, :, 0] + sgn * \ np.random.uniform(1, 2, (100, )).astype(np.float64) self.inputs = {'X': x, 'Y': y} self.attrs = {'axis': 1} self.outputs = { 'Out': np.maximum(self.inputs['X'], self.inputs['Y'].reshape(1, 100, 1)) } class TestElementwiseMaxOp_broadcast_2(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_max" x = np.random.uniform(0.5, 1, (1, 3, 100)).astype(np.float64) sgn = np.random.choice([-1, 1], (100, )).astype(np.float64) y = x[0, 0, :] + sgn * \ np.random.uniform(1, 2, (100, )).astype(np.float64) self.inputs = {'X': x, 'Y': y} self.outputs = { 'Out': np.maximum(self.inputs['X'], self.inputs['Y'].reshape(1, 1, 100)) } class TestElementwiseMaxOp_broadcast_3(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_max" x = np.random.uniform(0.5, 1, (2, 50, 2, 1)).astype(np.float64) sgn = np.random.choice([-1, 1], (50, 2)).astype(np.float64) y = x[0, :, :, 0] + sgn * \ np.random.uniform(1, 2, (50, 2)).astype(np.float64) self.inputs = {'X': x, 'Y': y} self.attrs = {'axis': 1} self.outputs = { 'Out': np.maximum(self.inputs['X'], self.inputs['Y'].reshape(1, 50, 2, 1)) } class TestElementwiseMaxOp_broadcast_4(TestElementwiseOp): def setUp(self): self.op_type = "elementwise_max" x = np.random.uniform(0.5, 1, (2, 3, 4, 5)).astype(np.float64) sgn = np.random.choice([-1, 1], (2, 3, 1, 5)).astype(np.float64) y = x + sgn * \ np.random.uniform(1, 2, (2, 3, 1, 5)).astype(np.float64) self.inputs = {'X': x, 'Y': y} self.outputs = {'Out': np.maximum(self.inputs['X'], self.inputs['Y'])} if __name__ == '__main__': unittest.main()