# 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 def huber_loss_forward(val, delta): abs_val = abs(val) if abs_val <= delta: return 0.5 * val * val else: return delta * (abs_val - 0.5 * delta) class TestHuberLossOp(OpTest): def setUp(self): self.op_type = 'huber_loss' samples_num = 64 delta = 1.0 self.inputs = { 'X': np.random.uniform(0, 1., (samples_num, 1)).astype('float32'), 'Y': np.random.uniform(0, 1., (samples_num, 1)).astype('float32'), } residual = self.inputs['Y'] - self.inputs['X'] loss = np.vectorize(huber_loss_forward)(residual, delta).astype('float32') self.attrs = {'delta': delta} self.outputs = { 'Residual': residual, 'Out': loss.reshape((samples_num, 1)) } def test_check_output(self): self.check_output() def test_check_grad_normal(self): self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.008) def test_check_grad_ingore_x(self): self.check_grad( ['Y'], 'Out', max_relative_error=0.008, no_grad_set=set("residual")) def test_check_grad_ingore_y(self): self.check_grad( ['X'], 'Out', max_relative_error=0.008, no_grad_set=set('residual')) if __name__ == '__main__': unittest.main()