# 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 division import unittest import numpy as np from op_test import OpTest def kldiv_loss(x, target, reduction): output = target * (np.log(target) - x) loss = np.where(target >= 0, output, np.zeros_like(x)) if reduction == "batchmean": return loss.sum() / x.shape[0] if reduction == "mean": return loss.mean() if reduction == "sum": return loss.sum() return loss class TestKLDivLossOp(OpTest): def setUp(self): self.initTestCase() self.op_type = 'kldiv_loss' x = np.random.uniform(-10, 10, self.x_shape).astype('float32') target = np.random.uniform(-10, 10, self.x_shape).astype('float32') self.attrs = {"reduction": self.reduction} self.inputs = { 'X': x, 'Target': target, } loss = kldiv_loss(x, target, self.reduction) self.outputs = {'Loss': loss.astype('float32')} def test_check_output(self): self.check_output() def test_check_grad(self): self.check_grad( ['X'], 'Loss', no_grad_set=set(["Target"]), max_relative_error=0.06) def initTestCase(self): self.x_shape = (4, 5, 5) self.reduction = 'batchmean' class TestKLDivLossOp2(TestKLDivLossOp): def initTestCase(self): self.x_shape = (3, 2, 7, 7) self.reduction = 'none' class TestKLDivLossOp3(TestKLDivLossOp): def initTestCase(self): self.x_shape = (2, 3, 5, 7, 9) self.reduction = 'mean' class TestKLDivLossOp4(TestKLDivLossOp): def initTestCase(self): self.x_shape = (5, 7) self.reduction = 'sum' if __name__ == "__main__": unittest.main()