| /********************************************************* |
| * |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you 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. |
| * |
| ************************************************************/ |
| |
| #include "../src/model/layer/batchnorm.h" |
| #include "gtest/gtest.h" |
| #include <iostream> |
| |
| using namespace singa; |
| |
| TEST(BatchNorm, Setup) { |
| BatchNorm batchnorm; |
| // EXPECT_EQ("BatchNorm", batchnorm.layer_type()); |
| |
| singa::LayerConf conf; |
| singa::BatchNormConf *batchnorm_conf = conf.mutable_batchnorm_conf(); |
| batchnorm_conf->set_factor(0.01); |
| batchnorm.Setup(Shape{2, 4, 4}, conf); |
| |
| EXPECT_FLOAT_EQ(0.01f, batchnorm.factor()); |
| EXPECT_EQ(2u, batchnorm.channels()); |
| EXPECT_EQ(4u, batchnorm.height()); |
| EXPECT_EQ(4u, batchnorm.width()); |
| } |
| |
| TEST(BatchNorm, Forward) { |
| BatchNorm batchnorm; |
| const float x[] = {1, 2, 3, 4}; |
| Tensor in(Shape{2, 2}); |
| in.CopyDataFromHostPtr(x, 2 * 2); |
| const float alpha_[] = {1, 1}; |
| Tensor alpha(Shape{2}); |
| alpha.CopyDataFromHostPtr(alpha_, 2); |
| |
| const float beta_[] = {2, 2}; |
| Tensor beta(Shape{2}); |
| beta.CopyDataFromHostPtr(beta_, 2); |
| singa::LayerConf conf; |
| singa::BatchNormConf *batchnorm_conf = conf.mutable_batchnorm_conf(); |
| batchnorm_conf->set_factor(1); |
| batchnorm.Setup(Shape{2}, conf); |
| batchnorm.set_bnScale(alpha); |
| batchnorm.set_bnBias(beta); |
| batchnorm.set_runningMean(beta); |
| batchnorm.set_runningVariance(beta); |
| Tensor out = batchnorm.Forward(kTrain, in); |
| const float *outptr = out.data<float>(); |
| const auto &shape = out.shape(); |
| EXPECT_EQ(2u, shape.size()); |
| EXPECT_EQ(2u, shape[0]); |
| EXPECT_EQ(2u, shape[1]); |
| EXPECT_NEAR(1.0f, outptr[0], 1e-4f); |
| EXPECT_NEAR(1.0f, outptr[1], 1e-4f); |
| EXPECT_NEAR(3.0f, outptr[2], 1e-4f); |
| EXPECT_NEAR(3.0f, outptr[3], 1e-4f); |
| } |
| |
| TEST(BatchNorm, Backward) { |
| BatchNorm batchnorm; |
| const float x[] = {1, 2, 3, 4}; |
| Tensor in(Shape{2, 2}); |
| in.CopyDataFromHostPtr(x, 2 * 2); |
| const float dy[] = {4, 3, 2, 1}; |
| Tensor dy_in(Shape{2, 2}); |
| dy_in.CopyDataFromHostPtr(dy, 2 * 2); |
| const float alpha_[] = {1, 1}; |
| Tensor alpha(Shape{2}); |
| alpha.CopyDataFromHostPtr(alpha_, 2); |
| |
| const float beta_[] = {0, 0}; |
| Tensor beta(Shape{2}); |
| beta.CopyDataFromHostPtr(beta_, 2); |
| singa::LayerConf conf; |
| singa::BatchNormConf *batchnorm_conf = conf.mutable_batchnorm_conf(); |
| batchnorm_conf->set_factor(1); |
| batchnorm.Setup(Shape{2}, conf); |
| batchnorm.set_bnScale(alpha); |
| batchnorm.set_bnBias(beta); |
| batchnorm.set_runningMean(beta); |
| batchnorm.set_runningVariance(beta); |
| Tensor out = batchnorm.Forward(kTrain, in); |
| auto ret = batchnorm.Backward(kTrain, dy_in); |
| Tensor dx = ret.first; |
| const auto & shape = dx.shape(); |
| EXPECT_EQ(2u, shape.size()); |
| EXPECT_EQ(2u, shape[0]); |
| EXPECT_EQ(2u, shape[1]); |
| const float *dxptr = ret.first.data<float>(); |
| EXPECT_NEAR(.0f, dxptr[0], 1e-4f); |
| EXPECT_NEAR(.0f, dxptr[1], 1e-4f); |
| EXPECT_NEAR(.0f, dxptr[2], 1e-4f); |
| EXPECT_NEAR(.0f, dxptr[3], 1e-4f); |
| |
| Tensor dbnScale = ret.second.at(0); |
| const float *dbnScaleptr = dbnScale.data<float>(); |
| const auto & dbnScaleShape = dbnScale.shape(); |
| EXPECT_EQ(1u, dbnScaleShape.size()); |
| EXPECT_EQ(2u, dbnScaleShape[0]); |
| |
| EXPECT_NEAR(-2.0f, dbnScaleptr[0], 1e-4f); |
| EXPECT_NEAR(-2.0f, dbnScaleptr[1], 1e-4f); |
| |
| Tensor dbnBias = ret.second.at(1); |
| const float *dbnBiasptr = dbnBias.data<float>(); |
| const auto & dbnBiasShape = dbnBias.shape(); |
| EXPECT_EQ(1u, dbnBiasShape.size()); |
| EXPECT_EQ(2u, dbnBiasShape[0]); |
| |
| EXPECT_NEAR(6.0f, dbnBiasptr[0], 1e-4f); |
| EXPECT_NEAR(4.0f, dbnBiasptr[1], 1e-4f); |
| } |