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test_opencl_pooling.cc
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test_opencl_pooling.cc
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/************************************************************
*
* 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/pooling.h"
#include "../src/model/layer/opencl_pooling.h"
#include "gtest/gtest.h"
#ifdef USE_OPENCL
using singa::OpenclDevice;
using singa::OpenclPooling;
using singa::Shape;
TEST(OpenclPooling, Setup) {
OpenclPooling pool;
EXPECT_EQ("OpenclPooling", pool.layer_type());
singa::LayerConf conf;
singa::PoolingConf *poolconf = conf.mutable_pooling_conf();
poolconf->set_pool(singa::PoolingConf_PoolMethod_MAX);
poolconf->set_kernel_h(1);
poolconf->set_kernel_w(2);
poolconf->set_pad_h(1);
poolconf->set_pad_w(0);
poolconf->set_stride_h(2);
poolconf->set_stride_w(1);
pool.Setup(Shape{1, 3, 3}, conf);
EXPECT_EQ(singa::PoolingConf_PoolMethod_MAX, pool.pool_method());
EXPECT_EQ(1u, pool.kernel_h());
EXPECT_EQ(2u, pool.kernel_w());
EXPECT_EQ(1u, pool.pad_h());
EXPECT_EQ(0u, pool.pad_w());
EXPECT_EQ(2u, pool.stride_h());
EXPECT_EQ(1u, pool.stride_w());
EXPECT_EQ(1u, pool.channels());
EXPECT_EQ(3u, pool.height());
EXPECT_EQ(3u, pool.width());
}
TEST(OpenclPooling, Forward) {
const size_t batchsize = 2, c = 1, h = 3, w = 3;
const float x[batchsize * c * h * w] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f,
7.0f, 8.0f, 9.0f, 1.0f, 2.0f, 3.0f,
4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f};
auto ocl = std::make_shared<OpenclDevice>();
singa::Tensor in(singa::Shape{batchsize, c, h, w}, ocl);
in.CopyDataFromHostPtr(x, batchsize * c * h * w);
OpenclPooling pool;
singa::LayerConf conf;
singa::PoolingConf *poolconf = conf.mutable_pooling_conf();
poolconf->set_pool(singa::PoolingConf_PoolMethod_MAX);
poolconf->set_kernel_h(2);
poolconf->set_kernel_w(2);
poolconf->set_pad_h(0);
poolconf->set_pad_w(0);
poolconf->set_stride_h(1);
poolconf->set_stride_w(1);
pool.Setup(Shape{1, 3, 3}, conf);
// Parameter "flag" does not influence pooling
singa::Tensor out1 = pool.Forward(singa::kTrain, in);
out1.ToHost();
const float *outptr1 = out1.data<float>();
// Input: 3*3; kernel: 2*2; stride: 1*1; no padding.
EXPECT_EQ(8u, out1.Size());
EXPECT_EQ(5.0f, outptr1[0]);
EXPECT_EQ(6.0f, outptr1[1]);
EXPECT_EQ(8.0f, outptr1[2]);
EXPECT_EQ(9.0f, outptr1[3]);
EXPECT_EQ(5.0f, outptr1[4]);
EXPECT_EQ(6.0f, outptr1[5]);
EXPECT_EQ(8.0f, outptr1[6]);
EXPECT_EQ(9.0f, outptr1[7]);
}
TEST(OpenclPooling, Backward) {
// src_data
const size_t batchsize = 2, c = 1, src_h = 3, src_w = 3;
const float x[batchsize * c * src_h * src_w] = {
1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f,
1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f};
auto ocl = std::make_shared<OpenclDevice>();
singa::Tensor in(singa::Shape{batchsize, c, src_h, src_w}, ocl);
in.CopyDataFromHostPtr(x, batchsize * c * src_h * src_w);
OpenclPooling pool;
singa::LayerConf conf;
singa::PoolingConf *poolconf = conf.mutable_pooling_conf();
poolconf->set_pool(singa::PoolingConf_PoolMethod_MAX);
poolconf->set_kernel_h(2);
poolconf->set_kernel_w(2);
poolconf->set_pad_h(0);
poolconf->set_pad_w(0);
poolconf->set_stride_h(1);
poolconf->set_stride_w(1);
pool.Setup(Shape{1, 3, 3}, conf);
singa::Tensor out1 = pool.Forward(singa::kTrain, in);
// grad
const size_t grad_h = 2, grad_w = 2;
const float dy[batchsize * c * grad_h * grad_w] = {0.1f, 0.2f, 0.3f, 0.4f,
0.1f, 0.2f, 0.3f, 0.4f};
singa::Tensor grad(singa::Shape{batchsize, c, grad_h, grad_w}, ocl);
grad.CopyDataFromHostPtr(dy, batchsize * c * grad_h * grad_w);
const auto ret = pool.Backward(singa::kTrain, grad);
singa::Tensor in_grad = ret.first;
in_grad.ToHost();
const float *dx = in_grad.data<float>();
EXPECT_EQ(18u, in_grad.Size());
EXPECT_EQ(0.0f, dx[0]);
EXPECT_EQ(0.0f, dx[1]);
EXPECT_EQ(0.0f, dx[2]);
EXPECT_EQ(0.0f, dx[3]);
EXPECT_EQ(0.1f, dx[4]);
EXPECT_EQ(0.2f, dx[5]);
EXPECT_EQ(0.0f, dx[6]);
EXPECT_EQ(0.3f, dx[7]);
EXPECT_EQ(0.4f, dx[8]);
EXPECT_EQ(0.0f, dx[9]);
EXPECT_EQ(0.0f, dx[10]);
EXPECT_EQ(0.0f, dx[11]);
EXPECT_EQ(0.0f, dx[12]);
EXPECT_EQ(0.1f, dx[13]);
EXPECT_EQ(0.2f, dx[14]);
EXPECT_EQ(0.0f, dx[15]);
EXPECT_EQ(0.3f, dx[16]);
EXPECT_EQ(0.4f, dx[17]);
}
#endif // USE_OPENCL