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提交 cdac60f6 编写于 作者: Q qijun
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add SpatialPyramidPoolLayer c++ support

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Showing with 562 addition and 182 deletion
paddle/cuda/include/hl_cnn.h
浏览文件 @ cdac60f6
......@@ -91,6 +91,7 @@ extern void hl_expand_feature2col(
* @param[in] paddingH padding height.
* @param[in] paddingW padding width.
* @param[out] tgtData output data.
* @param[in] tgtStride output data stride.
*
*/
extern void hl_maxpool_forward(
......@@ -100,7 +101,8 @@ extern void hl_maxpool_forward(
const int pooledH, const int pooledW,
const int sizeX, const int sizeY,
const int strideH, const int strideW,
const int paddingH, const int paddingW, real* tgtData);
const int paddingH, const int paddingW,
real* tgtData, const int tgtStride);
/**
* @brief Maximum pool backward.
......@@ -123,6 +125,7 @@ extern void hl_maxpool_forward(
* @param[in] paddingH padding height.
* @param[in] paddingW padding width.
* @param[out] targetGrad output grad.
* @param[in] outStride output grad data stride.
*
*/
extern void hl_maxpool_backward(
......@@ -135,7 +138,7 @@ extern void hl_maxpool_backward(
const int strideH, const int strideW,
const int paddingH, const int paddingW,
real scaleA, real scaleB,
real* targetGrad);
real* targetGrad, const int outStride);
/**
* @brief Averge pool forward.
......@@ -154,6 +157,7 @@ extern void hl_maxpool_backward(
* @param[in] paddingH padding height.
* @param[in] paddingW padding width.
* @param[out] tgtData output data.
* @param[in] tgtStride output data stride.
*
*/
extern void hl_avgpool_forward(
......@@ -163,7 +167,8 @@ extern void hl_avgpool_forward(
const int pooledH, const int pooledW,
const int sizeX, const int sizeY,
const int strideH, const int strideW,
const int paddingH, const int paddingW, real* tgtData);
const int paddingH, const int paddingW,
real* tgtData, const int tgtStride);
/**
* @brief Maximum pool backward.
......@@ -184,6 +189,7 @@ extern void hl_avgpool_forward(
* @param[in] scaleA scale.
* @param[in] scaleB scale.
* @param[out] backGrad output grad.
* @param[in] outStride output grad data stride.
*
*/
extern void hl_avgpool_backward(
......@@ -195,7 +201,7 @@ extern void hl_avgpool_backward(
const int strideH, const int strideW,
int paddingH, int paddingW,
real scaleA, real scaleB,
real* backGrad);
real* backGrad, const int outStride);
/**
* @brief Cross-map-respose normalize forward.
......
paddle/cuda/include/stub/hl_cnn_stub.h
浏览文件 @ cdac60f6
......@@ -44,7 +44,8 @@ inline void hl_maxpool_forward(
const int pooledH, const int pooledW,
const int sizeX, const int sizeY,
const int strideH, const int strideW,
const int paddingH, const int paddingW, real* tgtData) {}
const int paddingH, const int paddingW,
real* tgtData, const int tgtStride) {}
inline void hl_maxpool_backward(
const int frameCnt, const real* inputData,
......@@ -56,7 +57,7 @@ inline void hl_maxpool_backward(
const int strideH, const int strideW,
const int paddingH, const int paddingW,
real scaleA, real scaleB,
real* targetGrad) {}
real* targetGrad, const int outStride) {}
inline void hl_avgpool_forward(
const int frameCnt, const real* inputData,
......@@ -65,7 +66,8 @@ inline void hl_avgpool_forward(
const int pooledH, const int pooledW,
const int sizeX, const int sizeY,
const int strideH, const int strideW,
const int paddingH, const int paddingW, real* tgtData) {}
const int paddingH, const int paddingW,
real* tgtData, const int tgtStride) {}
inline void hl_avgpool_backward(
const int frameCnt, const real* outGrad,
......@@ -76,7 +78,7 @@ inline void hl_avgpool_backward(
const int strideH, const int strideW,
int paddingH, int paddingW,
real scaleA, real scaleB,
real* backGrad) {}
real* backGrad, const int outStride) {}
inline void hl_CMRNorm_forward(
size_t frameCnt, const real* in, real* scale, real* out,
......
paddle/cuda/src/hl_cuda_cnn.cu
浏览文件 @ cdac60f6
......@@ -152,7 +152,7 @@ __global__ void KeMaxPoolForward(const int nthreads, const real* inputData,
const int ksizeW, const int ksizeH,
const int strideH, const int strideW,
const int offsetH, const int offsetW,
real* tgtData) {
real* tgtData, const int tgtStride) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < nthreads) {
int pw = index % pooledW;
......@@ -173,7 +173,9 @@ __global__ void KeMaxPoolForward(const int nthreads, const real* inputData,
maxval = inputData[h * width + w];
}
}
tgtData[index] = maxval;
int tgtIndex = index % (pooledW * pooledH * channels) +
frameNum * tgtStride;
tgtData[tgtIndex] = maxval;
}
}
......@@ -184,7 +186,7 @@ void hl_maxpool_forward(const int frameCnt, const real* inputData,
const int sizeX, const int sizeY,
const int strideH, const int strideW,
const int paddingH, const int paddingW,
real* tgtData) {
real* tgtData, const int tgtStride) {
int num_kernels = pooledH * pooledW * channels * frameCnt;
int blocks = (num_kernels + 1024 - 1) / 1024;
......@@ -194,7 +196,7 @@ void hl_maxpool_forward(const int frameCnt, const real* inputData,
KeMaxPoolForward<<< grid, threads, 0, STREAM_DEFAULT >>>
(num_kernels, inputData, channels, height, width,
pooledH, pooledW, sizeX, sizeY, strideH, strideW,
paddingH, paddingW, tgtData);
paddingH, paddingW, tgtData, tgtStride);
CHECK_SYNC("hl_maxpool_forward failed");
}
......@@ -207,7 +209,7 @@ __global__ void KeMaxPoolBackward(const int nthreads, const real* inputData,
const int strideH, const int strideW,
const int padH, const int padW,
real scaleA, real scaleB,
real* targetGrad) {
real* targetGrad, const int outStride) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < nthreads) {
// find out the local index
......@@ -223,8 +225,8 @@ __global__ void KeMaxPoolBackward(const int nthreads, const real* inputData,
int pwend = offsetW >= 0 ? min(offsetW / strideW + 1, pooledW) : 0;
real gradient = 0;
real input = inputData[index];
outData += (frameNum * channels + offsetC) * pooledH * pooledW;
outGrad += (frameNum * channels + offsetC) * pooledH * pooledW;
outData += (frameNum * outStride + offsetC * pooledH * pooledW);
outGrad += (frameNum * outStride + offsetC * pooledH * pooledW);
for (int ph = phstart; ph < phend; ++ph) {
for (int pw = pwstart; pw < pwend; ++pw) {
if (input == outData[ph * pooledW + pw]) {
......@@ -246,7 +248,7 @@ void hl_maxpool_backward(const int frameCnt, const real* inputData,
const int strideH, const int strideW,
const int paddingH, const int paddingW,
real scaleA, real scaleB,
real* targetGrad) {
real* targetGrad, const int outStride) {
int num_kernels = height * width * channels * frameCnt;
int blocks = (num_kernels + 1024 - 1) / 1024;
......@@ -257,7 +259,7 @@ void hl_maxpool_backward(const int frameCnt, const real* inputData,
strideH, strideW,
paddingH, paddingW,
scaleA, scaleB,
targetGrad);
targetGrad, outStride);
CHECK_SYNC("hl_maxpool_backward");
}
......@@ -268,7 +270,7 @@ __global__ void KeAvgPoolForward(const int nthreads, const real* inputData,
const int sizeX, const int sizeY,
const int strideH, const int strideW,
const int padH, const int padW,
real* tgtData) {
real* tgtData, const int tgtStride) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < nthreads) {
int pw = index % pooledW;
......@@ -293,7 +295,9 @@ __global__ void KeAvgPoolForward(const int nthreads, const real* inputData,
aveval += inputData[h * width + w];
}
}
tgtData[index] = aveval / pool_size;
int tgtIndex = index % (pooledW * pooledH * channels) +
frameNum * tgtStride;
tgtData[tgtIndex] = aveval / pool_size;
}
}
......@@ -303,14 +307,15 @@ void hl_avgpool_forward(const int frameCnt, const real* inputData,
const int pooledH, const int pooledW,
const int sizeX, const int sizeY,
const int strideH, const int strideW,
const int paddingH, const int paddingW, real* tgtData) {
const int paddingH, const int paddingW,
real* tgtData, const int tgtStride) {
int num_kernels = pooledH * pooledW * channels * frameCnt;
int blocks = (num_kernels + 1024 - 1) / 1024;
KeAvgPoolForward<<< blocks, 1024, 0, STREAM_DEFAULT >>>
(num_kernels, inputData, channels,
height, width, pooledH, pooledW,
sizeX, sizeY, strideH, strideW,
paddingH, paddingW, tgtData);
paddingH, paddingW, tgtData, tgtStride);
CHECK_SYNC("hl_avgpool_forward failed");
}
......@@ -322,7 +327,7 @@ __global__ void KeAvgPoolBackward(const int nthreads, const real* outGrad,
const int strideH, const int strideW,
const int padH, const int padW,
real scaleA, real scaleB,
real* tgtGrad) {
real* tgtGrad, const int outStride) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < nthreads) {
int offsetW = index % width + padW;
......@@ -335,7 +340,8 @@ __global__ void KeAvgPoolBackward(const int nthreads, const real* outGrad,
int phend = offsetH >= 0 ? min(offsetH / strideH + 1, pooledH) : 0;
int pwend = offsetW >= 0 ? min(offsetW / strideW + 1, pooledW) : 0;
real gradient = 0;
outGrad += (frameNum * channels + offsetC) * pooledH * pooledW;
outGrad += (frameNum * outStride + offsetC * pooledH * pooledW);
for (int ph = phstart; ph < phend; ++ph) {
for (int pw = pwstart; pw < pwend; ++pw) {
......@@ -360,7 +366,7 @@ void hl_avgpool_backward(const int frameCnt, const real* outGrad,
const int strideH, const int strideW,
const int paddingH, const int paddingW,
real scaleA, real scaleB,
real* backGrad) {
real* backGrad, const int outStride) {
int num_kernels = height * width * channels * frameCnt;
int blocks = (num_kernels + 1024 - 1) / 1024;
......@@ -370,7 +376,7 @@ void hl_avgpool_backward(const int frameCnt, const real* outGrad,
strideH, strideW,
paddingH, paddingW,
scaleA, scaleB,
backGrad);
backGrad, outStride);
CHECK_SYNC("hl_avgpool_backward failed");
}
......
paddle/gserver/layers/PoolProjection.cpp 0 → 100644
浏览文件 @ cdac60f6
/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
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. */
#include "PoolProjection.h"
namespace paddle {
REGISTER_PROJECTION_CREATE_FUNC(pool2, &PoolProjection::create);
PoolProjection* PoolProjection::create(const ProjectionConfig& config,
ParameterPtr parameter, bool useGpu) {
const std::string& pool = config.pool_conf().pool_type();
if (pool == "max") {
return new MaxPoolProjection(config, parameter, useGpu);
} else if (pool == "avg") {
return new AvgPoolProjection(config, parameter, useGpu);
} else {
LOG(FATAL) << "Unknown pool type: " << pool;
return nullptr;
}
}
void MaxPoolProjection::forward() {
MatrixPtr inputV = in_->value;
MatrixPtr outV = out_->value;
outV->maxPoolForward(*inputV, imgSizeY_, imgSize_, channels_,
sizeX_, sizeY_, strideY_, stride_,
outputY_, outputX_, confPaddingY_, confPadding_);
}
void MaxPoolProjection::backward(const UpdateCallback& callback) {
(void)callback;
MatrixPtr outGrad = out_->grad;
MatrixPtr inputV = in_->value;
MatrixPtr outV = out_->value;
MatrixPtr inputGrad = in_->grad;
if (NULL == inputGrad) {
return;
}
inputGrad->maxPoolBackward(*inputV, imgSizeY_, imgSize_, *outGrad, *outV,
sizeX_, sizeY_,
strideY_, stride_, outputY_, outputX_, 1, 1,
confPaddingY_, confPadding_);
}
void AvgPoolProjection::forward() {
MatrixPtr inputV = in_->value;
MatrixPtr outV = out_->value;
outV->avgPoolForward(*inputV, imgSizeY_, imgSize_, channels_,
sizeX_, sizeY_, strideY_, stride_,
outputY_, outputX_, confPaddingY_, confPadding_);
}
void AvgPoolProjection::backward(const UpdateCallback& callback) {
(void)callback;
MatrixPtr outputGrad = out_->grad;
MatrixPtr inputGrad = in_->grad;
if (NULL == inputGrad) {
return;
}
inputGrad->avgPoolBackward(*outputGrad, imgSizeY_, imgSize_,
sizeX_, sizeY_, strideY_, stride_,
outputY_, outputX_, 1, 1,
confPaddingY_, confPadding_);
}
} // namespace paddle
paddle/gserver/layers/PoolProjection.h 0 → 100644
浏览文件 @ cdac60f6
/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
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. */
#pragma once
#include "Projection.h"
namespace paddle {
class PoolProjection : public Projection {
protected:
size_t imgSizeY_, imgSize_;
size_t outputY_, outputX_;
size_t strideY_, stride_;
size_t sizeY_, sizeX_;
int confPaddingY_, confPadding_;
size_t channels_;
std::string poolType_;
public:
PoolProjection(const ProjectionConfig& config, ParameterPtr parameter,
bool useGpu)
: Projection(config, parameter, useGpu) {
const PoolConfig& conf = config_.pool_conf();
poolType_ = conf.pool_type();
channels_ = conf.channels();
sizeX_ = conf.size_x();
stride_ = conf.stride();
outputX_ = conf.output_x();
imgSize_ = conf.img_size();
confPadding_ = conf.padding();
sizeY_ = conf.has_size_y() ? conf.size_y() : conf.size_x();
imgSizeY_ = conf.has_img_size_y() ? conf.img_size_y() : conf.img_size();
strideY_ = conf.has_stride_y() ? conf.stride_y() : conf.stride();
confPaddingY_ = conf.has_padding_y() ? conf.padding_y() : conf.padding();
outputY_ = conf.has_output_y() ? conf.output_y() : conf.output_x();
}
static PoolProjection* create(const ProjectionConfig& config,
ParameterPtr parameter, bool useGpu);
const std::string& getPoolType() const { return poolType_; }
};
class MaxPoolProjection : public PoolProjection {
public:
MaxPoolProjection(const ProjectionConfig& config, ParameterPtr parameter,
bool useGpu)
: PoolProjection(config, parameter, useGpu) {}
virtual void forward();
virtual void backward(const UpdateCallback& callback = nullptr);
};
class AvgPoolProjection : public PoolProjection {
public:
AvgPoolProjection(const ProjectionConfig& config, ParameterPtr parameter,
bool useGpu)
: PoolProjection(config, parameter, useGpu) {}
virtual void forward();
virtual void backward(const UpdateCallback& callback = nullptr);
};
} // namespace paddle
paddle/gserver/layers/Projection.h
浏览文件 @ cdac60f6
......@@ -12,12 +12,11 @@ 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. */
#pragma once
#include "paddle/parameter/Parameter.h"
#include "ModelConfig.pb.h"
#include "Layer.h"
#include "ModelConfig.pb.h"
#include "paddle/parameter/Parameter.h"
namespace paddle {
......@@ -28,6 +27,11 @@ namespace paddle {
Projection::registrar_.registerClass<__class_name>(#__type_name); \
})
#define REGISTER_PROJECTION_CREATE_FUNC(__type_name, createFunction) \
static InitFunction __reg_type_##__type_name([]() { \
Projection::registrar_.registerClass(#__type_name, createFunction); \
})
/**
* A projection takes one Argument as input, calculate the result and add it
* to output Argument.
......@@ -50,7 +54,8 @@ public:
registrar_;
/**
* Forward propagation. If backward() will be called, in and out must be kept valid until then.
* Forward propagation. If backward() will be called, in and out must be kept
* valid until then.
* @param in input of projection
* @param out output of projection
* @param passType PASS_TRAIN of PASS_TEST
......
paddle/gserver/layers/SpatialPyramidPoolLayer.cpp 0 → 100644
浏览文件 @ cdac60f6
/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
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. */
#include "SpatialPyramidPoolLayer.h"
namespace paddle {
REGISTER_LAYER(spp, SpatialPyramidPoolLayer);
ProjectionConfig SpatialPyramidPoolLayer::getConfig(size_t imgSizeW,
size_t imgSizeH,
size_t channels,
size_t pyramidLevel,
std::string& poolType) {
ProjectionConfig config;
config.set_type("pool2");
PoolConfig* conf = config.mutable_pool_conf();
conf->set_channels(channels);
conf->set_img_size(imgSizeW);
conf->set_img_size_y(imgSizeH);
conf->set_pool_type(poolType);
int numBins = std::pow(2, pyramidLevel);
int sizeH = std::ceil(imgSizeH / static_cast<double>(numBins));
int remainderH = sizeH * numBins - imgSizeH;
int paddingH = (remainderH + 1) / 2;
int outSizeH = outputSize(imgSizeH, sizeH, paddingH, sizeH);
int sizeW = std::ceil(imgSizeW / static_cast<double>(numBins));
int remainderW = sizeW * numBins - imgSizeW;
int paddingW = (remainderW + 1) / 2;
int outSizeW = outputSize(imgSizeW, sizeW, paddingW, sizeW);
conf->set_stride(sizeW);
conf->set_stride_y(sizeH);
conf->set_size_x(sizeW);
conf->set_size_y(sizeH);
conf->set_padding(paddingW);
conf->set_padding_y(paddingH);
conf->set_output_x(outSizeW);
conf->set_output_y(outSizeH);
config.set_output_size(outSizeH * outSizeW * channels);
return config;
}
void SpatialPyramidPoolLayer::splitInput(Argument& input, size_t height,
size_t width, bool useGpu) {
input.value = getInput(0).value;
if (passType_ != PASS_TEST && needGradient()) {
Matrix::resizeOrCreate(input.grad, height, width, /* trans */ false,
useGpu);
input.grad->zeroMem();
}
}
bool SpatialPyramidPoolLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
Layer::init(layerMap, parameterMap);
CHECK_EQ(config_.inputs_size(), 1);
const SppConfig& sppConf = config_.inputs(0).spp_conf();
pyramidHeight_ = sppConf.pyramid_height();
poolType_ = sppConf.pool_type();
channels_ = sppConf.channels();
imgSizeW_ = sppConf.img_size();
imgSizeH_ = sppConf.has_img_size_y() ? sppConf.img_size_y() : imgSizeW_;
poolProjections_.reserve(pyramidHeight_);
projCol_.reserve(pyramidHeight_);
projInput_.reserve(pyramidHeight_);
projOutput_.resize(pyramidHeight_);
size_t startCol = 0;
size_t endCol = 0;
for (size_t i = 0; i < pyramidHeight_; i++) {
poolProjections_.emplace_back(PoolProjection::create(
getConfig(imgSizeW_, imgSizeH_, channels_, i, poolType_),
nullptr, useGpu_));
endCol += poolProjections_[i]->getOutputSize();
projCol_.push_back(std::make_pair(startCol, endCol));
startCol = endCol;
projInput_.emplace_back(Argument());
}
outputSize_ = endCol;
return true;
}
void SpatialPyramidPoolLayer::forward(PassType passType) {
Layer::forward(passType);
int batchSize = getInput(0).getBatchSize();
resetOutput(batchSize, outputSize_);
for (size_t i = 0; i < pyramidHeight_; i++) {
size_t startCol = projCol_[i].first;
size_t endCol = projCol_[i].second;
projOutput_[i].value = output_.value->subColMatrix(startCol, endCol);
projOutput_[i].grad = output_.grad->subColMatrix(startCol, endCol);
splitInput(projInput_[i], getInput(0).value->getHeight(),
getInput(0).value->getWidth(), useGpu_);
}
for (size_t i = 0; i < pyramidHeight_; i++) {
poolProjections_[i]->forward(&projInput_[i], &projOutput_[i], passType);
}
}
void SpatialPyramidPoolLayer::backward(const UpdateCallback& callback) {
for (size_t i = 0; i < pyramidHeight_; i++) {
if (poolProjections_[i]) {
poolProjections_[i]->backward(callback);
getInput(0).grad->add(*projInput_[i].grad);
}
}
}
} // namespace paddle
paddle/gserver/layers/SpatialPyramidPoolLayer.h 0 → 100644
浏览文件 @ cdac60f6
/* Copyright (c) 2016 Baidu, Inc. All Rights Reserve.
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. */
#pragma once
#include "Layer.h"
#include "PoolProjection.h"
#include "paddle/utils/Logging.h"
namespace paddle {
class SpatialPyramidPoolLayer : public Layer {
protected:
size_t channels_;
size_t imgSizeW_;
size_t imgSizeH_;
size_t pyramidHeight_;
size_t outputSize_;
std::string poolType_;
std::vector<std::unique_ptr<PoolProjection>> poolProjections_;
std::vector<Argument> projInput_;
std::vector<Argument> projOutput_;
std::vector<std::pair<size_t, size_t>> projCol_;
public:
explicit SpatialPyramidPoolLayer(const LayerConfig& config) : Layer(config) {}
~SpatialPyramidPoolLayer() {}
virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
ProjectionConfig getConfig(size_t sizeX_, size_t sizeY_, size_t channels,
size_t pyamidLevel_, std::string& poolType_);
int outputSize(int imageSize, int windowSize, int padding, int stride) {
return (imageSize - windowSize + 2 * padding) / stride + 1;
}
virtual void forward(PassType passType);
virtual void backward(const UpdateCallback& callback = nullptr);
void splitInput(Argument& input, size_t height, size_t width, bool useGpu);
};
} // namespace paddle
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @ cdac60f6
......@@ -13,14 +13,14 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include <vector>
#include <string>
#include "paddle/gserver/layers/DataLayer.h"
#include <vector>
#include "ModelConfig.pb.h"
#include "paddle/gserver/layers/DataLayer.h"
#include "paddle/trainer/Trainer.h"
#include "TestUtil.h"
#include "LayerGradUtil.h"
#include "TestUtil.h"
using namespace paddle; // NOLINT
using namespace std; // NOLINT
......@@ -880,6 +880,32 @@ TEST(Layer, PoolLayer) {
#endif
}
void testSppLayer(const string& poolType, const int pyramidHeight, bool trans,
bool useGpu) {
TestConfig config;
config.layerConfig.set_type("spp");
config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
SppConfig* sppConfig = input->mutable_spp_conf();
sppConfig->set_pool_type(poolType);
sppConfig->set_pyramid_height(pyramidHeight);
sppConfig->set_channels(16);
sppConfig->set_img_size(10);
sppConfig->set_img_size_y(20);
testLayerGrad(config, "spp", 100, trans, useGpu);
}
TEST(Layer, SpatialPyramidPoolLayer) {
for (auto useGpu : {false, true}) {
testSppLayer("avg", 1, false, useGpu);
testSppLayer("avg", 3, false, useGpu);
testSppLayer("avg", 5, false, useGpu);
testSppLayer("max", 1, false, useGpu);
testSppLayer("max", 3, false, useGpu);
testSppLayer("avg", 5, false, useGpu);
}
}
TEST(Layer, rankCostLayer) {
TestConfig config;
config.layerConfig.set_type("rank-cost");
......
paddle/math/Matrix.cpp
浏览文件 @ cdac60f6
......@@ -13,19 +13,19 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "Matrix.h"
#include "MathFunctions.h"
#include "SparseMatrix.h"
#include "SparseRowMatrix.h"
#include "MathFunctions.h"
#include <cmath>
#include <float.h>
#include <algorithm>
#include <cmath>
#include "paddle/utils/Logging.h"
#include <string.h>
#include "hl_gpu.h"
#include "hl_table_apply.h"
#include "hl_top_k.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/ThreadLocal.h"
......@@ -349,13 +349,11 @@ void GpuMatrix::collectBias(Matrix& a, real scale) {
} else {
real* data = getData();
hl_sparse_matrix_s A_d = sMatPtr->sMatrix_.get();
hl_sparse_matrix_column_sum(data, A_d, sMatPtr->getHeight(),
width_, scale);
hl_sparse_matrix_column_sum(data, A_d, sMatPtr->getHeight(), width_, scale);
}
}
void GpuMatrix::sequenceAvgForward(Matrix& a,
const IVector& startsPos,
void GpuMatrix::sequenceAvgForward(Matrix& a, const IVector& startsPos,
int mode) {
size_t height = getHeight();
size_t width = getWidth();
......@@ -401,8 +399,8 @@ void GpuMatrix::mul(const GpuMatrix& a, const GpuMatrix& b, real scaleAB,
hl_trans_op_t transa = !a.isTransposed() ? HPPL_OP_N : HPPL_OP_T;
hl_trans_op_t transb = !b.isTransposed() ? HPPL_OP_N : HPPL_OP_T;
hl_matrix_mul(A_d, transa, B_d, transb, C_d, dimM, dimN, dimK,
scaleAB, scaleT, lda, ldb, ldc);
hl_matrix_mul(A_d, transa, B_d, transb, C_d, dimM, dimN, dimK, scaleAB,
scaleT, lda, ldb, ldc);
}
void GpuMatrix::mul(const GpuSparseMatrix& a, const GpuMatrix& b, real scaleAB,
......@@ -423,8 +421,8 @@ void GpuMatrix::mul(const GpuSparseMatrix& a, const GpuMatrix& b, real scaleAB,
hl_sparse_matrix_s A_d = a.sMatrix_.get();
real* B_d = b.data_;
real* C_d = data_;
hl_matrix_csr_mul_dense(A_d, transA, B_d, HPPL_OP_N, C_d, height_,
width_, b.height_, scaleAB, scaleT);
hl_matrix_csr_mul_dense(A_d, transA, B_d, HPPL_OP_N, C_d, height_, width_,
b.height_, scaleAB, scaleT);
}
void GpuMatrix::mul(const GpuMatrix& a, const GpuSparseMatrix& b, real scaleAB,
......@@ -445,11 +443,11 @@ void GpuMatrix::mul(const GpuMatrix& a, const GpuSparseMatrix& b, real scaleAB,
<< "Matrix dimensions are not equal";
}
if (b.format_ == SPARSE_CSC) {
hl_matrix_dense_mul_csc(A_d, HPPL_OP_N, B_d, transB, C_d, height_,
width_, a.width_, scaleAB, scaleT);
hl_matrix_dense_mul_csc(A_d, HPPL_OP_N, B_d, transB, C_d, height_, width_,
a.width_, scaleAB, scaleT);
} else {
hl_matrix_dense_mul_csr(A_d, HPPL_OP_N, B_d, transB, C_d, height_,
width_, a.width_, scaleAB, scaleT);
hl_matrix_dense_mul_csr(A_d, HPPL_OP_N, B_d, transB, C_d, height_, width_,
a.width_, scaleAB, scaleT);
}
}
......@@ -511,8 +509,8 @@ void GpuMatrix::selectRows(Matrix& table, IVector& ids) {
size_t tableSize = table.getHeight();
int* index = ids.getData();
hl_matrix_select_rows(a, stride_, table.getData(), table.stride_,
index, numSamples, tableSize, dim);
hl_matrix_select_rows(a, stride_, table.getData(), table.stride_, index,
numSamples, tableSize, dim);
#endif
}
......@@ -529,8 +527,8 @@ void GpuMatrix::addToRows(Matrix& table, IVector& ids) {
size_t tableSize = table.getHeight();
int* index = ids.getData();
hl_matrix_add_to_rows(table.getData(), table.stride_, a, stride_,
index, numSamples, tableSize, dim);
hl_matrix_add_to_rows(table.getData(), table.stride_, a, stride_, index,
numSamples, tableSize, dim);
#endif
}
......@@ -565,13 +563,8 @@ void GpuMatrix::rowMax(IVector& maxIds, Matrix& maxVal) {
CHECK_EQ(maxIds.getSize(), numSamples * beam);
CHECK_EQ(maxVal.getHeight(), numSamples);
hl_matrix_top_k(maxVal.getData(),
maxVal.getStride(),
maxIds.getData(),
this->getData(),
this->getStride(),
this->getWidth(),
beam,
hl_matrix_top_k(maxVal.getData(), maxVal.getStride(), maxIds.getData(),
this->getData(), this->getStride(), this->getWidth(), beam,
numSamples);
#endif
}
......@@ -599,8 +592,8 @@ void GpuMatrix::maxoutForward(Matrix& a, IVector& id, size_t channels,
real* output = getData();
int* idForGpu = id.getData();
hl_maxout_forward(input, output, idForGpu, batchSize, size,
size / channels, groups);
hl_maxout_forward(input, output, idForGpu, batchSize, size, size / channels,
groups);
}
void GpuMatrix::maxoutBackward(Matrix& a, IVector& id, size_t channels,
......@@ -615,8 +608,8 @@ void GpuMatrix::maxoutBackward(Matrix& a, IVector& id, size_t channels,
const real* output = a.getData();
const int* idForGpu = id.getData();
hl_maxout_backward(input, output, idForGpu, batchSize, size,
size / channels, groups);
hl_maxout_backward(input, output, idForGpu, batchSize, size, size / channels,
groups);
}
/*calulate the error of classification */
......@@ -632,8 +625,8 @@ void GpuMatrix::classificationError(MatrixPtr output, IVectorPtr label) {
real* recResult_d = data_;
int* label_d = label_ptr->getData();
hl_matrix_classification_error(output_d, label_d, recResult_d,
height_, output_ptr->width_);
hl_matrix_classification_error(output_d, label_d, recResult_d, height_,
output_ptr->width_);
}
/* copy -log(output[i * width + label]) to this->data[i] */
......@@ -702,8 +695,7 @@ void GpuMatrix::sequenceSoftmax(Matrix& output, const IVector& index) {
real* outputData = output.getData();
auto starts = index.getData();
int numSequences = index.getSize() - 1;
hl_sequence_softmax_forward(inputData, outputData,
starts, numSequences);
hl_sequence_softmax_forward(inputData, outputData, starts, numSequences);
}
void GpuMatrix::softmaxDerivative(Matrix& output, Matrix& sftmaxSum) {
......@@ -717,8 +709,7 @@ void GpuMatrix::softmaxDerivative(Matrix& output, Matrix& sftmaxSum) {
real* output_d = output.data_;
real* sftmaxSum_d = sftmaxSum.data_;
real* grad_d = data_;
hl_matrix_softmax_derivative(grad_d, output_d, sftmaxSum_d, height_,
width_);
hl_matrix_softmax_derivative(grad_d, output_d, sftmaxSum_d, height_, width_);
}
void GpuMatrix::softmaxBackward(Matrix& outputV) {
......@@ -778,15 +769,15 @@ void GpuMatrix::cosSim(Matrix& output1, Matrix& output2, real scale) {
real* out = getData();
real* x = output1.getData();
real* y = output2.getData();
hl_cossim(out, x, y,
dim, output1.getHeight(), output2.getHeight(), scale);
hl_cossim(out, x, y, dim, output1.getHeight(), output2.getHeight(), scale);
}
void GpuMatrix::cosSimDerivative(Matrix& output, Matrix& prevOut1,
Matrix& prevOut2, Matrix& prevGrad1,
Matrix& prevGrad2, real scale) {
CHECK(output.useGpu_ == true && prevOut1.useGpu_ == true &&
prevOut2.useGpu_ == true && prevGrad1.useGpu_ == true &&
prevGrad2.useGpu_ == true) << "Matrix type are not equal";
prevGrad2.useGpu_ == true)
<< "Matrix type are not equal";
CHECK_EQ(getWidth(), 1UL);
CHECK_EQ(output.getWidth(), 1UL);
......@@ -806,8 +797,7 @@ void GpuMatrix::cosSimDerivative(Matrix& output, Matrix& prevOut1,
real* prevOutY = prevOut2.getData();
real* prevGradX = prevGrad1.getData();
real* prevGradY = prevGrad2.getData();
hl_cossim_derivative(grad, out, prevOutX, prevOutY,
prevGradX, prevGradY, dim,
hl_cossim_derivative(grad, out, prevOutX, prevOutY, prevGradX, prevGradY, dim,
prevOut1.getHeight(), prevOut2.getHeight(), scale);
}
......@@ -859,8 +849,8 @@ void GpuMatrix::check(std::ostream& os, Matrix& refMat, bool printDiff) {
void GpuMatrix::convExpand(Matrix& feature, int feaImgHeight, int feaImgWidth,
int channels, int blockH, int blockW, int strideH,
int strideW, int paddingH, int paddingW,
int outputH, int outputW) {
int strideW, int paddingH, int paddingW, int outputH,
int outputW) {
CHECK(feature.useGpu_ == true) << "Matrix type are not equal";
CHECK_EQ(size_t(feaImgHeight * feaImgWidth * channels),
......@@ -870,17 +860,16 @@ void GpuMatrix::convExpand(Matrix& feature, int feaImgHeight, int feaImgWidth,
size_t elemCnt = outputH * outputW * blockH * blockW * channels;
CHECK_EQ(elemCnt, height_ * width_) << "Matrix dimensions are not equal";
hl_expand_feature2col(feature.getData(), channels, feaImgHeight,
feaImgWidth, blockH, blockW, strideH, strideW,
paddingH, paddingW, outputH, outputW,
getData());
hl_expand_feature2col(feature.getData(), channels, feaImgHeight, feaImgWidth,
blockH, blockW, strideH, strideW, paddingH, paddingW,
outputH, outputW, getData());
}
void GpuMatrix::convShrink(Matrix& expandFeat, int thisImgHeight,
int thisImgWidth, int channels, int blockH,
int blockW, int strideH, int strideW, int paddingH,
int paddingW, int outputH, int outputW,
real alpha, real beta) {
int paddingW, int outputH, int outputW, real alpha,
real beta) {
CHECK(expandFeat.useGpu_ == true) << "Matrix type are not equal";
CHECK_EQ(size_t(thisImgHeight * thisImgWidth * channels),
getHeight() * getWidth())
......@@ -889,18 +878,17 @@ void GpuMatrix::convShrink(Matrix& expandFeat, int thisImgHeight,
size_t elemCnt = outputH * outputW * blockW * blockH * channels;
CHECK(elemCnt == expandFeat.getHeight() * expandFeat.getWidth())
<< "Matrix dimensions are not equal";
hl_shrink_col2feature(
expandFeat.getData(), channels, thisImgHeight, thisImgWidth, blockH,
blockW, strideH, strideW, paddingH, paddingW, outputH, outputW,
getData(), alpha, beta);
hl_shrink_col2feature(expandFeat.getData(), channels, thisImgHeight,
thisImgWidth, blockH, blockW, strideH, strideW,
paddingH, paddingW, outputH, outputW, getData(), alpha,
beta);
}
void GpuMatrix::maxPoolForward(Matrix& inputMat, size_t imgSizeH,
size_t imgSizeW, size_t channels,
size_t sizeX, size_t sizeY,
size_t strideH, size_t strideW,
size_t outputH, size_t outputW,
size_t paddingH, size_t paddingW) {
size_t imgSizeW, size_t channels, size_t sizeX,
size_t sizeY, size_t strideH, size_t strideW,
size_t outputH, size_t outputW, size_t paddingH,
size_t paddingW) {
CHECK(inputMat.useGpu_ == true) << "Matrix type are not equal";
real* inputData = inputMat.getData();
......@@ -911,16 +899,15 @@ void GpuMatrix::maxPoolForward(Matrix& inputMat, size_t imgSizeH,
CHECK(height_ == inputMat.getHeight());
CHECK(width_ == outputH * outputW * channels);
hl_maxpool_forward(frameNum, inputData, channels, height, width,
outputH, outputW, sizeX, sizeY, strideH, strideW,
paddingH, paddingW, data_);
hl_maxpool_forward(frameNum, inputData, channels, height, width, outputH,
outputW, sizeX, sizeY, strideH, strideW, paddingH,
paddingW, data_, getStride());
}
void GpuMatrix::maxPoolBackward(Matrix& inputMat, size_t imgSizeH,
size_t imgSizeW, Matrix& outGrad, Matrix& outV,
size_t sizeX, size_t sizeY,
size_t strideH, size_t strideW,
size_t outputH, size_t outputW,
size_t sizeX, size_t sizeY, size_t strideH,
size_t strideW, size_t outputH, size_t outputW,
real scaleTargets, real scaleOutput,
size_t paddingH, size_t paddingW) {
CHECK(inputMat.useGpu_ == true && outGrad.useGpu_ == true &&
......@@ -940,19 +927,17 @@ void GpuMatrix::maxPoolBackward(Matrix& inputMat, size_t imgSizeH,
CHECK(outGrad.getHeight() == outV.getHeight() &&
outGrad.getWidth() == outV.getWidth());
hl_maxpool_backward(frameNum, inputData, outData, outDiff, channels,
height, width, outputH, outputW, sizeX, sizeY,
strideH, strideW, paddingH, paddingW,
scaleTargets, scaleOutput, data_);
hl_maxpool_backward(frameNum, inputData, outData, outDiff, channels, height,
width, outputH, outputW, sizeX, sizeY, strideH, strideW,
paddingH, paddingW, scaleTargets, scaleOutput, data_,
outGrad.getStride());
}
void GpuMatrix::avgPoolForward(Matrix& inputMat, size_t imgSizeH,
size_t imgSizeW, size_t channels,
size_t sizeX, size_t sizeY,
size_t strideH, size_t strideW,
size_t outputH, size_t outputW,
size_t paddingH, size_t paddingW) {
size_t imgSizeW, size_t channels, size_t sizeX,
size_t sizeY, size_t strideH, size_t strideW,
size_t outputH, size_t outputW, size_t paddingH,
size_t paddingW) {
CHECK(inputMat.useGpu_ == true) << "Matrix type are not equal";
real* inputData = inputMat.getData();
......@@ -963,18 +948,17 @@ void GpuMatrix::avgPoolForward(Matrix& inputMat, size_t imgSizeH,
CHECK(height_ == inputMat.getHeight());
CHECK(width_ == outputH * outputW * channels);
hl_avgpool_forward(frameNum, inputData, channels, height, width,
outputH, outputW, sizeX, sizeY,
strideH, strideW,
paddingH, paddingW, data_);
hl_avgpool_forward(frameNum, inputData, channels, height, width, outputH,
outputW, sizeX, sizeY, strideH, strideW, paddingH,
paddingW, data_, getStride());
}
void GpuMatrix::avgPoolBackward(Matrix& outGrad, size_t imgSizeH,
size_t imgSizeW, size_t sizeX, size_t sizeY,
size_t strideH, size_t strideW,
size_t outputH, size_t outputW,
real scaleTargets, real scaleOutput,
size_t paddingH, size_t paddingW) {
size_t strideH, size_t strideW, size_t outputH,
size_t outputW, real scaleTargets,
real scaleOutput, size_t paddingH,
size_t paddingW) {
CHECK(outGrad.useGpu_ == true) << "Matrix type are not equal";
real* outDiff = outGrad.getData();
......@@ -986,11 +970,10 @@ void GpuMatrix::avgPoolBackward(Matrix& outGrad, size_t imgSizeH,
CHECK(height_ == outGrad.getHeight());
CHECK(outGrad.getWidth() == outputH * outputW * channels);
hl_avgpool_backward(frameNum, outDiff, channels, height, width,
outputH, outputW, sizeX, sizeY,
strideH, strideW, paddingH, paddingW,
scaleTargets, scaleOutput,
data_);
hl_avgpool_backward(frameNum, outDiff, channels, height, width, outputH,
outputW, sizeX, sizeY, strideH, strideW, paddingH,
paddingW, scaleTargets, scaleOutput, data_,
outGrad.getStride());
}
void GpuMatrix::crossMapNormalFwd(Matrix& input, size_t imgSizeH,
......@@ -1005,8 +988,8 @@ void GpuMatrix::crossMapNormalFwd(Matrix& input, size_t imgSizeH,
CHECK(denoms.getHeight() == input.getHeight() &&
denoms.getWidth() == input.getWidth() && input.getHeight() == height_ &&
input.getWidth() == width_);
hl_CMRNorm_forward(num, input.getData(), denoms.getData(), data_,
channels, height, width, sizeX, scale, -pow);
hl_CMRNorm_forward(num, input.getData(), denoms.getData(), data_, channels,
height, width, sizeX, scale, -pow);
}
void GpuMatrix::crossMapNormalBwd(Matrix& localGrad, Matrix& denoms,
......@@ -1026,13 +1009,11 @@ void GpuMatrix::crossMapNormalBwd(Matrix& localGrad, Matrix& denoms,
denoms.getWidth() == localGrad.getWidth());
hl_CMRNorm_backward(num, preOutV.getData(), denoms.getData(),
localOutV.getData(), localGrad.getData(), data_,
channels, height, width, sizeX, -pow,
2.0f * pow * scale);
localOutV.getData(), localGrad.getData(), data_, channels,
height, width, sizeX, -pow, 2.0f * pow * scale);
}
void GpuMatrix::maxSequenceForward(Matrix& input,
const IVector& sequence,
void GpuMatrix::maxSequenceForward(Matrix& input, const IVector& sequence,
IVector& index) {
CHECK(dynamic_cast<GpuMatrix*>(&input));
CHECK(dynamic_cast<const GpuIVector*>(&sequence));
......@@ -1049,12 +1030,11 @@ void GpuMatrix::maxSequenceForward(Matrix& input,
CHECK_EQ(numSequences, sequence.getSize() - 1);
CHECK_EQ(numSequences * dim, index.getSize());
hl_max_sequence_forward(inputData, starts, outData, maxIndex,
numSequences, dim);
hl_max_sequence_forward(inputData, starts, outData, maxIndex, numSequences,
dim);
}
void GpuMatrix::maxSequenceBackward(Matrix& outputGrad,
const IVector& sequence,
void GpuMatrix::maxSequenceBackward(Matrix& outputGrad, const IVector& sequence,
IVector& index) {
CHECK(dynamic_cast<GpuMatrix*>(&outputGrad));
CHECK(dynamic_cast<const GpuIVector*>(&sequence));
......@@ -1111,9 +1091,8 @@ void GpuMatrix::contextProjectionBackwardData(MatrixPtr inputGrad,
real* inGrad = inputGrad->getData();
const int* starts = sequence.getData();
hl_context_projection_backward_data(outGrad, starts, inGrad,
numSequences, inputDim,
contextLength, contextStart);
hl_context_projection_backward_data(outGrad, starts, inGrad, numSequences,
inputDim, contextLength, contextStart);
}
void GpuMatrix::contextProjectionBackwardWeight(MatrixPtr weightGrad,
......@@ -1133,8 +1112,8 @@ void GpuMatrix::contextProjectionBackwardWeight(MatrixPtr weightGrad,
real* wtGrad = weightGrad->getData();
const int* starts = sequence.getData();
hl_context_projection_backward_weight(
outGrad, starts, wtGrad, numSequences, weightDim, totalPad, contextLength,
hl_context_projection_backward_weight(outGrad, starts, wtGrad, numSequences,
weightDim, totalPad, contextLength,
contextStart, beginPad);
}
......@@ -1147,8 +1126,7 @@ void GpuMatrix::paramReluForward(Matrix& data, Matrix& W) {
size_t numSamples = data.getHeight();
size_t partial_sum = numElements / (W.getHeight() * W.getWidth());
real* output = getData();
hl_param_relu_forward(output, input, w, numElements, numSamples,
partial_sum);
hl_param_relu_forward(output, input, w, numElements, numSamples, partial_sum);
}
void GpuMatrix::paramReluBackwardW(Matrix& oGrad, Matrix& data) {
......@@ -1160,8 +1138,8 @@ void GpuMatrix::paramReluBackwardW(Matrix& oGrad, Matrix& data) {
size_t numElements = data.getWidth();
size_t numSamples = data.getHeight();
size_t partial_sum = numElements / (this->getHeight() * this->getWidth());
hl_param_relu_backward_w(wgrad, ograd, input,
numElements, numSamples, partial_sum);
hl_param_relu_backward_w(wgrad, ograd, input, numElements, numSamples,
partial_sum);
}
void GpuMatrix::paramReluBackwardDiff(Matrix& oGrad, Matrix& data, Matrix& W) {
......@@ -1172,8 +1150,8 @@ void GpuMatrix::paramReluBackwardDiff(Matrix& oGrad, Matrix& data, Matrix& W) {
size_t numElements = data.getWidth();
size_t numSamples = data.getHeight();
size_t partial_sum = numElements / (W.getHeight() * W.getWidth());
hl_param_relu_backward_diff(ograd, input, w, diff,
numElements, numSamples, partial_sum);
hl_param_relu_backward_diff(ograd, input, w, diff, numElements, numSamples,
partial_sum);
}
void GpuMatrix::addColumnVector(const Matrix& b) {
......@@ -1422,8 +1400,8 @@ void CpuMatrix::transpose(MatrixPtr matTrans, bool memAlloc) {
void CpuMatrix::convExpand(Matrix& feature, int feaImgHeight, int feaImgWidth,
int channels, int blockH, int blockW, int strideH,
int strideW, int paddingH, int paddingW,
int outputH, int outputW) {
int strideW, int paddingH, int paddingW, int outputH,
int outputW) {
CHECK(feature.useGpu_ == false) << "Matrix type are not equal";
CHECK_EQ(size_t(feaImgHeight * feaImgWidth * channels),
......@@ -1463,8 +1441,8 @@ void CpuMatrix::convExpand(Matrix& feature, int feaImgHeight, int feaImgWidth,
void CpuMatrix::convShrink(Matrix& expandFeat, int thisImgHeight,
int thisImgWidth, int channels, int blockH,
int blockW, int strideH, int strideW, int paddingH,
int paddingW, int outputH, int outputW,
real alpha, real beta) {
int paddingW, int outputH, int outputW, real alpha,
real beta) {
CHECK(expandFeat.useGpu_ == false) << "Matrix type are not equal";
CHECK_EQ(size_t(thisImgHeight * thisImgWidth * channels),
getHeight() * getWidth())
......@@ -1501,11 +1479,10 @@ void CpuMatrix::convShrink(Matrix& expandFeat, int thisImgHeight,
}
void CpuMatrix::maxPoolForward(Matrix& inputMat, size_t imgSizeH,
size_t imgSizeW, size_t channels,
size_t sizeX, size_t sizeY,
size_t strideH, size_t strideW,
size_t outputH, size_t outputW,
size_t paddingH, size_t paddingW) {
size_t imgSizeW, size_t channels, size_t sizeX,
size_t sizeY, size_t strideH, size_t strideW,
size_t outputH, size_t outputW, size_t paddingH,
size_t paddingW) {
real* inputData = inputMat.getData();
real* outData = data_;
size_t num = inputMat.getHeight();
......@@ -1513,15 +1490,20 @@ void CpuMatrix::maxPoolForward(Matrix& inputMat, size_t imgSizeH,
size_t inHeight = imgSizeH;
CHECK(inHeight * inWidth == inputMat.getWidth() / channels);
CHECK_EQ(num, this->getHeight());
CHECK_EQ(channels*outputH*outputW, this->getWidth());
CHECK_EQ(channels * outputH * outputW, this->getWidth());
/* initialize the data_ */
for (size_t i = 0; i < height_ * width_; i++) {
outData[i] = -(real)FLT_MAX;
for (size_t i = 0; i < height_; i++) {
for (size_t j = 0; j < width_; j++) {
outData[i * getStride() + j] = -(real)FLT_MAX;
}
}
/* pool max one by one */
for (size_t n = 0; n < num; ++n) { // frame by frame
if (!isContiguous()) {
outData = data_ + n * getStride();
}
for (size_t c = 0; c < channels; ++c) { // channel by channel
for (size_t ph = 0; ph < outputH; ++ph) {
for (size_t pw = 0; pw < outputW; ++pw) {
......@@ -1564,6 +1546,10 @@ void CpuMatrix::maxPoolBackward(Matrix& image, size_t imgSizeH, size_t imgSizeW,
real* otData = outV.getData();
real* otGrad = outGrad.getData();
for (size_t n = 0; n < num; ++n) {
if (!outV.isContiguous()) {
otData = outV.getData() + n * outV.getStride();
otGrad = outGrad.getData() + n * outGrad.getStride();
}
for (size_t c = 0; c < channels; ++c) {
for (size_t ph = 0; ph < outputH; ++ph) {
for (size_t pw = 0; pw < outputW; ++pw) {
......@@ -1594,9 +1580,9 @@ void CpuMatrix::maxPoolBackward(Matrix& image, size_t imgSizeH, size_t imgSizeW,
void CpuMatrix::avgPoolForward(Matrix& input, size_t imgSizeH, size_t imgSizeW,
size_t channels, size_t sizeX, size_t sizeY,
size_t strideH, size_t strideW,
size_t outputH, size_t outputW,
size_t paddingH, size_t paddingW) {
size_t strideH, size_t strideW, size_t outputH,
size_t outputW, size_t paddingH,
size_t paddingW) {
// The main loop
size_t num = input.getHeight();
size_t inHeight = imgSizeH;
......@@ -1607,6 +1593,9 @@ void CpuMatrix::avgPoolForward(Matrix& input, size_t imgSizeH, size_t imgSizeW,
real* inData = input.getData();
for (size_t n = 0; n < num; ++n) {
if (!isContiguous()) {
tgtData = data_ + n * getStride();
}
for (size_t c = 0; c < channels; ++c) {
for (size_t ph = 0; ph < outputH; ++ph) {
for (size_t pw = 0; pw < outputW; ++pw) {
......@@ -1638,9 +1627,8 @@ void CpuMatrix::avgPoolForward(Matrix& input, size_t imgSizeH, size_t imgSizeW,
}
void CpuMatrix::avgPoolBackward(Matrix& input, size_t imgSizeH, size_t imgSizeW,
size_t sizeX, size_t sizeY,
size_t strideH, size_t strideW,
size_t outputH, size_t outputW,
size_t sizeX, size_t sizeY, size_t strideH,
size_t strideW, size_t outputH, size_t outputW,
real scaleTargets, real scaleOutput,
size_t paddingH, size_t paddingW) {
size_t num = input.getHeight();
......@@ -1650,6 +1638,9 @@ void CpuMatrix::avgPoolBackward(Matrix& input, size_t imgSizeH, size_t imgSizeW,
real* outData = getData();
for (size_t n = 0; n < num; ++n) {
if (!input.isContiguous()) {
inData = input.getData() + n * input.getStride();
}
for (size_t c = 0; c < channels; ++c) {
for (size_t ph = 0; ph < outputH; ++ph) {
for (size_t pw = 0; pw < outputW; ++pw) {
......@@ -1752,8 +1743,7 @@ void CpuMatrix::crossMapNormalBwd(Matrix& localGrad, Matrix& denoms,
* Output: output size is the number of input sequences (NOT input instances).
* output[i] is set to max_{for each instance in this sequence}{input[i]}
*/
void CpuMatrix::maxSequenceForward(Matrix& input,
const IVector& sequence,
void CpuMatrix::maxSequenceForward(Matrix& input, const IVector& sequence,
IVector& index) {
CHECK(dynamic_cast<CpuMatrix*>(&input));
CHECK(dynamic_cast<const CpuIVector*>(&sequence));
......@@ -1794,8 +1784,7 @@ void CpuMatrix::maxSequenceForward(Matrix& input,
}
}
void CpuMatrix::maxSequenceBackward(Matrix& outputGrad,
const IVector& sequence,
void CpuMatrix::maxSequenceBackward(Matrix& outputGrad, const IVector& sequence,
IVector& index) {
CHECK(dynamic_cast<CpuMatrix*>(&outputGrad));
CHECK(dynamic_cast<const CpuIVector*>(&sequence));
......@@ -2000,8 +1989,7 @@ void CpuMatrix::collectBias(Matrix& a, real scale) {
}
}
void CpuMatrix::sequenceAvgForward(Matrix& a,
const IVector& startsPos,
void CpuMatrix::sequenceAvgForward(Matrix& a, const IVector& startsPos,
int mode) {
size_t height = getHeight();
size_t width = getWidth();
......
proto/ModelConfig.proto.m4
浏览文件 @ cdac60f6
......@@ -120,6 +120,14 @@ message PoolConfig {
optional uint32 padding_y = 13 [default = 0];
}
message SppConfig {
required string pool_type = 1;
required uint32 pyramid_height = 2;
required uint32 channels = 3;
required uint32 img_size = 4;
optional uint32 img_size_y = 5;
}
message NormConfig {
// rnorm or cmrnorm
required string norm_type = 1;
......@@ -194,6 +202,9 @@ message ProjectionConfig {
optional ConvConfig conv_conf = 8;
optional int32 num_filters = 9;
// For pool
optional PoolConfig pool_conf = 10;
// For IdentityOffsetProjection
optional uint64 offset = 11 [default = 0];
}
......@@ -235,6 +246,7 @@ message LayerInputConfig {
// Set the argument name.
optional string input_layer_argument = 9;
optional MaxOutConfig maxout_conf = 10;
optional SppConfig spp_conf = 11;
}
message LayerConfig {
......
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