提交 8b5431d5 编写于 作者: D dangqingqing

padding operation

上级 495649af
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3 合并请求!11636[IMPORTANT] MKLDNN layout: Support for sum operator,!2081Release/0.10.0,!1094Padding Operation
...@@ -17,6 +17,7 @@ if(WITH_TESTING) ...@@ -17,6 +17,7 @@ if(WITH_TESTING)
# file(GLOB test_files . *OpTest.cpp) # file(GLOB test_files . *OpTest.cpp)
# add_executable(${test_bin} EXCLUDE_FROM_ALL ${test_files}) # add_executable(${test_bin} EXCLUDE_FROM_ALL ${test_files})
add_simple_unittest(CrossMapNormalOpTest) add_simple_unittest(CrossMapNormalOpTest)
add_simple_unittest(PadOpTest)
add_unittest(ContextProjectionOpTest add_unittest(ContextProjectionOpTest
ContextProjectionOpTest.cpp ContextProjectionOpTest.cpp
../gserver/tests/TestUtil.cpp) ../gserver/tests/TestUtil.cpp)
......
/* Copyright (c) 2016 PaddlePaddle Authors. 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 "PadOp.h"
#include "paddle/math/Vector.h"
namespace paddle {
template <>
void Pad<DEVICE_TYPE_CPU>(real* outputs,
const real* inputs,
const int num,
const int inC,
const int inH,
const int inW,
const int padc0,
const int padc1,
const int padh0,
const int padh1,
const int padw0,
const int padw1) {
int outC = inC + padc0 + padc1;
int outH = inH + padh0 + padh1;
int outW = inW + padw0 + padw1;
for (int i = 0; i < num; i++) {
for (int c = 0; c < inC; c++) {
for (int h = 0; h < inH; h++) {
int inoff = ((i * inC + c) * inH + h) * inW;
int outoff = ((i * outC + c + padc0) * outH + h + padh0) * outW + padw0;
memcpy(outputs + outoff, inputs + inoff, inW * sizeof(real));
}
}
}
}
template <>
void PadGrad<DEVICE_TYPE_CPU>(real* inGrad,
const real* outGrad,
const int num,
const int inC,
const int inH,
const int inW,
const int padc0,
const int padc1,
const int padh0,
const int padh1,
const int padw0,
const int padw1) {
int outC = inC + padc0 + padc1;
int outH = inH + padh0 + padh1;
int outW = inW + padw0 + padw1;
for (int i = 0; i < num; i++) {
for (int c = 0; c < inC; c++) {
for (int h = 0; h < inH; h++) {
int inoff = ((i * inC + c) * inH + h) * inW;
int outoff = ((i * outC + c + padc0) * outH + h + padh0) * outW + padw0;
CpuVector inG = CpuVector(inW, inGrad + inoff);
CpuVector outG = CpuVector(inW, const_cast<real*>(outGrad + outoff));
inG += outG;
}
}
}
}
/**
* \param inputs[0] input value.
* \param outputs[0] output value.
*/
template <DeviceType Device>
class PadFunc : public FunctionBase {
public:
void init(const FuncConfig& config) override {
padc0_ = config.get<int>("padc0");
padc1_ = config.get<int>("padc1");
padh0_ = config.get<int>("padh0");
padh1_ = config.get<int>("padh1");
padw0_ = config.get<int>("padw0");
padw1_ = config.get<int>("padw1");
}
void calc(const Arguments& inputs,
const Arguments& outputs,
const Arguments& inouts) override {
CHECK_EQ(1, inputs.size());
CHECK_EQ(1, outputs.size());
CHECK_EQ(0, inouts.size());
size_t num = inputs[0].dims_[0];
size_t inC = inputs[0].dims_[1];
size_t inH = inputs[0].dims_[2];
size_t inW = inputs[0].dims_[3];
Pad<Device>(outputs[0].getData(),
inputs[0].getData(),
num,
inC,
inH,
inW,
padc0_,
padc1_,
padh0_,
padh1_,
padw0_,
padw1_);
}
private:
int padc0_;
int padc1_;
int padh0_;
int padh1_;
int padw0_;
int padw1_;
};
/**
* \param inputs[0] input grad.
* \param outputs[0] output grad.
*/
template <DeviceType Device>
class PadGradFunc : public FunctionBase {
public:
void init(const FuncConfig& config) override {
padc0_ = config.get<int>("padc0");
padc1_ = config.get<int>("padc1");
padh0_ = config.get<int>("padh0");
padh1_ = config.get<int>("padh1");
padw0_ = config.get<int>("padw0");
padw1_ = config.get<int>("padw1");
}
void calc(const Arguments& inputs,
const Arguments& outputs,
const Arguments& inouts) override {
CHECK_EQ(1, inputs.size());
CHECK_EQ(0, outputs.size());
CHECK_EQ(1, inouts.size());
size_t n = inouts[0].dims_[0];
size_t inC = inouts[0].dims_[1];
size_t inH = inouts[0].dims_[2];
size_t inW = inouts[0].dims_[3];
PadGrad<Device>(inouts[0].getData(),
inputs[0].getData(),
n,
inC,
inH,
inW,
padc0_,
padc1_,
padh0_,
padh1_,
padw0_,
padw1_);
}
private:
int padc0_;
int padc1_;
int padh0_;
int padh1_;
int padw0_;
int padw1_;
};
REGISTER_TYPED_FUNC(Pad, CPU, PadFunc);
REGISTER_TYPED_FUNC(PadGrad, CPU, PadGradFunc);
#ifndef PADDLE_ONLY_CPU
REGISTER_TYPED_FUNC(Pad, GPU, PadFunc);
REGISTER_TYPED_FUNC(PadGrad, GPU, PadGradFunc);
#endif
} // namespace paddle
/* Copyright (c) 2016 PaddlePaddle Authors. 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 "Function.h"
namespace paddle {
/**
* \brief This funtion pads zeros to inputs according to the specify dimension.
* The data structure of image data is NCHW.
*
* \param[out] outputs save results.
* \param[in] inputs input data.
* \param[in] num batch size of input data.
* \param[in] inC channel number of input data.
* \param[in] inH height of input data.
* \param[in] inH with of input data.
* \param[in] padc0 how many values to add before the data in dimension of
* channel.
* \param[in] padc1 how many values to add after the data in dimension of
* channel.
* \param[in] padh0 how many values to add before the data in dimension of
* height.
* \param[in] padh1 how many values to add after the data in dimension of
* height.
* \param[in] padw0 how many values to add before the data in dimension of
* width.
* \param[in] padw1 how many values to add after the data in dimension of
* width.
*
*/
template <DeviceType Device>
void Pad(real* outputs,
const real* inputs,
const int num,
const int inC,
const int inH,
const int inW,
const int padc0,
const int padc1,
const int padh0,
const int padh1,
const int padw0,
const int padw1);
/**
* \brief Padding operation backward.
* The data structure of image data is NCHW.
*
* \param[out] inGrad gradients of previous layer.
* \param[in] outGrad output gradients.
* \param[in] num batch size of input data.
* \param[in] inC channel number of input data.
* \param[in] inH height of input data.
* \param[in] inH with of input data.
* \param[in] padc0 how many values to add before the data in dimension of
* channel.
* \param[in] padc1 how many values to add after the data in dimension of
* channel.
* \param[in] padh0 how many values to add before the data in dimension of
* height.
* \param[in] padh1 how many values to add after the data in dimension of
* height.
* \param[in] padw0 how many values to add before the data in dimension of
* width.
* \param[in] padw1 how many values to add after the data in dimension of
* width.
*
*/
template <DeviceType Device>
void PadGrad(real* inGrad,
const real* outGrad,
const int num,
const int inC,
const int inH,
const int inW,
const int padc0,
const int padc1,
const int padh0,
const int padh1,
const int padw0,
const int padw1);
} // namespace paddle
/* Copyright (c) 2016 PaddlePaddle Authors. 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 "hl_base.h"
#include "PadOp.h"
namespace paddle {
__global__ void KePad(real* outputs, const real* inputs,
int inC, int inH, int inW,
int padc, int padh, int padw,
int outC, int outH, int outW, int nthreads) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < nthreads) {
const int w = idx % inW;
const int h = (idx / inW) % inH;
const int c = (idx / inW / inH) % inC;
const int n = idx / inW / inH / inC;
const int off = ((n * outC + c + padc) * outH + h + padh) * outW + padw + w;
outputs[off] = inputs[idx];
}
}
template <>
void Pad<DEVICE_TYPE_GPU>(real* outputs,
const real* inputs,
const int num,
const int inC,
const int inH,
const int inW,
const int padc0,
const int padc1,
const int padh0,
const int padh1,
const int padw0,
const int padw1) {
size_t nth = num * inC * inH * inW;
int blockSize = 1024;
int gridSize = (nth + 1024 - 1) / 1024;
int outC = inC + padc0 + padc1;
int outH = inH + padh0 + padh1;
int outW = inW + padw0 + padw1;
KePad<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
(outputs, inputs, inC, inH, inW, padc0, padh0, padw0,
outC, outH, outW, nth);
CHECK_SYNC("Pad");
}
__global__ void KePadDiff(real* inGrad, const real* outGrad,
int inC, int inH, int inW,
int padc, int padh, int padw,
int outC, int outH, int outW, int nthreads) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < nthreads) {
const int w = idx % inW;
const int h = (idx / inW) % inH;
const int c = (idx / inW / inH) % inC;
const int n = idx / inW / inH / inC;
const int off = ((n * outC + c + padc) * outH + h + padh) * outW + padw + w;
inGrad[idx] += outGrad[off];
}
}
template <>
void PadGrad<DEVICE_TYPE_GPU>(real* inGrad,
const real* outGrad,
const int num,
const int inC,
const int inH,
const int inW,
const int padc0,
const int padc1,
const int padh0,
const int padh1,
const int padw0,
const int padw1) {
int nth = num * inC * inH * inW;
int blockSize = 1024;
int gridSize = (nth + 1024 - 1) / 1024;
int outC = inC + padc0 + padc1;
int outH = inH + padh0 + padh1;
int outW = inW + padw0 + padw1;
KePadDiff <<<gridSize, blockSize, 0, STREAM_DEFAULT>>>
(inGrad, outGrad, inC, inH, inW, padc0, padh0, padw0,
outC, outH, outW, nth);
CHECK_SYNC("PadGrad");
}
} // namespace paddle
/* Copyright (c) 2016 PaddlePaddle Authors. 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 <gtest/gtest.h>
#include "FunctionTest.h"
namespace paddle {
TEST(Pad, real) {
for (size_t numSamples : {5, 32}) {
for (size_t channels : {1, 5, 32}) {
for (size_t imgSizeH : {5, 33, 100}) {
for (size_t imgSizeW : {5, 32, 96}) {
VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
<< " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
FunctionCompare compare("Pad",
FuncConfig()
.set("padc0", 2)
.set("padc1", 3)
.set("padh0", 1)
.set("padh1", 2)
.set("padw0", 3)
.set("padw1", 2));
Dims inDims{numSamples, channels, imgSizeH, imgSizeW};
Dims outDims{numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};
compare.cmpWithArg(
{Tensor(nullptr, inDims)}, {Tensor(nullptr, outDims)}, {});
}
}
}
}
}
// TEST(PadGrad, real) {
// for (size_t numSamples : {5, 32}) {
// for (size_t channels : {1, 5, 32}) {
// for (size_t imgSizeH : {5, 33, 100}) {
// for (size_t imgSizeW : {5, 32, 96}) {
// VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
// << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
//
// FunctionCompare compare("PadGrad",
// FuncConfig()
// .set("padc0", 2).set("padc1", 3)
// .set("padh0", 1).set("padh1", 2)
// .set("padw0", 3).set("padw1", 2));
// Dims inDims{numSamples, channels, imgSizeH, imgSizeW};
// Dims outDims{numSamples, channels + 5, imgSizeH + 3, imgSizeW + 5};
// compare.cmpWithArg({Tensor(nullptr, inDims)},
// {Tensor(nullptr, outDims)},
// {});
// }
// }
// }
// }
//}
} // namespace paddle
/* Copyright (c) 2016 PaddlePaddle Authors. 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 "PadLayer.h"
#include "paddle/utils/Stat.h"
namespace paddle {
REGISTER_LAYER(pad, PadLayer);
bool PadLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
/* Initialize the basic parent class */
Layer::init(layerMap, parameterMap);
auto& pad_conf = config_.inputs(0).pad_conf();
auto& img_conf = pad_conf.image_conf();
CHECK_EQ(config_.inputs_size(), 1);
inDims_.push_back(0);
inDims_.push_back(img_conf.channels());
inDims_.push_back(img_conf.has_img_size_y() ? img_conf.img_size_y()
: img_conf.img_size());
inDims_.push_back(img_conf.img_size());
CHECK_EQ(2UL, pad_conf.pad_c_size());
CHECK_EQ(2UL, pad_conf.pad_h_size());
CHECK_EQ(2UL, pad_conf.pad_w_size());
padc_.push_back(pad_conf.pad_c(0));
padc_.push_back(pad_conf.pad_c(1));
padh_.push_back(pad_conf.pad_h(0));
padh_.push_back(pad_conf.pad_h(1));
padw_.push_back(pad_conf.pad_w(0));
padw_.push_back(pad_conf.pad_w(1));
outDims_.resize(4);
setOutDims(0);
createFunction(forward_,
"Pad",
FuncConfig()
.set("padc0", padc_[0])
.set("padc1", padc_[1])
.set("padh0", padh_[0])
.set("padh1", padh_[1])
.set("padw0", padw_[0])
.set("padw1", padw_[1]));
createFunction(backward_,
"PadGrad",
FuncConfig()
.set("padc0", padc_[0])
.set("padc1", padc_[1])
.set("padh0", padh_[0])
.set("padh1", padh_[1])
.set("padw0", padw_[0])
.set("padw1", padw_[1]));
return true;
}
void PadLayer::setOutDims(int batchSize) {
outDims_[0] = batchSize;
outDims_[1] = inDims_[1] + padc_[0] + padc_[1];
outDims_[2] = inDims_[2] + padh_[0] + padh_[1];
outDims_[3] = inDims_[3] + padw_[0] + padw_[1];
}
void PadLayer::setTensorDim(int batchSize) {
CHECK_EQ(inputLayers_.size(), 1UL);
inDims_[0] = batchSize;
int h = inputLayers_[0]->getOutput().getFrameHeight();
if (h != 0) inDims_[2];
int w = inputLayers_[0]->getOutput().getFrameWidth();
if (w != 0) inDims_[3];
setOutDims(batchSize);
}
void PadLayer::forward(PassType passType) {
Layer::forward(passType);
MatrixPtr input = inputLayers_[0]->getOutputValue();
size_t batchSize = input->getHeight();
setTensorDim(batchSize);
int size = outDims_[1] * outDims_[2] * outDims_[3];
resetOutput(batchSize, size);
MatrixPtr outV = getOutputValue();
REGISTER_TIMER_INFO("PadForward", getName().c_str());
forward_[0]->calc({Tensor(input->getData(), inDims_)},
{Tensor(outV->getData(), outDims_)},
{});
}
void PadLayer::backward(const UpdateCallback& callback) {
(void)callback;
MatrixPtr preGrad = inputLayers_[0]->getOutputGrad();
if (NULL == preGrad) {
return;
}
MatrixPtr outGrad = getOutputGrad();
REGISTER_TIMER_INFO("PadBackward", getName().c_str());
backward_[0]->calc({Tensor(outGrad->getData(), outDims_)},
{},
{Tensor(preGrad->getData(), inDims_)});
}
} // namespace paddle
/* Copyright (c) 2016 PaddlePaddle Authors. 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"
namespace paddle {
/**
* @brief response normalization across feature maps
* namely normalize in number of size_ channels
*/
class PadLayer : public Layer {
public:
explicit PadLayer(const LayerConfig& config) : Layer(config) {}
~PadLayer() {}
bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
void forward(PassType passType);
void backward(const UpdateCallback& callback = nullptr);
protected:
void setOutDims(int batchSize);
void setTensorDim(int batchSize);
std::vector<int> padc_;
std::vector<int> padh_;
std::vector<int> padw_;
Dims inDims_;
Dims outDims_;
};
} // namespace paddle
...@@ -32,1534 +32,1580 @@ DECLARE_double(checkgrad_eps); ...@@ -32,1534 +32,1580 @@ DECLARE_double(checkgrad_eps);
DECLARE_bool(thread_local_rand_use_global_seed); DECLARE_bool(thread_local_rand_use_global_seed);
DECLARE_bool(prev_batch_state); DECLARE_bool(prev_batch_state);
TEST(Operator, dot_mul) { // TEST(Operator, dot_mul) {
// TestConfig config;
// config.layerConfig.set_size(10);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
// operatorConf.set_type("dot_mul");
// operatorConf.set_dotmul_scale(-1);
//
// testOperatorGrad(config, operatorConf, 100, false, false);
// }
//
// TEST(Projection, context) {
// for (auto contextStart : {-5, -3, -1, 0, 3}) {
// for (auto contextLength : {1, 2, 5, 7}) {
// for (auto batchSize : {1, 2, 5, 20, 50}) {
// for (auto trainablePadding : {false, true}) {
// LOG(INFO) << " contextStart=" << contextStart
// << " contextLength=" << contextLength
// << " batchSize=" << batchSize
// << " trainablePadding=" << trainablePadding;
// ProjectionConfig conf;
// conf.set_type("context");
// conf.set_input_size(10);
// conf.set_context_start(contextStart);
// conf.set_context_length(contextLength);
// conf.set_trainable_padding(trainablePadding);
// conf.set_output_size(conf.context_length() * conf.input_size());
// int pad =
// std::max(0, -conf.context_start()) +
// std::max(0, conf.context_start() + conf.context_length() - 1);
// for (auto useGpu : {false, true}) {
// testProjectionGrad(
// conf,
// INPUT_SEQUENCE_DATA,
// trainablePadding ? conf.input_size() * pad : 0,
// batchSize,
// useGpu,
// contextStart + contextLength <= 1); // = testState
// }
// }
// }
// }
// }
// }
//
// TEST(Projection, trans_fc) {
// ProjectionConfig conf;
// conf.set_type("trans_fc");
// conf.set_input_size(50);
// conf.set_output_size(20);
// for (auto useGpu : {false, true}) {
// testProjectionGrad(conf,
// INPUT_DATA,
// /* parameterSize */ 1000,
// /* batchSize */ 100,
// useGpu);
// }
// }
//
// TEST(Projection, fc) {
// ProjectionConfig conf;
// conf.set_type("fc");
// conf.set_input_size(10);
// conf.set_output_size(20);
// for (auto useGpu : {false, true}) {
// testProjectionGrad(conf,
// INPUT_DATA,
// /* parameterSize */ 200,
// /* batchSize */ 100,
// useGpu);
// }
// }
//
// TEST(Projection, dot_mul) {
// ProjectionConfig conf;
// conf.set_type("dot_mul");
// conf.set_input_size(20);
// conf.set_output_size(20);
// for (auto useGpu : {false, true}) {
// testProjectionGrad(conf,
// INPUT_DATA,
// /* parameterSize */ 20,
// /* batchSize */ 100,
// useGpu);
// }
// }
//
// TEST(Projection, table) {
// ProjectionConfig conf;
// conf.set_type("table");
// conf.set_input_size(10);
// conf.set_output_size(20);
// for (auto useGpu : {false, true}) {
// testProjectionGrad(conf,
// INPUT_LABEL,
// /* parameterSize */ 200,
// /* batchSize */ 100,
// useGpu);
// }
// }
//
// TEST(Projection, identity) {
// ProjectionConfig conf;
// conf.set_type("identity");
// conf.set_input_size(10);
// conf.set_output_size(10);
// for (auto useGpu : {false, true}) {
// testProjectionGrad(conf,
// INPUT_DATA,
// /* parameterSize */ 0,
// /* batchSize */ 100,
// useGpu);
// }
// }
//
// TEST(Projection, scaling) {
// ProjectionConfig conf;
// conf.set_type("scaling");
// conf.set_input_size(10);
// conf.set_output_size(10);
// for (auto useGpu : {false}) {
// testProjectionGrad(conf,
// INPUT_DATA,
// /* parameterSize */ 1,
// /* batchSize */ 100,
// useGpu);
// }
// }
//
// void testProjectionConv(size_t groups) {
// const int NUM_FILTERS = 18;
// const int FILTER_SIZE = 2;
// const int FILTER_SIZE_Y = 3;
// const int CHANNELS = 3;
// const int IMAGE_SIZE = 16;
//
// ProjectionConfig conf;
// conf.set_type("conv");
// conf.set_num_filters(NUM_FILTERS);
//
// ConvConfig* conv = conf.mutable_conv_conf();
// conv->set_filter_size(FILTER_SIZE);
// conv->set_filter_size_y(FILTER_SIZE_Y);
// conv->set_channels(CHANNELS);
// conv->set_padding(0);
// conv->set_padding_y(1);
// conv->set_stride(2);
// conv->set_stride_y(2);
// conv->set_groups(groups);
// conv->set_filter_channels(conv->channels() / conv->groups());
// conv->set_img_size(IMAGE_SIZE);
// int output_x = outputSize(conv->img_size(),
// conv->filter_size(),
// conv->padding(),
// conv->stride(),
// /* caffeMode */ true);
// int output_y = outputSize(conv->img_size(),
// conv->filter_size_y(),
// conv->padding_y(),
// conv->stride_y(),
// /* caffeMode */ true);
// conv->set_output_x(output_x);
// conf.set_input_size(IMAGE_SIZE * IMAGE_SIZE * CHANNELS);
// conf.set_output_size(output_x * output_y * NUM_FILTERS);
//
// testProjectionGrad(conf,
// INPUT_DATA,
// /* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE
// *
// FILTER_SIZE_Y / groups,
// /* batchSize */ 100,
// true,
// false,
// NUM_FILTERS,
// true);
// }
//
// #ifndef PADDLE_ONLY_CPU
// TEST(Projection, conv) {
// testProjectionConv(1);
// testProjectionConv(3);
// }
// #endif
//
// TEST(Layer, BilinearInterpLayer) {
// TestConfig config;
// config.layerConfig.set_type("bilinear_interp");
// config.biasSize = 0;
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});
//
// LayerInputConfig* input = config.layerConfig.add_inputs();
// BilinearInterpConfig* bilinear = input->mutable_bilinear_interp_conf();
// ImageConfig* image = bilinear->mutable_image_conf();
// image->set_img_size(32);
// image->set_img_size_y(32);
// image->set_channels(4);
//
// for (auto useGpu : {false, true}) {
// for (auto outSize : {32, 64}) {
// bilinear->set_out_size_x(outSize);
// bilinear->set_out_size_y(outSize);
// testLayerGrad(config, "bilinear_interp", 10, false, useGpu);
// }
// }
// }
//
// TEST(Layer, concat) {
// TestConfig config;
// config.biasSize = 0;
// config.layerConfig.set_type("concat");
// config.layerConfig.set_size(15);
// config.layerConfig.set_active_type("sigmoid");
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0});
// config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "concat", 100, false, useGpu);
// }
// }
//
// TEST(Layer, AddtoLayer) {
// TestConfig config;
// config.biasSize = 0;
// config.layerConfig.set_type("addto");
// config.layerConfig.set_size(10);
// config.layerConfig.set_active_type("sigmoid");
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "addto", 100, false, useGpu);
// }
// }
//
// TEST(Layer, CRFLayer) {
// TestConfig config;
// config.layerConfig.set_type("crf");
// config.layerConfig.set_size(10);
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 120});
// config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// // Not support GPU now
// testLayerGrad(config,
// "crf",
// 100,
// /* trans */ false,
// /* useGpu */ false,
// false /*useWeight*/,
// 0.03 /*epsilon*/);
// }
//
// TEST(Layer, CTCLayer) {
// TestConfig config;
// config.layerConfig.set_type("ctc");
// config.layerConfig.set_norm_by_times(false);
// config.layerConfig.set_size(10);
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0});
// config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "ctc", 100, /* trans */ false, /* useGpu */
// useGpu);
// }
// }
//
// TEST(Layer, cosSimLayer) {
// TestConfig config;
// config.layerConfig.set_type("cos");
// config.layerConfig.set_size(1);
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 50, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "cos", 100, false, useGpu);
// }
// }
//
// TEST(Layer, CosSimVecMatLayer) {
// TestConfig config;
// config.layerConfig.set_type("cos_vm");
// config.layerConfig.set_size(5); // output size
// config.layerConfig.set_cos_scale(2.0);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 0});
// config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "cos_vm", 100, false, useGpu);
// }
// }
//
// void testConvLayer(const string& type, bool trans, bool useGpu) {
// TestConfig config;
// config.biasSize = 16;
// config.layerConfig.set_type(type);
// config.layerConfig.set_num_filters(16);
// config.layerConfig.set_partial_sum(1);
// config.layerConfig.set_shared_biases(true);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 384, 288});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// ConvConfig* conv = input->mutable_conv_conf();
// conv->set_filter_size(2);
// conv->set_filter_size_y(3);
// conv->set_channels(3);
// conv->set_padding(0);
// conv->set_padding_y(1);
// conv->set_stride(2);
// conv->set_stride_y(2);
// conv->set_groups(1);
// conv->set_filter_channels(conv->channels() / conv->groups());
// conv->set_img_size(16);
// conv->set_img_size_y(8);
// conv->set_output_x(outputSize(conv->img_size(),
// conv->filter_size(),
// conv->padding(),
// conv->stride(),
// /* caffeMode */ true));
// conv->set_output_y(outputSize(conv->img_size_y(),
// conv->filter_size_y(),
// conv->padding_y(),
// conv->stride_y(),
// /* caffeMode */ true));
// config.layerConfig.set_size(conv->output_x() * conv->output_y() *
// config.layerConfig.num_filters());
//
// testLayerGrad(config, "conv", 100, trans, useGpu);
// // Use small batch_size and useWeight=true to test biasGrad
// testLayerGrad(config, "conv", 2, trans, useGpu, true, 0.02);
// }
//
// TEST(Layer, convLayer) {
// testConvLayer("exconv", /* trans= */ false, /* useGpu= */ false);
// #ifndef PADDLE_ONLY_CPU
// testConvLayer("exconv", /* trans= */ false, /* useGpu= */ true);
// testConvLayer("cudnn_conv", /* trans= */ false, /* useGpu= */ true);
// #endif
// }
//
// void testConvTransLayer(const string& type, bool trans, bool useGpu) {
// TestConfig config;
// config.biasSize = 3;
// config.layerConfig.set_type(type);
// config.layerConfig.set_num_filters(3);
// config.layerConfig.set_partial_sum(1);
// config.layerConfig.set_shared_biases(true);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 288});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// ConvConfig* conv = input->mutable_conv_conf();
// conv->set_filter_size(2);
// conv->set_filter_size_y(3);
// conv->set_channels(16);
// conv->set_padding(0);
// conv->set_padding_y(1);
// conv->set_stride(2);
// conv->set_stride_y(2);
// conv->set_groups(1);
// conv->set_filter_channels(3 / conv->groups());
// conv->set_img_size(16);
// conv->set_output_x(outputSize(conv->img_size(),
// conv->filter_size(),
// conv->padding(),
// conv->stride(),
// /* caffeMode */ true));
//
// config.layerConfig.set_size(conv->img_size() * conv->img_size() *
// config.layerConfig.num_filters());
//
// testLayerGrad(config, "convTrans", 100, trans, useGpu);
// // Use small batch_size and useWeight=true to test biasGrad
// testLayerGrad(config, "convTrans", 2, trans, useGpu, true, 0.02);
// }
//
// TEST(Layer, convTransLayer) {
// for (auto useGpu : {false, true}) {
// testConvTransLayer("exconvt", /* trans= */ false, /* useGpu= */ useGpu);
// }
// }
//
// TEST(Layer, blockExpandLayer) {
// TestConfig config;
// config.biasSize = 0;
// config.layerConfig.set_type("blockexpand");
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 6144, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// BlockExpandConfig* blockExpand = input->mutable_block_expand_conf();
// blockExpand->set_img_size_x(64);
// blockExpand->set_img_size_y(32);
// blockExpand->set_channels(3);
// blockExpand->set_padding_x(0);
// blockExpand->set_padding_y(0);
// blockExpand->set_block_x(4);
// blockExpand->set_block_y(32);
// blockExpand->set_stride_x(2);
// blockExpand->set_stride_y(2);
// blockExpand->set_output_x(outputSize(blockExpand->img_size_x(),
// blockExpand->block_x(),
// blockExpand->padding_x(),
// blockExpand->stride_x(),
// /* caffeMode */ false));
// blockExpand->set_output_y(outputSize(blockExpand->img_size_y(),
// blockExpand->block_y(),
// blockExpand->padding_y(),
// blockExpand->stride_y(),
// /* caffeMode */ false));
// config.layerConfig.set_size(blockExpand->block_x() * blockExpand->block_y()
// *
// blockExpand->channels());
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "blockexpand", 100, false, useGpu);
// }
// }
//
// TEST(Layer, maxoutLayer) {
// TestConfig config;
// config.biasSize = 0;
// config.layerConfig.set_type("maxout");
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// MaxOutConfig* maxout = input->mutable_maxout_conf();
// ImageConfig* image = maxout->mutable_image_conf();
//
// image->set_img_size(32);
// image->set_img_size_y(32);
// image->set_channels(4);
// maxout->set_groups(2);
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "maxout", 10, false, useGpu);
// }
// }
// void testFcLayer(string format, size_t nnz) {
// TestConfig config;
// config.biasSize = 4096;
// config.layerConfig.set_type("fc");
// config.layerConfig.set_size(4096);
// config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_drop_rate(0.1);
//
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_0", 8192, nnz, ParaSparse(format)});
// config.layerConfig.add_inputs();
//
// LOG(INFO) << config.inputDefs[0].sparse.sparse << " "
// << config.inputDefs[0].sparse.format;
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config,
// "fc",
// 100,
// /* trans */ false,
// useGpu,
// /* weight */ true);
// }
// }
//
// TEST(Layer, fcLayer) {
// testFcLayer("", 4096 * 4096 * 2);
// testFcLayer("csc", 4096 * 40);
// testFcLayer("csr", 4096 * 40);
// }
//
// TEST(Layer, SelectiveFullyConnectedLayer) {
// TestConfig config;
// size_t nin = 16;
// size_t nout = 256;
// config.layerConfig.set_type("selective_fc");
// config.layerConfig.set_size(nout);
// config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_has_selected_colums(true);
// config.layerConfig.set_selective_fc_pass_generation(false);
// config.biasSize = nout;
//
// config.inputDefs.push_back({INPUT_DATA, "input0", nin, nin * nout});
// config.layerConfig.add_inputs();
// config.inputDefs.push_back(
// {INPUT_SPARSE_NON_VALUE_DATA, "index", nout, 0, ParaSparse("csr",
// true)});
// config.layerConfig.add_inputs();
//
// testLayerGrad(config,
// "selective_fc",
// 100,
// /* trans= */ false,
// /* useGup= */ false,
// false);
// #ifndef PADDLE_ONLY_CPU
// testLayerGrad(config,
// "selective_fc",
// 100,
// /* trans= */ false,
// /* useGup= */ true,
// false);
// #endif
// }
//
// TEST(Layer, DataNormLayer) {
// TestConfig config;
// config.layerConfig.set_type("data_norm");
// config.layerConfig.set_size(20);
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 100});
// config.inputDefs.back().isStatic = true;
// config.layerConfig.add_inputs();
//
// for (auto strategy : {"z-score", "min-max", "decimal-scaling"}) {
// config.layerConfig.set_data_norm_strategy(strategy);
// // The parameters are static, so not support GPU now
// testLayerGrad(config,
// "data_norm",
// 200,
// /* trans */ false,
// /* useGpu */ false);
// }
// }
//
// TEST(Layer, hsigmoidLayer) {
// TestConfig config;
// config.layerConfig.set_type("hsigmoid");
// config.layerConfig.set_num_classes(5);
// config.layerConfig.set_size(1);
// config.biasSize = config.layerConfig.num_classes() - 1;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 200});
// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 5, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// // Not support GPU now
// testLayerGrad(config, "hsigmoid", 100, /* trans */ false, /* useGpu */
// false);
// }
//
// TEST(Layer, multi_cross) {
// TestConfig config;
// config.layerConfig.set_type("multi-class-cross-entropy");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(
// config, "multi-class-cross-entropy", 100, /* trans */ false, useGpu);
// }
// }
//
// TEST(Layer, multi_binary_label_sparse_mat) {
// TestConfig config;
// config.layerConfig.set_type("multi_binary_label_cross_entropy");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50,
// 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config,
// "multi_binary_label_cross_entropy",
// 100,
// /* trans */ false,
// useGpu);
// }
// }
//
// TEST(layer, multi_binary_label_id) {
// TestConfig config;
// config.layerConfig.set_type("multi_binary_label_cross_entropy");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config,
// "multi_binary_label_cross_entropy",
// 100,
// /* trans */ false,
// useGpu);
// }
// }
//
// TEST(Layer, multi_cross_with_selfnorm) {
// TestConfig config;
// config.layerConfig.set_type("multi_class_cross_entropy_with_selfnorm");
// config.layerConfig.set_softmax_selfnorm_alpha(0.1);
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// // Not support GPU now
// testLayerGrad(config,
// "multi_class_cross_entropy_with_selfnorm",
// 100,
// /* trans */ false,
// /* useGpu */ false);
// }
//
// TEST(Layer, multi_cross_soft) {
// TestConfig config;
// config.layerConfig.set_type("soft_binary_class_cross_entropy");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config,
// "soft_binary_class_cross_entropy",
// 100,
// /* trans */ false,
// useGpu);
// }
// }
//
// TEST(Layer, square_error) {
// TestConfig config;
// config.layerConfig.set_type("square_error");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu);
// }
// }
//
// TEST(Layer, sparse_square_error) {
// TestConfig config;
// config.layerConfig.set_type("square_error");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50,
// 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// // "GpuSparseMatrix" as label is not supported
// testLayerGrad(config,
// "square_error",
// 100,
// /* trans */ false,
// /* useGpu */ false);
// }
//
// TEST(Layer, sparse_float_square_error) {
// TestConfig config;
// config.layerConfig.set_type("square_error");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
// config.inputDefs.push_back({INPUT_SPARSE_FLOAT_VALUE_DATA, "layer_1", 50,
// 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// // "GpuSparseMatrix" as label is not supported
// testLayerGrad(config,
// "square_error",
// 100,
// /* trans */ false,
// /* useGpu */ false);
// }
//
// TEST(Layer, square_error_weighted) {
// TestConfig config;
// config.layerConfig.set_type("square_error");
// config.biasSize = 0;
// config.testAccumulate = false;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu);
// }
// }
//
// TEST(Layer, huber_two_class) {
// TestConfig config;
// config.layerConfig.set_type("huber");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.inputDefs.push_back({INPUT_LABEL, "layer_1", 2, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "huber", 100, /* trans */ false, useGpu);
// }
// }
//
// void testExpandLayer(string trans_type, bool hasSubseq) {
// TestConfig config;
// config.layerConfig.set_type("expand");
//
// config.inputDefs.push_back(
// {trans_type == "non-seq" ? INPUT_DENSE_DIM_DATA : INPUT_SEQUENCE_DATA,
// "layer_0",
// 10,
// 0});
// config.inputDefs.push_back(
// {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA,
// "layer_1",
// 10,
// 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// config.layerConfig.set_trans_type(trans_type);
// LOG(INFO) << " trans_type=" << trans_type << " hasSubseq=" << hasSubseq;
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "expand", 30, false, useGpu);
// }
// }
//
// TEST(Layer, ExpandLayer) {
// testExpandLayer("non-seq", false); // non-seq expand to seq
// testExpandLayer("non-seq", true); // non-seq expand to hasSubseq
// testExpandLayer("seq", true); // seq expand to hasSubseq
// }
//
// void testDegradeLayer(bool hasSubseq, string layer_type, string trans_type) {
// TestConfig config;
// config.layerConfig.set_type(layer_type);
// config.layerConfig.set_size(10);
// config.biasSize = 0;
//
// config.inputDefs.push_back(
// {hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA,
// "layer_0",
// 10,
// 0});
// config.layerConfig.add_inputs();
// config.layerConfig.set_trans_type(trans_type);
//
// auto testDegradeLayerGrad = [](TestConfig& config, string layer_type) {
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, layer_type, 100, false, useGpu);
// }
// };
//
// if (layer_type == "average") {
// for (auto strategy : {"average", "sum", "squarerootn"}) {
// LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type
// << " average_strategy=" << strategy;
// config.layerConfig.set_average_strategy(strategy);
// testDegradeLayerGrad(config, layer_type);
// }
// } else {
// LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type;
// testDegradeLayerGrad(config, layer_type);
// }
// }
//
// TEST(Layer, MaxLayer) {
// testDegradeLayer(false, "max", "non-seq"); // seq max to non-seq
// testDegradeLayer(true, "max", "non-seq"); // hasSubseq max to non-seq
// testDegradeLayer(true, "max", "seq"); // hasSubseq max to seq
// }
//
// TEST(Layer, SequenceLastInstanceLayer) {
// testDegradeLayer(false,
// "seqlastins",
// "non-seq"); // seq seqlastins to non-seq
// testDegradeLayer(true,
// "seqlastins",
// "non-seq"); // hasSubseq seqlastins to non-seq
// testDegradeLayer(true, "seqlastins", "seq"); // hasSubseq seqlastins to
// seq
// }
//
// TEST(Layer, AverageLayer) {
// testDegradeLayer(false, "average", "non-seq"); // seq average to non-seq
// testDegradeLayer(true, "average", "non-seq"); // hasSubseq average to
// non-seq
// testDegradeLayer(true, "average", "seq"); // hasSubseq average to seq
// }
//
// TEST(Layer, SequenceConcatLayer) {
// TestConfig config;
// config.layerConfig.set_type("seqconcat");
// config.layerConfig.set_size(10);
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0});
// config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "seqconcat", 100, false, useGpu);
// }
// }
//
// TEST(Layer, SequenceReshapeLayer) {
// TestConfig config;
// config.layerConfig.set_type("seqreshape");
// config.layerConfig.set_size(10);
//
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 100, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "seqreshape", 100, false, useGpu);
// }
// }
//
// TEST(Layer, ConvShiftLayer) {
// TestConfig config;
// config.layerConfig.set_type("conv_shift");
// config.layerConfig.set_size(10);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 3, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// // Not support GPU now
// testLayerGrad(config, "conv_shift", 100, false, false);
// }
//
// TEST(Layer, PowerLayer) {
// TestConfig config;
// config.layerConfig.set_type("power");
// config.layerConfig.set_size(10);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "power", 100, false, useGpu);
// }
// }
//
// TEST(Layer, ConvexCombinationLayer) {
// TestConfig config;
// config.layerConfig.set_type("convex_comb");
// config.layerConfig.set_size(20);
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "convex_comb", 100, false, useGpu);
// }
// }
//
// TEST(Layer, InterpolationLayer) {
// TestConfig config;
// config.layerConfig.set_type("interpolation");
// config.layerConfig.set_size(10);
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_2", 10, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "interpolation", 100, false, useGpu);
// }
// }
//
// TEST(Layer, OuterProdLayer) {
// TestConfig config;
// config.layerConfig.set_type("out_prod");
// config.layerConfig.set_size(100);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "out_prod", 100, false, useGpu);
// }
// }
//
// TEST(Layer, SlopeInterceptLayer) {
// TestConfig config;
// config.layerConfig.set_type("slope_intercept");
// config.layerConfig.set_size(10);
// config.layerConfig.set_slope(1.0);
// config.layerConfig.set_intercept(0.1);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "slope_intercept", 100, false, useGpu);
// }
// }
//
// TEST(Layer, ScalingLayer) {
// TestConfig config;
// config.layerConfig.set_type("scaling");
// config.layerConfig.set_size(10);
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.layerConfig.add_inputs();
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "scaling", 100, false, useGpu);
// }
// }
//
// void testNormLayer(const string& normType, bool trans, bool useGpu) {
// TestConfig config;
// config.layerConfig.set_type("norm");
// config.layerConfig.set_active_type("relu");
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1568, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// NormConfig* norm = input->mutable_norm_conf();
// norm->set_norm_type(normType);
// norm->set_channels(16);
// norm->set_size(5);
// norm->set_scale(0.001);
// norm->set_pow(0.75);
// norm->set_blocked(0);
// norm->set_img_size(14);
// norm->set_img_size_y(7);
// norm->set_output_x(norm->img_size());
// norm->set_output_y(norm->img_size_y());
// if (norm->norm_type() == "cmrnorm" ||
// norm->norm_type() == "cmrnorm-projection") {
// norm->set_scale(norm->scale() / norm->size());
// } else {
// norm->set_scale(norm->scale() / (norm->size() * norm->size()));
// }
//
// config.layerConfig.set_size(norm->output_x() * norm->output_y() *
// norm->channels());
// config.biasSize = 0;
//
// testLayerGrad(config, "norm", 100, trans, useGpu);
// }
//
// TEST(Layer, NormLayer) {
// testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */
// true);
// testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */
// false);
// }
//
// void setPoolConfig(TestConfig* config,
// PoolConfig* pool,
// const string& poolType) {
// (*config).biasSize = 0;
// (*config).layerConfig.set_type("pool");
// (*config).layerConfig.set_num_filters(16);
//
// int kw = 3, kh = 3;
// int pw = 0, ph = 0;
// int sw = 2, sh = 2;
// pool->set_pool_type(poolType);
// pool->set_channels(16);
// pool->set_size_x(kw);
// pool->set_size_y(kh);
// pool->set_start(0);
// pool->set_padding(pw);
// pool->set_padding_y(ph);
// pool->set_stride(sw);
// pool->set_stride_y(sh);
//
// int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false);
// int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false);
// pool->set_output_x(ow);
// pool->set_output_y(oh);
// }
//
// void testPoolLayer(const string& poolType, bool trans, bool useGpu) {
// TestConfig config;
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 3136, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// PoolConfig* pool = input->mutable_pool_conf();
//
// pool->set_img_size(14);
// pool->set_img_size_y(14);
// setPoolConfig(&config, pool, poolType);
// config.layerConfig.set_size(pool->output_x() * pool->output_y() *
// pool->channels());
//
// testLayerGrad(config, "pool", 100, trans, useGpu);
// }
//
// #ifndef PADDLE_ONLY_CPU
// void testPoolLayer2(const string& poolType, bool trans, bool useGpu) {
// TestConfig config;
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0});
// LayerInputConfig* input = config.layerConfig.add_inputs();
// PoolConfig* pool = input->mutable_pool_conf();
//
// pool->set_size_y(4);
// pool->set_stride_y(3);
// pool->set_img_size(10);
// pool->set_img_size_y(20);
// setPoolConfig(&config, pool, poolType);
// pool->set_output_y((pool->img_size_y() - pool->start() - pool->size_y()) /
// ((float)pool->stride_y()) +
// 1.5);
// config.layerConfig.set_size(pool->output_x() * pool->output_y() *
// pool->channels());
//
// testLayerGrad(config, "pool", 100, trans, useGpu);
// }
// #endif
//
// TEST(Layer, PoolLayer) {
// testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ false);
// testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ false);
//
// #ifndef PADDLE_ONLY_CPU
// testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ true);
// testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ true);
// testPoolLayer("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true);
// testPoolLayer("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
// testPoolLayer2("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true);
// testPoolLayer2("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
// #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);
// ImageConfig* imageConfig = sppConfig->mutable_image_conf();
// imageConfig->set_channels(16);
// imageConfig->set_img_size(10);
// imageConfig->set_img_size_y(20);
// int outputSize = (std::pow(4, sppConfig->pyramid_height()) - 1) / (4 - 1);
// config.layerConfig.set_size(outputSize * imageConfig->channels());
// testLayerGrad(config, "spp", 100, trans, useGpu);
// }
//
// TEST(Layer, SpatialPyramidPoolLayer) {
// for (auto useGpu : {false, true}) {
// for (auto pyramidHeight : {1, 2, 3}) {
// testSppLayer("avg-projection", pyramidHeight, false, useGpu);
// testSppLayer("max-projection", pyramidHeight, false, useGpu);
// }
// }
// }
//
// TEST(Layer, rankCostLayer) {
// TestConfig config;
// config.layerConfig.set_type("rank-cost");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0});
// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "rank-cost", 100, false, useGpu);
// }
// }
//
// TEST(Layer, sumCostLayer) {
// TestConfig config;
// config.layerConfig.set_type("sum_cost");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "sum_cost", 100, false, useGpu);
// }
// }
//
// TEST(Layer, weightedRankCostLayer) {
// TestConfig config;
// config.layerConfig.set_type("rank-cost");
// config.biasSize = 0;
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0});
// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0});
// config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_3", 1, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "weighted-rank-cost", 100, false, useGpu);
// }
// }
//
// TEST(Layer, TensorLayer) {
// TestConfig config;
// config.layerConfig.set_type("tensor");
// config.layerConfig.set_size(10);
// config.layerConfig.set_active_type("sigmoid");
// config.biasSize = config.layerConfig.size();
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 250});
// config.inputDefs.push_back({INPUT_DATA, "layer_1", 5, 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "tensor", 100, false, useGpu);
// }
// }
//
// TEST(Layer, RecurrentLayer) {
// TestConfig config;
// config.layerConfig.set_type("recurrent");
// config.layerConfig.set_size(4);
// config.layerConfig.set_active_type("tanh");
// config.biasSize = 4;
//
// config.inputDefs.push_back(
// {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 4, /* paraSize= */ 16});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// for (auto reversed : {false, true}) {
// config.layerConfig.set_reversed(reversed);
// config.testState = !reversed;
// testLayerGrad(config, "recurrent", 50, /* trans= */ false, useGpu);
// }
// }
// }
//
// TEST(Layer, LstmLayer) {
// TestConfig config;
// config.layerConfig.set_type("lstmemory");
// config.layerConfig.set_size(4);
// config.layerConfig.set_active_type("tanh");
// config.layerConfig.set_active_state_type("sigmoid");
// config.layerConfig.set_active_gate_type("sigmoid");
// config.biasSize = 28;
//
// config.inputDefs.push_back(
// {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 64});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// for (auto reversed : {false, true}) {
// config.layerConfig.set_reversed(reversed);
// config.testState = !reversed;
// testLayerGrad(config, "lstmemory", 100, /* trans= */ false, useGpu);
// }
// }
// for (auto useGpu : {true}) {
// config.testBatchState = true;
// config.layerConfig.set_reversed(false);
// testLayerGrad(config, "lstmemory", 10, /* trans= */ false, useGpu);
// }
// }
//
// TEST(Layer, MDLstmLayer) {
// TestConfig config;
// config.layerConfig.set_type("mdlstmemory");
// config.layerConfig.set_size(4);
// config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_active_state_type("sigmoid");
// config.layerConfig.set_active_gate_type("sigmoid");
// config.biasSize = 4 * 9;
//
// config.inputDefs.push_back(
// {INPUT_SEQUENCE_MDIM_DATA, "layer_0", 4 * 5, 4 * 4 * 5});
// config.layerConfig.add_inputs();
// config.layerConfig.add_directions(true);
// config.layerConfig.add_directions(true);
//
// for (auto useGpu : {false, true}) {
// for (int i = 0; i < 2; i++) {
// for (int j = 0; j < 2; j++) {
// config.layerConfig.set_directions(0, bool(i));
// config.layerConfig.set_directions(1, bool(j));
// testLayerGrad(config, "mdlstmemory", 100, false, useGpu);
// }
// }
// }
// }
//
// TEST(Layer, ParameterReluLayer) {
// auto testParameterReluLayer = [&](size_t inputSize, size_t channels) {
// TestConfig config;
// config.layerConfig.set_type("prelu");
// config.inputDefs.push_back({INPUT_DATA, "layer_0", inputSize, channels});
// config.layerConfig.add_inputs();
// config.layerConfig.set_size(inputSize);
// config.layerConfig.set_partial_sum(inputSize /
// channels); // size of feature map
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "prelu", 100, false, useGpu);
// }
// };
//
// testParameterReluLayer(192, 1);
// testParameterReluLayer(192, 3);
// testParameterReluLayer(192, 192);
// }
//
// TEST(Layer, ResizeLayer) {
// TestConfig config;
// config.biasSize = 0;
// config.layerConfig.set_type("resize");
// config.layerConfig.set_size(64);
//
// config.inputDefs.push_back({INPUT_DATA, "layer_0", 16, 0});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "resize", 100, false, useGpu);
// }
// }
//
// TEST(Layer, NCELayer) {
// TestConfig config;
// size_t numClasses = 4;
// config.layerConfig.set_type("nce");
// config.layerConfig.set_size(1);
// config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_num_classes(numClasses);
// config.biasSize = numClasses;
//
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 16 *
// numClasses});
// config.inputDefs.push_back(
// {INPUT_LABEL, "label", /* dim= */ numClasses, /* paraSize= */ 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto withWeight : {false, true}) {
// if (withWeight) {
// config.inputDefs.push_back(
// {INPUT_DATA_TARGET, "weight", /* dim= */ 1, /* paraSize= */ 0});
// config.layerConfig.add_inputs();
// }
//
// for (auto isIdLabel : {false, true}) {
// config.inputDefs[1] = {
// isIdLabel ? INPUT_LABEL : INPUT_SPARSE_NON_VALUE_DATA,
// "label",
// /* dim= */ numClasses,
// /* paraSize= */ 0};
//
// for (auto withDist : {false, true}) {
// config.layerConfig.clear_neg_sampling_dist();
// if (withDist) {
// double sum = 0;
// for (size_t i = 0; i < numClasses; ++i) {
// real p = rand(); // NOLINT use rand_r
// config.layerConfig.add_neg_sampling_dist(p);
// sum += p;
// }
// for (size_t i = 0; i < numClasses; ++i) {
// real p = config.layerConfig.neg_sampling_dist(i) / sum;
// config.layerConfig.set_neg_sampling_dist(i, p);
// }
// }
// LOG(INFO) << "NCELayer "
// << " isIdLabel=" << isIdLabel << " withWeight=" <<
// withWeight
// << " withDist=" << withDist;
// // Not support GPU now
// testLayerGrad(config,
// "nce",
// 100,
// /* trans= */ false,
// /* useGpu */ false);
// }
// }
// }
// }
//
// TEST(Layer, GatedRecurrentLayer) {
// TestConfig config;
// config.layerConfig.set_type("gated_recurrent");
// config.layerConfig.set_size(4);
// config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_active_gate_type("sigmoid");
// config.biasSize = 12;
//
// config.inputDefs.push_back(
// {INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48});
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// for (auto reversed : {false, true}) {
// config.layerConfig.set_reversed(reversed);
// config.testState = !reversed;
// testLayerGrad(config, "gated_recurrent", 100, /* trans= */ false,
// useGpu);
// }
// }
// }
//
// TEST(Layer, GruStepLayer) {
// TestConfig config;
// config.layerConfig.set_type("gru_step");
// config.layerConfig.set_size(4);
// config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_active_gate_type("sigmoid");
// config.biasSize = 12;
//
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48});
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "gruStep", 100, /* trans= */ false, useGpu);
// }
// }
//
// TEST(Layer, LstmStepLayer) {
// TestConfig config;
// config.layerConfig.set_type("lstm_step");
// config.layerConfig.set_size(4);
// config.layerConfig.set_active_type("sigmoid");
// config.layerConfig.set_active_state_type("sigmoid");
// config.layerConfig.set_active_gate_type("sigmoid");
// config.biasSize = 12;
// config.testAccumulate = false;
//
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 0});
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "lstmStep", 100, /* trans= */ false, useGpu);
// }
// }
//
// void testBatchNormLayer(const string& type, bool trans, bool useGpu) {
// TestConfig config;
// const int CHANNELS = 10;
// const int IMG_SIZE = 16;
// const int IMG_SIZE_Y = 8;
// size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y;
// config.layerConfig.set_type(type);
// config.layerConfig.set_size(size);
// config.layerConfig.set_active_type("sigmoid");
// config.biasSize = CHANNELS;
// config.inputDefs.push_back({INPUT_DATA,
// "layer_0",
// /* dim= */ size,
// /* paraSize= */ CHANNELS});
//
// config.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean", 1,
// CHANNELS});
// config.inputDefs.back().isStatic = true;
// config.inputDefs.push_back({INPUT_DATA, "layer_2_running_var", 1,
// CHANNELS});
// config.inputDefs.back().isStatic = true;
//
// LayerInputConfig* input = config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// ImageConfig* img_conf = input->mutable_image_conf();
// img_conf->set_channels(CHANNELS);
// img_conf->set_img_size(IMG_SIZE);
// img_conf->set_img_size_y(IMG_SIZE_Y);
//
// testLayerGrad(config,
// "batch_norm",
// 64,
// /* trans= */ trans,
// useGpu,
// /* useWeight */ true);
// }
//
// TEST(Layer, BatchNormalizationLayer) {
// testBatchNormLayer("batch_norm", false, false);
// #ifndef PADDLE_ONLY_CPU
// testBatchNormLayer("batch_norm", false, true);
// if (hl_get_cudnn_lib_version() >= int(4000)) {
// testBatchNormLayer("cudnn_batch_norm", false, true);
// }
// #endif
// }
//
// TEST(Operator, conv) {
// TestConfig config;
// const int NUM_FILTERS = 16;
// const int FILTER_SIZE = 2;
// const int FILTER_SIZE_Y = 3;
// const int CHANNELS = 3;
// const int IMAGE_SIZE = 16;
// const int IMAGE_SIZE_Y = 8;
// OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
// operatorConf.set_type("conv");
// ConvConfig* conv = operatorConf.mutable_conv_conf();
// operatorConf.set_num_filters(NUM_FILTERS);
// conv->set_filter_size(FILTER_SIZE);
// conv->set_filter_size_y(FILTER_SIZE_Y);
// conv->set_channels(CHANNELS);
// conv->set_padding(0);
// conv->set_padding_y(1);
// conv->set_stride(2);
// conv->set_stride_y(2);
// conv->set_groups(1);
// conv->set_filter_channels(conv->channels() / conv->groups());
// conv->set_img_size(IMAGE_SIZE);
// conv->set_img_size_y(IMAGE_SIZE_Y);
// conv->set_output_x(outputSize(conv->img_size(),
// conv->filter_size(),
// conv->padding(),
// conv->stride(),
// /* caffeMode */ true));
// conv->set_output_y(outputSize(conv->img_size_y(),
// conv->filter_size_y(),
// conv->padding_y(),
// conv->stride_y(),
// /* caffeMode */ true));
// config.layerConfig.set_size(conv->output_x() * conv->output_y() *
// NUM_FILTERS);
//
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE_Y * CHANNELS, 0});
// config.inputDefs.push_back(
// {INPUT_DATA,
// "layer_1",
// FILTER_SIZE * FILTER_SIZE_Y * CHANNELS * NUM_FILTERS,
// 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// testOperatorGrad(config, operatorConf, 100, /*useGpu*/ true, false);
// }
//
// TEST(Layer, FeatureMapExpandLayer) {
// TestConfig config;
// config.layerConfig.set_type("featmap_expand");
// const int CHANNELS = 10;
// const int INPUT_SIZE = 100;
// config.layerConfig.set_size(INPUT_SIZE * CHANNELS);
// config.layerConfig.set_num_filters(CHANNELS);
// config.inputDefs.push_back({INPUT_SEQUENCE_DATA,
// "layer_0",
// /* dim= */ INPUT_SIZE,
// /* paraSize= */ 0});
// config.layerConfig.add_inputs();
// for (auto useGpu : {false, true}) {
// testLayerGrad(config,
// "featmap_expand",
// /*batch_size*/ 100,
// /* trans= */ false,
// useGpu,
// /* useWeight */ true);
// }
// }
//
// TEST(Layer, MultiplexLayer) {
// TestConfig config;
// const int LAYER_SIZE = 100;
// config.layerConfig.set_type("multiplex");
// config.layerConfig.set_size(LAYER_SIZE);
//
// config.inputDefs.push_back({INPUT_LABEL, "layer_0", 2, 0});
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_1", /* dim= */ LAYER_SIZE, /* paraSize= */ 0});
// config.inputDefs.push_back(
// {INPUT_DATA, "layer_2", /* dim= */ LAYER_SIZE, /* paraSize= */ 0});
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
// config.layerConfig.add_inputs();
//
// for (auto useGpu : {false, true}) {
// testLayerGrad(config, "multiplex", 512, /* trans= */ false, useGpu);
// }
// }
//
TEST(Layer, PadLayer) {
TestConfig config; TestConfig config;
config.layerConfig.set_size(10);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
operatorConf.set_type("dot_mul");
operatorConf.set_dotmul_scale(-1);
testOperatorGrad(config, operatorConf, 100, false, false);
}
TEST(Projection, context) {
for (auto contextStart : {-5, -3, -1, 0, 3}) {
for (auto contextLength : {1, 2, 5, 7}) {
for (auto batchSize : {1, 2, 5, 20, 50}) {
for (auto trainablePadding : {false, true}) {
LOG(INFO) << " contextStart=" << contextStart
<< " contextLength=" << contextLength
<< " batchSize=" << batchSize
<< " trainablePadding=" << trainablePadding;
ProjectionConfig conf;
conf.set_type("context");
conf.set_input_size(10);
conf.set_context_start(contextStart);
conf.set_context_length(contextLength);
conf.set_trainable_padding(trainablePadding);
conf.set_output_size(conf.context_length() * conf.input_size());
int pad =
std::max(0, -conf.context_start()) +
std::max(0, conf.context_start() + conf.context_length() - 1);
for (auto useGpu : {false, true}) {
testProjectionGrad(
conf,
INPUT_SEQUENCE_DATA,
trainablePadding ? conf.input_size() * pad : 0,
batchSize,
useGpu,
contextStart + contextLength <= 1); // = testState
}
}
}
}
}
}
TEST(Projection, trans_fc) {
ProjectionConfig conf;
conf.set_type("trans_fc");
conf.set_input_size(50);
conf.set_output_size(20);
for (auto useGpu : {false, true}) {
testProjectionGrad(conf,
INPUT_DATA,
/* parameterSize */ 1000,
/* batchSize */ 100,
useGpu);
}
}
TEST(Projection, fc) {
ProjectionConfig conf;
conf.set_type("fc");
conf.set_input_size(10);
conf.set_output_size(20);
for (auto useGpu : {false, true}) {
testProjectionGrad(conf,
INPUT_DATA,
/* parameterSize */ 200,
/* batchSize */ 100,
useGpu);
}
}
TEST(Projection, dot_mul) {
ProjectionConfig conf;
conf.set_type("dot_mul");
conf.set_input_size(20);
conf.set_output_size(20);
for (auto useGpu : {false, true}) {
testProjectionGrad(conf,
INPUT_DATA,
/* parameterSize */ 20,
/* batchSize */ 100,
useGpu);
}
}
TEST(Projection, table) {
ProjectionConfig conf;
conf.set_type("table");
conf.set_input_size(10);
conf.set_output_size(20);
for (auto useGpu : {false, true}) {
testProjectionGrad(conf,
INPUT_LABEL,
/* parameterSize */ 200,
/* batchSize */ 100,
useGpu);
}
}
TEST(Projection, identity) {
ProjectionConfig conf;
conf.set_type("identity");
conf.set_input_size(10);
conf.set_output_size(10);
for (auto useGpu : {false, true}) {
testProjectionGrad(conf,
INPUT_DATA,
/* parameterSize */ 0,
/* batchSize */ 100,
useGpu);
}
}
TEST(Projection, scaling) {
ProjectionConfig conf;
conf.set_type("scaling");
conf.set_input_size(10);
conf.set_output_size(10);
for (auto useGpu : {false}) {
testProjectionGrad(conf,
INPUT_DATA,
/* parameterSize */ 1,
/* batchSize */ 100,
useGpu);
}
}
void testProjectionConv(size_t groups) {
const int NUM_FILTERS = 18;
const int FILTER_SIZE = 2;
const int FILTER_SIZE_Y = 3;
const int CHANNELS = 3;
const int IMAGE_SIZE = 16;
ProjectionConfig conf;
conf.set_type("conv");
conf.set_num_filters(NUM_FILTERS);
ConvConfig* conv = conf.mutable_conv_conf();
conv->set_filter_size(FILTER_SIZE);
conv->set_filter_size_y(FILTER_SIZE_Y);
conv->set_channels(CHANNELS);
conv->set_padding(0);
conv->set_padding_y(1);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_groups(groups);
conv->set_filter_channels(conv->channels() / conv->groups());
conv->set_img_size(IMAGE_SIZE);
int output_x = outputSize(conv->img_size(),
conv->filter_size(),
conv->padding(),
conv->stride(),
/* caffeMode */ true);
int output_y = outputSize(conv->img_size(),
conv->filter_size_y(),
conv->padding_y(),
conv->stride_y(),
/* caffeMode */ true);
conv->set_output_x(output_x);
conf.set_input_size(IMAGE_SIZE * IMAGE_SIZE * CHANNELS);
conf.set_output_size(output_x * output_y * NUM_FILTERS);
testProjectionGrad(conf,
INPUT_DATA,
/* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE *
FILTER_SIZE_Y / groups,
/* batchSize */ 100,
true,
false,
NUM_FILTERS,
true);
}
#ifndef PADDLE_ONLY_CPU
TEST(Projection, conv) {
testProjectionConv(1);
testProjectionConv(3);
}
#endif
TEST(Layer, BilinearInterpLayer) {
TestConfig config;
config.layerConfig.set_type("bilinear_interp");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
BilinearInterpConfig* bilinear = input->mutable_bilinear_interp_conf();
ImageConfig* image = bilinear->mutable_image_conf();
image->set_img_size(32);
image->set_img_size_y(32);
image->set_channels(4);
for (auto useGpu : {false, true}) {
for (auto outSize : {32, 64}) {
bilinear->set_out_size_x(outSize);
bilinear->set_out_size_y(outSize);
testLayerGrad(config, "bilinear_interp", 10, false, useGpu);
}
}
}
TEST(Layer, concat) {
TestConfig config;
config.biasSize = 0;
config.layerConfig.set_type("concat");
config.layerConfig.set_size(15);
config.layerConfig.set_active_type("sigmoid");
config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0});
config.layerConfig.add_inputs();
config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "concat", 100, false, useGpu);
}
}
TEST(Layer, AddtoLayer) {
TestConfig config;
config.biasSize = 0;
config.layerConfig.set_type("addto");
config.layerConfig.set_size(10);
config.layerConfig.set_active_type("sigmoid");
config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
config.layerConfig.add_inputs();
config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "addto", 100, false, useGpu);
}
}
TEST(Layer, CRFLayer) {
TestConfig config;
config.layerConfig.set_type("crf");
config.layerConfig.set_size(10);
config.biasSize = 0;
config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 120});
config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
// Not support GPU now
testLayerGrad(config,
"crf",
100,
/* trans */ false,
/* useGpu */ false,
false /*useWeight*/,
0.03 /*epsilon*/);
}
TEST(Layer, CTCLayer) {
TestConfig config;
config.layerConfig.set_type("ctc");
config.layerConfig.set_norm_by_times(false);
config.layerConfig.set_size(10);
config.biasSize = 0;
config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0});
config.inputDefs.push_back({INPUT_SEQUENCE_LABEL, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "ctc", 100, /* trans */ false, /* useGpu */ useGpu);
}
}
TEST(Layer, cosSimLayer) {
TestConfig config;
config.layerConfig.set_type("cos");
config.layerConfig.set_size(1);
config.biasSize = 0; config.biasSize = 0;
config.layerConfig.set_type("pad");
config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0}); int c = 4;
config.inputDefs.push_back({INPUT_DATA, "layer_1", 50, 0}); int h = 31;
config.layerConfig.add_inputs(); int w = 36;
config.layerConfig.add_inputs(); size_t size = c * h * w;
config.inputDefs.push_back({INPUT_DATA, "layer_0", size, 0});
for (auto useGpu : {false, true}) {
testLayerGrad(config, "cos", 100, false, useGpu);
}
}
TEST(Layer, CosSimVecMatLayer) {
TestConfig config;
config.layerConfig.set_type("cos_vm");
config.layerConfig.set_size(5); // output size
config.layerConfig.set_cos_scale(2.0);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 0});
config.layerConfig.add_inputs();
config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "cos_vm", 100, false, useGpu);
}
}
void testConvLayer(const string& type, bool trans, bool useGpu) {
TestConfig config;
config.biasSize = 16;
config.layerConfig.set_type(type);
config.layerConfig.set_num_filters(16);
config.layerConfig.set_partial_sum(1);
config.layerConfig.set_shared_biases(true);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 384, 288});
LayerInputConfig* input = config.layerConfig.add_inputs(); LayerInputConfig* input = config.layerConfig.add_inputs();
ConvConfig* conv = input->mutable_conv_conf(); PadConfig* pad = input->mutable_pad_conf();
conv->set_filter_size(2); ImageConfig* image = pad->mutable_image_conf();
conv->set_filter_size_y(3);
conv->set_channels(3); image->set_channels(c);
conv->set_padding(0); image->set_img_size(h);
conv->set_padding_y(1); image->set_img_size_y(w);
conv->set_stride(2); pad->add_pad_c(1);
conv->set_stride_y(2); pad->add_pad_c(2);
conv->set_groups(1); pad->add_pad_h(2);
conv->set_filter_channels(conv->channels() / conv->groups()); pad->add_pad_h(3);
conv->set_img_size(16); pad->add_pad_w(3);
conv->set_img_size_y(8); pad->add_pad_w(5);
conv->set_output_x(outputSize(conv->img_size(),
conv->filter_size(),
conv->padding(),
conv->stride(),
/* caffeMode */ true));
conv->set_output_y(outputSize(conv->img_size_y(),
conv->filter_size_y(),
conv->padding_y(),
conv->stride_y(),
/* caffeMode */ true));
config.layerConfig.set_size(conv->output_x() * conv->output_y() *
config.layerConfig.num_filters());
testLayerGrad(config, "conv", 100, trans, useGpu);
// Use small batch_size and useWeight=true to test biasGrad
testLayerGrad(config, "conv", 2, trans, useGpu, true, 0.02);
}
TEST(Layer, convLayer) {
testConvLayer("exconv", /* trans= */ false, /* useGpu= */ false);
#ifndef PADDLE_ONLY_CPU
testConvLayer("exconv", /* trans= */ false, /* useGpu= */ true);
testConvLayer("cudnn_conv", /* trans= */ false, /* useGpu= */ true);
#endif
}
void testConvTransLayer(const string& type, bool trans, bool useGpu) {
TestConfig config;
config.biasSize = 3;
config.layerConfig.set_type(type);
config.layerConfig.set_num_filters(3);
config.layerConfig.set_partial_sum(1);
config.layerConfig.set_shared_biases(true);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 288});
LayerInputConfig* input = config.layerConfig.add_inputs();
ConvConfig* conv = input->mutable_conv_conf();
conv->set_filter_size(2);
conv->set_filter_size_y(3);
conv->set_channels(16);
conv->set_padding(0);
conv->set_padding_y(1);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_groups(1);
conv->set_filter_channels(3 / conv->groups());
conv->set_img_size(16);
conv->set_output_x(outputSize(conv->img_size(),
conv->filter_size(),
conv->padding(),
conv->stride(),
/* caffeMode */ true));
config.layerConfig.set_size(conv->img_size() * conv->img_size() *
config.layerConfig.num_filters());
testLayerGrad(config, "convTrans", 100, trans, useGpu);
// Use small batch_size and useWeight=true to test biasGrad
testLayerGrad(config, "convTrans", 2, trans, useGpu, true, 0.02);
}
TEST(Layer, convTransLayer) {
for (auto useGpu : {false, true}) {
testConvTransLayer("exconvt", /* trans= */ false, /* useGpu= */ useGpu);
}
}
TEST(Layer, blockExpandLayer) {
TestConfig config;
config.biasSize = 0;
config.layerConfig.set_type("blockexpand");
config.inputDefs.push_back({INPUT_DATA, "layer_0", 6144, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
BlockExpandConfig* blockExpand = input->mutable_block_expand_conf();
blockExpand->set_img_size_x(64);
blockExpand->set_img_size_y(32);
blockExpand->set_channels(3);
blockExpand->set_padding_x(0);
blockExpand->set_padding_y(0);
blockExpand->set_block_x(4);
blockExpand->set_block_y(32);
blockExpand->set_stride_x(2);
blockExpand->set_stride_y(2);
blockExpand->set_output_x(outputSize(blockExpand->img_size_x(),
blockExpand->block_x(),
blockExpand->padding_x(),
blockExpand->stride_x(),
/* caffeMode */ false));
blockExpand->set_output_y(outputSize(blockExpand->img_size_y(),
blockExpand->block_y(),
blockExpand->padding_y(),
blockExpand->stride_y(),
/* caffeMode */ false));
config.layerConfig.set_size(blockExpand->block_x() * blockExpand->block_y() *
blockExpand->channels());
for (auto useGpu : {false, true}) {
testLayerGrad(config, "blockexpand", 100, false, useGpu);
}
}
TEST(Layer, maxoutLayer) {
TestConfig config;
config.biasSize = 0;
config.layerConfig.set_type("maxout");
config.inputDefs.push_back({INPUT_DATA, "layer_0", 4096, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
MaxOutConfig* maxout = input->mutable_maxout_conf();
ImageConfig* image = maxout->mutable_image_conf();
image->set_img_size(32);
image->set_img_size_y(32);
image->set_channels(4);
maxout->set_groups(2);
for (auto useGpu : {false, true}) {
testLayerGrad(config, "maxout", 10, false, useGpu);
}
}
void testFcLayer(string format, size_t nnz) {
TestConfig config;
config.biasSize = 4096;
config.layerConfig.set_type("fc");
config.layerConfig.set_size(4096);
config.layerConfig.set_active_type("sigmoid");
config.layerConfig.set_drop_rate(0.1);
config.inputDefs.push_back(
{INPUT_DATA, "layer_0", 8192, nnz, ParaSparse(format)});
config.layerConfig.add_inputs();
LOG(INFO) << config.inputDefs[0].sparse.sparse << " "
<< config.inputDefs[0].sparse.format;
for (auto useGpu : {false, true}) {
testLayerGrad(config,
"fc",
100,
/* trans */ false,
useGpu,
/* weight */ true);
}
}
TEST(Layer, fcLayer) {
testFcLayer("", 4096 * 4096 * 2);
testFcLayer("csc", 4096 * 40);
testFcLayer("csr", 4096 * 40);
}
TEST(Layer, SelectiveFullyConnectedLayer) {
TestConfig config;
size_t nin = 16;
size_t nout = 256;
config.layerConfig.set_type("selective_fc");
config.layerConfig.set_size(nout);
config.layerConfig.set_active_type("sigmoid");
config.layerConfig.set_has_selected_colums(true);
config.layerConfig.set_selective_fc_pass_generation(false);
config.biasSize = nout;
config.inputDefs.push_back({INPUT_DATA, "input0", nin, nin * nout});
config.layerConfig.add_inputs();
config.inputDefs.push_back(
{INPUT_SPARSE_NON_VALUE_DATA, "index", nout, 0, ParaSparse("csr", true)});
config.layerConfig.add_inputs();
testLayerGrad(config,
"selective_fc",
100,
/* trans= */ false,
/* useGup= */ false,
false);
#ifndef PADDLE_ONLY_CPU
testLayerGrad(config,
"selective_fc",
100,
/* trans= */ false,
/* useGup= */ true,
false);
#endif
}
TEST(Layer, DataNormLayer) {
TestConfig config;
config.layerConfig.set_type("data_norm");
config.layerConfig.set_size(20);
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 20, 100});
config.inputDefs.back().isStatic = true;
config.layerConfig.add_inputs();
for (auto strategy : {"z-score", "min-max", "decimal-scaling"}) {
config.layerConfig.set_data_norm_strategy(strategy);
// The parameters are static, so not support GPU now
testLayerGrad(config,
"data_norm",
200,
/* trans */ false,
/* useGpu */ false);
}
}
TEST(Layer, hsigmoidLayer) {
TestConfig config;
config.layerConfig.set_type("hsigmoid");
config.layerConfig.set_num_classes(5);
config.layerConfig.set_size(1);
config.biasSize = config.layerConfig.num_classes() - 1;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 200});
config.inputDefs.push_back({INPUT_LABEL, "layer_1", 5, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
// Not support GPU now
testLayerGrad(config, "hsigmoid", 100, /* trans */ false, /* useGpu */ false);
}
TEST(Layer, multi_cross) {
TestConfig config;
config.layerConfig.set_type("multi-class-cross-entropy");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(
config, "multi-class-cross-entropy", 100, /* trans */ false, useGpu);
}
}
TEST(Layer, multi_binary_label_sparse_mat) {
TestConfig config;
config.layerConfig.set_type("multi_binary_label_cross_entropy");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config,
"multi_binary_label_cross_entropy",
100,
/* trans */ false,
useGpu);
}
}
TEST(layer, multi_binary_label_id) {
TestConfig config;
config.layerConfig.set_type("multi_binary_label_cross_entropy");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config,
"multi_binary_label_cross_entropy",
100,
/* trans */ false,
useGpu);
}
}
TEST(Layer, multi_cross_with_selfnorm) {
TestConfig config;
config.layerConfig.set_type("multi_class_cross_entropy_with_selfnorm");
config.layerConfig.set_softmax_selfnorm_alpha(0.1);
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
config.inputDefs.push_back({INPUT_LABEL, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
// Not support GPU now
testLayerGrad(config,
"multi_class_cross_entropy_with_selfnorm",
100,
/* trans */ false,
/* useGpu */ false);
}
TEST(Layer, multi_cross_soft) {
TestConfig config;
config.layerConfig.set_type("soft_binary_class_cross_entropy");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config,
"soft_binary_class_cross_entropy",
100,
/* trans */ false,
useGpu);
}
}
TEST(Layer, square_error) {
TestConfig config;
config.layerConfig.set_type("square_error");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu);
}
}
TEST(Layer, sparse_square_error) {
TestConfig config;
config.layerConfig.set_type("square_error");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
config.inputDefs.push_back({INPUT_SPARSE_NON_VALUE_DATA, "layer_1", 50, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
// "GpuSparseMatrix" as label is not supported
testLayerGrad(config,
"square_error",
100,
/* trans */ false,
/* useGpu */ false);
}
TEST(Layer, sparse_float_square_error) {
TestConfig config;
config.layerConfig.set_type("square_error");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 50, 0});
config.inputDefs.push_back({INPUT_SPARSE_FLOAT_VALUE_DATA, "layer_1", 50, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
// "GpuSparseMatrix" as label is not supported
testLayerGrad(config,
"square_error",
100,
/* trans */ false,
/* useGpu */ false);
}
TEST(Layer, square_error_weighted) {
TestConfig config;
config.layerConfig.set_type("square_error");
config.biasSize = 0;
config.testAccumulate = false;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0});
config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "square_error", 100, /* trans */ false, useGpu);
}
}
TEST(Layer, huber_two_class) {
TestConfig config;
config.layerConfig.set_type("huber");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
config.inputDefs.push_back({INPUT_LABEL, "layer_1", 2, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "huber", 100, /* trans */ false, useGpu);
}
}
void testExpandLayer(string trans_type, bool hasSubseq) {
TestConfig config;
config.layerConfig.set_type("expand");
config.inputDefs.push_back(
{trans_type == "non-seq" ? INPUT_DENSE_DIM_DATA : INPUT_SEQUENCE_DATA,
"layer_0",
10,
0});
config.inputDefs.push_back(
{hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA,
"layer_1",
10,
0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
config.layerConfig.set_trans_type(trans_type);
LOG(INFO) << " trans_type=" << trans_type << " hasSubseq=" << hasSubseq;
for (auto useGpu : {false, true}) {
testLayerGrad(config, "expand", 30, false, useGpu);
}
}
TEST(Layer, ExpandLayer) {
testExpandLayer("non-seq", false); // non-seq expand to seq
testExpandLayer("non-seq", true); // non-seq expand to hasSubseq
testExpandLayer("seq", true); // seq expand to hasSubseq
}
void testDegradeLayer(bool hasSubseq, string layer_type, string trans_type) {
TestConfig config;
config.layerConfig.set_type(layer_type);
config.layerConfig.set_size(10);
config.biasSize = 0;
config.inputDefs.push_back(
{hasSubseq ? INPUT_HASSUB_SEQUENCE_DATA : INPUT_SEQUENCE_DATA,
"layer_0",
10,
0});
config.layerConfig.add_inputs();
config.layerConfig.set_trans_type(trans_type);
auto testDegradeLayerGrad = [](TestConfig& config, string layer_type) {
for (auto useGpu : {false, true}) {
testLayerGrad(config, layer_type, 100, false, useGpu);
}
};
if (layer_type == "average") {
for (auto strategy : {"average", "sum", "squarerootn"}) {
LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type
<< " average_strategy=" << strategy;
config.layerConfig.set_average_strategy(strategy);
testDegradeLayerGrad(config, layer_type);
}
} else {
LOG(INFO) << " hasSubseq=" << hasSubseq << " trans_type=" << trans_type;
testDegradeLayerGrad(config, layer_type);
}
}
TEST(Layer, MaxLayer) {
testDegradeLayer(false, "max", "non-seq"); // seq max to non-seq
testDegradeLayer(true, "max", "non-seq"); // hasSubseq max to non-seq
testDegradeLayer(true, "max", "seq"); // hasSubseq max to seq
}
TEST(Layer, SequenceLastInstanceLayer) {
testDegradeLayer(false,
"seqlastins",
"non-seq"); // seq seqlastins to non-seq
testDegradeLayer(true,
"seqlastins",
"non-seq"); // hasSubseq seqlastins to non-seq
testDegradeLayer(true, "seqlastins", "seq"); // hasSubseq seqlastins to seq
}
TEST(Layer, AverageLayer) {
testDegradeLayer(false, "average", "non-seq"); // seq average to non-seq
testDegradeLayer(true, "average", "non-seq"); // hasSubseq average to non-seq
testDegradeLayer(true, "average", "seq"); // hasSubseq average to seq
}
TEST(Layer, SequenceConcatLayer) {
TestConfig config;
config.layerConfig.set_type("seqconcat");
config.layerConfig.set_size(10);
config.biasSize = 0;
config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 10, 0});
config.layerConfig.add_inputs();
config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_1", 10, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "seqconcat", 100, false, useGpu);
}
}
TEST(Layer, SequenceReshapeLayer) {
TestConfig config;
config.layerConfig.set_type("seqreshape");
config.layerConfig.set_size(10);
config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "layer_0", 100, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "seqreshape", 100, false, useGpu);
}
}
TEST(Layer, ConvShiftLayer) {
TestConfig config;
config.layerConfig.set_type("conv_shift");
config.layerConfig.set_size(10);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
config.inputDefs.push_back({INPUT_DATA, "layer_1", 3, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
// Not support GPU now
testLayerGrad(config, "conv_shift", 100, false, false);
}
TEST(Layer, PowerLayer) {
TestConfig config;
config.layerConfig.set_type("power");
config.layerConfig.set_size(10);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "power", 100, false, useGpu);
}
}
TEST(Layer, ConvexCombinationLayer) {
TestConfig config;
config.layerConfig.set_type("convex_comb");
config.layerConfig.set_size(20);
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 0});
config.inputDefs.push_back({INPUT_DATA, "layer_1", 100, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "convex_comb", 100, false, useGpu);
}
}
TEST(Layer, InterpolationLayer) {
TestConfig config;
config.layerConfig.set_type("interpolation");
config.layerConfig.set_size(10);
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
config.inputDefs.push_back({INPUT_DATA, "layer_2", 10, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "interpolation", 100, false, useGpu);
}
}
TEST(Layer, OuterProdLayer) {
TestConfig config;
config.layerConfig.set_type("out_prod");
config.layerConfig.set_size(100);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
config.layerConfig.add_inputs();
config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "out_prod", 100, false, useGpu);
}
}
TEST(Layer, SlopeInterceptLayer) {
TestConfig config;
config.layerConfig.set_type("slope_intercept");
config.layerConfig.set_size(10);
config.layerConfig.set_slope(1.0);
config.layerConfig.set_intercept(0.1);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "slope_intercept", 100, false, useGpu);
}
}
TEST(Layer, ScalingLayer) {
TestConfig config;
config.layerConfig.set_type("scaling");
config.layerConfig.set_size(10);
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
config.layerConfig.add_inputs();
config.inputDefs.push_back({INPUT_DATA, "layer_1", 10, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "scaling", 100, false, useGpu);
}
}
void testNormLayer(const string& normType, bool trans, bool useGpu) {
TestConfig config;
config.layerConfig.set_type("norm");
config.layerConfig.set_active_type("relu");
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1568, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
NormConfig* norm = input->mutable_norm_conf();
norm->set_norm_type(normType);
norm->set_channels(16);
norm->set_size(5);
norm->set_scale(0.001);
norm->set_pow(0.75);
norm->set_blocked(0);
norm->set_img_size(14);
norm->set_img_size_y(7);
norm->set_output_x(norm->img_size());
norm->set_output_y(norm->img_size_y());
if (norm->norm_type() == "cmrnorm" ||
norm->norm_type() == "cmrnorm-projection") {
norm->set_scale(norm->scale() / norm->size());
} else {
norm->set_scale(norm->scale() / (norm->size() * norm->size()));
}
config.layerConfig.set_size(norm->output_x() * norm->output_y() *
norm->channels());
config.biasSize = 0;
testLayerGrad(config, "norm", 100, trans, useGpu);
}
TEST(Layer, NormLayer) {
testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */ true);
testNormLayer("cmrnorm-projection", /* trans= */ false, /* useGpu= */ false);
}
void setPoolConfig(TestConfig* config,
PoolConfig* pool,
const string& poolType) {
(*config).biasSize = 0;
(*config).layerConfig.set_type("pool");
(*config).layerConfig.set_num_filters(16);
int kw = 3, kh = 3;
int pw = 0, ph = 0;
int sw = 2, sh = 2;
pool->set_pool_type(poolType);
pool->set_channels(16);
pool->set_size_x(kw);
pool->set_size_y(kh);
pool->set_start(0);
pool->set_padding(pw);
pool->set_padding_y(ph);
pool->set_stride(sw);
pool->set_stride_y(sh);
int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false);
int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false);
pool->set_output_x(ow);
pool->set_output_y(oh);
}
void testPoolLayer(const string& poolType, bool trans, bool useGpu) {
TestConfig config;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 3136, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
PoolConfig* pool = input->mutable_pool_conf();
pool->set_img_size(14);
pool->set_img_size_y(14);
setPoolConfig(&config, pool, poolType);
config.layerConfig.set_size(pool->output_x() * pool->output_y() *
pool->channels());
testLayerGrad(config, "pool", 100, trans, useGpu);
}
#ifndef PADDLE_ONLY_CPU
void testPoolLayer2(const string& poolType, bool trans, bool useGpu) {
TestConfig config;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
PoolConfig* pool = input->mutable_pool_conf();
pool->set_size_y(4);
pool->set_stride_y(3);
pool->set_img_size(10);
pool->set_img_size_y(20);
setPoolConfig(&config, pool, poolType);
pool->set_output_y((pool->img_size_y() - pool->start() - pool->size_y()) /
((float)pool->stride_y()) +
1.5);
config.layerConfig.set_size(pool->output_x() * pool->output_y() *
pool->channels());
testLayerGrad(config, "pool", 100, trans, useGpu);
}
#endif
TEST(Layer, PoolLayer) {
testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ false);
testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ false);
#ifndef PADDLE_ONLY_CPU
testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ true);
testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ true);
testPoolLayer("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true);
testPoolLayer("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
testPoolLayer2("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true);
testPoolLayer2("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
#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);
ImageConfig* imageConfig = sppConfig->mutable_image_conf();
imageConfig->set_channels(16);
imageConfig->set_img_size(10);
imageConfig->set_img_size_y(20);
int outputSize = (std::pow(4, sppConfig->pyramid_height()) - 1) / (4 - 1);
config.layerConfig.set_size(outputSize * imageConfig->channels());
testLayerGrad(config, "spp", 100, trans, useGpu);
}
TEST(Layer, SpatialPyramidPoolLayer) {
for (auto useGpu : {false, true}) {
for (auto pyramidHeight : {1, 2, 3}) {
testSppLayer("avg-projection", pyramidHeight, false, useGpu);
testSppLayer("max-projection", pyramidHeight, false, useGpu);
}
}
}
TEST(Layer, rankCostLayer) {
TestConfig config;
config.layerConfig.set_type("rank-cost");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0});
config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "rank-cost", 100, false, useGpu);
}
}
TEST(Layer, sumCostLayer) {
TestConfig config;
config.layerConfig.set_type("sum_cost");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "sum_cost", 100, false, useGpu);
}
}
TEST(Layer, weightedRankCostLayer) {
TestConfig config;
config.layerConfig.set_type("rank-cost");
config.biasSize = 0;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0});
config.inputDefs.push_back({INPUT_DATA, "layer_1", 1, 0});
config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_2", 1, 0});
config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_3", 1, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "weighted-rank-cost", 100, false, useGpu);
}
}
TEST(Layer, TensorLayer) {
TestConfig config;
config.layerConfig.set_type("tensor");
config.layerConfig.set_size(10);
config.layerConfig.set_active_type("sigmoid");
config.biasSize = config.layerConfig.size();
config.inputDefs.push_back({INPUT_DATA, "layer_0", 5, 250});
config.inputDefs.push_back({INPUT_DATA, "layer_1", 5, 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "tensor", 100, false, useGpu);
}
}
TEST(Layer, RecurrentLayer) {
TestConfig config;
config.layerConfig.set_type("recurrent");
config.layerConfig.set_size(4);
config.layerConfig.set_active_type("tanh");
config.biasSize = 4;
config.inputDefs.push_back(
{INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 4, /* paraSize= */ 16});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
for (auto reversed : {false, true}) {
config.layerConfig.set_reversed(reversed);
config.testState = !reversed;
testLayerGrad(config, "recurrent", 50, /* trans= */ false, useGpu);
}
}
}
TEST(Layer, LstmLayer) {
TestConfig config;
config.layerConfig.set_type("lstmemory");
config.layerConfig.set_size(4);
config.layerConfig.set_active_type("tanh");
config.layerConfig.set_active_state_type("sigmoid");
config.layerConfig.set_active_gate_type("sigmoid");
config.biasSize = 28;
config.inputDefs.push_back(
{INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 64});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
for (auto reversed : {false, true}) {
config.layerConfig.set_reversed(reversed);
config.testState = !reversed;
testLayerGrad(config, "lstmemory", 100, /* trans= */ false, useGpu);
}
}
for (auto useGpu : {true}) {
config.testBatchState = true;
config.layerConfig.set_reversed(false);
testLayerGrad(config, "lstmemory", 10, /* trans= */ false, useGpu);
}
}
TEST(Layer, MDLstmLayer) {
TestConfig config;
config.layerConfig.set_type("mdlstmemory");
config.layerConfig.set_size(4);
config.layerConfig.set_active_type("sigmoid");
config.layerConfig.set_active_state_type("sigmoid");
config.layerConfig.set_active_gate_type("sigmoid");
config.biasSize = 4 * 9;
config.inputDefs.push_back(
{INPUT_SEQUENCE_MDIM_DATA, "layer_0", 4 * 5, 4 * 4 * 5});
config.layerConfig.add_inputs();
config.layerConfig.add_directions(true);
config.layerConfig.add_directions(true);
for (auto useGpu : {false, true}) {
for (int i = 0; i < 2; i++) {
for (int j = 0; j < 2; j++) {
config.layerConfig.set_directions(0, bool(i));
config.layerConfig.set_directions(1, bool(j));
testLayerGrad(config, "mdlstmemory", 100, false, useGpu);
}
}
}
}
TEST(Layer, ParameterReluLayer) {
auto testParameterReluLayer = [&](size_t inputSize, size_t channels) {
TestConfig config;
config.layerConfig.set_type("prelu");
config.inputDefs.push_back({INPUT_DATA, "layer_0", inputSize, channels});
config.layerConfig.add_inputs();
config.layerConfig.set_size(inputSize);
config.layerConfig.set_partial_sum(inputSize /
channels); // size of feature map
for (auto useGpu : {false, true}) {
testLayerGrad(config, "prelu", 100, false, useGpu);
}
};
testParameterReluLayer(192, 1);
testParameterReluLayer(192, 3);
testParameterReluLayer(192, 192);
}
TEST(Layer, ResizeLayer) {
TestConfig config;
config.biasSize = 0;
config.layerConfig.set_type("resize");
config.layerConfig.set_size(64);
config.inputDefs.push_back({INPUT_DATA, "layer_0", 16, 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "resize", 100, false, useGpu);
}
}
TEST(Layer, NCELayer) {
TestConfig config;
size_t numClasses = 4;
config.layerConfig.set_type("nce");
config.layerConfig.set_size(1);
config.layerConfig.set_active_type("sigmoid");
config.layerConfig.set_num_classes(numClasses);
config.biasSize = numClasses;
config.inputDefs.push_back(
{INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 16 * numClasses});
config.inputDefs.push_back(
{INPUT_LABEL, "label", /* dim= */ numClasses, /* paraSize= */ 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto withWeight : {false, true}) {
if (withWeight) {
config.inputDefs.push_back(
{INPUT_DATA_TARGET, "weight", /* dim= */ 1, /* paraSize= */ 0});
config.layerConfig.add_inputs();
}
for (auto isIdLabel : {false, true}) {
config.inputDefs[1] = {
isIdLabel ? INPUT_LABEL : INPUT_SPARSE_NON_VALUE_DATA,
"label",
/* dim= */ numClasses,
/* paraSize= */ 0};
for (auto withDist : {false, true}) {
config.layerConfig.clear_neg_sampling_dist();
if (withDist) {
double sum = 0;
for (size_t i = 0; i < numClasses; ++i) {
real p = rand(); // NOLINT use rand_r
config.layerConfig.add_neg_sampling_dist(p);
sum += p;
}
for (size_t i = 0; i < numClasses; ++i) {
real p = config.layerConfig.neg_sampling_dist(i) / sum;
config.layerConfig.set_neg_sampling_dist(i, p);
}
}
LOG(INFO) << "NCELayer "
<< " isIdLabel=" << isIdLabel << " withWeight=" << withWeight
<< " withDist=" << withDist;
// Not support GPU now
testLayerGrad(config,
"nce",
100,
/* trans= */ false,
/* useGpu */ false);
}
}
}
}
TEST(Layer, GatedRecurrentLayer) {
TestConfig config;
config.layerConfig.set_type("gated_recurrent");
config.layerConfig.set_size(4);
config.layerConfig.set_active_type("sigmoid");
config.layerConfig.set_active_gate_type("sigmoid");
config.biasSize = 12;
config.inputDefs.push_back(
{INPUT_SEQUENCE_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
for (auto reversed : {false, true}) {
config.layerConfig.set_reversed(reversed);
config.testState = !reversed;
testLayerGrad(config, "gated_recurrent", 100, /* trans= */ false, useGpu);
}
}
}
TEST(Layer, GruStepLayer) {
TestConfig config;
config.layerConfig.set_type("gru_step");
config.layerConfig.set_size(4);
config.layerConfig.set_active_type("sigmoid");
config.layerConfig.set_active_gate_type("sigmoid");
config.biasSize = 12;
config.inputDefs.push_back(
{INPUT_DATA, "layer_0", /* dim= */ 12, /* paraSize= */ 48});
config.inputDefs.push_back(
{INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "gruStep", 100, /* trans= */ false, useGpu);
}
}
TEST(Layer, LstmStepLayer) {
TestConfig config;
config.layerConfig.set_type("lstm_step");
config.layerConfig.set_size(4);
config.layerConfig.set_active_type("sigmoid");
config.layerConfig.set_active_state_type("sigmoid");
config.layerConfig.set_active_gate_type("sigmoid");
config.biasSize = 12;
config.testAccumulate = false;
config.inputDefs.push_back(
{INPUT_DATA, "layer_0", /* dim= */ 16, /* paraSize= */ 0});
config.inputDefs.push_back(
{INPUT_DATA, "layer_1", /* dim= */ 4, /* paraSize= */ 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "lstmStep", 100, /* trans= */ false, useGpu);
}
}
void testBatchNormLayer(const string& type, bool trans, bool useGpu) {
TestConfig config;
const int CHANNELS = 10;
const int IMG_SIZE = 16;
const int IMG_SIZE_Y = 8;
size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y;
config.layerConfig.set_type(type);
config.layerConfig.set_size(size);
config.layerConfig.set_active_type("sigmoid");
config.biasSize = CHANNELS;
config.inputDefs.push_back({INPUT_DATA,
"layer_0",
/* dim= */ size,
/* paraSize= */ CHANNELS});
config.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean", 1, CHANNELS});
config.inputDefs.back().isStatic = true;
config.inputDefs.push_back({INPUT_DATA, "layer_2_running_var", 1, CHANNELS});
config.inputDefs.back().isStatic = true;
LayerInputConfig* input = config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
ImageConfig* img_conf = input->mutable_image_conf();
img_conf->set_channels(CHANNELS);
img_conf->set_img_size(IMG_SIZE);
img_conf->set_img_size_y(IMG_SIZE_Y);
testLayerGrad(config,
"batch_norm",
64,
/* trans= */ trans,
useGpu,
/* useWeight */ true);
}
TEST(Layer, BatchNormalizationLayer) {
testBatchNormLayer("batch_norm", false, false);
#ifndef PADDLE_ONLY_CPU
testBatchNormLayer("batch_norm", false, true);
if (hl_get_cudnn_lib_version() >= int(4000)) {
testBatchNormLayer("cudnn_batch_norm", false, true);
}
#endif
}
TEST(Operator, conv) {
TestConfig config;
const int NUM_FILTERS = 16;
const int FILTER_SIZE = 2;
const int FILTER_SIZE_Y = 3;
const int CHANNELS = 3;
const int IMAGE_SIZE = 16;
const int IMAGE_SIZE_Y = 8;
OperatorConfig& operatorConf = *config.layerConfig.add_operator_confs();
operatorConf.set_type("conv");
ConvConfig* conv = operatorConf.mutable_conv_conf();
operatorConf.set_num_filters(NUM_FILTERS);
conv->set_filter_size(FILTER_SIZE);
conv->set_filter_size_y(FILTER_SIZE_Y);
conv->set_channels(CHANNELS);
conv->set_padding(0);
conv->set_padding_y(1);
conv->set_stride(2);
conv->set_stride_y(2);
conv->set_groups(1);
conv->set_filter_channels(conv->channels() / conv->groups());
conv->set_img_size(IMAGE_SIZE);
conv->set_img_size_y(IMAGE_SIZE_Y);
conv->set_output_x(outputSize(conv->img_size(),
conv->filter_size(),
conv->padding(),
conv->stride(),
/* caffeMode */ true));
conv->set_output_y(outputSize(conv->img_size_y(),
conv->filter_size_y(),
conv->padding_y(),
conv->stride_y(),
/* caffeMode */ true));
config.layerConfig.set_size(conv->output_x() * conv->output_y() *
NUM_FILTERS);
config.inputDefs.push_back(
{INPUT_DATA, "layer_0", IMAGE_SIZE * IMAGE_SIZE_Y * CHANNELS, 0});
config.inputDefs.push_back(
{INPUT_DATA,
"layer_1",
FILTER_SIZE * FILTER_SIZE_Y * CHANNELS * NUM_FILTERS,
0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
testOperatorGrad(config, operatorConf, 100, /*useGpu*/ true, false);
}
TEST(Layer, FeatureMapExpandLayer) {
TestConfig config;
config.layerConfig.set_type("featmap_expand");
const int CHANNELS = 10;
const int INPUT_SIZE = 100;
config.layerConfig.set_size(INPUT_SIZE * CHANNELS);
config.layerConfig.set_num_filters(CHANNELS);
config.inputDefs.push_back({INPUT_SEQUENCE_DATA,
"layer_0",
/* dim= */ INPUT_SIZE,
/* paraSize= */ 0});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config,
"featmap_expand",
/*batch_size*/ 100,
/* trans= */ false,
useGpu,
/* useWeight */ true);
}
}
TEST(Layer, MultiplexLayer) {
TestConfig config;
const int LAYER_SIZE = 100;
config.layerConfig.set_type("multiplex");
config.layerConfig.set_size(LAYER_SIZE);
config.inputDefs.push_back({INPUT_LABEL, "layer_0", 2, 0});
config.inputDefs.push_back(
{INPUT_DATA, "layer_1", /* dim= */ LAYER_SIZE, /* paraSize= */ 0});
config.inputDefs.push_back(
{INPUT_DATA, "layer_2", /* dim= */ LAYER_SIZE, /* paraSize= */ 0});
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) { for (auto useGpu : {false, true}) {
testLayerGrad(config, "multiplex", 512, /* trans= */ false, useGpu); testLayerGrad(config, "pad", 10, false, useGpu);
} }
} }
......
...@@ -255,6 +255,13 @@ message PriorBoxConfig { ...@@ -255,6 +255,13 @@ message PriorBoxConfig {
repeated float variance = 4; repeated float variance = 4;
} }
message PadConfig {
required ImageConfig image_conf = 1;
repeated uint32 pad_c = 2;
repeated uint32 pad_h = 3;
repeated uint32 pad_w = 4;
}
message LayerInputConfig { message LayerInputConfig {
required string input_layer_name = 1; required string input_layer_name = 1;
optional string input_parameter_name = 2; optional string input_parameter_name = 2;
...@@ -271,6 +278,7 @@ message LayerInputConfig { ...@@ -271,6 +278,7 @@ message LayerInputConfig {
optional MaxOutConfig maxout_conf = 11; optional MaxOutConfig maxout_conf = 11;
optional SppConfig spp_conf = 12; optional SppConfig spp_conf = 12;
optional PriorBoxConfig priorbox_conf = 13; optional PriorBoxConfig priorbox_conf = 13;
optional PadConfig pad_conf = 14;
} }
message LayerConfig { message LayerConfig {
......
...@@ -493,6 +493,7 @@ class Input(Cfg): ...@@ -493,6 +493,7 @@ class Input(Cfg):
block_expand=None, block_expand=None,
maxout=None, maxout=None,
spp=None, spp=None,
pad=None,
format=None, format=None,
nnz=None, nnz=None,
is_static=None, is_static=None,
...@@ -844,6 +845,12 @@ class SpatialPyramidPool(Cfg): ...@@ -844,6 +845,12 @@ class SpatialPyramidPool(Cfg):
self.add_keys(locals()) self.add_keys(locals())
@config_class
class Pad(Cfg):
def __init__(self, channels, pad_c, pad_h, pad_w):
self.add_keys(locals())
@config_class @config_class
class Norm(Cfg): class Norm(Cfg):
def __init__(self, def __init__(self,
...@@ -1842,6 +1849,25 @@ class SpatialPyramidPoolLayer(LayerBase): ...@@ -1842,6 +1849,25 @@ class SpatialPyramidPoolLayer(LayerBase):
self.set_cnn_layer(name, 1, output_x, spp_conf.image_conf.channels) self.set_cnn_layer(name, 1, output_x, spp_conf.image_conf.channels)
@config_layer('pad')
class PadLayer(LayerBase):
def __init__(self, name, inputs, **xargs):
super(PadLayer, self).__init__(name, 'pad', 0, inputs=inputs, **xargs)
pad = self.inputs[0].pad
self.config.inputs[0].pad_conf.pad_c.extend(pad.pad_c)
self.config.inputs[0].pad_conf.pad_h.extend(pad.pad_h)
self.config.inputs[0].pad_conf.pad_w.extend(pad.pad_w)
input_layer = self.get_input_layer(0)
image_conf = self.config.inputs[0].pad_conf.image_conf
parse_image(pad, input_layer.name, image_conf)
out_ch = pad.channels + pad.pad_c[0] + pad.pad_c[1]
out_h = image_conf.img_size_y + pad.pad_h[0] + pad.pad_h[1]
out_w = image_conf.img_size + pad.pad_w[0] + pad.pad_w[1]
self.set_cnn_layer(name, out_h, out_w, out_ch)
self.config.size = out_ch * out_h * out_w
@config_layer('batch_norm') @config_layer('batch_norm')
class BatchNormLayer(LayerBase): class BatchNormLayer(LayerBase):
layer_type = 'batch_norm' layer_type = 'batch_norm'
......
...@@ -170,6 +170,7 @@ class LayerType(object): ...@@ -170,6 +170,7 @@ class LayerType(object):
BLOCK_EXPAND = "blockexpand" BLOCK_EXPAND = "blockexpand"
MAXOUT = "maxout" MAXOUT = "maxout"
SPP_LAYER = "spp" SPP_LAYER = "spp"
PAD_LAYER = "pad"
PRINT_LAYER = "print" PRINT_LAYER = "print"
PRIORBOX_LAYER = "priorbox" PRIORBOX_LAYER = "priorbox"
...@@ -3488,9 +3489,6 @@ def conv_projection(input, ...@@ -3488,9 +3489,6 @@ def conv_projection(input,
groups=1, groups=1,
param_attr=None): param_attr=None):
""" """
ConvProjection with a layer as input.
It performs element-wise multiplication with weight.
Different from img_conv_layer and conv_op, conv_projection is an Projection, Different from img_conv_layer and conv_op, conv_projection is an Projection,
which can be used in mixed_layer and conat_layer. It use cudnn to implement which can be used in mixed_layer and conat_layer. It use cudnn to implement
conv and only support GPU mode. conv and only support GPU mode.
...@@ -3499,7 +3497,7 @@ def conv_projection(input, ...@@ -3499,7 +3497,7 @@ def conv_projection(input,
.. code-block:: python .. code-block:: python
proj = conv_projection(img=input1, proj = conv_projection(input=input1,
filter_size=3, filter_size=3,
num_filters=64, num_filters=64,
num_channels=64) num_channels=64)
...@@ -3582,6 +3580,84 @@ def conv_projection(input, ...@@ -3582,6 +3580,84 @@ def conv_projection(input,
return proj return proj
@wrap_name_default("pad")
@layer_support()
def pad_layer(input,
pad_c=None,
pad_h=None,
pad_w=None,
name=None,
layer_attr=None):
"""
This operation pads zeros to the input data according to pad_c,pad_h
and pad_w. pad_c, pad_h, pad_w specifies the which dimension and size
of padding. And the input data shape is NCHW.
For example, pad_c=[2,3] means padding 2 zeros before the
input data and 3 zeros after the input data in channel dimension.
pad_h means padding zeros in height dimension. pad_w means padding zeros
in width dimension.
.. code-block:: python
pad = pad_layer(input=ipt,
pad_c=[4,4],
pad_h=[0,0],
pad_w=[2,2])
:param input: layer's input.
:type input: LayerOutput
:param pad_c: padding size in channel dimension.
:type pad_c: list|None
:param pad_h: padding size in height dimension.
:type pad_h: list|None
:param pad_w: padding size in width dimension.
:type pad_w: list|None
:param layer_attr: Extra Layer Attribute.
:type layer_attr: ExtraLayerAttribute
:param name: layer name.
:type name: basestring
:return: LayerOutput object.
:rtype: LayerOutput
"""
if pad_c is not None:
assert isinstance(pad_c, collections.Sequence) and len(pad_c) == 2
else:
pad_c = [0, 0]
if pad_h is not None:
assert isinstance(pad_h, collections.Sequence) and len(pad_h) == 2
else:
pad_h = [0, 0]
if pad_w is not None:
assert isinstance(pad_w, collections.Sequence) and len(pad_w) == 2
else:
pad_w = [0, 0]
assert input.num_filters is not None
in_ch = input.num_filters
out_ch = in_ch + pad_c[0] + pad_c[1]
l = Layer(
name=name,
type=LayerType.PAD_LAYER,
inputs=Input(
input.name,
pad=Pad(
channels=in_ch,
pad_c=pad_c,
pad_h=pad_h,
pad_w=pad_w, )),
**ExtraLayerAttribute.to_kwargs(layer_attr))
return LayerOutput(
name,
layer_type=LayerType.PAD_LAYER,
parents=[input],
num_filters=out_ch,
size=l.config.size)
@wrap_name_default() @wrap_name_default()
@layer_support() @layer_support()
def conv_shift_layer(a, b, name=None, layer_attr=None): def conv_shift_layer(a, b, name=None, layer_attr=None):
......
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
data = data_layer(name='data', size=2304, height=48, width=42)
conv = img_conv_layer(
input=data,
filter_size=3,
num_channels=1,
num_filters=16,
padding=1,
act=LinearActivation(),
bias_attr=True)
pool = img_pool_layer(
input=conv, num_channels=8, pool_size=2, stride=2, pool_type=MaxPooling())
pad = pad_layer(input=pool, pad_c=[2, 3], pad_h=[1, 2], pad_w=[3, 1])
outputs(pad)
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