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提交 17b4cea4 编写于 作者: T Tao Luo 提交者： GitHub
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Merge pull request #4759 from tensor-tang/merge_grad 

Merge mkldnn output grad
上级 e593113a 7a7c8fd9
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Showing with 285 addition and 88 deletion
paddle/gserver/layers/Layer.h
浏览文件 @ 17b4cea4
......@@ -86,6 +86,7 @@ protected:
/// Also used in 'use_mkldnn' case.
std::vector<Argument> outputOtherDevice_;
/// If there are several outputs, map them by each name.
/// MKLDNNLayer use it only to merge output grad
std::map<std::string, Argument*> outputMap_;
/// Used to merge grad on different devices.
MatrixPtr tmpGrad_;
......@@ -325,6 +326,11 @@ public:
outputMap_[name] = output;
}
/**
* Get the output map size, if layer has multi-output.
*/
size_t getOutputMapSize() { return outputMap_.size(); }
/**
* Get the output based on layer's name.
*/
......
paddle/gserver/layers/MKLDNNConvLayer.cpp
浏览文件 @ 17b4cea4
......@@ -225,8 +225,6 @@ void MKLDNNConvLayer::resetFwdPipeline(
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
pipeline.clear();
if (cvtInVal_) {
pipeline.push_back(*cvtInVal_);
}
......@@ -245,7 +243,7 @@ void MKLDNNConvLayer::resetFwdPipeline(
void MKLDNNConvLayer::resetInValue(
std::shared_ptr<conv_fwd::primitive_desc>& pd, MKLDNNMatrixPtr& in) {
const MatrixPtr& inMat = inputLayers_[0]->getOutput().value;
const MatrixPtr& inMat = inputLayers_[0]->getOutputValue();
in = MKLDNNMatrix::create(inMat, pd->src_primitive_desc());
// create buffer and reorder if input value do not match
......@@ -310,15 +308,20 @@ void MKLDNNConvLayer::resetOutValue(
const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).value;
memory::dims outDims = memory::dims{bs_, oc_, oh_, ow_};
cpuOutVal_ = MKLDNNMatrix::create(cpuOut, outDims, format::nchw, engine_);
if (cpuOutVal_->getPrimitiveDesc() != out->getPrimitiveDesc()) {
if (cpuOutVal_->getPrimitiveDesc() != pd->dst_primitive_desc()) {
out = MKLDNNMatrix::create(nullptr, pd->dst_primitive_desc());
cvtOutVal_ = MKLDNNMatrix::createReorder(out, cpuOutVal_);
CHECK(cvtOutVal_) << "should not be emptry";
CHECK(cvtOutVal_) << "should not be empty";
} else {
// CPU output share the same data of MKLDNN output
cpuOut->setData(out->getData());
cpuOutVal_ = out;
}
// when output is cpu device, change the mkldnn output value and make them
// share the same data. Then if next layer use inputlayer->getOuputValue()
// to achieve the input value, it will get the right data.
output_.value = std::dynamic_pointer_cast<Matrix>(cpuOutVal_);
return;
}
output_.value = std::dynamic_pointer_cast<Matrix>(out);
}
void MKLDNNConvLayer::resetBwdWgtPD(
......@@ -412,8 +415,6 @@ void MKLDNNConvLayer::resetBwdPipeline(
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
pipeline.clear();
if (cvtOutGrad_) {
pipeline.push_back(*cvtOutGrad_);
}
......@@ -446,28 +447,27 @@ void MKLDNNConvLayer::resetBwdPipeline(
void MKLDNNConvLayer::resetOutGrad(
std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD, MKLDNNMatrixPtr& out) {
const MatrixPtr& outMat = output_.grad;
out = MKLDNNMatrix::create(outMat, wgtPD->diff_dst_primitive_desc());
CHECK(outVal_ != nullptr &&
out->getPrimitiveDesc() == outVal_->getPrimitiveDesc())
<< "primitive desc of out grad and value should be equal";
// TODO(TJ): merge outgrad
// create reorder if has output grad does not match
cpuOutGrad_ = nullptr;
cvtOutGrad_ = nullptr;
if (!outputIsOnlyMKLDNN()) {
CHECK(outVal_ != nullptr &&
outVal_->getPrimitiveDesc() == wgtPD->diff_dst_primitive_desc())
<< "primitive desc of out grad and value should be equal";
if (outputIsOnlyMKLDNN()) {
MKLDNNLayer::resetOutGrad(out, outVal_->getPrimitiveDesc());
} else {
const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).grad;
outMat->setData(cpuOut->getData());
// same PrimitiveDesc with cpuInVal_
CHECK(cpuOutVal_);
cpuOutGrad_ = MKLDNNMatrix::create(cpuOut, cpuOutVal_->getPrimitiveDesc());
if (cpuOutGrad_->getPrimitiveDesc() == out->getPrimitiveDesc()) {
out = cpuOutGrad_;
} else {
out = MKLDNNMatrix::create(nullptr, wgtPD->diff_dst_primitive_desc());
// create reorder if primitive desc does not match
if (cpuOutGrad_->getPrimitiveDesc() != outVal_->getPrimitiveDesc()) {
out = MKLDNNMatrix::create(output_.grad, outVal_->getPrimitiveDesc());
cvtOutGrad_ = MKLDNNMatrix::createReorder(cpuOutGrad_, out);
CHECK(cvtOutGrad_);
} else {
// share the same data of CPU output
output_.grad->setData(cpuOut->getData());
out = cpuOutGrad_;
}
}
}
......@@ -496,32 +496,30 @@ void MKLDNNConvLayer::resetWgtBiasGrad(
void MKLDNNConvLayer::resetInGrad(
std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
MKLDNNMatrixPtr& in) {
in = nullptr;
cpuInGrad_ = nullptr;
cvtInGrad_ = nullptr;
if (dataPD == nullptr) {
return;
}
// TODO(TJ): use outputMaps_ ways to get the inGrad_ when merge outgrad done
in = MKLDNNMatrix::create(inputLayers_[0]->getOutput().grad,
dataPD->diff_src_primitive_desc());
CHECK(nullptr != inVal_ &&
in->getPrimitiveDesc() == inVal_->getPrimitiveDesc())
<< "primitive desc of input grad and value should be equal";
// create reorder if has output grad does not match
cpuInGrad_ = nullptr;
cvtInGrad_ = nullptr;
if (!inputIsOnlyMKLDNN()) {
if (inputIsOnlyMKLDNN()) {
MKLDNNLayer::resetInGrad(in, dataPD->diff_src_primitive_desc());
CHECK(nullptr != inVal_ &&
in->getPrimitiveDesc() == inVal_->getPrimitiveDesc())
<< "primitive desc of input grad and value should be equal";
} else {
const MatrixPtr& cpuIn = getInputGrad(0, CPU_DEVICE);
// same PrimitiveDesc with cpuInVal_
CHECK(cpuInVal_);
cpuInGrad_ = MKLDNNMatrix::create(cpuIn, cpuInVal_->getPrimitiveDesc());
if (cpuInGrad_->getPrimitiveDesc() != in->getPrimitiveDesc()) {
const MatrixPtr& dnnIn = getInputGrad(0, MKLDNN_DEVICE);
in = MKLDNNMatrix::create(dnnIn, in->getPrimitiveDesc());
in = cpuInGrad_;
// create reorder if PrimitiveDesc does not match
if (cpuInGrad_->getPrimitiveDesc() != dataPD->diff_src_primitive_desc()) {
in = MKLDNNMatrix::create(getInputGrad(0, MKLDNN_DEVICE),
dataPD->diff_src_primitive_desc());
cvtInGrad_ = MKLDNNMatrix::createReorder(in, cpuInGrad_);
CHECK(cvtInGrad_);
} else {
in = cpuInGrad_;
}
}
}
......
paddle/gserver/layers/MKLDNNFcLayer.cpp
浏览文件 @ 17b4cea4
......@@ -180,10 +180,10 @@ void MKLDNNFcLayer::resetWgtBiasValue(MKLDNNMatrixPtr& wgt,
void MKLDNNFcLayer::resetOutValue(MKLDNNMatrixPtr& out) {
out = MKLDNNMatrix::create(output_.value, {bs_, oc_}, format::nc, engine_);
if (!outputIsOnlyMKLDNN()) {
// fc cpu output value do not need create convert
// just share point
// fc cpu output value do not need create convert, just share data
getOutput(CPU_DEVICE).value->setData(out->getData());
}
output_.value = std::dynamic_pointer_cast<Matrix>(out);
}
void MKLDNNFcLayer::resetFwdPD(std::shared_ptr<fc_fwd::primitive_desc>& pd,
......@@ -214,8 +214,6 @@ void MKLDNNFcLayer::resetFwdPipeline(
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
pipeline.clear();
if (bias) {
fwd_.reset(new fc_fwd(*pd, *in, *wgt, *bias, *out));
} else {
......@@ -237,19 +235,14 @@ void MKLDNNFcLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
}
void MKLDNNFcLayer::resetOutGrad(MKLDNNMatrixPtr& out) {
// TODO(TJ): merge outgrad
int device = outputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE;
output_.grad->setData(getOutput(device).grad->getData());
// for MKLDNN device:
// can not directly cast outputgrad to mkldnnmatrix,
// since each layer can not write the inputgrad to mkldnn inputgrad.
// So just create from matrix with outputvalue format.
// for CPU device:
// fc do not need to convert from cpu device since output is always nc format
// only need create from cpu device
CHECK(outVal_);
out =
MKLDNNMatrix::create(getOutput(device).grad, outVal_->getPrimitiveDesc());
if (outputIsOnlyMKLDNN()) {
MKLDNNLayer::resetOutGrad(out, outVal_->getPrimitiveDesc());
} else {
const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).grad;
output_.grad->setData(cpuOut->getData());
out = MKLDNNMatrix::create(cpuOut, outVal_->getPrimitiveDesc());
}
}
void MKLDNNFcLayer::resetWgtBiasGrad(MKLDNNMatrixPtr& wgt,
......@@ -267,13 +260,11 @@ void MKLDNNFcLayer::resetWgtBiasGrad(MKLDNNMatrixPtr& wgt,
void MKLDNNFcLayer::resetInGrad(MKLDNNMatrixPtr& in) {
in = nullptr;
const MatrixPtr& inGrad = inputLayers_[0]->getOutput().grad;
if (inGrad == nullptr) {
if (inputLayers_[0]->getOutput().grad == nullptr) {
return;
}
// TODO(TJ): use outputMaps_ ways to get the inGrad_ when merge outgrad done
CHECK(inVal_);
in = MKLDNNMatrix::create(inGrad, inVal_->getPrimitiveDesc());
MKLDNNLayer::resetInGrad(in, inVal_->getPrimitiveDesc());
}
void MKLDNNFcLayer::resetBwdWgtPD(
......@@ -314,7 +305,6 @@ void MKLDNNFcLayer::resetBwdPipeline(
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
pipeline.clear();
CHECK(inVal_);
if (bias) {
bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVal_, *out, *wgt, *bias));
......
paddle/gserver/layers/MKLDNNLayer.h
浏览文件 @ 17b4cea4
......@@ -65,6 +65,17 @@ protected:
MKLDNNMatrixPtr biasVal_;
MKLDNNMatrixPtr biasGrad_;
// merge grad primitive
std::shared_ptr<mkldnn::primitive> mergeGrad_;
std::vector<mkldnn::primitive> pipelineMergeGrad_;
// tmp input argument to save input grad, only used to merge grad
Argument tmpInArg_;
// since mkldnn sum do not support different formats:
// can refer to https://github.com/01org/mkl-dnn/issues/134
// so need create reorder manually and save tmp MKLDNNMatrix
MKLDNNMatrixPtr tmpOutGrad_;
std::shared_ptr<mkldnn::primitive> tmpCvt_;
public:
explicit MKLDNNLayer(const LayerConfig& config)
: Layer(config),
......@@ -99,6 +110,7 @@ public:
if (!Layer::init(layerMap, parameterMap)) {
return false;
}
setOutputMap();
checkCPUOutputsNumber();
stream_.reset(new MKLDNNStream());
......@@ -118,12 +130,9 @@ public:
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn forward";
// reset when input total sizes changed, not only the batchsize
inputElemenCnt_ = elemenCnt;
pipelineFwd_.clear();
reshape(bs_, ic_, ih_, iw_, oc_, oh_, ow_);
resetFwd(pipelineFwd_, inVal_, wgtVal_, biasVal_, outVal_);
if (outVal_) {
// change original output value to mkldnn output value
output_.value = std::dynamic_pointer_cast<Matrix>(outVal_);
}
convertWeightsFromPaddle();
needResetBwd_ = true;
}
......@@ -144,9 +153,18 @@ public:
void backward(const UpdateCallback& callback) override {
if (needResetBwd_) {
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn backward";
pipelineBwd_.clear();
pipelineMergeGrad_.clear();
mergeGrad_ = nullptr;
resetBwd(pipelineBwd_, inGrad_, wgtGrad_, biasGrad_, outGrad_);
needResetBwd_ = false;
}
// merge grad must before backward activation
if (mergeGrad_) {
REGISTER_TIMER_INFO("MergeBpGrad", getName().c_str());
stream_->submit(pipelineMergeGrad_);
}
{
REGISTER_TIMER_INFO("BpActTimer", getName().c_str());
backwardActivation();
......@@ -247,6 +265,76 @@ protected:
}
}
/**
* reset the output grad matrix from primitive desc.
* and reset the merge grad primitive if needed.
* note: when this layer has serval outputs,
* it could not be mixed with cpu device,
* since it can not get memory desc from cpu device.
*/
virtual void resetOutGrad(MKLDNNMatrixPtr& out,
mkldnn::memory::primitive_desc pd) {
CHECK(outputIsOnlyMKLDNN()) << "do not support mixed with other device yet";
mergeGrad_ = nullptr;
pipelineMergeGrad_.clear();
out = MKLDNNMatrix::create(output_.grad, pd);
if (outputMap_.size() <= 1) {
return;
}
std::vector<double> scales(outputMap_.size(), 1.0);
std::vector<mkldnn::memory::primitive_desc> srcPDs;
std::vector<mkldnn::primitive::at> srcs;
for (auto it = outputMap_.begin(); it != outputMap_.end(); ++it) {
MKLDNNMatrixPtr src =
std::dynamic_pointer_cast<MKLDNNMatrix>(it->second->grad);
VLOG(MKLDNN_BASE) << getName() << " has output grad " << it->first;
CHECK(src) << "should be MKLDNNMatrix";
auto srcDims = src->getDims();
auto dstDims = out->getDims();
CHECK_EQ(srcDims.size(), dstDims.size());
for (size_t i = 0; i < srcDims.size(); ++i) {
CHECK_EQ(srcDims[i], dstDims[i]);
}
srcPDs.push_back(src->getPrimitiveDesc());
srcs.push_back(*src);
}
// TODO(TJ): remove me when mkldnn sum support different formats
for (size_t i = 1; i < srcPDs.size(); ++i) {
CHECK(srcPDs[0] == srcPDs[i]);
}
tmpOutGrad_ = nullptr;
tmpCvt_ = nullptr;
if (out->getPrimitiveDesc() != srcPDs[0]) {
tmpOutGrad_ = MKLDNNMatrix::create(nullptr, srcPDs[0]);
tmpCvt_ = MKLDNNMatrix::createReorder(tmpOutGrad_, out);
CHECK(tmpCvt_);
pipelineMergeGrad_.push_back(*tmpCvt_);
} else {
tmpOutGrad_ = out;
}
auto sumPD = mkldnn::sum::primitive_desc(
tmpOutGrad_->getMemoryDesc(), scales, srcPDs);
mergeGrad_.reset(new mkldnn::sum(sumPD, srcs, *tmpOutGrad_));
pipelineMergeGrad_.insert(pipelineMergeGrad_.begin(), *mergeGrad_);
}
/**
* reset input grad from primitive desc.
* this function is avaiable for input is only mkldnn
* or input do not care cpu device
*/
virtual void resetInGrad(MKLDNNMatrixPtr& in,
mkldnn::memory::primitive_desc pd) {
LayerPtr& input = inputLayers_[0];
const MatrixPtr& grad =
input->getOutputMapSize() > 1 ? nullptr : input->getOutput().grad;
in = MKLDNNMatrix::create(grad, pd);
Argument& arg = input->getOutput(this->getName());
arg.grad = std::dynamic_pointer_cast<Matrix>(in);
}
/**
* print info about sizes
*/
......@@ -334,6 +422,16 @@ private:
}
}
/**
* Set output map of prev layers.
*/
void setOutputMap() {
outputMap_.clear();
for (size_t i = 0; i < inputLayers_.size(); ++i) {
inputLayers_[i]->setOutput(getName(), &tmpInArg_);
}
}
/**
* Check the cpu device number of outputOtherDevice_.
* should have only one at most.
......
paddle/gserver/layers/MKLDNNPoolLayer.cpp
浏览文件 @ 17b4cea4
......@@ -142,14 +142,16 @@ void MKLDNNPoolLayer::resetOutValue(MKLDNNMatrixPtr& out) {
const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).value;
cpuOutVal_ = MKLDNNMatrix::create(cpuOut, outDims, format::nchw, engine_);
if (cpuOutVal_->getPrimitiveDesc() != out->getPrimitiveDesc()) {
out = MKLDNNMatrix::create(nullptr, out->getPrimitiveDesc());
cvtOutVal_ = MKLDNNMatrix::createReorder(out, cpuOutVal_);
CHECK(cvtOutVal_) << "should not be emptry";
} else {
// CPU output share the same data of MKLDNN output
cpuOut->setData(out->getData());
cpuOutVal_ = out;
}
output_.value = std::dynamic_pointer_cast<Matrix>(cpuOutVal_);
return;
}
output_.value = std::dynamic_pointer_cast<Matrix>(outVal_);
}
void MKLDNNPoolLayer::resetFwdPD(std::shared_ptr<pool_fwd::primitive_desc>& pd,
......@@ -187,7 +189,6 @@ void MKLDNNPoolLayer::resetFwdPipeline(
std::shared_ptr<pool_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& out) {
pipeline.clear();
fwd_ = workspace_
? std::make_shared<pool_fwd>(pool_fwd(*pd, *in, *out, *workspace_))
: std::make_shared<pool_fwd>(pool_fwd(*pd, *in, *out));
......@@ -205,17 +206,17 @@ void MKLDNNPoolLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
resetInGrad(in);
}
void MKLDNNPoolLayer::resetOutGrad(MKLDNNMatrixPtr& out) {
CHECK(outVal_) << "Should have output value";
out = MKLDNNMatrix::create(output_.grad, outVal_->getPrimitiveDesc());
// create reorder if output value has cpu device and pd do not match
cpuOutGrad_ = nullptr;
cvtOutGrad_ = nullptr;
if (!outputIsOnlyMKLDNN()) {
CHECK(outVal_);
if (outputIsOnlyMKLDNN()) {
MKLDNNLayer::resetOutGrad(out, outVal_->getPrimitiveDesc());
} else {
const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).grad;
cpuOutGrad_ = MKLDNNMatrix::create(
cpuOut, memory::dims{bs_, oc_, oh_, ow_}, format::nchw, engine_);
if (cpuOutGrad_->getPrimitiveDesc() != out->getPrimitiveDesc()) {
if (cpuOutGrad_->getPrimitiveDesc() != outVal_->getPrimitiveDesc()) {
out = MKLDNNMatrix::create(output_.grad, outVal_->getPrimitiveDesc());
cvtOutGrad_ = MKLDNNMatrix::createReorder(cpuOutGrad_, out);
CHECK(cvtOutGrad_) << "should not be emptry";
} else {
......@@ -228,12 +229,11 @@ void MKLDNNPoolLayer::resetOutGrad(MKLDNNMatrixPtr& out) {
void MKLDNNPoolLayer::resetInGrad(MKLDNNMatrixPtr& in) {
in = nullptr;
const MatrixPtr& inGrad = inputLayers_[0]->getOutput().grad;
if (inGrad == nullptr) {
if (inputLayers_[0]->getOutput().grad == nullptr) {
return;
}
CHECK(inVal_);
in = MKLDNNMatrix::create(inGrad, inVal_->getPrimitiveDesc());
MKLDNNLayer::resetInGrad(in, inVal_->getPrimitiveDesc());
}
void MKLDNNPoolLayer::resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd,
......@@ -261,7 +261,6 @@ void MKLDNNPoolLayer::resetBwdPipeline(
std::shared_ptr<pool_bwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& out) {
pipeline.clear();
if (cvtOutGrad_) {
pipeline.push_back(*cvtOutGrad_);
}
......
paddle/gserver/tests/MKLDNNTester.cpp
浏览文件 @ 17b4cea4
......@@ -124,8 +124,8 @@ void MKLDNNTester::randomTopDiffs() {
void MKLDNNTester::checkForward() {
VLOG(MKLDNN_ALL) << "Check Forward";
printTopDatas();
double delta = compareMatrix(dnnLayer_->getOutput(CPU_DEVICE).value,
refLayer_->getOutputValue());
double delta =
compareMatrix(dnnLayer_->getOutputValue(), refLayer_->getOutputValue());
EXPECT_LE(fabs(delta), eps_);
}
......
paddle/trainer/tests/CMakeLists.txt
浏览文件 @ 17b4cea4
......@@ -39,15 +39,18 @@ add_test(NAME test_CompareTwoNets
################ test_CompareMKLDNNandCPU ######################
if(WITH_MKLDNN)
add_unittest_without_exec(test_CompareMKLDNNandCPU
test_CompareTwoNets.cpp)
add_test(NAME test_CompareMKLDNNandCPU
COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/
${CMAKE_CURRENT_BINARY_DIR}/test_CompareMKLDNNandCPU
--config_file_a=trainer/tests/sample_trainer_config_simple_net.conf --use_mkldnn_a=True
--config_file_b=trainer/tests/sample_trainer_config_simple_net.conf --use_mkldnn_b=False
--use_gpu=False
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
macro(gen_command VAR_NAME CONFIG_FILE)
set(${VAR_NAME} "${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh" "-d" "${PADDLE_SOURCE_DIR}/python/"
"${CMAKE_CURRENT_BINARY_DIR}/test_CompareMKLDNNandCPU --use_gpu=False"
"--config_file_a=trainer/tests/${CONFIG_FILE} --use_mkldnn_a=True"
"--config_file_b=trainer/tests/${CONFIG_FILE} --use_mkldnn_b=False"
"WORKING_DIRECTORY" "${PADDLE_SOURCE_DIR}/paddle/")
endmacro()
add_unittest_without_exec(test_CompareMKLDNNandCPU test_CompareTwoNets.cpp)
gen_command(compare_simple_net "sample_trainer_config_simple_net.conf")
gen_command(compare_branch_net "sample_trainer_config_branch_net.conf")
add_test(NAME test_CompareMKLDNNandCPU_simple_net COMMAND ${compare_simple_net})
add_test(NAME test_CompareMKLDNNandCPU_branch_net COMMAND ${compare_branch_net})
endif()
############### test_CompareTwoOpts ###################
......
paddle/trainer/tests/sample_trainer_config_branch_net.conf 0 → 100644
浏览文件 @ 17b4cea4
# Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
################################### Data Configuration ###################################
TrainData(ProtoData(files = "trainer/tests/mnist.list"))
################################### Algorithm Configuration ###################################
settings(batch_size = 256,
learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
################################### Network Configuration ###################################
data = data_layer(name ="input", size=784)
tmp = img_conv_layer(input=data,
num_channels=1,
filter_size=3,
num_filters=32,
padding=1,
shared_biases=True,
act=ReluActivation())
a1 = img_conv_layer(input=tmp,
filter_size=1,
num_filters=32,
padding=0,
shared_biases=True,
act=ReluActivation())
a2 = img_conv_layer(input=tmp,
filter_size=3,
num_filters=32,
padding=1,
shared_biases=True,
act=ReluActivation())
tmp = concat_layer(input=[a1, a2])
tmp = img_pool_layer(input=tmp,
num_channels=64,
pool_size=3,
stride=2,
padding=1,
pool_type=AvgPooling())
b1 = img_conv_layer(input=tmp,
filter_size=3,
num_filters=64,
padding=1,
shared_biases=True,
act=ReluActivation())
b1 = img_pool_layer(input=b1,
pool_size=3,
stride=1,
padding=1,
pool_type=MaxPooling())
b2 = img_conv_layer(input=tmp,
filter_size=5,
num_filters=64,
padding=2,
shared_biases=True,
act=ReluActivation())
b2 = img_pool_layer(input=b2,
pool_size=5,
stride=1,
padding=2,
pool_type=MaxPooling())
tmp = addto_layer(input=[b1, b2],
act=ReluActivation(),
bias_attr=False)
tmp = img_pool_layer(input=tmp,
pool_size=3,
stride=2,
padding=1,
pool_type=MaxPooling())
tmp = fc_layer(input=tmp, size=64,
bias_attr=False,
act=TanhActivation())
output = fc_layer(input=tmp, size=10,
bias_attr=True,
act=SoftmaxActivation())
lbl = data_layer(name ="label", size=10)
cost = classification_cost(input=output, label=lbl)
outputs(cost)
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