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Merge pull request #11461 from dkurt:dnn_reduce_mem_consumption

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This commit is contained in:
Alexander Alekhin 2018-05-11 09:47:03 +00:00
commit ed150bd97a
6 changed files with 55 additions and 35 deletions
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20
modules/dnn/src/caffe/caffe_importer.cpp
View File

@ -250,16 +250,13 @@ public:
blobShapeFromProto(pbBlob, shape);
dstBlob.create((int)shape.size(), &shape[0], CV_32F);
float *dstData = dstBlob.ptr<float>();
if (pbBlob.data_size())
{
// Single precision floats.
CV_Assert(pbBlob.data_size() == (int)dstBlob.total());
CV_DbgAssert(pbBlob.GetDescriptor()->FindFieldByLowercaseName("data")->cpp_type() == FieldDescriptor::CPPTYPE_FLOAT);
for (int i = 0; i < pbBlob.data_size(); i++)
dstData[i] = pbBlob.data(i);
Mat(dstBlob.dims, &dstBlob.size[0], CV_32F, (void*)pbBlob.data().data()).copyTo(dstBlob);
}
else
{
@ -288,11 +285,18 @@ public:
if (li == netBinary.layer_size() || netBinary.layer(li).blobs_size() == 0)
return;
const caffe::LayerParameter &binLayer = netBinary.layer(li);
layerParams.blobs.resize(binLayer.blobs_size());
for (int bi = 0; bi < binLayer.blobs_size(); bi++)
caffe::LayerParameter* binLayer = netBinary.mutable_layer(li);
const int numBlobs = binLayer->blobs_size();
layerParams.blobs.resize(numBlobs);
for (int bi = 0; bi < numBlobs; bi++)
{
blobFromProto(binLayer.blobs(bi), layerParams.blobs[bi]);
blobFromProto(binLayer->blobs(bi), layerParams.blobs[bi]);
}
binLayer->clear_blobs();
CV_Assert(numBlobs == binLayer->blobs().ClearedCount());
for (int bi = 0; bi < numBlobs; bi++)
{
delete binLayer->mutable_blobs()->ReleaseCleared();
}
}

40
modules/dnn/src/caffe/caffe_io.cpp
View File

@ -132,7 +132,7 @@ void UpgradeV0PaddingLayers(const NetParameter& param,
NetParameter* param_upgraded_pad);
// Upgrade a single V0LayerConnection to the V1LayerParameter format.
bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection,
bool UpgradeV0LayerParameter(V1LayerParameter* v0_layer_connection,
V1LayerParameter* layer_param);
V1LayerParameter_LayerType UpgradeV0LayerType(const string& type);
@ -149,9 +149,9 @@ bool NetNeedsV1ToV2Upgrade(const NetParameter& net_param);
// Perform all necessary transformations to upgrade a NetParameter with
// deprecated V1LayerParameters.
bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param);
bool UpgradeV1Net(NetParameter* net_param);
bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
bool UpgradeV1LayerParameter(V1LayerParameter* v1_layer_param,
LayerParameter* layer_param);
const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type);
@ -194,7 +194,7 @@ bool UpgradeV0Net(const NetParameter& v0_net_param_padding_layers,
net_param->set_name(v0_net_param.name());
}
for (int i = 0; i < v0_net_param.layers_size(); ++i) {
is_fully_compatible &= UpgradeV0LayerParameter(v0_net_param.layers(i),
is_fully_compatible &= UpgradeV0LayerParameter(v0_net_param.mutable_layers(i),
net_param->add_layers());
}
for (int i = 0; i < v0_net_param.input_size(); ++i) {
@ -268,8 +268,10 @@ void UpgradeV0PaddingLayers(const NetParameter& param,
}
}
bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection,
bool UpgradeV0LayerParameter(V1LayerParameter* v0_layer_connection_,
V1LayerParameter* layer_param) {
CV_Assert(v0_layer_connection_ != NULL);
const V1LayerParameter& v0_layer_connection = *v0_layer_connection_;
bool is_fully_compatible = true;
layer_param->Clear();
for (int i = 0; i < v0_layer_connection.bottom_size(); ++i) {
@ -287,9 +289,7 @@ bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection,
if (v0_layer_param.has_type()) {
layer_param->set_type(UpgradeV0LayerType(type));
}
for (int i = 0; i < v0_layer_param.blobs_size(); ++i) {
layer_param->add_blobs()->CopyFrom(v0_layer_param.blobs(i));
}
layer_param->mutable_blobs()->Swap(v0_layer_connection_->mutable_blobs());
for (int i = 0; i < v0_layer_param.blobs_lr_size(); ++i) {
layer_param->add_blobs_lr(v0_layer_param.blobs_lr(i));
}
@ -770,8 +770,7 @@ bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
if (NetNeedsV1ToV2Upgrade(*param)) {
LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
<< "V1LayerParameter: " << param_file;
NetParameter original_param(*param);
if (!UpgradeV1Net(original_param, param)) {
if (!UpgradeV1Net(param)) {
success = false;
LOG(ERROR) << "Warning: had one or more problems upgrading "
<< "V1LayerParameter (see above); continuing anyway.";
@ -791,23 +790,24 @@ bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
return success;
}
bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param) {
bool UpgradeV1Net(NetParameter* net_param) {
// V1LayerParameter layers -> LayerParameter layer
CV_Assert(net_param != NULL);
bool is_fully_compatible = true;
if (v1_net_param.layer_size() > 0) {
if (net_param->layer_size() > 0) {
LOG(ERROR) << "Input NetParameter to be upgraded already specifies 'layer' "
<< "fields; these will be ignored for the upgrade.";
is_fully_compatible = false;
}
net_param->CopyFrom(v1_net_param);
net_param->clear_layers();
net_param->clear_layer();
for (int i = 0; i < v1_net_param.layers_size(); ++i) {
if (!UpgradeV1LayerParameter(v1_net_param.layers(i),
for (int i = 0; i < net_param->layers_size(); ++i) {
if (!UpgradeV1LayerParameter(net_param->mutable_layers(i),
net_param->add_layer())) {
LOG(ERROR) << "Upgrade of input layer " << i << " failed.";
is_fully_compatible = false;
}
}
net_param->clear_layers();
return is_fully_compatible;
}
@ -834,8 +834,10 @@ void UpgradeNetBatchNorm(NetParameter* net_param) {
}
}
bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
bool UpgradeV1LayerParameter(V1LayerParameter* v1_layer_param_,
LayerParameter* layer_param) {
CV_Assert(v1_layer_param_ != NULL);
const V1LayerParameter& v1_layer_param = *v1_layer_param_;
layer_param->Clear();
bool is_fully_compatible = true;
for (int i = 0; i < v1_layer_param.bottom_size(); ++i) {
@ -856,9 +858,7 @@ bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
if (v1_layer_param.has_type()) {
layer_param->set_type(UpgradeV1LayerType(v1_layer_param.type()));
}
for (int i = 0; i < v1_layer_param.blobs_size(); ++i) {
layer_param->add_blobs()->CopyFrom(v1_layer_param.blobs(i));
}
layer_param->mutable_blobs()->Swap(v1_layer_param_->mutable_blobs());
for (int i = 0; i < v1_layer_param.param_size(); ++i) {
while (layer_param->param_size() <= i) { layer_param->add_param(); }
layer_param->mutable_param(i)->set_name(v1_layer_param.param(i));

10
modules/dnn/src/layers/convolution_layer.cpp
View File

@ -169,7 +169,8 @@ class ConvolutionLayerImpl CV_FINAL : public BaseConvolutionLayerImpl
{
public:
enum { VEC_ALIGN = 8, DFT_TYPE = CV_32F };
Mat weightsMat, weightsMat_doubles;
Mat weightsMat;
std::vector<double> weightsMultipliers;
std::vector<float> biasvec;
std::vector<float> reluslope;
Ptr<ActivationLayer> activ;
@ -259,7 +260,7 @@ public:
wm = wm_aligned;
}
weightsMat = wm;
weightsMat.convertTo(weightsMat_doubles, CV_64F);
weightsMultipliers.assign(outCn, 1.0);
Mat biasMat = hasBias() ? blobs[1].reshape(1, outCn) : Mat();
biasvec.resize(outCn+2);
@ -335,13 +336,14 @@ public:
if (!w.empty())
{
Mat originWeights = blobs[0].reshape(1, outCn);
for (int i = 0; i < outCn; ++i)
{
double wi = w.at<float>(i);
cv::multiply(slice(weightsMat_doubles, i), wi, slice(weightsMat_doubles, i));
weightsMultipliers[i] *= wi;
cv::multiply(originWeights.row(i), weightsMultipliers[i], weightsMat.row(i));
biasvec[i] *= wi;
}
weightsMat_doubles.convertTo(weightsMat, weightsMat.type());
}
if (!b.empty())

10
modules/dnn/src/tensorflow/tf_graph_simplifier.cpp
View File

@ -612,7 +612,7 @@ void RemoveIdentityOps(tensorflow::GraphDef& net)
Mat getTensorContent(const tensorflow::TensorProto &tensor)
{
std::string content = tensor.tensor_content();
const std::string& content = tensor.tensor_content();
switch (tensor.dtype())
{
case tensorflow::DT_FLOAT:
@ -681,6 +681,14 @@ Mat getTensorContent(const tensorflow::TensorProto &tensor)
return Mat();
}
void releaseTensor(tensorflow::TensorProto* tensor)
{
if (!tensor->mutable_tensor_content()->empty())
{
delete tensor->release_tensor_content();
}
}
CV__DNN_EXPERIMENTAL_NS_END
}} // namespace dnn, namespace cv

2
modules/dnn/src/tensorflow/tf_graph_simplifier.hpp
View File

@ -23,6 +23,8 @@ void simplifySubgraphs(tensorflow::GraphDef& net);
Mat getTensorContent(const tensorflow::TensorProto &tensor);
void releaseTensor(tensorflow::TensorProto* tensor);
CV__DNN_EXPERIMENTAL_NS_END
}} // namespace dnn, namespace cv

8
modules/dnn/src/tensorflow/tf_importer.cpp
View File

@ -677,7 +677,9 @@ void TFImporter::populateNet(Net dstNet)
layers_to_ignore.insert(next_layers[0].first);
}
kernelFromTensor(getConstBlob(layer, value_id), layerParams.blobs[0]);
const tensorflow::TensorProto& kernelTensor = getConstBlob(layer, value_id);
kernelFromTensor(kernelTensor, layerParams.blobs[0]);
releaseTensor(const_cast<tensorflow::TensorProto*>(&kernelTensor));
int* kshape = layerParams.blobs[0].size.p;
if (type == "DepthwiseConv2dNative")
{
@ -788,7 +790,9 @@ void TFImporter::populateNet(Net dstNet)
}
int kernel_blob_index = -1;
blobFromTensor(getConstBlob(layer, value_id, -1, &kernel_blob_index), layerParams.blobs[0]);
const tensorflow::TensorProto& kernelTensor = getConstBlob(layer, value_id, -1, &kernel_blob_index);
blobFromTensor(kernelTensor, layerParams.blobs[0]);
releaseTensor(const_cast<tensorflow::TensorProto*>(&kernelTensor));
if (kernel_blob_index == 1) { // In this case output is computed by x*W formula - W should be transposed
Mat data = layerParams.blobs[0].t();