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Added Torch blob reader and torch convolution test.

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pull/265/head
Vitaliy Lyudvichenko 10 years ago
parent 2905c03581
commit 8df8936810
3 changed files with 83 additions and 29 deletions
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  1. 3
      modules/dnn/include/opencv2/dnn/dnn.hpp
  2. 74
      modules/dnn/src/torch/torch_importer.cpp
  3. 35
      modules/dnn/test/test_torch_importer.cpp

3
modules/dnn/include/opencv2/dnn/dnn.hpp
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@ -92,6 +92,9 @@ namespace dnn
CV_EXPORTS Ptr<Importer> createTorchImporter(const String &filename, bool isBinary = true);
CV_EXPORTS Blob readTorchMat(const String &filename, bool isBinary = true);
//Layer factory allows to create instances of registered layers.
class CV_EXPORTS LayerRegister
{

74
modules/dnn/src/torch/torch_importer.cpp
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@ -53,12 +53,18 @@ struct TorchImporter : public ::cv::dnn::Importer
struct Module
{
String thName, type;
String thName, apiType;
dnn::LayerParams params;
std::vector<Module*> modules;
Module(const String &_thName, const String &_type = String())
: thName(_thName), type(_type) {}
Module(const String &_thName, const String &_apiType = String())
: thName(_thName), apiType(_apiType) {}
~Module()
{
for (size_t i = 0; i < modules.size(); i++)
delete modules[i];
}
};
Module *rootModule;
@ -67,6 +73,9 @@ struct TorchImporter : public ::cv::dnn::Importer
TorchImporter(String filename, bool isBinary)
{
rootModule = curModule = NULL;
moduleCounter = 0;
file = THDiskFile_new(filename.c_str(), "r", 0);
CV_Assert(file && THFile_isOpened(file));
@ -206,7 +215,7 @@ struct TorchImporter : public ::cv::dnn::Importer
double *buf = storageMat.ptr<double>();
THFile_readLongRaw(file, (long*)buf, size);
for (size_t i = 0; i < (size_t)size; i++)
for (size_t i = (size_t)size; i-- > 0; )
buf[i] = ((long*)buf)[i];
}
break;
@ -295,7 +304,7 @@ struct TorchImporter : public ::cv::dnn::Importer
std::cout << "scalarParams:\n";
std::cout << scalarParams;
std::cout << "#" << tensorParams.size() << "tensorParams:\n";
std::cout << "#" << tensorParams.size() << " tensorParams:\n";
std::map<String,Blob>::const_iterator it;
for (it = tensorParams.begin(); it != tensorParams.end(); it++)
std::cout << it->first << ": Tensor " << it->second.shape() << "\n";
@ -348,7 +357,8 @@ struct TorchImporter : public ::cv::dnn::Importer
//allocate Blob
Mat srcMat(ndims, (int*)isizes, typeTensor , storages[indexStorage].ptr() + offset, (size_t*)ssteps);
int dstType = (typeTensor == CV_64F) ? CV_64F : CV_32F;
//int dstType = (typeTensor == CV_64F) ? CV_64F : CV_32F;
int dstType = CV_32F;
Blob blob;
blob.create(BlobShape(ndims, isizes), dstType);
@ -357,7 +367,7 @@ struct TorchImporter : public ::cv::dnn::Importer
tensors.insert(std::make_pair(indexTensor, blob));
}
bool isNNClass(const String &className, String &nnName)
static bool isNNClass(const String &className, String &nnName)
{
const char *prefixes[] = {"nn.", "cunn.", "cudnn.", "fbcunn.", NULL};
@ -373,7 +383,7 @@ struct TorchImporter : public ::cv::dnn::Importer
return false;
}
void convertTorchKernelsParams(const Dict &torchParams, cv::dnn::LayerParams &layerParams)
static void convertTorchKernelsParams(const Dict &torchParams, cv::dnn::LayerParams &layerParams)
{
layerParams.set("kernel_h", torchParams.get<int>("kH"));
layerParams.set("kernel_w", torchParams.get<int>("kW"));
@ -418,7 +428,7 @@ struct TorchImporter : public ::cv::dnn::Importer
}
else if (nnName == "SpatialConvolution")
{
newModule->type = "Convolution";
newModule->apiType = "Convolution";
readTorchTable(scalarParams, tensorParams);
CV_Assert(tensorParams.count("weight"));
@ -436,7 +446,7 @@ struct TorchImporter : public ::cv::dnn::Importer
}
else if (nnName == "SpatialMaxPooling" || nnName == "SpatialAveragePooling")
{
newModule->type = "Pooling";
newModule->apiType = "Pooling";
readTorchTable(scalarParams, tensorParams);
if (nnName == "SpatialMaxPooling")
@ -449,7 +459,7 @@ struct TorchImporter : public ::cv::dnn::Importer
}
else if (nnName == "Linear")
{
newModule->type = "InnerProduct";
newModule->apiType = "InnerProduct";
readTorchTable(scalarParams, tensorParams);
CV_Assert(tensorParams.count("weight"));
@ -467,7 +477,7 @@ struct TorchImporter : public ::cv::dnn::Importer
}
else if (nnName == "Reshape")
{
newModule->type = "Reshape";
newModule->apiType = "Reshape";
readTorchTable(scalarParams, tensorParams);
CV_Assert(scalarParams.has("size"));
@ -491,6 +501,7 @@ struct TorchImporter : public ::cv::dnn::Importer
}
else
{
delete newModule;
readTorchTable(scalarParams, tensorParams);
CV_Error(Error::StsNotImplemented, "Unknown nn class \"" + className + "\"");
}
@ -504,7 +515,6 @@ struct TorchImporter : public ::cv::dnn::Importer
void readObject()
{
int typeidx = readInt();
std::cout << "typeidx: " << typeidx << "\n";
if (typeidx == TYPE_TORCH)
{
@ -541,11 +551,11 @@ struct TorchImporter : public ::cv::dnn::Importer
if (module == NULL)
return prevLayerId;
if (module->type.length())
if (module->apiType.length())
{
int newLayerId = this->net.addLayer(generateLayerName(module->type), module->type, module->params);
int newLayerId = this->net.addLayer(generateLayerName(module->apiType), module->apiType, module->params);
net.connect(prevLayerId, prevOutNum, newLayerId, 0);
std::cout << "added " << module->thName << " i.e. " << module->type << "\n";
std::cout << "added " << module->thName << " i.e. " << module->apiType << "\n";
return newLayerId;
}
else
@ -584,16 +594,16 @@ struct TorchImporter : public ::cv::dnn::Importer
void populateNet(Net net)
{
this->net = net;
THFile_seek(file, 0);
readedIndexes.clear();
storages.clear();
if (rootModule == NULL)
{
rootModule = new Module("Sequential");
curModule = rootModule;
rootModule = new Module("Sequential");
curModule = rootModule;
readObject();
THFile_seek(file, 0);
readObject();
}
moduleCounter = 0;
this->net = net;
fill(rootModule);
}
};
@ -603,6 +613,16 @@ CV_EXPORTS Ptr<Importer> createTorchImporter(const String &filename, bool isBina
return Ptr<Importer>(new TorchImporter(filename, isBinary));
}
CV_EXPORTS Blob readTorchMat(const String &filename, bool isBinary)
{
Ptr<TorchImporter> importer(new TorchImporter(filename, isBinary));
importer->readObject();
CV_Assert(importer->tensors.size() == 1);
return importer->tensors.begin()->second;
}
#else //ENABLE_TORCH_IMPORTER
CV_EXPORTS Ptr<Importer> createTorchImporter(const String&, bool)
@ -611,6 +631,12 @@ CV_EXPORTS Ptr<Importer> createTorchImporter(const String&, bool)
return Ptr<Importer>();
}
CV_EXPORTS Blob readTorchMat(const String &filename, bool isBinary)
{
CV_Error(Error::StsNotImplemented, "Module was build without Torch importer");
return Blob();
}
#endif //ENABLE_TORCH_IMPORTER
}
}

35
modules/dnn/test/test_torch_importer.cpp
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@ -17,7 +17,8 @@ static std::string getOpenCVExtraDir()
template<typename TStr>
static std::string getTestFile(TStr filename)
{
return (getOpenCVExtraDir() + "/dnn/") + filename;
//return (getOpenCVExtraDir() + "/dnn/") + filename;
return String("/home/vitaliy/th/") + filename;
}
TEST(Torch_Importer, simple_read)
@ -25,13 +26,37 @@ TEST(Torch_Importer, simple_read)
Net net;
Ptr<Importer> importer;
ASSERT_NO_THROW( importer = createTorchImporter("/home/vitaliy/th/conv1.txt", false) );
//ASSERT_NO_THROW( importer = createTorchImporter("L:\\home\\vitaliy\\th\\conv1.txt", false) );
ASSERT_NO_THROW( importer = createTorchImporter(getTestFile("conv1.txt"), false) );
ASSERT_TRUE( importer != NULL );
ASSERT_NO_THROW( importer->populateNet(net) );
}
static Blob convertBlob(const Blob &inp, int type)
{
Mat tmpMat;
inp.getMatRef().convertTo(tmpMat, type);
Blob res;
res.create(inp.shape(), type);
res.fill(inp.shape(), type, (void*)tmpMat.data);
return res;
}
TEST(Torch_Importer, run_convolution)
{
Net net;
Ptr<Importer> importer = createTorchImporter(getTestFile("run_conv_net.txt"), false);
ASSERT_TRUE(importer != NULL);
importer->populateNet(net);
//ASSERT_NO_THROW( importer->populateNet(net) );
Blob inp = convertBlob( readTorchMat(getTestFile("run_conv_input.txt"), false), CV_32F );
Blob outRef = convertBlob( readTorchMat(getTestFile("run_conv_output.txt"), false), CV_32F );
net.setBlob(".0", inp);
net.forward();
Blob out = net.getBlob("l1_Convolution");
normAssert(outRef, out);
}
}

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