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Merge pull request #14671 from alalek:dnn_test_fast_read

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This commit is contained in:
Alexander Alekhin 2019-05-30 06:48:34 +00:00
commit 8ea2f59d33
5 changed files with 31 additions and 33 deletions
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26
modules/dnn/test/test_caffe_importer.cpp
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@ -83,17 +83,17 @@ TEST(Test_Caffe, memory_read)
const string proto = findDataFile("dnn/bvlc_googlenet.prototxt", false);
const string model = findDataFile("dnn/bvlc_googlenet.caffemodel", false);
string dataProto;
ASSERT_TRUE(readFileInMemory(proto, dataProto));
string dataModel;
ASSERT_TRUE(readFileInMemory(model, dataModel));
std::vector<char> dataProto;
readFileContent(proto, dataProto);
std::vector<char> dataModel;
readFileContent(model, dataModel);
Net net = readNetFromCaffe(dataProto.c_str(), dataProto.size());
Net net = readNetFromCaffe(dataProto.data(), dataProto.size());
net.setPreferableBackend(DNN_BACKEND_OPENCV);
ASSERT_FALSE(net.empty());
Net net2 = readNetFromCaffe(dataProto.c_str(), dataProto.size(),
dataModel.c_str(), dataModel.size());
Net net2 = readNetFromCaffe(dataProto.data(), dataProto.size(),
dataModel.data(), dataModel.size());
ASSERT_FALSE(net2.empty());
}
@ -124,13 +124,13 @@ TEST_P(Reproducibility_AlexNet, Accuracy)
const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
if (readFromMemory)
{
string dataProto;
ASSERT_TRUE(readFileInMemory(proto, dataProto));
string dataModel;
ASSERT_TRUE(readFileInMemory(model, dataModel));
std::vector<char> dataProto;
readFileContent(proto, dataProto);
std::vector<char> dataModel;
readFileContent(model, dataModel);
net = readNetFromCaffe(dataProto.c_str(), dataProto.size(),
dataModel.c_str(), dataModel.size());
net = readNetFromCaffe(dataProto.data(), dataProto.size(),
dataModel.data(), dataModel.size());
}
else
net = readNetFromCaffe(proto, model);

2
modules/dnn/test/test_common.hpp
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@ -59,7 +59,7 @@ void normAssertDetections(
double confThreshold = 0.0, double scores_diff = 1e-5,
double boxes_iou_diff = 1e-4);
bool readFileInMemory(const std::string& filename, std::string& content);
void readFileContent(const std::string& filename, CV_OUT std::vector<char>& content);
#ifdef HAVE_INF_ENGINE
bool validateVPUType();

16
modules/dnn/test/test_common.impl.hpp
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@ -158,23 +158,21 @@ void normAssertDetections(
testBoxes, comment, confThreshold, scores_diff, boxes_iou_diff);
}
bool readFileInMemory(const std::string& filename, std::string& content)
void readFileContent(const std::string& filename, CV_OUT std::vector<char>& content)
{
std::ios::openmode mode = std::ios::in | std::ios::binary;
const std::ios::openmode mode = std::ios::in | std::ios::binary;
std::ifstream ifs(filename.c_str(), mode);
if (!ifs.is_open())
return false;
ASSERT_TRUE(ifs.is_open());
content.clear();
ifs.seekg(0, std::ios::end);
content.reserve(ifs.tellg());
const size_t sz = ifs.tellg();
content.resize(sz);
ifs.seekg(0, std::ios::beg);
content.assign((std::istreambuf_iterator<char>(ifs)),
std::istreambuf_iterator<char>());
return true;
ifs.read((char*)content.data(), sz);
ASSERT_FALSE(ifs.fail());
}

8
modules/dnn/test/test_darknet_importer.cpp
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@ -93,11 +93,11 @@ TEST(Test_Darknet, read_yolo_voc_stream)
}
// Import from bytes array.
{
std::string cfg, weights;
readFileInMemory(cfgFile, cfg);
readFileInMemory(weightsFile, weights);
std::vector<char> cfg, weights;
readFileContent(cfgFile, cfg);
readFileContent(weightsFile, weights);
Net net = readNetFromDarknet(&cfg[0], cfg.size(), &weights[0], weights.size());
Net net = readNetFromDarknet(cfg.data(), cfg.size(), weights.data(), weights.size());
net.setInput(inp);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat out = net.forward();

12
modules/dnn/test/test_tf_importer.cpp
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@ -96,17 +96,17 @@ public:
if (memoryLoad)
{
// Load files into a memory buffers
string dataModel;
ASSERT_TRUE(readFileInMemory(netPath, dataModel));
std::vector<char> dataModel;
readFileContent(netPath, dataModel);
string dataConfig;
std::vector<char> dataConfig;
if (hasText)
{
ASSERT_TRUE(readFileInMemory(netConfig, dataConfig));
readFileContent(netConfig, dataConfig);
}
net = readNetFromTensorflow(dataModel.c_str(), dataModel.size(),
dataConfig.c_str(), dataConfig.size());
net = readNetFromTensorflow(dataModel.data(), dataModel.size(),
dataConfig.data(), dataConfig.size());
}
else
net = readNetFromTensorflow(netPath, netConfig);