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Enable some tests for clDNN plugin from Intel's Inference Engine

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This commit is contained in:
Dmitry Kurtaev 2018-04-19 15:04:57 +03:00
parent 7ea5029ae5
commit bd77d100e1
6 changed files with 81 additions and 69 deletions
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4
cmake/OpenCVDetectInferenceEngine.cmake
View File

@ -15,6 +15,10 @@ macro(ie_fail)
return()
endmacro()
if(NOT HAVE_CXX11)
ie_fail()
endif()
if(NOT INF_ENGINE_ROOT_DIR OR NOT EXISTS "${INF_ENGINE_ROOT_DIR}/include/inference_engine.hpp")
set(ie_root_paths "${INF_ENGINE_ROOT_DIR}")
if(DEFINED ENV{INTEL_CVSDK_DIR})

6
modules/dnn/perf/perf_net.cpp
View File

@ -95,24 +95,18 @@ PERF_TEST_P_(DNNTestNetwork, AlexNet)
PERF_TEST_P_(DNNTestNetwork, GoogLeNet)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
"", Mat(cv::Size(224, 224), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, ResNet_50)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
"resnet_50.yml", Mat(cv::Size(224, 224), CV_32FC3));
}
PERF_TEST_P_(DNNTestNetwork, SqueezeNet_v1_1)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
"squeezenet_v1_1.yml", Mat(cv::Size(227, 227), CV_32FC3));
}

9
modules/dnn/src/dnn.cpp
View File

@ -1255,6 +1255,15 @@ struct Net::Impl
if (weightableLayer->_biases)
weightableLayer->_biases = convertFp16(weightableLayer->_biases);
}
else
{
for (const auto& weights : {"weights", "biases"})
{
auto it = ieNode->layer->blobs.find(weights);
if (it != ieNode->layer->blobs.end())
it->second = convertFp16(it->second);
}
}
}
ieNode->connect(ld.inputBlobsWrappers, ld.outputBlobsWrappers);

60
modules/dnn/src/layers/prior_box_layer.cpp
View File

@ -295,6 +295,19 @@ public:
return false;
}
void finalize(const std::vector<Mat*> &inputs, std::vector<Mat> &outputs) CV_OVERRIDE
{
CV_Assert(inputs.size() > 1, inputs[0]->dims == 4, inputs[1]->dims == 4);
int layerWidth = inputs[0]->size[3];
int layerHeight = inputs[0]->size[2];
int imageWidth = inputs[1]->size[3];
int imageHeight = inputs[1]->size[2];
_stepY = _stepY == 0 ? (static_cast<float>(imageHeight) / layerHeight) : _stepY;
_stepX = _stepX == 0 ? (static_cast<float>(imageWidth) / layerWidth) : _stepX;
}
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inps, OutputArrayOfArrays outs, OutputArrayOfArrays internals)
{
@ -310,16 +323,6 @@ public:
int _imageWidth = inputs[1].size[3];
int _imageHeight = inputs[1].size[2];
float stepX, stepY;
if (_stepX == 0 || _stepY == 0)
{
stepX = static_cast<float>(_imageWidth) / _layerWidth;
stepY = static_cast<float>(_imageHeight) / _layerHeight;
} else {
stepX = _stepX;
stepY = _stepY;
}
if (umat_offsetsX.empty())
{
Mat offsetsX(1, _offsetsX.size(), CV_32FC1, &_offsetsX[0]);
@ -339,8 +342,8 @@ public:
ocl::Kernel kernel("prior_box", ocl::dnn::prior_box_oclsrc);
kernel.set(0, (int)nthreads);
kernel.set(1, (float)stepX);
kernel.set(2, (float)stepY);
kernel.set(1, (float)_stepX);
kernel.set(2, (float)_stepY);
kernel.set(3, ocl::KernelArg::PtrReadOnly(umat_offsetsX));
kernel.set(4, ocl::KernelArg::PtrReadOnly(umat_offsetsY));
kernel.set(5, (int)_offsetsX.size());
@ -410,15 +413,6 @@ public:
int _imageWidth = inputs[1]->size[3];
int _imageHeight = inputs[1]->size[2];
float stepX, stepY;
if (_stepX == 0 || _stepY == 0) {
stepX = static_cast<float>(_imageWidth) / _layerWidth;
stepY = static_cast<float>(_imageHeight) / _layerHeight;
} else {
stepX = _stepX;
stepY = _stepY;
}
float* outputPtr = outputs[0].ptr<float>();
float _boxWidth, _boxHeight;
for (size_t h = 0; h < _layerHeight; ++h)
@ -431,8 +425,8 @@ public:
_boxHeight = _boxHeights[i];
for (int j = 0; j < _offsetsX.size(); ++j)
{
float center_x = (w + _offsetsX[j]) * stepX;
float center_y = (h + _offsetsY[j]) * stepY;
float center_x = (w + _offsetsX[j]) * _stepX;
float center_y = (h + _offsetsY[j]) * _stepY;
outputPtr = addPrior(center_x, center_y, _boxWidth, _boxHeight, _imageWidth,
_imageHeight, _bboxesNormalized, outputPtr);
}
@ -495,7 +489,7 @@ public:
ieLayer->params["aspect_ratio"] += format(",%f", _aspectRatios[i]);
}
ieLayer->params["flip"] = _flip ? "1" : "0";
ieLayer->params["flip"] = "0"; // We already flipped aspect ratios.
ieLayer->params["clip"] = _clip ? "1" : "0";
CV_Assert(!_variance.empty());
@ -503,12 +497,20 @@ public:
for (int i = 1; i < _variance.size(); ++i)
ieLayer->params["variance"] += format(",%f", _variance[i]);
ieLayer->params["step"] = _stepX == _stepY ? format("%f", _stepX) : "0";
ieLayer->params["step_h"] = _stepY;
ieLayer->params["step_w"] = _stepX;
if (_stepX == _stepY)
{
ieLayer->params["step"] = format("%f", _stepX);
ieLayer->params["step_h"] = "0.0";
ieLayer->params["step_w"] = "0.0";
}
else
{
ieLayer->params["step"] = "0.0";
ieLayer->params["step_h"] = format("%f", _stepY);
ieLayer->params["step_w"] = format("%f", _stepX);
}
CV_Assert(_offsetsX.size() == 1, _offsetsY.size() == 1, _offsetsX[0] == _offsetsY[0]);
ieLayer->params["offset"] = format("%f", _offsetsX[0]);;
ieLayer->params["offset"] = format("%f", _offsetsX[0]);
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif // HAVE_INF_ENGINE

10
modules/dnn/src/op_inf_engine.cpp
View File

@ -233,9 +233,17 @@ InferenceEngine::StatusCode
InfEngineBackendNet::getLayerByName(const char *layerName, InferenceEngine::CNNLayerPtr &out,
InferenceEngine::ResponseDesc *resp) noexcept
{
CV_Error(Error::StsNotImplemented, "");
for (auto& l : layers)
{
if (l->name == layerName)
{
out = l;
return InferenceEngine::StatusCode::OK;
}
}
CV_Error(Error::StsObjectNotFound, cv::format("Cannot find a layer %s", layerName));
return InferenceEngine::StatusCode::NOT_FOUND;
}
void InfEngineBackendNet::setTargetDevice(InferenceEngine::TargetDevice device) noexcept
{

59
modules/dnn/test/test_backends.cpp
View File

@ -23,9 +23,9 @@ public:
}
void processNet(const std::string& weights, const std::string& proto,
Size inpSize, const std::string& outputLayer,
Size inpSize, const std::string& outputLayer = "",
const std::string& halideScheduler = "",
double l1 = 1e-5, double lInf = 1e-4)
double l1 = 0.0, double lInf = 0.0)
{
// Create a common input blob.
int blobSize[] = {1, 3, inpSize.height, inpSize.width};
@ -36,9 +36,9 @@ public:
}
void processNet(std::string weights, std::string proto,
Mat inp, const std::string& outputLayer,
Mat inp, const std::string& outputLayer = "",
std::string halideScheduler = "",
double l1 = 1e-5, double lInf = 1e-4)
double l1 = 0.0, double lInf = 0.0)
{
if (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL)
{
@ -49,6 +49,16 @@ public:
throw SkipTestException("OpenCL is not available/disabled in OpenCV");
}
}
if (target == DNN_TARGET_OPENCL_FP16)
{
l1 = l1 == 0.0 ? 4e-3 : l1;
lInf = lInf == 0.0 ? 2e-2 : lInf;
}
else
{
l1 = l1 == 0.0 ? 1e-5 : l1;
lInf = lInf == 0.0 ? 1e-4 : lInf;
}
weights = findDataFile(weights, false);
if (!proto.empty())
proto = findDataFile(proto, false);
@ -71,31 +81,28 @@ public:
Mat out = net.forward(outputLayer).clone();
if (outputLayer == "detection_out")
normAssertDetections(outDefault, out, "First run", 0.2);
normAssertDetections(outDefault, out, "First run", 0.2, l1, lInf);
else
normAssert(outDefault, out, "First run", l1, lInf);
// Test 2: change input.
inp *= 0.1f;
float* inpData = (float*)inp.data;
for (int i = 0; i < inp.size[0] * inp.size[1]; ++i)
{
Mat slice(inp.size[2], inp.size[3], CV_32F, inpData);
cv::flip(slice, slice, 1);
inpData += slice.total();
}
netDefault.setInput(inp);
net.setInput(inp);
outDefault = netDefault.forward(outputLayer).clone();
out = net.forward(outputLayer).clone();
if (outputLayer == "detection_out")
checkDetections(outDefault, out, "Second run", l1, lInf);
normAssertDetections(outDefault, out, "Second run", 0.2, l1, lInf);
else
normAssert(outDefault, out, "Second run", l1, lInf);
}
void checkDetections(const Mat& out, const Mat& ref, const std::string& msg,
float l1, float lInf, int top = 5)
{
top = std::min(std::min(top, out.size[2]), out.size[3]);
std::vector<cv::Range> range(4, cv::Range::all());
range[2] = cv::Range(0, top);
normAssert(out(range), ref(range));
}
};
TEST_P(DNNTestNetwork, AlexNet)
@ -110,8 +117,6 @@ TEST_P(DNNTestNetwork, AlexNet)
TEST_P(DNNTestNetwork, ResNet_50)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
Size(224, 224), "prob",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_resnet_50.yml" :
@ -120,8 +125,6 @@ TEST_P(DNNTestNetwork, ResNet_50)
TEST_P(DNNTestNetwork, SqueezeNet_v1_1)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
Size(227, 227), "prob",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_squeezenet_v1_1.yml" :
@ -130,8 +133,6 @@ TEST_P(DNNTestNetwork, SqueezeNet_v1_1)
TEST_P(DNNTestNetwork, GoogLeNet)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("");
processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
Size(224, 224), "prob");
}
@ -180,7 +181,7 @@ TEST_P(DNNTestNetwork, SSD_VGG16)
{
if (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL ||
backend == DNN_BACKEND_HALIDE && target == DNN_TARGET_CPU ||
backend == DNN_BACKEND_INFERENCE_ENGINE)
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
throw SkipTestException("");
processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel",
"dnn/ssd_vgg16.prototxt", Size(300, 300), "detection_out");
@ -189,30 +190,24 @@ TEST_P(DNNTestNetwork, SSD_VGG16)
TEST_P(DNNTestNetwork, OpenPose_pose_coco)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
double l1 = target == DNN_TARGET_OPENCL_FP16 ? 3e-5 : 1e-5;
double lInf = target == DNN_TARGET_OPENCL_FP16 ? 3e-3 : 1e-4;
processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
Size(368, 368), "", "", l1, lInf);
Size(368, 368));
}
TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
double l1 = target == DNN_TARGET_OPENCL_FP16 ? 4e-5 : 1e-5;
double lInf = target == DNN_TARGET_OPENCL_FP16 ? 7e-3 : 1e-4;
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
Size(368, 368), "", "", l1, lInf);
Size(368, 368));
}
TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
double l1 = target == DNN_TARGET_OPENCL_FP16 ? 5e-5 : 1e-5;
double lInf = target == DNN_TARGET_OPENCL_FP16 ? 5e-3 : 1e-4;
// The same .caffemodel but modified .prototxt
// See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi_faster_4_stages.prototxt",
Size(368, 368), "", "", l1, lInf);
Size(368, 368));
}
TEST_P(DNNTestNetwork, OpenFace)