Merge pull request opencv#11351 from dkurt:dnn_enable_inf_engine_tests

Author: alalek

cmake/OpenCVDetectInferenceEngine.cmake

@@ -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})

modules/dnn/perf/perf_net.cpp

@@ -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));
 }

modules/dnn/src/dnn.cpp

@@ -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);

modules/dnn/src/layers/prior_box_layer.cpp

@@ -295,6 +295,19 @@ class PriorBoxLayerImpl CV_FINAL : public PriorBoxLayer
  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 @@ class PriorBoxLayerImpl CV_FINAL : public PriorBoxLayer
  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 @@ class PriorBoxLayerImpl CV_FINAL : public PriorBoxLayer
 
  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 @@ class PriorBoxLayerImpl CV_FINAL : public PriorBoxLayer
  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 @@ class PriorBoxLayerImpl CV_FINAL : public PriorBoxLayer
  _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);
  }
@@ -495,20 +489,28 @@ class PriorBoxLayerImpl CV_FINAL : public PriorBoxLayer
  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());
  ieLayer->params["variance"] = format("%f", _variance[0]);
  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

modules/dnn/src/op_inf_engine.cpp

@@ -233,8 +233,16 @@ InferenceEngine::StatusCode
 InfEngineBackendNet::getLayerByName(const char *layerName, InferenceEngine::CNNLayerPtr &out,
  InferenceEngine::ResponseDesc *resp) noexcept
 {
- CV_Error(Error::StsNotImplemented, "");
- return InferenceEngine::StatusCode::OK;
+ 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

modules/dnn/test/test_backends.cpp

@@ -23,9 +23,9 @@ class DNNTestNetwork : public TestWithParam <tuple<DNNBackend, DNNTarget> >
  }
 
  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 @@ class DNNTestNetwork : public TestWithParam <tuple<DNNBackend, DNNTarget> >
  }
 
  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 @@ class DNNTestNetwork : public TestWithParam <tuple<DNNBackend, DNNTarget> >
  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 @@ class DNNTestNetwork : public TestWithParam <tuple<DNNBackend, DNNTarget> >
  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)