Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge pull request #13387 from dkurt:dnn_minor_ie_fixes

Browse Source
pull/13436/head
Alexander Alekhin 6 years ago
parent aa666dfa9c 53f6198f27
commit eb1f7797e4
7 changed files with 83 additions and 29 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 6
      modules/dnn/src/op_inf_engine.cpp
  2. 3
      modules/dnn/src/op_inf_engine.hpp
  3. 1
      modules/dnn/test/test_caffe_importer.cpp
  4. 2
      modules/dnn/test/test_darknet_importer.cpp
  5. 2
      modules/dnn/test/test_halide_layers.cpp
  6. 94
      modules/dnn/test/test_layers.cpp
  7. 4
      modules/dnn/test/test_torch_importer.cpp

6
modules/dnn/src/op_inf_engine.cpp
Unescape View File

@ -152,6 +152,7 @@ InfEngineBackendNet::InfEngineBackendNet()
{
targetDevice = InferenceEngine::TargetDevice::eCPU;
precision = InferenceEngine::Precision::FP32;
hasNetOwner = false;
}
InfEngineBackendNet::InfEngineBackendNet(InferenceEngine::CNNNetwork& net)
@ -162,6 +163,7 @@ InfEngineBackendNet::InfEngineBackendNet(InferenceEngine::CNNNetwork& net)
outputs = net.getOutputsInfo();
layers.resize(net.layerCount()); // A hack to execute InfEngineBackendNet::layerCount correctly.
netOwner = net;
hasNetOwner = true;
}
void InfEngineBackendNet::Release() noexcept
@ -178,12 +180,12 @@ void InfEngineBackendNet::setPrecision(InferenceEngine::Precision p) noexcept
InferenceEngine::Precision InfEngineBackendNet::getPrecision() noexcept
{
return precision;
return hasNetOwner ? netOwner.getPrecision() : precision;
}
InferenceEngine::Precision InfEngineBackendNet::getPrecision() const noexcept
{
return precision;
return hasNetOwner ? netOwner.getPrecision() : precision;
}
// Assume that outputs of network is unconnected blobs.

3
modules/dnn/src/op_inf_engine.hpp
Unescape View File

@ -134,6 +134,9 @@ private:
InferenceEngine::InferRequest infRequest;
// In case of models from Model Optimizer we need to manage their lifetime.
InferenceEngine::CNNNetwork netOwner;
// There is no way to check if netOwner is initialized or not so we use
// a separate flag to determine if the model has been loaded from IR.
bool hasNetOwner;
std::string name;

1
modules/dnn/test/test_caffe_importer.cpp
Unescape View File

@ -471,6 +471,7 @@ TEST(Test_Caffe, shared_weights)
net.setInput(blob_1, "input_1");
net.setInput(blob_2, "input_2");
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat sum = net.forward();

2
modules/dnn/test/test_darknet_importer.cpp
Unescape View File

@ -306,7 +306,7 @@ TEST_P(Test_Darknet_nets, TinyYoloVoc)
// batch size 1
testDarknetModel(config_file, weights_file, ref.rowRange(0, 2), scoreDiff, iouDiff);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018040000
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018040000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_MYRIAD)
#endif
// batch size 2

2
modules/dnn/test/test_halide_layers.cpp
Unescape View File

@ -166,7 +166,7 @@ TEST_P(Deconvolution, Accuracy)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_CPU &&
dilation.width == 2 && dilation.height == 2)
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018040000
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018040000
if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_CPU &&
hasBias && group != 1)
throw SkipTestException("Test is disabled for OpenVINO 2018R4");

94
modules/dnn/test/test_layers.cpp
Unescape View File

@ -137,7 +137,7 @@ TEST_P(Test_Caffe_layers, Convolution)
TEST_P(Test_Caffe_layers, DeConvolution)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018040000
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018040000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_CPU)
throw SkipTestException("Test is disabled for OpenVINO 2018R4");
#endif
@ -918,8 +918,11 @@ INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_DWconv_Prelu, Combine(Values(3, 6), Val
// Using Intel's Model Optimizer generate .xml and .bin files:
// ./ModelOptimizer -w /path/to/caffemodel -d /path/to/prototxt \
// -p FP32 -i -b ${batch_size} -o /path/to/output/folder
TEST(Layer_Test_Convolution_DLDT, Accuracy)
typedef testing::TestWithParam<Target> Layer_Test_Convolution_DLDT;
TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
{
Target targetId = GetParam();
Net netDefault = readNet(_tf("layer_convolution.caffemodel"), _tf("layer_convolution.prototxt"));
Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin"));
@ -930,17 +933,29 @@ TEST(Layer_Test_Convolution_DLDT, Accuracy)
Mat outDefault = netDefault.forward();
net.setInput(inp);
Mat out = net.forward();
net.setPreferableTarget(targetId);
if (targetId != DNN_TARGET_MYRIAD)
{
Mat out = net.forward();
normAssert(outDefault, out);
normAssert(outDefault, out);
std::vector<int> outLayers = net.getUnconnectedOutLayers();
ASSERT_EQ(net.getLayer(outLayers[0])->name, "output_merge");
ASSERT_EQ(net.getLayer(outLayers[0])->type, "Concat");
std::vector<int> outLayers = net.getUnconnectedOutLayers();
ASSERT_EQ(net.getLayer(outLayers[0])->name, "output_merge");
ASSERT_EQ(net.getLayer(outLayers[0])->type, "Concat");
}
else
{
// An assertion is expected because the model is in FP32 format but
// Myriad plugin supports only FP16 models.
ASSERT_ANY_THROW(net.forward());
}
}
TEST(Layer_Test_Convolution_DLDT, setInput_uint8)
TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8)
{
Target targetId = GetParam();
Mat inp = blobFromNPY(_tf("blob.npy"));
Mat inputs[] = {Mat(inp.dims, inp.size, CV_8U), Mat()};
@ -951,12 +966,25 @@ TEST(Layer_Test_Convolution_DLDT, setInput_uint8)
for (int i = 0; i < 2; ++i)
{
Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin"));
net.setPreferableTarget(targetId);
net.setInput(inputs[i]);
outs[i] = net.forward();
ASSERT_EQ(outs[i].type(), CV_32F);
if (targetId != DNN_TARGET_MYRIAD)
{
outs[i] = net.forward();
ASSERT_EQ(outs[i].type(), CV_32F);
}
else
{
// An assertion is expected because the model is in FP32 format but
// Myriad plugin supports only FP16 models.
ASSERT_ANY_THROW(net.forward());
}
}
normAssert(outs[0], outs[1]);
if (targetId != DNN_TARGET_MYRIAD)
normAssert(outs[0], outs[1]);
}
INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Convolution_DLDT,
testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE)));
// 1. Create a .prototxt file with the following network:
// layer {
@ -980,14 +1008,17 @@ TEST(Layer_Test_Convolution_DLDT, setInput_uint8)
// net.save('/path/to/caffemodel')
//
// 3. Convert using ModelOptimizer.
typedef testing::TestWithParam<tuple<int, int> > Test_DLDT_two_inputs;
typedef testing::TestWithParam<tuple<int, int, Target> > Test_DLDT_two_inputs;
TEST_P(Test_DLDT_two_inputs, as_IR)
{
int firstInpType = get<0>(GetParam());
int secondInpType = get<1>(GetParam());
// TODO: It looks like a bug in Inference Engine.
Target targetId = get<2>(GetParam());
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018040000
if (secondInpType == CV_8U)
throw SkipTestException("");
throw SkipTestException("Test is enabled starts from OpenVINO 2018R4");
#endif
Net net = readNet(_tf("net_two_inputs.xml"), _tf("net_two_inputs.bin"));
int inpSize[] = {1, 2, 3};
@ -998,11 +1029,21 @@ TEST_P(Test_DLDT_two_inputs, as_IR)
net.setInput(firstInp, "data");
net.setInput(secondInp, "second_input");
Mat out = net.forward();
net.setPreferableTarget(targetId);
if (targetId != DNN_TARGET_MYRIAD)
{
Mat out = net.forward();
Mat ref;
cv::add(firstInp, secondInp, ref, Mat(), CV_32F);
normAssert(out, ref);
Mat ref;
cv::add(firstInp, secondInp, ref, Mat(), CV_32F);
normAssert(out, ref);
}
else
{
// An assertion is expected because the model is in FP32 format but
// Myriad plugin supports only FP16 models.
ASSERT_ANY_THROW(net.forward());
}
}
TEST_P(Test_DLDT_two_inputs, as_backend)
@ -1010,6 +1051,8 @@ TEST_P(Test_DLDT_two_inputs, as_backend)
static const float kScale = 0.5f;
static const float kScaleInv = 1.0f / kScale;
Target targetId = get<2>(GetParam());
Net net;
LayerParams lp;
lp.type = "Eltwise";
@ -1018,9 +1061,9 @@ TEST_P(Test_DLDT_two_inputs, as_backend)
int eltwiseId = net.addLayerToPrev(lp.name, lp.type, lp); // connect to a first input
net.connect(0, 1, eltwiseId, 1); // connect to a second input
int inpSize[] = {1, 2, 3};
Mat firstInp(3, &inpSize[0], get<0>(GetParam()));
Mat secondInp(3, &inpSize[0], get<1>(GetParam()));
int inpSize[] = {1, 2, 3, 4};
Mat firstInp(4, &inpSize[0], get<0>(GetParam()));
Mat secondInp(4, &inpSize[0], get<1>(GetParam()));
randu(firstInp, 0, 255);
randu(secondInp, 0, 255);
@ -1028,15 +1071,20 @@ TEST_P(Test_DLDT_two_inputs, as_backend)
net.setInput(firstInp, "data", kScale);
net.setInput(secondInp, "second_input", kScaleInv);
net.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
net.setPreferableTarget(targetId);
Mat out = net.forward();
Mat ref;
addWeighted(firstInp, kScale, secondInp, kScaleInv, 0, ref, CV_32F);
normAssert(out, ref);
// Output values are in range [0, 637.5].
double l1 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.06 : 1e-6;
double lInf = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.3 : 1e-5;
normAssert(out, ref, "", l1, lInf);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_DLDT_two_inputs, Combine(
Values(CV_8U, CV_32F), Values(CV_8U, CV_32F)
Values(CV_8U, CV_32F), Values(CV_8U, CV_32F),
testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE))
));
class UnsupportedLayer : public Layer

4
modules/dnn/test/test_torch_importer.cpp
Unescape View File

@ -136,7 +136,7 @@ TEST_P(Test_Torch_layers, run_reshape_change_batch_size)
TEST_P(Test_Torch_layers, run_reshape)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018040000
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018040000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("Test is disabled for OpenVINO 2018R4");
#endif
@ -172,7 +172,7 @@ TEST_P(Test_Torch_layers, run_depth_concat)
TEST_P(Test_Torch_layers, run_deconv)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018040000
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018040000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("Test is disabled for OpenVINO 2018R4");
#endif

Write Preview
Loading…
Cancel
Save