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Merge pull request #14277 from dkurt:ie_enable_tests_2019r1

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pull/14313/head
Alexander Alekhin 6 years ago
parent a9ddf12187 a2bbfa1db5
commit 227ec19929
9 changed files with 70 additions and 98 deletions
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  1. 27
      modules/dnn/src/dnn.cpp
  2. 14
      modules/dnn/src/layers/elementwise_layers.cpp
  3. 4
      modules/dnn/src/layers/flatten_layer.cpp
  4. 49
      modules/dnn/src/layers/padding_layer.cpp
  5. 2
      modules/dnn/src/layers/pooling_layer.cpp
  6. 7
      modules/dnn/test/test_backends.cpp
  7. 13
      modules/dnn/test/test_halide_layers.cpp
  8. 2
      modules/dnn/test/test_onnx_importer.cpp
  9. 50
      modules/dnn/test/test_tf_importer.cpp

27
modules/dnn/src/dnn.cpp
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@ -1160,12 +1160,6 @@ struct Net::Impl
continue;
currLayer->unsetAttached();
Ptr<PoolingLayer> poolingLayer = currLayer.dynamicCast<PoolingLayer>();
if( !poolingLayer.empty() )
{
poolingLayer->computeMaxIdx = true;
}
}
layersTimings.clear();
@ -2082,30 +2076,11 @@ struct Net::Impl
}
}
}
// the optimization #2. if there is no layer that takes max pooling layer's computed
// max indices (and only some semantical segmentation networks might need this;
// many others only take the maximum values), then we switch the max pooling
// layer to the faster operating mode.
Ptr<PoolingLayer> poolingLayer = ld.layerInstance.dynamicCast<PoolingLayer>();
if( !poolingLayer.empty() && !ld.consumers.empty() )
{
size_t i = 0, nconsumers = ld.consumers.size();
for( ; i < nconsumers; i++ )
if( ld.consumers[i].oid > 0 )
break;
// if there is no layer that takes the second output pin of the pooling layer
// on input then we don't need to compute the indices
if( i >= nconsumers )
{
poolingLayer->computeMaxIdx = false;
printf_(("\tsimplified pooling layer %s\n", poolingLayer->name.c_str()));
}
}
if (preferableBackend != DNN_BACKEND_OPENCV)
continue; // Go to the next layer.
// the optimization #3. if there is concat layer that concatenates channels
// the optimization #2. if there is concat layer that concatenates channels
// from the inputs together (i.e. axis == 1) then we make the inputs of
// the concat layer to write to the concatenation output buffer
// (and so we eliminate the concatenation layer, because the channels

14
modules/dnn/src/layers/elementwise_layers.cpp
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@ -256,8 +256,11 @@ struct ReLUFunctor
bool supportBackend(int backendId, int)
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE;
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
return slope >= 0 || !INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2019R1);
#endif
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
@ -741,8 +744,11 @@ struct AbsValFunctor
bool supportBackend(int backendId, int)
{
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE ||
backendId == DNN_BACKEND_INFERENCE_ENGINE;
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
return !INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2019R1);
#endif
return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE;
}
void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const

4
modules/dnn/src/layers/flatten_layer.cpp
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@ -159,8 +159,8 @@ public:
InferenceEngine::Builder::Layer ieLayer(name);
ieLayer.setName(name);
ieLayer.setType("Flatten");
ieLayer.getParameters()["axis"] = _startAxis;
ieLayer.getParameters()["end_axis"] = _endAxis;
ieLayer.getParameters()["axis"] = (size_t)_startAxis;
ieLayer.getParameters()["end_axis"] = _endAxis; // Do not cast to size_t because it might be negative.
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));

49
modules/dnn/src/layers/padding_layer.cpp
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@ -12,6 +12,7 @@ Implementation of padding layer, which adds paddings to input blob.
#include "../precomp.hpp"
#include "layers_common.hpp"
#include "../op_halide.hpp"
#include "../op_inf_engine.hpp"
#include <vector>
namespace cv
@ -68,28 +69,36 @@ public:
// Compute dstRanges.
const MatSize& inpShape = inputs[0].size;
dstRanges.resize(paddings.size());
int offset = 0;
if (inputDims != -1 && inputs[0].dims != inputDims)
{
dstRanges.insert(dstRanges.begin(), Range::all());
offset = 1;
paddings.insert(paddings.begin(), std::make_pair(0, 0));
}
dstRanges.resize(paddings.size());
for (int i = 0; i < paddings.size(); ++i)
{
dstRanges[offset + i].start = paddings[i].first;
dstRanges[offset + i].end = paddings[i].first + inpShape[offset + i];
dstRanges[i].start = paddings[i].first;
dstRanges[i].end = paddings[i].first + inpShape[i];
}
// Add the rest of dimensions.
for (int i = dstRanges.size(); i < inputs[0].dims; ++i)
{
dstRanges.push_back(Range::all());
paddings.push_back(std::make_pair(0, 0));
}
inputDims = -1; // Next time paddings are filled for all the dimensions.
}
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
return INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) &&
(preferableTarget != DNN_TARGET_MYRIAD ||
(dstRanges.size() == 4 && paddings[0].first == 0 && paddings[0].second == 0));
#endif
return backendId == DNN_BACKEND_OPENCV ||
(backendId == DNN_BACKEND_HALIDE && haveHalide() && dstRanges.size() == 4);
}
@ -109,7 +118,7 @@ public:
{
std::vector<float> paddingValue_fp32(1, paddingValue);
std::vector<int16_t> paddingValue_fp16(1);
convertFp16(paddingValue_fp32, paddingValue_fp16);
cv::convertFp16(paddingValue_fp32, paddingValue_fp16);
outputs[0].setTo(paddingValue_fp16[0]);
}
else
@ -173,6 +182,32 @@ public:
return Ptr<BackendNode>();
}
virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
InferenceEngine::Builder::Layer ieLayer(name);
ieLayer.setName(name);
ieLayer.setType("Pad");
std::vector<int> begins(paddings.size(), 0), ends(paddings.size(), 0);
for (int i = 0; i < paddings.size(); ++i)
{
begins[i] = paddings[i].first;
ends[i] = paddings[i].second;
}
ieLayer.getParameters()["pads_begin"] = begins;
ieLayer.getParameters()["pads_end"] = ends;
ieLayer.getParameters()["pad_mode"] = paddingType;
if (paddingType == "constant")
ieLayer.getParameters()["pad_value"] = paddingValue;
ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
#endif
return Ptr<BackendNode>();
}
private:
std::vector<std::pair<int, int> > paddings; // Pairs pad before, pad after.
std::vector<Range> dstRanges;

2
modules/dnn/src/layers/pooling_layer.cpp
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@ -140,7 +140,7 @@ public:
#ifdef HAVE_OPENCL
poolOp.release();
#endif
computeMaxIdx = type == MAX;
computeMaxIdx = type == MAX && outputs.size() == 2;
}
virtual bool supportBackend(int backendId) CV_OVERRIDE

7
modules/dnn/test/test_backends.cpp
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@ -289,12 +289,7 @@ TEST_P(DNNTestNetwork, OpenFace)
#if INF_ENGINE_VER_MAJOR_EQ(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("Test is disabled for Myriad targets");
#elif INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
)
throw SkipTestException("Test is disabled for MyriadX target");
#else
#elif INF_ENGINE_VER_MAJOR_EQ(2018030000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("Test has been fixed in OpenVINO 2018R4");
#endif

13
modules/dnn/test/test_halide_layers.cpp
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@ -561,12 +561,6 @@ TEST_P(ReLU, Accuracy)
float negativeSlope = get<0>(GetParam());
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE
&& negativeSlope < 0
)
throw SkipTestException("Test is disabled");
#endif
LayerParams lp;
lp.set("negative_slope", negativeSlope);
@ -589,13 +583,6 @@ TEST_P(NoParamActivation, Accuracy)
LayerParams lp;
lp.type = get<0>(GetParam());
lp.name = "testLayer";
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE
&& lp.type == "AbsVal"
)
throw SkipTestException("Test is disabled");
#endif
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, NoParamActivation, Combine(

2
modules/dnn/test/test_onnx_importer.cpp
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@ -379,7 +379,7 @@ TEST_P(Test_ONNX_nets, LResNet100E_IR)
lInf = 0.035;
}
else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_CPU) {
l1 = 4.5e-5;
l1 = 4.6e-5;
lInf = 1.9e-4;
}
testONNXModels("LResNet100E_IR", pb, l1, lInf);

50
modules/dnn/test/test_tf_importer.cpp
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@ -140,10 +140,6 @@ TEST_P(Test_TensorFlow_layers, padding)
TEST_P(Test_TensorFlow_layers, padding_same)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("Test is disabled for DLIE");
#endif
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
@ -251,10 +247,6 @@ TEST_P(Test_TensorFlow_layers, reshape)
TEST_P(Test_TensorFlow_layers, flatten)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("Test is disabled for DLIE");
#endif
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2
@ -267,11 +259,6 @@ TEST_P(Test_TensorFlow_layers, flatten)
TEST_P(Test_TensorFlow_layers, unfused_flatten)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("Test is disabled for DLIE");
#endif
runTensorFlowNet("unfused_flatten");
runTensorFlowNet("unfused_flatten_unknown_batch");
}
@ -320,11 +307,14 @@ class Test_TensorFlow_nets : public DNNTestLayer {};
TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
{
checkBackend();
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
)
throw SkipTestException("Test is disabled for MyriadX");
#endif
checkBackend();
std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false);
std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false);
std::string imgPath = findDataFile("dnn/street.png", false);
@ -333,30 +323,18 @@ TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
resize(imread(imgPath), inp, Size(300, 300));
inp = blobFromImage(inp, 1.0f / 127.5, Size(), Scalar(127.5, 127.5, 127.5), true);
std::vector<String> outNames(3);
outNames[0] = "concat";
outNames[1] = "concat_1";
outNames[2] = "detection_out";
std::vector<Mat> refs(outNames.size());
for (int i = 0; i < outNames.size(); ++i)
{
std::string path = findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco." + outNames[i] + ".npy", false);
refs[i] = blobFromNPY(path);
}
Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco.detection_out.npy", false));
Net net = readNetFromTensorflow(netPath, netConfig);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
Mat out = net.forward();
std::vector<Mat> output;
net.forward(output, outNames);
normAssert(refs[0].reshape(1, 1), output[0].reshape(1, 1), "", 1e-5, 1.5e-4);
normAssert(refs[1].reshape(1, 1), output[1].reshape(1, 1), "", 1e-5, 3e-4);
normAssertDetections(refs[2], output[2], "", 0.2);
double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0043 : default_l1;
double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.037 : default_lInf;
normAssertDetections(ref, out, "", 0.2, scoreDiff, iouDiff);
}
TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
@ -597,10 +575,6 @@ TEST_P(Test_TensorFlow_layers, fp16_weights)
TEST_P(Test_TensorFlow_layers, fp16_padding_same)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("Test is disabled for DLIE");
#endif
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X

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