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Merge branch '3.4' into merge-3.4

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This commit is contained in:
Alexander Alekhin
2018-12-22 05:40:15 +00:00
parent 578ea4bae9 c0e11bb50e
commit 1dee705074
29 changed files with 995 additions and 1879 deletions
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modules/dnn/include/opencv2/dnn/dnn.hpp
+2 -1
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@@ -750,6 +750,7 @@ CV__DNN_INLINE_NS_BEGIN
* @brief Reads a network model stored in <a href="http://torch.ch">Torch7</a> framework's format.
* @param model path to the file, dumped from Torch by using torch.save() function.
* @param isBinary specifies whether the network was serialized in ascii mode or binary.
* @param evaluate specifies testing phase of network. If true, it's similar to evaluate() method in Torch.
* @returns Net object.
*
* @note Ascii mode of Torch serializer is more preferable, because binary mode extensively use `long` type of C language,
@@ -771,7 +772,7 @@ CV__DNN_INLINE_NS_BEGIN
*
* Also some equivalents of these classes from cunn, cudnn, and fbcunn may be successfully imported.
*/
CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true);
CV_EXPORTS_W Net readNetFromTorch(const String &model, bool isBinary = true, bool evaluate = true);
/**
* @brief Read deep learning network represented in one of the supported formats.
modules/dnn/include/opencv2/dnn/version.hpp
+1 -1
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@@ -6,7 +6,7 @@
#define OPENCV_DNN_VERSION_HPP
/// Use with major OpenCV version only.
#define OPENCV_DNN_API_VERSION 20181205
#define OPENCV_DNN_API_VERSION 20181221
#if !defined CV_DOXYGEN && !defined CV_DNN_DONT_ADD_INLINE_NS
#define CV__DNN_INLINE_NS __CV_CAT(dnn4_v, OPENCV_DNN_API_VERSION)
modules/dnn/src/layers/mvn_layer.cpp
+6
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@@ -116,9 +116,15 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
#ifdef HAVE_INF_ENGINE
if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
return !zeroDev && eps <= 1e-7f;
#else
return !zeroDev && (preferableTarget == DNN_TARGET_CPU || eps <= 1e-7f);
#endif
else
#endif // HAVE_INF_ENGINE
return backendId == DNN_BACKEND_OPENCV;
}
modules/dnn/src/onnx/onnx_importer.cpp
+5 -6
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@@ -420,31 +420,30 @@ void ONNXImporter::populateNet(Net dstNet)
}
else if (layer_type == "Sub")
{
Mat blob = (-1.0f) * getBlob(node_proto, constBlobs, 1);
blob = blob.reshape(1, 1);
Mat blob = getBlob(node_proto, constBlobs, 1);
if (blob.total() == 1) {
layerParams.type = "Power";
layerParams.set("shift", blob.at<float>(0));
layerParams.set("shift", -blob.at<float>(0));
}
else {
layerParams.type = "Scale";
layerParams.set("has_bias", true);
layerParams.blobs.push_back(blob);
layerParams.blobs.push_back(-1.0f * blob.reshape(1, 1));
}
}
else if (layer_type == "Div")
{
Mat blob = getBlob(node_proto, constBlobs, 1);
CV_Assert_N(blob.type() == CV_32F, blob.total());
divide(1.0, blob, blob);
if (blob.total() == 1)
{
layerParams.set("scale", blob.at<float>(0));
layerParams.set("scale", 1.0f / blob.at<float>(0));
layerParams.type = "Power";
}
else
{
layerParams.type = "Scale";
divide(1.0, blob, blob);
layerParams.blobs.push_back(blob);
layerParams.set("bias_term", false);
}
modules/dnn/src/op_inf_engine.hpp
+3 -2
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@@ -26,10 +26,11 @@
#define INF_ENGINE_RELEASE_2018R2 2018020000
#define INF_ENGINE_RELEASE_2018R3 2018030000
#define INF_ENGINE_RELEASE_2018R4 2018040000
#define INF_ENGINE_RELEASE_2018R5 2018050000
#ifndef INF_ENGINE_RELEASE
#warning("IE version have not been provided via command-line. Using 2018R4 by default")
#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2018R4
#warning("IE version have not been provided via command-line. Using 2018R5 by default")
#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2018R5
#endif
#define INF_ENGINE_VER_MAJOR_GT(ver) (((INF_ENGINE_RELEASE) / 10000) > ((ver) / 10000))
modules/dnn/src/torch/torch_importer.cpp
+8 -5
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@@ -129,13 +129,15 @@ struct TorchImporter
Module *rootModule;
Module *curModule;
int moduleCounter;
bool testPhase;
TorchImporter(String filename, bool isBinary)
TorchImporter(String filename, bool isBinary, bool evaluate)
{
CV_TRACE_FUNCTION();
rootModule = curModule = NULL;
moduleCounter = 0;
testPhase = evaluate;
file = cv::Ptr<THFile>(THDiskFile_new(filename, "r", 0), THFile_free);
CV_Assert(file && THFile_isOpened(file));
@@ -680,7 +682,8 @@ struct TorchImporter
layerParams.blobs.push_back(tensorParams["bias"].second);
}
if (nnName == "InstanceNormalization")
bool trainPhase = scalarParams.get<bool>("train", false);
if (nnName == "InstanceNormalization" || (trainPhase && !testPhase))
{
cv::Ptr<Module> mvnModule(new Module(nnName));
mvnModule->apiType = "MVN";
@@ -1243,18 +1246,18 @@ struct TorchImporter
Mat readTorchBlob(const String &filename, bool isBinary)
{
TorchImporter importer(filename, isBinary);
TorchImporter importer(filename, isBinary, true);
importer.readObject();
CV_Assert(importer.tensors.size() == 1);
return importer.tensors.begin()->second;
}
Net readNetFromTorch(const String &model, bool isBinary)
Net readNetFromTorch(const String &model, bool isBinary, bool evaluate)
{
CV_TRACE_FUNCTION();
TorchImporter importer(model, isBinary);
TorchImporter importer(model, isBinary, evaluate);
Net net;
importer.populateNet(net);
return net;
modules/dnn/test/test_backends.cpp
+2 -2
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@@ -226,9 +226,9 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
TEST_P(DNNTestNetwork, OpenFace)
{
#if defined(INF_ENGINE_RELEASE)
#if INF_ENGINE_RELEASE < 2018030000
#if (INF_ENGINE_RELEASE < 2018030000 || INF_ENGINE_RELEASE == 2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
throw SkipTestException("");
#elif INF_ENGINE_RELEASE < 2018040000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("Test is enabled starts from OpenVINO 2018R4");
modules/dnn/test/test_ie_models.cpp
+8
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@@ -190,6 +190,14 @@ TEST_P(DNNTestOpenVINO, models)
modelName == "landmarks-regression-retail-0009" ||
modelName == "semantic-segmentation-adas-0001")))
throw SkipTestException("");
#elif INF_ENGINE_RELEASE == 2018050000
if (modelName == "single-image-super-resolution-0063" ||
modelName == "single-image-super-resolution-1011" ||
modelName == "single-image-super-resolution-1021" ||
(target == DNN_TARGET_OPENCL_FP16 && modelName == "face-reidentification-retail-0095") ||
(target == DNN_TARGET_MYRIAD && (modelName == "license-plate-recognition-barrier-0001" ||
modelName == "semantic-segmentation-adas-0001")))
throw SkipTestException("");
#endif
#endif
modules/dnn/test/test_layers.cpp
+4
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@@ -295,6 +295,10 @@ TEST_P(Test_Caffe_layers, Eltwise)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
throw SkipTestException("Test is disabled for OpenVINO 2018R5");
#endif
testLayerUsingCaffeModels("layer_eltwise");
}
modules/dnn/test/test_onnx_importer.cpp
+22 -2
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@@ -164,6 +164,8 @@ TEST_P(Test_ONNX_layers, MultyInputs)
TEST_P(Test_ONNX_layers, DynamicReshape)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
testONNXModels("dynamic_reshape");
}
@@ -249,6 +251,10 @@ TEST_P(Test_ONNX_nets, VGG16)
else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL) {
lInf = 1.2e-4;
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
l1 = 0.131;
#endif
testONNXModels("vgg16", pb, l1, lInf);
}
@@ -327,7 +333,7 @@ TEST_P(Test_ONNX_nets, CNN_MNIST)
TEST_P(Test_ONNX_nets, MobileNet_v2)
{
// output range: [-166; 317]
const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.38 : 7e-5;
const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.4 : 7e-5;
const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 2.87 : 5e-4;
testONNXModels("mobilenetv2", pb, l1, lInf);
}
@@ -350,7 +356,17 @@ TEST_P(Test_ONNX_nets, LResNet100E_IR)
TEST_P(Test_ONNX_nets, Emotion_ferplus)
{
testONNXModels("emotion_ferplus", pb);
double l1 = default_l1;
double lInf = default_lInf;
// Output values are in range [-2.01109, 2.11111]
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
l1 = 0.007;
else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.021;
lInf = 0.034;
}
testONNXModels("emotion_ferplus", pb, l1, lInf);
}
TEST_P(Test_ONNX_nets, Inception_v2)
@@ -371,6 +387,10 @@ TEST_P(Test_ONNX_nets, DenseNet121)
TEST_P(Test_ONNX_nets, Inception_v1)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
#endif
testONNXModels("inception_v1", pb);
}
modules/dnn/test/test_tf_importer.cpp
+12
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@@ -241,6 +241,10 @@ TEST_P(Test_TensorFlow_layers, unfused_flatten)
TEST_P(Test_TensorFlow_layers, leaky_relu)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
throw SkipTestException("");
#endif
runTensorFlowNet("leaky_relu_order1");
runTensorFlowNet("leaky_relu_order2");
runTensorFlowNet("leaky_relu_order3");
@@ -383,6 +387,10 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN)
TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("Unstable test case");
#endif
checkBackend();
std::string proto = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pbtxt", false);
std::string model = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pb", false);
@@ -560,6 +568,10 @@ TEST_P(Test_TensorFlow_layers, slice)
if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
(target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
#endif
runTensorFlowNet("slice_4d");
}
modules/dnn/test/test_torch_importer.cpp
+13 -8
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@@ -73,7 +73,7 @@ class Test_Torch_layers : public DNNTestLayer
{
public:
void runTorchNet(const String& prefix, String outLayerName = "",
bool check2ndBlob = false, bool isBinary = false,
bool check2ndBlob = false, bool isBinary = false, bool evaluate = true,
double l1 = 0.0, double lInf = 0.0)
{
String suffix = (isBinary) ? ".dat" : ".txt";
@@ -84,7 +84,7 @@ public:
checkBackend(backend, target, &inp, &outRef);
Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary);
Net net = readNetFromTorch(_tf(prefix + "_net" + suffix), isBinary, evaluate);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
@@ -114,7 +114,7 @@ TEST_P(Test_Torch_layers, run_convolution)
// Output reference values are in range [23.4018, 72.0181]
double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.08 : default_l1;
double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.42 : default_lInf;
runTorchNet("net_conv", "", false, true, l1, lInf);
runTorchNet("net_conv", "", false, true, true, l1, lInf);
}
TEST_P(Test_Torch_layers, run_pool_max)
@@ -147,7 +147,7 @@ TEST_P(Test_Torch_layers, run_reshape)
TEST_P(Test_Torch_layers, run_reshape_single_sample)
{
// Reference output values in range [14.4586, 18.4492].
runTorchNet("net_reshape_single_sample", "", false, false,
runTorchNet("net_reshape_single_sample", "", false, false, true,
(target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.0073 : default_l1,
(target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.025 : default_lInf);
}
@@ -166,7 +166,7 @@ TEST_P(Test_Torch_layers, run_concat)
TEST_P(Test_Torch_layers, run_depth_concat)
{
runTorchNet("net_depth_concat", "", false, true, 0.0,
runTorchNet("net_depth_concat", "", false, true, true, 0.0,
target == DNN_TARGET_OPENCL_FP16 ? 0.021 : 0.0);
}
@@ -182,6 +182,7 @@ TEST_P(Test_Torch_layers, run_deconv)
TEST_P(Test_Torch_layers, run_batch_norm)
{
runTorchNet("net_batch_norm", "", false, true);
runTorchNet("net_batch_norm_train", "", false, true, false);
}
TEST_P(Test_Torch_layers, net_prelu)
@@ -216,7 +217,7 @@ TEST_P(Test_Torch_layers, net_conv_gemm_lrn)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
runTorchNet("net_conv_gemm_lrn", "", false, true,
runTorchNet("net_conv_gemm_lrn", "", false, true, true,
target == DNN_TARGET_OPENCL_FP16 ? 0.046 : 0.0,
target == DNN_TARGET_OPENCL_FP16 ? 0.023 : 0.0);
}
@@ -266,9 +267,9 @@ class Test_Torch_nets : public DNNTestLayer {};
TEST_P(Test_Torch_nets, OpenFace_accuracy)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
#if defined(INF_ENGINE_RELEASE) && (INF_ENGINE_RELEASE < 2018030000 || INF_ENGINE_RELEASE == 2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
throw SkipTestException("");
#endif
checkBackend();
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
@@ -389,6 +390,10 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
// -model models/instance_norm/feathers.t7
TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
#endif
checkBackend();
std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
"dnn/fast_neural_style_instance_norm_feathers.t7"};