potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-30 15:53:03 +04:00
Code Packages Releases Wiki Activity

Merge pull request #13932 from l-bat:MyriadX_master_dldt

Browse Source 
* Fix precision in tests for MyriadX

* Fix ONNX tests

* Add output range in ONNX tests

* Skip tests on Myriad OpenVINO 2018R5

* Add detect MyriadX

* Add detect MyriadX on OpenVINO R5

* Skip tests on Myriad next version of OpenVINO

* dnn(ie): VPU type from environment variable

* dnn(test): validate VPU type

* dnn(test): update DLIE test skip conditions
This commit is contained in:
Lubov Batanina
2019-03-29 16:42:58 +03:00
committed by Alexander Alekhin
parent 982e6fc721
commit 7d3d6bc4e2
19 changed files with 1024 additions and 417 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/test/test_backends.cpp
+66 -18
View File
@@ -2,7 +2,7 @@
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2018, Intel Corporation, all rights reserved.
// Copyright (C) 2018-2019, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
#include "test_precomp.hpp"
@@ -157,21 +157,29 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
float diffScores = (target == DNN_TARGET_OPENCL_FP16) ? 6e-3 : 0.0;
processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
inp, "detection_out", "", diffScores);
float diffScores = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1.5e-2 : 0.0;
float diffSquares = (target == DNN_TARGET_MYRIAD) ? 0.063 : 0.0;
float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.252 : 0.0;
processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
inp, "detection_out", "", diffScores, diffSquares, detectionConfThresh);
}
TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height)
{
if (backend == DNN_BACKEND_HALIDE)
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
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 560), Scalar(127.5, 127.5, 127.5), false);
float diffScores = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.029 : 0.0;
float diffSquares = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09 : 0.0;
processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
inp, "detection_out", "", diffScores, diffSquares);
inp, "detection_out", "", diffScores, diffSquares);
}
TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
@@ -180,16 +188,22 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.011 : 0.0;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.06 : 0.0;
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.095 : 0.0;
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.09 : 0.0;
float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.216 : 0.2;
processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt",
inp, "detection_out", "", l1, lInf);
inp, "detection_out", "", l1, lInf, detectionConfThresh);
}
TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow_Different_Width_Height)
{
if (backend == DNN_BACKEND_HALIDE)
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
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat inp = blobFromImage(sample, 1.0f, Size(300, 560), Scalar(), false);
float l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.012 : 0.0;
@@ -215,32 +229,54 @@ TEST_P(DNNTestNetwork, SSD_VGG16)
if (backend == DNN_BACKEND_HALIDE && target == DNN_TARGET_CPU)
throw SkipTestException("");
double scoreThreshold = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0325 : 0.0;
const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.032 : 0.0;
Mat sample = imread(findDataFile("dnn/street.png", false));
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel",
"dnn/ssd_vgg16.prototxt", inp, "detection_out", "", scoreThreshold);
"dnn/ssd_vgg16.prototxt", inp, "detection_out", "", scoreThreshold, lInf);
}
TEST_P(DNNTestNetwork, OpenPose_pose_coco)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
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 OpenVINO <= 2018R5 + MyriadX target");
#endif
const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.0056 : 0.0;
const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.072 : 0.0;
processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
Size(46, 46));
Size(46, 46), "", "", l1, lInf);
}
TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
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 OpenVINO <= 2018R5 + MyriadX target");
#endif
// output range: [-0.001, 0.97]
const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.012 : 0.0;
const float lInf = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16) ? 0.16 : 0.0;
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
Size(46, 46));
Size(46, 46), "", "", l1, lInf);
}
TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
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 OpenVINO <= 2018R5 + MyriadX target");
#endif
// 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",
@@ -250,17 +286,24 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
TEST_P(DNNTestNetwork, OpenFace)
{
#if defined(INF_ENGINE_RELEASE)
#if INF_ENGINE_RELEASE == 2018050000
#if INF_ENGINE_VER_MAJOR_EQ(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
#elif INF_ENGINE_RELEASE < 2018040000
throw SkipTestException("Test is disabled for Myriad targets");
#elif INF_ENGINE_VER_MAJOR_GT(2018050000)
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
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
throw SkipTestException("Test is enabled starts from OpenVINO 2018R4");
throw SkipTestException("Test has been fixed in OpenVINO 2018R4");
#endif
#endif
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "");
const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.0024 : 0.0;
const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.0071 : 0.0;
processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "", "", l1, lInf);
}
TEST_P(DNNTestNetwork, opencv_face_detector)
@@ -275,6 +318,11 @@ TEST_P(DNNTestNetwork, opencv_face_detector)
TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
{
#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
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
Mat sample = imread(findDataFile("dnn/street.png", false));
@@ -289,7 +337,7 @@ TEST_P(DNNTestNetwork, DenseNet_121)
{
if (backend == DNN_BACKEND_HALIDE)
throw SkipTestException("");
// Reference output values are in range [-3.807, 4.605]
float l1 = 0.0, lInf = 0.0;
if (target == DNN_TARGET_OPENCL_FP16)
{
@@ -297,7 +345,7 @@ TEST_P(DNNTestNetwork, DenseNet_121)
}
else if (target == DNN_TARGET_MYRIAD)
{
l1 = 6e-2; lInf = 0.27;
l1 = 0.1; lInf = 0.6;
}
processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Size(224, 224), "", "", l1, lInf);
}