mirror of
https://github.com/opencv/opencv.git
synced 2024-11-26 04:00:30 +08:00
f96f934426
* Update Intel's Inference Engine deep learning backend * Remove cpu_extension dependency * Update Darknet accuracy tests
308 lines
12 KiB
C++
308 lines
12 KiB
C++
// This file is part of OpenCV project.
|
|
// 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.
|
|
// Third party copyrights are property of their respective owners.
|
|
|
|
#include "test_precomp.hpp"
|
|
#include "opencv2/core/ocl.hpp"
|
|
|
|
namespace opencv_test { namespace {
|
|
|
|
class DNNTestNetwork : public TestWithParam <tuple<DNNBackend, DNNTarget> >
|
|
{
|
|
public:
|
|
dnn::Backend backend;
|
|
dnn::Target target;
|
|
|
|
DNNTestNetwork()
|
|
{
|
|
backend = (dnn::Backend)(int)get<0>(GetParam());
|
|
target = (dnn::Target)(int)get<1>(GetParam());
|
|
}
|
|
|
|
void processNet(const std::string& weights, const std::string& proto,
|
|
Size inpSize, const std::string& outputLayer = "",
|
|
const std::string& halideScheduler = "",
|
|
double l1 = 0.0, double lInf = 0.0)
|
|
{
|
|
// Create a common input blob.
|
|
int blobSize[] = {1, 3, inpSize.height, inpSize.width};
|
|
Mat inp(4, blobSize, CV_32FC1);
|
|
randu(inp, 0.0f, 1.0f);
|
|
|
|
processNet(weights, proto, inp, outputLayer, halideScheduler, l1, lInf);
|
|
}
|
|
|
|
void processNet(std::string weights, std::string proto,
|
|
Mat inp, const std::string& outputLayer = "",
|
|
std::string halideScheduler = "",
|
|
double l1 = 0.0, double lInf = 0.0)
|
|
{
|
|
if (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL)
|
|
{
|
|
#ifdef HAVE_OPENCL
|
|
if (!cv::ocl::useOpenCL())
|
|
#endif
|
|
{
|
|
throw SkipTestException("OpenCL is not available/disabled in OpenCV");
|
|
}
|
|
}
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
|
|
{
|
|
if (!checkMyriadTarget())
|
|
{
|
|
throw SkipTestException("Myriad is not available/disabled in OpenCV");
|
|
}
|
|
}
|
|
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
|
|
{
|
|
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);
|
|
|
|
// Create two networks - with default backend and target and a tested one.
|
|
Net netDefault = readNet(weights, proto);
|
|
Net net = readNet(weights, proto);
|
|
|
|
netDefault.setInput(inp);
|
|
Mat outDefault = netDefault.forward(outputLayer).clone();
|
|
|
|
net.setInput(inp);
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
if (backend == DNN_BACKEND_HALIDE && !halideScheduler.empty())
|
|
{
|
|
halideScheduler = findDataFile(halideScheduler, false);
|
|
net.setHalideScheduler(halideScheduler);
|
|
}
|
|
Mat out = net.forward(outputLayer).clone();
|
|
|
|
check(outDefault, out, outputLayer, l1, lInf, "First run");
|
|
|
|
// Test 2: change input.
|
|
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();
|
|
check(outDefault, out, outputLayer, l1, lInf, "Second run");
|
|
}
|
|
|
|
void check(Mat& ref, Mat& out, const std::string& outputLayer, double l1, double lInf, const char* msg)
|
|
{
|
|
if (outputLayer == "detection_out")
|
|
{
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
|
|
{
|
|
// Inference Engine produces detections terminated by a row which starts from -1.
|
|
out = out.reshape(1, out.total() / 7);
|
|
int numDetections = 0;
|
|
while (numDetections < out.rows && out.at<float>(numDetections, 0) != -1)
|
|
{
|
|
numDetections += 1;
|
|
}
|
|
out = out.rowRange(0, numDetections);
|
|
}
|
|
normAssertDetections(ref, out, msg, 0.2, l1, lInf);
|
|
}
|
|
else
|
|
normAssert(ref, out, msg, l1, lInf);
|
|
}
|
|
};
|
|
|
|
TEST_P(DNNTestNetwork, AlexNet)
|
|
{
|
|
processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
|
|
Size(227, 227), "prob",
|
|
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_alexnet.yml" :
|
|
"dnn/halide_scheduler_alexnet.yml");
|
|
}
|
|
|
|
TEST_P(DNNTestNetwork, ResNet_50)
|
|
{
|
|
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" :
|
|
"dnn/halide_scheduler_resnet_50.yml");
|
|
}
|
|
|
|
TEST_P(DNNTestNetwork, SqueezeNet_v1_1)
|
|
{
|
|
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" :
|
|
"dnn/halide_scheduler_squeezenet_v1_1.yml");
|
|
}
|
|
|
|
TEST_P(DNNTestNetwork, GoogLeNet)
|
|
{
|
|
processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
|
|
Size(224, 224), "prob");
|
|
}
|
|
|
|
TEST_P(DNNTestNetwork, Inception_5h)
|
|
{
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE) throw SkipTestException("");
|
|
processNet("dnn/tensorflow_inception_graph.pb", "", Size(224, 224), "softmax2",
|
|
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_inception_5h.yml" :
|
|
"dnn/halide_scheduler_inception_5h.yml");
|
|
}
|
|
|
|
TEST_P(DNNTestNetwork, ENet)
|
|
{
|
|
if ((backend == DNN_BACKEND_INFERENCE_ENGINE) ||
|
|
(backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL_FP16))
|
|
throw SkipTestException("");
|
|
processNet("dnn/Enet-model-best.net", "", Size(512, 512), "l367_Deconvolution",
|
|
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_enet.yml" :
|
|
"dnn/halide_scheduler_enet.yml",
|
|
2e-5, 0.15);
|
|
}
|
|
|
|
TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
|
|
{
|
|
if (backend == DNN_BACKEND_HALIDE)
|
|
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 l1 = (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL_FP16) ? 0.0007 : 0.0;
|
|
float lInf = (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL_FP16) ? 0.011 : 0.0;
|
|
|
|
processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
|
|
inp, "detection_out", "", l1, lInf);
|
|
}
|
|
|
|
// TODO: update MobileNet model.
|
|
TEST_P(DNNTestNetwork, MobileNet_SSD_TensorFlow)
|
|
{
|
|
if (backend == DNN_BACKEND_HALIDE ||
|
|
backend == DNN_BACKEND_INFERENCE_ENGINE)
|
|
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 l1 = (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL_FP16) ? 0.008 : 0.0;
|
|
float lInf = (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL_FP16) ? 0.06 : 0.0;
|
|
processNet("dnn/ssd_mobilenet_v1_coco.pb", "dnn/ssd_mobilenet_v1_coco.pbtxt",
|
|
inp, "detection_out", "", l1, lInf);
|
|
}
|
|
|
|
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) ? 0.0252 : 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);
|
|
}
|
|
|
|
TEST_P(DNNTestNetwork, OpenPose_pose_coco)
|
|
{
|
|
if (backend == DNN_BACKEND_HALIDE ||
|
|
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
|
|
throw SkipTestException("");
|
|
processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
|
|
Size(368, 368));
|
|
}
|
|
|
|
TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
|
|
{
|
|
if (backend == DNN_BACKEND_HALIDE ||
|
|
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
|
|
throw SkipTestException("");
|
|
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
|
|
Size(368, 368));
|
|
}
|
|
|
|
TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
|
|
{
|
|
if (backend == DNN_BACKEND_HALIDE ||
|
|
backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
|
|
throw SkipTestException("");
|
|
// 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));
|
|
}
|
|
|
|
TEST_P(DNNTestNetwork, OpenFace)
|
|
{
|
|
if (backend == DNN_BACKEND_HALIDE ||
|
|
backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU)
|
|
throw SkipTestException("");
|
|
processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "");
|
|
}
|
|
|
|
TEST_P(DNNTestNetwork, opencv_face_detector)
|
|
{
|
|
if (backend == DNN_BACKEND_HALIDE)
|
|
throw SkipTestException("");
|
|
Size inpSize;
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
|
|
inpSize = Size(300, 300);
|
|
Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
|
|
Mat inp = blobFromImage(img, 1.0, inpSize, Scalar(104.0, 177.0, 123.0), false, false);
|
|
processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt",
|
|
inp, "detection_out");
|
|
}
|
|
|
|
TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
|
|
{
|
|
if (backend == DNN_BACKEND_HALIDE ||
|
|
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL) ||
|
|
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16))
|
|
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 l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.008 : 0.0;
|
|
float lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.07 : 0.0;
|
|
processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "dnn/ssd_inception_v2_coco_2017_11_17.pbtxt",
|
|
inp, "detection_out", "", l1, lInf);
|
|
}
|
|
|
|
TEST_P(DNNTestNetwork, DenseNet_121)
|
|
{
|
|
if ((backend == DNN_BACKEND_HALIDE) ||
|
|
(backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL_FP16) ||
|
|
(backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL_FP16 ||
|
|
target == DNN_TARGET_MYRIAD)))
|
|
throw SkipTestException("");
|
|
processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Size(224, 224), "", "caffe");
|
|
}
|
|
|
|
const tuple<DNNBackend, DNNTarget> testCases[] = {
|
|
#ifdef HAVE_HALIDE
|
|
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_CPU),
|
|
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_HALIDE, DNN_TARGET_OPENCL),
|
|
#endif
|
|
#ifdef HAVE_INF_ENGINE
|
|
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_CPU),
|
|
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL),
|
|
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_OPENCL_FP16),
|
|
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_INFERENCE_ENGINE, DNN_TARGET_MYRIAD),
|
|
#endif
|
|
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_DEFAULT, DNN_TARGET_OPENCL),
|
|
tuple<DNNBackend, DNNTarget>(DNN_BACKEND_DEFAULT, DNN_TARGET_OPENCL_FP16)
|
|
};
|
|
|
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, testing::ValuesIn(testCases));
|
|
|
|
}} // namespace
|