potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-31 08:13:04 +04:00
Code Packages Releases Wiki Activity

Load networks from intermediate representation of Intel's Deep learning deployment toolkit.

Browse Source
This commit is contained in:
Dmitry Kurtaev
2018-03-16 19:27:04 +03:00
parent e06d1e8083
commit 7972f47ed4
7 changed files with 301 additions and 145 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/test/test_backends.cpp
+20 -34
View File
@@ -10,19 +10,6 @@
namespace opencv_test { namespace {
static void loadNet(const std::string& weights, const std::string& proto,
const std::string& framework, Net* net)
{
if (framework == "caffe")
*net = cv::dnn::readNetFromCaffe(proto, weights);
else if (framework == "torch")
*net = cv::dnn::readNetFromTorch(weights);
else if (framework == "tensorflow")
*net = cv::dnn::readNetFromTensorflow(weights, proto);
else
CV_Error(Error::StsNotImplemented, "Unknown framework " + framework);
}
class DNNTestNetwork : public TestWithParam <tuple<DNNBackend, DNNTarget> >
{
public:
@@ -37,7 +24,7 @@ public:
void processNet(const std::string& weights, const std::string& proto,
Size inpSize, const std::string& outputLayer,
const std::string& framework, const std::string& halideScheduler = "",
const std::string& halideScheduler = "",
double l1 = 1e-5, double lInf = 1e-4)
{
// Create a common input blob.
@@ -45,12 +32,12 @@ public:
Mat inp(4, blobSize, CV_32FC1);
randu(inp, 0.0f, 1.0f);
processNet(weights, proto, inp, outputLayer, framework, halideScheduler, l1, lInf);
processNet(weights, proto, inp, outputLayer, halideScheduler, l1, lInf);
}
void processNet(std::string weights, std::string proto,
Mat inp, const std::string& outputLayer,
const std::string& framework, std::string halideScheduler = "",
std::string halideScheduler = "",
double l1 = 1e-5, double lInf = 1e-4)
{
if (backend == DNN_BACKEND_DEFAULT && target == DNN_TARGET_OPENCL)
@@ -67,9 +54,8 @@ public:
proto = findDataFile(proto, false);
// Create two networks - with default backend and target and a tested one.
Net netDefault, net;
loadNet(weights, proto, framework, &netDefault);
loadNet(weights, proto, framework, &net);
Net netDefault = readNet(weights, proto);
Net net = readNet(weights, proto);
netDefault.setInput(inp);
Mat outDefault = netDefault.forward(outputLayer).clone();
@@ -115,7 +101,7 @@ public:
TEST_P(DNNTestNetwork, AlexNet)
{
processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
Size(227, 227), "prob", "caffe",
Size(227, 227), "prob",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_alexnet.yml" :
"dnn/halide_scheduler_alexnet.yml");
}
@@ -123,7 +109,7 @@ TEST_P(DNNTestNetwork, AlexNet)
TEST_P(DNNTestNetwork, ResNet_50)
{
processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
Size(224, 224), "prob", "caffe",
Size(224, 224), "prob",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_resnet_50.yml" :
"dnn/halide_scheduler_resnet_50.yml");
}
@@ -131,7 +117,7 @@ TEST_P(DNNTestNetwork, ResNet_50)
TEST_P(DNNTestNetwork, SqueezeNet_v1_1)
{
processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
Size(227, 227), "prob", "caffe",
Size(227, 227), "prob",
target == DNN_TARGET_OPENCL ? "dnn/halide_scheduler_opencl_squeezenet_v1_1.yml" :
"dnn/halide_scheduler_squeezenet_v1_1.yml");
}
@@ -139,13 +125,13 @@ TEST_P(DNNTestNetwork, SqueezeNet_v1_1)
TEST_P(DNNTestNetwork, GoogLeNet)
{
processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
Size(224, 224), "prob", "caffe");
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", "tensorflow",
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");
}
@@ -153,7 +139,7 @@ TEST_P(DNNTestNetwork, Inception_5h)
TEST_P(DNNTestNetwork, ENet)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE) throw SkipTestException("");
processNet("dnn/Enet-model-best.net", "", Size(512, 512), "l367_Deconvolution", "torch",
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);
@@ -166,7 +152,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
processNet("dnn/MobileNetSSD_deploy.caffemodel", "dnn/MobileNetSSD_deploy.prototxt",
inp, "detection_out", "caffe");
inp, "detection_out");
}
TEST_P(DNNTestNetwork, MobileNet_SSD_TensorFlow)
@@ -175,7 +161,7 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_TensorFlow)
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);
processNet("dnn/ssd_mobilenet_v1_coco.pb", "dnn/ssd_mobilenet_v1_coco.pbtxt",
inp, "detection_out", "tensorflow");
inp, "detection_out");
}
TEST_P(DNNTestNetwork, SSD_VGG16)
@@ -185,21 +171,21 @@ TEST_P(DNNTestNetwork, SSD_VGG16)
backend == DNN_BACKEND_INFERENCE_ENGINE)
throw SkipTestException("");
processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel",
"dnn/ssd_vgg16.prototxt", Size(300, 300), "detection_out", "caffe");
"dnn/ssd_vgg16.prototxt", Size(300, 300), "detection_out");
}
TEST_P(DNNTestNetwork, OpenPose_pose_coco)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
Size(368, 368), "", "caffe");
Size(368, 368), "");
}
TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
Size(368, 368), "", "caffe");
Size(368, 368), "");
}
TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
@@ -208,13 +194,13 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
// 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), "", "caffe");
Size(368, 368), "");
}
TEST_P(DNNTestNetwork, OpenFace)
{
if (backend == DNN_BACKEND_HALIDE) throw SkipTestException("");
processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "", "torch");
processNet("dnn/openface_nn4.small2.v1.t7", "", Size(96, 96), "");
}
TEST_P(DNNTestNetwork, opencv_face_detector)
@@ -223,7 +209,7 @@ TEST_P(DNNTestNetwork, opencv_face_detector)
Mat img = imread(findDataFile("gpu/lbpcascade/er.png", false));
Mat inp = blobFromImage(img, 1.0, Size(), Scalar(104.0, 177.0, 123.0), false, false);
processNet("dnn/opencv_face_detector.caffemodel", "dnn/opencv_face_detector.prototxt",
inp, "detection_out", "caffe");
inp, "detection_out");
}
TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
@@ -232,7 +218,7 @@ TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
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);
processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "dnn/ssd_inception_v2_coco_2017_11_17.pbtxt",
inp, "detection_out", "tensorflow");
inp, "detection_out");
}
const tuple<DNNBackend, DNNTarget> testCases[] = {