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Merge pull request #20359 from mpashchenkov:mp/onnx-tests

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G-API: ONNX. Skip tests.

* imread for every test

* Changed name for Yolo function
pull/20400/head
Maxim Pashchenkov 4 years ago committed by GitHub
parent aaad1791d9
commit ed2a698392
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1 changed files with 62 additions and 45 deletions
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  1. 107
      modules/gapi/test/infer/gapi_infer_onnx_test.cpp

107
modules/gapi/test/infer/gapi_infer_onnx_test.cpp
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@ -67,17 +67,17 @@ struct ONNXInitPath {
static ONNXInitPath g_init_path; static ONNXInitPath g_init_path;
cv::Mat initMatrixRandU(const int type, const cv::Size& sz_in) { cv::Mat initMatrixRandU(const int type, const cv::Size& sz_in) {
const cv::Mat in_mat1 = cv::Mat(sz_in, type); const cv::Mat in_mat = cv::Mat(sz_in, type);
if (CV_MAT_DEPTH(type) < CV_32F) { if (CV_MAT_DEPTH(type) < CV_32F) {
cv::randu(in_mat1, cv::Scalar::all(0), cv::Scalar::all(255)); cv::randu(in_mat, cv::Scalar::all(0), cv::Scalar::all(255));
} else { } else {
const int fscale = 256; // avoid bits near ULP, generate stable test input const int fscale = 256; // avoid bits near ULP, generate stable test input
cv::Mat in_mat32s(in_mat1.size(), CV_MAKE_TYPE(CV_32S, CV_MAT_CN(type))); cv::Mat in_mat32s(in_mat.size(), CV_MAKE_TYPE(CV_32S, CV_MAT_CN(type)));
cv::randu(in_mat32s, cv::Scalar::all(0), cv::Scalar::all(255 * fscale)); cv::randu(in_mat32s, cv::Scalar::all(0), cv::Scalar::all(255 * fscale));
in_mat32s.convertTo(in_mat1, type, 1.0f / fscale, 0); in_mat32s.convertTo(in_mat, type, 1.0f / fscale, 0);
} }
return in_mat1; return in_mat;
} }
} // anonymous namespace } // anonymous namespace
namespace opencv_test namespace opencv_test
@ -319,15 +319,13 @@ public:
size_t num_in, num_out; size_t num_in, num_out;
std::vector<cv::Mat> out_gapi; std::vector<cv::Mat> out_gapi;
std::vector<cv::Mat> out_onnx; std::vector<cv::Mat> out_onnx;
cv::Mat in_mat1; cv::Mat in_mat;
ONNXtest() { ONNXtest() {
initTestDataPath(); initTestDataPath();
env = Ort::Env(ORT_LOGGING_LEVEL_WARNING, "test"); env = Ort::Env(ORT_LOGGING_LEVEL_WARNING, "test");
memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault); memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
out_gapi.resize(1); out_gapi.resize(1);
// FIXME: It should be an image from own (gapi) directory in opencv extra
in_mat1 = cv::imread(findDataFile("cv/dpm/cat.png"));
} }
template<typename T> template<typename T>
@ -463,13 +461,9 @@ public:
cv::Rect(cv::Point{70, 10}, cv::Size{20, 260}), cv::Rect(cv::Point{70, 10}, cv::Size{20, 260}),
cv::Rect(cv::Point{5, 15}, cv::Size{200, 160}), cv::Rect(cv::Point{5, 15}, cv::Size{200, 160}),
}; };
cv::Mat m_in_y; const cv::Size sz{640, 480};
cv::Mat m_in_uv; const cv::Mat m_in_y = initMatrixRandU(CV_8UC1, sz);
virtual void SetUp() { const cv::Mat m_in_uv = initMatrixRandU(CV_8UC2, sz / 2);
cv::Size sz{640, 480};
m_in_y = initMatrixRandU(CV_8UC1, sz);
m_in_uv = initMatrixRandU(CV_8UC2, sz / 2);
}
}; };
class ONNXGRayScale : public ONNXtest { class ONNXGRayScale : public ONNXtest {
@ -545,20 +539,20 @@ class ONNXYoloV3 : public ONNXWithRemap {
public: public:
std::vector<cv::Mat> ins; std::vector<cv::Mat> ins;
private: void constructYoloInputs(const cv::Mat& src) {
virtual void SetUp() {
const int yolo_in_h = 416; const int yolo_in_h = 416;
const int yolo_in_w = 416; const int yolo_in_w = 416;
cv::Mat yolov3_input, shape, prep_mat; cv::Mat yolov3_input, shape, prep_mat;
cv::resize(in_mat1, yolov3_input, cv::Size(yolo_in_w, yolo_in_h)); cv::resize(src, yolov3_input, cv::Size(yolo_in_w, yolo_in_h));
shape.create(cv::Size(2, 1), CV_32F); shape.create(cv::Size(2, 1), CV_32F);
float* ptr = shape.ptr<float>(); float* ptr = shape.ptr<float>();
ptr[0] = in_mat1.cols; ptr[0] = src.cols;
ptr[1] = in_mat1.rows; ptr[1] = src.rows;
preprocess(yolov3_input, prep_mat); preprocess(yolov3_input, prep_mat);
ins = {prep_mat, shape}; ins = {prep_mat, shape};
} }
private:
void preprocess(const cv::Mat& src, cv::Mat& dst) { void preprocess(const cv::Mat& src, cv::Mat& dst) {
cv::Mat cvt; cv::Mat cvt;
src.convertTo(cvt, CV_32F, 1.f / 255.f); src.convertTo(cvt, CV_32F, 1.f / 255.f);
@ -571,9 +565,10 @@ private:
TEST_F(ONNXClassification, Infer) TEST_F(ONNXClassification, Infer)
{ {
useModel("classification/squeezenet/model/squeezenet1.0-9"); useModel("classification/squeezenet/model/squeezenet1.0-9");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
// ONNX_API code // ONNX_API code
cv::Mat processed_mat; cv::Mat processed_mat;
preprocess(in_mat1, processed_mat); preprocess(in_mat, processed_mat);
infer<float>(processed_mat, out_onnx); infer<float>(processed_mat, out_onnx);
// G_API code // G_API code
G_API_NET(SqueezNet, <cv::GMat(cv::GMat)>, "squeeznet"); G_API_NET(SqueezNet, <cv::GMat(cv::GMat)>, "squeeznet");
@ -583,7 +578,7 @@ TEST_F(ONNXClassification, Infer)
// NOTE: We have to normalize U8 tensor // NOTE: We have to normalize U8 tensor
// so cfgMeanStd() is here // so cfgMeanStd() is here
auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std }); auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std });
comp.apply(cv::gin(in_mat1), comp.apply(cv::gin(in_mat),
cv::gout(out_gapi.front()), cv::gout(out_gapi.front()),
cv::compile_args(cv::gapi::networks(net))); cv::compile_args(cv::gapi::networks(net)));
// Validate // Validate
@ -593,9 +588,10 @@ TEST_F(ONNXClassification, Infer)
TEST_F(ONNXClassification, InferTensor) TEST_F(ONNXClassification, InferTensor)
{ {
useModel("classification/squeezenet/model/squeezenet1.0-9"); useModel("classification/squeezenet/model/squeezenet1.0-9");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
// Create tensor // Create tensor
cv::Mat tensor; cv::Mat tensor;
preprocess(in_mat1, tensor); preprocess(in_mat, tensor);
// ONNX_API code // ONNX_API code
infer<float>(tensor, out_onnx); infer<float>(tensor, out_onnx);
// G_API code // G_API code
@ -614,10 +610,11 @@ TEST_F(ONNXClassification, InferTensor)
TEST_F(ONNXClassification, InferROI) TEST_F(ONNXClassification, InferROI)
{ {
useModel("classification/squeezenet/model/squeezenet1.0-9"); useModel("classification/squeezenet/model/squeezenet1.0-9");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
const auto ROI = rois.at(0); const auto ROI = rois.at(0);
// ONNX_API code // ONNX_API code
cv::Mat roi_mat; cv::Mat roi_mat;
preprocess(in_mat1(ROI), roi_mat); preprocess(in_mat(ROI), roi_mat);
infer<float>(roi_mat, out_onnx); infer<float>(roi_mat, out_onnx);
// G_API code // G_API code
G_API_NET(SqueezNet, <cv::GMat(cv::GMat)>, "squeeznet"); G_API_NET(SqueezNet, <cv::GMat(cv::GMat)>, "squeeznet");
@ -628,7 +625,7 @@ TEST_F(ONNXClassification, InferROI)
// NOTE: We have to normalize U8 tensor // NOTE: We have to normalize U8 tensor
// so cfgMeanStd() is here // so cfgMeanStd() is here
auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std }); auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std });
comp.apply(cv::gin(in_mat1, ROI), comp.apply(cv::gin(in_mat, ROI),
cv::gout(out_gapi.front()), cv::gout(out_gapi.front()),
cv::compile_args(cv::gapi::networks(net))); cv::compile_args(cv::gapi::networks(net)));
// Validate // Validate
@ -638,10 +635,11 @@ TEST_F(ONNXClassification, InferROI)
TEST_F(ONNXClassification, InferROIList) TEST_F(ONNXClassification, InferROIList)
{ {
useModel("classification/squeezenet/model/squeezenet1.0-9"); useModel("classification/squeezenet/model/squeezenet1.0-9");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
// ONNX_API code // ONNX_API code
for (size_t i = 0; i < rois.size(); ++i) { for (size_t i = 0; i < rois.size(); ++i) {
cv::Mat roi_mat; cv::Mat roi_mat;
preprocess(in_mat1(rois[i]), roi_mat); preprocess(in_mat(rois[i]), roi_mat);
infer<float>(roi_mat, out_onnx); infer<float>(roi_mat, out_onnx);
} }
// G_API code // G_API code
@ -653,7 +651,7 @@ TEST_F(ONNXClassification, InferROIList)
// NOTE: We have to normalize U8 tensor // NOTE: We have to normalize U8 tensor
// so cfgMeanStd() is here // so cfgMeanStd() is here
auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std }); auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std });
comp.apply(cv::gin(in_mat1, rois), comp.apply(cv::gin(in_mat, rois),
cv::gout(out_gapi), cv::gout(out_gapi),
cv::compile_args(cv::gapi::networks(net))); cv::compile_args(cv::gapi::networks(net)));
// Validate // Validate
@ -663,10 +661,11 @@ TEST_F(ONNXClassification, InferROIList)
TEST_F(ONNXClassification, Infer2ROIList) TEST_F(ONNXClassification, Infer2ROIList)
{ {
useModel("classification/squeezenet/model/squeezenet1.0-9"); useModel("classification/squeezenet/model/squeezenet1.0-9");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
// ONNX_API code // ONNX_API code
for (size_t i = 0; i < rois.size(); ++i) { for (size_t i = 0; i < rois.size(); ++i) {
cv::Mat roi_mat; cv::Mat roi_mat;
preprocess(in_mat1(rois[i]), roi_mat); preprocess(in_mat(rois[i]), roi_mat);
infer<float>(roi_mat, out_onnx); infer<float>(roi_mat, out_onnx);
} }
// G_API code // G_API code
@ -678,7 +677,7 @@ TEST_F(ONNXClassification, Infer2ROIList)
// NOTE: We have to normalize U8 tensor // NOTE: We have to normalize U8 tensor
// so cfgMeanStd() is here // so cfgMeanStd() is here
auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std }); auto net = cv::gapi::onnx::Params<SqueezNet> { model_path }.cfgMeanStd({ mean }, { std });
comp.apply(cv::gin(in_mat1, rois), comp.apply(cv::gin(in_mat, rois),
cv::gout(out_gapi), cv::gout(out_gapi),
cv::compile_args(cv::gapi::networks(net))); cv::compile_args(cv::gapi::networks(net)));
// Validate // Validate
@ -688,9 +687,10 @@ TEST_F(ONNXClassification, Infer2ROIList)
TEST_F(ONNXWithRemap, InferDynamicInputTensor) TEST_F(ONNXWithRemap, InferDynamicInputTensor)
{ {
useModel("object_detection_segmentation/tiny-yolov2/model/tinyyolov2-8"); useModel("object_detection_segmentation/tiny-yolov2/model/tinyyolov2-8");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
// Create tensor // Create tensor
cv::Mat cvt, rsz, tensor; cv::Mat cvt, rsz, tensor;
cv::resize(in_mat1, rsz, cv::Size{416, 416}); cv::resize(in_mat, rsz, cv::Size{416, 416});
rsz.convertTo(cvt, CV_32F, 1.f / 255.f); rsz.convertTo(cvt, CV_32F, 1.f / 255.f);
toCHW(cvt, tensor); toCHW(cvt, tensor);
tensor = tensor.reshape(1, {1, 3, 416, 416}); tensor = tensor.reshape(1, {1, 3, 416, 416});
@ -714,9 +714,10 @@ TEST_F(ONNXWithRemap, InferDynamicInputTensor)
TEST_F(ONNXGRayScale, InferImage) TEST_F(ONNXGRayScale, InferImage)
{ {
useModel("body_analysis/emotion_ferplus/model/emotion-ferplus-8"); useModel("body_analysis/emotion_ferplus/model/emotion-ferplus-8");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
// ONNX_API code // ONNX_API code
cv::Mat prep_mat; cv::Mat prep_mat;
preprocess(in_mat1, prep_mat); preprocess(in_mat, prep_mat);
infer<float>(prep_mat, out_onnx); infer<float>(prep_mat, out_onnx);
// G_API code // G_API code
G_API_NET(EmotionNet, <cv::GMat(cv::GMat)>, "emotion-ferplus"); G_API_NET(EmotionNet, <cv::GMat(cv::GMat)>, "emotion-ferplus");
@ -725,7 +726,7 @@ TEST_F(ONNXGRayScale, InferImage)
cv::GComputation comp(cv::GIn(in), cv::GOut(out)); cv::GComputation comp(cv::GIn(in), cv::GOut(out));
auto net = cv::gapi::onnx::Params<EmotionNet> { model_path } auto net = cv::gapi::onnx::Params<EmotionNet> { model_path }
.cfgNormalize({ false }); // model accepts 0..255 range in FP32; .cfgNormalize({ false }); // model accepts 0..255 range in FP32;
comp.apply(cv::gin(in_mat1), comp.apply(cv::gin(in_mat),
cv::gout(out_gapi.front()), cv::gout(out_gapi.front()),
cv::compile_args(cv::gapi::networks(net))); cv::compile_args(cv::gapi::networks(net)));
// Validate // Validate
@ -735,8 +736,9 @@ TEST_F(ONNXGRayScale, InferImage)
TEST_F(ONNXWithRemap, InferMultiOutput) TEST_F(ONNXWithRemap, InferMultiOutput)
{ {
useModel("object_detection_segmentation/ssd-mobilenetv1/model/ssd_mobilenet_v1_10"); useModel("object_detection_segmentation/ssd-mobilenetv1/model/ssd_mobilenet_v1_10");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
// ONNX_API code // ONNX_API code
const auto prep_mat = in_mat1.reshape(1, {1, in_mat1.rows, in_mat1.cols, in_mat1.channels()}); const auto prep_mat = in_mat.reshape(1, {1, in_mat.rows, in_mat.cols, in_mat.channels()});
infer<uint8_t>(prep_mat, out_onnx); infer<uint8_t>(prep_mat, out_onnx);
cv::Mat onnx_conv_out({1, 1, 200, 7}, CV_32F); cv::Mat onnx_conv_out({1, 1, 200, 7}, CV_32F);
remapToIESSDOut({out_onnx[3], out_onnx[0], out_onnx[2], out_onnx[1]}, onnx_conv_out); remapToIESSDOut({out_onnx[3], out_onnx[0], out_onnx[2], out_onnx[1]}, onnx_conv_out);
@ -750,7 +752,7 @@ TEST_F(ONNXWithRemap, InferMultiOutput)
auto net = cv::gapi::onnx::Params<MobileNet>{ model_path } auto net = cv::gapi::onnx::Params<MobileNet>{ model_path }
.cfgOutputLayers({"detection_output"}) .cfgOutputLayers({"detection_output"})
.cfgPostProc({cv::GMatDesc{CV_32F, {1, 1, 200, 7}}}, remapSSDPorts); .cfgPostProc({cv::GMatDesc{CV_32F, {1, 1, 200, 7}}}, remapSSDPorts);
comp.apply(cv::gin(in_mat1), comp.apply(cv::gin(in_mat),
cv::gout(out_gapi.front()), cv::gout(out_gapi.front()),
cv::compile_args(cv::gapi::networks(net))); cv::compile_args(cv::gapi::networks(net)));
// Validate // Validate
@ -760,12 +762,13 @@ TEST_F(ONNXWithRemap, InferMultiOutput)
TEST_F(ONNXMediaFrame, InferBGR) TEST_F(ONNXMediaFrame, InferBGR)
{ {
useModel("classification/squeezenet/model/squeezenet1.0-9"); useModel("classification/squeezenet/model/squeezenet1.0-9");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
// ONNX_API code // ONNX_API code
cv::Mat processed_mat; cv::Mat processed_mat;
preprocess(in_mat1, processed_mat); preprocess(in_mat, processed_mat);
infer<float>(processed_mat, out_onnx); infer<float>(processed_mat, out_onnx);
// G_API code // G_API code
auto frame = MediaFrame::Create<TestMediaBGR>(in_mat1); auto frame = MediaFrame::Create<TestMediaBGR>(in_mat);
G_API_NET(SqueezNet, <cv::GMat(cv::GMat)>, "squeeznet"); G_API_NET(SqueezNet, <cv::GMat(cv::GMat)>, "squeeznet");
cv::GFrame in; cv::GFrame in;
cv::GMat out = cv::gapi::infer<SqueezNet>(in); cv::GMat out = cv::gapi::infer<SqueezNet>(in);
@ -783,6 +786,7 @@ TEST_F(ONNXMediaFrame, InferBGR)
TEST_F(ONNXMediaFrame, InferYUV) TEST_F(ONNXMediaFrame, InferYUV)
{ {
useModel("classification/squeezenet/model/squeezenet1.0-9"); useModel("classification/squeezenet/model/squeezenet1.0-9");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
const auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv); const auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv);
// ONNX_API code // ONNX_API code
cv::Mat pp; cv::Mat pp;
@ -808,10 +812,11 @@ TEST_F(ONNXMediaFrame, InferYUV)
TEST_F(ONNXMediaFrame, InferROIBGR) TEST_F(ONNXMediaFrame, InferROIBGR)
{ {
useModel("classification/squeezenet/model/squeezenet1.0-9"); useModel("classification/squeezenet/model/squeezenet1.0-9");
auto frame = MediaFrame::Create<TestMediaBGR>(in_mat1); in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
auto frame = MediaFrame::Create<TestMediaBGR>(in_mat);
// ONNX_API code // ONNX_API code
cv::Mat roi_mat; cv::Mat roi_mat;
preprocess(in_mat1(rois.front()), roi_mat); preprocess(in_mat(rois.front()), roi_mat);
infer<float>(roi_mat, out_onnx); infer<float>(roi_mat, out_onnx);
// G_API code // G_API code
G_API_NET(SqueezNet, <cv::GMat(cv::GMat)>, "squeeznet"); G_API_NET(SqueezNet, <cv::GMat(cv::GMat)>, "squeeznet");
@ -832,6 +837,7 @@ TEST_F(ONNXMediaFrame, InferROIBGR)
TEST_F(ONNXMediaFrame, InferROIYUV) TEST_F(ONNXMediaFrame, InferROIYUV)
{ {
useModel("classification/squeezenet/model/squeezenet1.0-9"); useModel("classification/squeezenet/model/squeezenet1.0-9");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
const auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv); const auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv);
// ONNX_API code // ONNX_API code
cv::Mat pp; cv::Mat pp;
@ -858,11 +864,12 @@ TEST_F(ONNXMediaFrame, InferROIYUV)
TEST_F(ONNXMediaFrame, InferListBGR) TEST_F(ONNXMediaFrame, InferListBGR)
{ {
useModel("classification/squeezenet/model/squeezenet1.0-9"); useModel("classification/squeezenet/model/squeezenet1.0-9");
const auto frame = MediaFrame::Create<TestMediaBGR>(in_mat1); in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
const auto frame = MediaFrame::Create<TestMediaBGR>(in_mat);
// ONNX_API code // ONNX_API code
for (size_t i = 0; i < rois.size(); ++i) { for (size_t i = 0; i < rois.size(); ++i) {
cv::Mat roi_mat; cv::Mat roi_mat;
preprocess(in_mat1(rois[i]), roi_mat); preprocess(in_mat(rois[i]), roi_mat);
infer<float>(roi_mat, out_onnx); infer<float>(roi_mat, out_onnx);
} }
// G_API code // G_API code
@ -884,6 +891,7 @@ TEST_F(ONNXMediaFrame, InferListBGR)
TEST_F(ONNXMediaFrame, InferListYUV) TEST_F(ONNXMediaFrame, InferListYUV)
{ {
useModel("classification/squeezenet/model/squeezenet1.0-9"); useModel("classification/squeezenet/model/squeezenet1.0-9");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
const auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv); const auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv);
// ONNX_API code // ONNX_API code
cv::Mat pp; cv::Mat pp;
@ -911,8 +919,9 @@ TEST_F(ONNXMediaFrame, InferListYUV)
TEST_F(ONNXRCNN, InferWithDisabledOut) TEST_F(ONNXRCNN, InferWithDisabledOut)
{ {
useModel("object_detection_segmentation/faster-rcnn/model/FasterRCNN-10"); useModel("object_detection_segmentation/faster-rcnn/model/FasterRCNN-10");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
cv::Mat pp; cv::Mat pp;
preprocess(in_mat1, pp); preprocess(in_mat, pp);
// ONNX_API code // ONNX_API code
infer<float>(pp, out_onnx, {"6379", "6383"}); infer<float>(pp, out_onnx, {"6379", "6383"});
// G_API code // G_API code
@ -937,11 +946,12 @@ TEST_F(ONNXRCNN, InferWithDisabledOut)
TEST_F(ONNXMediaFrame, InferList2BGR) TEST_F(ONNXMediaFrame, InferList2BGR)
{ {
useModel("classification/squeezenet/model/squeezenet1.0-9"); useModel("classification/squeezenet/model/squeezenet1.0-9");
const auto frame = MediaFrame::Create<TestMediaBGR>(in_mat1); in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
const auto frame = MediaFrame::Create<TestMediaBGR>(in_mat);
// ONNX_API code // ONNX_API code
for (size_t i = 0; i < rois.size(); ++i) { for (size_t i = 0; i < rois.size(); ++i) {
cv::Mat roi_mat; cv::Mat roi_mat;
preprocess(in_mat1(rois[i]), roi_mat); preprocess(in_mat(rois[i]), roi_mat);
infer<float>(roi_mat, out_onnx); infer<float>(roi_mat, out_onnx);
} }
// G_API code // G_API code
@ -963,6 +973,7 @@ TEST_F(ONNXMediaFrame, InferList2BGR)
TEST_F(ONNXMediaFrame, InferList2YUV) TEST_F(ONNXMediaFrame, InferList2YUV)
{ {
useModel("classification/squeezenet/model/squeezenet1.0-9"); useModel("classification/squeezenet/model/squeezenet1.0-9");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
const auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv); const auto frame = MediaFrame::Create<TestMediaNV12>(m_in_y, m_in_uv);
// ONNX_API code // ONNX_API code
cv::Mat pp; cv::Mat pp;
@ -991,6 +1002,8 @@ TEST_F(ONNXMediaFrame, InferList2YUV)
TEST_F(ONNXYoloV3, InferConstInput) TEST_F(ONNXYoloV3, InferConstInput)
{ {
useModel("object_detection_segmentation/yolov3/model/yolov3-10"); useModel("object_detection_segmentation/yolov3/model/yolov3-10");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
constructYoloInputs(in_mat);
// ONNX_API code // ONNX_API code
infer<float>(ins, out_onnx); infer<float>(ins, out_onnx);
// G_API code // G_API code
@ -1022,6 +1035,8 @@ TEST_F(ONNXYoloV3, InferBSConstInput)
// and all input layer names are specified. // and all input layer names are specified.
// Const input has the advantage. It is expected behavior. // Const input has the advantage. It is expected behavior.
useModel("object_detection_segmentation/yolov3/model/yolov3-10"); useModel("object_detection_segmentation/yolov3/model/yolov3-10");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
constructYoloInputs(in_mat);
// Tensor with incorrect image size // Tensor with incorrect image size
// is used for check case when InputLayers and constInput have same names // is used for check case when InputLayers and constInput have same names
cv::Mat bad_shape; cv::Mat bad_shape;
@ -1059,8 +1074,9 @@ TEST_F(ONNXYoloV3, InferBSConstInput)
TEST_F(ONNXRCNN, ConversionInt64to32) TEST_F(ONNXRCNN, ConversionInt64to32)
{ {
useModel("object_detection_segmentation/faster-rcnn/model/FasterRCNN-10"); useModel("object_detection_segmentation/faster-rcnn/model/FasterRCNN-10");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
cv::Mat dst; cv::Mat dst;
preprocess(in_mat1, dst); preprocess(in_mat, dst);
// ONNX_API code // ONNX_API code
infer<float>(dst, out_onnx); infer<float>(dst, out_onnx);
// G_API code // G_API code
@ -1087,6 +1103,7 @@ TEST_F(ONNXRCNN, ConversionInt64to32)
TEST_F(ONNXWithRemap, InferOutReallocation) TEST_F(ONNXWithRemap, InferOutReallocation)
{ {
useModel("object_detection_segmentation/ssd-mobilenetv1/model/ssd_mobilenet_v1_10"); useModel("object_detection_segmentation/ssd-mobilenetv1/model/ssd_mobilenet_v1_10");
in_mat = cv::imread(findDataFile("cv/dpm/cat.png", false));
// G_API code // G_API code
G_API_NET(MobileNet, <cv::GMat(cv::GMat)>, "ssd_mobilenet"); G_API_NET(MobileNet, <cv::GMat(cv::GMat)>, "ssd_mobilenet");
auto net = cv::gapi::onnx::Params<MobileNet>{model_path} auto net = cv::gapi::onnx::Params<MobileNet>{model_path}
@ -1096,7 +1113,7 @@ TEST_F(ONNXWithRemap, InferOutReallocation)
cv::GMat out1; cv::GMat out1;
out1 = cv::gapi::infer<MobileNet>(in); out1 = cv::gapi::infer<MobileNet>(in);
cv::GComputation comp(cv::GIn(in), cv::GOut(out1)); cv::GComputation comp(cv::GIn(in), cv::GOut(out1));
EXPECT_THROW(comp.apply(cv::gin(in_mat1), EXPECT_THROW(comp.apply(cv::gin(in_mat),
cv::gout(out_gapi[0]), cv::gout(out_gapi[0]),
cv::compile_args(cv::gapi::networks(net))), std::exception); cv::compile_args(cv::gapi::networks(net))), std::exception);
} }

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