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opencv/modules/dnn/test/test_onnx_importer.cpp
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uwezkhan 69d2303531 Merge pull request #29345 from uwezkhan:onnx-tile-bounds 
bound tile axis and repeats length in onnx parseTile #29345

OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1385

parseTile sizes repeats_vec from the input-0 rank, then fills it from fields of the model that are never checked against that size. In the tile-1 path the axis taken from the third input indexes repeats_vec directly, and in the tile>1 path the loop writes one entry per element of the repeats tensor. A crafted ONNX with an out-of-range axis, or a repeats tensor longer than the input rank, writes past repeats_vec while loading the model through readNetFromONNX.

The fix runs axis through normalize_axis, the same helper the squeeze and concat paths in this file already use, so a negative or oversized axis is rejected before the write, and it checks the repeats length equals the input rank before the loop. Keeping both bounds in the parser puts the check next to the write instead of trusting the model to be well formed. Before, a repeats tensor shorter than the rank was silently accepted; after, it is rejected, which matches the ONNX rule that repeats carries one entry per input dimension.

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-06-26 12:39:04 +03:00

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// 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) 2018-2019, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
#include "test_precomp.hpp"
#include "npy_blob.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#include <numeric>
namespace opencv_test { namespace {
void yoloPostProcessing(
std::vector<Mat>& outs,
std::vector<int>& keep_classIds,
std::vector<float>& keep_confidences,
std::vector<Rect2d>& keep_boxes,
float conf_threshold,
float iou_threshold,
const std::string& model_name,
const int nc=80);
template<typename TString>
static std::string _tf(TString filename, bool required = true)
{
return findDataFile(std::string("dnn/onnx/") + filename, required);
}
class Test_ONNX_layers : public DNNTestLayer
{
public:
bool required;
Test_ONNX_layers() : required(true) { }
enum Extension
{
npy,
pb
};
void testInputShapes(const Net& net, const std::vector<Mat>& inps)
{
std::vector<MatShape> inLayerShapes;
std::vector<MatShape> outLayerShapes;
net.getLayerShapes(MatShape(), 0, inLayerShapes, outLayerShapes);
ASSERT_EQ(inLayerShapes.size(), inps.size());
for (int i = 0; i < inps.size(); ++i) {
bool hasDynamicShapes = inLayerShapes[i].empty();
if (hasDynamicShapes)
continue;
if (inLayerShapes[i].size() == 1) { // 1D input
ASSERT_EQ(shape(inLayerShapes[i][0], 1), shape(inps[i]));
} else {
// Compare all axes except batch dimension which is variable.
inLayerShapes[i][0] = inps[i].size[0];
ASSERT_EQ(inLayerShapes[i], shape(inps[i]));
}
}
}
void testONNXModels(const String& basename, const Extension ext = npy,
double l1 = 0, double lInf = 0, const bool useSoftmax = false,
bool checkNoFallbacks = true, int numInps = 1,
bool testShapes = true, bool useWinograd = true)
{
String onnxmodel = _tf("models/" + basename + ".onnx", required);
std::vector<Mat> inps(numInps);
Mat ref;
if (ext == npy) {
for (int i = 0; i < numInps; ++i)
inps[i] = blobFromNPY(_tf("data/input_" + basename + (numInps > 1 ? format("_%d", i) : "") + ".npy"));
ref = blobFromNPY(_tf("data/output_" + basename + ".npy"));
}
else if (ext == pb) {
for (int i = 0; i < numInps; ++i)
inps[i] = readTensorFromONNX(_tf("data/input_" + basename + (numInps > 1 ? format("_%d", i) : "") + ".pb"));
ref = readTensorFromONNX(_tf("data/output_" + basename + ".pb"));
}
else
CV_Error(Error::StsUnsupportedFormat, "Unsupported extension");
checkBackend(&inps[0], &ref);
Net net = readNetFromONNX(onnxmodel);
ASSERT_FALSE(net.empty());
if (testShapes)
testInputShapes(net, inps);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.enableWinograd(useWinograd);
std::vector<String> inputNames;
for (int i = 0; i < numInps; ++i)
inputNames.push_back(format("%d", i));
net.setInputsNames(inputNames);
for (int i = 0; i < numInps; ++i)
net.setInput(inps[i], inputNames[i]);
Mat out = net.forward("");
EXPECT_EQ(shape(out), shape(ref));
if (useSoftmax)
{
LayerParams lp;
Net netSoftmax;
netSoftmax.addLayerToPrev("softmaxLayer", "Softmax", lp);
netSoftmax.setPreferableBackend(DNN_BACKEND_OPENCV);
netSoftmax.setInput(out);
out = netSoftmax.forward();
netSoftmax.setInput(ref);
ref = netSoftmax.forward();
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
{
l1 = std::max(l1, 1.4e-3);
lInf = std::max(lInf, 8e-3);
}
normAssert(ref, out, basename.c_str(), l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
if (checkNoFallbacks)
expectNoFallbacksFromIE(net);
}
};
TEST_P(Test_ONNX_layers, InstanceNorm)
{
if (target == DNN_TARGET_MYRIAD)
testONNXModels("instancenorm", npy, 0, 0, false, false);
else
testONNXModels("instancenorm", npy);
}
TEST_P(Test_ONNX_layers, MaxPooling)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testONNXModels("maxpooling", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, MaxPooling_2)
{
testONNXModels("two_maxpooling", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, Convolution)
{
testONNXModels("convolution");
testONNXModels("conv_asymmetric_pads");
}
TEST_P(Test_ONNX_layers, Convolution_variable_weight)
{
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // not supported
if (backend == DNN_BACKEND_VKCOM)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); // not supported
String basename = "conv_variable_w";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
for (int i = 0; i < 2; i++)
{
Mat input = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_0.npy"));
Mat weights = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_1.npy"));
Mat ref = blobFromNPY(_tf("data/output_" + basename + format("_%d", i) + ".npy"));
net.setInput(input, "0");
net.setInput(weights, "1");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
}
}
TEST_P(Test_ONNX_layers, Convolution_variable_weight_bias)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// openvino/src/plugins/intel_myriad/common/src/ngraph/transformations/extract_dynamic_batch/slice_convolution.cpp:14 Expecting operation v1::GroupConvolution GroupConvolution_6904725 (Reshape_17[0]:f32{1,4,5,5}, Reshape_6904719[0]:f32{4,1,1,2,2}) -> (f32{1,4,4,4}) to have constant kernel, got Reshape_6904719[0]:f32{4,1,1,2,2}
// openvino\src\plugins\intel_myriad\common\src\ngraph\transformations\extract_dynamic_batch\slice_convolution.cpp:15 Expecting operation v1::GroupConvolution GroupConvolution_6904692 (Reshape_17[0]:f32{1,4,5,5}, Reshape_6904686[0]:f32{4,1,1,2,2}) -> (f32{1,4,4,4}) to have constant kernel, got Reshape_6904686[0]:f32{4,1,1,2,2}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
// accuracy (depends on OpenCL version / HW)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
#elif defined(INF_ENGINE_RELEASE)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU &&
getInferenceEngineCPUType() == CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
if (backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // supports only <= 2 inputs
if (backend == DNN_BACKEND_VKCOM)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); // not supported
String basename = "conv_variable_wb";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
for (int i = 0; i < 2; i++)
{
Mat input = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_0.npy"));
Mat weights = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_1.npy"));
Mat bias = blobFromNPY(_tf("data/input_" + basename + format("_%d", i) + "_2.npy"));
Mat ref = blobFromNPY(_tf("data/output_" + basename + format("_%d", i) + ".npy"));
net.setInput(input, "0");
net.setInput(weights, "1");
net.setInput(bias, "bias");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
}
}
TEST_P(Test_ONNX_layers, Gather)
{
testONNXModels("gather", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, Gather_Scalar)
{
testONNXModels("gather_scalar", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, GatherMulti)
{
// GPU plugin unsupported slice for constant
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
testONNXModels("gather_multi", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, Gather_shared_indices) {
testONNXModels("gather_shared_indices", npy, 0, 0, false, false, 1);
}
TEST_P(Test_ONNX_layers, Two_resizes_with_shared_subgraphs) {
testONNXModels("two_resizes_with_shared_subgraphs", npy, 0, 0, false, false, 3, /*testShapes*/ false);
}
TEST_P(Test_ONNX_layers, Convolution3D)
{
if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16)
{
// CUDA_FP16: cuDNN did not return a suitable algorithm for convolution.
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
}
testONNXModels("conv3d");
}
TEST_P(Test_ONNX_layers, Convolution3D_bias)
{
if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16)
{
// CUDA_FP16: cuDNN did not return a suitable algorithm for convolution.
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
}
testONNXModels("conv3d_bias");
testONNXModels("conv3d_depthwise_bias"); // kernel 1x1
}
TEST_P(Test_ONNX_layers, Two_convolution)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
// Reference output values are in range [-0.855, 0.611]
testONNXModels("two_convolution");
}
TEST_P(Test_ONNX_layers, Deconvolution)
{
testONNXModels("deconvolution", npy, 0, 0, false, false);
testONNXModels("two_deconvolution", npy, 0, 0, false, false);
testONNXModels("deconvolution_group", npy, 0, 0, false, false);
testONNXModels("deconvolution_output_shape", npy, 0, 0, false, false);
if (target != DNN_TARGET_CUDA_FP16) // bug
testONNXModels("deconv_adjpad_2d", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, Deconvolution3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/frontend/frontend.cpp:592 Failed to compile layer "2":
// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively)
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2":
// [ GENERAL_ERROR ] vpu/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively)
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#endif
if (backend == DNN_BACKEND_OPENCV)
throw SkipTestException("OpenCV backend is not supported"); // FIXIT use tags
if (backend == DNN_BACKEND_VKCOM)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN);
testONNXModels("deconv3d");
}
TEST_P(Test_ONNX_layers, Deconvolution3D_bias)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/frontend/frontend.cpp:592 Failed to compile layer "3":
// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 3@weights Const data got different desc and content byte sizes (270 and 810 respectively)
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2":
// [ GENERAL_ERROR ] vpu/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively)
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#endif
if (backend == DNN_BACKEND_OPENCV)
throw SkipTestException("OpenCV backend is not supported"); // FIXIT use tags
if (backend == DNN_BACKEND_VKCOM)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN);
testONNXModels("deconv3d_bias");
}
TEST_P(Test_ONNX_layers, Deconvolution3D_pad)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/frontend/frontend.cpp:592 Failed to compile layer "3":
// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 3@weights Const data got different desc and content byte sizes (108 and 432 respectively)
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2":
// [ GENERAL_ERROR ] vpu/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively)
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#endif
if (backend == DNN_BACKEND_OPENCV)
throw SkipTestException("OpenCV backend is not supported"); // FIXIT use tags
if (backend == DNN_BACKEND_VKCOM)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN);
testONNXModels("deconv3d_pad");
}
TEST_P(Test_ONNX_layers, Deconvolution3D_adjpad)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/frontend/frontend.cpp:592 Failed to compile layer "3":
// [ GENERAL_ERROR ] openvino/src/plugins/intel_myriad/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 3@weights Const data got different desc and content byte sizes (90 and 180 respectively)
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// [ GENERAL_ERROR ] vpu/graph_transformer/src/frontend/frontend.cpp:439 Failed to compile layer "2":
// [ GENERAL_ERROR ] vpu/graph_transformer/src/model/model.cpp:198 duplicateData error: while duplicating 2@weights Const data got different desc and content byte sizes (162 and 486 respectively)
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#endif
if (backend == DNN_BACKEND_OPENCV)
throw SkipTestException("OpenCV backend is not supported"); // FIXIT use tags
if (backend == DNN_BACKEND_VKCOM)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN);
testONNXModels("deconv3d_adjpad");
}
TEST_P(Test_ONNX_layers, Dropout)
{
testONNXModels("dropout");
}
TEST_P(Test_ONNX_layers, Linear)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
testONNXModels("linear");
}
TEST_P(Test_ONNX_layers, ReLU)
{
testONNXModels("ReLU");
}
TEST_P(Test_ONNX_layers, PReLU)
{
testONNXModels("PReLU_slope");
}
TEST_P(Test_ONNX_layers, Clip)
{
testONNXModels("clip", npy);
}
TEST_P(Test_ONNX_layers, Clip_init)
{
testONNXModels("clip_init_min_max");
testONNXModels("clip_init_min");
testONNXModels("clip_init_max");
}
TEST_P(Test_ONNX_layers, Shape)
{
testONNXModels("shape_of_constant");
}
TEST_P(Test_ONNX_layers, ReduceMean)
{
testONNXModels("reduce_mean");
testONNXModels("reduce_mean_axis1");
testONNXModels("reduce_mean_axis2");
}
TEST_P(Test_ONNX_layers, ReduceSum)
{
testONNXModels("reduce_sum");
testONNXModels("reduce_sum_axis_dynamic_batch");
}
TEST_P(Test_ONNX_layers, ReduceMax)
{
testONNXModels("reduce_max");
}
TEST_P(Test_ONNX_layers, ReduceMax_axis_0)
{
testONNXModels("reduce_max_axis_0");
}
TEST_P(Test_ONNX_layers, ReduceMax_axis_1)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// [ GENERAL_ERROR ] AssertionFailed: !out.networkInputs.empty()
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testONNXModels("reduce_max_axis_1");
}
TEST_P(Test_ONNX_layers, Min)
{
testONNXModels("min", npy, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, ArgLayer)
{
if (backend != DNN_BACKEND_OPENCV || target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported"); // FIXIT use tags
testONNXModels("argmax");
testONNXModels("argmin");
}
TEST_P(Test_ONNX_layers, Scale)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// accuracy (inf/nan)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// accuracy
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
// IE exception: mkldnn_node.cpp:238 Ngraph operation Reshape with name ReduceMean_0 has dynamic output shape on 0 port, but CPU plug-in supports only static shape
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// Ngraph operation Reshape with name ReduceMean_0 has dynamic output shape on 0 port, but CPU plug-in supports only static shape
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testONNXModels("scale");
}
TEST_P(Test_ONNX_layers, Scale_broadcast)
{
if (backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // doesn't support broadcasting
testONNXModels("scale_broadcast", npy, 0, 0, false, true, 3);
}
TEST_P(Test_ONNX_layers, Scale_broadcast_mid)
{
if (backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // doesn't support broadcasting
testONNXModels("scale_broadcast_mid", npy, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, ReduceMean3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
#endif
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported"); // FIXIT use tags
if (backend == DNN_BACKEND_VKCOM)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN);
testONNXModels("reduce_mean3d");
}
TEST_P(Test_ONNX_layers, MaxPooling_Sigmoid)
{
testONNXModels("maxpooling_sigmoid");
}
TEST_P(Test_ONNX_layers, Cast)
{
testONNXModels("cast");
}
TEST_P(Test_ONNX_layers, Power)
{
testONNXModels("pow2", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, Exp)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("exp");
}
TEST_P(Test_ONNX_layers, Elementwise_Ceil)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
testONNXModels("ceil");
}
TEST_P(Test_ONNX_layers, Elementwise_Floor)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
testONNXModels("floor");
}
TEST_P(Test_ONNX_layers, Elementwise_Log)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
testONNXModels("log");
}
TEST_P(Test_ONNX_layers, Elementwise_Round)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
testONNXModels("round");
}
TEST_P(Test_ONNX_layers, Elementwise_Sqrt)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
testONNXModels("sqrt");
#endif
}
TEST_P(Test_ONNX_layers, Elementwise_not)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
testONNXModels("not");
}
TEST_P(Test_ONNX_layers, Compare_EQ)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// IE exception: Function contains several inputs and outputs with one friendly name!
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
// IE exception: Function contains several inputs and outputs with one friendly name!
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
testONNXModels("equal");
}
TEST_P(Test_ONNX_layers, Compare_GT)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// IE exception: Function contains several inputs and outputs with one friendly name!
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
// IE exception: Function contains several inputs and outputs with one friendly name!
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
testONNXModels("greater");
}
TEST_P(Test_ONNX_layers, Greater_input_dtype_int64) {
testONNXModels("greater_input_dtype_int64");
}
TEST_P(Test_ONNX_layers, Compare_LT)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// IE exception: Function contains several inputs and outputs with one friendly name!
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
// IE exception: Function contains several inputs and outputs with one friendly name!
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
testONNXModels("less");
}
TEST_P(Test_ONNX_layers, Compare_GTorEQ)
{
testONNXModels("greater_or_equal");
}
TEST_P(Test_ONNX_layers, Compare_LEorEQ)
{
testONNXModels("less_or_equal");
}
TEST_P(Test_ONNX_layers, CompareSameDims_EQ)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// IE exception: Function contains several inputs and outputs with one friendly name!
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
// IE exception: Function contains several inputs and outputs with one friendly name!
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
testONNXModels("equal_same_dims", npy, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, CompareSameDims_GT)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// IE exception: Function contains several inputs and outputs with one friendly name!
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
// IE exception: Function contains several inputs and outputs with one friendly name!
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
testONNXModels("greater_same_dims", npy, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, CompareSameDims_LT)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// IE exception: Function contains several inputs and outputs with one friendly name!
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
// IE exception: Function contains several inputs and outputs with one friendly name!
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
testONNXModels("less_same_dims", npy, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, Concatenation)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
testONNXModels("concatenation");
testONNXModels("concat_const_blobs");
}
TEST_P(Test_ONNX_layers, CumSumExclusiveInplace)
{
testONNXModels("cumsum_exclusive_inplace");
}
TEST_P(Test_ONNX_layers, Range)
{
testONNXModels("range_float");
testONNXModels("range_float_negative");
}
TEST_P(Test_ONNX_layers, Eltwise3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
#endif
testONNXModels("eltwise3d");
}
TEST_P(Test_ONNX_layers, AveragePooling)
{
testONNXModels("average_pooling");
}
TEST_P(Test_ONNX_layers, MaxPooling3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// accuracy
if (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
// IE exception: [ GENERAL_ERROR ] AssertionFailed: !expired()
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// accuracy
if (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
// IE exception: [ GENERAL_ERROR ] AssertionFailed: !expired()
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
#endif
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported"); // FIXIT use tags
if (backend == DNN_BACKEND_VKCOM)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN);
testONNXModels("max_pool3d", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, AvePooling3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
#endif
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported"); // FIXIT use tags
if (backend == DNN_BACKEND_VKCOM)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN);
testONNXModels("ave_pool3d");
}
TEST_P(Test_ONNX_layers, PoolConv3D)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
#endif
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported"); // FIXIT use tags
if (backend == DNN_BACKEND_VKCOM)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN);
if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16)
{
// CUDA_FP16: cuDNN did not return a suitable algorithm for convolution.
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
}
testONNXModels("pool_conv_3d");
}
TEST_P(Test_ONNX_layers, BatchNormalization)
{
testONNXModels("batch_norm");
}
TEST_P(Test_ONNX_layers, BatchNormalization3D)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
testONNXModels("batch_norm_3d");
}
TEST_P(Test_ONNX_layers, BatchNormalizationUnfused)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception
#endif
testONNXModels("frozenBatchNorm2d");
}
TEST_P(Test_ONNX_layers, BatchNormalizationSubgraph)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception
#endif
testONNXModels("batch_norm_subgraph");
}
TEST_P(Test_ONNX_layers, NormalizeFusionSubgraph)
{
testONNXModels("normalize_fusion");
}
TEST_P(Test_ONNX_layers, Transpose)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
testONNXModels("transpose");
}
TEST_P(Test_ONNX_layers, Multiplication)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("mul");
}
TEST_P(Test_ONNX_layers, MatMul_2d)
{
testONNXModels("matmul_2d");
}
TEST_P(Test_ONNX_layers, MatMul_3d)
{
testONNXModels("matmul_3d");
}
TEST_P(Test_ONNX_layers, MatMul_4d)
{
testONNXModels("matmul_4d");
}
TEST_P(Test_ONNX_layers, MatMul_2d_init)
{
testONNXModels("matmul_2d_init");
}
TEST_P(Test_ONNX_layers, MatMul_3d_init)
{
testONNXModels("matmul_3d_init");
}
TEST_P(Test_ONNX_layers, MatMul_4d_init)
{
testONNXModels("matmul_4d_init");
}
TEST_P(Test_ONNX_layers, MatMul_init_2)
{
testONNXModels("matmul_init_2");
}
TEST_P(Test_ONNX_layers, MatMul_init_bcast)
{
testONNXModels("matmul_init_bcast");
}
TEST_P(Test_ONNX_layers, MatMul_bcast_3dx2d) {
testONNXModels("matmul_bcast");
}
TEST_P(Test_ONNX_layers, MatMulAdd)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// accuracy
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
testONNXModels("matmul_add");
}
TEST_P(Test_ONNX_layers, Expand)
{
testONNXModels("expand");
}
TEST_P(Test_ONNX_layers, ExpandIdentity) {
testONNXModels("expand_identity");
}
TEST_P(Test_ONNX_layers, ExpandBatch) {
testONNXModels("expand_batch");
}
TEST_P(Test_ONNX_layers, ExpandChannels) {
testONNXModels("expand_channels");
}
TEST_P(Test_ONNX_layers, ExpandNegBatch) {
testONNXModels("expand_neg_batch");
}
TEST_P(Test_ONNX_layers, ExpandHW)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("expand_hw");
}
TEST_P(Test_ONNX_layers, Constant)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testONNXModels("constant");
}
TEST_P(Test_ONNX_layers, Padding)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
testONNXModels("padding", npy, 0, 0, false, false);
#else
testONNXModels("padding");
#endif
}
TEST_P(Test_ONNX_layers, Resize)
{
testONNXModels("resize_nearest");
testONNXModels("tf_half_pixel_for_nn");
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("resize_bilinear");
}
TEST_P(Test_ONNX_layers, ResizeUnfused)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("upsample_unfused_torch1.2");
testONNXModels("upsample_unfused_opset9_torch1.4");
testONNXModels("resize_nearest_unfused_opset11_torch1.4");
testONNXModels("resize_nearest_unfused_opset11_torch1.3");
testONNXModels("resize_bilinear_unfused_opset11_torch1.4");
}
TEST_P(Test_ONNX_layers, ResizeUnfusedTwoInputs)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2023000000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
testONNXModels("upsample_unfused_two_inputs_opset9_torch1.4", npy, 0, 0, false, true, 2);
testONNXModels("upsample_unfused_two_inputs_opset11_torch1.4", npy, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, MultyInputs)
{
testONNXModels("multy_inputs", npy, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, Broadcast)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("channel_broadcast", npy, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, DynamicResize)
{
testONNXModels("dynamic_resize_9", npy, 0, 0, false, true, 2);
testONNXModels("dynamic_resize_10", npy, 0, 0, false, true, 2);
testONNXModels("dynamic_resize_11", npy, 0, 0, false, true, 2);
testONNXModels("dynamic_resize_13", npy, 0, 0, false, true, 2);
testONNXModels("dynamic_resize_scale_9", npy, 0, 0, false, true, 2);
testONNXModels("dynamic_resize_scale_10", npy, 0, 0, false, true, 2);
testONNXModels("dynamic_resize_scale_11", npy, 0, 0, false, true, 2);
testONNXModels("dynamic_resize_scale_13", npy, 0, 0, false, true, 2);
testONNXModels("resize_size_opset11");
testONNXModels("resize_size_opset13");
}
TEST_P(Test_ONNX_layers, Resize_HumanSeg)
{
testONNXModels("resize_humanseg");
}
TEST_P(Test_ONNX_layers, Div)
{
const String model = _tf("models/div.onnx");
Net net = readNetFromONNX(model);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
// Reference output values range is -68.80928, 2.991873. So to avoid computational
// difference for FP16 we'll perform reversed division (just swap inputs).
Mat inp1 = blobFromNPY(_tf("data/input_div_1.npy"));
Mat inp2 = blobFromNPY(_tf("data/input_div_0.npy"));
Mat ref = blobFromNPY(_tf("data/output_div.npy"));
cv::divide(1.0, ref, ref);
checkBackend(&inp1, &ref);
net.setInput(inp1, "0");
net.setInput(inp2, "1");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
// NaryEltwise layer suuports only CPU for now
testONNXModels("div_test_1x1", npy, 0, 0, false, false, 2);
}
TEST_P(Test_ONNX_layers, DynamicReshape)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("dynamic_reshape");
testONNXModels("dynamic_reshape_opset_11");
testONNXModels("flatten_by_prod");
testONNXModels("flatten_const");
testONNXModels("flatten_axis_numaxes");
}
TEST_P(Test_ONNX_layers, Reshape)
{
testONNXModels("unsqueeze");
testONNXModels("unsqueeze_opset_13");
}
TEST_P(Test_ONNX_layers, Unsqueeze_Neg_Axes)
{
testONNXModels("unsqueeze_neg_axes");
}
TEST_P(Test_ONNX_layers, Squeeze)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
testONNXModels("squeeze");
testONNXModels("squeeze_axes_op13");
testONNXModels("squeeze_no_axes");
}
TEST_P(Test_ONNX_layers, ReduceL2)
{
testONNXModels("reduceL2");
testONNXModels("reduceL2_subgraph");
testONNXModels("reduceL2_subgraph_2");
testONNXModels("reduceL2_subgraph2_2");
}
TEST_P(Test_ONNX_layers, Split)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2023000000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
testONNXModels("split_0");
testONNXModels("split_1");
testONNXModels("split_2");
testONNXModels("split_3");
testONNXModels("split_4");
testONNXModels("split_5");
testONNXModels("split_6");
testONNXModels("split_neg_axis");
}
// Mul inside with 0-d tensor, output should be A x 1, but is 1 x A. PR #22652
TEST_P(Test_ONNX_layers, DISABLED_Split_sizes_0d)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
testONNXModels("split_sizes");
}
TEST_P(Test_ONNX_layers, Slice)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)
testONNXModels("slice", npy, 0, 0, false, false);
#else
testONNXModels("slice");
testONNXModels("slice_neg_starts");
testONNXModels("slice_opset_11");
testONNXModels("slice_neg_steps", pb);
#endif
}
TEST_P(Test_ONNX_layers, Slice_Steps_2DInput)
{
testONNXModels("slice_opset_11_steps_2d");
}
TEST_P(Test_ONNX_layers, Slice_Steps_3DInput)
{
testONNXModels("slice_opset_11_steps_3d");
}
TEST_P(Test_ONNX_layers, Slice_Steps_4DInput)
{
testONNXModels("slice_opset_11_steps_4d");
}
TEST_P(Test_ONNX_layers, Slice_Steps_5DInput)
{
testONNXModels("slice_opset_11_steps_5d");
}
TEST_P(Test_ONNX_layers, Slice_Nonseq_Axes)
{
testONNXModels("slice_nonseq_axes");
testONNXModels("slice_nonseq_axes_steps");
testONNXModels("slice_nonseq_miss_axes_steps");
}
TEST_P(Test_ONNX_layers, Slice_Neg_Axes)
{
testONNXModels("slice_neg_axes");
testONNXModels("slice_neg_axes_steps");
testONNXModels("slice_neg_miss_axes_steps");
}
TEST_P(Test_ONNX_layers, Softmax)
{
testONNXModels("softmax");
testONNXModels("log_softmax", npy, 0, 0, false, false);
testONNXModels("softmax_unfused");
}
TEST_P(Test_ONNX_layers, Split_EltwiseMax)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2023000000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
testONNXModels("split_max");
}
TEST_P(Test_ONNX_layers, LSTM_Activations)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// IE exception: Node Block1326/lstm/reshape_0/permute was not assigned on any pointed device
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// IE Exception: Ngraph operation Reshape with name Block1237_Output_0_before_reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
#endif
testONNXModels("lstm_cntk_tanh", pb, 0, 0, false, false);
}
// disabled due to poor handling of 1-d mats
TEST_P(Test_ONNX_layers, DISABLED_LSTM)
{
testONNXModels("lstm", npy, 0, 0, false, false);
}
// disabled due to poor handling of 1-d mats
TEST_P(Test_ONNX_layers, DISABLED_LSTM_bidirectional)
{
testONNXModels("lstm_bidirectional", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, LSTM_hidden)
{
testONNXModels("hidden_lstm", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, LSTM_hidden_bidirectional)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// IE exception: Node Transpose_45 was not assigned on any pointed device.
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
#endif
testONNXModels("hidden_lstm_bi", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, GRU)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// IE exception: Node GRU_22 was not assigned on any pointed device
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
#endif
testONNXModels("gru", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, gru_cell_batchsize_50_seqlen_1)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// IE exception: Node GRU_22 was not assigned on any pointed device
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
#endif
if(backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
testONNXModels("gru_cell_batchsize_50_seqlen_1", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, gru_cell_batchsize_5_seqlen_5)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// IE exception: Node GRU_22 was not assigned on any pointed device
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
#endif
if(backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
testONNXModels("gru_cell_batchsize_5_seqlen_5", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, gru_cell_batchsize_1_seqlen_50)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// IE exception: Node GRU_22 was not assigned on any pointed device
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
#endif
if(backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
testONNXModels("gru_cell_batchsize_1_seqlen_50", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, GRU_bidirectional)
{
testONNXModels("gru_bi", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, LSTM_cell_forward)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// accuracy!
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// Ngraph operation Reshape with name LSTM_16/lstm_y/reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testONNXModels("lstm_cell_forward", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, LSTM_cell_bidirectional)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// Ngraph operation Reshape with name LSTM_16/lstm_y/reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testONNXModels("lstm_cell_bidirectional", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, LSTM_cell_with_peepholes)
{
testONNXModels("lstm_cell_with_peepholes", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, LSTM_cell_batchsize_50_seqlen_1)
{
if(backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
testONNXModels("lstm_cell_batchsize_50_seqlen_1", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, LSTM_cell_batchsize_1_seqlen_50)
{
if(backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
testONNXModels("lstm_cell_batchsize_1_seqlen_50", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, LSTM_cell_batchsize_5_seqlen_5)
{
if(backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
testONNXModels("lstm_cell_batchsize_5_seqlen_5", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, LSTM_init_h0_c0)
{
if(backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
testONNXModels("lstm_init_h0_c0", npy, 0, 0, false, false, 3);
}
// epsilon is larger because onnx does not match with torch/opencv exactly
TEST_P(Test_ONNX_layers, LSTM_layout_seq)
{
if(backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
testONNXModels("lstm_layout_0", npy, 0.005, 0.005, false, false, 3);
}
// epsilon is larger because onnx does not match with torch/opencv exactly
TEST_P(Test_ONNX_layers, LSTM_layout_batch)
{
if(backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
testONNXModels("lstm_layout_1", npy, 0.005, 0.005, false, false, 3);
}
TEST_P(Test_ONNX_layers, DISABLED_Einsum_1D)
{
testONNXModels("einsum_1d", npy, 0, 0, false, false, 2);
}
TEST_P(Test_ONNX_layers, Einsum_2D)
{
testONNXModels("einsum_2d", npy, 0, 0, false, false, 2);
}
TEST_P(Test_ONNX_layers, Einsum_2D_Ellipses)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
testONNXModels("einsum_2d_ellipses", npy, 0, 0, false, false, 2);
}
TEST_P(Test_ONNX_layers, Einsum_3D)
{
testONNXModels("einsum_3d", npy, 0, 0, false, false, 2);
}
TEST_P(Test_ONNX_layers, Einsum_4D)
{
testONNXModels("einsum_4d", npy, 0, 0, false, false, 2);
}
TEST_P(Test_ONNX_layers, Einsum_5D)
{
testONNXModels("einsum_5d", npy, 0, 0, false, false, 2);
}
TEST_P(Test_ONNX_layers, DISABLED_Einsum_InnerProduct)
{
testONNXModels("einsum_inner", npy, 0, 0, false, false, 2);
}
TEST_P(Test_ONNX_layers, DISABLED_Einsum_HadamardProduct)
{
testONNXModels("einsum_hadamard", npy, 0, 0, false, false, 2);
}
TEST_P(Test_ONNX_layers, Einsum_Batch_Diagonal)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
testONNXModels("einsum_batch_diagonal", npy, 0, 0, false, false, 1);
}
TEST_P(Test_ONNX_layers, Einsum_Sum)
{
testONNXModels("einsum_sum", npy, 0, 0, false, false, 1);
}
TEST_P(Test_ONNX_layers, Einsum_transpose)
{
testONNXModels("einsum_transpose", npy, 0, 0, false, false, 1);
}
TEST_P(Test_ONNX_layers, Einsum_const_inputs) {
testONNXModels("einsum_const_inputs", npy, 0, 0, false, false, 1);
}
TEST_P(Test_ONNX_layers, Pad2d_Unfused)
{
testONNXModels("ReflectionPad2d");
testONNXModels("ZeroPad2d");
}
TEST_P(Test_ONNX_layers, LinearWithConstant)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2020040000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
#endif
if (backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
testONNXModels("lin_with_constant");
}
TEST_P(Test_ONNX_layers, MatmulWithTwoInputs)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2020040000)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
#endif
testONNXModels("matmul_with_two_inputs");
}
TEST_P(Test_ONNX_layers, ResizeOpset11_Torch1_6)
{
testONNXModels("resize_opset11_torch1.6");
}
TEST_P(Test_ONNX_layers, Mish)
{
testONNXModels("mish");
testONNXModels("mish_no_softplus");
}
TEST_P(Test_ONNX_layers, CalculatePads)
{
testONNXModels("calc_pads");
}
TEST_P(Test_ONNX_layers, Conv1d)
{
testONNXModels("conv1d");
}
TEST_P(Test_ONNX_layers, Conv1d_bias)
{
testONNXModels("conv1d_bias");
}
TEST_P(Test_ONNX_layers, Conv1d_variable_weight)
{
if (backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // not supported
if (backend == DNN_BACKEND_VKCOM)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); // not supported
String basename = "conv1d_variable_w";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat input = blobFromNPY(_tf("data/input_" + basename + "_0.npy"));
Mat weights = blobFromNPY(_tf("data/input_" + basename + "_1.npy"));
Mat ref = blobFromNPY(_tf("data/output_" + basename + ".npy"));
net.setInput(input, "0");
net.setInput(weights, "1");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
}
TEST_P(Test_ONNX_layers, Conv1d_variable_weight_bias)
{
if (backend == DNN_BACKEND_CUDA)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); // not supported
if (backend == DNN_BACKEND_VKCOM)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN); // not supported
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (target == DNN_TARGET_CPU && getInferenceEngineCPUType() == CV_DNN_INFERENCE_ENGINE_CPU_TYPE_ARM_COMPUTE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_ARM_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
String basename = "conv1d_variable_wb";
Net net = readNetFromONNX(_tf("models/" + basename + ".onnx"));
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
Mat input = blobFromNPY(_tf("data/input_" + basename + "_0.npy"));
Mat weights = blobFromNPY(_tf("data/input_" + basename + "_1.npy"));
Mat bias = blobFromNPY(_tf("data/input_" + basename + "_2.npy"));
Mat ref = blobFromNPY(_tf("data/output_" + basename + ".npy"));
net.setInput(input, "0");
net.setInput(weights, "1");
net.setInput(bias, "bias");
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
}
TEST_P(Test_ONNX_layers, GatherMultiOutput)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// IE Exception: Ngraph operation Reshape with name 6 has dynamic output shape on 0 port, but CPU plug-in supports only static shape
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2021030000)
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE);
#endif
testONNXModels("gather_multi_output", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, DynamicAxes_squeeze_and_conv)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
#if INF_ENGINE_VER_MAJOR_LT(2021000000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
#endif
testONNXModels("squeeze_and_conv_dynamic_axes");
}
TEST_P(Test_ONNX_layers, DynamicAxes_unsqueeze_and_conv)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
#if INF_ENGINE_VER_MAJOR_LT(2021000000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
#endif
testONNXModels("unsqueeze_and_conv_dynamic_axes");
}
TEST_P(Test_ONNX_layers, DynamicAxes_gather)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
#if INF_ENGINE_VER_MAJOR_LT(2021000000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
#endif
testONNXModels("gather_dynamic_axes", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, DynamicAxes_gather_scalar)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// accuracy
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// accuracy
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
#elif defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
#if INF_ENGINE_VER_MAJOR_LT(2021000000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
#endif
testONNXModels("gather_scalar_dynamic_axes", npy, 0, 0, false, false);
}
TEST_P(Test_ONNX_layers, DynamicAxes_slice)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
#if INF_ENGINE_VER_MAJOR_LT(2021000000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
#endif
testONNXModels("slice_dynamic_axes");
}
TEST_P(Test_ONNX_layers, DynamicAxes_slice_opset_11)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
#if INF_ENGINE_VER_MAJOR_LT(2021000000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
#endif
testONNXModels("slice_opset_11_dynamic_axes");
}
TEST_P(Test_ONNX_layers, DynamicAxes_resize_opset11_torch16)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
#if INF_ENGINE_VER_MAJOR_LT(2021000000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
#endif
testONNXModels("resize_opset11_torch1.6_dynamic_axes");
}
TEST_P(Test_ONNX_layers, DynamicAxes_average_pooling)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
#if INF_ENGINE_VER_MAJOR_LT(2021000000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
#endif
testONNXModels("average_pooling_dynamic_axes");
}
TEST_P(Test_ONNX_layers, DynamicAxes_maxpooling_sigmoid)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
#if INF_ENGINE_VER_MAJOR_LT(2021000000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
#endif
testONNXModels("maxpooling_sigmoid_dynamic_axes");
}
TEST_P(Test_ONNX_layers, DynamicAxes_dynamic_batch)
{
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
#if INF_ENGINE_VER_MAJOR_LT(2021000000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
#endif
testONNXModels("dynamic_batch");
}
TEST_P(Test_ONNX_layers, MaxPool1d)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
{
// 2021.4: [ GENERAL_ERROR ] AssertionFailed: !expired()
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
testONNXModels("maxpooling_1d");
}
TEST_P(Test_ONNX_layers, MaxPoolSigmoid1d)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
testONNXModels("maxpooling_sigmoid_1d");
}
TEST_P(Test_ONNX_layers, MaxPool1d_Twise)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
testONNXModels("two_maxpooling_1d");
}
TEST_P(Test_ONNX_layers, AvePool1d)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
testONNXModels("average_pooling_1d");
}
TEST_P(Test_ONNX_layers, PoolConv1d)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#endif
testONNXModels("pool_conv_1d");
}
TEST_P(Test_ONNX_layers, ConvResizePool1d)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// IE Exception: Ngraph operation Reshape with name 15 has dynamic output shape on 0 port, but CPU plug-in supports only static shape
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
#endif
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#if INF_ENGINE_VER_MAJOR_EQ(2021030000)
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // exception
#endif
}
#endif
const double lInf = (target == DNN_TARGET_CPU_FP16) ? 0.024 : default_lInf;
testONNXModels("conv_resize_pool_1d", npy, default_l1, lInf);
}
TEST_P(Test_ONNX_layers, DepthWiseAdd)
{
testONNXModels("depthwiseconv_add");
}
TEST_P(Test_ONNX_layers, DepthStride2)
{
testONNXModels("depthwise_stride2");
}
TEST_P(Test_ONNX_layers, SubFromConst)
{
testONNXModels("sub_from_const1");
testONNXModels("sub_from_const_eltwise");
testONNXModels("sub_from_const_broadcast");
}
TEST_P(Test_ONNX_layers, DivConst)
{
testONNXModels("div_const");
}
TEST_P(Test_ONNX_layers, Gemm)
{
testONNXModels("gemm_no_transB");
testONNXModels("gemm_transB_0");
testONNXModels("gemm_first_const");
}
TEST_P(Test_ONNX_layers, Gemm_bias)
{
testONNXModels("gemm_vector_bias");
}
TEST_P(Test_ONNX_layers, Quantized_Convolution)
{
// The difference of QOperator and QDQ format:
// https://onnxruntime.ai/docs/performance/quantization.html#onnx-quantization-representation-format.
{
SCOPED_TRACE("QOperator quantized model.");
testONNXModels("quantized_conv_uint8_weights", npy, 0.004, 0.02);
testONNXModels("quantized_conv_int8_weights", npy, 0.03, 0.5);
testONNXModels("quantized_conv_per_channel_weights", npy, 0.06, 0.4);
testONNXModels("quantized_conv_asymmetric_pads_int8_weights");
}
{
SCOPED_TRACE("QDQ quantized model.");
testONNXModels("quantized_conv_uint8_weights_qdq", npy, 0.004, 0.02);
testONNXModels("quantized_conv_int8_weights_qdq", npy, 0.03, 0.5);
testONNXModels("quantized_conv_per_channel_weights_qdq", npy, 0.06, 0.4);
}
}
TEST_P(Test_ONNX_layers, Quantized_MatMul)
{
testONNXModels("quantized_matmul_uint8_weights", npy, 0.005, 0.007);
testONNXModels("quantized_matmul_int8_weights", npy, 0.06, 0.2);
testONNXModels("quantized_matmul_per_channel_weights", npy, 0.06, 0.22);
}
TEST_P(Test_ONNX_layers, Quantized_Gemm)
{
testONNXModels("quantized_gemm", npy);
}
TEST_P(Test_ONNX_layers, Quantized_MatMul_Variable_Weights)
{
// Unsupported
EXPECT_THROW(
{
testONNXModels("quantized_matmul_variable_inputs");
}, cv::Exception);
}
TEST_P(Test_ONNX_layers, Quantized_Eltwise)
{
testONNXModels("quantized_eltwise");
}
TEST_P(Test_ONNX_layers, Quantized_Eltwise_Scalar)
{
testONNXModels("quantized_eltwise_scalar");
}
TEST_P(Test_ONNX_layers, Quantized_Eltwise_Broadcast)
{
testONNXModels("quantized_eltwise_broadcast");
}
TEST_P(Test_ONNX_layers, Quantized_LeakyReLU)
{
testONNXModels("quantized_leaky_relu");
}
TEST_P(Test_ONNX_layers, Quantized_Sigmoid)
{
testONNXModels("quantized_sigmoid");
}
TEST_P(Test_ONNX_layers, Quantized_MaxPool)
{
testONNXModels("quantized_maxpool");
}
TEST_P(Test_ONNX_layers, Quantized_AvgPool)
{
testONNXModels("quantized_avgpool");
}
TEST_P(Test_ONNX_layers, Quantized_Split)
{
testONNXModels("quantized_split");
}
TEST_P(Test_ONNX_layers, Quantized_Pad)
{
testONNXModels("quantized_padding");
}
TEST_P(Test_ONNX_layers, Quantized_Reshape)
{
testONNXModels("quantized_reshape");
}
TEST_P(Test_ONNX_layers, Quantized_Transpose)
{
testONNXModels("quantized_transpose");
}
TEST_P(Test_ONNX_layers, Quantized_Squeeze)
{
testONNXModels("quantized_squeeze");
}
TEST_P(Test_ONNX_layers, Quantized_Unsqueeze)
{
testONNXModels("quantized_unsqueeze");
}
TEST_P(Test_ONNX_layers, Quantized_Resize)
{
testONNXModels("quantized_resize_nearest");
double l1 = backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ? 0.0013 : 2e-4;
testONNXModels("quantized_resize_bilinear", npy, l1, 0.003);
l1 = backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ? 0.0013 : 3e-4;
testONNXModels("quantized_resize_bilinear_align", npy, l1, 0.003);
}
TEST_P(Test_ONNX_layers, Quantized_Concat)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
testONNXModels("quantized_concat");
testONNXModels("quantized_concat_const_blob");
}
TEST_P(Test_ONNX_layers, Quantized_Constant)
{
testONNXModels("quantized_constant", npy, 0.002, 0.008);
}
TEST_P(Test_ONNX_layers, OutputRegistration)
{
testONNXModels("output_registration", npy, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, QLinearSoftmax)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
testONNXModels("qlinearsoftmax_v11", npy, 0.002, 0.002); // 2D coerced
testONNXModels("qlinearsoftmax_v13", npy, 0.002, 0.002);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_ONNX_layers, dnnBackendsAndTargets());
class Test_ONNX_nets : public Test_ONNX_layers
{
public:
Test_ONNX_nets() { required = false; }
};
TEST_P(Test_ONNX_nets, Alexnet)
{
#if defined(OPENCV_32BIT_CONFIGURATION) && (defined(HAVE_OPENCL) || defined(_WIN32))
applyTestTag(CV_TEST_TAG_MEMORY_2GB);
#else
applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
#endif
const String model = _tf("models/alexnet.onnx", false);
Net net = readNetFromONNX(model);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.enableWinograd(false);
Mat inp = imread(_tf("../grace_hopper_227.png"));
Mat ref = blobFromNPY(_tf("../caffe_alexnet_prob.npy"));
checkBackend(&inp, &ref);
net.setInput(blobFromImage(inp, 1.0f, Size(227, 227), Scalar(), false));
ASSERT_FALSE(net.empty());
Mat out = net.forward();
normAssert(out, ref, "", default_l1, default_lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(Test_ONNX_nets, RAFT)
{
applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_DEBUG_VERYLONG, CV_TEST_TAG_MEMORY_2GB);
std::string weight_path = _tf("models/optical_flow_estimation_raft_2023aug.onnx", false);
std::string img0_path = findDataFile(std::string("gpu/opticalflow/frame0.png"));
std::string img1_path = findDataFile(std::string("gpu/opticalflow/frame1.png"));
Size target_size{480, 360};
auto img0 = imread(img0_path);
auto img1 = imread(img1_path);
auto blob0 = blobFromImage(img0, 1.0, target_size, 0, true);
auto blob1 = blobFromImage(img1, 1.0, target_size, 0, true);
auto net = readNet(weight_path);
net.setInput(blob0, "0");
net.setInput(blob1, "1");
std::vector<std::string> outnames{"12007", "12006"};
std::vector<Mat> outs;
net.forward(outs, outnames);
// output 12006 is not checked to save space in opencv_extra since its ref is > 1MB,
// and output 12006 is calculated from 12007 so checking 12007 is sufficient.
std::string ref_12700_path = _tf("data/output_optical_flow_estimation_raft_2023aug.npy");
auto ref0 = blobFromNPY(ref_12700_path);
normAssert(ref0, outs[0], "", 1.5e-3, 3.2e-2);
}
TEST_P(Test_ONNX_nets, Squeezenet)
{
testONNXModels("squeezenet", pb);
}
TEST_P(Test_ONNX_nets, Googlenet)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// accuracy
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// accuracy
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
const String model = _tf("models/googlenet.onnx", false);
Net net = readNetFromONNX(model);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
if (target == DNN_TARGET_CPU_FP16)
net.enableWinograd(false);
std::vector<Mat> images;
images.push_back( imread(_tf("../googlenet_0.png")) );
images.push_back( imread(_tf("../googlenet_1.png")) );
Mat inp = blobFromImages(images, 1.0f, Size(), Scalar(), false);
Mat ref = blobFromNPY(_tf("../googlenet_prob.npy"));
checkBackend(&inp, &ref);
net.setInput(inp);
ASSERT_FALSE(net.empty());
Mat out = net.forward();
normAssert(ref, out, "", default_l1, default_lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(Test_ONNX_nets, CaffeNet)
{
#if defined(OPENCV_32BIT_CONFIGURATION) && (defined(HAVE_OPENCL) || defined(_WIN32))
applyTestTag(CV_TEST_TAG_MEMORY_2GB);
#else
applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019030000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
testONNXModels("caffenet", pb);
}
TEST_P(Test_ONNX_nets, RCNN_ILSVRC13)
{
#if defined(OPENCV_32BIT_CONFIGURATION) && (defined(HAVE_OPENCL) || defined(_WIN32))
applyTestTag(CV_TEST_TAG_MEMORY_2GB);
#else
applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019030000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
// Reference output values are in range [-4.992, -1.161]
testONNXModels("rcnn_ilsvrc13", pb, 0.0046);
}
TEST_P(Test_ONNX_nets, VGG16_bn)
{
applyTestTag(CV_TEST_TAG_MEMORY_6GB); // > 2.3Gb
// output range: [-16; 27], after Softmax [0; 0.67]
const double lInf = (target == DNN_TARGET_MYRIAD) ? 0.038 : default_lInf;
testONNXModels("vgg16-bn", pb, default_l1, lInf, true);
}
TEST_P(Test_ONNX_nets, ZFNet)
{
applyTestTag(CV_TEST_TAG_MEMORY_2GB);
testONNXModels("zfnet512", pb);
}
TEST_P(Test_ONNX_nets, ResNet18v1)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
// output range: [-16; 22], after Softmax [0, 0.51]
testONNXModels("resnet18v1", pb, default_l1, default_lInf, true, target != DNN_TARGET_MYRIAD);
}
TEST_P(Test_ONNX_nets, ResNet50v1)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
// output range: [-67; 75], after Softmax [0, 0.98]
size_t hwm0 = getTopMemoryUsageMB();
testONNXModels("resnet50v1", pb, default_l1, default_lInf, true, target != DNN_TARGET_MYRIAD);
size_t hwm1 = getTopMemoryUsageMB();
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_CPU)
{
EXPECT_LE(hwm1 - hwm0, 350) << "Top allocated memory";
}
}
TEST_P(Test_ONNX_nets, ResNet50_Int8)
{
testONNXModels("resnet50_int8", pb, default_l1, default_lInf, true);
}
TEST_P(Test_ONNX_nets, ResNet101_DUC_HDC)
{
applyTestTag(CV_TEST_TAG_VERYLONG);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL)
{
if (backend == DNN_BACKEND_OPENCV)
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_OPENCL : CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
throw SkipTestException("Test is disabled for OpenCL targets");
}
testONNXModels("resnet101_duc_hdc", pb);
}
TEST_P(Test_ONNX_nets, TinyYolov2)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
if (cvtest::skipUnstableTests)
throw SkipTestException("Skip unstable test");
#if defined(INF_ENGINE_RELEASE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019
&& (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16)
)
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X,
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ?
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER :
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
// output range: [-11; 8]
double l1 = default_l1, lInf = default_lInf;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16)
{
l1 = 0.02;
lInf = 0.2;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.018;
lInf = 0.16;
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.018f; lInf = 0.16f;
}
#endif
testONNXModels("tiny_yolo2", pb, l1, lInf, false, true, 1, true, false);
}
TEST_P(Test_ONNX_nets, CNN_MNIST)
{
// output range: [-1952; 6574], after Softmax [0; 1]
testONNXModels("cnn_mnist", pb, default_l1, default_lInf, true);
}
TEST_P(Test_ONNX_nets, MobileNet_v2)
{
// output range: [-166; 317], after Softmax [0; 1]
testONNXModels("mobilenetv2", pb, default_l1, default_lInf, true);
}
TEST_P(Test_ONNX_nets, MobileNet_v2_FP16)
{
testONNXModels("mobilenetv2_fp16", npy, default_l1, default_lInf, true);
}
TEST_P(Test_ONNX_nets, LResNet100E_IR)
{
applyTestTag(
#if defined(OPENCV_32BIT_CONFIGURATION) && defined(HAVE_OPENCL)
CV_TEST_TAG_MEMORY_2GB,
#else
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
#endif
CV_TEST_TAG_DEBUG_VERYLONG
);
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
double l1 = default_l1, lInf = default_lInf;
// output range: [-3; 3]
bool useWinograd = true;
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.009;
lInf = 0.035;
}
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_CPU)
{
l1 = 4.6e-5;
lInf = 1.9e-4;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.009;
lInf = 0.04;
}
else if (target == DNN_TARGET_CPU_FP16)
{
useWinograd = false;
l1 = 0.009;
lInf = 0.035;
}
testONNXModels("LResNet100E_IR", pb, l1, lInf, false, true, 1, true, useWinograd);
}
TEST_P(Test_ONNX_nets, Emotion_ferplus)
{
#if defined(INF_ENGINE_RELEASE)
if (target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X,
backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ?
CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER :
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
#endif
double l1 = default_l1;
double lInf = default_lInf;
bool useWinograd = true;
// Output values are in range [-2.011, 2.111]
if ((backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) || (target == DNN_TARGET_CUDA_FP16))
l1 = 0.007;
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.021;
lInf = 0.034;
}
else if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_CPU || target == DNN_TARGET_OPENCL)) {
l1 = 2.4e-4;
lInf = 6e-4;
}
else if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_CPU_FP16)
{
useWinograd = false;
l1 = 0.007;
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.013f; lInf = 0.035f;
}
#endif
testONNXModels("emotion_ferplus", pb, l1, lInf, false, true, 1, true, useWinograd);
}
TEST_P(Test_ONNX_nets, Inception_v2)
{
testONNXModels("inception_v2", pb, default_l1, default_lInf, true);
}
TEST_P(Test_ONNX_nets, DenseNet121)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
// output range: [-87; 138], after Softmax [0; 1]
testONNXModels("densenet121", pb, default_l1, default_lInf, true, target != DNN_TARGET_MYRIAD);
}
TEST_P(Test_ONNX_nets, Inception_v1)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
#endif
testONNXModels("inception_v1", pb);
}
TEST_P(Test_ONNX_nets, Shufflenet)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
}
#endif
testONNXModels("shufflenet", pb);
}
TEST_P(Test_ONNX_nets, Resnet34_kinetics)
{
applyTestTag(CV_TEST_TAG_DEBUG_VERYLONG);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// IE exception: Failed to allocate graph: MYRIAD device is not opened
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
// accuracy
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
);
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
{
// IE exception: Function contains several inputs and outputs with one friendly name!
if (target == DNN_TARGET_MYRIAD)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
}
#elif defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER); // Only CPU on DLIE backend is supported
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // Only CPU on DLIE backend is supported
#endif
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported"); // FIXIT use tags
if (backend == DNN_BACKEND_VKCOM)
applyTestTag(CV_TEST_TAG_DNN_SKIP_VULKAN);
String onnxmodel = findDataFile("dnn/resnet-34_kinetics.onnx", false);
Mat image0 = imread(findDataFile("dnn/dog416.png"));
Mat image1 = imread(findDataFile("dnn/street.png"));
Mat ref0 = blobFromNPY(_tf("data/output_kinetics0.npy"));
Mat ref1 = blobFromNPY(_tf("data/output_kinetics1.npy"));
std::vector<Mat> images_0(16, image0);
std::vector<Mat> images_1(16, image1);
Mat blob0 = blobFromImages(images_0, 1.0, Size(112, 112), Scalar(114.7748, 107.7354, 99.4750), true, true);
Mat blob1 = blobFromImages(images_1, 1.0, Size(112, 112), Scalar(114.7748, 107.7354, 99.4750), true, true);
Net permute;
LayerParams lp;
int order[] = {1, 0, 2, 3};
lp.set("order", DictValue::arrayInt<int*>(&order[0], 4));
permute.addLayerToPrev("perm", "Permute", lp);
permute.setPreferableBackend(backend);
permute.setPreferableTarget(target);
permute.setInput(blob0);
Mat input0 = permute.forward().clone();
permute.setInput(blob1);
Mat input1 = permute.forward().clone();
int dims[] = {1, 3, 16, 112, 112};
input0 = input0.reshape(0, 5, &dims[0]);
input1 = input1.reshape(0, 5, &dims[0]);
Net net = readNetFromONNX(onnxmodel);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
// output range [-5, 11]
float l1 = 0.0013;
float lInf = 0.009;
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.02;
lInf = 0.07;
}
if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.01;
lInf = 0.06;
}
testInputShapes(net, {input0});
checkBackend(&input0, &ref0);
net.setInput(input0);
Mat out = net.forward().clone();
normAssert(ref0, out, "", l1, lInf);
checkBackend(&input1, &ref1);
net.setInput(input1);
out = net.forward().clone();
normAssert(ref1, out, "", l1, lInf);
expectNoFallbacksFromIE(net);
}
TEST_P(Test_ONNX_layers, CumSum)
{
testONNXModels("cumsum_1d_exclusive_1");
testONNXModels("cumsum_1d_reverse");
testONNXModels("cumsum_1d_exclusive_1_reverse");
testONNXModels("cumsum_2d_dim_1");
testONNXModels("cumsum_3d_dim_2");
}
static void testYOLO(const std::string& weightPath, const std::vector<int>& refClassIds,
const std::vector<float>& refScores, const std::vector<Rect2d>& refBoxes,
Image2BlobParams imgParams, float conf_threshold = 0.3, float iou_threshold = 0.5,
double scores_diff = 1e-5, double boxes_iou_diff = 1e-4, const std::string test_name = "")
{
std::string imgPath = _tf("../dog_orig_size.png");
Mat img = imread(imgPath);
Mat inp = blobFromImageWithParams(img, imgParams);
Net net = readNet(weightPath);
net.setInput(inp);
std::vector<Mat> outs;
net.forward(outs, net.getUnconnectedOutLayersNames());
// Retrieve
std::vector<int> keep_classIds;
std::vector<float> keep_confidences;
std::vector<Rect2d> keep_boxes;
yoloPostProcessing(outs, keep_classIds, keep_confidences, keep_boxes, conf_threshold, iou_threshold, test_name);
normAssertDetections(
refClassIds, refScores, refBoxes,
keep_classIds, keep_confidences, keep_boxes,
"", 0.0, scores_diff, boxes_iou_diff);
}
void yoloPostProcessing(
std::vector<Mat>& outs,
std::vector<int>& keep_classIds,
std::vector<float>& keep_confidences,
std::vector<Rect2d>& keep_boxes,
float conf_threshold,
float iou_threshold,
const std::string& model_name,
const int nc
){
// Retrieve
std::vector<int> classIds;
std::vector<float> confidences;
std::vector<Rect2d> boxes;
if (model_name == "yolov8" || model_name == "yolov10" ||
model_name == "yolov9")
{
cv::transposeND(outs[0], {0, 2, 1}, outs[0]);
}
if (model_name == "yolonas"){
// outs contains 2 elemets of shape [1, 8400, nc] and [1, 8400, 4]. Concat them to get [1, 8400, nc+4]
Mat concat_out;
// squeeze the first dimension
outs[0] = outs[0].reshape(1, outs[0].size[1]);
outs[1] = outs[1].reshape(1, outs[1].size[1]);
cv::hconcat(outs[1], outs[0], concat_out);
outs[0] = concat_out;
// remove the second element
outs.pop_back();
// unsqueeze the first dimension
outs[0] = outs[0].reshape(0, std::vector<int>{1, outs[0].size[0], outs[0].size[1]});
}
// assert if last dim is nc+5 or nc+4
CV_CheckEQ(outs[0].dims, 3, "Invalid output shape. The shape should be [1, #anchors, nc+5 or nc+4]");
CV_CheckEQ((outs[0].size[2] == nc + 5 || outs[0].size[2] == nc + 4), true, "Invalid output shape: ");
for (auto preds : outs){
preds = preds.reshape(1, preds.size[1]); // [1, 8400, 85] -> [8400, 85]
for (int i = 0; i < preds.rows; ++i)
{
// filter out non object
float obj_conf = (model_name == "yolov8" || model_name == "yolonas" ||
model_name == "yolov9" || model_name == "yolov10") ? 1.0f : preds.at<float>(i, 4) ;
if (obj_conf < conf_threshold)
continue;
Mat scores = preds.row(i).colRange((model_name == "yolov8" || model_name == "yolonas" || model_name == "yolov9" || model_name == "yolov10") ? 4 : 5, preds.cols);
double conf;
Point maxLoc;
minMaxLoc(scores, 0, &conf, 0, &maxLoc);
conf = (model_name == "yolov8" || model_name == "yolonas" || model_name == "yolov9" || model_name == "yolov10") ? conf : conf * obj_conf;
if (conf < conf_threshold)
continue;
// get bbox coords
float* det = preds.ptr<float>(i);
double cx = det[0];
double cy = det[1];
double w = det[2];
double h = det[3];
// [x1, y1, x2, y2]
if (model_name == "yolonas" || model_name == "yolov10"){
boxes.push_back(Rect2d(cx, cy, w, h));
} else {
boxes.push_back(Rect2d(cx - 0.5 * w, cy - 0.5 * h,
cx + 0.5 * w, cy + 0.5 * h));
}
classIds.push_back(maxLoc.x);
confidences.push_back(conf);
}
}
// NMS
std::vector<int> keep_idx;
NMSBoxes(boxes, confidences, conf_threshold, iou_threshold, keep_idx);
for (auto i : keep_idx)
{
keep_classIds.push_back(classIds[i]);
keep_confidences.push_back(confidences[i]);
keep_boxes.push_back(boxes[i]);
}
}
TEST_P(Test_ONNX_nets, YOLOv10)
{
std::string weightPath = _tf("models/yolov10s.onnx", false);
Size targetSize{640, 480};
float conf_threshold = 0.50;
float iou_threshold = 0.50;
std::vector<int> refClassIds{1, 16, 7};
std::vector<float> refScores{0.9510f, 0.9454f, 0.8404f};
std::vector<Rect2d> refBoxes{
Rect2d(105.5014, 112.8838, 472.9274, 350.0603),
Rect2d(109.8231, 185.7994, 258.5916, 452.9302),
Rect2d(388.5018, 62.1034, 576.6399, 143.3986)
};
Image2BlobParams imgParams(
Scalar::all(1 / 255.0),
targetSize,
Scalar::all(0),
true,
CV_32F,
DNN_LAYOUT_NCHW,
DNN_PMODE_LETTERBOX,
Scalar::all(114)
);
testYOLO(
weightPath, refClassIds, refScores, refBoxes,
imgParams, conf_threshold, iou_threshold,
1.0e-4, 1.0e-4, "yolov10");
}
TEST_P(Test_ONNX_nets, YOLOv9)
{
std::string weightPath = _tf("models/yolov9t.onnx", false);
Size targetSize{640, 480};
float conf_threshold = 0.50;
float iou_threshold = 0.50;
std::vector<int> refClassIds{1, 16, 2}; // wrong class mapping for yolov9
std::vector<float> refScores{0.959274f, 0.901125f, 0.559396f};
std::vector<Rect2d> refBoxes{
Rect2d(106.255, 107.927, 472.497, 350.309),
Rect2d(108.633, 185.256, 259.287, 450.672),
Rect2d(390.701, 62.1454, 576.928, 141.795)
};
Image2BlobParams imgParams(
Scalar::all(1 / 255.0),
targetSize,
Scalar::all(0),
true,
CV_32F,
DNN_LAYOUT_NCHW,
DNN_PMODE_LETTERBOX,
Scalar::all(114)
);
testYOLO(
weightPath, refClassIds, refScores, refBoxes,
imgParams, conf_threshold, iou_threshold,
1.0e-4, 1.0e-4, "yolov9");
}
TEST_P(Test_ONNX_nets, YOLOX)
{
applyTestTag(CV_TEST_TAG_DEBUG_VERYLONG);
std::string weightPath = _tf("models/yolox_s_inf_decoder.onnx", false);
Size targetSize{640, 640};
float conf_threshold = 0.50;
float iou_threshold = 0.50;
std::vector<int> refClassIds{1, 16, 7};
std::vector<float> refScores{0.9649f, 0.9163f, 0.6879f};
std::vector<Rect2d> refBoxes{
Rect2d(105.5384, 179.4100, 470.6339, 428.5553),
Rect2d(111.4482, 263.4098, 258.7438, 526.1140),
Rect2d(389.1421, 143.9286, 577.9495, 222.0294)
};
Image2BlobParams imgParams(
Scalar::all(1),
targetSize,
Scalar::all(0),
true,
CV_32F,
DNN_LAYOUT_NCHW,
DNN_PMODE_LETTERBOX,
Scalar::all(114)
);
testYOLO(
weightPath, refClassIds, refScores, refBoxes,
imgParams, conf_threshold, iou_threshold,
1.0e-4, 1.0e-4);
}
TEST_P(Test_ONNX_nets, YOLONas)
{
// model information: https://dl.opencv.org/models/yolo-nas/Readme.md
std::string weightPath = _tf("models/yolo_nas_s.onnx", false);
Size targetSize{640, 640};
float conf_threshold = 0.50;
float iou_threshold = 0.50;
std::vector<int> refClassIds{1, 16, 7};
std::vector<float> refScores{0.9720f, 0.9283f, 0.8990f};
// [x1, y1, x2, y2]
std::vector<Rect2d> refBoxes{
Rect2d(105.516, 173.696, 471.323, 430.433),
Rect2d(109.241, 263.406, 259.872, 531.858),
Rect2d(390.153, 142.492, 574.932, 222.709)
};
Image2BlobParams imgParams(
Scalar::all(1/255.0),
targetSize,
Scalar::all(0),
false,
CV_32F,
DNN_LAYOUT_NCHW,
DNN_PMODE_LETTERBOX,
Scalar::all(114)
);
testYOLO(
weightPath, refClassIds, refScores, refBoxes,
imgParams, conf_threshold, iou_threshold,
1.0e-4, 1.0e-4, "yolonas");
}
TEST_P(Test_ONNX_nets, YOLOv8)
{
std::string weightPath = _tf("models/yolov8n.onnx", false);
Size targetSize{640, 640};
float conf_threshold = 0.25;
float iou_threshold = 0.50;
std::vector<int> refClassIds{16, 1, 2};
std::vector<float> refScores{0.9332f, 0.8959f, 0.6157f};
// [x1, y1, x2, y2]
std::vector<Rect2d> refBoxes{
Rect2d(108.8965, 261.9094, 257.1633, 530.3049),
Rect2d(110.4020, 192.9843, 473.4418, 429.5965),
Rect2d(389.1603, 143.2506, 577.3542, 223.0615),
};
Image2BlobParams imgParams(
Scalar::all(1/255.0),
targetSize,
Scalar::all(0),
true,
CV_32F,
DNN_LAYOUT_NCHW,
DNN_PMODE_LETTERBOX,
Scalar::all(114)
);
testYOLO(
weightPath, refClassIds, refScores, refBoxes,
imgParams, conf_threshold, iou_threshold,
1.0e-4, 1.0e-4, "yolov8");
}
// This test is mainly to test:
// 1. identity node with constant input
// 2. limited support to range operator (all inputs are constant)
// 3. parseExpand with multiple broadcast axes
// 4. 1D mat dimension issue with the output of range operator
TEST_P(Test_ONNX_nets, YOLOv7)
{
applyTestTag(
CV_TEST_TAG_MEMORY_2GB,
CV_TEST_TAG_DEBUG_VERYLONG
);
std::string weightPath = _tf("models/yolov7.onnx", false);
// Reference, which is collected with input size of 640x640
std::vector<int> refClassIds{1, 16, 7};
std::vector<float> refScores{0.9614331f, 0.9589417f, 0.8679074f};
// [x1, y1, x2, y2] x 3
std::vector<Rect2d> refBoxes{Rect2d(105.973236f, 150.16716f, 472.59012f, 466.48834f),
Rect2d(109.97953f, 246.17862f, 259.83676f, 600.76624f),
Rect2d(385.96185f, 83.02809f, 576.07355f, 189.82793f)};
Size targetSize{640, 640};
Image2BlobParams imgParams(
Scalar::all(1/255.0),
targetSize,
Scalar::all(0),
true,
CV_32F,
DNN_LAYOUT_NCHW,
DNN_PMODE_NULL,
Scalar::all(0)
);
testYOLO(weightPath, refClassIds, refScores, refBoxes, imgParams);
}
TEST_P(Test_ONNX_nets, YOLOv6)
{
std::string weightPath = _tf("models/yolov6n.onnx", false);
Size targetSize{640, 640};
float conf_threshold = 0.30;
float iou_threshold = 0.50;
std::vector<int> refClassIds{1, 16, 7, 1};
std::vector<float> refScores{0.95031f, 0.87123f, 0.65453f, 0.34142f};
// [x1, y1, x2, y2] x 3
std::vector<Rect2d> refBoxes{Rect2d(98.84, 177.91, 473.29, 431.19),
Rect2d(109.80, 265.50, 258.86, 531.97),
Rect2d(387.79, 141.61, 576.98, 223.52),
Rect2d(105.62, 199.24, 218.37, 389.84),
};
Image2BlobParams imgParams(
Scalar::all(1/255.0),
targetSize,
Scalar::all(0),
true,
CV_32F,
DNN_LAYOUT_NCHW,
DNN_PMODE_LETTERBOX,
Scalar::all(114)
);
testYOLO(
weightPath, refClassIds, refScores, refBoxes,
imgParams, conf_threshold, iou_threshold,
1.0e-4, 1.0e-3);
}
TEST_P(Test_ONNX_nets, YOLOv5n)
{
std::string weightPath = findDataFile("dnn/yolov5n.onnx", false);
// Reference, which is collected with input size of 640x640
std::vector<int> refClassIds{16, 2, 1};
std::vector<float> refScores{0.749053f, 0.616853f, 0.32506f};
// [x1, y1, x2, y2] x 4
std::vector<Rect2d> refBoxes{Rect2d(108.088f, 239.293f, 266.196f, 607.658f),
Rect2d(392.028f, 89.9233f, 579.152f, 190.447f),
Rect2d(120.278f, 159.76, 214.481f, 241.473f)};
Size targetSize{640, 640};
Image2BlobParams imgParams(
Scalar::all(1/255.0),
targetSize,
Scalar::all(0),
true,
CV_32F,
DNN_LAYOUT_NCHW,
DNN_PMODE_NULL,
Scalar::all(0)
);
testYOLO(weightPath, refClassIds, refScores, refBoxes, imgParams);
}
TEST_P(Test_ONNX_layers, Tile)
{
testONNXModels("tile", pb);
// tile-1 (opset 1) form with a negative axis; the parser must normalize it
// against the input rank instead of indexing the repeats buffer directly.
testONNXModels("tile_neg_axis");
}
TEST_P(Test_ONNX_layers, Gelu)
{
testONNXModels("gelu");
testONNXModels("gelu_approximation");
}
TEST_P(Test_ONNX_layers, OpenAI_CLIP_head)
{
testONNXModels("clip-vit-base-head");
}
TEST_P(Test_ONNX_layers, where_node)
{
testONNXModels("where_layer");
}
TEST_P(Test_ONNX_layers, Gemm_all_attributes) {
testONNXModels("test_gemm_all_attributes", pb, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, Gemm_alpha) {
testONNXModels("test_gemm_alpha", pb, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, Gemm_beta) {
testONNXModels("test_gemm_beta", pb, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, Gemm_default_matrix_bias) {
testONNXModels("test_gemm_default_matrix_bias", pb, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, Gemm_default_no_bias) {
testONNXModels("test_gemm_default_no_bias", pb, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, Gemm_default_scalar_bias) {
testONNXModels("test_gemm_default_scalar_bias", pb, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, Gemm_default_single_elem_vector_bias) {
testONNXModels("test_gemm_default_single_elem_vector_bias", pb, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, Gemm_default_vector_bias) {
testONNXModels("test_gemm_default_vector_bias", pb, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, Gemm_default_zero_bias) {
testONNXModels("test_gemm_default_zero_bias", pb, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, Gemm_transposeA) {
testONNXModels("test_gemm_transposeA", pb, 0, 0, false, true, 2);
}
TEST_P(Test_ONNX_layers, Gemm_transposeB) {
testONNXModels("test_gemm_transposeB", pb, 0, 0, false, true, 2);
}
// Note: These tests are converted from onnx/onnx so that they have constant shape as input.
// TODO: They can be moved into conformance tests once dynamic input is properly supported.
TEST_P(Test_ONNX_layers, Expand_dim_changed) {
testONNXModels("test_expand_dim_changed", pb, 0, 0, false, true, 1);
}
TEST_P(Test_ONNX_layers, Expand_dim_unchanged) {
testONNXModels("test_expand_dim_unchanged", pb, 0, 0, false, true, 1);
}
TEST_P(Test_ONNX_layers, Expand_shape_model1) {
testONNXModels("test_expand_shape_model1", pb, 0, 0, false, true, 1);
}
TEST_P(Test_ONNX_layers, Expand_shape_model2) {
testONNXModels("test_expand_shape_model2", pb, 0, 0, false, true, 1);
}
TEST_P(Test_ONNX_layers, Expand_shape_model3) {
testONNXModels("test_expand_shape_model3", pb, 0, 0, false, true, 1);
}
TEST_P(Test_ONNX_layers, Expand_shape_model4) {
testONNXModels("test_expand_shape_model4", pb, 0, 0, false, true, 1);
}
TEST_P(Test_ONNX_layers, Attention) {
testONNXModels("attention");
}
TEST_P(Test_ONNX_layers, AttentionSingleHead) {
testONNXModels("attention_single_head");
}
TEST_P(Test_ONNX_layers, PyTorchAttentionSingleHead){
testONNXModels("pytorch_attention_single_head");
}
TEST_P(Test_ONNX_layers, PyTorchUnflatten){
testONNXModels("unflatten");
}
TEST_P(Test_ONNX_nets, ViT_B_32) {
applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_DEBUG_LONG);
const std::string model_path = _tf("models/vit_b_32.onnx", false);
auto net = readNet(model_path);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
auto image = imread(_tf("../googlenet_0.png"));
auto blob = blobFromImage(image, 1.f, Size(224, 224));
auto ref = blobFromNPY(_tf("data/output_vit_b_32.npy"));
checkBackend(&blob, &ref);
net.setInput(blob);
auto out = net.forward();
double l1 = default_l1;
double lInf = default_lInf;
if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.01;
lInf = 0.06;
}
if (target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.008;
lInf = 0.04;
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
if (target == DNN_TARGET_CPU) {
l1 = 6e-5; // Expected: (normL1) <= (l1), actual: 4.31208e-05 vs 1e-05
lInf = 0.0003; // Expected: (normInf) <= (lInf), actual: 0.000194907 vs 0.0001
} else if (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16) {
l1 = 0.0092; // Expected: (normL1) <= (l1), actual: 0.00918349 vs 4.4e-05
lInf = 0.056; // Expected: (normInf) <= (lInf), actual: 0.0556431 vs 0.0002
}
}
normAssert(ref, out, "ViTB_32", l1, lInf);
}
TEST_P(Test_ONNX_nets, VitTrack) {
auto image = imread(_tf("../dog_orig_size.png"));
auto input0 = blobFromImage(image, 1.f, Size(128, 128));
auto input1 = blobFromImage(image, 1.f, Size(256, 256));
auto net = readNet(_tf("models/object_tracking_vittrack_2023sep.onnx", false));
net.setInput(input0, "template");
net.setInput(input1, "search");
std::vector<std::string> output_names{"output1", "output2", "output3"};
std::vector<Mat> outputs;
net.forward(outputs, output_names);
auto ref_output1 = blobFromNPY(_tf("data/output_object_tracking_vittrack_2023sep_0.npy"));
auto ref_output2 = blobFromNPY(_tf("data/output_object_tracking_vittrack_2023sep_1.npy"));
auto ref_output3 = blobFromNPY(_tf("data/output_object_tracking_vittrack_2023sep_2.npy"));
normAssert(ref_output1, outputs[0], "VitTrack output1", 3e-5, 3e-4);
normAssert(ref_output2, outputs[1], "VitTrack output2", 3e-5, 2e-4);
normAssert(ref_output3, outputs[2], "VitTrack output3", 3e-4, 9e-4);
}
TEST_P(Test_ONNX_layers, LayerNormNoFusion) {
testONNXModels("layer_norm_no_fusion");
}
TEST_P(Test_ONNX_layers, MatMulAddFusion) {
double l1 = (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) ? 0.0018 : default_l1;
double lInf = (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) ? 0.011 : default_lInf;
testONNXModels("biased_matmul", npy, l1, lInf);
}
TEST_P(Test_ONNX_layers, ClipDivSharedConstant) {
testONNXModels("clip_div_shared_constant");
}
TEST_P(Test_ONNX_layers, TopK) {
auto test = [&](const std::string &basename, double l1 = 0, double lInf = 0) {
std::string onnxmodel = _tf("models/" + basename + ".onnx", true);
Mat input = readTensorFromONNX(_tf("data/input_" + basename + ".pb"));
Mat output_ref_val = readTensorFromONNX(_tf("data/output_" + basename + "_0.pb")),
output_ref_ind = readTensorFromONNX(_tf("data/output_" + basename + "_1.pb"));
checkBackend(&input, &output_ref_val);
checkBackend(&input, &output_ref_ind);
Net net = readNetFromONNX(onnxmodel);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(input);
std::vector<Mat> outputs;
net.forward(outputs, std::vector<std::string>{"values", "indices"});
Mat output_res_val = outputs.front(),
output_res_ind = outputs.back();
output_res_ind.convertTo(output_res_ind, CV_32S); // TODO: remove this conversion on 5.x
normAssert(output_ref_val, output_res_val, (basename + " values").c_str(), l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
normAssert(output_ref_ind, output_res_ind, (basename + " indices").c_str(), l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
expectNoFallbacksFromIE(net);
};
test("top_k");
test("top_k_negative_axis");
test("top_k_smallest");
}
TEST_P(Test_ONNX_layers, RandomNormalLike_basic)
{
Net net = readNetFromONNX(findDataFile("dnn/onnx/models/random_normal_like.onnx", true));
Mat input(2, 3, CV_32F, Scalar(0));
net.setInput(input);
Mat out = net.forward();
EXPECT_EQ(out.rows, 2);
EXPECT_EQ(out.cols, 3);
EXPECT_EQ(out.type(), CV_32F);
double minVal, maxVal;
minMaxLoc(out, &minVal, &maxVal);
EXPECT_NE(minVal, 0.0);
EXPECT_NE(maxVal, 0.0);
EXPECT_NE(minVal, maxVal);
Mat out2 = net.forward();
EXPECT_EQ(countNonZero(out != out2), 0);
}
TEST_P(Test_ONNX_layers, RandomNormalLike_complex)
{
Net net = readNetFromONNX(findDataFile("dnn/onnx/models/random_normal_like_complex.onnx", true));
Mat input(2, 3, CV_32F, Scalar(0));
net.setInput(input);
Mat out = net.forward();
EXPECT_EQ(out.rows, 2);
EXPECT_EQ(out.cols, 3);
EXPECT_EQ(out.type(), CV_32F);
double minVal, maxVal;
minMaxLoc(out, &minVal, &maxVal);
EXPECT_NE(minVal, maxVal);
net.setInput(input);
Mat out2 = net.forward();
EXPECT_EQ(countNonZero(out != out2), 0);
}
INSTANTIATE_TEST_CASE_P(/**/, Test_ONNX_nets, dnnBackendsAndTargets());
}} // namespace
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