mirror of
https://github.com/opencv/opencv.git
synced 2024-11-25 11:40:44 +08:00
1610 lines
56 KiB
C++
1610 lines
56 KiB
C++
// This file is part of OpenCV project.
|
|
// It is subject to the license terms in the LICENSE file found in the top-level directory
|
|
// of this distribution and at http://opencv.org/license.html.
|
|
|
|
// Copyright (C) 2018-2019, 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>
|
|
namespace opencv_test { namespace {
|
|
|
|
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 testONNXModels(const String& basename, const Extension ext = npy,
|
|
const double l1 = 0, const float lInf = 0, const bool useSoftmax = false,
|
|
bool checkNoFallbacks = true, int numInps = 1)
|
|
{
|
|
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());
|
|
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
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("");
|
|
|
|
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();
|
|
}
|
|
normAssert(ref, out, "", 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);
|
|
|
|
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 ((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);
|
|
|
|
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)
|
|
{
|
|
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("gather");
|
|
// 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_scalar", npy, 0, 0, false, false);
|
|
}
|
|
|
|
TEST_P(Test_ONNX_layers, Convolution3D)
|
|
{
|
|
testONNXModels("conv3d");
|
|
}
|
|
|
|
TEST_P(Test_ONNX_layers, Convolution3D_bias)
|
|
{
|
|
testONNXModels("conv3d_bias");
|
|
}
|
|
|
|
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);
|
|
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(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
|
|
|
|
testONNXModels("deconv3d");
|
|
}
|
|
|
|
TEST_P(Test_ONNX_layers, Deconvolution3D_bias)
|
|
{
|
|
#if 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
|
|
|
|
testONNXModels("deconv3d_bias");
|
|
}
|
|
|
|
TEST_P(Test_ONNX_layers, Deconvolution3D_pad)
|
|
{
|
|
#if 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
|
|
|
|
testONNXModels("deconv3d_pad");
|
|
}
|
|
|
|
TEST_P(Test_ONNX_layers, Deconvolution3D_adjpad)
|
|
{
|
|
#if 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
|
|
|
|
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, 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");
|
|
}
|
|
|
|
TEST_P(Test_ONNX_layers, ReduceMax)
|
|
{
|
|
testONNXModels("reduce_max");
|
|
testONNXModels("reduce_max_axis_0");
|
|
testONNXModels("reduce_max_axis_1");
|
|
}
|
|
|
|
TEST_P(Test_ONNX_layers, Scale)
|
|
{
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
testONNXModels("scale");
|
|
testONNXModels("scale_broadcast", npy, 0, 0, false, true, 3);
|
|
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
|
|
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
|
|
|
|
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, 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, 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
|
|
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(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
|
|
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
|
|
|
|
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
|
|
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
|
|
|
|
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
|
|
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
|
|
|
|
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)
|
|
{
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
|
|
testONNXModels("matmul_2d");
|
|
testONNXModels("matmul_3d");
|
|
testONNXModels("matmul_4d");
|
|
}
|
|
|
|
TEST_P(Test_ONNX_layers, MatMulAdd)
|
|
{
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
|
|
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");
|
|
testONNXModels("expand_identity");
|
|
testONNXModels("expand_batch");
|
|
testONNXModels("expand_channels");
|
|
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");
|
|
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 (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("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_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);
|
|
}
|
|
|
|
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);
|
|
expectNoFallbacksFromIE(net);
|
|
}
|
|
|
|
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");
|
|
}
|
|
|
|
TEST_P(Test_ONNX_layers, Reshape)
|
|
{
|
|
testONNXModels("unsqueeze");
|
|
testONNXModels("unsqueeze_opset_13");
|
|
}
|
|
|
|
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");
|
|
}
|
|
|
|
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 (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_1");
|
|
testONNXModels("split_2");
|
|
testONNXModels("split_3");
|
|
testONNXModels("split_4");
|
|
testONNXModels("split_sizes");
|
|
testONNXModels("split_neg_axis");
|
|
}
|
|
|
|
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");
|
|
#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, Softmax)
|
|
{
|
|
testONNXModels("softmax");
|
|
testONNXModels("log_softmax", npy, 0, 0, false, false);
|
|
testONNXModels("softmax_unfused");
|
|
}
|
|
|
|
TEST_P(Test_ONNX_layers, Split_EltwiseMax)
|
|
{
|
|
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_max");
|
|
}
|
|
|
|
TEST_P(Test_ONNX_layers, LSTM_Activations)
|
|
{
|
|
#if 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);
|
|
}
|
|
|
|
TEST_P(Test_ONNX_layers, LSTM)
|
|
{
|
|
testONNXModels("lstm", npy, 0, 0, false, false);
|
|
}
|
|
|
|
TEST_P(Test_ONNX_layers, 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)
|
|
{
|
|
testONNXModels("hidden_lstm_bi", npy, 0, 0, false, false);
|
|
}
|
|
|
|
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
|
|
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");
|
|
}
|
|
|
|
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)
|
|
{
|
|
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_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");
|
|
}
|
|
|
|
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");
|
|
}
|
|
|
|
TEST_P(Test_ONNX_layers, DynamicAxes_gather_scalar)
|
|
{
|
|
#if 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
|
|
);
|
|
#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 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");
|
|
}
|
|
|
|
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_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
|
|
testONNXModels("conv_resize_pool_1d");
|
|
}
|
|
|
|
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");
|
|
}
|
|
|
|
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);
|
|
|
|
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, Squeezenet)
|
|
{
|
|
testONNXModels("squeezenet", pb);
|
|
}
|
|
|
|
TEST_P(Test_ONNX_nets, Googlenet)
|
|
{
|
|
#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
|
|
#if 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);
|
|
#endif
|
|
|
|
const String model = _tf("models/googlenet.onnx", false);
|
|
|
|
Net net = readNetFromONNX(model);
|
|
ASSERT_FALSE(net.empty());
|
|
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
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]
|
|
testONNXModels("resnet50v1", pb, default_l1, default_lInf, true, target != DNN_TARGET_MYRIAD);
|
|
}
|
|
|
|
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)
|
|
{
|
|
l1 = 0.02;
|
|
lInf = 0.2;
|
|
}
|
|
#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);
|
|
}
|
|
|
|
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, 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_LONG
|
|
);
|
|
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;
|
|
double lInf = default_lInf;
|
|
// output range: [-3; 3]
|
|
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;
|
|
}
|
|
testONNXModels("LResNet100E_IR", pb, l1, lInf);
|
|
}
|
|
|
|
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;
|
|
|
|
// Output values are in range [-2.011, 2.111]
|
|
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_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;
|
|
}
|
|
#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);
|
|
}
|
|
|
|
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)
|
|
{
|
|
#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
|
|
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 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);
|
|
}
|
|
#endif
|
|
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU)
|
|
throw SkipTestException("Only CPU is supported"); // FIXIT use tags
|
|
|
|
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;
|
|
}
|
|
|
|
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);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_ONNX_nets, dnnBackendsAndTargets());
|
|
|
|
}} // namespace
|