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opencv/modules/dnn/test/test_backends.cpp
Vadim Pisarevsky 3cd57ea09e
Merge pull request #26056 from vpisarev:new_dnn_engine 
New dnn engine #26056

This is the 1st PR with the new engine; CI is green and PR is ready to be merged, I think.
Merge together with https://github.com/opencv/opencv_contrib/pull/3794

---

**Known limitations:**
* [solved] OpenVINO is temporarily disabled, but is probably easy to restore (it's not a deal breaker to merge this PR, I guess)
* The new engine does not support any backends nor any targets except for the default CPU implementation. But it's possible to choose the old engine when loading a model, then all the functionality is available.
* [Caffe patch is here: #26208] The new engine only supports ONNX. When a model is constructed manually or is loaded from a file of different format (.tf, .tflite, .caffe, .darknet), the old engine is used.
* Even in the case of ONNX some layers are not supported by the new engine, such as all quantized layers (including DequantizeLinear, QuantizeLinear, QLinearConv etc.), LSTM, GRU, .... It's planned, of course, to have full support for ONNX by OpenCV 5.0 gold release. When a loaded model contains unsupported layers, we switch to the old engine automatically  (at ONNX parsing time, not at `forward()` time).
* Some layers , e.g. Expat, are only partially supported by the new engine. In the case of unsupported flavours it switches to the old engine automatically (at ONNX parsing time, not at `forward()` time).
* 'Concat' graph optimization is disabled. The optimization eliminates Concat layer and instead makes the layers that generate tensors to be concatenated to write the outputs to the final destination. Of course, it's only possible when `axis=0` or `axis=N=1`. The optimization is not compatible with dynamic shapes since we need to know in advance where to store the tensors. Because some of the layer implementations have been modified to become more compatible with the new engine, the feature appears to be broken even when the old engine is used.
* Some `dnn::Net` API is not available with the new engine. Also, shape inference may return false if some of the output or intermediate tensors' shapes cannot be inferred without running the model. Probably this can be fixed by a dummy run of the model with zero inputs.
* Some overloads of `dnn::Net::getFLOPs()` and `dnn::Net::getMemoryConsumption()` are not exposed any longer in wrapper generators; but the most useful overloads are exposed (and checked by Java tests).
* [in progress] A few Einsum tests related to empty shapes have been disabled due to crashes in the tests and in Einsum implementations. The code and the tests need to be repaired.
* OpenCL implementation of Deconvolution is disabled. It's very bad and very slow anyway; need to be completely revised.
* Deconvolution3D test is now skipped, because it was only supported by CUDA and OpenVINO backends, both of which are not supported by the new engine.
* Some tests, such as FastNeuralStyle, checked that the in the case of CUDA backend there is no fallback to CPU. Currently all layers in the new engine are processed on CPU, so there are many fallbacks. The checks, therefore, have been temporarily disabled.

---

- [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
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2024-10-16 15:28:19 +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 "opencv2/core/ocl.hpp"
namespace opencv_test { namespace {
class DNNTestNetwork : public DNNTestLayer
{
public:
void processNet(const std::string& weights, const std::string& proto,
Size inpSize, const std::string& outputLayer = "",
double l1 = 0.0, double lInf = 0.0)
{
// Create a common input blob.
int blobSize[] = {1, 3, inpSize.height, inpSize.width};
Mat inp(4, blobSize, CV_32FC1);
randu(inp, 0.0f, 1.0f);
processNet(weights, proto, inp, outputLayer, l1, lInf);
}
void processNet(std::string weights, std::string proto,
Mat inp, const std::string& outputLayer = "",
double l1 = 0.0, double lInf = 0.0, double detectionConfThresh = 0.2, bool useWinograd = true)
{
checkBackend();
l1 = l1 ? l1 : default_l1;
lInf = lInf ? lInf : default_lInf;
weights = findDataFile(weights, false);
if (!proto.empty())
proto = findDataFile(proto);
// Create two networks - with default backend and target and a tested one.
Net netDefault = readNet(weights, proto);
netDefault.setPreferableBackend(DNN_BACKEND_OPENCV);
netDefault.setInput(inp);
Mat outDefault = netDefault.forward(outputLayer).clone();
net = readNet(weights, proto);
net.setInput(inp);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
if (target == DNN_TARGET_CPU_FP16)
net.enableWinograd(false);
Mat out = net.forward(outputLayer).clone();
check(outDefault, out, outputLayer, l1, lInf, detectionConfThresh, "First run");
// Test 2: change input.
float* inpData = (float*)inp.data;
for (int i = 0; i < inp.size[0] * inp.size[1]; ++i)
{
Mat slice(inp.size[2], inp.size[3], CV_32F, inpData);
cv::flip(slice, slice, 1);
inpData += slice.total();
}
netDefault.setInput(inp);
net.setInput(inp);
outDefault = netDefault.forward(outputLayer).clone();
out = net.forward(outputLayer).clone();
check(outDefault, out, outputLayer, l1, lInf, detectionConfThresh, "Second run");
}
void check(Mat& ref, Mat& out, const std::string& outputLayer, double l1, double lInf,
double detectionConfThresh, const char* msg)
{
if (outputLayer == "detection_out")
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
{
// Inference Engine produces detections terminated by a row which starts from -1.
out = out.reshape(1, out.total() / 7);
int numDetections = 0;
while (numDetections < out.rows && out.at<float>(numDetections, 0) != -1)
{
numDetections += 1;
}
out = out.rowRange(0, numDetections);
}
normAssertDetections(ref, out, msg, detectionConfThresh, l1, lInf);
}
else
normAssert(ref, out, msg, l1, lInf);
}
Net net;
};
TEST_P(DNNTestNetwork, DISABLED_YOLOv8n) {
processNet("dnn/onnx/models/yolov8n.onnx", "", Size(640, 640), "output0");
expectNoFallbacksFromIE(net);
expectNoFallbacksFromCUDA(net);
}
TEST_P(DNNTestNetwork, AlexNet)
{
applyTestTag(CV_TEST_TAG_MEMORY_1GB);
processNet("dnn/bvlc_alexnet.caffemodel", "dnn/bvlc_alexnet.prototxt",
Size(227, 227), "prob");
expectNoFallbacksFromIE(net);
expectNoFallbacksFromCUDA(net);
}
TEST_P(DNNTestNetwork, ResNet_50)
{
applyTestTag(
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
CV_TEST_TAG_DEBUG_VERYLONG
);
processNet("dnn/ResNet-50-model.caffemodel", "dnn/ResNet-50-deploy.prototxt",
Size(224, 224), "prob");
expectNoFallbacksFromIE(net);
expectNoFallbacksFromCUDA(net);
}
TEST_P(DNNTestNetwork, SqueezeNet_v1_1)
{
processNet("dnn/squeezenet_v1.1.caffemodel", "dnn/squeezenet_v1.1.prototxt",
Size(227, 227), "prob");
expectNoFallbacksFromIE(net);
expectNoFallbacksFromCUDA(net);
}
TEST_P(DNNTestNetwork, GoogLeNet)
{
applyTestTag(target == DNN_TARGET_CPU ? "" : CV_TEST_TAG_MEMORY_512MB);
processNet("dnn/bvlc_googlenet.caffemodel", "dnn/bvlc_googlenet.prototxt",
Size(224, 224), "prob");
expectNoFallbacksFromIE(net);
expectNoFallbacksFromCUDA(net);
}
TEST_P(DNNTestNetwork, Inception_5h)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
double l1 = default_l1, lInf = default_lInf;
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && (target == DNN_TARGET_CPU || target == DNN_TARGET_OPENCL))
{
l1 = 1.72e-5;
lInf = 8e-4;
}
processNet("dnn/tensorflow_inception_graph.pb", "", Size(224, 224), "softmax2", l1, lInf);
expectNoFallbacksFromIE(net);
expectNoFallbacksFromCUDA(net);
}
TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
Mat sample = imread(findDataFile("dnn/street.png"));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), false);
float scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) ? 1.5e-2 : 0.0;
float iouDiff = (target == DNN_TARGET_MYRIAD) ? 0.063 : 0.0;
float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.262 : FLT_MIN;
processNet("dnn/MobileNetSSD_deploy_19e3ec3.caffemodel", "dnn/MobileNetSSD_deploy_19e3ec3.prototxt",
inp, "detection_out", scoreDiff, iouDiff, detectionConfThresh);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
// May hang on some configurations
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 Transpose with name conv15_2_mbox_conf_perm 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
);
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) &&
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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#elif defined(INF_ENGINE_RELEASE)
if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) &&
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_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
Mat sample = imread(findDataFile("dnn/street.png"));
Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 560), Scalar(127.5, 127.5, 127.5), false);
float scoreDiff = 0.0, iouDiff = 0.0;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16)
{
scoreDiff = 0.029;
iouDiff = 0.09;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
scoreDiff = 0.03;
iouDiff = 0.08;
}
processNet("dnn/MobileNetSSD_deploy_19e3ec3.caffemodel", "dnn/MobileNetSSD_deploy_19e3ec3.prototxt",
inp, "detection_out", scoreDiff, iouDiff);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow)
{
applyTestTag((target == DNN_TARGET_CPU || target == DNN_TARGET_CPU_FP16) ? "" : CV_TEST_TAG_MEMORY_512MB);
Mat sample = imread(findDataFile("dnn/street.png"));
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
float detectionConfThresh = (target == DNN_TARGET_MYRIAD) ? 0.216 : 0.2;
float scoreDiff = 0.0, iouDiff = 0.0;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16)
{
scoreDiff = 0.095;
iouDiff = 0.09;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
scoreDiff = 0.007;
iouDiff = 0.08;
}
processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt",
inp, "detection_out", scoreDiff, iouDiff, detectionConfThresh);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, MobileNet_SSD_v1_TensorFlow_Different_Width_Height)
{
#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 && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
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, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
Mat sample = imread(findDataFile("dnn/street.png"));
Mat inp = blobFromImage(sample, 1.0f, Size(300, 560), Scalar(), false);
float scoreDiff = 0.0, iouDiff = 0.0;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16)
{
scoreDiff = 0.013;
iouDiff = 0.06;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
scoreDiff = 0.007;
iouDiff = 0.06;
}
processNet("dnn/ssd_mobilenet_v1_coco_2017_11_17.pb", "dnn/ssd_mobilenet_v1_coco_2017_11_17.pbtxt",
inp, "detection_out", scoreDiff, iouDiff);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, MobileNet_SSD_v2_TensorFlow)
{
applyTestTag(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB);
Mat sample = imread(findDataFile("dnn/street.png"));
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
float scoreDiff = 2e-5, iouDiff = 0.0;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16)
{
scoreDiff = 0.013;
iouDiff = 0.062;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
scoreDiff = 0.02;
iouDiff = 0.07;
}
processNet("dnn/ssd_mobilenet_v2_coco_2018_03_29.pb", "dnn/ssd_mobilenet_v2_coco_2018_03_29.pbtxt",
inp, "detection_out", scoreDiff, iouDiff, 0.25);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, SSD_VGG16)
{
applyTestTag(
CV_TEST_TAG_MEMORY_2GB,
CV_TEST_TAG_LONG,
CV_TEST_TAG_DEBUG_VERYLONG
);
Mat sample = imread(findDataFile("dnn/street.png"));
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
float scoreDiff = 0.0, iouDiff = 0.0;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CPU_FP16)
{
scoreDiff = 0.04;
}
else if (target == DNN_TARGET_MYRIAD)
{
scoreDiff = 0.0325;
iouDiff = 0.032;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
scoreDiff = 0.03;
iouDiff = 0.13;
}
processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel",
"dnn/ssd_vgg16.prototxt", inp, "detection_out", scoreDiff,
iouDiff, 0.2, false);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, OpenPose_pose_coco)
{
applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
CV_TEST_TAG_DEBUG_LONG);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(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_VERSION);
#endif
const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.009 : 0.0;
const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.09 : 0.0;
processNet("dnn/openpose_pose_coco.caffemodel", "dnn/openpose_pose_coco.prototxt",
Size(46, 46), "", l1, lInf);
expectNoFallbacksFromIE(net);
expectNoFallbacksFromCUDA(net);
}
TEST_P(DNNTestNetwork, OpenPose_pose_mpi)
{
applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
CV_TEST_TAG_DEBUG_VERYLONG);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(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_VERSION);
#endif
// output range: [-0.001, 0.97]
const float l1 = (target == DNN_TARGET_MYRIAD) ? 0.02 : 0.0;
const float lInf = (target == DNN_TARGET_MYRIAD || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CPU_FP16) ? 0.2 : 0.0;
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi.prototxt",
Size(46, 46), "", l1, lInf);
expectNoFallbacksFromIE(net);
expectNoFallbacksFromCUDA(net);
}
TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
{
applyTestTag(CV_TEST_TAG_LONG, CV_TEST_TAG_MEMORY_1GB);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(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_VERSION);
#endif
// The same .caffemodel but modified .prototxt
// See https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/pose/poseParameters.cpp
processNet("dnn/openpose_pose_mpi.caffemodel", "dnn/openpose_pose_mpi_faster_4_stages.prototxt",
Size(46, 46));
expectNoFallbacksFromIE(net);
expectNoFallbacksFromCUDA(net);
}
TEST_P(DNNTestNetwork, YuNet)
{
Mat img = imread(findDataFile("gpu/lbpcascade/er.png"));
resize(img, img, Size(320, 320));
Mat inp = blobFromImage(img);
processNet("dnn/onnx/models/yunet-202303.onnx", "", inp);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, Inception_v2_SSD_TensorFlow)
{
applyTestTag(
(target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB),
CV_TEST_TAG_DEBUG_VERYLONG
);
#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);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
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, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
Mat sample = imread(findDataFile("dnn/street.png"));
Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
float scoreDiff = 0.0, iouDiff = 0.0;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16)
{
scoreDiff = 0.02;
iouDiff = 0.1;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
scoreDiff = 0.015;
iouDiff = 0.08;
}
processNet("dnn/ssd_inception_v2_coco_2017_11_17.pb", "dnn/ssd_inception_v2_coco_2017_11_17.pbtxt",
inp, "detection_out", scoreDiff, iouDiff);
expectNoFallbacksFromIE(net);
}
TEST_P(DNNTestNetwork, DenseNet_121)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
// Reference output values are in range [-3.807, 4.605]
float l1 = 0.0, lInf = 0.0;
if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_CPU_FP16)
{
l1 = 2e-2;
lInf = 9e-2;
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
lInf = 0.1f;
}
else if (target == DNN_TARGET_MYRIAD)
{
l1 = 0.1;
lInf = 0.6;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.008;
lInf = 0.06;
}
processNet("dnn/DenseNet_121.caffemodel", "dnn/DenseNet_121.prototxt", Size(224, 224), "", l1, lInf);
if (target != DNN_TARGET_MYRIAD || getInferenceEngineVPUType() != CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
expectNoFallbacksFromIE(net);
expectNoFallbacksFromCUDA(net);
}
TEST_P(DNNTestNetwork, FastNeuralStyle_eccv16)
{
applyTestTag(CV_TEST_TAG_MEMORY_512MB, CV_TEST_TAG_DEBUG_VERYLONG);
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);
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);
#if defined(INF_ENGINE_RELEASE)
#if INF_ENGINE_VER_MAJOR_LE(2018050000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_OPENCL)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#endif
Mat img = imread(findDataFile("dnn/googlenet_1.png"));
Mat inp = blobFromImage(img, 1.0, Size(224, 224), Scalar(0.0, 0.0, 0.0), true, false);
// Output image has values in range [0.0, 255.0].
float l1 = 5e-4, lInf = 1e-2;
if (target == DNN_TARGET_MYRIAD)
{
l1 = 0.4;
lInf = 7.46;
}
else if (target == DNN_TARGET_CUDA)
{
l1 = 8e-4;
lInf = 2e-2;
}
else if (target == DNN_TARGET_CUDA_FP16)
{
l1 = 0.9;
lInf = 16;
}
else if (target == DNN_TARGET_CPU_FP16)
{
l1 = 0.4;
lInf = 26.;
}
else if (target == DNN_TARGET_VULKAN)
{
l1 = 0.4;
lInf = 7.46;
}
else if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL)
{
l1 = 5.5e-4;
}
else if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.86;
lInf = 16;
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
{
l1 = 5e-3;
lInf = 5e-3;
}
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
{
lInf = 25;
}
#endif
processNet("dnn/mosaic-9.onnx", "", inp, "", l1, lInf);
#if defined(HAVE_INF_ENGINE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
expectNoFallbacksFromIE(net);
#endif
// BUG: https://github.com/opencv/opencv/issues/26306
// Temporarily disabled check for no "fallbacks", since the new engine does not support CUDA yet
//expectNoFallbacksFromCUDA(net);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, DNNTestNetwork, dnnBackendsAndTargets(/* withInferenceEngine = */ true,
/* obsolete_withHalide = */ false,
/* withCpuOCV = */ false,
/* withVkCom = */ true,
/* withCUDA = */ true));
/*
Backend tests of layers
*/
static void testLayer(Mat& input, Net& net, Backend backendId, Target targetId, bool skipCheck = false, bool randInput = true, double l1 = 0.0, double lInf = 0.0)
{
DNNTestLayer::checkBackend(backendId, targetId);
if (randInput)
randu(input, -1.0f, 1.0f);
net.setInput(input);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
Mat outputDefault = net.forward().clone();
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat output = net.forward().clone();
if (skipCheck)
return;
double default_l1, default_lInf;
DNNTestLayer::getDefaultThresholds(backendId, targetId, &default_l1, &default_lInf);
if (l1 == 0.0)
l1 = default_l1;
if (lInf == 0.0)
lInf = default_lInf;
normAssert(outputDefault, output, "", l1, lInf);
if (cvtest::debugLevel > 0 || testing::Test::HasFailure())
{
std::cout << "l1=" << l1 << " lInf=" << lInf << std::endl;
std::cout << outputDefault.reshape(1, outputDefault.total()).t() << std::endl;
std::cout << output.reshape(1, outputDefault.total()).t() << std::endl;
}
}
static void testLayer(LayerParams& params, Mat& input, Backend backendId, Target targetId, bool skipCheck = false, double l1 = 0.0, double lInf = 0.0)
{
Net net;
net.addLayerToPrev(params.name, params.type, params);
testLayer(input, net, backendId, targetId, skipCheck, true, l1, lInf);
}
class Test_layers_backends : public DNNTestLayer {};
////////////////////////////////////////////////////////////////////////////////
// Padding
////////////////////////////////////////////////////////////////////////////////
TEST_P(Test_layers_backends, Padding)
{
static const int kNumRuns = 10;
std::vector<int> paddings(8);
cv::RNG& rng = cv::theRNG();
for (int t = 0; t < kNumRuns; ++t)
{
for (int i = 0; i < paddings.size(); ++i)
paddings[i] = rng(5);
LayerParams lp;
lp.set("paddings", DictValue::arrayInt<int*>(&paddings[0], paddings.size()));
lp.type = "Padding";
lp.name = "testLayer";
int sz[] = {1 + (int)rng(10), 1 + (int)rng(10), 1 + (int)rng(10), 1 + (int)rng(10)};
Mat input(4, &sz[0], CV_32F);
testLayer(lp, input, backend, target);
}
}
////////////////////////////////////////////////////////////////////////////////
// Convolution
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Size, bool, tuple<Backend, Target> > > Convolution;
TEST_P(Convolution, Accuracy)
{
int inChannels = get<0>(GetParam())[0];
int outChannels = get<0>(GetParam())[1];
int group = get<0>(GetParam())[2];
Size inSize = get<1>(GetParam());
Size kernel = get<2>(GetParam());
Size stride = get<3>(GetParam());
Size pad = get<4>(GetParam());
Size dilation = get<5>(GetParam());
bool hasBias = get<6>(GetParam());
Backend backendId = get<0>(get<7>(GetParam()));
Target targetId = get<1>(get<7>(GetParam()));
bool skipCheck = false;
int sz[] = {outChannels, inChannels / group, kernel.height, kernel.width};
Mat weights(4, &sz[0], CV_32F);
randu(weights, -1.0f, 1.0f);
LayerParams lp;
lp.set("kernel_w", kernel.width);
lp.set("kernel_h", kernel.height);
lp.set("pad_w", pad.width);
lp.set("pad_h", pad.height);
lp.set("stride_w", stride.width);
lp.set("stride_h", stride.height);
lp.set("dilation_w", dilation.width);
lp.set("dilation_h", dilation.height);
lp.set("num_output", outChannels);
lp.set("group", group);
lp.set("bias_term", hasBias);
lp.type = "Convolution";
lp.name = "testLayer";
lp.blobs.push_back(weights);
if (hasBias)
{
Mat bias(1, outChannels, CV_32F);
randu(bias, -1.0f, 1.0f);
lp.blobs.push_back(bias);
}
int inpSz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &inpSz[0], CV_32F);
testLayer(lp, input, backendId, targetId, skipCheck);
if (skipCheck)
throw SkipTestException("Skip checks in unstable test");
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Convolution, testing::Combine(
/*in channels, out channels, group*/
testing::Values(Vec3i(6, 4, 1), Vec3i(6, 9, 1),
Vec3i(6, 4, 2), Vec3i(6, 9, 3)),
/*in size*/ testing::Values(Size(5, 6)),
/*kernel*/ testing::Values(Size(3, 1), Size(1, 3)),
/*stride*/ testing::Values(Size(1, 1), Size(2, 2)),
/*pad*/ testing::Values(Size(1, 0), Size(0, 1)),
/*dilation*/ testing::Values(Size(1, 1), Size(2, 2)),
/*has bias*/ testing::Bool(),
dnnBackendsAndTargets()
));
////////////////////////////////////////////////////////////////////////////////
// Deconvolution
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Vec4i, bool, tuple<Backend, Target> > > Deconvolution;
TEST_P(Deconvolution, Accuracy)
{
int inChannels = get<0>(GetParam())[0];
int outChannels = get<0>(GetParam())[1];
int group = get<0>(GetParam())[2];
Size inSize = get<1>(GetParam());
Size kernel = get<2>(GetParam());
Size pad = get<3>(GetParam());
Size dilation = get<4>(GetParam());
Size stride = Size(get<5>(GetParam())[0], get<5>(GetParam())[1]);
Size adjPad = Size(get<5>(GetParam())[2], get<5>(GetParam())[3]);
bool hasBias = get<6>(GetParam());
Backend backendId = get<0>(get<7>(GetParam()));
Target targetId = get<1>(get<7>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
&& inChannels == 6 && outChannels == 4 && group == 1
&& kernel == Size(3, 1) && pad == Size(0, 1)
&& stride == Size(1, 1) && dilation == Size(1, 1))
applyTestTag(targetId == 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
);
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
&& inChannels == 6 && outChannels == 4 && group == 1
&& kernel == Size(1, 3) && pad == Size(1, 0)
&& stride == Size(1, 1) && dilation == Size(1, 1))
applyTestTag(targetId == 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_GE(2019010000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
&& inChannels == 6 && outChannels == 4 && group == 1
&& kernel == Size(1, 3) && pad == Size(1, 0)
&& stride == Size(1, 1) && dilation == Size(1, 1))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
#endif
if (targetId == DNN_TARGET_CUDA_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
int sz[] = {inChannels, outChannels / group, kernel.height, kernel.width};
Mat weights(4, &sz[0], CV_32F);
randu(weights, -1.0f, 1.0f);
LayerParams lp;
lp.set("kernel_w", kernel.width);
lp.set("kernel_h", kernel.height);
lp.set("pad_w", pad.width);
lp.set("pad_h", pad.height);
lp.set("stride_w", stride.width);
lp.set("stride_h", stride.height);
lp.set("dilation_w", dilation.width);
lp.set("dilation_h", dilation.height);
lp.set("adj_w", adjPad.width);
lp.set("adj_h", adjPad.height);
lp.set("num_output", outChannels);
lp.set("group", group);
lp.set("bias_term", hasBias);
lp.type = "Deconvolution";
lp.name = "testLayer";
lp.blobs.push_back(weights);
if (hasBias)
{
Mat bias(1, outChannels, CV_32F);
randu(bias, -1.0f, 1.0f);
lp.blobs.push_back(bias);
}
int inpSz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &inpSz[0], CV_32F);
testLayer(lp, input, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Deconvolution, testing::Combine(
/*in channels, out channels, group*/
testing::Values(Vec3i(6, 4, 1), Vec3i(6, 9, 3)),
/*in size*/ testing::Values(Size(5, 6)),
/*kernel*/ testing::Values(Size(3, 1), Size(1, 3)),
/*pad*/ testing::Values(Size(1, 0), Size(0, 1)),
/*dilation*/ testing::Values(Size(1, 1)),
/*stride, adj. pad*/ testing::Values(Vec4i(1,1, 0,0), Vec4i(2,2, 1,0), Vec4i(1,2, 0,1)),
/*has bias*/ testing::Bool(),
dnnBackendsAndTargets()
));
////////////////////////////////////////////////////////////////////////////////
// LRN
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<Vec3i, int, Vec3f, bool, std::string, tuple<Backend, Target> > > LRN;
TEST_P(LRN, Accuracy)
{
int inChannels = get<0>(GetParam())[0];
Size inSize = Size(get<0>(GetParam())[1], get<0>(GetParam())[2]);
int localSize = get<1>(GetParam());
float alpha = get<2>(GetParam())[0];
float beta = get<2>(GetParam())[1];
float bias = get<2>(GetParam())[2];
bool normBySize = get<3>(GetParam());
std::string nrmType = get<4>(GetParam());
Backend backendId = get<0>(get<5>(GetParam()));
Target targetId = get<1>(get<5>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if ((inSize.width == 5 || inSize.height == 5) && targetId == DNN_TARGET_MYRIAD &&
nrmType == "ACROSS_CHANNELS")
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
#endif
LayerParams lp;
lp.set("norm_region", nrmType);
lp.set("local_size", localSize);
lp.set("alpha", alpha);
lp.set("beta", beta);
lp.set("bias", bias);
lp.set("norm_by_size", normBySize);
lp.type = "LRN";
lp.name = "testLayer";
int sz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &sz[0], CV_32F);
double l1 = 0.0, lInf = 0.0;
// The OpenCL kernels use the native_ math functions which have
// implementation defined accuracy, so we use relaxed thresholds. See
// https://github.com/opencv/opencv/issues/9821 for more details.
if (targetId == DNN_TARGET_OPENCL)
{
l1 = 0.01;
lInf = 0.01;
}
testLayer(lp, input, backendId, targetId, false, l1, lInf);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, LRN, testing::Combine(
/*input ch,w,h*/ testing::Values(Vec3i(6, 5, 8), Vec3i(7, 11, 6)),
/*local size*/ testing::Values(3, 5),
testing::Values(Vec3f(0.9f, 1.0f, 1.1f), Vec3f(0.9f, 1.1f, 1.0f),
/*alpha, beta, bias*/ Vec3f(1.0f, 0.9f, 1.1f), Vec3f(1.0f, 1.1f, 0.9f),
Vec3f(1.1f, 0.9f, 1.0f), Vec3f(1.1f, 1.0f, 0.9f)),
/*norm_by_size*/ testing::Bool(),
/*norm_type*/ testing::Values("ACROSS_CHANNELS", "WITHIN_CHANNEL"),
dnnBackendsAndTargets()
));
////////////////////////////////////////////////////////////////////////////////
// Average pooling
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, Size, Size, Size, tuple<Backend, Target> > > AvePooling;
TEST_P(AvePooling, Accuracy)
{
int inChannels = get<0>(GetParam());
Size outSize = get<1>(GetParam());; // Input size will be computed from parameters.
Size kernel = get<2>(GetParam());
Size stride = get<3>(GetParam());
Backend backendId = get<0>(get<4>(GetParam()));
Target targetId = get<1>(get<4>(GetParam()));
#if defined(INF_ENGINE_RELEASE)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
&& kernel == Size(1, 1) && (stride == Size(1, 1) || stride == Size(2, 2)))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
#endif
const int inWidth = (outSize.width - 1) * stride.width + kernel.width;
const int inHeight = (outSize.height - 1) * stride.height + kernel.height;
LayerParams lp;
lp.set("pool", "ave");
lp.set("kernel_w", kernel.width);
lp.set("kernel_h", kernel.height);
lp.set("stride_w", stride.width);
lp.set("stride_h", stride.height);
lp.type = "Pooling";
lp.name = "testLayer";
int sz[] = {1, inChannels, inHeight, inWidth};
Mat input(4, &sz[0], CV_32F);
testLayer(lp, input, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, AvePooling, testing::Combine(
/*in channels*/ testing::Values(3, 4),
/*out size*/ testing::Values(Size(1, 1), Size(2, 2), Size(3, 2), Size(4, 7)),
/*kernel*/ testing::Values(Size(1, 1), Size(2, 2), Size(3, 3), Size(3, 2)),
/*stride*/ testing::Values(Size(1, 1), Size(2, 2), Size(3, 2)),
dnnBackendsAndTargets()
));
////////////////////////////////////////////////////////////////////////////////
// Maximum pooling
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, Size, Size, Size, Size, tuple<Backend, Target> > > MaxPooling;
TEST_P(MaxPooling, Accuracy)
{
int inChannels = get<0>(GetParam());
Size inSize = get<1>(GetParam());
Size kernel = get<2>(GetParam());
Size stride = get<3>(GetParam());
Size pad = get<4>(GetParam());
Backend backendId = get<0>(get<5>(GetParam()));
Target targetId = get<1>(get<5>(GetParam()));
// https://github.com/openvinotoolkit/openvino/issues/18731
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && stride != Size(1, 1)) {
int ow = ceil(static_cast<float>(inSize.width + 2 * pad.width - kernel.width) / stride.width);
int oh = ceil(static_cast<float>(inSize.height + 2 * pad.height - kernel.height) / stride.height);
if (ow * stride.width >= inSize.width + pad.width || oh * stride.height >= inSize.height + pad.height)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
}
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
&& (stride == Size(1, 1) || stride == Size(2, 2))
&& (pad == Size(0, 1) || pad == Size(1, 1))
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == 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
LayerParams lp;
lp.set("pool", "max");
lp.set("kernel_w", kernel.width);
lp.set("kernel_h", kernel.height);
lp.set("stride_w", stride.width);
lp.set("stride_h", stride.height);
lp.set("pad_w", pad.width);
lp.set("pad_h", pad.height);
lp.type = "Pooling";
lp.name = "testLayer";
int sz[] = {1, inChannels, inSize.height, inSize.width};
Mat input(4, &sz[0], CV_32F);
testLayer(lp, input, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, MaxPooling, testing::Combine(
/*in channels*/ testing::Values(3, 4),
/*in size*/ testing::Values(Size(5, 5), Size(7, 6)),
/*kernel*/ testing::Values(Size(2, 2), Size(3, 3), Size(3, 2)),
/*stride*/ testing::Values(Size(1, 1), Size(2, 2), Size(3, 2)),
/*pad*/ testing::Values(Size(0, 0), Size(1, 1), Size(0, 1)),
dnnBackendsAndTargets()
));
////////////////////////////////////////////////////////////////////////////////
// Fully-connected
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, int, Size, int, bool, tuple<Backend, Target> > > FullyConnected;
TEST_P(FullyConnected, Accuracy)
{
int batch = get<0>(GetParam());
int inChannels = get<1>(GetParam());
Size inSize = get<2>(GetParam());
int outChannels = get<3>(GetParam());
bool hasBias = get<4>(GetParam());
Backend backendId = get<0>(get<5>(GetParam()));
Target targetId = get<1>(get<5>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && (targetId == DNN_TARGET_OPENCL_FP16 ||
(targetId == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X))) {
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
}
#endif
// https://github.com/openvinotoolkit/openvino/issues/19436
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL_FP16 && batch == 16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2023000000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL && batch == 16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL);
#endif
Mat weights(outChannels, inChannels * inSize.height * inSize.width, CV_32F);
randu(weights, -1.0f, 1.0f);
Mat bias(1, outChannels, CV_32F);
randu(bias, -1.0f, 1.0f);
LayerParams lp;
lp.set("num_output", outChannels);
lp.set("bias_term", hasBias);
lp.blobs.push_back(weights);
lp.blobs.push_back(bias);
lp.type = "InnerProduct";
lp.name = "testLayer";
int sz[] = {batch, inChannels, inSize.height, inSize.width};
Mat input(4, &sz[0], CV_32F);
double l1 = 0.0;
double lInf = 0.0;
#if defined(INF_ENGINE_RELEASE)
if (targetId == DNN_TARGET_MYRIAD)
{
l1 = 0.015;
lInf = 0.025;
}
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.01;
if (INF_ENGINE_VER_MAJOR_GE(2023000000))
lInf = 0.016;
}
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL)
{
l1 = 5e-3;
lInf = INF_ENGINE_VER_MAJOR_GE(2023000000) ? 0.016 : 7e-3;
}
#endif
if (targetId == DNN_TARGET_CUDA_FP16)
l1 = 0.015;
testLayer(lp, input, backendId, targetId, false, l1, lInf);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, FullyConnected, testing::Combine(
/*batch*/ testing::Values(1, 2, 4, 8, 16),
/*in channels*/ testing::Values(3, 4),
/*in size*/ testing::Values(Size(5, 4), Size(4, 5), Size(1, 1)),
/*out channels*/ testing::Values(3, 4),
/*has bias*/ testing::Bool(),
dnnBackendsAndTargets()
));
////////////////////////////////////////////////////////////////////////////////
// SoftMax
////////////////////////////////////////////////////////////////////////////////
typedef TestWithParam<tuple<int, tuple<Backend, Target> > > SoftMax;
TEST_P(SoftMax, Accuracy)
{
int inChannels = get<0>(GetParam());
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
LayerParams lp;
lp.type = "Softmax";
lp.name = "testLayer";
int sz[] = {1, inChannels, 1, 1};
Mat input(4, &sz[0], CV_32F);
testLayer(lp, input, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, SoftMax, testing::Combine(
testing::Values(3, 4, 5, 1024),
dnnBackendsAndTargets()
));
//////////////////////////////////////////////////////////////////////////////
// Max pooling - unpooling
//////////////////////////////////////////////////////////////////////////////
TEST_P(Test_layers_backends, MaxPoolUnpool)
{
#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
LayerParams pool;
pool.set("pool", "max");
pool.set("kernel_w", 2);
pool.set("kernel_h", 2);
pool.set("stride_w", 2);
pool.set("stride_h", 2);
pool.set("pad_w", 0);
pool.set("pad_h", 0);
pool.type = "Pooling";
pool.name = "testPool";
LayerParams unpool;
unpool.set("pool_k_w", 2);
unpool.set("pool_k_h", 2);
unpool.set("pool_stride_w", 2);
unpool.set("pool_stride_h", 2);
unpool.set("pool_pad_w", 0);
unpool.set("pool_pad_h", 0);
unpool.type = "MaxUnpool";
unpool.name = "testUnpool";
Net net;
int poolId = net.addLayer(pool.name, pool.type, pool);
net.connect(0, 0, poolId, 0);
int unpoolId = net.addLayer(unpool.name, unpool.type, unpool);
net.connect(poolId, 0, unpoolId, 0);
net.connect(poolId, 1, unpoolId, 1);
int sz[] = {1, 1, 4, 4};
Mat input(4, &sz[0], CV_32F);
testLayer(input, net, backend, target);
}
////////////////////////////////////////////////////////////////////////////////
// AvePooling + in-place layers
////////////////////////////////////////////////////////////////////////////////
static const int kNumChannels = 3;
void testInPlaceActivation(LayerParams& lp, Backend backendId, Target targetId, double l1 = 0.0, double lInf = 0.0)
{
EXPECT_FALSE(lp.name.empty());
LayerParams pool;
pool.set("pool", "ave");
pool.set("kernel_w", 2);
pool.set("kernel_h", 2);
pool.set("stride_w", 2);
pool.set("stride_h", 2);
pool.type = "Pooling";
pool.name = "ave_pool";
Net net;
int poolId = net.addLayer(pool.name, pool.type, pool);
net.connect(0, 0, poolId, 0);
net.addLayerToPrev(lp.name, lp.type, lp);
int sz[] = {1, kNumChannels, 10, 10};
Mat input(4, &sz[0], CV_32F);
testLayer(input, net, backendId, targetId, false, true, l1, lInf);
}
typedef TestWithParam<tuple<bool, bool, float, tuple<Backend, Target> > > BatchNorm;
TEST_P(BatchNorm, Accuracy)
{
bool hasWeights = get<0>(GetParam());
bool hasBias = get<1>(GetParam());
float epsilon = get<2>(GetParam());
Backend backendId = get<0>(get<3>(GetParam()));
Target targetId = get<1>(get<3>(GetParam()));
LayerParams lp;
lp.set("has_weight", hasWeights);
lp.set("has_bias", hasBias);
lp.set("eps", epsilon);
lp.type = "BatchNorm";
lp.name = "testLayer";
lp.blobs.reserve(4);
for (int i = 0; i < 3; ++i)
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F));
if (hasBias || hasWeights)
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F));
for (int i = 0; i < lp.blobs.size(); ++i)
randu(lp.blobs[i], 0.0f, 1.0f);
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, BatchNorm, testing::Combine(
/*has weights*/ testing::Bool(),
/*has bias*/ testing::Bool(),
/*epsilon*/ testing::Values(1e-3f, 1e-5f),
dnnBackendsAndTargets()
));
typedef TestWithParam<tuple<float, tuple<Backend, Target> > > ReLU;
TEST_P(ReLU, Accuracy)
{
float negativeSlope = get<0>(GetParam());
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019020000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD && negativeSlope < 0)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
LayerParams lp;
lp.set("negative_slope", negativeSlope);
lp.type = "ReLU";
lp.name = "testLayer";
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, ReLU, testing::Combine(
/*negative slope*/ testing::Values(2.0f, 0.3f, -0.1f, 0.0f),
dnnBackendsAndTargets()
));
typedef TestWithParam<tuple<std::string, tuple<Backend, Target> > > NoParamActivation;
TEST_P(NoParamActivation, Accuracy)
{
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
std::string layer_type = get<0>(GetParam());
LayerParams lp;
lp.type = layer_type;
lp.name = "testLayer";
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, NoParamActivation, testing::Combine(
/*type*/ testing::Values("TanH", "Sigmoid", "AbsVal", "BNLL", "Swish", "Mish"),
dnnBackendsAndTargets()
));
typedef TestWithParam<tuple<Vec3f, tuple<Backend, Target> > > Power;
TEST_P(Power, Accuracy)
{
float power = get<0>(GetParam())[0];
float scale = get<0>(GetParam())[1];
float shift = get<0>(GetParam())[2];
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
LayerParams lp;
lp.set("power", power);
lp.set("scale", scale);
lp.set("shift", shift);
lp.type = "Power";
lp.name = "testLayer";
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Power, testing::Combine(
/*power, scale, shift*/ testing::Values(Vec3f(0.9f, 1.0f, 1.1f), Vec3f(0.9f, 1.1f, 1.0f),
Vec3f(1.0f, 0.9f, 1.1f), Vec3f(1.0f, 1.1f, 0.9f),
Vec3f(1.1f, 0.9f, 1.0f), Vec3f(1.1f, 1.0f, 0.9f)),
dnnBackendsAndTargets()
));
typedef TestWithParam<tuple<Vec3f, tuple<Backend, Target> > > Exp;
TEST_P(Exp, Accuracy)
{
float base = get<0>(GetParam())[0];
float scale = get<0>(GetParam())[1];
float shift = get<0>(GetParam())[2];
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
LayerParams lp;
lp.set("base", base);
lp.set("scale", scale);
lp.set("shift", shift);
lp.type = "Exp";
lp.name = "testLayer";
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Exp, testing::Combine(
/*base, scale, shift*/ testing::Values(Vec3f(0.9f, -1.0f, 1.1f), Vec3f(0.9f, 1.1f, -1.0f),
Vec3f(-1.0f, 0.9f, 1.1f), Vec3f(-1.0f, 1.1f, 0.9f),
Vec3f(1.1f, 0.9f, -1.0f), Vec3f(1.1f, -1.0f, 0.9f)),
dnnBackendsAndTargets()
));
TEST_P(Test_layers_backends, ChannelsPReLU)
{
LayerParams lp;
lp.type = "ChannelsPReLU";
lp.name = "testLayer";
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F));
randu(lp.blobs[0], -1.0f, 1.0f);
testInPlaceActivation(lp, backend, target);
}
typedef TestWithParam<tuple<bool, tuple<Backend, Target> > > Scale;
TEST_P(Scale, Accuracy)
{
bool hasBias = get<0>(GetParam());
Backend backendId = get<0>(get<1>(GetParam()));
Target targetId = get<1>(get<1>(GetParam()));
LayerParams lp;
lp.set("bias_term", hasBias);
lp.type = "Scale";
lp.name = "testLayer";
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F));
randu(lp.blobs[0], -1.0f, 1.0f);
if (hasBias)
{
lp.blobs.push_back(Mat(1, kNumChannels, CV_32F));
randu(lp.blobs[1], -1.0f, 1.0f);
}
testInPlaceActivation(lp, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Scale, testing::Combine(
testing::Bool(),
dnnBackendsAndTargets()
));
////////////////////////////////////////////////////////////////////////////////
// Concat layer
////////////////////////////////////////////////////////////////////////////////
//
// input --- conv --- concat --- output
// `--- conv ----^ ^ ^
// `---- ... ------' '
// `-----------------'
typedef TestWithParam<tuple<Vec3i, Vec3i, tuple<Backend, Target> > > Concat;
TEST_P(Concat, Accuracy)
{
Vec3i inSize = get<0>(GetParam());
Vec3i numChannels = get<1>(GetParam());
Backend backendId = get<0>(get<2>(GetParam()));
Target targetId = get<1>(get<2>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
&& inSize == Vec3i(1, 4, 5) && numChannels == Vec3i(1, 6, 2)
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); // crash
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_CPU
&& inSize == Vec3i(1, 4, 5) && numChannels == Vec3i(1, 6, 2)
)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION); // TODO: IE_CPU
#endif
Net net;
std::vector<int> convLayerIds;
convLayerIds.reserve(numChannels.channels);
for (int i = 0, n = numChannels.channels; i < n; ++i)
{
if (!numChannels[i])
break;
int sz[] = {numChannels[i], inSize[0], 1, 1};
Mat weights(4, &sz[0], CV_32F);
randu(weights, -1.0f, 1.0f);
LayerParams convParam;
convParam.set("kernel_w", 1);
convParam.set("kernel_h", 1);
convParam.set("num_output", numChannels[i]);
convParam.set("bias_term", false);
convParam.type = "Convolution";
std::ostringstream ss;
ss << "convLayer" << i;
convParam.name = ss.str();
convParam.blobs.push_back(weights);
int layerId = net.addLayer(convParam.name, convParam.type, convParam);
convLayerIds.push_back(layerId);
net.connect(0, 0, layerId, 0);
}
LayerParams concatParam;
concatParam.type = "Concat";
concatParam.name = "testLayer";
int concatId = net.addLayer(concatParam.name, concatParam.type, concatParam);
net.connect(0, 0, concatId, 0);
for (int i = 0; i < convLayerIds.size(); ++i)
{
net.connect(convLayerIds[i], 0, concatId, i + 1);
}
int sz[] = {1, inSize[0], inSize[1], inSize[2]};
Mat input(4, &sz[0], CV_32F);
testLayer(input, net, backendId, targetId);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Concat, testing::Combine(
/*input size*/ testing::Values(Vec3i(1, 4, 5), Vec3i(2, 8, 6)),
/*channels*/ testing::Values(Vec3i(2, 0, 0), Vec3i(3, 4, 0), Vec3i(1, 6, 2)),
dnnBackendsAndTargets()
));
////////////////////////////////////////////////////////////////////////////////
// Element-wise layers
////////////////////////////////////////////////////////////////////////////////
//
// input --- conv --- eltwise --- output
// `--- conv ----^ ^ ^
// `---- ... ------' '
// `-----------------'
typedef TestWithParam<tuple<Vec3i, std::string, int, bool, tuple<Backend, Target> > > Eltwise;
TEST_P(Eltwise, Accuracy)
{
Vec3i inSize = get<0>(GetParam());
std::string op = get<1>(GetParam());
int numConv = get<2>(GetParam());
bool weighted = get<3>(GetParam());
Backend backendId = get<0>(get<4>(GetParam()));
Target targetId = get<1>(get<4>(GetParam()));
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
// accuracy
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL &&
inSize == Vec3i(1, 4, 5) && op == "sum" && numConv == 1 && !weighted)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL &&
inSize == Vec3i(2, 8, 6) && op == "sum" && numConv == 1 && !weighted)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD &&
inSize == Vec3i(1, 4, 5))
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && numConv > 1)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_OPENCL &&
op == "sum" && numConv == 1 && !weighted)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
#endif
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && numConv > 1)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif
bool convInputShift = 1;
int numEltwiseInputs = numConv;
if (op == "div")
{
numConv = 1;
convInputShift = 0; // first input is convolution
}
Net net;
std::vector<int> convLayerIds(numConv);
for (int i = 0; i < numConv; ++i)
{
int sz[] = {inSize[0], inSize[0], 1, 1};
Mat weights(4, &sz[0], CV_32F);
randu(weights, -1.0f, 1.0f);
LayerParams convParam;
convParam.set("kernel_w", 1);
convParam.set("kernel_h", 1);
convParam.set("num_output", inSize[0]);
convParam.set("bias_term", false);
convParam.type = "Convolution";
std::ostringstream ss;
ss << "convLayer" << i;
convParam.name = ss.str();
convParam.blobs.push_back(weights);
convLayerIds[i] = net.addLayer(convParam.name, convParam.type, convParam);
net.connect(0, 0, convLayerIds[i], 0);
}
LayerParams eltwiseParam;
eltwiseParam.set("operation", op);
if (op == "sum" && weighted)
{
RNG& rng = cv::theRNG();
std::vector<float> coeff(1 + numConv);
for (int i = 0; i < coeff.size(); ++i)
{
coeff[i] = rng.uniform(-2.0f, 2.0f);
}
eltwiseParam.set("coeff", DictValue::arrayReal<float*>(&coeff[0], coeff.size()));
}
eltwiseParam.type = "Eltwise";
eltwiseParam.name = "testLayer";
int eltwiseId = net.addLayer(eltwiseParam.name, eltwiseParam.type, eltwiseParam);
if (convInputShift == 1)
net.connect(0, 0, eltwiseId, 0);
for (int i = 0; i < numConv; ++i)
{
net.connect(convLayerIds[i], 0, eltwiseId, i + convInputShift);
}
if (convInputShift == 0)
net.connect(0, 0, eltwiseId, numConv);
for (int i = numConv; i < numEltwiseInputs; ++i)
{
net.connect(0, 0, eltwiseId, i + 1);
}
int sz[] = {1, inSize[0], inSize[1], inSize[2]};
Mat input(4, &sz[0], CV_32F);
if (op == "div")
randu(input, 1.0f, 1.0f); // ensure no divisor value has absouluate value of less than 0.5
testLayer(input, net, backendId, targetId, /*skipCheck*/false, (op == "div") ? false : true);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, Eltwise, testing::Combine(
/*input size*/ testing::Values(Vec3i(1, 4, 5), Vec3i(2, 8, 6)),
/*operation*/ testing::Values("prod", "sum", "div", "max", "min"),
/*num convs*/ testing::Values(1, 2, 3),
/*weighted(for sum only)*/ testing::Bool(),
dnnBackendsAndTargets()
));
////////////////////////////////////////////////////////////////////////////////
// Element-wise layers
////////////////////////////////////////////////////////////////////////////////
using NaryEltwiseConcat = TestWithParam<tuple<std::vector<int>, tuple<Backend, Target>>>;
TEST_P(NaryEltwiseConcat, Accuracy) {
auto param = GetParam();
std::vector<int> input_shape = get<0>(param);
auto backend_id = get<0>(get<1>(param));
auto target_id = get<1>(get<1>(param));
/* Build the following net:
<1x4x84>
/
[Input] -+-> Mul(B<1x84>) -> Concat(axis=1) -> [Output]
| |
+-> Sigmoid ----------+
*/
Net net;
std::vector<int> mul_B_shape(input_shape.size() - 1, 1);
mul_B_shape.back() = input_shape.back();
Mat mul_B(mul_B_shape, CV_32FC1);
randn(mul_B, 0.f, 1.f);
LayerParams mul_B_lp;
mul_B_lp.name = "mul_B";
mul_B_lp.type = "Const";
mul_B_lp.blobs.push_back(mul_B);
int id_mul_B = net.addLayer(mul_B_lp.name, mul_B_lp.type, mul_B_lp);
LayerParams mul_lp;
mul_lp.name = "mul";
mul_lp.type = "NaryEltwise";
mul_lp.set("operation", "mul");
int id_mul = net.addLayer(mul_lp.name, mul_lp.type, mul_lp);
net.connect(0, 0, id_mul, 0);
net.connect(id_mul_B, 0, id_mul, 1);
LayerParams sigmoid_lp;
sigmoid_lp.name = "sigmoid";
sigmoid_lp.type = "Sigmoid";
int id_sigmoid = net.addLayer(sigmoid_lp.name, sigmoid_lp.type, sigmoid_lp);
net.connect(0, 0, id_sigmoid, 0);
LayerParams concat_lp;
concat_lp.name = "concat";
concat_lp.type = "Concat";
concat_lp.set("axis", 1);
int id_concat = net.addLayer(concat_lp.name, concat_lp.type, concat_lp);
net.connect(id_mul, 0, id_concat, 0);
net.connect(id_sigmoid, 0, id_concat, 1);
// Run test
Mat input(input_shape, CV_32FC1);
testLayer(input, net, backend_id, target_id, false);
}
INSTANTIATE_TEST_CASE_P(Layer_Test_Backends, NaryEltwiseConcat, testing::Combine(
testing::Values(std::vector<int>{1, 4, 84}),
dnnBackendsAndTargets())
);
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_layers_backends, dnnBackendsAndTargets());
}} // namespace
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