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opencv/modules/dnn/test/test_backends.cpp
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Vadim Pisarevsky 1b483ffea6 Merge pull request #28585 from vpisarev:dnn_block_layout_v5 
Block layout-based convolution in DNN #28585

merge together with https://github.com/opencv/opencv_extra/pull/1321

Some core parts of the new engine in DNN module have been revised substantially:

1. all tests seem to pass, except for `Test_Graph_Simplifier.ResizeSubgraph`, which has been disabled because it does not take the newly added `TransformLayoutLayer` into account. The test should be reworked perhaps.
1. convolution and related operations (maxpool/avgpool) now use so-called block layout (`DATA_LAYOUT_BLOCK`), where `NxCxHxW` tensors are represented  as `NxC1xHxWxC0`, where `C1=(C + C0-1)/C0` and `C0` is a power-of-two (usually 4, 8, 16 or 32).
1. graph is now pre-processed and `TransformLayoutLayer` is inserted to convert data from NCHW or NHWC layout to the block layout or vice versa. The transformations are done in a lazy way only when they are really needed. For example, in the whole Resnet only 2 transformations are performed.
1. transformer-based models and other models that do not use convolutions will run as usual, without going to block layout.
1. there is yet another graph preprocessing stage added that embeds constant weights/scale and bias into convolution and batch norm layers.
1. 'batchnorm', 'activation' and 'adding a residual' are now fused with convolution, just like in the old engine. That brings some noticeable acceleration.
1. optimized convolution kernels have been added.
     * depthwise convolution, as well as maxpool and avgpool support C0=4, 8, 16 etc. _as long as_  C0 is divisible by the number of fp32 lanes in a SIMD register of the target platform (e.g. on ARM with NEON there must be `C0 % 4 == 0`, on x64 with AVX2 `C0 % 8 == 0`).
     * non-depthwise convolution only supports C0=8 for now. C0=8 seems to be a sweetspot for ARM with NEON, x64 with AVX2 or RISC-V with RVV (with 128- or 256-bit registers). For some platforms with dedicated matrix accelerators C0=16 or even C0=32 might be more efficient, but we could add the respective kernels later.
     * only fp32 kernels have been added. fp16/bf16 kernels might be added a little later.

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [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
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
- [x] Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
2026-03-13 17:09:27 +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);
// BUG: https://github.com/opencv/opencv/issues/26349
Mat outDefault;
if(netDefault.getMainGraph())
outDefault = netDefault.forward().clone();
else
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);
// BUG: https://github.com/opencv/opencv/issues/26349
Mat out;
if(net.getMainGraph())
out = net.forward().clone();
else
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);
if(netDefault.getMainGraph())
outDefault = netDefault.forward().clone();
else
outDefault = netDefault.forward(outputLayer).clone();
if(net.getMainGraph())
out = net.forward().clone();
else
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 = 6e-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
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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