mirror of
https://github.com/opencv/opencv.git
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96947c30c0
backport of commit:6111935835
partial backport of commit:dd5976162b
930 lines
33 KiB
C++
930 lines
33 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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//
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// Copyright (C) 2017-2019, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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// This tests doesn't require any external data. They just compare outputs of
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// layers using different computation backends. Input and parameters are random.
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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using namespace cv;
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using namespace cv::dnn;
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using namespace testing;
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static void test(Mat& input, Net& net, Backend backendId, Target targetId, bool skipCheck = false, bool randInput = true)
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{
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DNNTestLayer::checkBackend(backendId, targetId);
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if (randInput)
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randu(input, -1.0f, 1.0f);
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net.setInput(input);
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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Mat outputDefault = net.forward().clone();
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net.setPreferableBackend(backendId);
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net.setPreferableTarget(targetId);
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Mat outputHalide = net.forward().clone();
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if (skipCheck)
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return;
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double l1, lInf;
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DNNTestLayer::getDefaultThresholds(backendId, targetId, &l1, &lInf);
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#if 0
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std::cout << "l1=" << l1 << " lInf=" << lInf << std::endl;
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std::cout << outputDefault.reshape(1, outputDefault.total()).t() << std::endl;
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std::cout << outputHalide.reshape(1, outputDefault.total()).t() << std::endl;
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#endif
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normAssert(outputDefault, outputHalide, "", l1, lInf);
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}
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static void test(LayerParams& params, Mat& input, Backend backendId, Target targetId, bool skipCheck = false)
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{
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Net net;
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net.addLayerToPrev(params.name, params.type, params);
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test(input, net, backendId, targetId, skipCheck);
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}
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static inline testing::internal::ParamGenerator<tuple<Backend, Target> > dnnBackendsAndTargetsWithHalide()
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{
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return dnnBackendsAndTargets(true, true, false); // OpenCV/CPU is used as reference
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}
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class Test_Halide_layers : public DNNTestLayer {};
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////////////////////////////////////////////////////////////////////////////////
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// Padding
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////////////////////////////////////////////////////////////////////////////////
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TEST_P(Test_Halide_layers, Padding)
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{
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static const int kNumRuns = 10;
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std::vector<int> paddings(8);
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cv::RNG& rng = cv::theRNG();
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for (int t = 0; t < kNumRuns; ++t)
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{
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for (int i = 0; i < paddings.size(); ++i)
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paddings[i] = rng(5);
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LayerParams lp;
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lp.set("paddings", DictValue::arrayInt<int*>(&paddings[0], paddings.size()));
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lp.type = "Padding";
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lp.name = "testLayer";
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int sz[] = {1 + (int)rng(10), 1 + (int)rng(10), 1 + (int)rng(10), 1 + (int)rng(10)};
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Mat input(4, &sz[0], CV_32F);
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test(lp, input, backend, target);
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}
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}
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////////////////////////////////////////////////////////////////////////////////
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// Convolution
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////////////////////////////////////////////////////////////////////////////////
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typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Size, bool, tuple<Backend, Target> > > Convolution;
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TEST_P(Convolution, Accuracy)
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{
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int inChannels = get<0>(GetParam())[0];
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int outChannels = get<0>(GetParam())[1];
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int group = get<0>(GetParam())[2];
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Size inSize = get<1>(GetParam());
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Size kernel = get<2>(GetParam());
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Size stride = get<3>(GetParam());
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Size pad = get<4>(GetParam());
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Size dilation = get<5>(GetParam());
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bool hasBias = get<6>(GetParam());
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Backend backendId = get<0>(get<7>(GetParam()));
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Target targetId = get<1>(get<7>(GetParam()));
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bool skipCheck = false;
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int sz[] = {outChannels, inChannels / group, kernel.height, kernel.width};
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Mat weights(4, &sz[0], CV_32F);
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randu(weights, -1.0f, 1.0f);
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LayerParams lp;
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lp.set("kernel_w", kernel.width);
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lp.set("kernel_h", kernel.height);
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lp.set("pad_w", pad.width);
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lp.set("pad_h", pad.height);
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lp.set("stride_w", stride.width);
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lp.set("stride_h", stride.height);
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lp.set("dilation_w", dilation.width);
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lp.set("dilation_h", dilation.height);
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lp.set("num_output", outChannels);
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lp.set("group", group);
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lp.set("bias_term", hasBias);
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lp.type = "Convolution";
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lp.name = "testLayer";
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lp.blobs.push_back(weights);
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if (hasBias)
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{
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Mat bias(1, outChannels, CV_32F);
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randu(bias, -1.0f, 1.0f);
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lp.blobs.push_back(bias);
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}
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int inpSz[] = {1, inChannels, inSize.height, inSize.width};
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Mat input(4, &inpSz[0], CV_32F);
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test(lp, input, backendId, targetId, skipCheck);
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if (skipCheck)
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throw SkipTestException("Skip checks in unstable test");
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}
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INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Convolution, Combine(
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/*in channels, out channels, group*/
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Values(Vec3i(6, 4, 1), Vec3i(6, 9, 1),
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Vec3i(6, 4, 2), Vec3i(6, 9, 3)),
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/*in size*/ Values(Size(5, 6)),
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/*kernel*/ Values(Size(3, 1), Size(1, 3)),
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/*stride*/ Values(Size(1, 1), Size(2, 2)),
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/*pad*/ Values(Size(1, 0), Size(0, 1)),
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/*dilation*/ Values(Size(1, 1), Size(2, 2)),
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/*has bias*/ Bool(),
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dnnBackendsAndTargetsWithHalide()
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));
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////////////////////////////////////////////////////////////////////////////////
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// Deconvolution
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////////////////////////////////////////////////////////////////////////////////
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typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Vec4i, bool, tuple<Backend, Target> > > Deconvolution;
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TEST_P(Deconvolution, Accuracy)
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{
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int inChannels = get<0>(GetParam())[0];
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int outChannels = get<0>(GetParam())[1];
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int group = get<0>(GetParam())[2];
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Size inSize = get<1>(GetParam());
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Size kernel = get<2>(GetParam());
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Size pad = get<3>(GetParam());
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Size dilation = get<4>(GetParam());
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Size stride = Size(get<5>(GetParam())[0], get<5>(GetParam())[1]);
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Size adjPad = Size(get<5>(GetParam())[2], get<5>(GetParam())[3]);
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bool hasBias = get<6>(GetParam());
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Backend backendId = get<0>(get<7>(GetParam()));
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Target targetId = get<1>(get<7>(GetParam()));
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
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&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
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&& inChannels == 6 && outChannels == 4 && group == 1
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&& kernel == Size(1, 3) && pad == Size(1, 0)
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&& stride == Size(1, 1) && dilation == Size(1, 1))
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
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#endif
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int sz[] = {inChannels, outChannels / group, kernel.height, kernel.width};
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Mat weights(4, &sz[0], CV_32F);
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randu(weights, -1.0f, 1.0f);
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LayerParams lp;
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lp.set("kernel_w", kernel.width);
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lp.set("kernel_h", kernel.height);
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lp.set("pad_w", pad.width);
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lp.set("pad_h", pad.height);
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lp.set("stride_w", stride.width);
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lp.set("stride_h", stride.height);
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lp.set("dilation_w", dilation.width);
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lp.set("dilation_h", dilation.height);
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lp.set("adj_w", adjPad.width);
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lp.set("adj_h", adjPad.height);
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lp.set("num_output", outChannels);
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lp.set("group", group);
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lp.set("bias_term", hasBias);
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lp.type = "Deconvolution";
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lp.name = "testLayer";
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lp.blobs.push_back(weights);
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if (hasBias)
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{
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Mat bias(1, outChannels, CV_32F);
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randu(bias, -1.0f, 1.0f);
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lp.blobs.push_back(bias);
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}
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int inpSz[] = {1, inChannels, inSize.height, inSize.width};
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Mat input(4, &inpSz[0], CV_32F);
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test(lp, input, backendId, targetId);
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}
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INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Deconvolution, Combine(
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/*in channels, out channels, group*/
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Values(Vec3i(6, 4, 1), Vec3i(6, 9, 3)),
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/*in size*/ Values(Size(5, 6)),
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/*kernel*/ Values(Size(3, 1), Size(1, 3)),
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/*pad*/ Values(Size(1, 0), Size(0, 1)),
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/*dilation*/ Values(Size(1, 1)),
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/*stride, adj. pad*/ Values(Vec4i(1,1, 0,0), Vec4i(2,2, 1,0), Vec4i(1,2, 0,1)),
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/*has bias*/ Bool(),
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dnnBackendsAndTargetsWithHalide()
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));
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////////////////////////////////////////////////////////////////////////////////
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// LRN
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////////////////////////////////////////////////////////////////////////////////
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typedef TestWithParam<tuple<Vec3i, int, Vec3f, bool, std::string, tuple<Backend, Target> > > LRN;
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TEST_P(LRN, Accuracy)
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{
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int inChannels = get<0>(GetParam())[0];
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Size inSize = Size(get<0>(GetParam())[1], get<0>(GetParam())[2]);
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int localSize = get<1>(GetParam());
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float alpha = get<2>(GetParam())[0];
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float beta = get<2>(GetParam())[1];
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float bias = get<2>(GetParam())[2];
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bool normBySize = get<3>(GetParam());
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std::string nrmType = get<4>(GetParam());
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Backend backendId = get<0>(get<5>(GetParam()));
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Target targetId = get<1>(get<5>(GetParam()));
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if ((inSize.width == 5 || inSize.height == 5) && targetId == DNN_TARGET_MYRIAD &&
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nrmType == "ACROSS_CHANNELS")
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
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LayerParams lp;
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lp.set("norm_region", nrmType);
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lp.set("local_size", localSize);
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lp.set("alpha", alpha);
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lp.set("beta", beta);
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lp.set("bias", bias);
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lp.set("norm_by_size", normBySize);
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lp.type = "LRN";
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lp.name = "testLayer";
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int sz[] = {1, inChannels, inSize.height, inSize.width};
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Mat input(4, &sz[0], CV_32F);
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test(lp, input, backendId, targetId);
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}
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INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, LRN, Combine(
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/*input ch,w,h*/ Values(Vec3i(6, 5, 8), Vec3i(7, 11, 6)),
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/*local size*/ Values(3, 5),
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Values(Vec3f(0.9f, 1.0f, 1.1f), Vec3f(0.9f, 1.1f, 1.0f),
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/*alpha, beta, bias*/ Vec3f(1.0f, 0.9f, 1.1f), Vec3f(1.0f, 1.1f, 0.9f),
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Vec3f(1.1f, 0.9f, 1.0f), Vec3f(1.1f, 1.0f, 0.9f)),
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/*norm_by_size*/ Bool(),
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/*norm_type*/ Values("ACROSS_CHANNELS", "WITHIN_CHANNEL"),
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dnnBackendsAndTargetsWithHalide()
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));
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////////////////////////////////////////////////////////////////////////////////
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// Average pooling
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////////////////////////////////////////////////////////////////////////////////
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typedef TestWithParam<tuple<int, Size, Size, Size, tuple<Backend, Target> > > AvePooling;
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TEST_P(AvePooling, Accuracy)
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{
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int inChannels = get<0>(GetParam());
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Size outSize = get<1>(GetParam());; // Input size will be computed from parameters.
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Size kernel = get<2>(GetParam());
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Size stride = get<3>(GetParam());
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Backend backendId = get<0>(get<4>(GetParam()));
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Target targetId = get<1>(get<4>(GetParam()));
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#if defined(INF_ENGINE_RELEASE)
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
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&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
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&& kernel == Size(1, 1) && (stride == Size(1, 1) || stride == Size(2, 2)))
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
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#endif
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const int inWidth = (outSize.width - 1) * stride.width + kernel.width;
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const int inHeight = (outSize.height - 1) * stride.height + kernel.height;
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LayerParams lp;
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lp.set("pool", "ave");
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lp.set("kernel_w", kernel.width);
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lp.set("kernel_h", kernel.height);
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lp.set("stride_w", stride.width);
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lp.set("stride_h", stride.height);
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lp.type = "Pooling";
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lp.name = "testLayer";
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int sz[] = {1, inChannels, inHeight, inWidth};
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Mat input(4, &sz[0], CV_32F);
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test(lp, input, backendId, targetId);
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}
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INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, AvePooling, Combine(
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/*in channels*/ Values(3, 4),
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/*out size*/ Values(Size(1, 1), Size(2, 2), Size(3, 2), Size(4, 7)),
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/*kernel*/ Values(Size(1, 1), Size(2, 2), Size(3, 3), Size(3, 2)),
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/*stride*/ Values(Size(1, 1), Size(2, 2), Size(3, 2)),
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dnnBackendsAndTargetsWithHalide()
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));
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////////////////////////////////////////////////////////////////////////////////
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// Maximum pooling
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////////////////////////////////////////////////////////////////////////////////
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typedef TestWithParam<tuple<int, Size, Size, Size, Size, tuple<Backend, Target> > > MaxPooling;
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TEST_P(MaxPooling, Accuracy)
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{
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int inChannels = get<0>(GetParam());
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Size inSize = get<1>(GetParam());
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Size kernel = get<2>(GetParam());
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Size stride = get<3>(GetParam());
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Size pad = get<4>(GetParam());
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Backend backendId = get<0>(get<5>(GetParam()));
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Target targetId = get<1>(get<5>(GetParam()));
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
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&& inSize == Size(7, 6) && kernel == Size(3, 2)
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&& (stride == Size(1, 1) || stride == Size(2, 2))
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&& (pad == Size(0, 1) || pad == Size(1, 1))
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)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
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&& (kernel == Size(2, 2) || kernel == Size(3, 2))
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&& stride == Size(1, 1) && (pad == Size(0, 0) || pad == Size(0, 1))
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)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
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&& getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
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&& (stride == Size(1, 1) || stride == Size(2, 2))
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&& (pad == Size(0, 1) || pad == Size(1, 1))
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)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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LayerParams lp;
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lp.set("pool", "max");
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lp.set("kernel_w", kernel.width);
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lp.set("kernel_h", kernel.height);
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lp.set("stride_w", stride.width);
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lp.set("stride_h", stride.height);
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lp.set("pad_w", pad.width);
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lp.set("pad_h", pad.height);
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lp.type = "Pooling";
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lp.name = "testLayer";
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int sz[] = {1, inChannels, inSize.height, inSize.width};
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Mat input(4, &sz[0], CV_32F);
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test(lp, input, backendId, targetId);
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}
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INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, MaxPooling, Combine(
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/*in channels*/ Values(3, 4),
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/*in size*/ Values(Size(5, 5), Size(7, 6)),
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/*kernel*/ Values(Size(2, 2), Size(3, 3), Size(3, 2)),
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/*stride*/ Values(Size(1, 1), Size(2, 2), Size(3, 2)),
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/*pad*/ Values(Size(0, 0), Size(1, 1), Size(0, 1)),
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dnnBackendsAndTargetsWithHalide()
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));
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////////////////////////////////////////////////////////////////////////////////
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// Fully-connected
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////////////////////////////////////////////////////////////////////////////////
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typedef TestWithParam<tuple<int, Size, int, bool, tuple<Backend, Target> > > FullyConnected;
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TEST_P(FullyConnected, Accuracy)
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{
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int inChannels = get<0>(GetParam());
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Size inSize = get<1>(GetParam());
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int outChannels = get<2>(GetParam());
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bool hasBias = get<3>(GetParam());
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Backend backendId = get<0>(get<4>(GetParam()));
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Target targetId = get<1>(get<4>(GetParam()));
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if ((backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
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backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && (targetId == DNN_TARGET_OPENCL_FP16 ||
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(targetId == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X))) {
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
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}
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Mat weights(outChannels, inChannels * inSize.height * inSize.width, CV_32F);
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randu(weights, -1.0f, 1.0f);
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|
|
|
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[] = {1, inChannels, inSize.height, inSize.width};
|
|
Mat input(4, &sz[0], CV_32F);
|
|
test(lp, input, backendId, targetId);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, FullyConnected, Combine(
|
|
/*in channels*/ Values(3, 4),
|
|
/*in size*/ Values(Size(5, 4), Size(4, 5), Size(1, 1)),
|
|
/*out channels*/ Values(3, 4),
|
|
/*has bias*/ Bool(),
|
|
dnnBackendsAndTargetsWithHalide()
|
|
));
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// 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);
|
|
test(lp, input, backendId, targetId);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, SoftMax, Combine(
|
|
Values(3, 4, 5, 1024),
|
|
dnnBackendsAndTargetsWithHalide()
|
|
));
|
|
|
|
//////////////////////////////////////////////////////////////////////////////
|
|
// Max pooling - unpooling
|
|
//////////////////////////////////////////////////////////////////////////////
|
|
TEST_P(Test_Halide_layers, MaxPoolUnpool)
|
|
{
|
|
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);
|
|
|
|
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);
|
|
test(input, net, backend, target);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// AvePooling + in-place layers
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
static const int kNumChannels = 3;
|
|
|
|
void testInPlaceActivation(LayerParams& lp, Backend backendId, Target targetId)
|
|
{
|
|
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);
|
|
test(input, net, backendId, targetId);
|
|
}
|
|
|
|
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_Halide, BatchNorm, Combine(
|
|
/*has weights*/ Bool(),
|
|
/*has bias*/ Bool(),
|
|
/*epsilon*/ Values(1e-3f, 1e-5f),
|
|
dnnBackendsAndTargetsWithHalide()
|
|
));
|
|
|
|
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_Halide, ReLU, Combine(
|
|
/*negative slope*/ Values(2.0f, 0.3f, -0.1f, 0.0f),
|
|
dnnBackendsAndTargetsWithHalide()
|
|
));
|
|
|
|
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()));
|
|
|
|
LayerParams lp;
|
|
lp.type = get<0>(GetParam());
|
|
lp.name = "testLayer";
|
|
testInPlaceActivation(lp, backendId, targetId);
|
|
}
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, NoParamActivation, Combine(
|
|
/*type*/ Values("TanH", "Sigmoid", "AbsVal", "BNLL", "Swish", "Mish"),
|
|
dnnBackendsAndTargetsWithHalide()
|
|
));
|
|
|
|
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_Halide, Power, Combine(
|
|
/*power, scale, shift*/ 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)),
|
|
dnnBackendsAndTargetsWithHalide()
|
|
));
|
|
|
|
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_Halide, Exp, Combine(
|
|
/*base, scale, shift*/ 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)),
|
|
dnnBackendsAndTargetsWithHalide()
|
|
));
|
|
|
|
TEST_P(Test_Halide_layers, 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_Halide, Scale, Combine(
|
|
Bool(),
|
|
dnnBackendsAndTargetsWithHalide()
|
|
));
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// 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);
|
|
test(input, net, backendId, targetId);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Concat, Combine(
|
|
/*input size*/ Values(Vec3i(1, 4, 5), Vec3i(2, 8, 6)),
|
|
/*channels*/ Values(Vec3i(2, 0, 0), Vec3i(3, 4, 0), Vec3i(1, 6, 2)),
|
|
dnnBackendsAndTargetsWithHalide()
|
|
));
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// 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_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)
|
|
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)
|
|
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)
|
|
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
|
|
test(input, net, backendId, targetId, /*skipCheck*/false, (op == "div") ? false : true);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Eltwise, Combine(
|
|
/*input size*/ Values(Vec3i(1, 4, 5), Vec3i(2, 8, 6)),
|
|
/*operation*/ Values("prod", "sum", "div", "max"),
|
|
/*num convs*/ Values(1, 2, 3),
|
|
/*weighted(for sum only)*/ Bool(),
|
|
dnnBackendsAndTargetsWithHalide()
|
|
));
|
|
|
|
////////////////////////////////////////////////////////////////////////////
|
|
// Mixed backends
|
|
////////////////////////////////////////////////////////////////////////////
|
|
#ifdef HAVE_HALIDE
|
|
TEST(MixedBackends_Halide_Default_Halide, Accuracy)
|
|
{
|
|
// Just a layer that supports Halide backend.
|
|
LayerParams lrn;
|
|
lrn.type = "LRN";
|
|
lrn.name = "testLRN";
|
|
|
|
// Some of layers that doesn't supports Halide backend yet.
|
|
LayerParams mvn;
|
|
mvn.type = "MVN";
|
|
mvn.name = "testMVN";
|
|
|
|
// Halide layer again.
|
|
LayerParams lrn2;
|
|
lrn2.type = "LRN";
|
|
lrn2.name = "testLRN2";
|
|
|
|
Net net;
|
|
int lrnId = net.addLayer(lrn.name, lrn.type, lrn);
|
|
net.connect(0, 0, lrnId, 0);
|
|
net.addLayerToPrev(mvn.name, mvn.type, mvn);
|
|
net.addLayerToPrev(lrn2.name, lrn2.type, lrn2);
|
|
|
|
int sz[] = {4, 3, 5, 6};
|
|
Mat input(4, &sz[0], CV_32F);
|
|
randu(input, -1.0f, 1.0f);
|
|
net.setInput(input);
|
|
net.setPreferableBackend(DNN_BACKEND_OPENCV);
|
|
Mat outputDefault = net.forward().clone();
|
|
|
|
net.setPreferableBackend(DNN_BACKEND_HALIDE);
|
|
net.setInput(input);
|
|
Mat outputHalide = net.forward().clone();
|
|
normAssert(outputDefault, outputHalide);
|
|
|
|
net.setPreferableTarget(DNN_TARGET_OPENCL);
|
|
net.setInput(input);
|
|
outputHalide = net.forward().clone();
|
|
normAssert(outputDefault, outputHalide);
|
|
}
|
|
#endif // HAVE_HALIDE
|
|
|
|
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_Halide_layers, dnnBackendsAndTargetsWithHalide());
|
|
|
|
}} // namespace
|