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715 lines
22 KiB
715 lines
22 KiB
// 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, 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 cvtest |
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{ |
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#ifdef HAVE_HALIDE |
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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(LayerParams& params, Mat& input) |
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{ |
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randu(input, -1.0f, 1.0f); |
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Net net; |
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int lid = net.addLayer(params.name, params.type, params); |
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net.connect(0, 0, lid, 0); |
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net.setInput(input); |
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Mat outputDefault = net.forward(params.name).clone(); |
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net.setPreferableBackend(DNN_BACKEND_HALIDE); |
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Mat outputHalide = net.forward(params.name).clone(); |
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normAssert(outputDefault, outputHalide); |
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} |
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//////////////////////////////////////////////////////////////////////////////// |
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// Padding |
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//////////////////////////////////////////////////////////////////////////////// |
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TEST(Padding_Halide, Accuracy) |
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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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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] = rand() % 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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Mat input({1 + rand() % 10, 1 + rand() % 10, 1 + rand() % 10, 1 + rand() % 10}, CV_32F); |
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test(lp, input); |
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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> > 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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Mat weights({outChannels, inChannels / group, kernel.height, kernel.width}, 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({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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Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F); |
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test(lp, input); |
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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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)); |
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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> > 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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Mat weights({inChannels, outChannels / group, kernel.height, kernel.width}, 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({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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Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F); |
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test(lp, input); |
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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), Size(2, 2)), |
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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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)); |
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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> > 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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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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Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F); |
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test(lp, input); |
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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,*/ Vec3f(1.0f, 0.9f, 1.1f), Vec3f(1.0f, 1.1f, 0.9f), |
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/*bias */ 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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)); |
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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> > 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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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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Mat input({1, inChannels, inHeight, inWidth}, CV_32F); |
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test(lp, input); |
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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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)); |
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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> > 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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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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Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F); |
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test(lp, input); |
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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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)); |
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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> > 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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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); |
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randu(bias, -1.0f, 1.0f); |
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LayerParams lp; |
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lp.set("num_output", outChannels); |
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lp.set("bias_term", hasBias); |
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lp.blobs.push_back(weights); |
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lp.blobs.push_back(bias); |
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lp.type = "InnerProduct"; |
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lp.name = "testLayer"; |
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Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F); |
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test(lp, input); |
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} |
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INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, FullyConnected, Combine( |
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/*in channels*/ Values(3, 4), |
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/*in size*/ Values(Size(5, 4), Size(4, 5), Size(1, 1)), |
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/*out channels*/ Values(3, 4), |
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/*has bias*/ Bool() |
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)); |
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//////////////////////////////////////////////////////////////////////////////// |
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// SoftMax |
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//////////////////////////////////////////////////////////////////////////////// |
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typedef TestWithParam<tuple<int> > SoftMax; |
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TEST_P(SoftMax, Accuracy) |
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{ |
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int inChannels = get<0>(GetParam()); |
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LayerParams lp; |
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lp.type = "SoftMax"; |
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lp.name = "testLayer"; |
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Mat input({1, inChannels, 1, 1}, CV_32F); |
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test(lp, input); |
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} |
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INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, SoftMax, Values(3, 4, 5, 1024)); |
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////////////////////////////////////////////////////////////////////////////// |
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// Max pooling - unpooling |
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////////////////////////////////////////////////////////////////////////////// |
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TEST(MaxPoolUnpool_Halide, Accuracy) |
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{ |
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LayerParams pool; |
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pool.set("pool", "max"); |
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pool.set("kernel_w", 2); |
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pool.set("kernel_h", 2); |
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pool.set("stride_w", 2); |
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pool.set("stride_h", 2); |
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pool.set("pad_w", 0); |
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pool.set("pad_h", 0); |
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pool.type = "Pooling"; |
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pool.name = "testPool"; |
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LayerParams unpool; |
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unpool.set("pool_k_w", 2); |
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unpool.set("pool_k_h", 2); |
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unpool.set("pool_stride_w", 2); |
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unpool.set("pool_stride_h", 2); |
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unpool.set("pool_pad_w", 0); |
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unpool.set("pool_pad_h", 0); |
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unpool.type = "MaxUnpool"; |
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unpool.name = "testUnpool"; |
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Net net; |
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int poolId = net.addLayer(pool.name, pool.type, pool); |
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net.connect(0, 0, poolId, 0); |
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int unpoolId = net.addLayer(unpool.name, unpool.type, unpool); |
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net.connect(poolId, 0, unpoolId, 0); |
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net.connect(poolId, 1, unpoolId, 1); |
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Mat input({1, 1, 4, 4}, CV_32F); |
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randu(input, -1.0f, 1.0f); |
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net.setInput(input); |
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Mat outputDefault = net.forward("testUnpool").clone(); |
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net.setPreferableBackend(DNN_BACKEND_HALIDE); |
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net.setInput(input); |
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Mat outputHalide = net.forward("testUnpool").clone(); |
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normAssert(outputDefault, outputHalide); |
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} |
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//////////////////////////////////////////////////////////////////////////////// |
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// AvePooling + in-place layers |
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//////////////////////////////////////////////////////////////////////////////// |
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static const int kNumChannels = 3; |
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void testInPlaceActivation(LayerParams& lp) |
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{ |
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EXPECT_FALSE(lp.name.empty()); |
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LayerParams pool; |
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pool.set("pool", "ave"); |
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pool.set("kernel_w", 2); |
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pool.set("kernel_h", 2); |
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pool.set("stride_w", 2); |
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pool.set("stride_h", 2); |
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pool.type = "Pooling"; |
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Net net; |
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int poolId = net.addLayer(pool.name, pool.type, pool); |
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net.connect(0, 0, poolId, 0); |
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net.addLayerToPrev(lp.name, lp.type, lp); |
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Mat input({1, kNumChannels, 10, 10}, CV_32F); |
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randu(input, -1.0f, 1.0f); |
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net.setInput(input); |
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Mat outputDefault = net.forward(lp.name).clone(); |
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net.setInput(input); |
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net.setPreferableBackend(DNN_BACKEND_HALIDE); |
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Mat outputHalide = net.forward(lp.name).clone(); |
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normAssert(outputDefault, outputHalide); |
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} |
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typedef TestWithParam<tuple<bool, bool, float> > BatchNorm; |
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TEST_P(BatchNorm, Accuracy) |
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{ |
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bool hasWeights = get<0>(GetParam()); |
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bool hasBias = get<1>(GetParam()); |
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float epsilon = get<2>(GetParam()); |
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LayerParams lp; |
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lp.set("has_weight", hasWeights); |
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lp.set("has_bias", hasBias); |
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lp.set("eps", epsilon); |
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lp.type = "BatchNorm"; |
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lp.name = "testLayer"; |
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lp.blobs.reserve(4); |
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for (int i = 0; i < 3; ++i) |
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lp.blobs.push_back(Mat({kNumChannels}, CV_32F)); |
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if (hasBias || hasWeights) |
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lp.blobs.push_back(Mat({kNumChannels}, CV_32F)); |
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for (Mat& m : lp.blobs) |
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randu(m, 0.0f, 1.0f); |
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testInPlaceActivation(lp); |
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} |
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INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, BatchNorm, Combine( |
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/*has weights*/ Bool(), |
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/*has bias*/ Bool(), |
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/*epsilon*/ Values(1e-3f, 1e-5f) |
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)); |
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typedef TestWithParam<tuple<float> > ReLU; |
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TEST_P(ReLU, Accuracy) |
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{ |
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float negativeSlope = get<0>(GetParam()); |
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LayerParams lp; |
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lp.set("negative_slope", negativeSlope); |
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lp.type = "ReLU"; |
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lp.name = "testLayer"; |
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testInPlaceActivation(lp); |
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} |
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INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, ReLU, Values( |
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/*negative slope*/ 2.0f, 0.3f, -0.1f, 0.0f |
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)); |
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typedef TestWithParam<tuple<std::string> > NoParamActivation; |
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TEST_P(NoParamActivation, Accuracy) |
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{ |
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LayerParams lp; |
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lp.type = get<0>(GetParam()); |
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lp.name = "testLayer"; |
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testInPlaceActivation(lp); |
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} |
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INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, NoParamActivation, Values( |
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/*type*/ "TanH", "Sigmoid", "AbsVal", "BNLL" |
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)); |
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typedef TestWithParam<tuple<Vec3f> > Power; |
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TEST_P(Power, Accuracy) |
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{ |
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float power = get<0>(GetParam())[0]; |
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float scale = get<0>(GetParam())[1]; |
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float shift = get<0>(GetParam())[2]; |
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LayerParams lp; |
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lp.set("power", power); |
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lp.set("scale", scale); |
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lp.set("shift", shift); |
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lp.type = "Power"; |
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lp.name = "testLayer"; |
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testInPlaceActivation(lp); |
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} |
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INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Power, |
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/*power, scale, shift*/ Values(Vec3f(0.9f, 1.0f, 1.1f), Vec3f(0.9f, 1.1f, 1.0f), |
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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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); |
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TEST(ChannelsPReLU, Accuracy) |
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{ |
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LayerParams lp; |
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lp.type = "ChannelsPReLU"; |
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lp.name = "testLayer"; |
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lp.blobs.push_back(Mat({kNumChannels}, CV_32F)); |
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randu(lp.blobs[0], -1.0f, 1.0f); |
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testInPlaceActivation(lp); |
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} |
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typedef TestWithParam<tuple<bool> > Scale; |
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TEST_P(Scale, Accuracy) |
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{ |
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bool hasBias = get<0>(GetParam()); |
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LayerParams lp; |
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lp.set("bias_term", hasBias); |
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lp.type = "Scale"; |
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lp.name = "testLayer"; |
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lp.blobs.push_back(Mat({kNumChannels}, CV_32F)); |
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randu(lp.blobs[0], -1.0f, 1.0f); |
|
if (hasBias) |
|
{ |
|
lp.blobs.push_back(Mat({kNumChannels}, CV_32F)); |
|
randu(lp.blobs[1], -1.0f, 1.0f); |
|
} |
|
testInPlaceActivation(lp); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Scale, Values(true, false)); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// Concat layer |
|
//////////////////////////////////////////////////////////////////////////////// |
|
// |
|
// input --- conv --- concat --- output |
|
// `--- conv ----^ ^ ^ |
|
// `---- ... ------' ' |
|
// `-----------------' |
|
typedef TestWithParam<tuple<Vec3i, Vec3i> > Concat; |
|
TEST_P(Concat, Accuracy) |
|
{ |
|
Vec3i inSize = get<0>(GetParam()); |
|
Vec3i numChannels = get<1>(GetParam()); |
|
|
|
Net net; |
|
|
|
std::vector<int> convLayerIds; |
|
convLayerIds.reserve(numChannels.channels); |
|
for (int i = 0, n = numChannels.channels; i < n; ++i) |
|
{ |
|
if (!numChannels[i]) |
|
break; |
|
|
|
Mat weights({numChannels[i], inSize[0], 1, 1}, 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); |
|
} |
|
|
|
Mat input({1, inSize[0], inSize[1], inSize[2]}, CV_32F); |
|
randu(input, -1.0f, 1.0f); |
|
|
|
net.setInput(input); |
|
Mat outputDefault = net.forward(concatParam.name).clone(); |
|
|
|
net.setPreferableBackend(DNN_BACKEND_HALIDE); |
|
Mat outputHalide = net.forward(concatParam.name).clone(); |
|
normAssert(outputDefault, outputHalide); |
|
} |
|
|
|
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)) |
|
)); |
|
|
|
//////////////////////////////////////////////////////////////////////////////// |
|
// Element-wise layers |
|
//////////////////////////////////////////////////////////////////////////////// |
|
// |
|
// input --- conv --- eltwise --- output |
|
// `--- conv ----^ ^ ^ |
|
// `---- ... ------' ' |
|
// `-----------------' |
|
typedef TestWithParam<tuple<Vec3i, std::string, int, bool> > 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()); |
|
|
|
Net net; |
|
|
|
std::vector<int> convLayerIds(numConv); |
|
for (int i = 0; i < numConv; ++i) |
|
{ |
|
Mat weights({inSize[0], inSize[0], 1, 1}, 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); |
|
net.connect(0, 0, eltwiseId, 0); |
|
for (int i = 0; i < numConv; ++i) |
|
{ |
|
net.connect(convLayerIds[i], 0, eltwiseId, i + 1); |
|
} |
|
|
|
Mat input({1, inSize[0], inSize[1], inSize[2]}, CV_32F); |
|
randu(input, -1.0f, 1.0f); |
|
|
|
net.setInput(input); |
|
Mat outputDefault = net.forward(eltwiseParam.name).clone(); |
|
|
|
net.setPreferableBackend(DNN_BACKEND_HALIDE); |
|
Mat outputHalide = net.forward(eltwiseParam.name).clone(); |
|
normAssert(outputDefault, outputHalide); |
|
} |
|
|
|
INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Eltwise, Combine( |
|
/*input size*/ Values(Vec3i(1, 4, 5), Vec3i(2, 8, 6)), |
|
/*operation*/ Values("prod", "sum", "max"), |
|
/*num convs*/ Values(1, 2, 3), |
|
/*weighted(for sum only)*/ Bool() |
|
)); |
|
|
|
//////////////////////////////////////////////////////////////////////////// |
|
// Mixed backends |
|
//////////////////////////////////////////////////////////////////////////// |
|
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); |
|
|
|
Mat input({4, 3, 5, 6}, CV_32F); |
|
randu(input, -1.0f, 1.0f); |
|
net.setInput(input); |
|
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 |
|
|
|
} // namespace cvtest
|
|
|