// This file is part of OpenCV project. // It is subject to the license terms in the LICENSE file found in the top-level directory // of this distribution and at http://opencv.org/license.html. // // Copyright (C) 2017, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. // This tests doesn't require any external data. They just compare outputs of // layers using different computation backends. Input and parameters are random. #include "test_precomp.hpp" namespace cvtest { #ifdef HAVE_HALIDE using namespace cv; using namespace cv::dnn; using namespace testing; static void test(LayerParams& params, Mat& input) { randu(input, -1.0f, 1.0f); Net net; int lid = net.addLayer(params.name, params.type, params); net.connect(0, 0, lid, 0); net.setInput(input); Mat outputDefault = net.forward(params.name).clone(); net.setPreferableBackend(DNN_BACKEND_HALIDE); Mat outputHalide = net.forward(params.name).clone(); normAssert(outputDefault, outputHalide); } //////////////////////////////////////////////////////////////////////////////// // Convolution //////////////////////////////////////////////////////////////////////////////// typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Size, bool> > Convolution; TEST_P(Convolution, Accuracy) { int inChannels = get<0>(GetParam())[0]; int outChannels = get<0>(GetParam())[1]; int group = get<0>(GetParam())[2]; Size inSize = get<1>(GetParam()); Size kernel = get<2>(GetParam()); Size stride = get<3>(GetParam()); Size pad = get<4>(GetParam()); Size dilation = get<5>(GetParam()); bool hasBias = get<6>(GetParam()); Mat weights({outChannels, inChannels / group, kernel.height, kernel.width}, CV_32F); randu(weights, -1.0f, 1.0f); LayerParams lp; lp.set("kernel_w", kernel.width); lp.set("kernel_h", kernel.height); lp.set("pad_w", pad.width); lp.set("pad_h", pad.height); lp.set("stride_w", stride.width); lp.set("stride_h", stride.height); lp.set("dilation_w", dilation.width); lp.set("dilation_h", dilation.height); lp.set("num_output", outChannels); lp.set("group", group); lp.set("bias_term", hasBias); lp.type = "Convolution"; lp.name = "testLayer"; lp.blobs.push_back(weights); if (hasBias) { Mat bias({outChannels}, CV_32F); randu(bias, -1.0f, 1.0f); lp.blobs.push_back(bias); } Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F); test(lp, input); } INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Convolution, Combine( /*in channels, out channels, group*/ Values(Vec3i(6, 4, 1), Vec3i(6, 9, 1), Vec3i(6, 4, 2), Vec3i(6, 9, 3)), /*in size*/ Values(Size(5, 6)), /*kernel*/ Values(Size(3, 1), Size(1, 3)), /*stride*/ Values(Size(1, 1), Size(2, 2)), /*pad*/ Values(Size(1, 0), Size(0, 1)), /*dilation*/ Values(Size(1, 1), Size(2, 2)), /*has bias*/ Bool() )); //////////////////////////////////////////////////////////////////////////////// // Deconvolution //////////////////////////////////////////////////////////////////////////////// typedef TestWithParam<tuple<Vec3i, Size, Size, Size, Size, Vec4i, bool> > Deconvolution; TEST_P(Deconvolution, Accuracy) { int inChannels = get<0>(GetParam())[0]; int outChannels = get<0>(GetParam())[1]; int group = get<0>(GetParam())[2]; Size inSize = get<1>(GetParam()); Size kernel = get<2>(GetParam()); Size pad = get<3>(GetParam()); Size dilation = get<4>(GetParam()); Size stride = Size(get<5>(GetParam())[0], get<5>(GetParam())[1]); Size adjPad = Size(get<5>(GetParam())[2], get<5>(GetParam())[3]); bool hasBias = get<6>(GetParam()); Mat weights({outChannels, inChannels / group, kernel.height, kernel.width}, CV_32F); randu(weights, -1.0f, 1.0f); LayerParams lp; lp.set("kernel_w", kernel.width); lp.set("kernel_h", kernel.height); lp.set("pad_w", pad.width); lp.set("pad_h", pad.height); lp.set("stride_w", stride.width); lp.set("stride_h", stride.height); lp.set("dilation_w", dilation.width); lp.set("dilation_h", dilation.height); lp.set("adj_w", adjPad.width); lp.set("adj_h", adjPad.height); lp.set("num_output", outChannels); lp.set("group", group); lp.set("bias_term", hasBias); lp.type = "Deconvolution"; lp.name = "testLayer"; lp.blobs.push_back(weights); if (hasBias) { Mat bias({outChannels}, CV_32F); randu(bias, -1.0f, 1.0f); lp.blobs.push_back(bias); } Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F); test(lp, input); } INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Deconvolution, Combine( /*in channels, out channels, group*/ Values(Vec3i(6, 4, 1), Vec3i(6, 9, 1)), /*in size*/ Values(Size(5, 6)), /*kernel*/ Values(Size(3, 1), Size(1, 3)), /*pad*/ Values(Size(1, 0), Size(0, 1)), /*dilation*/ Values(Size(1, 1), Size(2, 2)), /*stride, adj. pad*/ Values(Vec4i(1,1, 0,0), Vec4i(2,2, 1,0), Vec4i(1,2, 0,1)), /*has bias*/ Bool() )); //////////////////////////////////////////////////////////////////////////////// // LRN //////////////////////////////////////////////////////////////////////////////// typedef TestWithParam<tuple<Vec3i, int, Vec3f, bool, std::string> > LRN; TEST_P(LRN, Accuracy) { int inChannels = get<0>(GetParam())[0]; Size inSize = Size(get<0>(GetParam())[1], get<0>(GetParam())[2]); int localSize = get<1>(GetParam()); float alpha = get<2>(GetParam())[0]; float beta = get<2>(GetParam())[1]; float bias = get<2>(GetParam())[2]; bool normBySize = get<3>(GetParam()); std::string nrmType = get<4>(GetParam()); LayerParams lp; lp.set("norm_region", nrmType); lp.set("local_size", localSize); lp.set("alpha", alpha); lp.set("beta", beta); lp.set("bias", bias); lp.set("norm_by_size", normBySize); lp.type = "LRN"; lp.name = "testLayer"; Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F); test(lp, input); } INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, LRN, Combine( /*input ch,w,h*/ Values(Vec3i(6, 5, 8), Vec3i(7, 11, 6)), /*local size*/ Values(3, 5), Values(Vec3f(0.9f, 1.0f, 1.1f), Vec3f(0.9f, 1.1f, 1.0f), /*alpha, beta,*/ Vec3f(1.0f, 0.9f, 1.1f), Vec3f(1.0f, 1.1f, 0.9f), /*bias */ Vec3f(1.1f, 0.9f, 1.0f), Vec3f(1.1f, 1.0f, 0.9f)), /*norm_by_size*/ Bool(), /*norm_type*/ Values("ACROSS_CHANNELS", "WITHIN_CHANNEL") )); //////////////////////////////////////////////////////////////////////////////// // Average pooling //////////////////////////////////////////////////////////////////////////////// typedef TestWithParam<tuple<int, Size, Size, Size> > AvePooling; TEST_P(AvePooling, Accuracy) { int inChannels = get<0>(GetParam()); Size outSize = get<1>(GetParam());; // Input size will be computed from parameters. Size kernel = get<2>(GetParam()); Size stride = get<3>(GetParam()); const int inWidth = (outSize.width - 1) * stride.width + kernel.width; const int inHeight = (outSize.height - 1) * stride.height + kernel.height; LayerParams lp; lp.set("pool", "ave"); lp.set("kernel_w", kernel.width); lp.set("kernel_h", kernel.height); lp.set("stride_w", stride.width); lp.set("stride_h", stride.height); lp.type = "Pooling"; lp.name = "testLayer"; Mat input({1, inChannels, inHeight, inWidth}, CV_32F); test(lp, input); } INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, AvePooling, Combine( /*in channels*/ Values(3, 4), /*out size*/ Values(Size(1, 1), Size(2, 2), Size(3, 2), Size(4, 7)), /*kernel*/ Values(Size(1, 1), Size(2, 2), Size(3, 3), Size(3, 2)), /*stride*/ Values(Size(1, 1), Size(2, 2), Size(3, 2)) )); //////////////////////////////////////////////////////////////////////////////// // Maximum pooling //////////////////////////////////////////////////////////////////////////////// typedef TestWithParam<tuple<int, Size, Size, Size, Size> > MaxPooling; TEST_P(MaxPooling, Accuracy) { int inChannels = get<0>(GetParam()); Size inSize = get<1>(GetParam()); Size kernel = get<2>(GetParam()); Size stride = get<3>(GetParam()); Size pad = get<4>(GetParam()); LayerParams lp; lp.set("pool", "max"); lp.set("kernel_w", kernel.width); lp.set("kernel_h", kernel.height); lp.set("stride_w", stride.width); lp.set("stride_h", stride.height); lp.set("pad_w", pad.width); lp.set("pad_h", pad.height); lp.type = "Pooling"; lp.name = "testLayer"; Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F); test(lp, input); } INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, MaxPooling, Combine( /*in channels*/ Values(3, 4), /*in size*/ Values(Size(5, 5), Size(7, 6)), /*kernel*/ Values(Size(2, 2), Size(3, 3), Size(3, 2)), /*stride*/ Values(Size(1, 1), Size(2, 2), Size(3, 2)), /*pad*/ Values(Size(0, 0), Size(1, 1), Size(0, 1)) )); //////////////////////////////////////////////////////////////////////////////// // Fully-connected //////////////////////////////////////////////////////////////////////////////// typedef TestWithParam<tuple<int, Size, int, bool> > FullyConnected; TEST_P(FullyConnected, Accuracy) { int inChannels = get<0>(GetParam()); Size inSize = get<1>(GetParam()); int outChannels = get<2>(GetParam()); bool hasBias = get<3>(GetParam()); Mat weights(outChannels, inChannels * inSize.height * inSize.width, CV_32F); randu(weights, -1.0f, 1.0f); Mat bias(1, outChannels, CV_32F); randu(bias, -1.0f, 1.0f); LayerParams lp; lp.set("num_output", outChannels); lp.set("bias_term", hasBias); lp.blobs.push_back(weights); lp.blobs.push_back(bias); lp.type = "InnerProduct"; lp.name = "testLayer"; Mat input({1, inChannels, inSize.height, inSize.width}, CV_32F); test(lp, input); } 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() )); //////////////////////////////////////////////////////////////////////////////// // SoftMax //////////////////////////////////////////////////////////////////////////////// typedef TestWithParam<tuple<int> > SoftMax; TEST_P(SoftMax, Accuracy) { int inChannels = get<0>(GetParam()); LayerParams lp; lp.type = "SoftMax"; lp.name = "testLayer"; Mat input({1, inChannels, 1, 1}, CV_32F); test(lp, input); } INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, SoftMax, Values(3, 4, 5, 1024)); ////////////////////////////////////////////////////////////////////////////// // Max pooling - unpooling ////////////////////////////////////////////////////////////////////////////// TEST(MaxPoolUnpool_Halide, Accuracy) { 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); Mat input({1, 1, 4, 4}, CV_32F); randu(input, -1.0f, 1.0f); net.setInput(input); Mat outputDefault = net.forward("testUnpool").clone(); net.setPreferableBackend(DNN_BACKEND_HALIDE); net.setInput(input); Mat outputHalide = net.forward("testUnpool").clone(); normAssert(outputDefault, outputHalide); } //////////////////////////////////////////////////////////////////////////////// // AvePooling + in-place layers //////////////////////////////////////////////////////////////////////////////// static const int kNumChannels = 3; void testInPlaceActivation(LayerParams& lp) { 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"; Net net; int poolId = net.addLayer(pool.name, pool.type, pool); net.connect(0, 0, poolId, 0); net.addLayerToPrev(lp.name, lp.type, lp); Mat input({1, kNumChannels, 10, 10}, CV_32F); randu(input, -1.0f, 1.0f); net.setInput(input); Mat outputDefault = net.forward(lp.name).clone(); net.setInput(input); net.setPreferableBackend(DNN_BACKEND_HALIDE); Mat outputHalide = net.forward(lp.name).clone(); normAssert(outputDefault, outputHalide); } typedef TestWithParam<tuple<bool, bool, float> > BatchNorm; TEST_P(BatchNorm, Accuracy) { bool hasWeights = get<0>(GetParam()); bool hasBias = get<1>(GetParam()); float epsilon = get<2>(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({kNumChannels}, CV_32F)); if (hasBias || hasWeights) lp.blobs.push_back(Mat({kNumChannels}, CV_32F)); for (Mat& m : lp.blobs) randu(m, 0.0f, 1.0f); testInPlaceActivation(lp); } INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, BatchNorm, Combine( /*has weights*/ Bool(), /*has bias*/ Bool(), /*epsilon*/ Values(1e-3f, 1e-5f) )); typedef TestWithParam<tuple<float> > ReLU; TEST_P(ReLU, Accuracy) { float negativeSlope = get<0>(GetParam()); LayerParams lp; lp.set("negative_slope", negativeSlope); lp.type = "ReLU"; lp.name = "testLayer"; testInPlaceActivation(lp); } INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, ReLU, Values( /*negative slope*/ 2.0f, 0.3f, -0.1f, 0.0f )); typedef TestWithParam<tuple<std::string> > NoParamActivation; TEST_P(NoParamActivation, Accuracy) { LayerParams lp; lp.type = get<0>(GetParam()); lp.name = "testLayer"; testInPlaceActivation(lp); } INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, NoParamActivation, Values( /*type*/ "TanH", "Sigmoid", "AbsVal", "BNLL" )); typedef TestWithParam<tuple<Vec3f> > Power; TEST_P(Power, Accuracy) { float power = get<0>(GetParam())[0]; float scale = get<0>(GetParam())[1]; float shift = get<0>(GetParam())[2]; LayerParams lp; lp.set("power", power); lp.set("scale", scale); lp.set("shift", shift); lp.type = "Power"; lp.name = "testLayer"; testInPlaceActivation(lp); } INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, Power, /*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)) ); TEST(ChannelsPReLU, Accuracy) { LayerParams lp; lp.type = "ChannelsPReLU"; lp.name = "testLayer"; lp.blobs.push_back(Mat({kNumChannels}, CV_32F)); randu(lp.blobs[0], -1.0f, 1.0f); testInPlaceActivation(lp); } typedef TestWithParam<tuple<bool> > Scale; TEST_P(Scale, Accuracy) { bool hasBias = get<0>(GetParam()); LayerParams lp; lp.set("bias_term", hasBias); lp.type = "Scale"; lp.name = "testLayer"; lp.blobs.push_back(Mat({kNumChannels}, CV_32F)); 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() )); #endif // HAVE_HALIDE } // namespace cvtest