// 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. #include "perf_precomp.hpp" #include namespace opencv_test { struct Layer_Slice : public TestBaseWithParam > { template void test_slice(const int* inputShape, const int* begin, const int* end) { int backendId = get<0>(GetParam()); int targetId = get<1>(GetParam()); Mat input(DIMS, inputShape, CV_32FC1, Scalar::all(0)); for (int i = 0; i < (int)input.total(); ++i) input.ptr()[i] = (float)(i & 4095); std::vector range(DIMS); for (int i = 0; i < DIMS; ++i) range[i] = Range(begin[i], end[i]); Net net; LayerParams lp; lp.type = "Slice"; lp.name = "testLayer"; lp.set("begin", DictValue::arrayInt((int*)&begin[0], DIMS)); lp.set("end", DictValue::arrayInt((int*)&end[0], DIMS)); net.addLayerToPrev(lp.name, lp.type, lp); // warmup { net.setInput(input); net.setPreferableBackend(backendId); net.setPreferableTarget(targetId); Mat out = net.forward(); EXPECT_GT(cv::norm(out, NORM_INF), 0); #if 0 //normAssert(out, input(range)); cout << input(range).clone().reshape(1, 1) << endl; cout << out.reshape(1, 1) << endl; #endif } TEST_CYCLE() { Mat res = net.forward(); } SANITY_CHECK_NOTHING(); } }; static std::set nary_eltwise_cuda_deny_ops = {"equal", "greater", "less", "mean", "pow", "sub"}; struct Layer_NaryEltwise : public TestBaseWithParam > { void test_layer(const std::vector& a_shape, const std::vector& b_shape, const String op, bool isRef = false) { int backendId = get<0>(GetParam()); int targetId = get<1>(GetParam()); if (!isRef && backendId == DNN_BACKEND_CUDA) { if (a_shape.size() != b_shape.size()) throw SkipTestException("The test is skipped because inputs with different shape size are not supported."); for(int i = 0; i < a_shape.size(); i++) if (a_shape[i] != b_shape[i] && a_shape[i] != 1 && b_shape[i] != 1) throw SkipTestException("The test is skipped because inputs are not supported."); if (nary_eltwise_cuda_deny_ops.find(op) != nary_eltwise_cuda_deny_ops.end()) throw SkipTestException("The operator '" + op + "' is skipped because is not support with cuda currently."); } Mat a(a_shape, CV_32FC1); Mat b(b_shape, CV_32FC1); Scalar mean = 0.f; Scalar std = 1.f; randn(a, mean, std); randn(b, mean, std); Net net; LayerParams lp; if (isRef) lp.type = "Eltwise"; else lp.type = "NaryEltwise"; lp.name = "testLayer"; lp.set("operation", op); int id = net.addLayerToPrev(lp.name, lp.type, lp); net.connect(0, 1, id, 1); // warmup { std::vector inpNames(2); inpNames[0] = "a"; inpNames[1] = "b"; net.setInputsNames(inpNames); net.setInput(a, inpNames[0]); net.setInput(b, inpNames[1]); net.setPreferableBackend(backendId); net.setPreferableTarget(targetId); Mat out = net.forward(); } TEST_CYCLE() { Mat res = net.forward(); } SANITY_CHECK_NOTHING(); } int N = 8; int C = 256; int H = 128; int W = 100; }; PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_add) { test_layer({N, C, H, W}, {N, C, H, W}, "add"); } PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_div) { test_layer({N, C, H, W}, {N, C, H, W}, "div"); } PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_div) { test_layer({N, C, H, W}, {N, C, H, W}, "div", true); } PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_equal) { test_layer({N, C, H, W}, {N, C, H, W}, "equal"); } PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_greater) { test_layer({N, C, H, W}, {N, C, H, W}, "greater"); } PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_less) { test_layer({N, C, H, W}, {N, C, H, W}, "less"); } PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_max) { test_layer({N, C, H, W}, {N, C, H, W}, "max"); } PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_max) { test_layer({N, C, H, W}, {N, C, H, W}, "max", true); } PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_mean) { test_layer({N, C, H, W}, {N, C, H, W}, "mean"); } PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_min) { test_layer({N, C, H, W}, {N, C, H, W}, "min"); } PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_min) { test_layer({N, C, H, W}, {N, C, H, W}, "min", true); } PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_mul) { test_layer({N, C, H, W}, {N, C, H, W}, "mul"); } PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_mul) { test_layer({N, C, H, W}, {N, C, H, W}, "prod", true); } PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_pow) { test_layer({N, C, H, W}, {N, C, H, W}, "pow"); } PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_sub) { test_layer({N, C, H, W}, {N, C, H, W}, "sub"); } PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_sum) { test_layer({N, C, H, W}, {N, C, H, W}, "sum"); } PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_sum) { test_layer({N, C, H, W}, {N, C, H, W}, "sum", true); } PERF_TEST_P_(Layer_NaryEltwise, NCHW_C_sum) { test_layer({N, C, H, W}, {C, 1, 1}, "sum"); } PERF_TEST_P_(Layer_NaryEltwise, NHWC_C) { test_layer({N, H, W, C}, {1, C}, "sum"); } PERF_TEST_P_(Layer_NaryEltwise, NHWC_H) { test_layer({N, H, W, C}, {1, H, 1, 1}, "sum"); } PERF_TEST_P_(Layer_Slice, YOLOv4_tiny_1) { const int inputShape[4] = {1, 64, 104, 104}; const int begin[] = {0, 32, 0, 0}; const int end[] = {1, 64, 104, 104}; test_slice<4>(inputShape, begin, end); } PERF_TEST_P_(Layer_Slice, YOLOv4_tiny_2) { const int inputShape[4] = {1, 128, 52, 52}; const int begin[] = {0, 64, 0, 0}; const int end[] = {1, 128, 52, 52}; test_slice<4>(inputShape, begin, end); } PERF_TEST_P_(Layer_Slice, YOLOv4_tiny_3) { const int inputShape[4] = {1, 256, 26, 26}; const int begin[] = {0, 128, 0, 0}; const int end[] = {1, 256, 26, 26}; test_slice<4>(inputShape, begin, end); } PERF_TEST_P_(Layer_Slice, FastNeuralStyle_eccv16) { const int inputShape[4] = {1, 128, 80, 100}; const int begin[] = {0, 0, 2, 2}; const int end[] = {1, 128, 76, 96}; test_slice<4>(inputShape, begin, end); } struct Layer_Scatter : public TestBaseWithParam > { void test_layer(const std::vector& shape, const String reduction = "none", int axis = 0) { int backendId = get<0>(GetParam()); int targetId = get<1>(GetParam()); Mat data(shape, CV_32FC1); Mat indices(shape, CV_32FC1); Mat updates(shape, CV_32FC1); Scalar mean = 0.f; Scalar std = 1.f; randn(data, mean, std); randu(indices, 0, shape[axis]); randn(updates, mean, std); indices.convertTo(indices, CV_32SC1, 1, -1); Net net; LayerParams lp; lp.type = "Scatter"; lp.name = "testLayer"; lp.set("reduction", reduction); lp.set("axis", axis); int id = net.addLayerToPrev(lp.name, lp.type, lp); net.connect(0, 0, id, 0); net.connect(0, 1, id, 1); net.connect(0, 2, id, 2); // warmup { std::vector inpNames(3); inpNames[0] = "data"; inpNames[1] = "indices"; inpNames[2] = "updates"; net.setInputsNames(inpNames); net.setInput(data, inpNames[0]); net.setInput(indices, inpNames[1]); net.setInput(updates, inpNames[2]); net.setPreferableBackend(backendId); net.setPreferableTarget(targetId); Mat out = net.forward(); } TEST_CYCLE() { Mat res = net.forward(); } SANITY_CHECK_NOTHING(); } int N = 8; int C = 256; int H = 128; int W = 100; }; PERF_TEST_P_(Layer_Scatter, DISABLED_Scatter) { test_layer({N, C, H, W}); } PERF_TEST_P_(Layer_Scatter, DISABLED_Scatter_add) { test_layer({N, C, H, W}, "add"); } struct Layer_ScatterND : public TestBaseWithParam > { void test_layer(const std::vector& shape, const String reduction = "none") { int backendId = get<0>(GetParam()); int targetId = get<1>(GetParam()); std::vector indices_shape(shape); indices_shape.push_back(int(shape.size())); Mat data(shape, CV_32FC1); Mat indices(indices_shape, CV_32FC1); Mat updates(shape, CV_32FC1); Scalar mean = 0.f; Scalar std = 1.f; randn(data, mean, std); randn(updates, mean, std); // initialize the indices with index tuples like [0...N, 0...C, 0...H, 0...W] std::vector current_index_tuple(shape.size()); int total = data.total(); std::vector indices_step; for (int i = 0; i < indices.dims; i++) { int step = indices.step.p[i] / sizeof(float); indices_step.push_back(step); } int t, j, idx, offset_at_idx, offset; for (int i = 0; i < total; i++) { t = i; for (j = shape.size() - 1; j >= 0; j--) { idx = t / shape[j]; offset_at_idx = (int)(t - idx * shape[j]); current_index_tuple[j] = offset_at_idx; t = idx; } offset = 0; for (j = 0; j < shape.size(); j++) offset += current_index_tuple[j] * indices_step[j]; for (j = 0; j < shape.size(); j++) indices.at(offset + j) = current_index_tuple[j]; } Net net; LayerParams lp; lp.type = "ScatterND"; lp.name = "testLayer"; lp.set("reduction", reduction); int id = net.addLayerToPrev(lp.name, lp.type, lp); net.connect(0, 0, id, 0); net.connect(0, 1, id, 1); net.connect(0, 2, id, 2); // warmup { std::vector inpNames(3); inpNames[0] = "data"; inpNames[1] = "indices"; inpNames[2] = "updates"; net.setInputsNames(inpNames); net.setInput(data, inpNames[0]); net.setInput(indices, inpNames[1]); net.setInput(updates, inpNames[2]); net.setPreferableBackend(backendId); net.setPreferableTarget(targetId); Mat out = net.forward(); } TEST_CYCLE() { Mat res = net.forward(); } SANITY_CHECK_NOTHING(); } int N = 8; int C = 256; int H = 128; int W = 100; }; PERF_TEST_P_(Layer_ScatterND, DISABLED_ScatterND) { test_layer({N, C, H ,W}); } PERF_TEST_P_(Layer_ScatterND, DISABLED_ScatterND_add) { test_layer({N, C, H , W}, "add"); } struct Layer_LayerNorm : public TestBaseWithParam > { void test_layer(const std::vector& x_shape) { int backendId = get<0>(GetParam()); int targetId = get<1>(GetParam()); Mat x(x_shape, CV_32FC1); Mat scale(x_shape.back(), 1, CV_32FC1); Mat b(x_shape.back(), 1, CV_32FC1); randu(x, 0.f, 1.f); randu(scale, 0.f, 1.f); randu(b, 0.f, 1.f); Net net; LayerParams lp; lp.type = "LayerNormalization"; lp.name = "testLayer"; lp.set("axis", 2); lp.set("hasBias", true); int id = net.addLayerToPrev(lp.name, lp.type, lp); net.connect(0, 0, id, 0); net.connect(0, 1, id, 1); net.connect(0, 2, id, 2); // warmup { std::vector inpNames(3); inpNames[0] = "x"; inpNames[1] = "scale"; inpNames[2] = "b"; net.setInputsNames(inpNames); net.setInput(x, inpNames[0]); net.setInput(scale, inpNames[1]); net.setInput(b, inpNames[2]); net.setPreferableBackend(backendId); net.setPreferableTarget(targetId); Mat out = net.forward(); } TEST_CYCLE() { Mat res = net.forward(); } SANITY_CHECK_NOTHING(); } int N = 1; int H = 50; int W = 768; }; PERF_TEST_P_(Layer_LayerNorm, LayerNorm) { test_layer({N, H ,W}); } struct Layer_LayerNormExpanded : public TestBaseWithParam > { void test_layer(const std::vector& x_shape) { int backendId = get<0>(GetParam()); int targetId = get<1>(GetParam()); Mat x(x_shape, CV_32FC1); Mat scale(1, x_shape.back(), CV_32FC1); // transpose to pass shape check Mat b(1, x_shape.back(), CV_32FC1); // transpose to pass shape check randu(x, 0.f, 1.f); randu(scale, 0.f, 1.f); randu(b, 0.f, 1.f); // sub graph structure: // -> ReduceMean -> -> Pow(2) -> ReduceMean -> Add(epsilon) -> Sqrt -> // x Sub Div -> Mul(scale) -> Add(bias) // ---------------> -------------------------------------------------> Net net; LayerParams lp_rm; lp_rm.type = "Reduce"; lp_rm.name = "reducemean1"; lp_rm.set("reduce", "AVE"); std::vector deleteDims(1, x_shape.back()); lp_rm.set("deleted_dims", DictValue::arrayInt(&deleteDims[0], deleteDims.size())); std::vector targetDims(x_shape.begin(), x_shape.end()); targetDims[x_shape.size() - 1] = 1; lp_rm.set("target_dims", DictValue::arrayInt(&targetDims[0], targetDims.size())); int id_rm = net.addLayerToPrev(lp_rm.name, lp_rm.type, lp_rm); net.connect(0, 0, id_rm, 0); LayerParams lp_sub; lp_sub.type = "NaryEltwise"; lp_sub.name = "sub1"; lp_sub.set("operation", "sub"); int id_sub = net.addLayer(lp_sub.name, lp_sub.type, lp_sub); net.connect(0, 0, id_sub, 0); net.connect(id_rm, 0, id_sub, 1); Mat pow_const(1, 1, CV_32FC1); pow_const.at(0) = 2.f; LayerParams lp_pow_const; lp_pow_const.type = "Const"; lp_pow_const.name = "const1"; lp_pow_const.blobs.push_back(pow_const); int id_pow_const = net.addLayer(lp_pow_const.name, lp_pow_const.type, lp_pow_const); LayerParams lp_pow; lp_pow.type = "NaryEltwise"; lp_pow.name = "pow1"; lp_pow.set("operation", "pow"); int id_pow = net.addLayer(lp_pow.name, lp_pow.type, lp_pow); net.connect(id_sub, 0, id_pow, 0); net.connect(id_pow_const, 0, id_pow, 1); LayerParams lp_rm1; lp_rm1.type = "Reduce"; lp_rm1.name = "reducemean2"; lp_rm1.set("reduce", "AVE"); lp_rm1.set("deleted_dims", DictValue::arrayInt(&deleteDims[0], deleteDims.size())); lp_rm1.set("target_dims", DictValue::arrayInt(&targetDims[0], targetDims.size())); int id_rm1 = net.addLayer(lp_rm1.name, lp_rm1.type, lp_rm1); net.connect(id_pow, 0, id_rm1, 0); Mat add_const(1, 1, CV_32F); add_const.at(0) = 1e-5; LayerParams lp_add_const; lp_add_const.type = "Const"; lp_add_const.name = "const2"; lp_add_const.blobs.push_back(add_const); int id_add_const = net.addLayer(lp_add_const.name, lp_add_const.type, lp_add_const); LayerParams lp_add; lp_add.type = "NaryEltwise"; lp_add.name = "add1"; lp_add.set("operation", "add"); int id_add = net.addLayer(lp_add.name, lp_add.type, lp_add); net.connect(id_rm1, 0, id_add, 0); net.connect(id_add_const, 0, id_add, 1); LayerParams lp_sqrt; lp_sqrt.type = "Sqrt"; lp_sqrt.name = "sqrt1"; int id_sqrt = net.addLayer(lp_sqrt.name, lp_sqrt.type, lp_sqrt); net.connect(id_add, 0, id_sqrt, 0); LayerParams lp_div; lp_div.type = "NaryEltwise"; lp_div.name = "div1"; lp_div.set("operation", "div"); int id_div = net.addLayer(lp_div.name, lp_div.type, lp_div); net.connect(id_sub, 0, id_div, 0); net.connect(id_sqrt, 0, id_div, 1); LayerParams lp_mul; lp_mul.type = "NaryEltwise"; lp_mul.name = "mul1"; lp_mul.set("operation", "mul"); int id_mul = net.addLayer(lp_mul.name, lp_mul.type, lp_mul); net.connect(id_div, 0, id_mul, 0); net.connect(0, 1, id_mul, 1); LayerParams lp_add1; lp_add1.type = "NaryEltwise"; lp_add1.name = "add2"; lp_add1.set("operation", "add"); int id_add1 = net.addLayer(lp_add1.name, lp_add1.type, lp_add1); net.connect(id_mul, 0, id_add1, 0); net.connect(0, 2, id_add1, 1); // warmup { std::vector inpNames(3); inpNames[0] = "x"; inpNames[1] = "scale"; inpNames[2] = "b"; net.setInputsNames(inpNames); net.setInput(x, inpNames[0]); net.setInput(scale, inpNames[1]); net.setInput(b, inpNames[2]); net.setPreferableBackend(backendId); net.setPreferableTarget(targetId); Mat out = net.forward(); } TEST_CYCLE() { Mat res = net.forward(); } SANITY_CHECK_NOTHING(); } int N = 1; int H = 50; int W = 768; }; PERF_TEST_P_(Layer_LayerNormExpanded, DISABLED_LayerNormExpanded) { test_layer({N, H ,W}); } INSTANTIATE_TEST_CASE_P(/**/, Layer_Slice, dnnBackendsAndTargets(false, false)); INSTANTIATE_TEST_CASE_P(/**/, Layer_NaryEltwise, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU))); #ifdef HAVE_CUDA INSTANTIATE_TEST_CASE_P(CUDA, Layer_NaryEltwise, testing::Values(std::make_tuple(DNN_BACKEND_CUDA, DNN_TARGET_CUDA))); #endif INSTANTIATE_TEST_CASE_P(/**/, Layer_Scatter, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU))); INSTANTIATE_TEST_CASE_P(/**/, Layer_ScatterND, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU))); INSTANTIATE_TEST_CASE_P(/**/, Layer_LayerNorm, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU))); INSTANTIATE_TEST_CASE_P(/**/, Layer_LayerNormExpanded, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU))); typedef TestBaseWithParam > > Layer_FullyConnected; PERF_TEST_P_(Layer_FullyConnected, fc) { std::vector inpShape; inpShape.reserve(4); for (int i = 0; i < 4; ++i) { int dim = get<0>(GetParam())[i]; if (dim == 0) break; inpShape.push_back(dim); } Mat input(inpShape, CV_32F); randn(input, 0, 1); int axis = input.dims - 1; int outDims = get<1>(GetParam()); bool isMatMul = get<2>(GetParam()); int backendId = get<0>(get<3>(GetParam())); int targetId = get<1>(get<3>(GetParam())); std::vector weightShape; if (isMatMul) { weightShape = inpShape; weightShape[weightShape.size() - 2] = outDims; } else { weightShape = {outDims, (int)input.total(axis, input.dims)}; } Mat weights(weightShape, CV_32F); randn(weights, 0, 1); LayerParams lp; lp.set("axis", input.dims - 1); lp.set("is_matmul", weights.dims > 2); lp.set("bias_term", false); lp.set("num_output", (int)weights.total(0, weights.dims - 1)); lp.blobs.resize(1, weights); Net net; net.addLayerToPrev("matmul", "InnerProduct", lp); net.setInput(input); net.setPreferableBackend(backendId); net.setPreferableTarget(targetId); // warmup Mat output = net.forward(); TEST_CYCLE() { net.forward(); } SANITY_CHECK_NOTHING(); } INSTANTIATE_TEST_CASE_P(/**/, Layer_FullyConnected, Combine( Values( // input size Vec4i(5, 512, 384), Vec4i(5, 16, 512, 128) ), Values(256, 512, 1024), // output dimension testing::Bool(), // is_matmul dnnBackendsAndTargets() )); } // namespace