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429 lines
11 KiB
C++
429 lines
11 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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#include "perf_precomp.hpp"
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#include <opencv2/dnn/shape_utils.hpp>
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namespace opencv_test {
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struct Layer_Slice : public TestBaseWithParam<tuple<Backend, Target> >
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{
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template<int DIMS>
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void test_slice(const int* inputShape, const int* begin, const int* end)
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{
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int backendId = get<0>(GetParam());
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int targetId = get<1>(GetParam());
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Mat input(DIMS, inputShape, CV_32FC1, Scalar::all(0));
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for (int i = 0; i < (int)input.total(); ++i)
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input.ptr<float>()[i] = (float)(i & 4095);
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std::vector<Range> range(DIMS);
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for (int i = 0; i < DIMS; ++i)
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range[i] = Range(begin[i], end[i]);
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Net net;
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LayerParams lp;
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lp.type = "Slice";
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lp.name = "testLayer";
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lp.set("begin", DictValue::arrayInt<int*>((int*)&begin[0], DIMS));
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lp.set("end", DictValue::arrayInt<int*>((int*)&end[0], DIMS));
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net.addLayerToPrev(lp.name, lp.type, lp);
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// warmup
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{
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net.setInput(input);
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net.setPreferableBackend(backendId);
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net.setPreferableTarget(targetId);
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Mat out = net.forward();
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EXPECT_GT(cv::norm(out, NORM_INF), 0);
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#if 0
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//normAssert(out, input(range));
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cout << input(range).clone().reshape(1, 1) << endl;
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cout << out.reshape(1, 1) << endl;
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#endif
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}
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TEST_CYCLE()
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{
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Mat res = net.forward();
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}
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SANITY_CHECK_NOTHING();
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}
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};
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static std::set<std::string> nary_eltwise_cuda_deny_ops = {"add", "equal", "greater", "less", "mean", "mul", "pow", "sub"};
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struct Layer_NaryEltwise : public TestBaseWithParam<tuple<Backend, Target> >
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{
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void test_layer(const std::vector<int>& a_shape, const std::vector<int>& b_shape, const String op, bool isRef = false)
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{
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int backendId = get<0>(GetParam());
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int targetId = get<1>(GetParam());
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if (!isRef && backendId == DNN_BACKEND_CUDA)
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{
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if (a_shape != b_shape)
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throw SkipTestException("The test is skipped because inputs with different shapes are not supported.");
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if (nary_eltwise_cuda_deny_ops.find(op) != nary_eltwise_cuda_deny_ops.end())
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throw SkipTestException("The operator '" + op + "' is skipped because is not support with cuda currently.");
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}
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Mat a(a_shape, CV_32FC1);
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Mat b(b_shape, CV_32FC1);
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Scalar mean = 0.f;
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Scalar std = 1.f;
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randn(a, mean, std);
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randn(b, mean, std);
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Net net;
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LayerParams lp;
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if (isRef)
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lp.type = "Eltwise";
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else
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lp.type = "NaryEltwise";
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lp.name = "testLayer";
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lp.set("operation", op);
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int id = net.addLayerToPrev(lp.name, lp.type, lp);
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net.connect(0, 1, id, 1);
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// warmup
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{
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std::vector<String> inpNames(2);
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inpNames[0] = "a";
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inpNames[1] = "b";
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net.setInputsNames(inpNames);
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net.setInput(a, inpNames[0]);
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net.setInput(b, inpNames[1]);
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net.setPreferableBackend(backendId);
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net.setPreferableTarget(targetId);
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Mat out = net.forward();
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}
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TEST_CYCLE()
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{
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Mat res = net.forward();
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}
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SANITY_CHECK_NOTHING();
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}
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int N = 8;
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int C = 256;
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int H = 128;
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int W = 100;
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};
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_add)
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{
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test_layer({N, C, H, W}, {N, C, H, W}, "add");
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}
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_div)
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{
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test_layer({N, C, H, W}, {N, C, H, W}, "div");
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}
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_div)
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{
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test_layer({N, C, H, W}, {N, C, H, W}, "div", true);
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}
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_equal)
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{
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test_layer({N, C, H, W}, {N, C, H, W}, "equal");
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}
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_greater)
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{
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test_layer({N, C, H, W}, {N, C, H, W}, "greater");
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}
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_less)
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{
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test_layer({N, C, H, W}, {N, C, H, W}, "less");
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}
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_max)
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{
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test_layer({N, C, H, W}, {N, C, H, W}, "max");
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}
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_max)
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{
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test_layer({N, C, H, W}, {N, C, H, W}, "max", true);
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}
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_mean)
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{
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test_layer({N, C, H, W}, {N, C, H, W}, "mean");
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}
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_min)
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{
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test_layer({N, C, H, W}, {N, C, H, W}, "min");
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}
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_min)
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{
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test_layer({N, C, H, W}, {N, C, H, W}, "min", true);
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}
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_mul)
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{
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test_layer({N, C, H, W}, {N, C, H, W}, "mul");
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}
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_mul)
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{
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test_layer({N, C, H, W}, {N, C, H, W}, "prod", true);
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}
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_pow)
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{
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test_layer({N, C, H, W}, {N, C, H, W}, "pow");
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}
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_sub)
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{
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test_layer({N, C, H, W}, {N, C, H, W}, "sub");
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}
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_sum)
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{
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test_layer({N, C, H, W}, {N, C, H, W}, "sum");
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}
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_NCHW_ref_sum)
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{
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test_layer({N, C, H, W}, {N, C, H, W}, "sum", true);
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}
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PERF_TEST_P_(Layer_NaryEltwise, NCHW_C_sum)
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{
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test_layer({N, C, H, W}, {C, 1, 1}, "sum");
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}
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PERF_TEST_P_(Layer_NaryEltwise, NHWC_C)
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{
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test_layer({N, H, W, C}, {1, C}, "sum");
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}
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PERF_TEST_P_(Layer_Slice, YOLOv4_tiny_1)
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{
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const int inputShape[4] = {1, 64, 104, 104};
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const int begin[] = {0, 32, 0, 0};
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const int end[] = {1, 64, 104, 104};
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test_slice<4>(inputShape, begin, end);
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}
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PERF_TEST_P_(Layer_Slice, YOLOv4_tiny_2)
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{
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const int inputShape[4] = {1, 128, 52, 52};
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const int begin[] = {0, 64, 0, 0};
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const int end[] = {1, 128, 52, 52};
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test_slice<4>(inputShape, begin, end);
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}
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PERF_TEST_P_(Layer_Slice, YOLOv4_tiny_3)
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{
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const int inputShape[4] = {1, 256, 26, 26};
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const int begin[] = {0, 128, 0, 0};
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const int end[] = {1, 256, 26, 26};
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test_slice<4>(inputShape, begin, end);
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}
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PERF_TEST_P_(Layer_Slice, FastNeuralStyle_eccv16)
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{
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const int inputShape[4] = {1, 128, 80, 100};
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const int begin[] = {0, 0, 2, 2};
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const int end[] = {1, 128, 76, 96};
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test_slice<4>(inputShape, begin, end);
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}
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struct Layer_Scatter : public TestBaseWithParam<tuple<Backend, Target> >
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{
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void test_layer(const std::vector<int>& shape, const String reduction = "none", int axis = 0)
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{
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int backendId = get<0>(GetParam());
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int targetId = get<1>(GetParam());
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Mat data(shape, CV_32FC1);
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Mat indices(shape, CV_32FC1);
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Mat updates(shape, CV_32FC1);
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Scalar mean = 0.f;
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Scalar std = 1.f;
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randn(data, mean, std);
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randu(indices, 0, shape[axis]);
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randn(updates, mean, std);
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indices.convertTo(indices, CV_32SC1, 1, -1);
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Net net;
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LayerParams lp;
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lp.type = "Scatter";
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lp.name = "testLayer";
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lp.set("reduction", reduction);
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lp.set("axis", axis);
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int id = net.addLayerToPrev(lp.name, lp.type, lp);
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net.connect(0, 0, id, 0);
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net.connect(0, 1, id, 1);
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net.connect(0, 2, id, 2);
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// warmup
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{
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std::vector<String> inpNames(3);
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inpNames[0] = "data";
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inpNames[1] = "indices";
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inpNames[2] = "updates";
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net.setInputsNames(inpNames);
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net.setInput(data, inpNames[0]);
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net.setInput(indices, inpNames[1]);
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net.setInput(updates, inpNames[2]);
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net.setPreferableBackend(backendId);
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net.setPreferableTarget(targetId);
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Mat out = net.forward();
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}
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TEST_CYCLE()
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{
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Mat res = net.forward();
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}
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SANITY_CHECK_NOTHING();
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}
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int N = 8;
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int C = 256;
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int H = 128;
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int W = 100;
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};
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PERF_TEST_P_(Layer_Scatter, DISABLED_Scatter)
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{
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test_layer({N, C, H, W});
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}
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PERF_TEST_P_(Layer_Scatter, DISABLED_Scatter_add)
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{
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test_layer({N, C, H, W}, "add");
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}
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struct Layer_ScatterND : public TestBaseWithParam<tuple<Backend, Target> >
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{
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void test_layer(const std::vector<int>& shape, const String reduction = "none")
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{
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int backendId = get<0>(GetParam());
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int targetId = get<1>(GetParam());
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std::vector<int> indices_shape(shape);
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indices_shape.push_back(int(shape.size()));
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Mat data(shape, CV_32FC1);
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Mat indices(indices_shape, CV_32FC1);
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Mat updates(shape, CV_32FC1);
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Scalar mean = 0.f;
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Scalar std = 1.f;
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randn(data, mean, std);
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randn(updates, mean, std);
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// initialize the indices with index tuples like [0...N, 0...C, 0...H, 0...W]
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std::vector<int> current_index_tuple(shape.size());
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int total = data.total();
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std::vector<int> indices_step;
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for (int i = 0; i < indices.dims; i++)
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{
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int step = indices.step.p[i] / sizeof(float);
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indices_step.push_back(step);
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}
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int t, j, idx, offset_at_idx, offset;
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for (int i = 0; i < total; i++)
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{
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t = i;
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for (j = shape.size() - 1; j >= 0; j--)
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{
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idx = t / shape[j];
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offset_at_idx = (int)(t - idx * shape[j]);
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current_index_tuple[j] = offset_at_idx;
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t = idx;
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}
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offset = 0;
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for (j = 0; j < shape.size(); j++)
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offset += current_index_tuple[j] * indices_step[j];
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for (j = 0; j < shape.size(); j++)
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indices.at<float>(offset + j) = current_index_tuple[j];
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}
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Net net;
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LayerParams lp;
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lp.type = "ScatterND";
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lp.name = "testLayer";
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lp.set("reduction", reduction);
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int id = net.addLayerToPrev(lp.name, lp.type, lp);
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net.connect(0, 0, id, 0);
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net.connect(0, 1, id, 1);
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net.connect(0, 2, id, 2);
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// warmup
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{
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std::vector<String> inpNames(3);
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inpNames[0] = "data";
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inpNames[1] = "indices";
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inpNames[2] = "updates";
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net.setInputsNames(inpNames);
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net.setInput(data, inpNames[0]);
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net.setInput(indices, inpNames[1]);
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net.setInput(updates, inpNames[2]);
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net.setPreferableBackend(backendId);
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net.setPreferableTarget(targetId);
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Mat out = net.forward();
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}
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TEST_CYCLE()
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{
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Mat res = net.forward();
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}
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SANITY_CHECK_NOTHING();
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}
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int N = 8;
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int C = 256;
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int H = 128;
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int W = 100;
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};
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PERF_TEST_P_(Layer_ScatterND, DISABLED_ScatterND)
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{
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test_layer({N, C, H ,W});
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}
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PERF_TEST_P_(Layer_ScatterND, DISABLED_ScatterND_add)
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{
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test_layer({N, C, H , W}, "add");
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}
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INSTANTIATE_TEST_CASE_P(/**/, Layer_Slice, dnnBackendsAndTargets(false, false));
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INSTANTIATE_TEST_CASE_P(/**/, Layer_NaryEltwise, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
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#ifdef HAVE_CUDA
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INSTANTIATE_TEST_CASE_P(CUDA, Layer_NaryEltwise, testing::Values(std::make_tuple(DNN_BACKEND_CUDA, DNN_TARGET_CUDA)));
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#endif
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INSTANTIATE_TEST_CASE_P(/**/, Layer_Scatter, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
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INSTANTIATE_TEST_CASE_P(/**/, Layer_ScatterND, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
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} // namespace
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