2020-07-06 22:32:42 +08:00
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// 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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2023-02-10 19:33:59 +08:00
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static std::set<std::string> nary_eltwise_cuda_deny_ops = {"equal", "greater", "less", "mean", "pow", "sub"};
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2022-10-21 13:11:22 +08:00
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2022-07-19 11:14:05 +08:00
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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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2022-10-21 13:11:22 +08:00
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if (!isRef && backendId == DNN_BACKEND_CUDA)
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{
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2023-04-23 17:46:50 +08:00
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if (a_shape.size() != b_shape.size())
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throw SkipTestException("The test is skipped because inputs with different shape size are not supported.");
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for(int i = 0; i < a_shape.size(); i++)
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if (a_shape[i] != b_shape[i] && a_shape[i] != 1 && b_shape[i] != 1)
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throw SkipTestException("The test is skipped because inputs are not supported.");
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2022-10-21 13:11:22 +08:00
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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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2022-07-19 11:14:05 +08:00
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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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2020-07-06 22:32:42 +08:00
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2022-07-19 11:14:05 +08:00
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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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2020-07-06 22:32:42 +08:00
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2023-04-23 17:46:50 +08:00
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PERF_TEST_P_(Layer_NaryEltwise, NHWC_H)
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{
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test_layer({N, H, W, C}, {1, H, 1, 1}, "sum");
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}
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2020-07-06 22:32:42 +08:00
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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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2022-09-18 22:13:55 +08:00
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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++)
|
|
|
|
{
|
|
|
|
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<float>(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<String> 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");
|
|
|
|
}
|
|
|
|
|
2023-01-27 21:35:59 +08:00
|
|
|
struct Layer_LayerNorm : public TestBaseWithParam<tuple<Backend, Target> >
|
|
|
|
{
|
|
|
|
void test_layer(const std::vector<int>& 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<String> 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<tuple<Backend, Target> >
|
|
|
|
{
|
|
|
|
void test_layer(const std::vector<int>& 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<int> deleteDims(1, x_shape.back());
|
|
|
|
lp_rm.set("deleted_dims", DictValue::arrayInt(&deleteDims[0], deleteDims.size()));
|
|
|
|
std::vector<int> 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<float>(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<float>(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<String> 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});
|
|
|
|
}
|
|
|
|
|
2023-10-18 15:41:47 +08:00
|
|
|
struct Layer_GatherElements : public TestBaseWithParam<tuple<Backend, Target> >
|
|
|
|
{
|
|
|
|
void test_layer(const std::vector<int>& data_shape, const std::vector<int>& indices_shape, int axis = 0)
|
|
|
|
{
|
|
|
|
int backendId = get<0>(GetParam());
|
|
|
|
int targetId = get<1>(GetParam());
|
|
|
|
|
|
|
|
Mat data(data_shape, CV_32FC1);
|
|
|
|
Mat indices(indices_shape, CV_32FC1);
|
|
|
|
|
|
|
|
randu(data, 0.f, 1.f);
|
|
|
|
randu(indices, 0, data_shape[axis]);
|
|
|
|
|
|
|
|
Net net;
|
|
|
|
LayerParams lp;
|
|
|
|
lp.type = "GatherElements";
|
|
|
|
lp.name = "testLayer";
|
|
|
|
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);
|
|
|
|
|
|
|
|
// warmup
|
|
|
|
{
|
|
|
|
std::vector<String> inpNames(3);
|
|
|
|
inpNames[0] = "data";
|
|
|
|
inpNames[1] = "indices";
|
|
|
|
net.setInputsNames(inpNames);
|
|
|
|
net.setInput(data, inpNames[0]);
|
|
|
|
net.setInput(indices, inpNames[1]);
|
|
|
|
|
|
|
|
net.setPreferableBackend(backendId);
|
|
|
|
net.setPreferableTarget(targetId);
|
|
|
|
Mat out = net.forward();
|
|
|
|
}
|
|
|
|
|
|
|
|
TEST_CYCLE()
|
|
|
|
{
|
|
|
|
Mat res = net.forward();
|
|
|
|
}
|
|
|
|
|
|
|
|
SANITY_CHECK_NOTHING();
|
|
|
|
}
|
|
|
|
};
|
|
|
|
|
|
|
|
PERF_TEST_P_(Layer_GatherElements, GatherElements)
|
|
|
|
{
|
|
|
|
test_layer({2700, 1, 2914}, {2700, 1, 81}, 2);
|
|
|
|
}
|
|
|
|
|
2020-07-06 22:32:42 +08:00
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Layer_Slice, dnnBackendsAndTargets(false, false));
|
2022-07-19 11:14:05 +08:00
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Layer_NaryEltwise, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
|
2022-10-21 13:11:22 +08:00
|
|
|
#ifdef HAVE_CUDA
|
|
|
|
INSTANTIATE_TEST_CASE_P(CUDA, Layer_NaryEltwise, testing::Values(std::make_tuple(DNN_BACKEND_CUDA, DNN_TARGET_CUDA)));
|
|
|
|
#endif
|
2022-09-18 22:13:55 +08:00
|
|
|
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)));
|
2023-01-27 21:35:59 +08:00
|
|
|
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)));
|
2023-10-18 15:41:47 +08:00
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Layer_GatherElements, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
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2020-07-06 22:32:42 +08:00
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2023-08-03 14:13:42 +08:00
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typedef TestBaseWithParam<tuple<Vec4i, int, bool, tuple<Backend, Target> > > Layer_FullyConnected;
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PERF_TEST_P_(Layer_FullyConnected, fc)
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{
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std::vector<int> inpShape;
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inpShape.reserve(4);
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for (int i = 0; i < 4; ++i) {
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int dim = get<0>(GetParam())[i];
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if (dim == 0)
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break;
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inpShape.push_back(dim);
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}
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Mat input(inpShape, CV_32F);
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randn(input, 0, 1);
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int axis = input.dims - 1;
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int outDims = get<1>(GetParam());
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bool isMatMul = get<2>(GetParam());
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int backendId = get<0>(get<3>(GetParam()));
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int targetId = get<1>(get<3>(GetParam()));
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std::vector<int> weightShape;
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if (isMatMul) {
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weightShape = inpShape;
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weightShape[weightShape.size() - 2] = outDims;
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} else {
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weightShape = {outDims, (int)input.total(axis, input.dims)};
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}
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Mat weights(weightShape, CV_32F);
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randn(weights, 0, 1);
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LayerParams lp;
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lp.set("axis", input.dims - 1);
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lp.set("is_matmul", weights.dims > 2);
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lp.set("bias_term", false);
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lp.set("num_output", (int)weights.total(0, weights.dims - 1));
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lp.blobs.resize(1, weights);
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Net net;
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net.addLayerToPrev("matmul", "InnerProduct", lp);
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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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// warmup
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Mat output = net.forward();
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TEST_CYCLE()
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{
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net.forward();
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}
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SANITY_CHECK_NOTHING();
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}
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INSTANTIATE_TEST_CASE_P(/**/, Layer_FullyConnected, Combine(
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Values( // input size
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Vec4i(5, 512, 384),
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Vec4i(5, 16, 512, 128)
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),
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Values(256, 512, 1024), // output dimension
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testing::Bool(), // is_matmul
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dnnBackendsAndTargets()
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));
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2020-07-06 22:32:42 +08:00
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} // namespace
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