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12b8ed1443
Added more types support to dnn layers #25755 Added support of more types to dnn layers for CPU, CUDA and OpenVINO backends. Now most of the multi-type layers support uint8, int8, int32, int64, float32, float16, bool types. ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
1259 lines
39 KiB
C++
1259 lines
39 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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//
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// Copyright (C) 2017, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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#include "test_precomp.hpp"
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#include <opencv2/dnn/shape_utils.hpp>
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namespace opencv_test { namespace {
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int64_t getValueAt(const Mat &m, const int *indices)
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{
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if (m.type() == CV_Bool)
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return m.at<bool>(indices);
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else if (m.type() == CV_8U)
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return m.at<uint8_t>(indices);
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else if (m.type() == CV_8S)
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return m.at<int8_t>(indices);
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else if (m.type() == CV_32S)
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return m.at<int32_t>(indices);
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else if (m.type() == CV_64S)
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return m.at<int64_t>(indices);
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else
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CV_Error(Error::BadDepth, "Unsupported type");
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return -1;
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}
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int64_t getValueAt(const Mat &m, int index)
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{
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if (m.type() == CV_Bool)
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return m.ptr<bool>()[index];
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else if (m.type() == CV_8U)
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return m.ptr<uint8_t>()[index];
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else if (m.type() == CV_8S)
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return m.ptr<int8_t>()[index];
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else if (m.type() == CV_32S)
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return m.ptr<int32_t>()[index];
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else if (m.type() == CV_64S)
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return m.ptr<int64_t>()[index];
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else
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CV_Error(Error::BadDepth, "Unsupported type");
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return -1;
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}
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void fillRandom(Mat& m, int matType, Backend backend)
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{
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if (matType == CV_64S && backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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cv::randu(m, 1000000000, 1000000100); // Looks like OpenVINO uses int32 internal values for int64 operations
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else if (matType == CV_64S)
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cv::randu(m, 1000000000000000ll, 1000000000000100ll);
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else if (matType == CV_32S)
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cv::randu(m, 1000000000, 1000000100);
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else if (matType == CV_8S)
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cv::randu(m, -50, 50);
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else if (matType == CV_8U)
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cv::randu(m, 0, 100);
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else if (matType == CV_Bool)
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cv::randu(m, 0, 2);
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else
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CV_Error(Error::BadDepth, "Unsupported type");
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}
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typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_NaryEltwise_Int;
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TEST_P(Test_NaryEltwise_Int, random)
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{
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int matType = get<0>(GetParam());
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tuple<Backend, Target> backend_target= get<1>(GetParam());
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Backend backend = get<0>(backend_target);
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Target target = get<1>(backend_target);
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std::vector<int> inShape{2, 3, 4, 5};
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Mat input1(inShape, matType);
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Mat input2(inShape, matType);
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fillRandom(input1, matType, backend);
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fillRandom(input2, matType, backend);
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Net net;
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LayerParams lp;
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lp.type = "NaryEltwise";
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lp.name = "testLayer";
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if (matType == CV_Bool)
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lp.set("operation", "or");
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else
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lp.set("operation", "add");
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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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vector<String> inpNames(2);
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inpNames[0] = "input1";
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inpNames[1] = "input2";
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net.setInputsNames(inpNames);
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net.setInput(input1, inpNames[0]);
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net.setInput(input2, inpNames[1]);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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Mat re;
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re = net.forward();
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EXPECT_EQ(re.depth(), matType);
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EXPECT_EQ(re.size.dims(), 4);
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EXPECT_EQ(re.size[0], input1.size[0]);
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EXPECT_EQ(re.size[1], input1.size[1]);
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EXPECT_EQ(re.size[2], input1.size[2]);
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EXPECT_EQ(re.size[3], input1.size[3]);
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std::vector<int> reIndices(4);
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for (int i0 = 0; i0 < re.size[0]; ++i0)
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{
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reIndices[0] = i0;
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for (int i1 = 0; i1 < re.size[1]; ++i1)
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{
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reIndices[1] = i1;
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for (int i2 = 0; i2 < re.size[2]; ++i2)
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{
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reIndices[2] = i2;
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for (int i3 = 0; i3 < re.size[3]; ++i3)
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{
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reIndices[3] = i3;
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if (matType == CV_Bool)
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EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input1, reIndices.data()) | getValueAt(input2, reIndices.data()));
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else
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EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input1, reIndices.data()) + getValueAt(input2, reIndices.data()));
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}
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}
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}
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}
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}
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INSTANTIATE_TEST_CASE_P(/**/, Test_NaryEltwise_Int, Combine(
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testing::Values(CV_Bool, CV_8U, CV_8S, CV_32S, CV_64S),
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dnnBackendsAndTargets()
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));
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typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Const_Int;
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TEST_P(Test_Const_Int, random)
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{
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int matType = get<0>(GetParam());
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tuple<Backend, Target> backend_target= get<1>(GetParam());
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Backend backend = get<0>(backend_target);
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Target target = get<1>(backend_target);
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std::vector<int> inShape{2, 3, 4, 5};
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Mat input1(inShape, matType);
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Mat inputConst(inShape, matType);
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fillRandom(input1, matType, backend);
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fillRandom(inputConst, matType, backend);
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Net net;
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LayerParams lpConst;
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lpConst.type = "Const";
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lpConst.name = "constLayer";
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lpConst.blobs.push_back(inputConst);
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int idConst = net.addLayer(lpConst.name, lpConst.type, lpConst);
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LayerParams lp;
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lp.type = "NaryEltwise";
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lp.name = "testLayer";
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if (matType == CV_Bool)
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lp.set("operation", "or");
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else
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lp.set("operation", "add");
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int idSum = net.addLayer(lp.name, lp.type, lp);
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net.connect(0, 0, idSum, 0);
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net.connect(idConst, 0, idSum, 1);
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net.setInput(input1);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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Mat re;
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re = net.forward();
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EXPECT_EQ(re.depth(), matType);
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EXPECT_EQ(re.size.dims(), 4);
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EXPECT_EQ(re.size[0], input1.size[0]);
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EXPECT_EQ(re.size[1], input1.size[1]);
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EXPECT_EQ(re.size[2], input1.size[2]);
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EXPECT_EQ(re.size[3], input1.size[3]);
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std::vector<int> reIndices(4);
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for (int i0 = 0; i0 < re.size[0]; ++i0)
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{
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reIndices[0] = i0;
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for (int i1 = 0; i1 < re.size[1]; ++i1)
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{
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reIndices[1] = i1;
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for (int i2 = 0; i2 < re.size[2]; ++i2)
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{
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reIndices[2] = i2;
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for (int i3 = 0; i3 < re.size[3]; ++i3)
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{
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reIndices[3] = i3;
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if (matType == CV_Bool)
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EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input1, reIndices.data()) | getValueAt(inputConst, reIndices.data()));
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else
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EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input1, reIndices.data()) + getValueAt(inputConst, reIndices.data()));
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}
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}
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}
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}
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}
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INSTANTIATE_TEST_CASE_P(/**/, Test_Const_Int, Combine(
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testing::Values(CV_Bool, CV_8U, CV_8S, CV_32S, CV_64S),
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dnnBackendsAndTargets()
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));
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typedef testing::TestWithParam<tuple<int, int, tuple<Backend, Target> > > Test_ScatterND_Int;
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TEST_P(Test_ScatterND_Int, random)
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{
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int matType = get<0>(GetParam());
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int indicesType = get<1>(GetParam());
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tuple<Backend, Target> backend_target= get<2>(GetParam());
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Backend backend = get<0>(backend_target);
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Target target = get<1>(backend_target);
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std::vector<int> inShape{2, 3, 4, 5};
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Mat input(inShape, matType);
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fillRandom(input, matType, backend);
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std::vector<int64_t> indicesValues{0, 1, 2, 3,
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1, 2, 3, 4};
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std::vector<int64_t> updatesValues{25, 35};
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if (matType == CV_Bool)
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{
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updatesValues[0] = 1;
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updatesValues[1] = 0;
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}
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Mat indices(2, 4, indicesType);
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std::vector<int> updatesShape{2};
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Mat updates(updatesShape, matType);
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for (int i = 0; i < indicesValues.size(); ++i)
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{
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if (indicesType == CV_32S)
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indices.ptr<int32_t>()[i] = indicesValues[i];
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else
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indices.ptr<int64_t>()[i] = indicesValues[i];
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}
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for (int i = 0; i < updatesValues.size(); ++i)
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{
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if (matType == CV_32S)
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updates.ptr<int32_t>()[i] = updatesValues[i];
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else if (matType == CV_64S)
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updates.ptr<int64_t>()[i] = updatesValues[i];
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else if (matType == CV_8S)
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updates.ptr<int8_t>()[i] = updatesValues[i];
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else if (matType == CV_8U)
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updates.ptr<uint8_t>()[i] = updatesValues[i];
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else if (matType == CV_Bool)
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updates.ptr<bool>()[i] = updatesValues[i];
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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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int id = net.addLayerToPrev(lp.name, lp.type, lp);
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net.connect(0, 1, id, 1);
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net.connect(0, 2, id, 2);
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std::vector<String> inpNames(3);
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inpNames[0] = "scattedND_input";
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inpNames[1] = "scatterND_indices";
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inpNames[2] = "scatterND_updates";
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net.setInputsNames(inpNames);
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net.setInput(input, 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(backend);
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net.setPreferableTarget(target);
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Mat re;
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re = net.forward();
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EXPECT_EQ(re.depth(), matType);
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EXPECT_EQ(re.size.dims(), 4);
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ASSERT_EQ(shape(input), shape(re));
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std::vector<int> reIndices(4);
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for (int i0 = 0; i0 < input.size[0]; ++i0)
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{
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reIndices[0] = i0;
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for (int i1 = 0; i1 < input.size[1]; ++i1)
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{
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reIndices[1] = i1;
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for (int i2 = 0; i2 < input.size[2]; ++i2)
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{
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reIndices[2] = i2;
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for (int i3 = 0; i3 < input.size[3]; ++i3)
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{
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reIndices[3] = i3;
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if (reIndices[0] == indicesValues[0] &&
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reIndices[1] == indicesValues[1] &&
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reIndices[2] == indicesValues[2] &&
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reIndices[3] == indicesValues[3])
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{
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EXPECT_EQ(getValueAt(re, reIndices.data()), updatesValues[0]);
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}
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else if (reIndices[0] == indicesValues[4] &&
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reIndices[1] == indicesValues[5] &&
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reIndices[2] == indicesValues[6] &&
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reIndices[3] == indicesValues[7])
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{
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EXPECT_EQ(getValueAt(re, reIndices.data()), updatesValues[1]);
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}
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else
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{
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EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input, reIndices.data()));
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}
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}
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}
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}
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}
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}
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INSTANTIATE_TEST_CASE_P(/**/, Test_ScatterND_Int, Combine(
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testing::Values(CV_Bool, CV_8U, CV_8S, CV_32S, CV_64S),
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testing::Values(CV_32S, CV_64S),
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dnnBackendsAndTargets()
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));
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typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Concat_Int;
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TEST_P(Test_Concat_Int, random)
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{
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int matType = get<0>(GetParam());
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tuple<Backend, Target> backend_target= get<1>(GetParam());
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Backend backend = get<0>(backend_target);
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Target target = get<1>(backend_target);
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std::vector<int> inShape1{2, 3, 4, 5};
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Mat input1(inShape1, matType);
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fillRandom(input1, matType, backend);
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std::vector<int> inShape2{2, 2, 4, 5};
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Mat input2(inShape2, matType);
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fillRandom(input2, matType, backend);
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Net net;
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LayerParams lp;
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lp.type = "Concat";
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lp.name = "testLayer";
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lp.set<int>("axis", 1);
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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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vector<String> inpNames(2);
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inpNames[0] = "input1";
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inpNames[1] = "input2";
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net.setInputsNames(inpNames);
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net.setInput(input1, inpNames[0]);
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net.setInput(input2, inpNames[1]);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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Mat re;
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re = net.forward();
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EXPECT_EQ(re.depth(), matType);
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EXPECT_EQ(re.size.dims(), 4);
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EXPECT_EQ(re.size[0], input1.size[0]);
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EXPECT_EQ(re.size[1], input1.size[1] + input2.size[1]);
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EXPECT_EQ(re.size[2], input1.size[2]);
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EXPECT_EQ(re.size[3], input1.size[3]);
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std::vector<int> inIndices(4);
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std::vector<int> reIndices(4);
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for (int i0 = 0; i0 < re.size[0]; ++i0)
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{
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reIndices[0] = i0;
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inIndices[0] = i0;
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for (int i1 = 0; i1 < re.size[1]; ++i1)
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{
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reIndices[1] = i1;
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if (i1 < input1.size[1])
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inIndices[1] = i1;
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else
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inIndices[1] = i1 - input1.size[1];
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for (int i2 = 0; i2 < re.size[2]; ++i2)
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{
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reIndices[2] = i2;
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inIndices[2] = i2;
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for (int i3 = 0; i3 < re.size[3]; ++i3)
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{
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reIndices[3] = i3;
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inIndices[3] = i3;
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if (i1 < input1.size[1])
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{
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EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input1, inIndices.data()));
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}
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else
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{
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EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input2, inIndices.data()));
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}
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}
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}
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}
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}
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}
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INSTANTIATE_TEST_CASE_P(/**/, Test_Concat_Int, Combine(
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testing::Values(CV_Bool, CV_8U, CV_8S, CV_32S, CV_64S),
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dnnBackendsAndTargets()
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));
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typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_ArgMax_Int;
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TEST_P(Test_ArgMax_Int, random)
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{
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int matType = get<0>(GetParam());
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tuple<Backend, Target> backend_target= get<1>(GetParam());
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Backend backend = get<0>(backend_target);
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Target target = get<1>(backend_target);
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // There is a problem with OpenVINO and custom int64 layers. After model compilation the output tensor type changes from int64 to int32
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std::vector<int> inShape{5, 4, 3, 2};
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Mat input(inShape, matType);
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fillRandom(input, matType, backend);
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Net net;
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LayerParams lp;
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lp.type = "Arg";
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lp.name = "testLayer";
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lp.set("op", "max");
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lp.set<int>("keepdims", 0);
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lp.set<int>("axis", 1);
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net.addLayerToPrev(lp.name, lp.type, lp);
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net.setInput(input);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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Mat re;
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re = net.forward();
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EXPECT_EQ(re.depth(), CV_64S);
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EXPECT_EQ(re.size.dims(), 3);
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EXPECT_EQ(re.size[0], inShape[0]);
|
|
EXPECT_EQ(re.size[1], inShape[2]);
|
|
EXPECT_EQ(re.size[2], inShape[3]);
|
|
|
|
std::vector<int> inIndices(4);
|
|
std::vector<int> reIndices(3);
|
|
|
|
for (int i0 = 0; i0 < re.size[0]; ++i0)
|
|
{
|
|
inIndices[0] = i0;
|
|
reIndices[0] = i0;
|
|
for (int i1 = 0; i1 < re.size[1]; ++i1)
|
|
{
|
|
inIndices[2] = i1;
|
|
reIndices[1] = i1;
|
|
for (int i2 = 0; i2 < re.size[2]; ++i2)
|
|
{
|
|
inIndices[3] = i2;
|
|
reIndices[2] = i2;
|
|
|
|
int64_t max_value = -1000000000000000000l;
|
|
int64_t index = 0;
|
|
for (int j = 0; j < input.size[1]; ++j)
|
|
{
|
|
inIndices[1] = j;
|
|
int64_t cur_value = getValueAt(input, inIndices.data());
|
|
if (cur_value > max_value)
|
|
{
|
|
max_value = cur_value;
|
|
index = j;
|
|
}
|
|
}
|
|
EXPECT_EQ(getValueAt(re, reIndices.data()), index);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_ArgMax_Int, Combine(
|
|
testing::Values(CV_8U, CV_8S, CV_32S, CV_64S),
|
|
dnnBackendsAndTargets()
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Blank_Int;
|
|
TEST_P(Test_Blank_Int, random)
|
|
{
|
|
int matType = get<0>(GetParam());
|
|
tuple<Backend, Target> backend_target= get<1>(GetParam());
|
|
Backend backend = get<0>(backend_target);
|
|
Target target = get<1>(backend_target);
|
|
|
|
std::vector<int> inShape{2, 3, 4, 5};
|
|
Mat input(inShape, matType);
|
|
fillRandom(input, matType, backend);
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "Identity";
|
|
lp.name = "testLayer";
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
net.setInput(input);
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
Mat re;
|
|
re = net.forward();
|
|
EXPECT_EQ(re.depth(), matType);
|
|
EXPECT_EQ(re.size.dims(), 4);
|
|
EXPECT_EQ(re.size[0], 2);
|
|
EXPECT_EQ(re.size[1], 3);
|
|
EXPECT_EQ(re.size[2], 4);
|
|
EXPECT_EQ(re.size[3], 5);
|
|
|
|
std::vector<int> reIndices(4);
|
|
for (int i0 = 0; i0 < re.size[0]; ++i0)
|
|
{
|
|
reIndices[0] = i0;
|
|
for (int i1 = 0; i1 < re.size[1]; ++i1)
|
|
{
|
|
reIndices[1] = i1;
|
|
for (int i2 = 0; i2 < re.size[2]; ++i2)
|
|
{
|
|
reIndices[2] = i2;
|
|
for (int i3 = 0; i3 < re.size[3]; ++i3)
|
|
{
|
|
reIndices[3] = i3;
|
|
EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input, reIndices.data()));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Blank_Int, Combine(
|
|
testing::Values(CV_Bool, CV_8U, CV_8S, CV_32S, CV_64S),
|
|
dnnBackendsAndTargets()
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Expand_Int;
|
|
TEST_P(Test_Expand_Int, random)
|
|
{
|
|
int matType = get<0>(GetParam());
|
|
tuple<Backend, Target> backend_target= get<1>(GetParam());
|
|
Backend backend = get<0>(backend_target);
|
|
Target target = get<1>(backend_target);
|
|
|
|
std::vector<int> inShape{2, 3, 1, 5};
|
|
Mat input(inShape, matType);
|
|
fillRandom(input, matType, backend);
|
|
std::vector<int> outShape{2, 1, 4, 5};
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "Expand";
|
|
lp.name = "testLayer";
|
|
lp.set("shape", DictValue::arrayInt<int*>(&outShape[0], outShape.size()));
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
net.setInput(input);
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
Mat re;
|
|
re = net.forward();
|
|
EXPECT_EQ(re.depth(), matType);
|
|
EXPECT_EQ(re.size.dims(), 4);
|
|
EXPECT_EQ(re.size[0], 2);
|
|
EXPECT_EQ(re.size[1], 3);
|
|
EXPECT_EQ(re.size[2], 4);
|
|
EXPECT_EQ(re.size[3], 5);
|
|
|
|
std::vector<int> inIndices(4);
|
|
std::vector<int> reIndices(4);
|
|
for (int i0 = 0; i0 < re.size[0]; ++i0)
|
|
{
|
|
inIndices[0] = i0 % inShape[0];
|
|
reIndices[0] = i0;
|
|
for (int i1 = 0; i1 < re.size[1]; ++i1)
|
|
{
|
|
inIndices[1] = i1 % inShape[1];
|
|
reIndices[1] = i1;
|
|
for (int i2 = 0; i2 < re.size[2]; ++i2)
|
|
{
|
|
inIndices[2] = i2 % inShape[2];
|
|
reIndices[2] = i2;
|
|
for (int i3 = 0; i3 < re.size[3]; ++i3)
|
|
{
|
|
inIndices[3] = i3 % inShape[3];
|
|
reIndices[3] = i3;
|
|
EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input, inIndices.data()));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Expand_Int, Combine(
|
|
testing::Values(CV_Bool, CV_8U, CV_8S, CV_32S, CV_64S),
|
|
dnnBackendsAndTargets()
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Permute_Int;
|
|
TEST_P(Test_Permute_Int, random)
|
|
{
|
|
int matType = get<0>(GetParam());
|
|
tuple<Backend, Target> backend_target= get<1>(GetParam());
|
|
Backend backend = get<0>(backend_target);
|
|
Target target = get<1>(backend_target);
|
|
|
|
std::vector<int> inShape{2, 3, 4, 5};
|
|
Mat input(inShape, matType);
|
|
fillRandom(input, matType, backend);
|
|
std::vector<int> order{0, 2, 3, 1};
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "Permute";
|
|
lp.name = "testLayer";
|
|
lp.set("order", DictValue::arrayInt<int*>(&order[0], order.size()));
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
net.setInput(input);
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
Mat re;
|
|
re = net.forward();
|
|
EXPECT_EQ(re.depth(), matType);
|
|
EXPECT_EQ(re.size.dims(), 4);
|
|
EXPECT_EQ(re.size[0], 2);
|
|
EXPECT_EQ(re.size[1], 4);
|
|
EXPECT_EQ(re.size[2], 5);
|
|
EXPECT_EQ(re.size[3], 3);
|
|
|
|
std::vector<int> inIndices(4);
|
|
std::vector<int> reIndices(4);
|
|
for (int i0 = 0; i0 < input.size[0]; ++i0)
|
|
{
|
|
inIndices[0] = i0;
|
|
reIndices[0] = i0;
|
|
for (int i1 = 0; i1 < input.size[1]; ++i1)
|
|
{
|
|
inIndices[1] = i1;
|
|
reIndices[3] = i1;
|
|
for (int i2 = 0; i2 < input.size[2]; ++i2)
|
|
{
|
|
inIndices[2] = i2;
|
|
reIndices[1] = i2;
|
|
for (int i3 = 0; i3 < input.size[3]; ++i3)
|
|
{
|
|
inIndices[3] = i3;
|
|
reIndices[2] = i3;
|
|
EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input, inIndices.data()));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Permute_Int, Combine(
|
|
testing::Values(CV_Bool, CV_8U, CV_8S, CV_32S, CV_64S),
|
|
dnnBackendsAndTargets()
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<int, int, tuple<Backend, Target> > > Test_GatherElements_Int;
|
|
TEST_P(Test_GatherElements_Int, random)
|
|
{
|
|
int matType = get<0>(GetParam());
|
|
int indicesType = get<1>(GetParam());
|
|
tuple<Backend, Target> backend_target= get<2>(GetParam());
|
|
Backend backend = get<0>(backend_target);
|
|
Target target = get<1>(backend_target);
|
|
|
|
std::vector<int> inShape{2, 3, 4, 5};
|
|
Mat input(inShape, matType);
|
|
fillRandom(input, matType, backend);
|
|
|
|
std::vector<int> indicesShape{2, 3, 10, 5};
|
|
Mat indicesMat(indicesShape, indicesType);
|
|
cv::randu(indicesMat, 0, 4);
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "GatherElements";
|
|
lp.name = "testLayer";
|
|
lp.set("axis", 2);
|
|
int id = net.addLayerToPrev(lp.name, lp.type, lp);
|
|
net.connect(0, 1, id, 1);
|
|
|
|
std::vector<String> inpNames(2);
|
|
inpNames[0] = "gather_input";
|
|
inpNames[1] = "gather_indices";
|
|
net.setInputsNames(inpNames);
|
|
net.setInput(input, inpNames[0]);
|
|
net.setInput(indicesMat, inpNames[1]);
|
|
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
Mat re;
|
|
re = net.forward();
|
|
EXPECT_EQ(re.depth(), matType);
|
|
EXPECT_EQ(re.size.dims(), 4);
|
|
ASSERT_EQ(shape(indicesMat), shape(re));
|
|
|
|
std::vector<int> inIndices(4);
|
|
std::vector<int> reIndices(4);
|
|
for (int i0 = 0; i0 < input.size[0]; ++i0)
|
|
{
|
|
inIndices[0] = i0;
|
|
reIndices[0] = i0;
|
|
for (int i1 = 0; i1 < input.size[1]; ++i1)
|
|
{
|
|
inIndices[1] = i1;
|
|
reIndices[1] = i1;
|
|
for (int i2 = 0; i2 < indicesMat.size[2]; ++i2)
|
|
{
|
|
reIndices[2] = i2;
|
|
for (int i3 = 0; i3 < input.size[3]; ++i3)
|
|
{
|
|
inIndices[3] = i3;
|
|
reIndices[3] = i3;
|
|
inIndices[2] = getValueAt(indicesMat, reIndices.data());
|
|
EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input, inIndices.data()));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_GatherElements_Int, Combine(
|
|
testing::Values(CV_Bool, CV_8U, CV_8S, CV_32S, CV_64S),
|
|
testing::Values(CV_32S, CV_64S),
|
|
dnnBackendsAndTargets()
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<int, int, tuple<Backend, Target> > > Test_Gather_Int;
|
|
TEST_P(Test_Gather_Int, random)
|
|
{
|
|
int matType = get<0>(GetParam());
|
|
int indicesType = get<1>(GetParam());
|
|
tuple<Backend, Target> backend_target= get<2>(GetParam());
|
|
Backend backend = get<0>(backend_target);
|
|
Target target = get<1>(backend_target);
|
|
|
|
std::vector<int> inShape{5, 1};
|
|
Mat input(inShape, matType);
|
|
fillRandom(input, matType, backend);
|
|
|
|
std::vector<int> indices_shape = {1, 1};
|
|
Mat indicesMat = cv::Mat(indices_shape, indicesType, 0.0);
|
|
|
|
std::vector<int> output_shape = {5, 1};
|
|
cv::Mat outputRef = cv::Mat(output_shape, matType, input(cv::Range::all(), cv::Range(0, 1)).data);
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "Gather";
|
|
lp.name = "testLayer";
|
|
lp.set("axis", 1);
|
|
lp.set("real_ndims", 1);
|
|
int id = net.addLayerToPrev(lp.name, lp.type, lp);
|
|
net.connect(0, 1, id, 1);
|
|
|
|
std::vector<String> inpNames(2);
|
|
inpNames[0] = "gather_input";
|
|
inpNames[1] = "gather_indices";
|
|
net.setInputsNames(inpNames);
|
|
net.setInput(input, inpNames[0]);
|
|
net.setInput(indicesMat, inpNames[1]);
|
|
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
Mat re;
|
|
re = net.forward();
|
|
EXPECT_EQ(re.depth(), matType);
|
|
|
|
ASSERT_EQ(shape(outputRef), shape(re));
|
|
normAssert(outputRef, re);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Gather_Int, Combine(
|
|
testing::Values(CV_Bool, CV_8U, CV_8S, CV_32S, CV_64S),
|
|
testing::Values(CV_32S, CV_64S),
|
|
dnnBackendsAndTargets()
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<int, int, tuple<Backend, Target> > > Test_Cast_Int;
|
|
TEST_P(Test_Cast_Int, random)
|
|
{
|
|
int inMatType = get<0>(GetParam());
|
|
int outMatType = get<1>(GetParam());
|
|
tuple<Backend, Target> backend_target= get<2>(GetParam());
|
|
Backend backend = get<0>(backend_target);
|
|
Target target = get<1>(backend_target);
|
|
|
|
std::vector<int> inShape{2, 3, 4, 5};
|
|
Mat input(inShape, inMatType);
|
|
if (inMatType == CV_Bool || outMatType == CV_Bool)
|
|
cv::randu(input, 0, 1.1);
|
|
else
|
|
cv::randu(input, 0, 100);
|
|
Mat outputRef;
|
|
input.convertTo(outputRef, outMatType);
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "Cast";
|
|
lp.name = "testLayer";
|
|
lp.set("outputType", outMatType);
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
net.setInput(input);
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
Mat re;
|
|
re = net.forward();
|
|
EXPECT_EQ(re.depth(), outMatType);
|
|
EXPECT_EQ(re.size.dims(), 4);
|
|
|
|
ASSERT_EQ(shape(input), shape(re));
|
|
normAssert(outputRef, re);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Cast_Int, Combine(
|
|
testing::Values(CV_Bool, CV_8U, CV_8S, CV_32S, CV_64S),
|
|
testing::Values(CV_Bool, CV_8U, CV_8S, CV_32S, CV_64S),
|
|
dnnBackendsAndTargets()
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Pad_Int;
|
|
TEST_P(Test_Pad_Int, random)
|
|
{
|
|
int matType = get<0>(GetParam());
|
|
tuple<Backend, Target> backend_target= get<1>(GetParam());
|
|
Backend backend = get<0>(backend_target);
|
|
Target target = get<1>(backend_target);
|
|
|
|
std::vector<int> inShape{2, 3, 4, 5};
|
|
Mat input(inShape, matType);
|
|
fillRandom(input, matType, backend);
|
|
std::vector<int> paddings{0, 0, 0, 0, 1, 0, 0, 1};
|
|
int64_t padValue = matType == CV_Bool ? 1 : 25;
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "Padding";
|
|
lp.name = "testLayer";
|
|
lp.set("paddings", DictValue::arrayInt<int*>(&paddings[0], paddings.size()));
|
|
lp.set<double>("value", padValue);
|
|
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
net.setInput(input);
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
Mat re;
|
|
re = net.forward();
|
|
EXPECT_EQ(re.depth(), matType);
|
|
EXPECT_EQ(re.size.dims(), 4);
|
|
EXPECT_EQ(re.size[0], 2);
|
|
EXPECT_EQ(re.size[1], 3);
|
|
EXPECT_EQ(re.size[2], 5);
|
|
EXPECT_EQ(re.size[3], 6);
|
|
|
|
std::vector<int> reIndices(4);
|
|
std::vector<int> inIndices(4);
|
|
for (int i0 = 0; i0 < re.size[0]; ++i0)
|
|
{
|
|
reIndices[0] = i0;
|
|
inIndices[0] = i0;
|
|
for (int i1 = 0; i1 < re.size[1]; ++i1)
|
|
{
|
|
reIndices[1] = i1;
|
|
inIndices[1] = i1;
|
|
for (int i2 = 0; i2 < re.size[2]; ++i2)
|
|
{
|
|
reIndices[2] = i2;
|
|
inIndices[2] = i2 - 1;
|
|
for (int i3 = 0; i3 < re.size[3]; ++i3)
|
|
{
|
|
reIndices[3] = i3;
|
|
inIndices[3] = i3;
|
|
if (i2 < 1 || i3 >= input.size[3])
|
|
{
|
|
EXPECT_EQ(getValueAt(re, reIndices.data()), padValue);
|
|
}
|
|
else
|
|
{
|
|
EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input, inIndices.data()));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Pad_Int, Combine(
|
|
testing::Values(CV_Bool, CV_8U, CV_8S, CV_32S, CV_64S),
|
|
dnnBackendsAndTargets()
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Slice_Int;
|
|
TEST_P(Test_Slice_Int, random)
|
|
{
|
|
int matType = get<0>(GetParam());
|
|
tuple<Backend, Target> backend_target= get<1>(GetParam());
|
|
Backend backend = get<0>(backend_target);
|
|
Target target = get<1>(backend_target);
|
|
|
|
std::vector<int> inputShape{1, 16, 6, 8};
|
|
std::vector<int> begin{0, 4, 0, 0};
|
|
std::vector<int> end{1, 8, 6, 8};
|
|
Mat input(inputShape, matType);
|
|
fillRandom(input, matType, backend);
|
|
|
|
std::vector<Range> range(4);
|
|
for (int i = 0; i < 4; ++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]), 4));
|
|
lp.set("end", DictValue::arrayInt<int*>(&(end[0]), 4));
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
net.setInput(input);
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
Mat out = net.forward();
|
|
|
|
Mat gt = input(range);
|
|
EXPECT_EQ(out.size.dims(), 4);
|
|
EXPECT_EQ(out.size[0], gt.size[0]);
|
|
EXPECT_EQ(out.size[1], gt.size[1]);
|
|
EXPECT_EQ(out.size[2], gt.size[2]);
|
|
EXPECT_EQ(out.size[3], gt.size[3]);
|
|
for (int i = 0; i < out.total(); ++i)
|
|
EXPECT_EQ(getValueAt(out, i), getValueAt(gt, i));
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Slice_Int, Combine(
|
|
testing::Values(CV_Bool, CV_8U, CV_8S, CV_32S, CV_64S),
|
|
dnnBackendsAndTargets()
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Reshape_Int;
|
|
TEST_P(Test_Reshape_Int, random)
|
|
{
|
|
int matType = get<0>(GetParam());
|
|
tuple<Backend, Target> backend_target= get<1>(GetParam());
|
|
Backend backend = get<0>(backend_target);
|
|
Target target = get<1>(backend_target);
|
|
|
|
std::vector<int> inShape{2, 3, 4, 5};
|
|
std::vector<int> outShape{2, 3, 2, 10};
|
|
Mat input(inShape, matType);
|
|
fillRandom(input, matType, backend);
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "Reshape";
|
|
lp.name = "testLayer";
|
|
lp.set("dim", DictValue::arrayInt<int*>(&outShape[0], outShape.size()));
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
net.setInput(input);
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
Mat re;
|
|
re = net.forward();
|
|
EXPECT_EQ(re.depth(), matType);
|
|
EXPECT_EQ(re.size.dims(), 4);
|
|
EXPECT_EQ(re.size[0], outShape[0]);
|
|
EXPECT_EQ(re.size[1], outShape[1]);
|
|
EXPECT_EQ(re.size[2], outShape[2]);
|
|
EXPECT_EQ(re.size[3], outShape[3]);
|
|
|
|
for (int i = 0; i < input.total(); ++i)
|
|
EXPECT_EQ(getValueAt(re, i), getValueAt(input, i));
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Reshape_Int, Combine(
|
|
testing::Values(CV_Bool, CV_8U, CV_8S, CV_32S, CV_64S),
|
|
dnnBackendsAndTargets()
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Flatten_Int;
|
|
TEST_P(Test_Flatten_Int, random)
|
|
{
|
|
int matType = get<0>(GetParam());
|
|
tuple<Backend, Target> backend_target= get<1>(GetParam());
|
|
Backend backend = get<0>(backend_target);
|
|
Target target = get<1>(backend_target);
|
|
|
|
std::vector<int> inShape{2, 3, 4, 5};
|
|
Mat input(inShape, matType);
|
|
fillRandom(input, matType, backend);
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "Flatten";
|
|
lp.name = "testLayer";
|
|
lp.set("axis", 1);
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
net.setInput(input);
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
Mat re;
|
|
re = net.forward();
|
|
EXPECT_EQ(re.depth(), matType);
|
|
EXPECT_EQ(re.size.dims(), 2);
|
|
EXPECT_EQ(re.size[0], inShape[0]);
|
|
EXPECT_EQ(re.size[1], inShape[1] * inShape[2] * inShape[3]);
|
|
|
|
for (int i = 0; i < input.total(); ++i)
|
|
EXPECT_EQ(getValueAt(re, i), getValueAt(input, i));
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Flatten_Int, Combine(
|
|
testing::Values(CV_Bool, CV_8U, CV_8S, CV_32S, CV_64S),
|
|
dnnBackendsAndTargets()
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Tile_Int;
|
|
TEST_P(Test_Tile_Int, random)
|
|
{
|
|
int matType = get<0>(GetParam());
|
|
tuple<Backend, Target> backend_target= get<1>(GetParam());
|
|
Backend backend = get<0>(backend_target);
|
|
Target target = get<1>(backend_target);
|
|
|
|
std::vector<int> inShape{2, 3, 4, 5};
|
|
Mat input(inShape, matType);
|
|
fillRandom(input, matType, backend);
|
|
std::vector<int> repeats{1, 1, 2, 3};
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "Tile";
|
|
lp.name = "testLayer";
|
|
lp.set("repeats", DictValue::arrayInt<int*>(repeats.data(), repeats.size()));
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
net.setInput(input);
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
Mat re;
|
|
re = net.forward();
|
|
EXPECT_EQ(re.depth(), matType);
|
|
EXPECT_EQ(re.size.dims(), 4);
|
|
EXPECT_EQ(re.size[0], inShape[0] * repeats[0]);
|
|
EXPECT_EQ(re.size[1], inShape[1] * repeats[1]);
|
|
EXPECT_EQ(re.size[2], inShape[2] * repeats[2]);
|
|
EXPECT_EQ(re.size[3], inShape[3] * repeats[3]);
|
|
|
|
std::vector<int> inIndices(4);
|
|
std::vector<int> reIndices(4);
|
|
for (int i0 = 0; i0 < re.size[0]; ++i0)
|
|
{
|
|
inIndices[0] = i0 % inShape[0];
|
|
reIndices[0] = i0;
|
|
for (int i1 = 0; i1 < re.size[1]; ++i1)
|
|
{
|
|
inIndices[1] = i1 % inShape[1];
|
|
reIndices[1] = i1;
|
|
for (int i2 = 0; i2 < re.size[2]; ++i2)
|
|
{
|
|
inIndices[2] = i2 % inShape[2];
|
|
reIndices[2] = i2;
|
|
for (int i3 = 0; i3 < re.size[3]; ++i3)
|
|
{
|
|
inIndices[3] = i3 % inShape[3];
|
|
reIndices[3] = i3;
|
|
EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input, inIndices.data()));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Tile_Int, Combine(
|
|
testing::Values(CV_Bool, CV_8U, CV_8S, CV_32S, CV_64S),
|
|
dnnBackendsAndTargets()
|
|
));
|
|
|
|
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Reduce_Int;
|
|
TEST_P(Test_Reduce_Int, random)
|
|
{
|
|
int matType = get<0>(GetParam());
|
|
tuple<Backend, Target> backend_target= get<1>(GetParam());
|
|
Backend backend = get<0>(backend_target);
|
|
Target target = get<1>(backend_target);
|
|
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && matType == CV_64S)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // There is a problem with OpenVINO and custom int64 layers. After model compilation the output tensor type changes from int64 to int32
|
|
|
|
std::vector<int> inShape{5, 4, 3, 2};
|
|
Mat input(inShape, matType);
|
|
if (matType == CV_64S && backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
cv::randu(input, 100000000, 100000100); // Looks like OpenVINO uses int32 internal values for int64 operations
|
|
else if (matType == CV_64S)
|
|
cv::randu(input, 1000000000000000ll, 1000000000000100ll);
|
|
else if (matType == CV_32S)
|
|
cv::randu(input, 100000000, 100000100);
|
|
else if (matType == CV_8S)
|
|
cv::randu(input, -25, 25);
|
|
else if (matType == CV_8U)
|
|
cv::randu(input, 0, 50);
|
|
else
|
|
CV_Error(Error::BadDepth, "Unsupported type");
|
|
|
|
std::vector<int> axes{1};
|
|
Net net;
|
|
|
|
LayerParams lp;
|
|
lp.type = "Reduce";
|
|
lp.name = "testLayer";
|
|
lp.set("reduce", "SUM");
|
|
lp.set("keepdims", false);
|
|
lp.set("axes", DictValue::arrayInt<int*>(axes.data(), axes.size()));
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
net.setInput(input);
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
Mat re;
|
|
re = net.forward();
|
|
EXPECT_EQ(re.depth(), matType);
|
|
EXPECT_EQ(re.size.dims(), 3);
|
|
EXPECT_EQ(re.size[0], inShape[0]);
|
|
EXPECT_EQ(re.size[1], inShape[2]);
|
|
EXPECT_EQ(re.size[2], inShape[3]);
|
|
|
|
std::vector<int> inIndices(4);
|
|
std::vector<int> reIndices(3);
|
|
|
|
for (int i0 = 0; i0 < re.size[0]; ++i0)
|
|
{
|
|
inIndices[0] = i0;
|
|
reIndices[0] = i0;
|
|
for (int i1 = 0; i1 < re.size[1]; ++i1)
|
|
{
|
|
inIndices[2] = i1;
|
|
reIndices[1] = i1;
|
|
for (int i2 = 0; i2 < re.size[2]; ++i2)
|
|
{
|
|
inIndices[3] = i2;
|
|
reIndices[2] = i2;
|
|
|
|
int64_t value = 0;
|
|
for (int j = 0; j < input.size[1]; ++j)
|
|
{
|
|
inIndices[1] = j;
|
|
value += getValueAt(input, inIndices.data());
|
|
}
|
|
EXPECT_EQ(getValueAt(re, reIndices.data()), value);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Reduce_Int;
|
|
TEST_P(Test_Reduce_Int, two_axes)
|
|
{
|
|
int matType = get<0>(GetParam());
|
|
tuple<Backend, Target> backend_target= get<1>(GetParam());
|
|
Backend backend = get<0>(backend_target);
|
|
Target target = get<1>(backend_target);
|
|
|
|
if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && matType == CV_64S)
|
|
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); // There is a problem with OpenVINO and custom int64 layers. After model compilation the output tensor type changes from int64 to int32
|
|
|
|
std::vector<int> inShape{5, 4, 3, 2};
|
|
Mat input(inShape, matType);
|
|
if (matType == CV_64S && backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
|
|
cv::randu(input, 100000000, 100000100); // Looks like OpenVINO uses int32 internal values for int64 operations
|
|
else if (matType == CV_64S)
|
|
cv::randu(input, 1000000000000000ll, 1000000000000100ll);
|
|
else if (matType == CV_32S)
|
|
cv::randu(input, 100000000, 100000100);
|
|
else if (matType == CV_8S)
|
|
cv::randu(input, -15, 15);
|
|
else if (matType == CV_8U)
|
|
cv::randu(input, 0, 30);
|
|
else
|
|
CV_Error(Error::BadDepth, "Unsupported type");
|
|
|
|
std::vector<int> axes{1, 3};
|
|
|
|
Net net;
|
|
LayerParams lp;
|
|
lp.type = "Reduce";
|
|
lp.name = "testLayer";
|
|
lp.set("reduce", "SUM");
|
|
lp.set("keepdims", false);
|
|
lp.set("axes", DictValue::arrayInt<int*>(axes.data(), axes.size()));
|
|
net.addLayerToPrev(lp.name, lp.type, lp);
|
|
|
|
net.setInput(input);
|
|
net.setPreferableBackend(backend);
|
|
net.setPreferableTarget(target);
|
|
|
|
Mat re;
|
|
re = net.forward();
|
|
EXPECT_EQ(re.depth(), matType);
|
|
EXPECT_EQ(re.size.dims(), 2);
|
|
EXPECT_EQ(re.size[0], inShape[0]);
|
|
EXPECT_EQ(re.size[1], inShape[2]);
|
|
|
|
std::vector<int> inIndices(4);
|
|
std::vector<int> reIndices(2);
|
|
|
|
for (int i0 = 0; i0 < re.size[0]; ++i0)
|
|
{
|
|
inIndices[0] = i0;
|
|
reIndices[0] = i0;
|
|
for (int i1 = 0; i1 < re.size[1]; ++i1)
|
|
{
|
|
inIndices[2] = i1;
|
|
reIndices[1] = i1;
|
|
int64_t value = 0;
|
|
for (int i2 = 0; i2 < input.size[3]; ++i2)
|
|
{
|
|
inIndices[3] = i2;
|
|
|
|
for (int j = 0; j < input.size[1]; ++j)
|
|
{
|
|
inIndices[1] = j;
|
|
value += getValueAt(input, inIndices.data());
|
|
}
|
|
}
|
|
EXPECT_EQ(getValueAt(re, reIndices.data()), value);
|
|
}
|
|
}
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(/**/, Test_Reduce_Int, Combine(
|
|
testing::Values(CV_8U, CV_8S, CV_32S, CV_64S),
|
|
dnnBackendsAndTargets()
|
|
));
|
|
|
|
}} // namespace
|