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https://github.com/opencv/opencv.git
synced 2026-07-31 00:03:03 +04:00
Allowed int64 constants in ONNX parser (#25148)
* Removed automatic int64 to int32 conversion in ONNX parser * Fixed wrong rebase code * added tests, minor fixes * fixed Cast layer * Fixed Cast layer for fp16 backend * Fixed Cast layer for fp16 backend * Fixed Cast layer for fp16 backend * Allowed uint32, int64, uint64 types in OpenCL * Fixed Cast layer for fp16 backend * Use randu in test_int --------- Co-authored-by: Alexander Lyulkov <alexander.lyulkov@opencv.ai>
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alexlyulkov
GitHub
@@ -6,9 +6,21 @@
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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_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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typedef testing::TestWithParam<tuple<Backend, Target> > Test_int64_sum;
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TEST_P(Test_int64_sum, basic)
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{
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@@ -53,4 +65,302 @@ INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_int64_sum,
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dnnBackendsAndTargets()
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);
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typedef testing::TestWithParam<tuple<int, tuple<Backend, Target> > > Test_Expand_Int;
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TEST_P(Test_Expand_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, 1, 5};
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int64_t low = matType == CV_64S ? 1000000000000000ll : 1000000000;
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Mat input(inShape, matType);
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cv::randu(input, low, low + 100);
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std::vector<int> outShape{2, 1, 4, 5};
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Net net;
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LayerParams lp;
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lp.type = "Expand";
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lp.name = "testLayer";
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lp.set("shape", DictValue::arrayInt<int*>(&outShape[0], outShape.size()));
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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(), matType);
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EXPECT_EQ(re.size.dims(), 4);
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EXPECT_EQ(re.size[0], 2);
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EXPECT_EQ(re.size[1], 3);
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EXPECT_EQ(re.size[2], 4);
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EXPECT_EQ(re.size[3], 5);
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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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inIndices[0] = i0 % inShape[0];
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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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inIndices[1] = i1 % inShape[1];
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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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inIndices[2] = i2 % inShape[2];
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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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inIndices[3] = i3 % inShape[3];
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reIndices[3] = i3;
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EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input, 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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INSTANTIATE_TEST_CASE_P(/**/, Test_Expand_Int, Combine(
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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_Permute_Int;
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TEST_P(Test_Permute_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_CUDA)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
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std::vector<int> inShape{2, 3, 4, 5};
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int64_t low = matType == CV_64S ? 1000000000000000ll : 1000000000;
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Mat input(inShape, matType);
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cv::randu(input, low, low + 100);
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std::vector<int> order{0, 2, 3, 1};
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Net net;
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LayerParams lp;
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lp.type = "Permute";
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lp.name = "testLayer";
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lp.set("order", DictValue::arrayInt<int*>(&order[0], order.size()));
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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(), matType);
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EXPECT_EQ(re.size.dims(), 4);
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EXPECT_EQ(re.size[0], 2);
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EXPECT_EQ(re.size[1], 4);
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EXPECT_EQ(re.size[2], 5);
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EXPECT_EQ(re.size[3], 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 < input.size[0]; ++i0)
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{
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inIndices[0] = i0;
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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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inIndices[1] = i1;
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reIndices[3] = i1;
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for (int i2 = 0; i2 < input.size[2]; ++i2)
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{
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inIndices[2] = i2;
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reIndices[1] = i2;
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for (int i3 = 0; i3 < input.size[3]; ++i3)
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{
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inIndices[3] = i3;
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reIndices[2] = i3;
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EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input, 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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INSTANTIATE_TEST_CASE_P(/**/, Test_Permute_Int, Combine(
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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, int, tuple<Backend, Target> > > Test_GatherElements_Int;
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TEST_P(Test_GatherElements_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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int64_t low = matType == CV_64S ? 1000000000000000ll : 1000000000;
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Mat input(inShape, matType);
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cv::randu(input, low, low + 100);
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std::vector<int> indicesShape{2, 3, 10, 5};
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Mat indicesMat(indicesShape, indicesType);
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cv::randu(indicesMat, 0, 4);
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Net net;
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LayerParams lp;
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lp.type = "GatherElements";
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lp.name = "testLayer";
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lp.set("axis", 2);
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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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std::vector<String> inpNames(2);
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inpNames[0] = "gather_input";
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inpNames[1] = "gather_indices";
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net.setInputsNames(inpNames);
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net.setInput(input, inpNames[0]);
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net.setInput(indicesMat, 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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ASSERT_EQ(shape(indicesMat), shape(re));
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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 < input.size[0]; ++i0)
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{
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inIndices[0] = i0;
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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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inIndices[1] = i1;
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reIndices[1] = i1;
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for (int i2 = 0; i2 < indicesMat.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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inIndices[3] = i3;
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reIndices[3] = i3;
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inIndices[2] = getValueAt(indicesMat, reIndices.data());
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EXPECT_EQ(getValueAt(re, reIndices.data()), getValueAt(input, 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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INSTANTIATE_TEST_CASE_P(/**/, Test_GatherElements_Int, Combine(
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testing::Values(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, int, tuple<Backend, Target> > > Test_Gather_Int;
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TEST_P(Test_Gather_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{5, 1};
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int64_t low = matType == CV_64S ? 1000000000000000ll : 1000000000;
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Mat input(inShape, matType);
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cv::randu(input, low, low + 100);
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std::vector<int> indices_shape = {1, 1};
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Mat indicesMat = cv::Mat(indices_shape, indicesType, 0.0);
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std::vector<int> output_shape = {5, 1};
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cv::Mat outputRef = cv::Mat(output_shape, matType, input(cv::Range::all(), cv::Range(0, 1)).data);
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Net net;
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LayerParams lp;
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lp.type = "Gather";
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lp.name = "testLayer";
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lp.set("axis", 1);
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lp.set("real_ndims", 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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std::vector<String> inpNames(2);
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inpNames[0] = "gather_input";
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inpNames[1] = "gather_indices";
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net.setInputsNames(inpNames);
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net.setInput(input, inpNames[0]);
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net.setInput(indicesMat, 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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ASSERT_EQ(shape(outputRef), shape(re));
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normAssert(outputRef, re);
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}
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INSTANTIATE_TEST_CASE_P(/**/, Test_Gather_Int, Combine(
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testing::Values(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, int, tuple<Backend, Target> > > Test_Cast_Int;
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TEST_P(Test_Cast_Int, random)
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{
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int inMatType = get<0>(GetParam());
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int outMatType = 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, inMatType);
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cv::randu(input, 200, 300);
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Mat outputRef;
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input.convertTo(outputRef, outMatType);
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Net net;
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LayerParams lp;
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lp.type = "Cast";
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lp.name = "testLayer";
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lp.set("outputType", outMatType);
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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(), outMatType);
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EXPECT_EQ(re.size.dims(), 4);
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ASSERT_EQ(shape(input), shape(re));
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normAssert(outputRef, re);
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}
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INSTANTIATE_TEST_CASE_P(/**/, Test_Cast_Int, Combine(
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testing::Values(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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}} // namespace
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