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242 lines
9.3 KiB
242 lines
9.3 KiB
// 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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Test for TFLite models loading |
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*/ |
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#include "test_precomp.hpp" |
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#include "npy_blob.hpp" |
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#include <opencv2/dnn/layer.details.hpp> // CV_DNN_REGISTER_LAYER_CLASS |
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#include <opencv2/dnn/utils/debug_utils.hpp> |
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#include <opencv2/dnn/shape_utils.hpp> |
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#ifdef OPENCV_TEST_DNN_TFLITE |
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namespace opencv_test { namespace { |
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using namespace cv; |
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using namespace cv::dnn; |
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class Test_TFLite : public DNNTestLayer { |
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public: |
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void testModel(Net& net, const std::string& modelName, const Mat& input, double l1 = 0, double lInf = 0); |
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void testModel(const std::string& modelName, const Mat& input, double l1 = 0, double lInf = 0); |
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void testModel(const std::string& modelName, const Size& inpSize, double l1 = 0, double lInf = 0); |
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void testLayer(const std::string& modelName, double l1 = 0, double lInf = 0); |
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}; |
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void testInputShapes(const Net& net, const std::vector<Mat>& inps) { |
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std::vector<MatShape> inLayerShapes; |
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std::vector<MatShape> outLayerShapes; |
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net.getLayerShapes(MatShape(), 0, inLayerShapes, outLayerShapes); |
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ASSERT_EQ(inLayerShapes.size(), inps.size()); |
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for (int i = 0; i < inps.size(); ++i) { |
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ASSERT_EQ(inLayerShapes[i], shape(inps[i])); |
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} |
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} |
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void Test_TFLite::testModel(Net& net, const std::string& modelName, const Mat& input, double l1, double lInf) |
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{ |
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l1 = l1 ? l1 : default_l1; |
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lInf = lInf ? lInf : default_lInf; |
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net.setPreferableBackend(backend); |
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net.setPreferableTarget(target); |
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testInputShapes(net, {input}); |
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net.setInput(input); |
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std::vector<String> outNames = net.getUnconnectedOutLayersNames(); |
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std::vector<Mat> outs; |
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net.forward(outs, outNames); |
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ASSERT_EQ(outs.size(), outNames.size()); |
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for (int i = 0; i < outNames.size(); ++i) { |
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Mat ref = blobFromNPY(findDataFile(format("dnn/tflite/%s_out_%s.npy", modelName.c_str(), outNames[i].c_str()))); |
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normAssert(ref.reshape(1, 1), outs[i].reshape(1, 1), outNames[i].c_str(), l1, lInf); |
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} |
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} |
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void Test_TFLite::testModel(const std::string& modelName, const Mat& input, double l1, double lInf) |
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{ |
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Net net = readNet(findDataFile("dnn/tflite/" + modelName + ".tflite", false)); |
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testModel(net, modelName, input, l1, lInf); |
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} |
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void Test_TFLite::testModel(const std::string& modelName, const Size& inpSize, double l1, double lInf) |
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{ |
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Mat input = imread(findDataFile("cv/shared/lena.png")); |
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input = blobFromImage(input, 1.0 / 255, inpSize, 0, true); |
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testModel(modelName, input, l1, lInf); |
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} |
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void Test_TFLite::testLayer(const std::string& modelName, double l1, double lInf) |
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{ |
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Mat inp = blobFromNPY(findDataFile("dnn/tflite/" + modelName + "_inp.npy")); |
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Net net = readNet(findDataFile("dnn/tflite/" + modelName + ".tflite")); |
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testModel(net, modelName, inp, l1, lInf); |
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} |
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// https://google.github.io/mediapipe/solutions/face_mesh |
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TEST_P(Test_TFLite, face_landmark) |
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{ |
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if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16); |
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double l1 = 2e-5, lInf = 2e-4; |
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if (target == DNN_TARGET_CPU_FP16 || target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || |
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(backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)) |
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{ |
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l1 = 0.15; |
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lInf = 0.82; |
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} |
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testModel("face_landmark", Size(192, 192), l1, lInf); |
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} |
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// https://google.github.io/mediapipe/solutions/face_detection |
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TEST_P(Test_TFLite, face_detection_short_range) |
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{ |
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double l1 = 0, lInf = 2e-4; |
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if (target == DNN_TARGET_CPU_FP16 || target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || |
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(backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)) |
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{ |
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l1 = 0.04; |
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lInf = 0.8; |
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} |
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testModel("face_detection_short_range", Size(128, 128), l1, lInf); |
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} |
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// https://google.github.io/mediapipe/solutions/selfie_segmentation |
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TEST_P(Test_TFLite, selfie_segmentation) |
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{ |
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double l1 = 0, lInf = 0; |
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if (target == DNN_TARGET_CPU_FP16 || target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || |
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(backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)) |
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{ |
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l1 = 0.01; |
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lInf = 0.48; |
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} |
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testModel("selfie_segmentation", Size(256, 256), l1, lInf); |
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} |
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TEST_P(Test_TFLite, max_unpooling) |
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{ |
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if (backend == DNN_BACKEND_CUDA) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA); |
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2022010000) |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION); |
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#endif |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) { |
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if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
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if (target == DNN_TARGET_OPENCL) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
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if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH); |
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} |
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if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) |
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applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16); |
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// Due Max Unpoling is a numerically unstable operation and small difference between frameworks |
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// might lead to positional difference of maximal elements in the tensor, this test checks |
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// behavior of Max Unpooling layer only. |
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Net net = readNet(findDataFile("dnn/tflite/hair_segmentation.tflite", false)); |
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net.setPreferableBackend(backend); |
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net.setPreferableTarget(target); |
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Mat input = imread(findDataFile("cv/shared/lena.png")); |
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cvtColor(input, input, COLOR_BGR2RGBA); |
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input = input.mul(Scalar(1, 1, 1, 0)); |
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input = blobFromImage(input, 1.0 / 255); |
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testInputShapes(net, {input}); |
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net.setInput(input); |
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std::vector<std::vector<Mat> > outs; |
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net.forward(outs, {"p_re_lu_1", "max_pooling_with_argmax2d", "conv2d_86", "max_unpooling2d_2"}); |
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ASSERT_EQ(outs.size(), 4); |
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ASSERT_EQ(outs[0].size(), 1); |
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ASSERT_EQ(outs[1].size(), 2); |
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ASSERT_EQ(outs[2].size(), 1); |
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ASSERT_EQ(outs[3].size(), 1); |
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Mat poolInp = outs[0][0]; |
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Mat poolOut = outs[1][0]; |
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Mat poolIds = outs[1][1]; |
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Mat unpoolInp = outs[2][0]; |
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Mat unpoolOut = outs[3][0]; |
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ASSERT_EQ(poolInp.size, unpoolOut.size); |
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ASSERT_EQ(poolOut.size, poolIds.size); |
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ASSERT_EQ(poolOut.size, unpoolInp.size); |
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ASSERT_EQ(countNonZero(poolInp), poolInp.total()); |
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for (int c = 0; c < 32; ++c) { |
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float *poolInpData = poolInp.ptr<float>(0, c); |
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float *poolOutData = poolOut.ptr<float>(0, c); |
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float *poolIdsData = poolIds.ptr<float>(0, c); |
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float *unpoolInpData = unpoolInp.ptr<float>(0, c); |
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float *unpoolOutData = unpoolOut.ptr<float>(0, c); |
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for (int y = 0; y < 64; ++y) { |
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for (int x = 0; x < 64; ++x) { |
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int maxIdx = (y * 128 + x) * 2; |
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std::vector<int> indices{maxIdx + 1, maxIdx + 128, maxIdx + 129}; |
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std::string errMsg = format("Channel %d, y: %d, x: %d", c, y, x); |
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for (int idx : indices) { |
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if (poolInpData[idx] > poolInpData[maxIdx]) { |
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EXPECT_EQ(unpoolOutData[maxIdx], 0.0f) << errMsg; |
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maxIdx = idx; |
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} |
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} |
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EXPECT_EQ(poolInpData[maxIdx], poolOutData[y * 64 + x]) << errMsg; |
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if (backend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) { |
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EXPECT_EQ(poolIdsData[y * 64 + x], (float)maxIdx) << errMsg; |
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} |
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EXPECT_EQ(unpoolOutData[maxIdx], unpoolInpData[y * 64 + x]) << errMsg; |
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} |
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} |
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} |
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} |
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TEST_P(Test_TFLite, EfficientDet_int8) { |
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if (target != DNN_TARGET_CPU || (backend != DNN_BACKEND_OPENCV && |
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backend != DNN_BACKEND_TIMVX && backend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)) { |
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throw SkipTestException("Only OpenCV, TimVX and OpenVINO targets support INT8 on CPU"); |
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} |
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Net net = readNet(findDataFile("dnn/tflite/coco_efficientdet_lite0_v1_1.0_quant_2021_09_06.tflite", false)); |
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net.setPreferableBackend(backend); |
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net.setPreferableTarget(target); |
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Mat img = imread(findDataFile("dnn/dog416.png")); |
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Mat blob = blobFromImage(img, 1.0, Size(320, 320)); |
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net.setInput(blob); |
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Mat out = net.forward(); |
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Mat_<float> ref({3, 7}, { |
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0, 7, 0.62890625, 0.6014542579650879, 0.13300055265426636, 0.8977657556533813, 0.292389452457428, |
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0, 17, 0.56640625, 0.15983937680721283, 0.35905322432518005, 0.5155506730079651, 0.9409466981887817, |
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0, 1, 0.5, 0.14357104897499084, 0.2240825891494751, 0.7183101177215576, 0.9140362739562988 |
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}); |
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normAssertDetections(ref, out, "", 0.5, 0.05, 0.1); |
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} |
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TEST_P(Test_TFLite, replicate_by_pack) { |
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double l1 = 0, lInf = 0; |
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL) |
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{ |
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l1 = 4e-4; |
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lInf = 2e-3; |
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} |
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testLayer("replicate_by_pack", l1, lInf); |
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} |
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INSTANTIATE_TEST_CASE_P(/**/, Test_TFLite, dnnBackendsAndTargets()); |
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}} // namespace |
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#endif // OPENCV_TEST_DNN_TFLITE
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