opencv/modules/dnn/test/test_tflite_importer.cpp

// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.

/*
Test for TFLite models loading
*/

#include "test_precomp.hpp"
#include "npy_blob.hpp"

#include <opencv2/dnn/layer.details.hpp>  // CV_DNN_REGISTER_LAYER_CLASS
#include <opencv2/dnn/utils/debug_utils.hpp>
#include <opencv2/dnn/shape_utils.hpp>

#ifdef OPENCV_TEST_DNN_TFLITE

namespace opencv_test { namespace {

using namespace cv;
using namespace cv::dnn;

class Test_TFLite : public DNNTestLayer {
public:
    void testModel(Net& net, const std::string& modelName, const Mat& input, double l1 = 0, double lInf = 0);
    void testModel(const std::string& modelName, const Mat& input, double l1 = 0, double lInf = 0);
    void testModel(const std::string& modelName, const Size& inpSize, double l1 = 0, double lInf = 0);
    void testLayer(const std::string& modelName, double l1 = 0, double lInf = 0);
};

void testInputShapes(const Net& net, const std::vector<Mat>& inps) {
    std::vector<MatShape> inLayerShapes;
    std::vector<MatShape> outLayerShapes;
    net.getLayerShapes(MatShape(), 0, inLayerShapes, outLayerShapes);
    ASSERT_EQ(inLayerShapes.size(), inps.size());

    for (int i = 0; i < inps.size(); ++i) {
        ASSERT_EQ(inLayerShapes[i], shape(inps[i]));
    }
}

void Test_TFLite::testModel(Net& net, const std::string& modelName, const Mat& input, double l1, double lInf)
{
    l1 = l1 ? l1 : default_l1;
    lInf = lInf ? lInf : default_lInf;

    net.setPreferableBackend(backend);
    net.setPreferableTarget(target);

    testInputShapes(net, {input});
    net.setInput(input);

    std::vector<String> outNames = net.getUnconnectedOutLayersNames();

    std::vector<Mat> outs;
    net.forward(outs, outNames);

    ASSERT_EQ(outs.size(), outNames.size());
    for (int i = 0; i < outNames.size(); ++i) {
        Mat ref = blobFromNPY(findDataFile(format("dnn/tflite/%s_out_%s.npy", modelName.c_str(), outNames[i].c_str())));
        normAssert(ref.reshape(1, 1), outs[i].reshape(1, 1), outNames[i].c_str(), l1, lInf);
    }
}

void Test_TFLite::testModel(const std::string& modelName, const Mat& input, double l1, double lInf)
{
    Net net = readNet(findDataFile("dnn/tflite/" + modelName + ".tflite", false));
    testModel(net, modelName, input, l1, lInf);
}

void Test_TFLite::testModel(const std::string& modelName, const Size& inpSize, double l1, double lInf)
{
    Mat input = imread(findDataFile("cv/shared/lena.png"));
    input = blobFromImage(input, 1.0 / 255, inpSize, 0, true);
    testModel(modelName, input, l1, lInf);
}

void Test_TFLite::testLayer(const std::string& modelName, double l1, double lInf)
{
    Mat inp = blobFromNPY(findDataFile("dnn/tflite/" + modelName + "_inp.npy"));
    Net net = readNet(findDataFile("dnn/tflite/" + modelName + ".tflite"));
    testModel(net, modelName, inp, l1, lInf);
}

// https://google.github.io/mediapipe/solutions/face_mesh
TEST_P(Test_TFLite, face_landmark)
{
    if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16)
        applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
    double l1 = 2e-5, lInf = 2e-4;
    if (target == DNN_TARGET_CPU_FP16 || target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD ||
        (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL))
    {
        l1 = 0.15;
        lInf = 0.82;
    }
    testModel("face_landmark", Size(192, 192), l1, lInf);
}

// https://google.github.io/mediapipe/solutions/face_detection
TEST_P(Test_TFLite, face_detection_short_range)
{
    double l1 = 0, lInf = 2e-4;
    if (target == DNN_TARGET_CPU_FP16 || target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD ||
        (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL))
    {
        l1 = 0.04;
        lInf = 0.8;
    }
    testModel("face_detection_short_range", Size(128, 128), l1, lInf);
}

// https://google.github.io/mediapipe/solutions/selfie_segmentation
TEST_P(Test_TFLite, selfie_segmentation)
{
    double l1 = 0, lInf = 0;
    if (target == DNN_TARGET_CPU_FP16 || target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD ||
        (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL))
    {
        l1 = 0.01;
        lInf = 0.48;
    }
    testModel("selfie_segmentation", Size(256, 256), l1, lInf);
}

TEST_P(Test_TFLite, max_unpooling)
{
    if (backend == DNN_BACKEND_CUDA)
        applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);

#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2022010000)
        if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
            applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
#endif

    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) {
        if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
        if (target == DNN_TARGET_OPENCL)      applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
        if (target == DNN_TARGET_MYRIAD)      applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
    }

    if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);

    // Due Max Unpoling is a numerically unstable operation and small difference between frameworks
    // might lead to positional difference of maximal elements in the tensor, this test checks
    // behavior of Max Unpooling layer only.
    Net net = readNet(findDataFile("dnn/tflite/hair_segmentation.tflite", false));
    net.setPreferableBackend(backend);
    net.setPreferableTarget(target);

    Mat input = imread(findDataFile("cv/shared/lena.png"));
    cvtColor(input, input, COLOR_BGR2RGBA);
    input = input.mul(Scalar(1, 1, 1, 0));
    input = blobFromImage(input, 1.0 / 255);
    testInputShapes(net, {input});
    net.setInput(input);

    std::vector<std::vector<Mat> > outs;
    net.forward(outs, {"p_re_lu_1", "max_pooling_with_argmax2d", "conv2d_86", "max_unpooling2d_2"});

    ASSERT_EQ(outs.size(), 4);
    ASSERT_EQ(outs[0].size(), 1);
    ASSERT_EQ(outs[1].size(), 2);
    ASSERT_EQ(outs[2].size(), 1);
    ASSERT_EQ(outs[3].size(), 1);
    Mat poolInp = outs[0][0];
    Mat poolOut = outs[1][0];
    Mat poolIds = outs[1][1];
    Mat unpoolInp = outs[2][0];
    Mat unpoolOut = outs[3][0];

    ASSERT_EQ(poolInp.size, unpoolOut.size);
    ASSERT_EQ(poolOut.size, poolIds.size);
    ASSERT_EQ(poolOut.size, unpoolInp.size);

    ASSERT_EQ(countNonZero(poolInp), poolInp.total());

    for (int c = 0; c < 32; ++c) {
        float *poolInpData = poolInp.ptr<float>(0, c);
        float *poolOutData = poolOut.ptr<float>(0, c);
        float *poolIdsData = poolIds.ptr<float>(0, c);
        float *unpoolInpData = unpoolInp.ptr<float>(0, c);
        float *unpoolOutData = unpoolOut.ptr<float>(0, c);
        for (int y = 0; y < 64; ++y) {
            for (int x = 0; x < 64; ++x) {
                int maxIdx = (y * 128 + x) * 2;
                std::vector<int> indices{maxIdx + 1, maxIdx + 128, maxIdx + 129};
                std::string errMsg = format("Channel %d, y: %d, x: %d", c, y, x);
                for (int idx : indices) {
                    if (poolInpData[idx] > poolInpData[maxIdx]) {
                        EXPECT_EQ(unpoolOutData[maxIdx], 0.0f) << errMsg;
                        maxIdx = idx;
                    }
                }
                EXPECT_EQ(poolInpData[maxIdx], poolOutData[y * 64 + x]) << errMsg;
                if (backend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
                    EXPECT_EQ(poolIdsData[y * 64 + x], (float)maxIdx) << errMsg;
                }
                EXPECT_EQ(unpoolOutData[maxIdx], unpoolInpData[y * 64 + x]) << errMsg;
            }
        }
    }
}

TEST_P(Test_TFLite, EfficientDet_int8) {
    if (target != DNN_TARGET_CPU || (backend != DNN_BACKEND_OPENCV &&
        backend != DNN_BACKEND_TIMVX && backend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)) {
        throw SkipTestException("Only OpenCV, TimVX and OpenVINO targets support INT8 on CPU");
    }
    Net net = readNet(findDataFile("dnn/tflite/coco_efficientdet_lite0_v1_1.0_quant_2021_09_06.tflite", false));
    net.setPreferableBackend(backend);
    net.setPreferableTarget(target);

    Mat img = imread(findDataFile("dnn/dog416.png"));
    Mat blob = blobFromImage(img, 1.0, Size(320, 320));

    net.setInput(blob);
    Mat out = net.forward();
    Mat_<float> ref({3, 7}, {
        0, 7, 0.62890625, 0.6014542579650879, 0.13300055265426636, 0.8977657556533813, 0.292389452457428,
        0, 17, 0.56640625, 0.15983937680721283, 0.35905322432518005, 0.5155506730079651, 0.9409466981887817,
        0, 1, 0.5, 0.14357104897499084, 0.2240825891494751, 0.7183101177215576, 0.9140362739562988
    });
    normAssertDetections(ref, out, "", 0.5, 0.05, 0.1);
}

TEST_P(Test_TFLite, replicate_by_pack) {
    double l1 = 0, lInf = 0;
    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
    {
        l1 = 4e-4;
        lInf = 2e-3;
    }
    testLayer("replicate_by_pack", l1, lInf);
}

INSTANTIATE_TEST_CASE_P(/**/, Test_TFLite, dnnBackendsAndTargets());

}}  // namespace

#endif  // OPENCV_TEST_DNN_TFLITE