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https://github.com/opencv/opencv.git
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76350cd30f
TFLite models importer * initial commit * Refactor TFLiteImporter * Better FlatBuffers detection * Add permute before 4D->3D reshape * Track layers layout * TFLite Convolution2DTransposeBias layer * Skip TFLite tests without FlatBuffers * Fix check of FlatBuffers in tests. Add readNetFromTFLite from buffer * TFLite Max Unpooling test * Add skip for TFLite unpooling test * Revert DW convolution workaround * Fix ObjC bindings * Better errors handling * Regenerate TFLite schema using flatc * dnn(tflite): more checks, better logging * Checks for unimplemented fusion. Fix tests
124 lines
4.3 KiB
C++
124 lines
4.3 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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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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namespace opencv_test
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{
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using namespace cv;
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using namespace cv::dnn;
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void testModel(const std::string& modelName, const Mat& input, double norm = 1e-5) {
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#ifndef HAVE_FLATBUFFERS
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throw SkipTestException("FlatBuffers required for TFLite importer");
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#endif
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Net net = readNet(findDataFile("dnn/tflite/" + modelName + ".tflite", false));
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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(), norm);
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}
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}
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void testModel(const std::string& modelName, const Size& inpSize, double norm = 1e-5) {
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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, norm);
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}
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// https://google.github.io/mediapipe/solutions/face_mesh
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TEST(Test_TFLite, face_landmark)
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{
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testModel("face_landmark", Size(192, 192), 2e-5);
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}
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// https://google.github.io/mediapipe/solutions/face_detection
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TEST(Test_TFLite, face_detection_short_range)
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{
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testModel("face_detection_short_range", Size(128, 128));
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}
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// https://google.github.io/mediapipe/solutions/selfie_segmentation
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TEST(Test_TFLite, selfie_segmentation)
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{
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testModel("selfie_segmentation", Size(256, 256));
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}
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TEST(Test_TFLite, max_unpooling)
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{
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#ifndef HAVE_FLATBUFFERS
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throw SkipTestException("FlatBuffers required for TFLite importer");
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#endif
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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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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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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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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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EXPECT_EQ(poolIdsData[y * 64 + x], (float)maxIdx) << errMsg;
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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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}
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