test_torch_importer.cpp

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#ifdef ENABLE_TORCH_IMPORTER

#include "test_precomp.hpp"
#include "npy_blob.hpp"
#include <opencv2/dnn/shape_utils.hpp>

namespace cvtest
{

using namespace std;
using namespace testing;
using namespace cv;
using namespace cv::dnn;

template<typename TStr>
static std::string _tf(TStr filename, bool inTorchDir = true)
{
    String path = getOpenCVExtraDir() + "/dnn/";
    if (inTorchDir)
        path += "torch/";
    path += filename;
    return path;
}

TEST(Torch_Importer, simple_read)
{
    Net net;
    Ptr<Importer> importer;

    ASSERT_NO_THROW( importer = createTorchImporter(_tf("net_simple_net.txt"), false) );
    ASSERT_TRUE( importer != NULL );
    importer->populateNet(net);
}

static void runTorchNet(String prefix, String outLayerName = "",
                        bool check2ndBlob = false, bool isBinary = false)
{
    String suffix = (isBinary) ? ".dat" : ".txt";

    Net net;
    Ptr<Importer> importer = createTorchImporter(_tf(prefix + "_net" + suffix), isBinary);
    ASSERT_TRUE(importer != NULL);
    importer->populateNet(net);

    Mat inp, outRef;
    ASSERT_NO_THROW( inp = readTorchBlob(_tf(prefix + "_input" + suffix), isBinary) );
    ASSERT_NO_THROW( outRef = readTorchBlob(_tf(prefix + "_output" + suffix), isBinary) );

    net.setBlob(".0", inp);
    net.forward();
    if (outLayerName.empty())
        outLayerName = net.getLayerNames().back();
    Mat out = net.getBlob(outLayerName);

    normAssert(outRef, out);

    if (check2ndBlob)
    {
        Mat out2 = net.getBlob(outLayerName + ".1");
        Mat ref2 = readTorchBlob(_tf(prefix + "_output_2" + suffix), isBinary);
        normAssert(out2, ref2);
    }
}

TEST(Torch_Importer, run_convolution)
{
    runTorchNet("net_conv");
}

TEST(Torch_Importer, run_pool_max)
{
    runTorchNet("net_pool_max", "", true);
}

TEST(Torch_Importer, run_pool_ave)
{
    runTorchNet("net_pool_ave");
}

TEST(Torch_Importer, run_reshape)
{
    runTorchNet("net_reshape");
    runTorchNet("net_reshape_batch");
}

TEST(Torch_Importer, run_linear)
{
    runTorchNet("net_linear_2d");
}

TEST(Torch_Importer, run_paralel)
{
    runTorchNet("net_parallel", "l2_torchMerge");
}

TEST(Torch_Importer, run_concat)
{
    runTorchNet("net_concat", "l2_torchMerge");
}

TEST(Torch_Importer, run_deconv)
{
    runTorchNet("net_deconv");
}

TEST(Torch_Importer, run_batch_norm)
{
    runTorchNet("net_batch_norm");
}

TEST(Torch_Importer, net_prelu)
{
    runTorchNet("net_prelu");
}

TEST(Torch_Importer, net_cadd_table)
{
    runTorchNet("net_cadd_table");
}

TEST(Torch_Importer, ENet_accuracy)
{
    Net net;
    {
        const string model = findDataFile("dnn/Enet-model-best.net", false);
        Ptr<Importer> importer = createTorchImporter(model, true);
        ASSERT_TRUE(importer != NULL);
        importer->populateNet(net);
    }

    Mat sample = imread(_tf("street.png", false));
    Mat inputBlob = blobFromImage(sample, 1./255);

    net.setBlob("", inputBlob);
    net.forward();
    Mat out = net.getBlob(net.getLayerNames().back());
    Mat ref = blobFromNPY(_tf("torch_enet_prob.npy", false));
    // Due to numerical instability in Pooling-Unpooling layers (indexes jittering)
    // thresholds for ENet must be changed. Accuracy of resuults was checked on
    // Cityscapes dataset and difference in mIOU with Torch is 10E-4%
    normAssert(ref, out, "", 0.00044, 0.44);
}

}

#endif