dnnBackendsAndTargets()

));

structLayer_Softmax :publicTestBaseWithParam<tuple<Backend, Target> >

{

voidtest_layer(const std::vector<int>& shape,int axis)

{

int backendId = get<0>(GetParam());

int targetId = get<1>(GetParam());

Matdata(shape, CV_32FC1);

Scalar mean =0.f;

Scalar std =1.f;

randn(data, mean, std);

typedef TestBaseWithParam<tuple<std::vector<int>,int, tuple<Backend, Target> > > Layer_Softmax;

LayerParams lp;

lp.type ="Softmax";

lp.name ="testLayer";

lp.set("axis", axis);

std::vector<int> shape = get<0>(GetParam());

int axis = get<1>(GetParam());

int backendId = get<0>(get<2>(GetParam()));

int targetId = get<1>(get<2>(GetParam()));

net.addLayerToPrev(lp.name, lp.type, lp);

{

net.setInput(data);

net.setPreferableBackend(backendId);

net.setPreferableTarget(targetId);

Mat out = net.forward();

}

Matdata(shape, CV_32FC1);

Scalar mean =0.f;

Scalar std =1.f;

randn(data, mean, std);

TEST_CYCLE()

{

Mat res = net.forward();

}

Net net;

LayerParams lp;

lp.type ="Softmax";

lp.name ="testLayer";

lp.set("axis", axis);

SANITY_CHECK_NOTHING();

net.addLayerToPrev(lp.name, lp.type, lp);

{

net.setInput(data);

net.setPreferableBackend(backendId);

net.setPreferableTarget(targetId);

Mat out = net.forward();

}

};

TEST_CYCLE() {

Mat res = net.forward();

}

PERF_TEST_P_(Layer_Softmax, Softmax_small)

{

test_layer({16,50,50},2);

}

PERF_TEST_P_(Layer_Softmax, Softmax_middle)

{

test_layer({16,197,197},2);

}

PERF_TEST_P_(Layer_Softmax, Softmax_large)

{

test_layer({16,1080,1920,3},2);

SANITY_CHECK_NOTHING();

}

INSTANTIATE_TEST_CASE_P(/**/, Layer_Softmax, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));

INSTANTIATE_TEST_CASE_P(/**/, Layer_Softmax, Combine(

Values(// input size

std::vector<int>({16,50,50}),

std::vector<int>({16,197,197}),

std::vector<int>({16,1024,1024})

),

Values(0,1,2),// axis

dnnBackendsAndTargets(false,false,false,false,false,false,false,false)// only CPU

));

}// namespace