void testInPlaceActivation(LayerParams& lp)
{
EXPECT_FALSE(lp.name.empty());
LayerParams pool;
pool.set("pool", "ave");
pool.set("kernel_w", 2);
pool.set("kernel_h", 2);
pool.set("stride_w", 2);
pool.set("stride_h", 2);
pool.type = "Pooling";
Net net;
int poolId = net.addLayer(pool.name, pool.type, pool);
net.connect(0, 0, poolId, 0);
net.addLayerToPrev(lp.name, lp.type, lp);
Mat input({1, kNumChannels, 10, 10}, CV_32F);
randu(input, -1.0f, 1.0f);
net.setInput(input);
Mat outputDefault = net.forward(lp.name).clone();
net.setInput(input);
net.setPreferableBackend(DNN_BACKEND_HALIDE);
Mat outputHalide = net.forward(lp.name).clone();
normAssert(outputDefault, outputHalide);
}
////////////////////////////////////////////////////////////////////////////
// Mixed backends
////////////////////////////////////////////////////////////////////////////
TEST(MixedBackends_Halide_Default_Halide, Accuracy)
{
// Just a layer that supports Halide backend.
LayerParams lrn;
lrn.type = "LRN";
lrn.name = "testLRN";
// Some of layers that doesn't supports Halide backend yet.
LayerParams mvn;
mvn.type = "MVN";
mvn.name = "testMVN";
// Halide layer again.
LayerParams lrn2;
lrn2.type = "LRN";
lrn2.name = "testLRN2";
Net net;
int lrnId = net.addLayer(lrn.name, lrn.type, lrn);
net.connect(0, 0, lrnId, 0);
net.addLayerToPrev(mvn.name, mvn.type, mvn);
net.addLayerToPrev(lrn2.name, lrn2.type, lrn2);
Mat input({4, 3, 5, 6}, CV_32F);
randu(input, -1.0f, 1.0f);
net.setInput(input);
Mat outputDefault = net.forward().clone();
net.setPreferableBackend(DNN_BACKEND_HALIDE);
net.setInput(input);
Mat outputHalide = net.forward().clone();
normAssert(outputDefault, outputHalide);
net.setPreferableTarget(DNN_TARGET_OPENCL);
net.setInput(input);
outputHalide = net.forward().clone();
normAssert(outputDefault, outputHalide);
}
void testONNXModels(const String& basename, const Extension ext = npy,
const double l1 = 0, const float lInf = 0, const bool useSoftmax = false,
bool checkNoFallbacks = true)
{
String onnxmodel = _tf("models/" + basename + ".onnx");
Mat inp, ref;
if (ext == npy) {
inp = blobFromNPY(_tf("data/input_" + basename + ".npy"));
ref = blobFromNPY(_tf("data/output_" + basename + ".npy"));
}
else if (ext == pb) {
inp = readTensorFromONNX(_tf("data/input_" + basename + ".pb"));
ref = readTensorFromONNX(_tf("data/output_" + basename + ".pb"));
}
else
CV_Error(Error::StsUnsupportedFormat, "Unsupported extension");
checkBackend(&inp, &ref);
Net net = readNetFromONNX(onnxmodel);
ASSERT_FALSE(net.empty());
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
Mat out = net.forward("");
if (useSoftmax)
{
LayerParams lp;
Net netSoftmax;
netSoftmax.addLayerToPrev("softmaxLayer", "SoftMax", lp);
netSoftmax.setPreferableBackend(DNN_BACKEND_OPENCV);
netSoftmax.setInput(out);
out = netSoftmax.forward();
netSoftmax.setInput(ref);
ref = netSoftmax.forward();
}
normAssert(ref, out, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
if (checkNoFallbacks)
expectNoFallbacksFromIE(net);
}
