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uint8 inputs for deep learning networks

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This commit is contained in:
Dmitry Kurtaev
2018-06-04 23:51:28 +03:00
parent 6c4f618db5
commit 070393dfda
7 changed files with 408 additions and 76 deletions
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modules/dnn/test/test_layers.cpp
+69 -7
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@@ -291,7 +291,7 @@ TEST_P(Test_Caffe_layers, Fused_Concat)
TEST_P(Test_Caffe_layers, Eltwise)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
throw SkipTestException("");
testLayerUsingCaffeModels("layer_eltwise");
}
@@ -939,6 +939,25 @@ TEST(Layer_Test_Convolution_DLDT, Accuracy)
ASSERT_EQ(net.getLayer(outLayers[0])->type, "Concat");
}
TEST(Layer_Test_Convolution_DLDT, setInput_uint8)
{
Mat inp = blobFromNPY(_tf("blob.npy"));
Mat inputs[] = {Mat(inp.dims, inp.size, CV_8U), Mat()};
randu(inputs[0], 0, 255);
inputs[0].convertTo(inputs[1], CV_32F);
Mat outs[2];
for (int i = 0; i < 2; ++i)
{
Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin"));
net.setInput(inputs[i]);
outs[i] = net.forward();
ASSERT_EQ(outs[i].type(), CV_32F);
}
normAssert(outs[0], outs[1]);
}
// 1. Create a .prototxt file with the following network:
// layer {
// type: "Input" name: "data" top: "data"
@@ -961,22 +980,65 @@ TEST(Layer_Test_Convolution_DLDT, Accuracy)
// net.save('/path/to/caffemodel')
//
// 3. Convert using ModelOptimizer.
TEST(Test_DLDT, two_inputs)
typedef testing::TestWithParam<tuple<int, int> > Test_DLDT_two_inputs;
TEST_P(Test_DLDT_two_inputs, as_IR)
{
int firstInpType = get<0>(GetParam());
int secondInpType = get<1>(GetParam());
// TODO: It looks like a bug in Inference Engine.
if (secondInpType == CV_8U)
throw SkipTestException("");
Net net = readNet(_tf("net_two_inputs.xml"), _tf("net_two_inputs.bin"));
int inpSize[] = {1, 2, 3};
Mat firstInp(3, &inpSize[0], CV_32F);
Mat secondInp(3, &inpSize[0], CV_32F);
randu(firstInp, -1, 1);
randu(secondInp, -1, 1);
Mat firstInp(3, &inpSize[0], firstInpType);
Mat secondInp(3, &inpSize[0], secondInpType);
randu(firstInp, 0, 255);
randu(secondInp, 0, 255);
net.setInput(firstInp, "data");
net.setInput(secondInp, "second_input");
Mat out = net.forward();
normAssert(out, firstInp + secondInp);
Mat ref;
cv::add(firstInp, secondInp, ref, Mat(), CV_32F);
normAssert(out, ref);
}
TEST_P(Test_DLDT_two_inputs, as_backend)
{
static const float kScale = 0.5f;
static const float kScaleInv = 1.0f / kScale;
Net net;
LayerParams lp;
lp.type = "Eltwise";
lp.name = "testLayer";
lp.set("operation", "sum");
int eltwiseId = net.addLayerToPrev(lp.name, lp.type, lp); // connect to a first input
net.connect(0, 1, eltwiseId, 1); // connect to a second input
int inpSize[] = {1, 2, 3};
Mat firstInp(3, &inpSize[0], get<0>(GetParam()));
Mat secondInp(3, &inpSize[0], get<1>(GetParam()));
randu(firstInp, 0, 255);
randu(secondInp, 0, 255);
net.setInputsNames({"data", "second_input"});
net.setInput(firstInp, "data", kScale);
net.setInput(secondInp, "second_input", kScaleInv);
net.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
Mat out = net.forward();
Mat ref;
addWeighted(firstInp, kScale, secondInp, kScaleInv, 0, ref, CV_32F);
normAssert(out, ref);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_DLDT_two_inputs, Combine(
Values(CV_8U, CV_32F), Values(CV_8U, CV_32F)
));
class UnsupportedLayer : public Layer
{
public: