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Minor fixes in IE backend tests

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This commit is contained in:
Dmitry Kurtaev
2018-12-07 12:40:34 +03:00
parent f1dc26d7ce
commit 53f6198f27
7 changed files with 83 additions and 29 deletions
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modules/dnn/test/test_layers.cpp
+71 -23
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@@ -137,7 +137,7 @@ TEST_P(Test_Caffe_layers, Convolution)
TEST_P(Test_Caffe_layers, DeConvolution)
{
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018040000
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018040000
if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_CPU)
throw SkipTestException("Test is disabled for OpenVINO 2018R4");
#endif
@@ -918,8 +918,11 @@ INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_DWconv_Prelu, Combine(Values(3, 6), Val
// Using Intel's Model Optimizer generate .xml and .bin files:
// ./ModelOptimizer -w /path/to/caffemodel -d /path/to/prototxt \
// -p FP32 -i -b ${batch_size} -o /path/to/output/folder
TEST(Layer_Test_Convolution_DLDT, Accuracy)
typedef testing::TestWithParam<Target> Layer_Test_Convolution_DLDT;
TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
{
Target targetId = GetParam();
Net netDefault = readNet(_tf("layer_convolution.caffemodel"), _tf("layer_convolution.prototxt"));
Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin"));
@@ -930,17 +933,29 @@ TEST(Layer_Test_Convolution_DLDT, Accuracy)
Mat outDefault = netDefault.forward();
net.setInput(inp);
Mat out = net.forward();
net.setPreferableTarget(targetId);
normAssert(outDefault, out);
if (targetId != DNN_TARGET_MYRIAD)
{
Mat out = net.forward();
std::vector<int> outLayers = net.getUnconnectedOutLayers();
ASSERT_EQ(net.getLayer(outLayers[0])->name, "output_merge");
ASSERT_EQ(net.getLayer(outLayers[0])->type, "Concat");
normAssert(outDefault, out);
std::vector<int> outLayers = net.getUnconnectedOutLayers();
ASSERT_EQ(net.getLayer(outLayers[0])->name, "output_merge");
ASSERT_EQ(net.getLayer(outLayers[0])->type, "Concat");
}
else
{
// An assertion is expected because the model is in FP32 format but
// Myriad plugin supports only FP16 models.
ASSERT_ANY_THROW(net.forward());
}
}
TEST(Layer_Test_Convolution_DLDT, setInput_uint8)
TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8)
{
Target targetId = GetParam();
Mat inp = blobFromNPY(_tf("blob.npy"));
Mat inputs[] = {Mat(inp.dims, inp.size, CV_8U), Mat()};
@@ -951,12 +966,25 @@ TEST(Layer_Test_Convolution_DLDT, setInput_uint8)
for (int i = 0; i < 2; ++i)
{
Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin"));
net.setPreferableTarget(targetId);
net.setInput(inputs[i]);
outs[i] = net.forward();
ASSERT_EQ(outs[i].type(), CV_32F);
if (targetId != DNN_TARGET_MYRIAD)
{
outs[i] = net.forward();
ASSERT_EQ(outs[i].type(), CV_32F);
}
else
{
// An assertion is expected because the model is in FP32 format but
// Myriad plugin supports only FP16 models.
ASSERT_ANY_THROW(net.forward());
}
}
normAssert(outs[0], outs[1]);
if (targetId != DNN_TARGET_MYRIAD)
normAssert(outs[0], outs[1]);
}
INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Convolution_DLDT,
testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE)));
// 1. Create a .prototxt file with the following network:
// layer {
@@ -980,14 +1008,17 @@ TEST(Layer_Test_Convolution_DLDT, setInput_uint8)
// net.save('/path/to/caffemodel')
//
// 3. Convert using ModelOptimizer.
typedef testing::TestWithParam<tuple<int, int> > Test_DLDT_two_inputs;
typedef testing::TestWithParam<tuple<int, int, Target> > 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.
Target targetId = get<2>(GetParam());
#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018040000
if (secondInpType == CV_8U)
throw SkipTestException("");
throw SkipTestException("Test is enabled starts from OpenVINO 2018R4");
#endif
Net net = readNet(_tf("net_two_inputs.xml"), _tf("net_two_inputs.bin"));
int inpSize[] = {1, 2, 3};
@@ -998,11 +1029,21 @@ TEST_P(Test_DLDT_two_inputs, as_IR)
net.setInput(firstInp, "data");
net.setInput(secondInp, "second_input");
Mat out = net.forward();
net.setPreferableTarget(targetId);
if (targetId != DNN_TARGET_MYRIAD)
{
Mat out = net.forward();
Mat ref;
cv::add(firstInp, secondInp, ref, Mat(), CV_32F);
normAssert(out, ref);
Mat ref;
cv::add(firstInp, secondInp, ref, Mat(), CV_32F);
normAssert(out, ref);
}
else
{
// An assertion is expected because the model is in FP32 format but
// Myriad plugin supports only FP16 models.
ASSERT_ANY_THROW(net.forward());
}
}
TEST_P(Test_DLDT_two_inputs, as_backend)
@@ -1010,6 +1051,8 @@ TEST_P(Test_DLDT_two_inputs, as_backend)
static const float kScale = 0.5f;
static const float kScaleInv = 1.0f / kScale;
Target targetId = get<2>(GetParam());
Net net;
LayerParams lp;
lp.type = "Eltwise";
@@ -1018,9 +1061,9 @@ TEST_P(Test_DLDT_two_inputs, as_backend)
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()));
int inpSize[] = {1, 2, 3, 4};
Mat firstInp(4, &inpSize[0], get<0>(GetParam()));
Mat secondInp(4, &inpSize[0], get<1>(GetParam()));
randu(firstInp, 0, 255);
randu(secondInp, 0, 255);
@@ -1028,15 +1071,20 @@ TEST_P(Test_DLDT_two_inputs, as_backend)
net.setInput(firstInp, "data", kScale);
net.setInput(secondInp, "second_input", kScaleInv);
net.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
net.setPreferableTarget(targetId);
Mat out = net.forward();
Mat ref;
addWeighted(firstInp, kScale, secondInp, kScaleInv, 0, ref, CV_32F);
normAssert(out, ref);
// Output values are in range [0, 637.5].
double l1 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.06 : 1e-6;
double lInf = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.3 : 1e-5;
normAssert(out, ref, "", l1, lInf);
}
INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_DLDT_two_inputs, Combine(
Values(CV_8U, CV_32F), Values(CV_8U, CV_32F)
Values(CV_8U, CV_32F), Values(CV_8U, CV_32F),
testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE))
));
class UnsupportedLayer : public Layer