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Merge pull request #13692 from dkurt:dnn_do_not_crash_myriad_in_tests

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Alexander Alekhin
2019-01-28 18:34:20 +00:00
parent 02e9636f1b ff775b2e54
commit 3585522b24
5 changed files with 54 additions and 63 deletions
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modules/dnn/test/test_layers.cpp
+26 -41
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@@ -923,8 +923,9 @@ TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
{
Target targetId = GetParam();
std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
Net netDefault = readNet(_tf("layer_convolution.caffemodel"), _tf("layer_convolution.prototxt"));
Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin"));
Net net = readNet(_tf("layer_convolution" + suffix + ".xml"), _tf("layer_convolution" + suffix + ".bin"));
Mat inp = blobFromNPY(_tf("blob.npy"));
@@ -935,22 +936,15 @@ TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
net.setInput(inp);
net.setPreferableTarget(targetId);
if (targetId != DNN_TARGET_MYRIAD)
{
Mat out = net.forward();
Mat out = net.forward();
normAssert(outDefault, out);
double l1 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 1.4e-3 : 1e-5;
double lInf = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 1.8e-2 : 1e-4;
normAssert(outDefault, out, "", l1, lInf);
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());
}
std::vector<int> outLayers = net.getUnconnectedOutLayers();
ASSERT_EQ(net.getLayer(outLayers[0])->name, "output");
ASSERT_EQ(net.getLayer(outLayers[0])->type, "Convolution");
}
TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8)
@@ -962,23 +956,16 @@ TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8)
randu(inputs[0], 0, 255);
inputs[0].convertTo(inputs[1], CV_32F);
std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
Mat outs[2];
for (int i = 0; i < 2; ++i)
{
Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin"));
Net net = readNet(_tf("layer_convolution" + suffix + ".xml"), _tf("layer_convolution" + suffix + ".bin"));
net.setPreferableTarget(targetId);
net.setInput(inputs[i]);
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());
}
outs[i] = net.forward();
ASSERT_EQ(outs[i].type(), CV_32F);
}
if (targetId != DNN_TARGET_MYRIAD)
normAssert(outs[0], outs[1]);
@@ -1020,7 +1007,8 @@ TEST_P(Test_DLDT_two_inputs_3dim, as_IR)
throw SkipTestException("Test is enabled starts from OpenVINO 2018R4");
#endif
Net net = readNet(_tf("net_two_inputs.xml"), _tf("net_two_inputs.bin"));
std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
Net net = readNet(_tf("net_two_inputs" + suffix + ".xml"), _tf("net_two_inputs.bin"));
std::vector<int> inpSize = get<3>(GetParam());
Mat firstInp(3, inpSize.data(), firstInpType);
Mat secondInp(3, inpSize.data(), secondInpType);
@@ -1030,20 +1018,17 @@ TEST_P(Test_DLDT_two_inputs_3dim, as_IR)
net.setInput(firstInp, "data");
net.setInput(secondInp, "second_input");
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);
}
else
{
// An assertion is expected because the model is in FP32 format but
// Myriad plugin supports only FP16 models.
ASSERT_ANY_THROW(net.forward());
}
double l1 = ((targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) &&
(firstInpType == CV_32F || secondInpType == CV_32F)) ? 0.06 : 0.0;
double lInf = ((targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) &&
(firstInpType == CV_32F || secondInpType == CV_32F)) ? 0.23 : 0.0;
Mat out = net.forward();
Mat ref;
cv::add(firstInp, secondInp, ref, Mat(), CV_32F);
normAssert(out, ref, "", l1, lInf);
}
std::vector< std::vector<int> > list_sizes{ {1, 2, 3}, {3, 2, 1}, {5, 5, 5}, {13, 7, 11} };