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Enable more deep learning tests using Intel's Inference Engine backend

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This commit is contained in:
Dmitry Kurtaev
2018-08-27 15:45:44 +03:00
parent 6477262e63
commit 3e027df583
12 changed files with 167 additions and 83 deletions
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modules/dnn/test/test_caffe_importer.cpp
+18 -11
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@@ -374,14 +374,6 @@ TEST(Reproducibility_GoogLeNet_fp16, Accuracy)
TEST_P(Test_Caffe_nets, Colorization)
{
checkBackend();
if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16) ||
(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD) ||
(backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
throw SkipTestException("");
const float l1 = 4e-4;
const float lInf = 3e-3;
Mat inp = blobFromNPY(_tf("colorization_inp.npy"));
Mat ref = blobFromNPY(_tf("colorization_out.npy"));
Mat kernel = blobFromNPY(_tf("colorization_pts_in_hull.npy"));
@@ -398,11 +390,15 @@ TEST_P(Test_Caffe_nets, Colorization)
net.setInput(inp);
Mat out = net.forward();
// Reference output values are in range [-29.1, 69.5]
const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.21 : 4e-4;
const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 5.3 : 3e-3;
normAssert(out, ref, "", l1, lInf);
}
TEST(Reproducibility_DenseNet_121, Accuracy)
TEST_P(Test_Caffe_nets, DenseNet_121)
{
checkBackend();
const string proto = findDataFile("dnn/DenseNet_121.prototxt", false);
const string model = findDataFile("dnn/DenseNet_121.caffemodel", false);
@@ -411,12 +407,23 @@ TEST(Reproducibility_DenseNet_121, Accuracy)
Mat ref = blobFromNPY(_tf("densenet_121_output.npy"));
Net net = readNetFromCaffe(proto, model);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
Mat out = net.forward();
normAssert(out, ref);
// Reference is an array of 1000 values from a range [-6.16, 7.9]
float l1 = default_l1, lInf = default_lInf;
if (target == DNN_TARGET_OPENCL_FP16)
{
l1 = 0.017; lInf = 0.067;
}
else if (target == DNN_TARGET_MYRIAD)
{
l1 = 0.097; lInf = 0.52;
}
normAssert(out, ref, "", l1, lInf);
}
TEST(Test_Caffe, multiple_inputs)