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Merge pull request #15203 from l-bat:determine_inp_shape

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* Determine input shapes

* Add test

* Remove getInputShapes

* Fix model

* Fix constructors

* Add Caffe test

* Fix predict
This commit is contained in:
Lubov Batanina
2019-08-09 19:51:42 +03:00
committed by Alexander Alekhin
parent 358d69956a
commit f1ea9d86b9
5 changed files with 114 additions and 27 deletions
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modules/dnn/test/test_caffe_importer.cpp
+9 -10
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@@ -479,18 +479,17 @@ TEST_P(Test_Caffe_nets, DenseNet_121)
applyTestTag(CV_TEST_TAG_MEMORY_512MB);
checkBackend();
const string proto = findDataFile("dnn/DenseNet_121.prototxt", false);
const string model = findDataFile("dnn/DenseNet_121.caffemodel", false);
const string weights = findDataFile("dnn/DenseNet_121.caffemodel", false);
Mat inp = imread(_tf("dog416.png"));
inp = blobFromImage(inp, 1.0 / 255, Size(224, 224), Scalar(), true, true);
Model model(proto, weights);
model.setInputScale(1.0 / 255).setInputSwapRB(true).setInputCrop(true);
std::vector<Mat> outs;
Mat ref = blobFromNPY(_tf("densenet_121_output.npy"));
Net net = readNetFromCaffe(proto, model);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp);
Mat out = net.forward();
model.setPreferableBackend(backend);
model.setPreferableTarget(target);
model.predict(inp, outs);
// Reference is an array of 1000 values from a range [-6.16, 7.9]
float l1 = default_l1, lInf = default_lInf;
@@ -506,9 +505,9 @@ TEST_P(Test_Caffe_nets, DenseNet_121)
{
l1 = 0.11; lInf = 0.5;
}
normAssert(out, ref, "", l1, lInf);
normAssert(outs[0], ref, "", l1, lInf);
if (target != DNN_TARGET_MYRIAD || getInferenceEngineVPUType() != CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
expectNoFallbacksFromIE(net);
expectNoFallbacksFromIE(model);
}
TEST(Test_Caffe, multiple_inputs)