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Merge pull request #16575 from l-bat:flownet2

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Support FlowNet2 model

* Support DataAugmentation layer

* Fix warnings

* Fix comments

* Support Correlation layer

* TEST

* Support Correlation layer

* Supported Accum and FlowWarp layers

* Supported ChannelNorm layer

* Supported Resample with inputs.size() > 1

* Fixed comments

* Refactoring

* Added tests

* Add resample test

* Added asserts in resize layer

* Updated DataAugmentation layer

* Update convolution layer

* Refactoring

* Fix data augmentation layer

* Fix caffe importer

* Fix resize

* Switch to Mat ptr

* Remove useless resize type

* Used ResizeLayer in Accum

* Split ChannelNormLayer

* Delete duplicate assert

* Add sample

* Fix sample

* Added colormap
This commit is contained in:
Liubov Batanina
2020-05-19 15:29:50 +03:00
committed by GitHub
parent 322960f795
commit d991c22090
10 changed files with 829 additions and 13 deletions
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modules/dnn/test/test_layers.cpp
+101 -10
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@@ -97,29 +97,68 @@ class Test_Caffe_layers : public DNNTestLayer
{
public:
void testLayerUsingCaffeModels(const String& basename, bool useCaffeModel = false,
bool useCommonInputBlob = true, double l1 = 0.0,
double lInf = 0.0)
bool useCommonInputBlob = true, double l1 = 0.0, double lInf = 0.0,
int numInps = 1, int numOuts = 1)
{
CV_Assert_N(numInps >= 1, numInps <= 10, numOuts >= 1, numOuts <= 10);
String prototxt = _tf(basename + ".prototxt");
String caffemodel = _tf(basename + ".caffemodel");
String inpfile = (useCommonInputBlob) ? _tf("blob.npy") : _tf(basename + ".input.npy");
String outfile = _tf(basename + ".npy");
std::vector<Mat> inps, refs, outs;
Mat inp = blobFromNPY(inpfile);
Mat ref = blobFromNPY(outfile);
checkBackend(&inp, &ref);
if (numInps > 1)
{
for (int i = 0; i < numInps; i++)
{
String inpfile = _tf(basename + ".input_" + (i + '0') + ".npy");
inps.push_back(blobFromNPY(inpfile));
}
}
else
{
String inpfile = (useCommonInputBlob) ? _tf("blob.npy") : _tf(basename + ".input.npy");
inps.push_back(blobFromNPY(inpfile));
}
if (numOuts > 1)
{
for (int i = 0; i < numOuts; i++)
{
String outfile = _tf(basename + "_" + (i + '0') + ".npy");
refs.push_back(blobFromNPY(outfile));
}
}
else
{
String outfile = _tf(basename + ".npy");
refs.push_back(blobFromNPY(outfile));
}
Net net = readNetFromCaffe(prototxt, (useCaffeModel) ? caffemodel : String());
ASSERT_FALSE(net.empty());
checkBackend(&inps[0], &refs[0]);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(inp, "input");
Mat out = net.forward("output");
String inp_name = "input";
if (numInps > 1)
{
for (int i = 0; i < numInps; i++)
{
net.setInput(inps[i], inp_name + "_" + (i + '0'));
}
}
else
{
net.setInput(inps.back(), inp_name);
}
normAssert(ref, out, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
net.forward(outs);
for (int i = 0; i < refs.size(); i++)
{
normAssert(refs[i], outs[i], "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
}
}
};
@@ -568,6 +607,58 @@ TEST_F(Layer_RNN_Test, get_set_test)
EXPECT_EQ(shape(outputs[1]), shape(nT, nS, nH));
}
TEST_P(Test_Caffe_layers, Accum)
{
if (backend == DNN_BACKEND_OPENCV && target != DNN_TARGET_CPU)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL, CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
testLayerUsingCaffeModels("accum", false, false, 0.0, 0.0, 2);
testLayerUsingCaffeModels("accum_ref", false, false, 0.0, 0.0, 2);
}
TEST_P(Test_Caffe_layers, FlowWarp)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
testLayerUsingCaffeModels("flow_warp", false, false, 0.0, 0.0, 2);
}
TEST_P(Test_Caffe_layers, ChannelNorm)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
testLayerUsingCaffeModels("channel_norm", false, false);
}
TEST_P(Test_Caffe_layers, DataAugmentation)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
testLayerUsingCaffeModels("data_augmentation", true, false);
}
TEST_P(Test_Caffe_layers, Resample)
{
if (backend != DNN_BACKEND_OPENCV)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
testLayerUsingCaffeModels("nearest_2inps", false, false, 0.0, 0.0, 2);
testLayerUsingCaffeModels("nearest", false, false);
}
TEST_P(Test_Caffe_layers, Correlation)
{
if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER,
CV_TEST_TAG_DNN_SKIP_OPENCL, CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
testLayerUsingCaffeModels("correlation", false, false, 0.0, 0.0, 2);
}
TEST_P(Test_Caffe_layers, Convolution2Inputs)
{
testLayerUsingCaffeModels("conv_2_inps", true, false, 0.0, 0.0, 2);
}
TEST_P(Test_Caffe_layers, ROIPooling_Accuracy)
{
Net net = readNetFromCaffe(_tf("net_roi_pooling.prototxt"));