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Merge pull request #15032 from l-bat:reduce_mean

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* Added support for the ONNX "ReduceMean" Layer. (as this is the same as the GlobalAveragePool)

* Add ReduceMean test

* Fix ONNX importer

* Fix ReduceMean

* Add assert

* Split test

* Fix split test
This commit is contained in:
Lubov Batanina 2019-07-19 19:18:34 +03:00 committed by Alexander Alekhin
parent a8a71eb200
commit 781f4d439e
5 changed files with 52 additions and 15 deletions
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5
modules/dnn/src/dnn.cpp
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@ -2170,7 +2170,10 @@ struct Net::Impl
if (isAsync)
CV_Error(Error::StsNotImplemented, "Default implementation fallbacks in asynchronous mode");
CV_Assert(layer->supportBackend(DNN_BACKEND_OPENCV));
if (!layer->supportBackend(DNN_BACKEND_OPENCV))
CV_Error(Error::StsNotImplemented, format("Layer \"%s\" of type \"%s\" unsupported on OpenCV backend",
ld.name.c_str(), ld.type.c_str()));
if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))
{
std::vector<UMat> umat_inputBlobs = OpenCLBackendWrapper::getUMatVector(ld.inputBlobsWrappers);

4
modules/dnn/src/layers/layers_common.cpp
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@ -148,13 +148,12 @@ void getPoolingKernelParams(const LayerParams &params, std::vector<size_t>& kern
std::vector<size_t>& pads_begin, std::vector<size_t>& pads_end,
std::vector<size_t>& strides, cv::String &padMode)
{
util::getStrideAndPadding(params, pads_begin, pads_end, strides, padMode);
globalPooling = params.has("global_pooling") &&
params.get<bool>("global_pooling");
if (globalPooling)
{
util::getStrideAndPadding(params, pads_begin, pads_end, strides, padMode);
if(params.has("kernel_h") || params.has("kernel_w") || params.has("kernel_size"))
{
CV_Error(cv::Error::StsBadArg, "In global_pooling mode, kernel_size (or kernel_h and kernel_w) cannot be specified");
@ -171,6 +170,7 @@ void getPoolingKernelParams(const LayerParams &params, std::vector<size_t>& kern
else
{
util::getKernelSize(params, kernel);
util::getStrideAndPadding(params, pads_begin, pads_end, strides, padMode, kernel.size());
}
}

38
modules/dnn/src/onnx/onnx_importer.cpp
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@ -397,11 +397,33 @@ void ONNXImporter::populateNet(Net dstNet)
layerParams.set("ceil_mode", layerParams.has("pad_mode"));
layerParams.set("ave_pool_padded_area", framework_name == "pytorch");
}
else if (layer_type == "GlobalAveragePool" || layer_type == "GlobalMaxPool")
else if (layer_type == "GlobalAveragePool" || layer_type == "GlobalMaxPool" || layer_type == "ReduceMean")
{
CV_Assert(node_proto.input_size() == 1);
layerParams.type = "Pooling";
layerParams.set("pool", layer_type == "GlobalAveragePool" ? "AVE" : "MAX");
layerParams.set("global_pooling", true);
layerParams.set("pool", layer_type == "GlobalMaxPool"? "MAX" : "AVE");
layerParams.set("global_pooling", layer_type == "GlobalAveragePool" || layer_type == "GlobalMaxPool");
if (layer_type == "ReduceMean")
{
if (layerParams.get<int>("keepdims") == 0 || !layerParams.has("axes"))
CV_Error(Error::StsNotImplemented, "Unsupported mode of ReduceMean operation.");
MatShape inpShape = outShapes[node_proto.input(0)];
if (inpShape.size() != 4 && inpShape.size() != 5)
CV_Error(Error::StsNotImplemented, "Unsupported input shape of reduce_mean operation.");
DictValue axes = layerParams.get("axes");
CV_Assert(axes.size() <= inpShape.size() - 2);
std::vector<int> kernel_size(inpShape.size() - 2, 1);
for (int i = 0; i < axes.size(); i++) {
int axis = axes.get<int>(i);
CV_Assert_N(axis >= 2 + i, axis < inpShape.size());
kernel_size[axis - 2] = inpShape[axis];
}
layerParams.set("kernel_size", DictValue::arrayInt(&kernel_size[0], kernel_size.size()));
}
}
else if (layer_type == "Slice")
{
@ -747,11 +769,13 @@ void ONNXImporter::populateNet(Net dstNet)
if (axes.size() != 1)
CV_Error(Error::StsNotImplemented, "Multidimensional unsqueeze");
int dims[] = {1, -1};
MatShape inpShape = outShapes[node_proto.input(0)];
int axis = axes.getIntValue(0);
CV_Assert(0 <= axis && axis <= inpShape.size());
std::vector<int> outShape = inpShape;
outShape.insert(outShape.begin() + axis, 1);
layerParams.type = "Reshape";
layerParams.set("axis", axes.getIntValue(0));
layerParams.set("num_axes", 1);
layerParams.set("dim", DictValue::arrayInt(&dims[0], 2));
layerParams.set("dim", DictValue::arrayInt(&outShape[0], outShape.size()));
}
else if (layer_type == "Reshape")
{

12
modules/dnn/test/test_onnx_importer.cpp
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@ -162,6 +162,18 @@ TEST_P(Test_ONNX_layers, Clip)
testONNXModels("clip", npy);
}
TEST_P(Test_ONNX_layers, ReduceMean)
{
testONNXModels("reduce_mean");
}
TEST_P(Test_ONNX_layers, ReduceMean3D)
{
if (target != DNN_TARGET_CPU)
throw SkipTestException("Only CPU is supported");
testONNXModels("reduce_mean3d");
}
TEST_P(Test_ONNX_layers, MaxPooling_Sigmoid)
{
testONNXModels("maxpooling_sigmoid");

8
modules/dnn/test/test_tf_importer.cpp
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@ -350,11 +350,6 @@ TEST_P(Test_TensorFlow_layers, l2_normalize_3d)
runTensorFlowNet("l2_normalize_3d");
}
TEST_P(Test_TensorFlow_layers, Split)
{
runTensorFlowNet("split");
}
class Test_TensorFlow_nets : public DNNTestLayer {};
TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
@ -682,6 +677,9 @@ TEST_P(Test_TensorFlow_layers, lstm)
TEST_P(Test_TensorFlow_layers, split)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_2);
runTensorFlowNet("split");
if (backend == DNN_BACKEND_INFERENCE_ENGINE)
applyTestTag(CV_TEST_TAG_DNN_SKIP_IE);
runTensorFlowNet("split_equals");