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Add BatchNorm3d layer

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This commit is contained in:
Liubov Batanina 2019-04-29 10:29:34 +03:00
parent b998c06d08
commit dadb1473c1
3 changed files with 19 additions and 2 deletions
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7
modules/dnn/src/layers/batch_norm_layer.cpp
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@ -29,6 +29,8 @@ class BatchNormLayerImpl CV_FINAL : public BatchNormLayer
public:
Mat weights_, bias_;
UMat umat_weight, umat_bias;
mutable int dims;
BatchNormLayerImpl(const LayerParams& params)
{
@ -142,6 +144,7 @@ public:
std::vector<MatShape> &outputs,
std::vector<MatShape> &internals) const CV_OVERRIDE
{
dims = inputs[0].size();
if (!useGlobalStats && inputs[0][0] != 1)
CV_Error(Error::StsNotImplemented, "Batch normalization in training mode with batch size > 1");
Layer::getMemoryShapes(inputs, requiredOutputs, outputs, internals);
@ -150,9 +153,9 @@ public:
virtual bool supportBackend(int backendId) CV_OVERRIDE
{
return backendId == DNN_BACKEND_OPENCV ||
return (backendId == DNN_BACKEND_OPENCV && (dims == 4 || dims == 2)) ||
(backendId == DNN_BACKEND_HALIDE && haveHalide()) ||
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine());
(backendId == DNN_BACKEND_INFERENCE_ENGINE && haveInfEngine() && (preferableTarget == DNN_TARGET_CPU || dims == 4));
}
#ifdef HAVE_OPENCL

7
modules/dnn/test/test_onnx_importer.cpp
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@ -167,6 +167,13 @@ TEST_P(Test_ONNX_layers, BatchNormalization)
testONNXModels("batch_norm");
}
TEST_P(Test_ONNX_layers, BatchNormalization3D)
{
if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
throw SkipTestException("Only DLIE backend on CPU is supported");
testONNXModels("batch_norm_3d");
}
TEST_P(Test_ONNX_layers, Transpose)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE &&

7
modules/dnn/test/test_tf_importer.cpp
View File

@ -188,6 +188,13 @@ TEST_P(Test_TensorFlow_layers, batch_norm)
runTensorFlowNet("mvn_batch_norm_1x1");
}
TEST_P(Test_TensorFlow_layers, batch_norm3D)
{
if (backend != DNN_BACKEND_INFERENCE_ENGINE || target != DNN_TARGET_CPU)
throw SkipTestException("Only DLIE backend on CPU is supported");
runTensorFlowNet("batch_norm3d");
}
TEST_P(Test_TensorFlow_layers, slim_batch_norm)
{
if (backend == DNN_BACKEND_INFERENCE_ENGINE)