-#else
- InferenceEngine::LayerParams lp;
- lp.name = name;
- lp.type = "Convolution";
- lp.precision = InferenceEngine::Precision::FP32;
- std::shared_ptr<InferenceEngine::ConvolutionLayer> ieLayer(new InferenceEngine::ConvolutionLayer(lp));
-
-#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R3)
- ieLayer->_kernel.insert(InferenceEngine::X_AXIS, kernel.width);
- ieLayer->_kernel.insert(InferenceEngine::Y_AXIS, kernel.height);
- ieLayer->_stride.insert(InferenceEngine::X_AXIS, stride.width);
- ieLayer->_stride.insert(InferenceEngine::Y_AXIS, stride.height);
- ieLayer->_padding.insert(InferenceEngine::X_AXIS, pad.width);
- ieLayer->_padding.insert(InferenceEngine::Y_AXIS, pad.height);
- ieLayer->_pads_end.insert(InferenceEngine::X_AXIS, pad.width);
- ieLayer->_pads_end.insert(InferenceEngine::Y_AXIS, pad.height);
- ieLayer->_dilation.insert(InferenceEngine::X_AXIS, dilation.width);
- ieLayer->_dilation.insert(InferenceEngine::Y_AXIS, dilation.height);
- ieLayer->params["output"] = format("%d", outCn);
- ieLayer->params["kernel"] = format("%d,%d,%d,%d", outCn, inpGroupCn, kernel.height, kernel.width);
- ieLayer->params["pads_begin"] = format("%d,%d", pad.height, pad.width);
- ieLayer->params["pads_end"] = format("%d,%d", pad.height, pad.width);
- ieLayer->params["strides"] = format("%d,%d", stride.height, stride.width);
- ieLayer->params["dilations"] = format("%d,%d", dilation.height, dilation.width);
-#else
- ieLayer->_kernel_x = kernel.width;
- ieLayer->_kernel_y = kernel.height;
- ieLayer->_stride_x = stride.width;
- ieLayer->_stride_y = stride.height;
- ieLayer->_padding_x = pad.width;
- ieLayer->_padding_y = pad.height;
- ieLayer->_dilation_x = dilation.width;
- ieLayer->_dilation_y = dilation.height;
-#endif
- ieLayer->_out_depth = outCn;
- ieLayer->_group = group;
-
- ieLayer->_weights = ieWeights;
- if (ieBiases)
- ieLayer->_biases = ieBiases;
- return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-#endif
-#endif // HAVE_INF_ENGINE
- return Ptr<BackendNode>();