Merge pull request #12837 from dkurt:dnn_fix_ie

modules/dnn/src/dnn.cpp

@@ -1511,10 +1511,10 @@ struct Net::Impl
             CV_Assert(!ieNode.empty());
             ieNode->net = net;
 
+            auto weightableLayer = std::dynamic_pointer_cast<InferenceEngine::WeightableLayer>(ieNode->layer);
             if ((preferableTarget == DNN_TARGET_OPENCL_FP16 || preferableTarget == DNN_TARGET_MYRIAD) && !fused)
             {
                 ieNode->layer->precision = InferenceEngine::Precision::FP16;
-                auto weightableLayer = std::dynamic_pointer_cast<InferenceEngine::WeightableLayer>(ieNode->layer);
                 if (weightableLayer)
                 {
                     if (weightableLayer->_weights)
@@ -1532,7 +1532,13 @@ struct Net::Impl
                     }
                 }
             }
-
+            if (weightableLayer)
+            {
+                if (weightableLayer->_weights)
+                    weightableLayer->blobs["weights"] = weightableLayer->_weights;
+                if (weightableLayer->_biases)
+                    weightableLayer->blobs["biases"] = weightableLayer->_biases;
+            }
             ieNode->connect(ld.inputBlobsWrappers, ld.outputBlobsWrappers);
             net->addBlobs(ld.inputBlobsWrappers);
             net->addBlobs(ld.outputBlobsWrappers);

modules/dnn/src/layers/convolution_layer.cpp

@@ -449,15 +449,28 @@ public:
         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);
+#else
         ieLayer->_kernel_x = kernel.width;
         ieLayer->_kernel_y = kernel.height;
         ieLayer->_stride_x = stride.width;
         ieLayer->_stride_y = stride.height;
-        ieLayer->_out_depth = outCn;
         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 = wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW);
@@ -1659,15 +1672,28 @@ public:
         lp.precision = InferenceEngine::Precision::FP32;
         std::shared_ptr<InferenceEngine::DeconvolutionLayer> ieLayer(new InferenceEngine::DeconvolutionLayer(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);
+#else
         ieLayer->_kernel_x = kernel.width;
         ieLayer->_kernel_y = kernel.height;
         ieLayer->_stride_x = stride.width;
         ieLayer->_stride_y = stride.height;
-        ieLayer->_out_depth = numOutput;
         ieLayer->_padding_x = pad.width;
         ieLayer->_padding_y = pad.height;
         ieLayer->_dilation_x = dilation.width;
         ieLayer->_dilation_y = dilation.height;
+#endif
+        ieLayer->_out_depth = numOutput;
         ieLayer->_group = group;
 
         ieLayer->_weights = wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW);

modules/dnn/src/layers/pooling_layer.cpp

@@ -268,6 +268,16 @@ public:
         {
             lp.type = "Pooling";
             InferenceEngine::PoolingLayer* poolLayer = new InferenceEngine::PoolingLayer(lp);
+#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R3)
+            poolLayer->_kernel.insert(InferenceEngine::X_AXIS, kernel.width);
+            poolLayer->_kernel.insert(InferenceEngine::Y_AXIS, kernel.height);
+            poolLayer->_stride.insert(InferenceEngine::X_AXIS, stride.width);
+            poolLayer->_stride.insert(InferenceEngine::Y_AXIS, stride.height);
+            poolLayer->_padding.insert(InferenceEngine::X_AXIS, pad_l);
+            poolLayer->_padding.insert(InferenceEngine::Y_AXIS, pad_t);
+            poolLayer->_pads_end.insert(InferenceEngine::X_AXIS, pad_r);
+            poolLayer->_pads_end.insert(InferenceEngine::Y_AXIS, pad_b);
+#else
             poolLayer->_kernel_x = kernel.width;
             poolLayer->_kernel_y = kernel.height;
             poolLayer->_stride_x = stride.width;
@@ -276,6 +286,7 @@ public:
             poolLayer->_padding_y = pad_t;
             poolLayer->params["pad-r"] = format("%d", pad_r);
             poolLayer->params["pad-b"] = format("%d", pad_b);
+#endif
             poolLayer->_exclude_pad = type == AVE && padMode == "SAME";
             poolLayer->params["rounding-type"] = ceilMode ? "ceil" : "floor";
             poolLayer->_type = type == MAX ? InferenceEngine::PoolingLayer::PoolType::MAX :