dnn: fixed IE support on Windows

modules/dnn/src/layers/batch_norm_layer.cpp

@@ -283,7 +283,7 @@ public:
         lp.precision = InferenceEngine::Precision::FP32;
         std::shared_ptr<InferenceEngine::ScaleShiftLayer> ieLayer(new InferenceEngine::ScaleShiftLayer(lp));
 
-        const int numChannels = weights_.total();
+        const size_t numChannels = weights_.total();
         ieLayer->_weights = wrapToInfEngineBlob(weights_, {numChannels}, InferenceEngine::Layout::C);
         ieLayer->_biases = wrapToInfEngineBlob(bias_, {numChannels}, InferenceEngine::Layout::C);
 

modules/dnn/src/layers/convolution_layer.cpp

@@ -456,7 +456,7 @@ public:
         if (hasBias() || fusedBias)
         {
             Mat biasesMat({outCn}, CV_32F, &biasvec[0]);
-            ieLayer->_biases = wrapToInfEngineBlob(biasesMat, {outCn}, InferenceEngine::Layout::C);
+            ieLayer->_biases = wrapToInfEngineBlob(biasesMat, {(size_t)outCn}, InferenceEngine::Layout::C);
         }
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
 #endif  // HAVE_INF_ENGINE

modules/dnn/src/layers/fully_connected_layer.cpp

@@ -427,9 +427,9 @@ public:
         std::shared_ptr<InferenceEngine::FullyConnectedLayer> ieLayer(new InferenceEngine::FullyConnectedLayer(lp));
 
         ieLayer->_out_num = blobs[0].size[0];
-        ieLayer->_weights = wrapToInfEngineBlob(blobs[0], {blobs[0].size[0], blobs[0].size[1], 1, 1}, InferenceEngine::Layout::OIHW);
+        ieLayer->_weights = wrapToInfEngineBlob(blobs[0], {(size_t)blobs[0].size[0], (size_t)blobs[0].size[1], 1, 1}, InferenceEngine::Layout::OIHW);
         if (blobs.size() > 1)
-            ieLayer->_biases = wrapToInfEngineBlob(blobs[1], {ieLayer->_out_num}, InferenceEngine::Layout::C);
+            ieLayer->_biases = wrapToInfEngineBlob(blobs[1], {(size_t)ieLayer->_out_num}, InferenceEngine::Layout::C);
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
 #endif  // HAVE_INF_ENGINE
         return Ptr<BackendNode>();

modules/dnn/src/layers/normalize_bbox_layer.cpp

@@ -254,7 +254,7 @@ public:
         ieLayer->params["across_spatial"] = acrossSpatial ? "1" : "0";
         ieLayer->params["channel_shared"] = blobs[0].total() == 1 ? "1" : "0";
 
-        const int numChannels = blobs[0].total();
+        const size_t numChannels = blobs[0].total();
         ieLayer->blobs["weights"] = wrapToInfEngineBlob(blobs[0], {numChannels}, InferenceEngine::Layout::C);
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
 #endif  // HAVE_INF_ENGINE

modules/dnn/src/layers/scale_layer.cpp

@@ -178,7 +178,7 @@ public:
         lp.precision = InferenceEngine::Precision::FP32;
         std::shared_ptr<InferenceEngine::ScaleShiftLayer> ieLayer(new InferenceEngine::ScaleShiftLayer(lp));
 
-        const int numChannels = blobs[0].total();
+        const size_t numChannels = blobs[0].total();
         ieLayer->_weights = wrapToInfEngineBlob(blobs[0], {numChannels}, InferenceEngine::Layout::C);
         if (hasBias)
             ieLayer->_biases = wrapToInfEngineBlob(blobs[1], {numChannels}, InferenceEngine::Layout::C);