Release 18.08

1 files changed, 16 insertions, 14 deletions

diff --git a/src/armnn/layers/Pooling2dLayer.cpp b/src/armnn/layers/Pooling2dLayer.cpp
index ede37d7604..68049101e7 100644
--- a/src/armnn/layers/Pooling2dLayer.cpp
+++ b/src/armnn/layers/Pooling2dLayer.cpp
@@ -29,15 +29,10 @@ Pooling2dLayer* Pooling2dLayer::Clone(Graph& graph) const
     return CloneBase<Pooling2dLayer>(graph, m_Param, GetName());
 }
 
-void Pooling2dLayer::ValidateTensorShapesFromInputs()
+std::vector<TensorShape> Pooling2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
 {
-    ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr,
-                     "Pooling2dLayer: InputSlot must be connected to an OutputSlot");
-    ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection()->IsTensorInfoSet(),
-                     "Pooling2dLayer: TensorInfo must be set on connected InputSlot.");
-
-    IOutputSlot* input = GetInputSlot(0).GetConnection();
-    const TensorShape& inputShape = input->GetTensorInfo().GetShape();
+    BOOST_ASSERT(inputShapes.size() == 1);
+    const TensorShape& inputShape = inputShapes[0];
 
     // If we support multiple batch dimensions in the future, then this assert will need to change.
     BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Pooling2dLayer will always have 4D input.");
@@ -75,8 +70,8 @@ void Pooling2dLayer::ValidateTensorShapesFromInputs()
                         BOOST_ASSERT_MSG(false, "Unsupported Output Shape Rounding");
                 }
 
-                // Make sure that border operations will start from inside the input and not the padded area
-                // This is what both Caffe and CL does...
+                // MakeS sure that border operations will start from inside the input and not the padded area.
+                // This is what both Caffe and CL do...
                 if ((size - 1)*stride >= inSize + lowPad)
                 {
                     --size;
@@ -89,18 +84,25 @@ void Pooling2dLayer::ValidateTensorShapesFromInputs()
                             m_Param.m_PaddingMethod, m_Param.m_OutputShapeRounding);
         outHeight= CalcSize(inHeight, m_Param.m_PadTop, m_Param.m_PadBottom, m_Param.m_PoolHeight, m_Param.m_StrideY,
                             m_Param.m_PaddingMethod, m_Param.m_OutputShapeRounding);
-
-
     }
     unsigned int outChannels = inChannels;
     unsigned int outBatchSize = inBatchSize;
 
-    TensorShape shapeOut({outBatchSize, outChannels, outHeight, outWidth});
+    return std::vector<TensorShape>({ TensorShape({outBatchSize, outChannels, outHeight, outWidth}) });
+}
+
+void Pooling2dLayer::ValidateTensorShapesFromInputs()
+{
+    VerifyLayerConnections(1, CHECK_LOCATION());
+
+    auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
+
+    BOOST_ASSERT(inferredShapes.size() == 1);
 
     ConditionalThrowIfNotEqual<LayerValidationException>(
         "Pooling2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
         GetOutputSlot(0).GetTensorInfo().GetShape(),
-        shapeOut);
+        inferredShapes[0]);
 }
 
 } // namespace armnn