Release 18.05

1 files changed, 106 insertions, 0 deletions

diff --git a/src/armnn/layers/Pooling2dLayer.cpp b/src/armnn/layers/Pooling2dLayer.cpp
new file mode 100644
index 0000000000..ede37d7604
--- /dev/null
+++ b/src/armnn/layers/Pooling2dLayer.cpp
@@ -0,0 +1,106 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+#include "Pooling2dLayer.hpp"
+
+#include "LayerCloneBase.hpp"
+
+#include <armnn/TypesUtils.hpp>
+#include <backends/WorkloadData.hpp>
+#include <backends/WorkloadFactory.hpp>
+
+namespace armnn
+{
+
+Pooling2dLayer::Pooling2dLayer(const Pooling2dDescriptor& param, const char* name)
+    : LayerWithParameters(1, 1, LayerType::Pooling2d, param, name)
+{
+}
+
+std::unique_ptr<IWorkload> Pooling2dLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const
+{
+    Pooling2dQueueDescriptor descriptor;
+    return factory.CreatePooling2d(descriptor, PrepInfoAndDesc(descriptor, graph));
+}
+
+Pooling2dLayer* Pooling2dLayer::Clone(Graph& graph) const
+{
+    return CloneBase<Pooling2dLayer>(graph, m_Param, GetName());
+}
+
+void Pooling2dLayer::ValidateTensorShapesFromInputs()
+{
+    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();
+
+    // 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.");
+
+
+    unsigned int inWidth = inputShape[3];
+    unsigned int inHeight = inputShape[2];
+    unsigned int inChannels = inputShape[1];
+    unsigned int inBatchSize = inputShape[0];
+
+    bool isGlobalPooling = (m_Param.m_StrideX==0 && m_Param.m_StrideY==0);
+    unsigned int outWidth = 1;
+    unsigned int outHeight = 1;
+    if (!isGlobalPooling)
+    {
+        BOOST_ASSERT_MSG(m_Param.m_StrideX!=0 && m_Param.m_StrideY!=0,
+                         "Stride can only be zero when performing global pooling");
+
+        auto CalcSize = [](auto inSize, auto lowPad, auto highPad, auto poolSize, auto stride, auto padMethod,
+                           auto outputShapeRounding)
+            {
+                unsigned int readSize = inSize + lowPad + highPad - poolSize;
+                float div = static_cast<float>(readSize) / static_cast<float>(stride);
+
+                unsigned int size = 0;
+                switch (outputShapeRounding)
+                {
+                    case OutputShapeRounding::Ceiling:
+                        size = static_cast<unsigned int>(ceil(div)) + 1;
+                        break;
+                    case OutputShapeRounding ::Floor:
+                        size = static_cast<unsigned int>(floor(div)) + 1;
+                        break;
+                    default:
+                        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...
+                if ((size - 1)*stride >= inSize + lowPad)
+                {
+                    --size;
+                }
+
+                return size;
+            };
+
+        outWidth = CalcSize(inWidth, m_Param.m_PadLeft, m_Param.m_PadRight, m_Param.m_PoolWidth, m_Param.m_StrideX,
+                            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});
+
+    ConditionalThrowIfNotEqual<LayerValidationException>(
+        "Pooling2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
+        GetOutputSlot(0).GetTensorInfo().GetShape(),
+        shapeOut);
+}
+
+} // namespace armnn