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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#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());
}
std::vector<TensorShape> Pooling2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
{
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.");
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");
}
// 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;
}
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;
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(),
inferredShapes[0]);*/
}
} // namespace armnn
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