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//
// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "Pooling2DOperator.hpp"
TosaSerializationBasicBlock* ConvertAvgPool2DIgnoreValueToTosaOperator(const Layer* layer,
const std::vector<const TensorInfo*>& inputs,
const std::vector<const TensorInfo*>& outputs,
const Pooling2dDescriptor* poolDescriptor)
{
std::string padInputName = std::string("input0_");
std::string padOutputName = std::string("intermediate0_") + GetUniqueTosaMappingID();
std::string poolOutputName = std::string("output0_");
std::string blockName = std::string("Op_AVG_POOL2D_block_") + GetUniqueTosaMappingID();
// If a layer is present then the block will be used for execution, so input and output names need to be determined
// using the previous and following layers so the graph is connected correctly. For validation this doesn't matter.
if(layer != nullptr)
{
// Get the layers connected to the input slots and determine unique tensors names.
Layer& connectedInputLayer = layer->GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayer();
padInputName = GenerateUniqueName(connectedInputLayer, 0);
// Determine unique output tensor name.
poolOutputName = GenerateUniqueOutputName(*layer, 0);
}
std::vector<int> paddings;
if (poolDescriptor->m_DataLayout == DataLayout::NHWC)
{
paddings = {0,
0,
static_cast<int>(poolDescriptor->m_PadTop),
static_cast<int>(poolDescriptor->m_PadBottom),
static_cast<int>(poolDescriptor->m_PadLeft),
static_cast<int>(poolDescriptor->m_PadRight),
0,
0
};
}
else
{
paddings = {0,
0,
0,
0,
static_cast<int>(poolDescriptor->m_PadTop),
static_cast<int>(poolDescriptor->m_PadBottom),
static_cast<int>(poolDescriptor->m_PadLeft),
static_cast<int>(poolDescriptor->m_PadRight)
};
}
TosaPadAttribute padAttribute(paddings, 0, 0.0f);
auto* opPad = new TosaSerializationOperator(Op_PAD,
Attribute_PadAttribute,
&padAttribute,
{padInputName},
{padOutputName});
std::vector<int> pad = {0, 0, 0, 0};
std::vector<int> kernel = {static_cast<int>(poolDescriptor->m_PoolHeight),
static_cast<int>(poolDescriptor->m_PoolWidth)};
std::vector<int> stride = {static_cast<int>(poolDescriptor->m_StrideY),
static_cast<int>(poolDescriptor->m_StrideX)};
TosaPoolAttribute poolAttribute(pad, kernel, stride, 0, 0, ArmNNToDType(inputs[0]->GetDataType()));
auto* opPool = new TosaSerializationOperator(Op_AVG_POOL2D,
Attribute_PoolAttribute,
&poolAttribute,
{padOutputName},
{poolOutputName});
std::vector<TosaSerializationTensor*> tensors;
std::vector<int32_t> inputShape = GetTosaTensorShape(inputs[0]->GetShape());
DType inputDType = ArmNNToDType(inputs[0]->GetDataType());
// Only add input tensors if connected layer is an input layer.
// As intermediate or constant tensors will be created separately.
// There also can't be duplicate tensor.
if(padInputName.find("input0_") != std::string::npos)
{
tensors.push_back(new TosaSerializationTensor(padInputName, inputShape, inputDType, {}));
}
std::vector<int32_t> outputShape = GetTosaTensorShape(outputs[0]->GetShape());
DType outputDType = ArmNNToDType(outputs[0]->GetDataType());
std::vector<int32_t> intermediateShape;
if (poolDescriptor->m_DataLayout == DataLayout::NHWC)
{
intermediateShape = {inputShape[0],
inputShape[1] + paddings[2] + paddings[3],
inputShape[2] + paddings[4] + paddings[5],
inputShape[3]};
}
else
{
intermediateShape = {inputShape[0],
inputShape[1],
inputShape[2] + paddings[4] + paddings[5],
inputShape[3] + paddings[6] + paddings[7]};
}
tensors.push_back(new TosaSerializationTensor(padOutputName, intermediateShape, inputDType, {}));
tensors.push_back(new TosaSerializationTensor(poolOutputName, outputShape, outputDType, {}));
// operatorInputNames/operatorOutputNames ends up being the same as
// blockInputNames/blockOutputNames for one-to-one ArmNN to TOSA mappings
return new TosaSerializationBasicBlock(blockName, // name
mainName, // region name
{opPad, opPool}, // operators
tensors, // tensors
{padInputName}, // inputs
{poolOutputName}); // outputs
}
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