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//
// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "DelegateUtils.hpp"
#include <armnn/utility/IgnoreUnused.hpp>
#include <tensorflow/lite/builtin_ops.h>
#include <tensorflow/lite/c/builtin_op_data.h>
#include <tensorflow/lite/c/common.h>
#include <tensorflow/lite/minimal_logging.h>
namespace armnnDelegate
{
TfLiteStatus VisitFullyConnectedOperator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
TfLiteNode* tfLiteNode,
int nodeIndex,
int32_t operatorCode)
{
auto numInputs = tfLiteNode->inputs->size;
if (numInputs < 2)
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d",
2, numInputs, nodeIndex);
return kTfLiteError;
}
TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
bool biasEnabled = (numInputs == 3);
const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]];
if(!IsValid(&tfLiteTensors[tfLiteNode->inputs->data[0]]))
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ",
operatorCode, nodeIndex);
return kTfLiteError;
}
if (IsDynamicTensor(tfLiteInputTensor))
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Dynamic input tensors are not supported in node #%d: ",
nodeIndex);
return kTfLiteError;
}
const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
if(!IsValid(&tfLiteOutputTensor))
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ",
operatorCode, nodeIndex);
return kTfLiteError;
}
if (IsDynamicTensor(tfLiteOutputTensor))
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Dynamic output tensors are not supported in node #%d: ",
nodeIndex);
return kTfLiteError;
}
const TfLiteTensor& tfLiteWeightsTensor = tfLiteTensors[tfLiteNode->inputs->data[1]];
if(!IsValid(&tfLiteWeightsTensor))
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Invalid weights tensor in operator #%d node #%d: ",
operatorCode, nodeIndex);
return kTfLiteError;
}
if (IsDynamicTensor(tfLiteWeightsTensor))
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Dynamic weight tensors are not supported in node #%d: ",
nodeIndex);
return kTfLiteError;
}
const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor);
armnn::TensorInfo weightsTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteWeightsTensor);
// Fully Connected Layer accepts two dimensional weights input
int32_t weightsDimension = static_cast<int32_t>(weightsTensorInfo.GetNumDimensions());
if (weightsDimension != 2)
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Dimension #$d for Fully Connected weights is not supported by Armnn"
" in operator #%d node #%d: ", weightsDimension, operatorCode, nodeIndex);
return kTfLiteError;
}
armnn::TensorInfo biasTensorInfo;
if (biasEnabled)
{
const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]];
if(!IsValid(&tfLiteBiasTensor))
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Invalid bias tensor in operator #%d node #%d: ",
operatorCode, nodeIndex);
return kTfLiteError;
}
if (IsDynamicTensor(tfLiteBiasTensor))
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Dynamic bias tensors are not supported in node #%d: ",
nodeIndex);
return kTfLiteError;
}
biasTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteBiasTensor);
}
else
{
biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor));
}
armnn::TensorInfo reshapedTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
if (inputTensorInfo.GetNumDimensions() > 2)
{
// Calculate reshape to flatten to 2D [batch_size, input_size]
std::vector<unsigned int> reshapedDimensions(2);
reshapedDimensions[1] = weightsTensorInfo.GetShape()[1];
reshapedDimensions[0] = inputTensorInfo.GetNumElements() / reshapedDimensions[1];
if (inputTensorInfo.GetNumElements() % reshapedDimensions[1] != 0)
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Failed to deduce input tensor shape from filter size #%d #%d node #%d: ",
reshapedDimensions[1], operatorCode, nodeIndex);
return kTfLiteError;
}
reshapedTensorInfo.SetShape(armnn::TensorShape{ 2, reshapedDimensions.data() });
}
armnn::FullyConnectedDescriptor descriptor;
descriptor.m_TransposeWeightMatrix = true;
descriptor.m_BiasEnabled = biasEnabled;
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
tfLiteContext,
IsFullyConnectedSupported,
delegateData.m_Backends,
isSupported,
reshapedTensorInfo,
outputTensorInfo,
weightsTensorInfo,
biasTensorInfo,
descriptor);
};
if (!delegateData.m_Network)
{
validateFunc(outputTensorInfo, isSupported);
return isSupported ? kTfLiteOk : kTfLiteError;
}
auto weightsTensor = CreateConstTensor(&tfLiteWeightsTensor,
weightsTensorInfo,
armnn::Optional<armnn::PermutationVector&>());
armnn::IConnectableLayer* layer = nullptr;
if (biasEnabled)
{
const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]];
auto biasTensor = CreateConstTensor(&tfLiteBiasTensor,
biasTensorInfo,
armnn::Optional<armnn::PermutationVector&>());
layer = delegateData.m_Network->AddFullyConnectedLayer(descriptor,
weightsTensor,
armnn::Optional<armnn::ConstTensor>(biasTensor));
}
else
{
layer = delegateData.m_Network->AddFullyConnectedLayer(descriptor,
weightsTensor,
armnn::EmptyOptional());
}
ARMNN_ASSERT(layer != nullptr);
armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
outputSlot.SetTensorInfo(outputTensorInfo);
armnn::IConnectableLayer* reshapeLayer = nullptr;
if (inputTensorInfo.GetNumDimensions() > 2)
{
// Add reshape to flatten to 2D [batch_size, input_size]
armnn::ReshapeDescriptor reshapeDescriptor;
reshapeDescriptor.m_TargetShape = reshapedTensorInfo.GetShape();
reshapeLayer = delegateData.m_Network->AddReshapeLayer(reshapeDescriptor);
ARMNN_ASSERT(reshapeLayer != nullptr);
reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedTensorInfo);
// Connect
delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[0]]->Connect(reshapeLayer->GetInputSlot(0));
reshapeLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
delegateData.m_OutputSlotForNode[tfLiteNode->outputs->data[0]] = &outputSlot;
}
if (reshapeLayer == nullptr)
{
Connect(layer, tfLiteNode, delegateData);
}
auto* tfLiteNodeParameters = reinterpret_cast<TfLiteAddParams*>(tfLiteNode->builtin_data);
if (!tfLiteNodeParameters)
{
// No Activation
return kTfLiteOk;
}
// Check Activation
TfLiteFusedActivation activationType = tfLiteNodeParameters->activation;
return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData);
}
} // namespace armnnDelegate
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