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//
// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnn/utility/IgnoreUnused.hpp>
#include "DelegateUtils.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>
#include <numeric>
namespace armnnDelegate
{
TfLiteStatus CreateOutputTensorShape(const armnn::TensorInfo& inputTensorInfo,
const std::vector<int32_t>& targetShape,
armnn::ReshapeDescriptor& reshapeDesc)
{
std::vector<unsigned int> outputDims(targetShape.begin(), targetShape.end());
const auto stretchDim = std::find(targetShape.begin(), targetShape.end(), -1);
if (stretchDim != targetShape.end())
{
if (std::find(std::next(stretchDim), targetShape.end(), -1) != targetShape.end())
{
// Return kTfLiteError and log the error after returning
return kTfLiteError;
}
auto targetNumElements =
armnn::numeric_cast<unsigned int>(
std::accumulate(targetShape.begin(), targetShape.end(), -1, std::multiplies<int32_t>()));
auto stretchIndex = static_cast<size_t>(std::distance(targetShape.begin(), stretchDim));
outputDims[stretchIndex] = inputTensorInfo.GetNumElements() / targetNumElements;
}
armnn::TensorShape outputShape = armnn::TensorShape(static_cast<unsigned int>(outputDims.size()),
outputDims.data());
reshapeDesc.m_TargetShape = outputShape;
return kTfLiteOk;
}
TfLiteStatus VisitReshapeOperator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
TfLiteNode* tfLiteNode,
int nodeIndex,
int32_t operatorCode)
{
auto numInputs = tfLiteNode->inputs->size;
if (numInputs == 2)
{
TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
}
else
{
TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
}
TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
const TfLiteTensor& tfLiteInputTensor0 = tfLiteTensors[tfLiteNode->inputs->data[0]];
if (IsDynamicTensor(tfLiteInputTensor0))
{
TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext,
"TfLiteArmnnDelegate: Dynamic input tensors are not supported in "
"operator #%d node #%d: ",
operatorCode, nodeIndex);
return kTfLiteError;
}
const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
if (IsDynamicTensor(tfLiteOutputTensor))
{
TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext,
"TfLiteArmnnDelegate: Dynamic output tensors are not supported in "
"operator #%d node #%d: ",
operatorCode, nodeIndex);
return kTfLiteError;
}
const armnn::TensorInfo& inputTensorInfo0 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor0);
const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor);
armnn::ReshapeDescriptor reshapeDesc;
// The new shape can be defined by either a second input tensor or by a builtin option, we need to check for both.
if (numInputs == 2)
{
const TfLiteTensor& tfLiteShapeInputTensor = tfLiteTensors[tfLiteNode->inputs->data[1]];
if (IsDynamicTensor(tfLiteShapeInputTensor))
{
TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext,
"TfLiteArmnnDelegate: Dynamic input tensors are not supported in "
"operator #%d node #%d: ",
operatorCode, nodeIndex);
return kTfLiteError;
}
// Get the shape data out of the input tensor
std::vector<int32_t> targetShape;
auto* shapeTensorDataPtr = tflite::GetTensorData<int32_t>(&tfLiteShapeInputTensor);
auto shapeTensorNumValues = tfLiteShapeInputTensor.dims->data[0];
for (auto i=0; i < shapeTensorNumValues; ++i)
{
targetShape.push_back(*(shapeTensorDataPtr+i));
}
// Use the data to create the required tensor shape.
if (CreateOutputTensorShape(inputTensorInfo0, targetShape, reshapeDesc) != kTfLiteOk)
{
TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext,
"TfLiteArmnnDelegate: At most one component of shape can be -1 in: "
"operator #%d node #%d: ",
operatorCode, nodeIndex);
return kTfLiteError;
}
}
else if (tfLiteNode->builtin_data)
{
std::vector<int32_t> targetShape;
TfLiteReshapeParams* reshapeOptions =
reinterpret_cast<TfLiteReshapeParams*>(tfLiteNode->builtin_data);
for (int i=0; i < reshapeOptions->num_dimensions; ++i)
{
targetShape.push_back(reshapeOptions->shape[i]);
}
if (CreateOutputTensorShape(inputTensorInfo0, targetShape, reshapeDesc) != kTfLiteOk)
{
TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext,
"TfLiteArmnnDelegate: At most one component of shape can be -1 in: "
"operator #%d node #%d: ",
operatorCode, nodeIndex);
return kTfLiteError;
}
}
else
{
TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext,
"Target shape not defined in reshape parameters or input tensor. "
"At least one method required in operator #%d node #%d: ",
operatorCode, nodeIndex);
}
bool isSupported = false;
auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC(__func__,
tfLiteContext,
IsReshapeSupported,
delegateData.m_Backends,
isSupported,
inputTensorInfo0,
outInfo,
reshapeDesc);
};
if (!delegateData.m_Network)
{
validateFunc(outputTensorInfo, isSupported);
return isSupported ? kTfLiteOk : kTfLiteError;
}
armnn::IConnectableLayer* layer = delegateData.m_Network->AddReshapeLayer(reshapeDesc);
ARMNN_ASSERT(layer != nullptr);
armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
outputSlot.SetTensorInfo(outputTensorInfo);
// Connect
return Connect(layer, tfLiteNode, delegateData);
}
TfLiteStatus VisitSqueezeOperator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
TfLiteNode* tfLiteNode,
int nodeIndex,
int32_t operatorCode)
{
armnn::IgnoreUnused(delegateData,
tfLiteContext,
tfLiteNode,
nodeIndex,
operatorCode);
return kTfLiteError;
}
TfLiteStatus VisitExpandDimsOperator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
TfLiteNode* tfLiteNode,
int nodeIndex,
int32_t operatorCode)
{
armnn::IgnoreUnused(delegateData,
tfLiteContext,
tfLiteNode,
nodeIndex,
operatorCode);
return kTfLiteError;
}
} // namespace armnnDelegate
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