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//
// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#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>
#include <fmt/format.h>
namespace armnnDelegate
{
TfLiteStatus VisitSliceOperator(DelegateData& delegateData,
TfLiteContext* tfLiteContext,
TfLiteNode* tfLiteNode,
int nodeIndex,
int32_t sliceOperatorCode)
{
TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 3, nodeIndex));
TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
// Read inputs [input, begin, size]
int numInputs = tfLiteNode->inputs->size;
std::vector<const TfLiteTensor*> tfLiteInputs;
tfLiteInputs.reserve(numInputs);
const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
for (int i = 0; i < numInputs; i++)
{
const TfLiteTensor* inputTensor = &tfLiteTensors[tfLiteNode->inputs->data[i]];
tfLiteInputs.push_back(inputTensor);
if (!IsValid(tfLiteContext, *inputTensor, sliceOperatorCode, nodeIndex))
{
return kTfLiteError;
}
}
// We save the begin and size tensors in our descriptor. Therefore we have to read those values from inputs
int inputRank = tfLiteInputs[0]->dims->size;
auto ReadInt32Input = [&](int inputIndex, std::vector<int32_t>& outputData, const char* name) -> TfLiteStatus
{
if (tfLiteInputs[inputIndex]->type != kTfLiteInt32)
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: The %s Tensor of the Slice operation needs to "
"be of type int32. Operator: #%d node #%d: ",
name, sliceOperatorCode, nodeIndex);
return kTfLiteError;
}
int rank = tfLiteInputs[inputIndex]->dims->size;
if (rank != 1)
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: The %s Tensor of the Slice operation needs to "
"be a 1D-Tensor. Operator: #%d node #%d: ",
name, sliceOperatorCode, nodeIndex);
return kTfLiteError;
}
int numValues = tfLiteInputs[inputIndex]->dims->data[0];
if (numValues != inputRank)
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: The number of values in the %s Tensor of the "
"Slice operation needs to be equal to the rank of the Input Tensor. Operator: #%d node #%d: ",
name, sliceOperatorCode, nodeIndex);
return kTfLiteError;
}
// return tensor data
auto* tensorDataPtr = tflite::GetTensorData<int32_t>(tfLiteInputs[inputIndex]);
outputData.assign(tensorDataPtr, tensorDataPtr + numValues);
return kTfLiteOk;
};
std::vector<int32_t> signedBegin;
if (ReadInt32Input(1, signedBegin, "Begin") != kTfLiteOk)
{
return kTfLiteError;
}
std::vector<int32_t> signedSize;
if (ReadInt32Input(2, signedSize, "Size") != kTfLiteOk)
{
return kTfLiteError;
}
std::vector<uint32_t> begin({ signedBegin.begin(), signedBegin.end() });
std::vector<uint32_t> size(signedSize.size());
for (unsigned int i = 0; i < signedSize.size(); ++i)
{
int signedValue = signedSize[i];
if (signedValue < -1 || signedValue > tfLiteInputs[0]->dims->data[i] - signedBegin[i])
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnDelegate: Invalid value for Size. Size must be in range [-1, inputDimSize - begin] "
"[-1, %d] inclusive but was %d Operator: #%d node #%d: ",
tfLiteInputs[0]->dims->data[i] - signedBegin[i], signedValue, sliceOperatorCode,
nodeIndex);
return kTfLiteError;
}
if (signedValue == -1)
{
size[i] = tfLiteInputs[0]->dims->data[i] - signedBegin[i];
}
else
{
size[i] = static_cast<uint32_t>(signedValue);
}
}
// Write all data to the descriptor
armnn::SliceDescriptor descriptor(begin, size);
// Validate output
const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
if (!IsValid(tfLiteContext, tfLiteOutputTensor, sliceOperatorCode, nodeIndex))
{
return kTfLiteError;
}
const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(*tfLiteInputs[0]);
const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true);
bool isSupported = false;
armnn::BackendId setBackend;
auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
{
FORWARD_LAYER_SUPPORT_FUNC("SLICE",
tfLiteContext,
IsSliceSupported,
delegateData.m_Backends,
isSupported,
setBackend,
inputTensorInfo,
outInfo,
descriptor);
};
if (!delegateData.m_Network)
{
validateFunc(outputTensorInfo, isSupported);
return isSupported ? kTfLiteOk : kTfLiteError;
}
auto layerName = fmt::format("Slice:{}", nodeIndex);
// Add a Slice layer
armnn::IConnectableLayer* layer = delegateData.m_Network->AddSliceLayer(descriptor, layerName.c_str());
layer->SetBackendId(setBackend);
ARMNN_ASSERT(layer != nullptr);
armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
outputSlot.SetTensorInfo(outputTensorInfo);
// try to connect the Constant Inputs if there are any
if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk )
{
return kTfLiteError;
}
// Connect
return Connect(layer, tfLiteNode, delegateData);
}
} // namespace armnnDelegate
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