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//
// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <OpaqueDelegateUtils.hpp>
namespace armnnOpaqueDelegate
{
TfLiteStatus ValidateResizeOperator(DelegateData& delegateData,
TfLiteOpaqueContext* tfLiteContext,
const armnn::TensorInfo& inputInfo,
const armnn::TensorInfo& outputInfo,
const armnn::ResizeDescriptor& descriptor)
{
bool isSupported = false;
FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("RESIZE",
tfLiteContext,
IsResizeSupported,
delegateData.m_Backends,
isSupported,
armnn::BackendId(),
inputInfo,
outputInfo,
descriptor);
return isSupported ? kTfLiteOk : kTfLiteError;
}
TfLiteStatus VisitResizeOperator(DelegateData& delegateData,
TfLiteOpaqueContext* tfLiteContext,
TfLiteOpaqueNode* tfLiteNode,
int nodeIndex,
int32_t resizeOperatorCode)
{
TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
// Gather input indices and use to get input tensor.
auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode);
const int* inputTensors;
if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk)
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ",
nodeIndex);
return kTfLiteError;
}
// The first input contains the data of the image that should be resized [batch, height, width, channels]
const TfLiteOpaqueTensor* tfLiteInputTensor =
TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
if (IsDynamicTensor(tfLiteInputTensor))
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
resizeOperatorCode, nodeIndex);
return kTfLiteError;
}
// The second input contains a size tensor. The size tensor contains two integer values
// that describe the new height and width of the image [new_height, new_width]
const TfLiteOpaqueTensor* tfLiteSizeTensor =
TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]);
if (IsDynamicTensor(tfLiteSizeTensor))
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
resizeOperatorCode, nodeIndex);
return kTfLiteError;
}
// Gather output indices and use to get output tensors.
int numOutputs = 0;
const int* outputTensors;
if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk)
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ",
nodeIndex);
return kTfLiteError;
}
// The output tensor should have the shape [batch, new_height, new_width, channels]
const TfLiteOpaqueTensor* tfLiteOutputTensor =
TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
if (IsDynamicTensor(tfLiteOutputTensor))
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ",
resizeOperatorCode, nodeIndex);
return kTfLiteError;
}
const armnn::TensorInfo& inputTensorInfo =
GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
const armnn::TensorInfo& outputTensorInfo =
GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);
std::string layerName("Resize");
// Fill descriptor
armnn::ResizeDescriptor desc;
switch (resizeOperatorCode)
{
case kTfLiteBuiltinResizeBilinear:
{
desc.m_Method = armnn::ResizeMethod::Bilinear;
layerName += "Bilinear:" + std::to_string(nodeIndex);
TfLiteResizeBilinearParams* bilinearOptions =
reinterpret_cast<TfLiteResizeBilinearParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode));
desc.m_AlignCorners = bilinearOptions->align_corners;
desc.m_HalfPixelCenters = bilinearOptions->half_pixel_centers;
break;
}
case kTfLiteBuiltinResizeNearestNeighbor:
{
desc.m_Method = armnn::ResizeMethod::NearestNeighbor;
layerName += "NearestNeighbor:" + std::to_string(nodeIndex);
TfLiteResizeNearestNeighborParams* nearestNeighborOptions =
reinterpret_cast<TfLiteResizeNearestNeighborParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode));
desc.m_AlignCorners = nearestNeighborOptions->align_corners;
desc.m_HalfPixelCenters = nearestNeighborOptions->half_pixel_centers;
break;
}
default:
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: Unknown TfLite built in operation for Resize. "
"Given operator: #%d node #%d: ",
resizeOperatorCode, nodeIndex);
return kTfLiteError;
}
}
// In Arm NN the values of the size input tensor [new_height, new_width] is saved in the operator
// descriptor. We have to read it from the input tensor and write it to the descriptor.
auto* sizeTensorDataPtr = static_cast<int*>(TfLiteOpaqueTensorData(tfLiteSizeTensor));
auto sizeTensorNumDimensions = TfLiteOpaqueTensorNumDims(tfLiteSizeTensor);
// The size tensor is only a 1D tensor -> [new_height, new width]
if (sizeTensorNumDimensions != 1)
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: The Size-Input-Tensor of the Resize operation is not allowed to be a "
"dynamic tensor. Operator: #%d node #%d: ",
resizeOperatorCode, nodeIndex);
return kTfLiteError;
}
// Get number of values in the size tensor
auto sizeTensorNumValues = TfLiteOpaqueTensorDim(tfLiteSizeTensor,0);
if (sizeTensorNumValues == 0)
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: The Size-Input-Tensor of the Resize operation is not allowed to be a "
"dynamic tensor. Operator: #%d node #%d: ",
resizeOperatorCode, nodeIndex);
return kTfLiteError;
}
else if (sizeTensorNumValues != 2)
{
TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
tfLiteContext,
"TfLiteArmnnOpaqueDelegate: The Size-Input-Tensor of the Resize operation requires to "
"have a dimension of 2 [new_height, new width] but a tensor with a dimension of #%d was given. "
"Operator: #%d node #%d: ",
sizeTensorNumValues, resizeOperatorCode, nodeIndex);
return kTfLiteError;
}
// get size tensor data
std::vector<int32_t> sizeTensorData(sizeTensorDataPtr, sizeTensorDataPtr+sizeTensorNumValues);
desc.m_TargetHeight = static_cast<uint32_t> (sizeTensorData[0]);
desc.m_TargetWidth = static_cast<uint32_t> (sizeTensorData[1]);
desc.m_DataLayout = armnn::DataLayout::NHWC;
// No network pointer indicates that only support for this operator should be checked
if (!delegateData.m_Network)
{
return ValidateResizeOperator(delegateData,
tfLiteContext,
inputTensorInfo,
outputTensorInfo,
desc);
}
armnn::IConnectableLayer* resizeLayer = nullptr;
resizeLayer = delegateData.m_Network->AddResizeLayer(desc, layerName.c_str());
armnn::IOutputSlot& outputSlot = resizeLayer->GetOutputSlot(0);
outputSlot.SetTensorInfo(outputTensorInfo);
// try to connect the Constant Inputs if there are any
if(ProcessInputs(resizeLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk )
{
return kTfLiteError;
}
ARMNN_ASSERT(resizeLayer != nullptr);
return Connect(resizeLayer, tfLiteContext, tfLiteNode, delegateData);
}
} // namespace armnnOpaqueDelegate
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