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-rw-r--r--delegate/classic/src/Redefine.hpp187
1 files changed, 173 insertions, 14 deletions
diff --git a/delegate/classic/src/Redefine.hpp b/delegate/classic/src/Redefine.hpp
index 41c62c33c8..2c29083719 100644
--- a/delegate/classic/src/Redefine.hpp
+++ b/delegate/classic/src/Redefine.hpp
@@ -5,8 +5,6 @@
#pragma once
-#include <armnn/utility/IgnoreUnused.hpp>
-
#include <ClassicDelegateUtils.hpp>
#include <tensorflow/lite/builtin_ops.h>
@@ -231,13 +229,83 @@ TfLiteStatus VisitSqueezeOperator(DelegateData& delegateData,
int nodeIndex,
int32_t operatorCode)
{
- armnn::IgnoreUnused(delegateData,
- tfLiteContext,
- tfLiteNode,
- nodeIndex,
- operatorCode);
+ 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& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]];
+ if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex))
+ {
+ return kTfLiteError;
+ }
+
+ const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
+ if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex))
+ {
+ return kTfLiteError;
+ }
+
+ auto* options = reinterpret_cast<TfLiteSqueezeParams*>(tfLiteNode->builtin_data);
+
+ const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
+
+ std::vector<uint32_t> squeezeDim;
+ // A single negative dim index is interpreted as a negative index in python
+ // Meaning the index will be the shape size plus the negative index value
+ if (options->num_squeeze_dims == 1 && options->squeeze_dims[0] < 0)
+ {
+ int32_t dim = static_cast<int32_t>(inputTensorInfo.GetShape().GetNumDimensions()) + options->squeeze_dims[0];
+ squeezeDim.push_back(static_cast<uint32_t>(dim));
+ }
+ else
+ {
+ for (int32_t i = 0; i < options->num_squeeze_dims; ++i)
+ {
+ squeezeDim.push_back(static_cast<uint32_t>(options->squeeze_dims[i]));
+ }
+ }
+
+ armnn::TensorInfo outputTensorInfo = OutputShapeOfSqueeze(squeezeDim, inputTensorInfo);
+
+ armnn::ReshapeDescriptor reshapeDesc;
+ reshapeDesc.m_TargetShape = outputTensorInfo.GetShape();
+
+ bool isSupported = false;
+ armnn::BackendId setBackend;
+ auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
+ {
+ FORWARD_LAYER_SUPPORT_FUNC("SQUEEZE",
+ tfLiteContext,
+ IsReshapeSupported,
+ delegateData.m_Backends,
+ isSupported,
+ setBackend,
+ inputTensorInfo,
+ outInfo,
+ reshapeDesc);
+ };
+
+ if (!delegateData.m_Network)
+ {
+ validateFunc(outputTensorInfo, isSupported);
+ return isSupported ? kTfLiteOk : kTfLiteError;
+ }
+
+ armnn::IConnectableLayer* layer = delegateData.m_Network->AddReshapeLayer(reshapeDesc);
+ layer->SetBackendId(setBackend);
+ ARMNN_ASSERT(layer != nullptr);
+
+ armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
+ outputSlot.SetTensorInfo(outputTensorInfo);
- return kTfLiteError;
+ // 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);
}
TfLiteStatus VisitExpandDimsOperator(DelegateData& delegateData,
@@ -246,13 +314,104 @@ TfLiteStatus VisitExpandDimsOperator(DelegateData& delegateData,
int nodeIndex,
int32_t operatorCode)
{
- armnn::IgnoreUnused(delegateData,
- tfLiteContext,
- tfLiteNode,
- nodeIndex,
- operatorCode);
+ TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
+ TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
- return kTfLiteError;
+ const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
+ const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]];
+ if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex))
+ {
+ return kTfLiteError;
+ }
+
+ const TfLiteTensor& tfLiteAxisTensor = tfLiteTensors[tfLiteNode->inputs->data[1]];
+ if (!IsValid(tfLiteContext, tfLiteAxisTensor, operatorCode, nodeIndex))
+ {
+ return kTfLiteError;
+ }
+
+ const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
+ if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex))
+ {
+ return kTfLiteError;
+ }
+
+ const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
+ armnn::TensorInfo outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor);
+
+ auto* axisTensorData = tflite::GetTensorData<int32_t>(&tfLiteAxisTensor);
+ int32_t axis = axisTensorData[0];
+
+ int32_t inputDimSize = static_cast<int32_t>(inputTensorInfo.GetShape().GetNumDimensions());
+ if (axis > inputDimSize || axis < 0 - (inputDimSize + 1))
+ {
+ TF_LITE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnOpaqueDelegate: Axis must be in range "
+ "[0 - (inputDimSize + 1), inputDimSize] inclusive.");
+ return kTfLiteError;
+ }
+
+ if(axis < 0)
+ {
+ axis = inputDimSize + axis + 1;
+ }
+
+ std::vector<unsigned int> shape(static_cast<unsigned int>(inputDimSize) + 1);
+ unsigned int inputShapeIndex = 0;
+ for (unsigned int i = 0; i < static_cast<unsigned int>(inputDimSize + 1); ++i)
+ {
+ if (i == static_cast<unsigned int>(axis))
+ {
+ shape[i] = 1;
+ }
+ else
+ {
+ shape[i] = inputTensorInfo.GetShape()[inputShapeIndex];
+ ++inputShapeIndex;
+ }
+ }
+
+ armnn::ReshapeDescriptor reshapeDesc;
+ reshapeDesc.m_TargetShape = armnn::TensorShape(static_cast<unsigned int>(inputDimSize + 1), shape.data());
+
+ bool isSupported = false;
+ armnn::BackendId setBackend;
+ auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
+ {
+ FORWARD_LAYER_SUPPORT_FUNC("EXPAND_DIMS",
+ tfLiteContext,
+ IsReshapeSupported,
+ delegateData.m_Backends,
+ isSupported,
+ setBackend,
+ inputTensorInfo,
+ outInfo,
+ reshapeDesc);
+ };
+
+ if (!delegateData.m_Network)
+ {
+ validateFunc(outputTensorInfo, isSupported);
+ return isSupported ? kTfLiteOk : kTfLiteError;
+ }
+
+ armnn::IConnectableLayer* layer = delegateData.m_Network->AddReshapeLayer(reshapeDesc);
+ layer->SetBackendId(setBackend);
+ ARMNN_ASSERT(layer != nullptr);
+
+ armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
+ outputTensorInfo.SetShape(reshapeDesc.m_TargetShape);
+ 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
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-06 05:07:31 +0000