| // |
| // Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include <ClassicDelegateUtils.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 VisitCastOperator(DelegateData& delegateData, |
| TfLiteContext* tfLiteContext, |
| TfLiteNode* tfLiteNode, |
| int nodeIndex, |
| int32_t 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; |
| } |
| |
| const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); |
| 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("CAST", |
| tfLiteContext, |
| IsCastSupported, |
| delegateData.m_Backends, |
| isSupported, |
| setBackend, |
| inputTensorInfo, |
| outInfo); |
| }; |
| |
| // If the m_Network is a nullptr, this signals that a prerequisite TfLite callback is required to clarify the |
| // support for the operator |
| // If supported, VisitCastOperator will be called again to add the layer to the network as seen further below |
| if (!delegateData.m_Network) |
| { |
| validateFunc(outputTensorInfo, isSupported); |
| return isSupported ? kTfLiteOk : kTfLiteError; |
| } |
| |
| // Add a Cast layer |
| auto layerName = GetLayerName(armnn::LayerType::Cast, nodeIndex); |
| armnn::IConnectableLayer* layer = delegateData.m_Network->AddCastLayer(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, nodeIndex) != kTfLiteOk) |
| { |
| return kTfLiteError; |
| } |
| |
| // Connect |
| return Connect(layer, tfLiteNode, delegateData); |
| } |
| |
| 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 (!IsValid(tfLiteContext, tfLiteInputTensor0, operatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; |
| if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| const armnn::TensorInfo& inputTensorInfo0 = GetTensorInfoForTfLiteTensor(tfLiteInputTensor0); |
| const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); |
| |
| armnn::ReshapeDescriptor reshapeDesc; |
| std::vector<int32_t> targetShape; |
| |
| TfLiteReshapeParams* reshapeOptions = reinterpret_cast<TfLiteReshapeParams*>(tfLiteNode->builtin_data); |
| |
| // The new shape can be defined by either a second input tensor or by a builtin option, we need to check for both. |
| // Options might be set without valid data. we need to check the dimensions are in a valid range. |
| if (reshapeOptions && reshapeOptions->num_dimensions > 0 && reshapeOptions->num_dimensions <= 8) |
| { |
| for (int i=0; i < reshapeOptions->num_dimensions; ++i) |
| { |
| targetShape.push_back(reshapeOptions->shape[i]); |
| } |
| } |
| else if (numInputs == 2) |
| { |
| // Get shape from the second input tensor |
| const TfLiteTensor& tfLiteShapeInputTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; |
| if (!IsValid(tfLiteContext, tfLiteShapeInputTensor, operatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| if (tfLiteShapeInputTensor.dims->size != 1) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext, |
| "TfLiteArmnnDelegate: Target 'shape' input is not a 1D tensor in " |
| "operator #%d node #%d: Falling back to TfLiteOptions.", |
| operatorCode, nodeIndex); |
| } |
| else |
| { |
| // Get the shape data out of the input tensor |
| 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)); |
| } |
| } |
| } |
| 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); |
| return kTfLiteError; |
| } |
| |
| // Check the target shape to check if there is zero in the shape. |
| if (std::find(targetShape.begin(), targetShape.end(), 0) != targetShape.end() && |
| inputTensorInfo0.GetNumElements() != 0) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG(tfLiteContext, |
| "TfLiteArmnnDelegate: Input to reshape is a tensor with elements, " |
| "but the requested shape has 0. " |
| "operator #%d node #%d: ", |
| operatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| // 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; |
| } |
| |
| if (reshapeDesc.m_TargetShape.GetNumElements() != inputTensorInfo0.GetNumElements()) |
| { |
| TF_LITE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnDelegate: Reshape, number of elements in output shape does not match input " |
| "operator #%d node #%d: ", |
| operatorCode, nodeIndex); |
| return kTfLiteError; |
| } |
| |
| bool isSupported = false; |
| armnn::BackendId setBackend; |
| auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) |
| { |
| FORWARD_LAYER_SUPPORT_FUNC("RESHAPE", |
| tfLiteContext, |
| IsReshapeSupported, |
| delegateData.m_Backends, |
| isSupported, |
| setBackend, |
| inputTensorInfo0, |
| outInfo, |
| reshapeDesc); |
| }; |
| |
| if (!delegateData.m_Network) |
| { |
| validateFunc(outputTensorInfo, isSupported); |
| return isSupported ? kTfLiteOk : kTfLiteError; |
| } |
| |
| auto layerName = GetLayerName(armnn::LayerType::Reshape, nodeIndex); |
| armnn::IConnectableLayer* layer = delegateData.m_Network->AddReshapeLayer(reshapeDesc, 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, nodeIndex) != kTfLiteOk) |
| { |
| return kTfLiteError; |
| } |
| |
| // Connect |
| return Connect(layer, tfLiteNode, delegateData); |
| } |
| |
| TfLiteStatus VisitSqueezeOperator(DelegateData& delegateData, |
| TfLiteContext* tfLiteContext, |
| TfLiteNode* tfLiteNode, |
| int nodeIndex, |
| int32_t 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; |
| } |
| |
| auto layerName = GetLayerName(armnn::LayerType::Reshape, nodeIndex, "Squeeze"); |
| armnn::IConnectableLayer* layer = delegateData.m_Network->AddReshapeLayer(reshapeDesc, 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, nodeIndex) != kTfLiteOk) |
| { |
| return kTfLiteError; |
| } |
| |
| // Connect |
| return Connect(layer, tfLiteNode, delegateData); |
| } |
| |
| TfLiteStatus VisitExpandDimsOperator(DelegateData& delegateData, |
| TfLiteContext* tfLiteContext, |
| TfLiteNode* tfLiteNode, |
| int nodeIndex, |
| int32_t operatorCode) |
| { |
| TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, 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& 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; |
| } |
| |
| auto layerName = GetLayerName(armnn::LayerType::Reshape, nodeIndex, "ExpandDims"); |
| armnn::IConnectableLayer* layer = delegateData.m_Network->AddReshapeLayer(reshapeDesc, layerName.c_str()); |
| 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, nodeIndex) != kTfLiteOk) |
| { |
| return kTfLiteError; |
| } |
| |
| // Connect |
| return Connect(layer, tfLiteNode, delegateData); |
| } |
| |
| } // namespace armnnDelegate |