| // |
| // Copyright © 2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #pragma once |
| |
| #include <OpaqueDelegateUtils.hpp> |
| |
| namespace armnnOpaqueDelegate |
| { |
| |
| TfLiteStatus VisitCastOperator(DelegateData& delegateData, |
| TfLiteOpaqueContext* tfLiteContext, |
| TfLiteOpaqueNode* tfLiteNode, |
| int nodeIndex, |
| int32_t operatorCode) |
| { |
| TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); |
| int numInputs = 0; |
| const int* inputTensors; |
| if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk) |
| { |
| return kTfLiteError; |
| } |
| |
| // This layer only has 1 input, so we can directly assign tensor[0] to a new opaque tensor |
| const TfLiteOpaqueTensor* |
| tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[numInputs-1]); |
| if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| int numOutputs = 0; |
| const int* outputTensors; |
| if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk) |
| { |
| return kTfLiteError; |
| } |
| |
| // This layer only has 1 output, so we can directly assign tensor[0] to a new opaque tensor |
| const TfLiteOpaqueTensor* |
| tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[numOutputs-1]); |
| if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); |
| const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); |
| |
| bool isSupported = false; |
| armnn::BackendId setBackend; |
| auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported) { |
| FORWARD_LAYER_OPAQUE_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 |
| armnn::IConnectableLayer* layer = delegateData.m_Network->AddCastLayer(); |
| 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, tfLiteContext, tfLiteNode, delegateData); |
| } |
| |
| TfLiteStatus VisitReshapeOperator(DelegateData& delegateData, |
| TfLiteOpaqueContext* tfLiteContext, |
| TfLiteOpaqueNode* tfLiteNode, |
| int nodeIndex, |
| int32_t operatorCode) |
| { |
| auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode); |
| |
| 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)); |
| |
| // Gather input indices and use to get input tensor. |
| 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; |
| } |
| |
| const TfLiteOpaqueTensor* tfLiteInputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]); |
| if (!IsValid(tfLiteContext, tfLiteInputTensor, operatorCode, 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; |
| } |
| |
| const TfLiteOpaqueTensor* tfLiteOutputTensor = TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]); |
| if (!IsValid(tfLiteContext, tfLiteOutputTensor, operatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| const armnn::TensorInfo& inputTensorInfo0 = GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor); |
| const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true); |
| |
| armnn::ReshapeDescriptor reshapeDesc; |
| std::vector<int32_t> targetShape; |
| |
| auto* reshapeOptions = reinterpret_cast<TfLiteReshapeParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode)); |
| |
| // 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 TfLiteOpaqueTensor* tfLiteShapeInputTensor = |
| TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]); |
| if (!IsValid(tfLiteContext, tfLiteShapeInputTensor, operatorCode, nodeIndex)) |
| { |
| return kTfLiteError; |
| } |
| |
| int32_t numDims = TfLiteOpaqueTensorNumDims(tfLiteShapeInputTensor); |
| if (numDims != 1) |
| { |
| TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: 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 = static_cast<int32_t*>(TfLiteOpaqueTensorData(tfLiteShapeInputTensor)); |
| int32_t shapeTensorNumValues = TfLiteOpaqueTensorDim(tfLiteShapeInputTensor, 0); |
| for (int32_t i = 0; i < shapeTensorNumValues; ++i) |
| { |
| targetShape.push_back(shapeTensorDataPtr[i]); |
| } |
| } |
| } |
| else |
| { |
| TF_LITE_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: Target shape not defined in reshape parameters or input tensor. " |
| "At least one method required in 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_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: 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_OPAQUE_MAYBE_KERNEL_LOG( |
| tfLiteContext, |
| "TfLiteArmnnOpaqueDelegate: 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_OPAQUE_SUPPORT_FUNC("RESHAPE", |
| tfLiteContext, |
| IsReshapeSupported, |
| delegateData.m_Backends, |
| isSupported, |
| setBackend, |
| inputTensorInfo0, |
| 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); |
| |
| // try to connect the Constant Inputs if there are any |
| if(ProcessInputs(layer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk ) |
| { |
| return kTfLiteError; |
| } |
| |
| // Connect |
| return Connect(layer, tfLiteContext, tfLiteNode, delegateData); |
| } |
| |
| } |