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Diffstat (limited to 'delegate/classic/src/Convolution.hpp')
| -rw-r--r-- | delegate/classic/src/Convolution.hpp | 870 |
1 files changed, 870 insertions, 0 deletions
diff --git a/delegate/classic/src/Convolution.hpp b/delegate/classic/src/Convolution.hpp new file mode 100644 index 0000000000..f6a50615fc --- /dev/null +++ b/delegate/classic/src/Convolution.hpp @@ -0,0 +1,870 @@ +// +// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include <DelegateUtils.hpp> +#include "SharedFunctions.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 "tensorflow/lite/kernels/internal/tensor.h" + +namespace armnnDelegate +{ + +TfLiteStatus VisitConv2dOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + TfLiteNode* tfLiteNode, + int nodeIndex, + int32_t operatorCode) +{ + auto numInputs = tfLiteNode->inputs->size; + if (numInputs < 2) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", + 2, numInputs, nodeIndex); + return kTfLiteError; + } + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + + armnn::Convolution2dDescriptor descriptor; + const auto params = reinterpret_cast<TfLiteConvParams*>(tfLiteNode->builtin_data); + + bool biasEnabled = IsOptionalOperandPresent(tfLiteNode, 2); + descriptor.m_BiasEnabled = biasEnabled; + descriptor.m_StrideX = NonNegative(params->stride_width, nodeIndex); + descriptor.m_StrideY = NonNegative(params->stride_height, nodeIndex); + descriptor.m_DataLayout = armnn::DataLayout::NHWC; + descriptor.m_DilationX = NonNegative(params->dilation_width_factor, nodeIndex); + descriptor.m_DilationY = NonNegative(params->dilation_height_factor, nodeIndex); + + const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; + const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; + if(!IsValid(&tfLiteTensors[tfLiteNode->inputs->data[0]])) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteInputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; + if(!IsValid(&tfLiteOutputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteOutputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + + const TfLiteTensor& tfLiteFilterTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; + if(!IsValid(&tfLiteFilterTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Invalid filter tensor in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteFilterTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic filter tensors are not supported in node #%d: ", + nodeIndex); + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); + + auto* tfLiteNodeParameters = reinterpret_cast<TfLiteConvParams*>(tfLiteNode->builtin_data); + TfLiteFusedActivation activationType=kTfLiteActNone; + if (tfLiteNodeParameters) + { + activationType = tfLiteNodeParameters->activation; + TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo, + outputTensorInfo, activationType); + if(activationStatus != kTfLiteOk) + { + return kTfLiteError; + } + + } + + const armnn::TensorInfo& filterTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteFilterTensor); + + armnn::TensorInfo biasTensorInfo; + if(biasEnabled) + { + const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; + if(!IsValid(&tfLiteBiasTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Invalid bias tensor in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteBiasTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic bias tensors are not supported in node #%d: ", + nodeIndex); + return kTfLiteError; + } + biasTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteBiasTensor); + } + else + { + biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor)); + } + + armnn::Optional<armnn::TensorInfo> optionalBiasInfo(biasTensorInfo); + + // TfLite uses NHWC tensors + const unsigned int inputHeight = inputTensorInfo.GetShape()[1]; + const unsigned int inputWidth = inputTensorInfo.GetShape()[2]; + + const unsigned int filterHeight = filterTensorInfo.GetShape()[1]; + const unsigned int filterWidth = filterTensorInfo.GetShape()[2]; + + // Calculate padding + CalcPadding(inputHeight, filterHeight, descriptor.m_StrideY, descriptor.m_DilationY, + descriptor.m_PadTop, descriptor.m_PadBottom, params->padding); + CalcPadding(inputWidth, filterWidth, descriptor.m_StrideX, descriptor.m_DilationX, + descriptor.m_PadLeft, descriptor.m_PadRight, params->padding); + + armnn::BackendId setBackend; + if (!delegateData.m_Network) + { + bool isSupported = false; + FORWARD_LAYER_SUPPORT_FUNC("CONV2D", + tfLiteContext, + IsConvolution2dSupported, + delegateData.m_Backends, + isSupported, + setBackend, + inputTensorInfo, + outputTensorInfo, + descriptor, + filterTensorInfo, + optionalBiasInfo); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + // Set up filter and biases + armnn::IConnectableLayer* layer = delegateData.m_Network->AddConvolution2dLayer(descriptor); + layer->SetBackendId(setBackend); + + if(filterTensorInfo.IsConstant()) + { + auto filter = + CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[1]], + filterTensorInfo); + + armnn::IConnectableLayer *weightsLayer = delegateData.m_Network->AddConstantLayer(filter); + weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); + weightsLayer->GetOutputSlot(0).SetTensorInfo(filterTensorInfo); + } + + if (biasEnabled) + { + const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; + if(biasTensorInfo.IsConstant()) + { + auto biasTensor = CreateConstTensor(&tfLiteBiasTensor, biasTensorInfo); + armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biasTensor); + ARMNN_ASSERT(biasLayer != nullptr); + biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); + biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensorInfo); + } + } + + // The data input can also be constant, so we must check that this is also allocated to an input slot + if(inputTensorInfo.IsConstant()) + { + auto input = + CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[0]], + inputTensorInfo); + + armnn::IConnectableLayer *inputLayer = delegateData.m_Network->AddConstantLayer(input); + inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u)); + inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); + } + + ARMNN_ASSERT(layer != nullptr); + + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + + if(Connect(layer, tfLiteNode, delegateData) != kTfLiteOk) + { + return kTfLiteError; + } + + if (!tfLiteNodeParameters) + { + // No Activation + return kTfLiteOk; + } + // Check and Create activation + return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData); + +} + +// Conv3d is only correctly supported for external delegates from TF Lite v2.6, as there was a breaking bug in v2.5. +#if defined(ARMNN_POST_TFLITE_2_5) +TfLiteStatus VisitConv3dOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + TfLiteNode* tfLiteNode, + int nodeIndex, + int32_t operatorCode) +{ + auto numInputs = tfLiteNode->inputs->size; + if (numInputs < 2) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", + 2, numInputs, nodeIndex); + return kTfLiteError; + } + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + + armnn::Convolution3dDescriptor descriptor; + const auto params = reinterpret_cast<TfLiteConv3DParams*>(tfLiteNode->builtin_data); + + bool biasEnabled = IsOptionalOperandPresent(tfLiteNode, 2); + descriptor.m_BiasEnabled = biasEnabled; + descriptor.m_DataLayout = armnn::DataLayout::NDHWC; + descriptor.m_StrideX = NonNegative(params->stride_width, nodeIndex); + descriptor.m_StrideY = NonNegative(params->stride_height, nodeIndex); + descriptor.m_StrideZ = NonNegative(params->stride_depth, nodeIndex); + descriptor.m_DilationX = NonNegative(params->dilation_width_factor, nodeIndex); + descriptor.m_DilationY = NonNegative(params->dilation_height_factor, nodeIndex); + descriptor.m_DilationZ = NonNegative(params->dilation_depth_factor, 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 TfLiteTensor& tfLiteFilterTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; + if (!IsValid(tfLiteContext, tfLiteFilterTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); + + auto* tfLiteNodeParameters = reinterpret_cast<TfLiteConv3DParams*>(tfLiteNode->builtin_data); + TfLiteFusedActivation activationType=kTfLiteActNone; + if (tfLiteNodeParameters) + { + activationType = tfLiteNodeParameters->activation; + TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo, + outputTensorInfo, activationType); + if(activationStatus != kTfLiteOk) + { + return kTfLiteError; + } + + } + + const armnn::TensorInfo& filterTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteFilterTensor); + + armnn::TensorInfo biasTensorInfo; + if(biasEnabled) + { + const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; + if (!IsValid(tfLiteContext, tfLiteBiasTensor, operatorCode, nodeIndex)) + { + return kTfLiteError; + } + biasTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteBiasTensor); + } + else + { + biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor)); + } + + armnn::Optional<armnn::TensorInfo> optionalBiasInfo(biasTensorInfo); + + // TfLite uses NDHWC tensors + const unsigned int inputDepth = inputTensorInfo.GetShape()[1]; + const unsigned int inputHeight = inputTensorInfo.GetShape()[2]; + const unsigned int inputWidth = inputTensorInfo.GetShape()[3]; + + // Assuming the filter is DHWIO : Depth, Height, Width, OutputChannels, InputChannels + const unsigned int filterDepth = filterTensorInfo.GetShape()[0]; + const unsigned int filterHeight = filterTensorInfo.GetShape()[1]; + const unsigned int filterWidth = filterTensorInfo.GetShape()[2]; + + // Calculate padding + CalcPadding(inputDepth, filterDepth, descriptor.m_StrideZ, descriptor.m_DilationZ, + descriptor.m_PadFront, descriptor.m_PadBack, params->padding); + CalcPadding(inputHeight, filterHeight, descriptor.m_StrideY, descriptor.m_DilationY, + descriptor.m_PadTop, descriptor.m_PadBottom, params->padding); + CalcPadding(inputWidth, filterWidth, descriptor.m_StrideX, descriptor.m_DilationX, + descriptor.m_PadLeft, descriptor.m_PadRight, params->padding); + + // 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, VisitConvolutionOperator will be called again to add the layer to the network as seen below. + armnn::BackendId setBackend; + if (!delegateData.m_Network) + { + bool isSupported = false; + FORWARD_LAYER_SUPPORT_FUNC("CONV3D", + tfLiteContext, + IsConvolution3dSupported, + delegateData.m_Backends, + isSupported, + setBackend, + inputTensorInfo, + outputTensorInfo, + descriptor, + filterTensorInfo, + optionalBiasInfo); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + armnn::IConnectableLayer* layer = delegateData.m_Network->AddConvolution3dLayer(descriptor); + layer->SetBackendId(setBackend); + ARMNN_ASSERT(layer != nullptr); + + // Add a constant layer for weights and biases if inputs are constant, + // which are connected to the Convolution3d layer as inputs. + if (filterTensorInfo.IsConstant()) + { + auto filter = CreateConstTensor(&tfLiteFilterTensor, + filterTensorInfo); + + armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(filter); + ARMNN_ASSERT(weightsLayer != nullptr); + + weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); + weightsLayer->GetOutputSlot(0).SetTensorInfo(filterTensorInfo); + } + + if(biasEnabled) + { + const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; + if(biasTensorInfo.IsConstant()) + { + auto biases = CreateConstTensor(&tfLiteBiasTensor, + biasTensorInfo); + + armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biases); + ARMNN_ASSERT(biasLayer != nullptr); + + biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); + biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensorInfo); + } + } + + // The data input can also be constant, so we must check that this is also allocated to an input slot + if(inputTensorInfo.IsConstant()) + { + auto input = + CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[0]], + inputTensorInfo); + + armnn::IConnectableLayer *inputLayer = delegateData.m_Network->AddConstantLayer(input); + inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u)); + inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); + } + + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + + if(Connect(layer, tfLiteNode, delegateData) != kTfLiteOk) + { + return kTfLiteError; + } + + if (!tfLiteNodeParameters) + { + // No Activation + return kTfLiteOk; + } + + // Check and create activation + return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData); +} +#endif + +TfLiteStatus VisitDepthwiseConv2dOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + TfLiteNode* tfLiteNode, + int nodeIndex, + int32_t operatorCode) +{ + auto numInputs = tfLiteNode->inputs->size; + if (numInputs < 2) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, "TfLiteArmnnDelegate: Minimum number of inputs (%d != %d) in node #%d", + 2, numInputs, nodeIndex); + return kTfLiteError; + } + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + + bool biasEnabled = IsOptionalOperandPresent(tfLiteNode, 2); + + armnn::DepthwiseConvolution2dDescriptor descriptor; + const auto params = reinterpret_cast<TfLiteDepthwiseConvParams*>(tfLiteNode->builtin_data); + + descriptor.m_BiasEnabled = biasEnabled; + descriptor.m_StrideX = NonNegative(params->stride_width, nodeIndex); + descriptor.m_StrideY = NonNegative(params->stride_height, nodeIndex); + descriptor.m_DataLayout = armnn::DataLayout::NHWC; + descriptor.m_DilationX = NonNegative(params->dilation_width_factor, nodeIndex); + descriptor.m_DilationY = NonNegative(params->dilation_height_factor, nodeIndex); + + const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; + const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; + if(!IsValid(&tfLiteInputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteInputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; + if(!IsValid(&tfLiteOutputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteOutputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + + const TfLiteTensor& tfLiteFilterTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; + if(!IsValid(&tfLiteFilterTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Invalid filter tensor in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteFilterTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic filter tensors are not supported in node #%d: ", + nodeIndex); + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); + + auto* tfLiteNodeParameters = reinterpret_cast<TfLiteDepthwiseConvParams *>(tfLiteNode->builtin_data); + TfLiteFusedActivation activationType = kTfLiteActNone; + if (tfLiteNodeParameters) + { + activationType = tfLiteNodeParameters->activation; + TfLiteStatus activationStatus = ValidateFusedActivationOperator(delegateData, tfLiteContext, outputTensorInfo, + outputTensorInfo, activationType); + if(activationStatus != kTfLiteOk) + { + return kTfLiteError; + } + + } + + const armnn::TensorInfo& filterTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteFilterTensor); + + // Assuming input is NHWC + unsigned int inputHeight = inputTensorInfo.GetShape()[1]; + unsigned int inputWidth = inputTensorInfo.GetShape()[2]; + + // TensorflowLite weights come in the format [1, H, W, I * M] + unsigned int filterHeight = filterTensorInfo.GetShape()[1]; + unsigned int filterWidth = filterTensorInfo.GetShape()[2]; + + // Calculate padding + CalcPadding(inputHeight, filterHeight, descriptor.m_StrideY, descriptor.m_DilationY, + descriptor.m_PadTop, descriptor.m_PadBottom, params->padding); + CalcPadding(inputWidth, filterWidth, descriptor.m_StrideX, descriptor.m_DilationX, + descriptor.m_PadLeft, descriptor.m_PadRight, params->padding); + + armnn::TensorInfo biasTensorInfo; + if(biasEnabled) + { + const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; + if(!IsValid(&tfLiteBiasTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Invalid bias tensor in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteBiasTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic bias tensors are not supported in node #%d: ", + nodeIndex); + return kTfLiteError; + } + biasTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteBiasTensor); + } + else + { + biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor)); + } + + armnn::BackendId setBackend; + if (!delegateData.m_Network) + { + bool isSupported = false; + FORWARD_LAYER_SUPPORT_FUNC("DEPTHWISE_CONV2D", + tfLiteContext, + IsDepthwiseConvolutionSupported, + delegateData.m_Backends, + isSupported, + setBackend, + inputTensorInfo, + outputTensorInfo, + descriptor, + filterTensorInfo, + armnn::Optional<armnn::TensorInfo>(biasTensorInfo)); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + armnn::IConnectableLayer* layer = delegateData.m_Network->AddDepthwiseConvolution2dLayer(descriptor); + layer->SetBackendId(setBackend); + + if(filterTensorInfo.IsConstant()) + { + // For depthwise the weights layout is the same as for tflite [1, H, W, I*M]. No permutation required. + auto filter = CreateConstTensor(&tfLiteFilterTensor, filterTensorInfo); + + armnn::IConnectableLayer* weightsLayer = delegateData.m_Network->AddConstantLayer(filter); + weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u)); + weightsLayer->GetOutputSlot(0).SetTensorInfo(filterTensorInfo); + } + + if (biasEnabled) + { + const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; + if(biasTensorInfo.IsConstant()) + { + auto biasTensor = CreateConstTensor(&tfLiteBiasTensor, biasTensorInfo); + armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biasTensor); + ARMNN_ASSERT(biasLayer != nullptr); + biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); + biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensorInfo); + } + } + + // The data input can also be constant, so we must check that this is also allocated to an input slot + if(inputTensorInfo.IsConstant()) + { + auto input = + CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[0]], + inputTensorInfo); + + armnn::IConnectableLayer *inputLayer = delegateData.m_Network->AddConstantLayer(input); + inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u)); + inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); + } + + ARMNN_ASSERT(layer != nullptr); + + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + + if(Connect(layer, tfLiteNode, delegateData) != kTfLiteOk) + { + return kTfLiteError; + } + + if (!tfLiteNodeParameters) + { + // No Activation + return kTfLiteOk; + } + // Check and create activation + return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData); +} + +TfLiteStatus VisitTransposeConv2dOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + TfLiteNode* tfLiteNode, + int nodeIndex, + int32_t operatorCode) +{ + TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 3, nodeIndex)); + TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex)); + + armnn::TransposeConvolution2dDescriptor descriptor; + auto* parameters = reinterpret_cast<TfLiteTransposeConvParams*>(tfLiteNode->builtin_data); + descriptor.m_BiasEnabled = false; + descriptor.m_StrideX = NonNegative(parameters->stride_width, nodeIndex); + descriptor.m_StrideY = NonNegative(parameters->stride_height, nodeIndex); + descriptor.m_DataLayout = armnn::DataLayout::NHWC; + + const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors; + const TfLiteTensor& tfLiteOutputShapeTensor = tfLiteTensors[tfLiteNode->inputs->data[0]]; + if(!IsValid(&tfLiteOutputShapeTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteOutputShapeTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + + const armnn::TensorInfo outputShapeTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputShapeTensor); + std::vector<int32_t> outputShape(outputShapeTensorInfo.GetNumElements()); + if (outputShapeTensorInfo.GetDataType() == armnn::DataType::Signed32) + { + for(unsigned int i=0; i < outputShapeTensorInfo.GetNumElements(); i++) + { + outputShape[i] = ::tflite::GetTensorData<int32_t>(&tfLiteOutputShapeTensor)[i]; + } + } + + if (outputShapeTensorInfo.GetDataType() == armnn::DataType::QAsymmU8) + { + for(unsigned int i=0; i < outputShapeTensorInfo.GetNumElements(); i++) + { + outputShape[i] = ::tflite::GetTensorData<uint8_t>(&tfLiteOutputShapeTensor)[i]; + } + } + // Change from signed to unsigned int to store in TransposeConvolution2dDescriptor. + for (int dimension : outputShape) + { + descriptor.m_OutputShape.push_back(static_cast<unsigned int>(dimension)); + } + descriptor.m_OutputShapeEnabled = true; + + const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; + if(!IsValid(&tfLiteInputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Invalid input tensor in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteInputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + + const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]]; + if(!IsValid(&tfLiteOutputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Invalid output tensor in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteOutputTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + + const TfLiteTensor& tfLiteFilterTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; + if(!IsValid(&tfLiteFilterTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Invalid filter tensor in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteFilterTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic filter tensors are not supported in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor, true); + const armnn::TensorInfo& filterTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteFilterTensor); + + // TfLite uses NHWC tensors + const unsigned int inputHeight = inputTensorInfo.GetShape()[1]; + const unsigned int inputWidth = inputTensorInfo.GetShape()[2]; + + const unsigned int filterHeight = filterTensorInfo.GetShape()[1]; + const unsigned int filterWidth = filterTensorInfo.GetShape()[2]; + + // Calculate padding + CalcPadding(inputHeight, + filterHeight, + descriptor.m_StrideY, + 1, // dilation y + descriptor.m_PadTop, + descriptor.m_PadBottom, + parameters->padding); + CalcPadding(inputWidth, + filterWidth, + descriptor.m_StrideX, + 1, // dilation x + descriptor.m_PadLeft, + descriptor.m_PadRight, + parameters->padding); + + // Set up filter + auto filterTensor = CreateConstTensor(&tfLiteFilterTensor, + filterTensorInfo); + armnn::BackendId setBackend; + if (!delegateData.m_Network) + { + bool isSupported = false; + FORWARD_LAYER_SUPPORT_FUNC("TRANSPOSE_CONV2D", + tfLiteContext, + IsTransposeConvolution2dSupported, + delegateData.m_Backends, + isSupported, + setBackend, + inputTensorInfo, + outputTensorInfo, + descriptor, + filterTensorInfo, + armnn::EmptyOptional()); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + armnn::IConnectableLayer* layer = delegateData.m_Network->AddTransposeConvolution2dLayer(descriptor, + filterTensor, + armnn::EmptyOptional()); + layer->SetBackendId(setBackend); + ARMNN_ASSERT(layer != nullptr); + + // The data input can be constant, so we must check that this is allocated to an input slot + if(inputTensorInfo.IsConstant()) + { + auto input = + CreateConstTensor(&tfLiteContext->tensors[tfLiteNode->inputs->data[2]], + inputTensorInfo); + + armnn::IConnectableLayer *inputLayer = delegateData.m_Network->AddConstantLayer(input); + inputLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0u)); + inputLayer->GetOutputSlot(0).SetTensorInfo(inputTensorInfo); + } + + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + + // Connect + if (delegateData.m_OutputSlotForNode[static_cast<unsigned int>(tfLiteNode->inputs->data[2])] != nullptr) + { + delegateData.m_OutputSlotForNode[static_cast<unsigned int>(tfLiteNode->inputs->data[2])]-> + Connect(layer->GetInputSlot(0)); + } + + // Prepare output slots + for (unsigned int outputIndex = 0; outputIndex < layer->GetNumOutputSlots(); ++outputIndex) + { + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(outputIndex); + delegateData.m_OutputSlotForNode[static_cast<unsigned int>(tfLiteNode->outputs->data[outputIndex])] = + &outputSlot; + } + return kTfLiteOk; +} + +TfLiteStatus VisitConvolutionOperator(DelegateData& delegateData, + TfLiteContext* tfLiteContext, + TfLiteNode* tfLiteNode, + int nodeIndex, + int32_t operatorCode) +{ + switch(operatorCode) + { + case kTfLiteBuiltinConv2d: + return VisitConv2dOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); +// Conv3d is only correctly supported for external delegates from TF Lite v2.6, as there was a breaking bug in v2.5. +#if defined(ARMNN_POST_TFLITE_2_5) + case kTfLiteBuiltinConv3d: + return VisitConv3dOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); +#endif + case kTfLiteBuiltinDepthwiseConv2d: + return VisitDepthwiseConv2dOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); + case kTfLiteBuiltinTransposeConv: + return VisitTransposeConv2dOperator(delegateData, tfLiteContext, tfLiteNode, nodeIndex, operatorCode); + default: + return kTfLiteError; + } +} + +} // namespace armnnDelegate |