diff options
Diffstat (limited to 'delegate/src/FullyConnected.hpp')
| -rw-r--r-- | delegate/src/FullyConnected.hpp | 212 |
1 files changed, 210 insertions, 2 deletions
diff --git a/delegate/src/FullyConnected.hpp b/delegate/src/FullyConnected.hpp index ad981cd63b..f35f4c92b0 100644 --- a/delegate/src/FullyConnected.hpp +++ b/delegate/src/FullyConnected.hpp @@ -5,6 +5,8 @@ #pragma once +#include "DelegateUtils.hpp" + #include <tensorflow/lite/builtin_ops.h> #include <tensorflow/lite/c/builtin_op_data.h> #include <tensorflow/lite/c/common.h> @@ -19,7 +21,213 @@ TfLiteStatus VisitFullyConnectedOperator(DelegateData& delegateData, int nodeIndex, int32_t operatorCode) { - return kTfLiteError; + 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 = (numInputs == 3); + + 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 node #%d: ", + 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 node #%d: ", + nodeIndex); + return kTfLiteError; + } + + const TfLiteTensor& tfLiteWeightsTensor = tfLiteTensors[tfLiteNode->inputs->data[1]]; + if(!IsValid(&tfLiteWeightsTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Invalid weights tensor in operator #%d node #%d: ", + operatorCode, nodeIndex); + return kTfLiteError; + } + if (IsDynamicTensor(tfLiteWeightsTensor)) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dynamic weight tensors are not supported in node #%d: ", + nodeIndex); + return kTfLiteError; + } + + const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor); + + armnn::TensorInfo weightsTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteWeightsTensor); + // Fully Connected Layer accepts two dimensional weights input + int32_t weightsDimension = static_cast<int32_t>(weightsTensorInfo.GetNumDimensions()); + if (weightsDimension != 2) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Dimension #$d for Fully Connected weights is not supported by Armnn" + " in operator #%d node #%d: ", weightsDimension, operatorCode, nodeIndex); + return kTfLiteError; + } + + 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::FullyConnectedDescriptor descriptor; + descriptor.m_TransposeWeightMatrix = true; + descriptor.m_BiasEnabled = biasEnabled; + + bool isSupported = false; + auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported) + { + FORWARD_LAYER_SUPPORT_FUNC(__func__, + tfLiteContext, + IsFullyConnectedSupported, + delegateData.m_Backends, + isSupported, + inputTensorInfo, + outputTensorInfo, + weightsTensorInfo, + biasTensorInfo, + descriptor); + }; + + if (!delegateData.m_Network) + { + validateFunc(outputTensorInfo, isSupported); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + auto weightsTensor = CreateConstTensor(&tfLiteWeightsTensor, + weightsTensorInfo, + armnn::Optional<armnn::PermutationVector&>()); + + armnn::IConnectableLayer* layer = nullptr; + if (biasEnabled) + { + const TfLiteTensor& tfLiteBiasTensor = tfLiteTensors[tfLiteNode->inputs->data[2]]; + auto biasTensor = CreateConstTensor(&tfLiteBiasTensor, + biasTensorInfo, + armnn::Optional<armnn::PermutationVector&>()); + layer = delegateData.m_Network->AddFullyConnectedLayer(descriptor, + weightsTensor.first, + armnn::Optional<armnn::ConstTensor>(biasTensor.first)); + } + else + { + layer = delegateData.m_Network->AddFullyConnectedLayer(descriptor, + weightsTensor.first, + armnn::EmptyOptional()); + } + ARMNN_ASSERT(layer != nullptr); + + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + + armnn::IConnectableLayer* reshapeLayer = nullptr; + if (inputTensorInfo.GetNumDimensions() > 2) + { + // Add reshape to flatten to 2D [batch_size, input_size] + std::vector<unsigned int> reshapedDimensions(2); + reshapedDimensions[1] = weightsTensorInfo.GetShape()[1]; + reshapedDimensions[0] = inputTensorInfo.GetNumElements() / reshapedDimensions[1]; + + if (inputTensorInfo.GetNumElements() % reshapedDimensions[1] != 0) + { + TF_LITE_MAYBE_KERNEL_LOG( + tfLiteContext, + "TfLiteArmnnDelegate: Failed to deduce input tensor shape from filter size #%d #%d node #%d: ", + reshapedDimensions[1], operatorCode, nodeIndex); + return kTfLiteError; + } + + armnn::TensorInfo reshapedTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor); + reshapedTensorInfo.SetShape(armnn::TensorShape{ 2, reshapedDimensions.data() }); + + armnn::ReshapeDescriptor reshapeDescriptor; + reshapeDescriptor.m_TargetShape = reshapedTensorInfo.GetShape(); + reshapeLayer = delegateData.m_Network->AddReshapeLayer(reshapeDescriptor); + ARMNN_ASSERT(reshapeLayer != nullptr); + + reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedTensorInfo); + + // Connect + delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[0]]->Connect(reshapeLayer->GetInputSlot(0)); + reshapeLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0)); + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); + delegateData.m_OutputSlotForNode[tfLiteNode->outputs->data[0]] = &outputSlot; + } + + if (reshapeLayer == nullptr) + { + Connect(layer, tfLiteNode, delegateData); + } + + auto* tfLiteNodeParameters = reinterpret_cast<TfLiteAddParams*>(tfLiteNode->builtin_data); + if (!tfLiteNodeParameters) + { + // No Activation + return kTfLiteOk; + } + + // Check Activation + TfLiteFusedActivation activationType = tfLiteNodeParameters->activation; + return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData); } -} // namespace armnnDelegate +} // namespace armnnDelegate
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