diff options
Diffstat (limited to 'delegate/src/Convolution.hpp')
| -rw-r--r-- | delegate/src/Convolution.hpp | 167 |
1 files changed, 167 insertions, 0 deletions
diff --git a/delegate/src/Convolution.hpp b/delegate/src/Convolution.hpp index 96612e0214..a7d6c1de26 100644 --- a/delegate/src/Convolution.hpp +++ b/delegate/src/Convolution.hpp @@ -204,6 +204,168 @@ TfLiteStatus VisitConv2dOperator(DelegateData& 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 = tfLiteNode->inputs->size == 3 ? true : false; + 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); + + 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. + if (!delegateData.m_Network) + { + bool isSupported = false; + FORWARD_LAYER_SUPPORT_FUNC(__func__, + tfLiteContext, + IsConvolution3dSupported, + delegateData.m_Backends, + isSupported, + inputTensorInfo, + outputTensorInfo, + descriptor, + filterTensorInfo, + optionalBiasInfo); + return isSupported ? kTfLiteOk : kTfLiteError; + } + + armnn::IConnectableLayer* layer = delegateData.m_Network->AddConvolution3dLayer(descriptor); + 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 (tflite::IsConstantTensor(&tfLiteFilterTensor)) + { + auto filter = CreateConstTensor(&tfLiteFilterTensor, + filterTensorInfo, + armnn::Optional<armnn::PermutationVector&>()); + + 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(tflite::IsConstantTensor(&tfLiteBiasTensor)) + { + auto biases = CreateConstTensor(&tfLiteBiasTensor, + biasTensorInfo, + armnn::Optional<armnn::PermutationVector&>()); + + armnn::IConnectableLayer* biasLayer = delegateData.m_Network->AddConstantLayer(biases); + ARMNN_ASSERT(biasLayer != nullptr); + + biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u)); + biasLayer->GetOutputSlot(0).SetTensorInfo(biasTensorInfo); + } + } + + armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0); + outputSlot.SetTensorInfo(outputTensorInfo); + + Connect(layer, tfLiteNode, delegateData); + + auto* tfLiteNodeParameters = reinterpret_cast<TfLiteConv3DParams*>(tfLiteNode->builtin_data); + if (!tfLiteNodeParameters) + { + // No Activation + return kTfLiteOk; + } + + // Check activation + TfLiteFusedActivation activationType = tfLiteNodeParameters->activation; + return FusedActivation(tfLiteContext, tfLiteNode, activationType, layer, 0, delegateData); +} +#endif + TfLiteStatus VisitDepthwiseConv2dOperator(DelegateData& delegateData, TfLiteContext* tfLiteContext, TfLiteNode* tfLiteNode, @@ -581,6 +743,11 @@ TfLiteStatus VisitConvolutionOperator(DelegateData& delegateData, { 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: |