| // |
| // Copyright © 2022-2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "TransposeConv2dOperator.hpp" |
| |
| #include "layers/TransposeConvolution2dLayer.hpp" |
| |
| TosaSerializationBasicBlock* ConvertTransposeConv2dToTosaOperator(const Layer* layer, |
| const std::vector<const TensorInfo*>& inputs, |
| const std::vector<const TensorInfo*>& outputs, |
| const TransposeConvolution2dDescriptor* descriptor) |
| { |
| std::string input0Name = std::string("input0_"); |
| std::string input1Name = std::string("constant_") + GetUniqueTosaMappingID(); |
| std::string input2Name = std::string("constant_") + GetUniqueTosaMappingID(); |
| std::string outputName = std::string("output0_"); |
| std::string blockName = std::string("Op_TRANSPOSE_CONV2D_block_") + GetUniqueTosaMappingID(); |
| |
| // If a layer is present then the block will be used for execution, so input and output names need to be determined |
| // using the previous and following layers so the graph is connected correctly. For validation this doesn't matter. |
| if(layer != nullptr) |
| { |
| // Get the layers connected to the input slots and determine unique tensor names. |
| Layer& connectedInputLayer = layer->GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayer(); |
| input0Name = GenerateUniqueName(connectedInputLayer, 0); |
| |
| // Determine unique output tensor name. |
| outputName = GenerateUniqueOutputName(*layer, 0); |
| } |
| |
| std::vector<TosaSerializationTensor*> tensors; |
| std::vector<TosaSerializationOperator*> operators; |
| |
| // Setup input tensor |
| // Only add tensor if connected layer is an input layer. |
| // As intermediate or constant tensors will be created separately. |
| // There also can't be duplicate tensors. |
| if(input0Name.find("input0_") != std::string::npos) |
| { |
| std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape()); |
| DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType()); |
| |
| tensors.push_back(new TosaSerializationTensor(input0Name, inputShape0, inputDType0, {})); |
| } |
| |
| // Setup weights tensor, constant data will get copied during SetConstantTensorData |
| operators.push_back(new TosaSerializationOperator(Op_CONST, Attribute_NONE, nullptr, {}, {input1Name})); |
| |
| // During validation the TensorInfo can be retrieved from the inputs. |
| // During execution, it is only available through the layer so use m_Weight. |
| if(layer == nullptr) |
| { |
| std::vector<int32_t> inputShape1 = GetTosaTensorShape(inputs[1]->GetShape()); |
| DType inputDType1 = ArmNNToDType(inputs[1]->GetDataType()); |
| |
| tensors.push_back(new TosaSerializationTensor(input1Name, inputShape1, inputDType1, {})); |
| } |
| else |
| { |
| auto transposeConv2dLayer = PolymorphicDowncast<const TransposeConvolution2dLayer*>(layer); |
| |
| std::vector<int32_t> inputShape1 = GetTosaTensorShape( |
| transposeConv2dLayer->m_Weight->GetTensorInfo().GetShape()); |
| DType inputDType1 = ArmNNToDType(transposeConv2dLayer->m_Weight->GetTensorInfo().GetDataType()); |
| |
| std::vector<uint8_t> uint8Data = ConvertConstantTensorDataToBuffer(transposeConv2dLayer->m_Weight); |
| tensors.push_back(new TosaSerializationTensor(input1Name, inputShape1, inputDType1, uint8Data)); |
| } |
| |
| // Setup bias operator and tensor, constant data will get copied during SetConstantTensorData |
| operators.push_back(new TosaSerializationOperator(Op_CONST, Attribute_NONE, nullptr, {}, {input2Name})); |
| |
| // During validation the TensorInfo can be retrieved from the inputs. |
| // During execution, it is only available through the layer so use m_Bias. |
| if(layer == nullptr && descriptor->m_BiasEnabled) |
| { |
| std::vector<int32_t> inputShape2 = GetTosaTensorShape(inputs[2]->GetShape()); |
| DType inputDType2 = ArmNNToDType(inputs[2]->GetDataType()); |
| |
| tensors.push_back(new TosaSerializationTensor(input2Name, inputShape2, inputDType2, {})); |
| } |
| else if(descriptor->m_BiasEnabled) |
| { |
| auto transposeConv2dLayer = PolymorphicDowncast<const TransposeConvolution2dLayer*>(layer); |
| |
| std::vector<int32_t> inputShape2 = GetTosaTensorShape( |
| transposeConv2dLayer->m_Bias->GetTensorInfo().GetShape()); |
| DType inputDType2 = ArmNNToDType(transposeConv2dLayer->m_Bias->GetTensorInfo().GetDataType()); |
| |
| std::vector<uint8_t> uint8Data = ConvertConstantTensorDataToBuffer(transposeConv2dLayer->m_Bias); |
| tensors.push_back(new TosaSerializationTensor(input2Name, inputShape2, inputDType2, uint8Data)); |
| } |
| else |
| { |
| // If bias is disabled, create a constant bias tensor of 0's as three inputs are required. |
| // The size of the bias must match the channels dimension, so get the correct index. |
| unsigned int index = (descriptor->m_DataLayout == DataLayout::NHWC) ? 3 : 1; |
| |
| std::vector<uint8_t> uint8Data; |
| std::vector<float> data(outputs[0]->GetShape()[index], 0.0f); |
| |
| TosaSerializationHandler::ConvertF32toU8(data, uint8Data); |
| |
| tensors.push_back(new TosaSerializationTensor(input2Name, |
| {static_cast<int32_t>(outputs[0]->GetShape()[index])}, |
| DType_FP32, |
| uint8Data)); |
| } |
| |
| // Setup Output Tensor |
| std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape()); |
| DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType()); |
| |
| tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {})); |
| |
| // Set up TRANSPOSE_CONV2D operator |
| // The TOSA Reference Model pads the output shape, so it is added to output shape. |
| // In Arm NN we pad the input shape, so it is taken away. |
| // To offset this the negative padding value can be used. |
| std::vector<int> pad = {-static_cast<int>(descriptor->m_PadTop), |
| -static_cast<int>(descriptor->m_PadBottom), |
| -static_cast<int>(descriptor->m_PadLeft), |
| -static_cast<int>(descriptor->m_PadRight)}; |
| std::vector<int> stride = {static_cast<int>(descriptor->m_StrideY), |
| static_cast<int>(descriptor->m_StrideX)}; |
| |
| std::vector<int> outputShape; |
| // If available use shape in descriptor otherwise use output shape. |
| if (descriptor->m_OutputShape.size() == 4) |
| { |
| for (uint32_t i = 0; i < descriptor->m_OutputShape.size(); ++i) |
| { |
| outputShape.push_back(static_cast<int>(descriptor->m_OutputShape[i])); |
| } |
| } |
| else |
| { |
| for (uint32_t i = 0; i < outputs[0]->GetNumDimensions(); ++i) |
| { |
| outputShape.push_back(static_cast<int>(outputs[0]->GetShape()[i])); |
| } |
| } |
| |
| TosaTransposeConvAttribute attribute(pad, stride, outputShape, 0, 0, false); // input_zp, weight_zp, local_bound |
| |
| auto* op = new TosaSerializationOperator(Op_TRANSPOSE_CONV2D, |
| Attribute_TransposeConvAttribute, |
| &attribute, |
| {input0Name, input1Name, input2Name}, |
| {outputName}); |
| operators.push_back(op); |
| |
| // operatorInputNames/operatorOutputNames ends up being the same as |
| // blockInputNames/blockOutputNames for one-to-one ArmNN to TOSA mappings |
| return new TosaSerializationBasicBlock(blockName, // name |
| mainName, // region name |
| operators, // operators |
| tensors, // tensors |
| {input0Name, input1Name, input2Name}, // inputs |
| {outputName}); // outputs |
| } |