| // |
| // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "Conv2dOperator.hpp" |
| |
| TosaSerializationBasicBlock* ConvertConv2dToTosaOperator(const Layer* layer, |
| const std::vector<const TensorInfo*>& inputs, |
| const std::vector<const TensorInfo*>& outputs, |
| const Convolution2dDescriptor* conv2dDescriptor) |
| { |
| std::vector<std::string> inputNames; |
| std::string outputName = std::string("output0_"); |
| std::string blockName = std::string("Op_CONV2D_block_") + GetUniqueTosaMappingID(); |
| |
| // Set input names for validation purposes only. |
| if(layer == nullptr) |
| { |
| inputNames.emplace_back("input0_"); |
| inputNames.emplace_back("input1_"); |
| if(conv2dDescriptor->m_BiasEnabled) |
| { |
| inputNames.emplace_back("input2_"); |
| } |
| } |
| // 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. |
| else |
| { |
| // Get the layer connected to the input slot and determine unique tensor names. |
| for (uint32_t i = 0; i < inputs.size(); ++i) |
| { |
| Layer& connectedLayer = layer->GetInputSlot(i).GetConnectedOutputSlot()->GetOwningLayer(); |
| |
| std::string inputName = GenerateUniqueName(connectedLayer, i); |
| inputNames.push_back(inputName); |
| } |
| |
| // 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(inputNames[0].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(inputNames[0], inputShape0, inputDType0, {})); |
| } |
| |
| // Only add input tensors if weights and bias are not constant or if running validation. |
| // Constant tensors will be created in the ConvertConstantToTosaOperator function. |
| if(!inputs[1]->IsConstant() || layer == nullptr) |
| { |
| std::vector<int32_t> inputShape1 = GetTosaTensorShape(inputs[1]->GetShape()); |
| DType inputDType1 = ArmNNToDType(inputs[1]->GetDataType()); |
| |
| tensors.push_back(new TosaSerializationTensor(inputNames[1], inputShape1, inputDType1, {})); |
| } |
| |
| if(conv2dDescriptor->m_BiasEnabled) |
| { |
| if(!inputs[2]->IsConstant() || layer == nullptr) |
| { |
| std::vector<int32_t> inputShape2 = GetTosaTensorShape(inputs[2]->GetShape()); |
| DType inputDType2 = ArmNNToDType(inputs[2]->GetDataType()); |
| |
| tensors.push_back(new TosaSerializationTensor(inputNames[2], inputShape2, inputDType2, {})); |
| } |
| } |
| else |
| { |
| // If bias is disabled, create a constant bias of 0 as three inputs are required. |
| std::string constantName = std::string("constant_") + GetUniqueTosaMappingID(); |
| |
| operators.push_back(new TosaSerializationOperator(Op_CONST, Attribute_NONE, nullptr, {}, {constantName})); |
| |
| // The size of the bias must match the channels dimension, so get the correct index. |
| unsigned int index = (conv2dDescriptor->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(constantName, |
| {static_cast<int32_t>(outputs[0]->GetShape()[index])}, |
| DType_FP32, |
| uint8Data)); |
| inputNames.emplace_back(constantName); |
| } |
| |
| // 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 CONV2D operator |
| std::vector<int> pad = {static_cast<int>(conv2dDescriptor->m_PadTop), |
| static_cast<int>(conv2dDescriptor->m_PadBottom), |
| static_cast<int>(conv2dDescriptor->m_PadLeft), |
| static_cast<int>(conv2dDescriptor->m_PadRight)}; |
| std::vector<int> stride = {static_cast<int>(conv2dDescriptor->m_StrideY), |
| static_cast<int>(conv2dDescriptor->m_StrideX)}; |
| std::vector<int> dilation = {static_cast<int>(conv2dDescriptor->m_DilationY), |
| static_cast<int>(conv2dDescriptor->m_DilationX)}; |
| TosaConvAttribute attribute(pad, stride, dilation, 0, 0); |
| |
| auto* op = new TosaSerializationOperator(Op_CONV2D, |
| Attribute_ConvAttribute, |
| &attribute, |
| inputNames, |
| {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 |
| inputNames, // inputs |
| {outputName}); // outputs |
| } |