| /* |
| * Copyright (c) 2021 Arm Limited. All rights reserved. |
| * SPDX-License-Identifier: Apache-2.0 |
| * |
| * Licensed under the Apache License, Version 2.0 (the "License"); |
| * you may not use this file except in compliance with the License. |
| * You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| #include "Model.hpp" |
| #include "log_macros.h" |
| |
| #include <cinttypes> |
| |
| /* Initialise the model */ |
| arm::app::Model::~Model() |
| { |
| delete this->m_pInterpreter; |
| /** |
| * No clean-up function available for allocator in TensorFlow Lite Micro yet. |
| **/ |
| } |
| |
| arm::app::Model::Model() : |
| m_inited (false), |
| m_type(kTfLiteNoType) |
| { |
| this->m_pErrorReporter = tflite::GetMicroErrorReporter(); |
| } |
| |
| bool arm::app::Model::Init(uint8_t* tensorArenaAddr, |
| uint32_t tensorArenaSize, |
| const uint8_t* nnModelAddr, |
| uint32_t nnModelSize, |
| tflite::MicroAllocator* allocator) |
| { |
| /* Following tf lite micro example: |
| * Map the model into a usable data structure. This doesn't involve any |
| * copying or parsing, it's a very lightweight operation. */ |
| debug("loading model from @ 0x%p\n", nnModelAddr); |
| debug("model size: %" PRIu32 " bytes.\n", nnModelSize); |
| |
| this->m_pModel = ::tflite::GetModel(nnModelAddr); |
| |
| if (this->m_pModel->version() != TFLITE_SCHEMA_VERSION) { |
| this->m_pErrorReporter->Report( |
| "[ERROR] model's schema version %d is not equal " |
| "to supported version %d.", |
| this->m_pModel->version(), TFLITE_SCHEMA_VERSION); |
| return false; |
| } |
| |
| this->m_modelAddr = nnModelAddr; |
| this->m_modelSize = nnModelSize; |
| |
| /* Pull in only the operation implementations we need. |
| * This relies on a complete list of all the ops needed by this graph. |
| * An easier approach is to just use the AllOpsResolver, but this will |
| * incur some penalty in code space for op implementations that are not |
| * needed by this graph. |
| * static ::tflite::ops::micro::AllOpsResolver resolver; */ |
| /* NOLINTNEXTLINE(runtime-global-variables) */ |
| debug("loading op resolver\n"); |
| |
| this->EnlistOperations(); |
| |
| /* Create allocator instance, if it doesn't exist */ |
| this->m_pAllocator = allocator; |
| if (!this->m_pAllocator) { |
| /* Create an allocator instance */ |
| info("Creating allocator using tensor arena at 0x%p\n", tensorArenaAddr); |
| |
| this->m_pAllocator = tflite::MicroAllocator::Create( |
| tensorArenaAddr, |
| tensorArenaSize, |
| this->m_pErrorReporter); |
| |
| if (!this->m_pAllocator) { |
| printf_err("Failed to create allocator\n"); |
| return false; |
| } |
| debug("Created new allocator @ 0x%p\n", this->m_pAllocator); |
| } else { |
| debug("Using existing allocator @ 0x%p\n", this->m_pAllocator); |
| } |
| |
| this->m_pInterpreter = new ::tflite::MicroInterpreter( |
| this->m_pModel, this->GetOpResolver(), |
| this->m_pAllocator, this->m_pErrorReporter); |
| |
| if (!this->m_pInterpreter) { |
| printf_err("Failed to allocate interpreter\n"); |
| return false; |
| } |
| |
| /* Allocate memory from the tensor_arena for the model's tensors. */ |
| info("Allocating tensors\n"); |
| TfLiteStatus allocate_status = this->m_pInterpreter->AllocateTensors(); |
| |
| if (allocate_status != kTfLiteOk) { |
| printf_err("tensor allocation failed!\n"); |
| delete this->m_pInterpreter; |
| return false; |
| } |
| |
| /* Get information about the memory area to use for the model's input. */ |
| this->m_input.resize(this->GetNumInputs()); |
| for (size_t inIndex = 0; inIndex < this->GetNumInputs(); inIndex++) |
| this->m_input[inIndex] = this->m_pInterpreter->input(inIndex); |
| |
| this->m_output.resize(this->GetNumOutputs()); |
| for (size_t outIndex = 0; outIndex < this->GetNumOutputs(); outIndex++) |
| this->m_output[outIndex] = this->m_pInterpreter->output(outIndex); |
| |
| if (this->m_input.empty() || this->m_output.empty()) { |
| printf_err("failed to get tensors\n"); |
| return false; |
| } else { |
| this->m_type = this->m_input[0]->type; /* Input 0 should be the main input */ |
| |
| /* Clear the input & output tensors */ |
| for (size_t inIndex = 0; inIndex < this->GetNumInputs(); inIndex++) { |
| std::memset(this->m_input[inIndex]->data.data, 0, this->m_input[inIndex]->bytes); |
| } |
| for (size_t outIndex = 0; outIndex < this->GetNumOutputs(); outIndex++) { |
| std::memset(this->m_output[outIndex]->data.data, 0, this->m_output[outIndex]->bytes); |
| } |
| |
| this->LogInterpreterInfo(); |
| } |
| |
| this->m_inited = true; |
| return true; |
| } |
| |
| tflite::MicroAllocator* arm::app::Model::GetAllocator() |
| { |
| if (this->IsInited()) { |
| return this->m_pAllocator; |
| } |
| return nullptr; |
| } |
| |
| void arm::app::Model::LogTensorInfo(TfLiteTensor* tensor) |
| { |
| if (!tensor) { |
| printf_err("Invalid tensor\n"); |
| assert(tensor); |
| return; |
| } |
| |
| debug("\ttensor is assigned to 0x%p\n", tensor); |
| info("\ttensor type is %s\n", TfLiteTypeGetName(tensor->type)); |
| info("\ttensor occupies %zu bytes with dimensions\n", |
| tensor->bytes); |
| for (int i = 0 ; i < tensor->dims->size; ++i) { |
| info ("\t\t%d: %3d\n", i, tensor->dims->data[i]); |
| } |
| |
| TfLiteQuantization quant = tensor->quantization; |
| if (kTfLiteAffineQuantization == quant.type) { |
| auto* quantParams = (TfLiteAffineQuantization*)quant.params; |
| info("Quant dimension: %" PRIi32 "\n", quantParams->quantized_dimension); |
| for (int i = 0; i < quantParams->scale->size; ++i) { |
| info("Scale[%d] = %f\n", i, quantParams->scale->data[i]); |
| } |
| for (int i = 0; i < quantParams->zero_point->size; ++i) { |
| info("ZeroPoint[%d] = %d\n", i, quantParams->zero_point->data[i]); |
| } |
| } |
| } |
| |
| void arm::app::Model::LogInterpreterInfo() |
| { |
| if (!this->m_pInterpreter) { |
| printf_err("Invalid interpreter\n"); |
| return; |
| } |
| |
| info("Model INPUT tensors: \n"); |
| for (auto input : this->m_input) { |
| this->LogTensorInfo(input); |
| } |
| |
| info("Model OUTPUT tensors: \n"); |
| for (auto output : this->m_output) { |
| this->LogTensorInfo(output); |
| } |
| |
| info("Activation buffer (a.k.a tensor arena) size used: %zu\n", |
| this->m_pInterpreter->arena_used_bytes()); |
| |
| /* We expect there to be only one subgraph. */ |
| const uint32_t nOperators = tflite::NumSubgraphOperators(this->m_pModel, 0); |
| info("Number of operators: %" PRIu32 "\n", nOperators); |
| |
| const tflite::SubGraph* subgraph = this->m_pModel->subgraphs()->Get(0); |
| |
| auto* opcodes = this->m_pModel->operator_codes(); |
| |
| /* For each operator, display registration information. */ |
| for (size_t i = 0 ; i < nOperators; ++i) { |
| const tflite::Operator* op = subgraph->operators()->Get(i); |
| const tflite::OperatorCode* opcode = opcodes->Get(op->opcode_index()); |
| const TfLiteRegistration* reg = nullptr; |
| |
| tflite::GetRegistrationFromOpCode(opcode, this->GetOpResolver(), |
| this->m_pErrorReporter, ®); |
| std::string opName; |
| |
| if (reg) { |
| if (tflite::BuiltinOperator_CUSTOM == reg->builtin_code) { |
| opName = std::string(reg->custom_name); |
| } else { |
| opName = std::string(EnumNameBuiltinOperator( |
| tflite::BuiltinOperator(reg->builtin_code))); |
| } |
| } |
| info("\tOperator %zu: %s\n", i, opName.c_str()); |
| } |
| } |
| |
| bool arm::app::Model::IsInited() const |
| { |
| return this->m_inited; |
| } |
| |
| bool arm::app::Model::IsDataSigned() const |
| { |
| return this->GetType() == kTfLiteInt8; |
| } |
| |
| bool arm::app::Model::ContainsEthosUOperator() const |
| { |
| /* We expect there to be only one subgraph. */ |
| const uint32_t nOperators = tflite::NumSubgraphOperators(this->m_pModel, 0); |
| const tflite::SubGraph* subgraph = this->m_pModel->subgraphs()->Get(0); |
| const auto* opcodes = this->m_pModel->operator_codes(); |
| |
| /* check for custom operators */ |
| for (size_t i = 0; (i < nOperators); ++i) |
| { |
| const tflite::Operator* op = subgraph->operators()->Get(i); |
| const tflite::OperatorCode* opcode = opcodes->Get(op->opcode_index()); |
| |
| auto builtin_code = tflite::GetBuiltinCode(opcode); |
| if ((builtin_code == tflite::BuiltinOperator_CUSTOM) && |
| ( nullptr != opcode->custom_code()) && |
| ( "ethos-u" == std::string(opcode->custom_code()->c_str()))) |
| { |
| return true; |
| } |
| } |
| return false; |
| } |
| |
| bool arm::app::Model::RunInference() |
| { |
| bool inference_state = false; |
| if (this->m_pModel && this->m_pInterpreter) { |
| if (kTfLiteOk != this->m_pInterpreter->Invoke()) { |
| printf_err("Invoke failed.\n"); |
| } else { |
| inference_state = true; |
| } |
| } else { |
| printf_err("Error: No interpreter!\n"); |
| } |
| return inference_state; |
| } |
| |
| TfLiteTensor* arm::app::Model::GetInputTensor(size_t index) const |
| { |
| if (index < this->GetNumInputs()) { |
| return this->m_input.at(index); |
| } |
| return nullptr; |
| } |
| |
| TfLiteTensor* arm::app::Model::GetOutputTensor(size_t index) const |
| { |
| if (index < this->GetNumOutputs()) { |
| return this->m_output.at(index); |
| } |
| return nullptr; |
| } |
| |
| size_t arm::app::Model::GetNumInputs() const |
| { |
| if (this->m_pModel && this->m_pInterpreter) { |
| return this->m_pInterpreter->inputs_size(); |
| } |
| return 0; |
| } |
| |
| size_t arm::app::Model::GetNumOutputs() const |
| { |
| if (this->m_pModel && this->m_pInterpreter) { |
| return this->m_pInterpreter->outputs_size(); |
| } |
| return 0; |
| } |
| |
| |
| TfLiteType arm::app::Model::GetType() const |
| { |
| return this->m_type; |
| } |
| |
| TfLiteIntArray* arm::app::Model::GetInputShape(size_t index) const |
| { |
| if (index < this->GetNumInputs()) { |
| return this->m_input.at(index)->dims; |
| } |
| return nullptr; |
| } |
| |
| TfLiteIntArray* arm::app::Model::GetOutputShape(size_t index) const |
| { |
| if (index < this->GetNumOutputs()) { |
| return this->m_output.at(index)->dims; |
| } |
| return nullptr; |
| } |
| |
| bool arm::app::Model::ShowModelInfoHandler() |
| { |
| if (!this->IsInited()) { |
| printf_err("Model is not initialised! Terminating processing.\n"); |
| return false; |
| } |
| |
| PrintTensorFlowVersion(); |
| info("Model address: 0x%p", this->ModelPointer()); |
| info("Model size: %" PRIu32 " bytes.", this->ModelSize()); |
| info("Model info:\n"); |
| this->LogInterpreterInfo(); |
| |
| info("The model is optimised for Ethos-U NPU: %s.\n", this->ContainsEthosUOperator()? "yes": "no"); |
| |
| return true; |
| } |
| |
| const uint8_t* arm::app::Model::ModelPointer() |
| { |
| return this->m_modelAddr; |
| } |
| |
| uint32_t arm::app::Model::ModelSize() |
| { |
| return this->m_modelSize; |
| } |