diff options
Diffstat (limited to 'source/application/tensorflow-lite-micro')
5 files changed, 684 insertions, 0 deletions
diff --git a/source/application/tensorflow-lite-micro/Model.cc b/source/application/tensorflow-lite-micro/Model.cc new file mode 100644 index 0000000..0775467 --- /dev/null +++ b/source/application/tensorflow-lite-micro/Model.cc @@ -0,0 +1,332 @@ +/* + * 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 "hal.h" + +#include <cstdint> + +/* Initialise the model */ +arm::app::Model::~Model() +{ + if (this->_m_pInterpreter) { + 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 = &this->_m_uErrorReporter; +} + +bool arm::app::Model::Init(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. */ + const uint8_t* model_addr = ModelPointer(); + debug("loading model from @ 0x%p\n", model_addr); + this->_m_pModel = ::tflite::GetModel(model_addr); + + 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; + } + + /* 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 in %s\n", + ACTIVATION_BUF_SECTION_NAME); + + this->_m_pAllocator = tflite::MicroAllocator::Create( + this->GetTensorArena(), + this->GetActivationBufferSize(), + 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) { + this->_m_pErrorReporter->Report("[ERROR] allocateTensors() failed"); + 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 %u bytes with dimensions\n", + (uint32_t)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: %u\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()); + + const uint32_t nOperators = this->_m_pInterpreter->operators_size(); + info("Number of operators: %u\n", nOperators); + + /* For each operator, display registration information */ + for (uint32_t i = 0 ; i < nOperators; ++i) { + const tflite::NodeAndRegistration nodeReg = + this->_m_pInterpreter->node_and_registration(i); + const TfLiteRegistration* reg = nodeReg.registration; + 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 %u: %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::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 info:\n"); + this->LogInterpreterInfo(); + +#if defined(ARM_NPU) + info("Use of Arm uNPU is enabled\n"); +#else /* ARM_NPU */ + info("Use of Arm uNPU is disabled\n"); +#endif /* ARM_NPU */ + + return true; +} +namespace arm { +namespace app { + static uint8_t _tensor_arena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE; +} /* namespace app */ +} /* namespace arm */ + +size_t arm::app::Model::GetActivationBufferSize() +{ + return ACTIVATION_BUF_SZ; +} + +uint8_t *arm::app::Model::GetTensorArena() +{ + return _tensor_arena; +}
\ No newline at end of file diff --git a/source/application/tensorflow-lite-micro/TensorFlowLiteMicro.cc b/source/application/tensorflow-lite-micro/TensorFlowLiteMicro.cc new file mode 100644 index 0000000..ce36a8f --- /dev/null +++ b/source/application/tensorflow-lite-micro/TensorFlowLiteMicro.cc @@ -0,0 +1,47 @@ +/* + * 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 "TensorFlowLiteMicro.hpp" + +#include "hal.h" + +void PrintTensorFlowVersion() +{ + info("uTFL version: %u.%u.%u\n", TF_MAJOR_VERSION, TF_MINOR_VERSION, + TF_PATCH_VERSION); +} + +arm::app::QuantParams arm::app::GetTensorQuantParams(TfLiteTensor* tensor) +{ + arm::app::QuantParams params; + if (kTfLiteAffineQuantization == tensor->quantization.type) { + auto* quantParams = (TfLiteAffineQuantization*) (tensor->quantization.params); + if (quantParams && 0 == quantParams->quantized_dimension) { + if (quantParams->scale->size) { + params.scale = quantParams->scale->data[0]; + } + if (quantParams->zero_point->size) { + params.offset = quantParams->zero_point->data[0]; + } + } else if (tensor->params.scale != 0.0) { + /* Legacy tensorflow quantisation parameters */ + params.scale = tensor->params.scale; + params.offset = tensor->params.zero_point; + } + } + return params; +} + diff --git a/source/application/tensorflow-lite-micro/include/BufAttributes.hpp b/source/application/tensorflow-lite-micro/include/BufAttributes.hpp new file mode 100644 index 0000000..126172b --- /dev/null +++ b/source/application/tensorflow-lite-micro/include/BufAttributes.hpp @@ -0,0 +1,85 @@ +/* + * 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. + */ + +#ifndef BUF_ATTRIBUTES_HPP +#define BUF_ATTRIBUTES_HPP + +#ifdef __has_attribute +#define HAVE_ATTRIBUTE(x) __has_attribute(x) +#else /* __has_attribute */ +#define HAVE_ATTRIBUTE(x) 0 +#endif /* __has_attribute */ + +#if HAVE_ATTRIBUTE(aligned) || (defined(__GNUC__) && !defined(__clang__)) + +/* We want all buffers/sections to be aligned to 16 byte. */ +#define ALIGNMENT_REQ aligned(16) + +/* Model data section name. */ +#define MODEL_SECTION section("nn_model") + +/* Label section name */ +#define LABEL_SECTION section("labels") + +#ifndef ACTIVATION_BUF_SZ + #warning "ACTIVATION_BUF_SZ needs to be defined. Using default value" + #define ACTIVATION_BUF_SZ 0x00200000 +#endif /* ACTIVATION_BUF_SZ */ + +#ifndef ACTIVATION_BUF_SRAM_SZ + #warning "ACTIVATION_BUF_SRAM_SZ needs to be defined. Using default value = 0" + #define ACTIVATION_BUF_SRAM_SZ 0x00000000 +#endif /* ACTIVATION_BUF_SRAM_SZ */ + +/** + * Activation buffer aka tensor arena section name + * We have to place the tensor arena in different region based on its size. + * If it fits in SRAM, we place it there, and also mark it by giving it a + * different section name. The scatter file places the ZI data in DDR and + * the uninitialised region in the SRAM. + **/ +#define ACTIVATION_BUF_SECTION_SRAM section(".bss.NoInit.activation_buf") +#define ACTIVATION_BUF_SECTION_DRAM section("activation_buf") + +#if ACTIVATION_BUF_SZ > ACTIVATION_BUF_SRAM_SZ /* Will buffer not fit in SRAM? */ + #define ACTIVATION_BUF_SECTION ACTIVATION_BUF_SECTION_DRAM + #define ACTIVATION_BUF_SECTION_NAME ("DDR") +#else /* ACTIVATION_BUF_SZ > 0x00200000 */ + #define ACTIVATION_BUF_SECTION ACTIVATION_BUF_SECTION_SRAM + #define ACTIVATION_BUF_SECTION_NAME ("SRAM") +#endif /* ACTIVATION_BUF_SZ > 0x00200000 */ + +/* IFM section name. */ +#define IFM_BUF_SECTION section("ifm") + +/* Form the attributes, alignment is mandatory. */ +#define MAKE_ATTRIBUTE(x) __attribute__((ALIGNMENT_REQ, x)) +#define MODEL_TFLITE_ATTRIBUTE MAKE_ATTRIBUTE(MODEL_SECTION) +#define ACTIVATION_BUF_ATTRIBUTE MAKE_ATTRIBUTE(ACTIVATION_BUF_SECTION) +#define IFM_BUF_ATTRIBUTE MAKE_ATTRIBUTE(IFM_BUF_SECTION) +#define LABELS_ATTRIBUTE MAKE_ATTRIBUTE(LABEL_SECTION) + +#else /* HAVE_ATTRIBUTE(aligned) || (defined(__GNUC__) && !defined(__clang__)) */ + +#define MODEL_TFLITE_ATTRIBUTE +#define ACTIVATION_BUF_ATTRIBUTE +#define IFM_BUF_ATTRIBUTE +#define LABELS_ATTRIBUTE + +#endif /* HAVE_ATTRIBUTE(aligned) || (defined(__GNUC__) && !defined(__clang__)) */ + +#endif /* BUF_ATTRIBUTES_HPP */
\ No newline at end of file diff --git a/source/application/tensorflow-lite-micro/include/Model.hpp b/source/application/tensorflow-lite-micro/include/Model.hpp new file mode 100644 index 0000000..70cf9ca --- /dev/null +++ b/source/application/tensorflow-lite-micro/include/Model.hpp @@ -0,0 +1,142 @@ +/* + * 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. + */ +#ifndef MODEL_HPP +#define MODEL_HPP + +#include "TensorFlowLiteMicro.hpp" +#include "BufAttributes.hpp" + +#include <cstdint> + +namespace arm { +namespace app { + + /** + * @brief NN model class wrapping the underlying TensorFlow-Lite-Micro API. + */ + class Model { + public: + /** @brief Constructor. */ + Model(); + + /** @brief Destructor. */ + ~Model(); + + /** @brief Gets the pointer to the model's input tensor at given input index. */ + TfLiteTensor* GetInputTensor(size_t index) const; + + /** @brief Gets the pointer to the model's output tensor at given output index. */ + TfLiteTensor* GetOutputTensor(size_t index) const; + + /** @brief Gets the model's data type. */ + TfLiteType GetType() const; + + /** @brief Gets the pointer to the model's input shape. */ + TfLiteIntArray* GetInputShape(size_t index) const; + + /** @brief Gets the pointer to the model's output shape at given output index. */ + TfLiteIntArray* GetOutputShape(size_t index) const; + + /** @brief Gets the number of input tensors the model has. */ + size_t GetNumInputs() const; + + /** @brief Gets the number of output tensors the model has. */ + size_t GetNumOutputs() const; + + /** @brief Logs the tensor information to stdout. */ + void LogTensorInfo(TfLiteTensor* tensor); + + /** @brief Logs the interpreter information to stdout. */ + void LogInterpreterInfo(); + + /** @brief Initialise the model class object. + * @param[in] allocator Optional: a pre-initialised micro allocator pointer, + * if available. If supplied, this allocator will be used + * to create the interpreter instance. + * @return true if initialisation succeeds, false otherwise. + **/ + bool Init(tflite::MicroAllocator* allocator = nullptr); + + /** + * @brief Gets the allocator pointer for this instance. + * @return Pointer to a tflite::MicroAllocator object, if + * available; nullptr otherwise. + **/ + tflite::MicroAllocator* GetAllocator(); + + /** @brief Checks if this object has been initialised. */ + bool IsInited() const; + + /** @brief Checks if the model uses signed data. */ + bool IsDataSigned() const; + + /** @brief Runs the inference (invokes the interpreter). */ + bool RunInference(); + + /** @brief Model information handler common to all models. + * @return true or false based on execution success. + **/ + bool ShowModelInfoHandler(); + + /** @brief Gets a pointer to the tensor arena. */ + uint8_t* GetTensorArena(); + + protected: + /** @brief Gets the pointer to the NN model data array. + * @return Pointer of uint8_t type. + **/ + virtual const uint8_t* ModelPointer() = 0; + + /** @brief Gets the model size. + * @return size_t, size in bytes. + **/ + virtual size_t ModelSize() = 0; + + /** + * @brief Gets the op resolver for the model instance. + * @return const reference to a tflite::MicroOpResolver object. + **/ + virtual const tflite::MicroOpResolver& GetOpResolver() = 0; + + /** + * @brief Add all the operators required for the given model. + * Implementation of this should come from the use case. + * @return true is ops are successfully added, false otherwise. + **/ + virtual bool EnlistOperations() = 0; + + /** @brief Gets the total size of tensor arena available for use. */ + size_t GetActivationBufferSize(); + + private: + tflite::MicroErrorReporter _m_uErrorReporter; /* Error reporter object. */ + tflite::ErrorReporter* _m_pErrorReporter = nullptr; /* Pointer to the error reporter. */ + const tflite::Model* _m_pModel = nullptr; /* Tflite model pointer. */ + tflite::MicroInterpreter* _m_pInterpreter = nullptr; /* Tflite interpreter. */ + tflite::MicroAllocator* _m_pAllocator = nullptr; /* Tflite micro allocator. */ + bool _m_inited = false; /* Indicates whether this object has been initialised. */ + + std::vector<TfLiteTensor*> _m_input = {}; /* Model's input tensor pointers. */ + std::vector<TfLiteTensor*> _m_output = {}; /* Model's output tensor pointers. */ + TfLiteType _m_type = kTfLiteNoType;/* Model's data type. */ + + }; + +} /* namespace app */ +} /* namespace arm */ + +#endif /* MODEL_HPP */ diff --git a/source/application/tensorflow-lite-micro/include/TensorFlowLiteMicro.hpp b/source/application/tensorflow-lite-micro/include/TensorFlowLiteMicro.hpp new file mode 100644 index 0000000..677b4ba --- /dev/null +++ b/source/application/tensorflow-lite-micro/include/TensorFlowLiteMicro.hpp @@ -0,0 +1,78 @@ +/* + * 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. + */ +#ifndef TENSORFLOW_LITE_MICRO_LOCAL_HPP +#define TENSORFLOW_LITE_MICRO_LOCAL_HPP + +/* We include all our TensorFlow Lite Micro headers here */ + +/** + * TensorFlow Lite Micro sources can generate a lot of warnings from the usage + * of a single macro (TF_LITE_REMOVE_VIRTUAL_DELETE). Suppress the known ones + * here to prevent them from masking warnings that might be generated by our + * application sources. + */ +#if defined(__ARMCC_VERSION) && (__ARMCC_VERSION >= 6010050) + #pragma clang diagnostic push + #pragma clang diagnostic ignored "-Wunused-parameter" + #include "tensorflow/lite/micro/micro_mutable_op_resolver.h" + #include "tensorflow/lite/micro/micro_interpreter.h" + #include "tensorflow/lite/micro/micro_error_reporter.h" + #include "tensorflow/lite/micro/all_ops_resolver.h" + #pragma clang diagnostic pop +#elif defined(__GNUC__) + #pragma GCC diagnostic push + #pragma GCC diagnostic ignored "-Wunused-parameter" + #include "tensorflow/lite/micro/micro_mutable_op_resolver.h" + #include "tensorflow/lite/micro/micro_interpreter.h" + #include "tensorflow/lite/micro/micro_error_reporter.h" + #include "tensorflow/lite/micro/all_ops_resolver.h" + #pragma GCC diagnostic pop +#else + #include "tensorflow/lite/micro/micro_mutable_op_resolver.h" + #include "tensorflow/lite/micro/micro_interpreter.h" + #include "tensorflow/lite/micro/micro_error_reporter.h" + #include "tensorflow/lite/micro/all_ops_resolver.h" +#endif + +#include "tensorflow/lite/c/common.h" +#include "tensorflow/lite/micro/kernels/micro_ops.h" +#include "tensorflow/lite/schema/schema_generated.h" +#include "tensorflow/lite/version.h" + +#if defined (TESTS) + #include "tensorflow/lite/micro/test_helpers.h" +#endif /* defined (TESTS) */ + +namespace arm { +namespace app { + + struct QuantParams { + float scale = 1.0; + int offset = 0; + }; + + QuantParams GetTensorQuantParams(TfLiteTensor* tensor); + +} /* namespace app */ +} /* namespace arm */ + +/** + * @brief Prints the tensor flow version in use to stdout. + */ +void PrintTensorFlowVersion(); + +#endif /* TENSORFLOW_LITE_MICRO_LOCAL_HPP */ |