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Added examples
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‎CMakeLists.txt

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@@ -29,6 +29,15 @@ target_compile_definitions(
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COMPILE_DEFINITIONS TF_LITE_STATIC_MEMORY=1
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)
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set_target_properties(
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pico-tflmicro
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PROPERTIES
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COMPILE_FLAGS -fno-rtti
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COMPILE_FLAGS -fno-exceptions
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COMPILE_FLAGS -fno-threadsafe-statics
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COMPILE_FLAGS -nostdlib
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)
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target_link_libraries(
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pico-tflmicro
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pico_stdlib
@@ -234,6 +243,15 @@ target_compile_definitions(
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COMPILE_DEFINITIONS TF_LITE_STATIC_MEMORY=1
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)
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set_target_properties(
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pico-tflmicro_test
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PROPERTIES
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COMPILE_FLAGS -fno-rtti
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COMPILE_FLAGS -fno-exceptions
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COMPILE_FLAGS -fno-threadsafe-statics
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COMPILE_FLAGS -nostdlib
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)
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target_link_libraries(
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pico-tflmicro_test
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pico_stdlib
@@ -246,6 +264,10 @@ target_sources(pico-tflmicro_test
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${CMAKE_CURRENT_LIST_DIR}/src/tensorflow/lite/micro/testing/test_conv_model.h
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)
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add_subdirectory("examples/hello_world")
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add_subdirectory("examples/person_detection")
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add_subdirectory("examples/magic_wand")
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add_subdirectory("examples/micro_speech")
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add_subdirectory("tests/greedy_memory_planner_test")
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add_subdirectory("tests/kernel_activations_test")
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add_subdirectory("tests/kernel_add_test")

‎examples/hello_world/CMakeLists.txt

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cmake_minimum_required(VERSION 3.12)
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project(hello_world C CXX ASM)
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set(CMAKE_C_STANDARD 11)
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set(CMAKE_CXX_STANDARD 11)
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add_executable(hello_world_test "")
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target_include_directories(hello_world_test
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PRIVATE
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${CMAKE_CURRENT_LIST_DIR}/.
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)
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set_target_properties(
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hello_world_test
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PROPERTIES
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COMPILE_FLAGS -fno-rtti
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COMPILE_FLAGS -fno-exceptions
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COMPILE_FLAGS -fno-threadsafe-statics
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COMPILE_FLAGS -nostdlib
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)
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target_sources(hello_world_test
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PRIVATE
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${CMAKE_CURRENT_LIST_DIR}/hello_world_test.cpp
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${CMAKE_CURRENT_LIST_DIR}/model.cpp
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${CMAKE_CURRENT_LIST_DIR}/model.h
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)
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target_link_libraries(
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hello_world_test
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pico-tflmicro
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pico-tflmicro_test
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)
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pico_add_extra_outputs(hello_world_test)
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add_executable(hello_world "")
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target_include_directories(hello_world
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PRIVATE
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${CMAKE_CURRENT_LIST_DIR}/.
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)
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set_target_properties(
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hello_world
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PROPERTIES
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COMPILE_FLAGS -fno-rtti
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COMPILE_FLAGS -fno-exceptions
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COMPILE_FLAGS -fno-threadsafe-statics
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COMPILE_FLAGS -nostdlib
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)
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target_sources(hello_world
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PRIVATE
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${CMAKE_CURRENT_LIST_DIR}/constants.cpp
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${CMAKE_CURRENT_LIST_DIR}/main.cpp
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${CMAKE_CURRENT_LIST_DIR}/main_functions.cpp
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${CMAKE_CURRENT_LIST_DIR}/model.cpp
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${CMAKE_CURRENT_LIST_DIR}/output_handler.cpp
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${CMAKE_CURRENT_LIST_DIR}/constants.h
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${CMAKE_CURRENT_LIST_DIR}/main_functions.h
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${CMAKE_CURRENT_LIST_DIR}/model.h
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${CMAKE_CURRENT_LIST_DIR}/output_handler.h
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)
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target_link_libraries(
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hello_world
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pico-tflmicro
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)
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pico_add_extra_outputs(hello_world)
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add_executable(output_handler_test "")
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target_include_directories(output_handler_test
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PRIVATE
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${CMAKE_CURRENT_LIST_DIR}/.
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)
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set_target_properties(
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output_handler_test
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PROPERTIES
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COMPILE_FLAGS -fno-rtti
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COMPILE_FLAGS -fno-exceptions
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COMPILE_FLAGS -fno-threadsafe-statics
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COMPILE_FLAGS -nostdlib
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)
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target_sources(output_handler_test
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PRIVATE
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${CMAKE_CURRENT_LIST_DIR}/output_handler.cpp
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${CMAKE_CURRENT_LIST_DIR}/output_handler_test.cpp
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${CMAKE_CURRENT_LIST_DIR}/constants.h
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${CMAKE_CURRENT_LIST_DIR}/output_handler.h
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)
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target_link_libraries(
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output_handler_test
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pico-tflmicro
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pico-tflmicro_test
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)
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pico_add_extra_outputs(output_handler_test)
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‎examples/hello_world/constants.cpp

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/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "constants.h"
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// This is a small number so that it's easy to read the logs
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const int kInferencesPerCycle = 20;

‎examples/hello_world/constants.h

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/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_CONSTANTS_H_
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#define TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_CONSTANTS_H_
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// This constant represents the range of x values our model was trained on,
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// which is from 0 to (2 * Pi). We approximate Pi to avoid requiring additional
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// libraries.
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const float kXrange = 2.f * 3.14159265359f;
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// This constant determines the number of inferences to perform across the range
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// of x values defined above. Since each inference takes time, the higher this
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// number, the more time it will take to run through the entire range. The value
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// of this constant can be tuned so that one full cycle takes a desired amount
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// of time. Since different devices take different amounts of time to perform
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// inference, this value should be defined per-device.
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extern const int kInferencesPerCycle;
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#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_CONSTANTS_H_

‎examples/hello_world/hello_world_test.cpp

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/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <math.h>
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#include "tensorflow/lite/micro/all_ops_resolver.h"
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#include "model.h"
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#include "tensorflow/lite/micro/micro_error_reporter.h"
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#include "tensorflow/lite/micro/micro_interpreter.h"
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#include "tensorflow/lite/micro/testing/micro_test.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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#include "tensorflow/lite/version.h"
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TF_LITE_MICRO_TESTS_BEGIN
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TF_LITE_MICRO_TEST(LoadModelAndPerformInference) {
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// Define the input and the expected output
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float x = 0.0f;
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float y_true = sin(x);
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// Set up logging
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tflite::MicroErrorReporter micro_error_reporter;
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// Map the model into a usable data structure. This doesn't involve any
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// copying or parsing, it's a very lightweight operation.
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const tflite::Model* model = ::tflite::GetModel(g_model);
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if (model->version() != TFLITE_SCHEMA_VERSION) {
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TF_LITE_REPORT_ERROR(&micro_error_reporter,
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"Model provided is schema version %d not equal "
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"to supported version %d.\n",
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model->version(), TFLITE_SCHEMA_VERSION);
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}
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// This pulls in all the operation implementations we need
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tflite::AllOpsResolver resolver;
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// Create an area of memory to use for input, output, and intermediate arrays.
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// Minimum arena size, at the time of writing. After allocating tensors
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// you can retrieve this value by invoking interpreter.arena_used_bytes().
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const int model_arena_size = 754;
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/* Extra headroom for model + alignment + future interpreter changes */
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const int extra_arena_size = 554 + 16 + 100;
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const int tensor_arena_size = model_arena_size + extra_arena_size;
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uint8_t tensor_arena[tensor_arena_size];
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// Build an interpreter to run the model with
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tflite::MicroInterpreter interpreter(
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model, resolver, tensor_arena, tensor_arena_size, &micro_error_reporter);
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// Allocate memory from the tensor_arena for the model's tensors
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TF_LITE_MICRO_EXPECT_EQ(interpreter.AllocateTensors(), kTfLiteOk);
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// Alert for substantial increase in arena size usage.
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TF_LITE_MICRO_EXPECT_LE(interpreter.arena_used_bytes(),
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model_arena_size + 100);
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// Obtain a pointer to the model's input tensor
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TfLiteTensor* input = interpreter.input(0);
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// Make sure the input has the properties we expect
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TF_LITE_MICRO_EXPECT_NE(nullptr, input);
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// The property "dims" tells us the tensor's shape. It has one element for
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// each dimension. Our input is a 2D tensor containing 1 element, so "dims"
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// should have size 2.
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TF_LITE_MICRO_EXPECT_EQ(2, input->dims->size);
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// The value of each element gives the length of the corresponding tensor.
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// We should expect two single element tensors (one is contained within the
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// other).
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TF_LITE_MICRO_EXPECT_EQ(1, input->dims->data[0]);
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TF_LITE_MICRO_EXPECT_EQ(1, input->dims->data[1]);
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// The input is an 8 bit integer value
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TF_LITE_MICRO_EXPECT_EQ(kTfLiteInt8, input->type);
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// Get the input quantization parameters
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float input_scale = input->params.scale;
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int input_zero_point = input->params.zero_point;
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// Quantize the input from floating-point to integer
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int8_t x_quantized = x / input_scale + input_zero_point;
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// Place the quantized input in the model's input tensor
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input->data.int8[0] = x_quantized;
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// Run the model and check that it succeeds
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TfLiteStatus invoke_status = interpreter.Invoke();
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TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, invoke_status);
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// Obtain a pointer to the output tensor and make sure it has the
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// properties we expect. It should be the same as the input tensor.
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TfLiteTensor* output = interpreter.output(0);
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TF_LITE_MICRO_EXPECT_EQ(2, output->dims->size);
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TF_LITE_MICRO_EXPECT_EQ(1, output->dims->data[0]);
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TF_LITE_MICRO_EXPECT_EQ(1, output->dims->data[1]);
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TF_LITE_MICRO_EXPECT_EQ(kTfLiteInt8, output->type);
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// Get the output quantization parameters
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float output_scale = output->params.scale;
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int output_zero_point = output->params.zero_point;
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// Obtain the quantized output from model's output tensor
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int8_t y_pred_quantized = output->data.int8[0];
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// Dequantize the output from integer to floating-point
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float y_pred = (y_pred_quantized - output_zero_point) * output_scale;
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// Check if the output is within a small range of the expected output
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float epsilon = 0.05f;
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TF_LITE_MICRO_EXPECT_NEAR(y_true, y_pred, epsilon);
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// Run inference on several more values and confirm the expected outputs
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x = 1.f;
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y_true = sin(x);
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input->data.int8[0] = x / input_scale + input_zero_point;
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interpreter.Invoke();
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y_pred = (output->data.int8[0] - output_zero_point) * output_scale;
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TF_LITE_MICRO_EXPECT_NEAR(y_true, y_pred, epsilon);
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x = 3.f;
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y_true = sin(x);
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input->data.int8[0] = x / input_scale + input_zero_point;
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interpreter.Invoke();
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y_pred = (output->data.int8[0] - output_zero_point) * output_scale;
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TF_LITE_MICRO_EXPECT_NEAR(y_true, y_pred, epsilon);
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x = 5.f;
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y_true = sin(x);
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input->data.int8[0] = x / input_scale + input_zero_point;
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interpreter.Invoke();
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y_pred = (output->data.int8[0] - output_zero_point) * output_scale;
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TF_LITE_MICRO_EXPECT_NEAR(y_true, y_pred, epsilon);
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}
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TF_LITE_MICRO_TESTS_END

‎examples/hello_world/main.cpp

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/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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9+
Unless required by applicable law or agreed to in writing, software
10+
distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12+
See the License for the specific language governing permissions and
13+
limitations under the License.
14+
==============================================================================*/
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#include "main_functions.h"
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// This is the default main used on systems that have the standard C entry
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// point. Other devices (for example FreeRTOS or ESP32) that have different
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// requirements for entry code (like an app_main function) should specialize
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// this main.cc file in a target-specific subfolder.
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int main(int argc, char* argv[]) {
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setup();
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while (true) {
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loop();
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}
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}

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