128 lines
4.9 KiB
C++
128 lines
4.9 KiB
C++
// Based on https://github.com/lupyuen/tflite-bl602/blob/main/main_functions.cc
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/* Copyright 2020 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 "main_functions.h"
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#include "bflb_platform.h"
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#include "tensorflow/lite/micro/all_ops_resolver.h"
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#include "constants.h"
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#include "model.h"
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#include "output_handler.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/system_setup.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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// Globals, used for compatibility with Arduino-style sketches.
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namespace
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{
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tflite::ErrorReporter *error_reporter = nullptr;
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const tflite::Model *model = nullptr;
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tflite::MicroInterpreter *interpreter = nullptr;
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TfLiteTensor *input = nullptr;
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TfLiteTensor *output = nullptr;
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int inference_count = 0;
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constexpr int kTensorArenaSize = 2000;
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uint8_t tensor_arena[kTensorArenaSize];
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} // namespace
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// The name of this function is important for Arduino compatibility.
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void setup()
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{
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tflite::InitializeTarget();
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// Set up logging. Google style is to avoid globals or statics because of
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// lifetime uncertainty, but since this has a trivial destructor it's okay.
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// NOLINTNEXTLINE(runtime-global-variables)
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static tflite::MicroErrorReporter micro_error_reporter;
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error_reporter = µ_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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model = tflite::GetModel(g_model);
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if (model->version() != TFLITE_SCHEMA_VERSION) {
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TF_LITE_REPORT_ERROR(error_reporter,
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"Model provided is schema version %d not equal "
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"to supported version %d.",
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model->version(), TFLITE_SCHEMA_VERSION);
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return;
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}
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// This pulls in all the operation implementations we need.
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// NOLINTNEXTLINE(runtime-global-variables)
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static tflite::AllOpsResolver resolver;
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// Build an interpreter to run the model with.
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static tflite::MicroInterpreter static_interpreter(
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model, resolver, tensor_arena, kTensorArenaSize, error_reporter);
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interpreter = &static_interpreter;
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// Allocate memory from the tensor_arena for the model's tensors.
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TfLiteStatus allocate_status = interpreter->AllocateTensors();
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if (allocate_status != kTfLiteOk) {
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TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed");
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return;
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}
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// Obtain pointers to the model's input and output tensors.
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input = interpreter->input(0);
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output = interpreter->output(0);
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// Keep track of how many inferences we have performed.
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inference_count = 0;
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}
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// The name of this function is important for Arduino compatibility.
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void loop()
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{
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// Calculate an x value to feed into the model. We compare the current
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// inference_count to the number of inferences per cycle to determine
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// our position within the range of possible x values the model was
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// trained on, and use this to calculate a value.
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float position = static_cast<float>(inference_count) /
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static_cast<float>(kInferencesPerCycle);
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float x = position * kXrange;
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// Quantize the input from floating-point to integer
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int8_t x_quantized = x / input->params.scale + input->params.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 inference, and report any error
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TfLiteStatus invoke_status = interpreter->Invoke();
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if (invoke_status != kTfLiteOk) {
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TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed on x: %f\n",
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static_cast<double>(x));
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return;
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}
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// Obtain the quantized output from model's output tensor
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int8_t y_quantized = output->data.int8[0];
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// Dequantize the output from integer to floating-point
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float y = (y_quantized - output->params.zero_point) * output->params.scale;
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// Output the results. A custom HandleOutput function can be implemented
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// for each supported hardware target.
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printf("x=%f, y=%f\r\n", x, y); ////
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//// TODO: HandleOutput(error_reporter, x, y);
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// Increment the inference_counter, and reset it if we have reached
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// the total number per cycle
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inference_count += 1;
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if (inference_count >= kInferencesPerCycle)
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inference_count = 0;
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}
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