3.3.7
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# Hello World Example
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This example is designed to demonstrate the absolute basics of using [TensorFlow
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Lite for Microcontrollers](https://www.tensorflow.org/lite/microcontrollers).
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It includes the full end-to-end workflow of training a model, converting it for
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use with TensorFlow Lite for Microcontrollers for running inference on a
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microcontroller.
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The model is trained to replicate a `sine` function and generates a pattern of
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data to either blink LEDs or control an animation, depending on the capabilities
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of the device.
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## Deploy to ESP32
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The sample has been tested on ESP-IDF version `release/v4.2` and `release/v4.4` with the following devices:
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- [ESP32-DevKitC](http://esp-idf.readthedocs.io/en/latest/get-started/get-started-devkitc.html)
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- [ESP32-S3-DevKitC](https://docs.espressif.com/projects/esp-idf/en/latest/esp32s3/hw-reference/esp32s3/user-guide-devkitc-1.html)
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- [ESP-EYE](https://github.com/espressif/esp-who/blob/master/docs/en/get-started/ESP-EYE_Getting_Started_Guide.md)
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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;
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@@ -0,0 +1,32 @@
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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_
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@@ -0,0 +1,112 @@
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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 "tensorflow/lite/micro/micro_mutable_op_resolver.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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#include "model.h"
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#include "constants.h"
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#include "output_handler.h"
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// Globals, used for compatibility with Arduino-style sketches.
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namespace {
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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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// 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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MicroPrintf(
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"Model provided is schema version %d not equal to supported "
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"version %d.",
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model->version(), TFLITE_SCHEMA_VERSION
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);
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return;
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}
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// Pull in only the operation implementations we need.
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static tflite::MicroMutableOpResolver<1> resolver;
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if (resolver.AddFullyConnected() != kTfLiteOk) {
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return;
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}
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// Build an interpreter to run the model with.
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static tflite::MicroInterpreter static_interpreter(model, resolver, tensor_arena, kTensorArenaSize);
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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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MicroPrintf("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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// 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) / 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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MicroPrintf("Invoke failed on x: %f\n", 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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HandleOutput(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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}
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@@ -0,0 +1,126 @@
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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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||||
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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,
|
||||
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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// Automatically created from a TensorFlow Lite flatbuffer using the command:
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// xxd -i model.tflite > model.cc
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// This is a standard TensorFlow Lite model file that has been converted into a
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// C data array, so it can be easily compiled into a binary for devices that
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// don't have a file system.
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// See train/README.md for a full description of the creation process.
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#include "model.h"
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// Keep model aligned to 8 bytes to guarantee aligned 64-bit accesses.
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alignas(8) const unsigned char g_model[] = {
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||||
0x1c, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x14, 0x00, 0x20, 0x00, 0x1c, 0x00, 0x18, 0x00, 0x14, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x08, 0x00,
|
||||
0x04, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x98, 0x00, 0x00, 0x00, 0xc8, 0x00, 0x00, 0x00, 0x1c, 0x03, 0x00, 0x00, 0x2c, 0x03, 0x00, 0x00,
|
||||
0x30, 0x09, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x60, 0xf7, 0xff, 0xff, 0x10, 0x00, 0x00, 0x00, 0x18, 0x00,
|
||||
0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x44, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x73, 0x65, 0x72, 0x76, 0x65, 0x00, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00,
|
||||
0x73, 0x65, 0x72, 0x76, 0x69, 0x6e, 0x67, 0x5f, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x00, 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xbc, 0xff,
|
||||
0xff, 0xff, 0x09, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x34, 0x00, 0x01, 0x00, 0x00, 0x00,
|
||||
0x04, 0x00, 0x00, 0x00, 0x76, 0xfd, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x32, 0x5f, 0x69, 0x6e,
|
||||
0x70, 0x75, 0x74, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x04, 0x00, 0x08, 0x00, 0x00, 0x00,
|
||||
0x0b, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, 0x6d, 0x69, 0x6e, 0x5f, 0x72, 0x75, 0x6e, 0x74, 0x69, 0x6d, 0x65, 0x5f, 0x76, 0x65,
|
||||
0x72, 0x73, 0x69, 0x6f, 0x6e, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x50, 0x02, 0x00, 0x00, 0x48, 0x02, 0x00, 0x00, 0x34, 0x02, 0x00, 0x00, 0xdc, 0x01, 0x00, 0x00,
|
||||
0x8c, 0x01, 0x00, 0x00, 0x6c, 0x01, 0x00, 0x00, 0x5c, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00, 0x34, 0x00, 0x00, 0x00, 0x2c, 0x00, 0x00, 0x00, 0x24, 0x00,
|
||||
0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xfa, 0xfd, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x31, 0x2e, 0x35, 0x2e, 0x30, 0x00, 0x00, 0x00,
|
||||
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x84, 0xfd, 0xff, 0xff, 0x88, 0xfd, 0xff, 0xff, 0x8c, 0xfd, 0xff, 0xff, 0x22, 0xfe, 0xff, 0xff, 0x04, 0x00,
|
||||
0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x21, 0xa5, 0x8b, 0xca, 0x5e, 0x1d, 0xce, 0x42, 0x9d, 0xce, 0x1f, 0xb0, 0xdf, 0x54, 0x2f, 0x81, 0x3e, 0xfe, 0xff, 0xff,
|
||||
0x04, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0xee, 0xfc, 0x00, 0xec, 0x05, 0x17, 0xef, 0xec, 0xe6, 0xf8, 0x03, 0x01, 0x00, 0xfa, 0xf8, 0xf5, 0xdc, 0xeb,
|
||||
0x27, 0x14, 0xf1, 0xde, 0xe2, 0xdb, 0xf0, 0xde, 0x31, 0x06, 0x02, 0xe6, 0xee, 0xf9, 0x00, 0x16, 0x07, 0xe0, 0xfe, 0xff, 0xe9, 0x06, 0xe7, 0xef, 0x81, 0x1b,
|
||||
0x18, 0xea, 0xc9, 0x01, 0x0f, 0x00, 0xda, 0xf7, 0x0e, 0xec, 0x13, 0x1f, 0x04, 0x13, 0xb4, 0xe6, 0xfd, 0x06, 0xb9, 0xe0, 0x0d, 0xec, 0xf0, 0xde, 0xeb, 0xf7,
|
||||
0x05, 0x26, 0x1a, 0xe4, 0x6f, 0x1a, 0xea, 0x1e, 0x35, 0xdf, 0x1a, 0xf3, 0xf1, 0x19, 0x0f, 0x03, 0x1b, 0xe1, 0xde, 0x13, 0xf6, 0x19, 0xff, 0xf6, 0x1b, 0x18,
|
||||
0xf0, 0x1c, 0xda, 0x1b, 0x1b, 0x20, 0xe5, 0x1a, 0xf5, 0xff, 0x96, 0x0b, 0x00, 0x01, 0xcd, 0xde, 0x0d, 0xf6, 0x16, 0xe3, 0xed, 0xfc, 0x0e, 0xe9, 0xfa, 0xeb,
|
||||
0x5c, 0xfc, 0x1d, 0x02, 0x5b, 0xe2, 0xe1, 0xf5, 0x15, 0xec, 0xf4, 0x00, 0x13, 0x05, 0xec, 0x0c, 0x1d, 0x14, 0x0e, 0xe7, 0x0b, 0xf4, 0x19, 0x00, 0xd7, 0x05,
|
||||
0x27, 0x02, 0x15, 0xea, 0xea, 0x02, 0x9b, 0x00, 0x0c, 0xfa, 0xe8, 0xea, 0xfd, 0x00, 0x14, 0xfd, 0x0b, 0x02, 0xef, 0xee, 0x06, 0xee, 0x01, 0x0d, 0x06, 0xe6,
|
||||
0xf7, 0x11, 0xf7, 0x09, 0xf8, 0xf1, 0x21, 0xff, 0x0e, 0xf3, 0xec, 0x12, 0x26, 0x1d, 0xf2, 0xe9, 0x28, 0x18, 0xe0, 0xfb, 0xf3, 0xf4, 0x05, 0x1d, 0x1d, 0xfb,
|
||||
0xfd, 0x1e, 0xfc, 0x11, 0xe8, 0x07, 0x09, 0x03, 0x12, 0xf2, 0x36, 0xfb, 0xdc, 0x1c, 0xf9, 0xef, 0xf3, 0xe7, 0x6f, 0x0c, 0x1d, 0x00, 0x45, 0xfd, 0x0e, 0xf0,
|
||||
0x0b, 0x19, 0x1a, 0xfa, 0xe0, 0x19, 0x1f, 0x13, 0x36, 0x1c, 0x12, 0xeb, 0x3b, 0x0c, 0xb4, 0xcb, 0xe6, 0x13, 0xfa, 0xeb, 0xf1, 0x06, 0x1c, 0xfa, 0x18, 0xe5,
|
||||
0xeb, 0xcb, 0x0c, 0xf4, 0x4a, 0xff, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x75, 0x1c, 0x11, 0xe1, 0x0c, 0x81, 0xa5, 0x42, 0xfe, 0xd5,
|
||||
0xd4, 0xb2, 0x61, 0x78, 0x19, 0xdf, 0x66, 0xff, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x77, 0x0b, 0x00, 0x00,
|
||||
0x53, 0xf6, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x77, 0x0c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xd3, 0x06, 0x00, 0x00, 0x00, 0x00,
|
||||
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x72, 0x21, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x2f, 0x07, 0x00, 0x00, 0x67, 0xf5, 0xff, 0xff, 0x34, 0xf0, 0xff, 0xff,
|
||||
0x00, 0x00, 0x00, 0x00, 0xb2, 0xff, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xb5, 0x04, 0x00, 0x00, 0x78, 0x0a,
|
||||
0x00, 0x00, 0x2d, 0x06, 0x00, 0x00, 0x71, 0xf8, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x9a, 0x0a, 0x00, 0x00, 0xfe, 0xf7, 0xff, 0xff, 0x0e, 0x05, 0x00, 0x00,
|
||||
0xd4, 0x09, 0x00, 0x00, 0x47, 0xfe, 0xff, 0xff, 0xb6, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xac, 0xf7, 0xff, 0xff, 0x4b, 0xf9, 0xff, 0xff, 0x4a, 0x05,
|
||||
0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x8c, 0xef, 0xff, 0xff,
|
||||
0x84, 0xff, 0xff, 0xff, 0x88, 0xff, 0xff, 0xff, 0x0f, 0x00, 0x00, 0x00, 0x4d, 0x4c, 0x49, 0x52, 0x20, 0x43, 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74, 0x65, 0x64,
|
||||
0x2e, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x18, 0x00, 0x14, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x04, 0x00,
|
||||
0x0e, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0xdc, 0x00, 0x00, 0x00, 0xe0, 0x00, 0x00, 0x00, 0xe4, 0x00, 0x00, 0x00, 0x04, 0x00,
|
||||
0x00, 0x00, 0x6d, 0x61, 0x69, 0x6e, 0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x84, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
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||||
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|
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|
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|
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|
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|
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|
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|
||||
0x00, 0x00, 0xd8, 0xff, 0xff, 0xff, 0x06, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x0c, 0x00, 0x0c, 0x00, 0x0b, 0x00, 0x00, 0x00,
|
||||
0x00, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x72, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x72, 0x0c, 0x00, 0x10, 0x00, 0x0f, 0x00, 0x00, 0x00, 0x08, 0x00,
|
||||
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|
||||
};
|
||||
const int g_model_len = 2488;
|
||||
@@ -0,0 +1,31 @@
|
||||
/* Copyright 2020 The TensorFlow Authors. All Rights Reserved.
|
||||
|
||||
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.
|
||||
==============================================================================*/
|
||||
|
||||
// Automatically created from a TensorFlow Lite flatbuffer using the command:
|
||||
// xxd -i model.tflite > model.cc
|
||||
|
||||
// This is a standard TensorFlow Lite model file that has been converted into a
|
||||
// C data array, so it can be easily compiled into a binary for devices that
|
||||
// don't have a file system.
|
||||
|
||||
// See train/README.md for a full description of the creation process.
|
||||
|
||||
#ifndef TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_MODEL_H_
|
||||
#define TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_MODEL_H_
|
||||
|
||||
extern const unsigned char g_model[];
|
||||
extern const int g_model_len;
|
||||
|
||||
#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_MODEL_H_
|
||||
@@ -0,0 +1,22 @@
|
||||
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
|
||||
|
||||
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 "output_handler.h"
|
||||
#include "tensorflow/lite/micro/micro_log.h"
|
||||
|
||||
void HandleOutput(float x_value, float y_value) {
|
||||
// Log the current X and Y values
|
||||
MicroPrintf("x_value: %f, y_value: %f\n", static_cast<double>(x_value), static_cast<double>(y_value));
|
||||
}
|
||||
@@ -0,0 +1,24 @@
|
||||
/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
|
||||
|
||||
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_EXAMPLES_HELLO_WORLD_OUTPUT_HANDLER_H_
|
||||
#define TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_OUTPUT_HANDLER_H_
|
||||
|
||||
#include "tensorflow/lite/c/common.h"
|
||||
|
||||
// Called by the main loop to produce some output based on the x and y values
|
||||
void HandleOutput(float x_value, float y_value);
|
||||
|
||||
#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_OUTPUT_HANDLER_H_
|
||||
@@ -0,0 +1,8 @@
|
||||
name=TFLite Micro
|
||||
version=3.3.7
|
||||
author=Sanket Wadekar
|
||||
maintainer=Sanket Wadekar
|
||||
sentence=TensorFlow Lite for Microcontrollers
|
||||
paragraph=With this library you can train and deploy Machine Learning models on esp32 series of microcontrollers.
|
||||
url=https://www.tensorflow.org/lite/microcontrollers
|
||||
architectures=esp32,esp32s3
|
||||
@@ -0,0 +1,6 @@
|
||||
/*
|
||||
* SPDX-FileCopyrightText: 2023 Espressif Systems (Shanghai) CO LTD
|
||||
*
|
||||
* SPDX-License-Identifier: Apache-2.0
|
||||
*/
|
||||
// This header file is kept to avoid Invalid library error prints while compiling TFLite Micro examples.
|
||||
@@ -0,0 +1,10 @@
|
||||
/*
|
||||
* SPDX-FileCopyrightText: 2023 Espressif Systems (Shanghai) CO LTD
|
||||
*
|
||||
* SPDX-License-Identifier: Apache-2.0
|
||||
*/
|
||||
#pragma once
|
||||
#include <utility>
|
||||
/*
|
||||
The example sketches include "tensorflow/lite/micro/micro_interpreter.h" which internally include "utility.h" header file when compiling examples for Arduino (when -DARDUINO flag is passed to the compiler). This header file does not exist in esp32-arduino core. Hence, keeping this file here as a workaround and including an alternate header file.
|
||||
*/
|
||||
Reference in New Issue
Block a user