本来是想找一篇中文教程,不过只看到一个英文官方的,也行吧,虽然效率会慢丢丢。
GitHub - espressif/esp-tflite-micro: TensorFlow Lite Micro for Espressif Chipsets
看了一圈,有个中文的:
esp-dl/README_cn.md at master · espressif/esp-dl · GitHub
目前阶段还是照着做,然后写一点感想。。。后面有机会和能力再深度写一下。
之前都是用micropython,觉得简单。不过涉及到tensorflow lite的好像都是原厂环境。。。
没办法,搞了一阵终于把环境弄出来了。可以Hello了。。。
继续往机器学习的方向走,遇到的最大的问题就是不管是tensorflow还是tflite-micro,都无法下载。但是网上的很多教程都是先要拿到tensorflow lite。然后基于tensorflow lite的例子,送到idf环境去编译。
后面看了历险记(参考中)那个文章,说是现在乐鑫自己也有tflite的例子,可以曲线救国,先跑通乐鑫自己的例子,然后用python下的tensorflow生成lite模型,最后用xxd命令转换成model.cc。这个model.cc刚好又是乐鑫里面带了的。这样就可以闭环了。
照着他写的一步步来,晚上把乐鑫自己的hello world跑通了,还是很简单。(真不知道网上这些大神哪里找的教程,我是翻了半天都没翻到)
又试了下用tensorflow生成模型(生成模型和转换是在ubuntu上做的哈):
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import mathx_values = np.random.uniform(low=0, high=2 * math.pi, size=1000).astype(np.float32)np.random.shuffle(x_values)y_values = np.sin(x_values).astype(np.float32)plt.plot(x_values, y_values,"b.")
plt.show()y_values += 0.1 * np.random.randn(*y_values.shape)plt.plot(x_values, y_values, "b.")
plt.show()TRAIN_SPLIT = int(0.6 * 1000)
TEST_SPLIT = int(0.2 * 1000 + TRAIN_SPLIT)x_train, x_validate, x_test = np.split(x_values, [TRAIN_SPLIT, TEST_SPLIT])
y_train, y_validate, y_test = np.split(y_values, [TRAIN_SPLIT, TEST_SPLIT])plt.plot(x_train, y_train, "b.", label="Train")
plt.plot(x_validate, y_validate, "y.", label="Validate")
plt.plot(x_test, y_test, "r.", label="Test")
plt.legend()
plt.show()model = tf.keras.Sequential()# First layer takes a scalar input and feeds it through 16 "neurons". The
# neurons decide whether to activate based on the 'relu' activation function.
model.add(tf.keras.layers.Dense(16, activation='relu', input_shape=(1,)))# The new second and third layer will help the network learn more complex
# representations
model.add(tf.keras.layers.Dense(16, activation='relu'))# Final layer is a single neuron, since we want to output a single value
model.add(tf.keras.layers.Dense(1))# Compile the model using the standard 'adam' optimizer and the mean squared
# error or 'mse' loss function for regression.
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
model.summary()model.fit(x_values,y_values,epochs=400,validation_split=0.2,batch_size=64,verbose=2)
save_dir = './modes/test'
tf.saved_model.save(model, save_dir)converter = tf.lite.TFLiteConverter.from_saved_model(save_dir)
tflite_model = converter.convert()import pathlibtflite_model_file = pathlib.Path('model.tflite')
tflite_model_file.write_bytes(tflite_model)def representative_dataset(num_samples=500):for i in range(num_samples):yield [x_values[i].reshape(1, 1)]saved_model_dir = "./modes/test"
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
converter.representative_dataset = representative_dataset
tflite_quant_model = converter.convert()
open("sine_model_quantized.tflite","wb").write(tflite_quant_model)
运行python之后生成sine_model_quantized.tflite
按照例程中说的安装xdd
sudo apt install xxd
然后运行转换命令。xdd命令也没什么特别的,就是在Linux中用于以二进制或十六进制显示文件的内容。-i 选项允许以C语言风格的数组形式输出文件内容。就是把二进制转成C的数组。
xxd -i sine_model_quantized.tflite > model.cc
之后把model.cc拷贝回esp32的hello world环境。但是代码要稍微改改,主要是要把模型数组换成g_model,否则要报错。
model.cc
#include "model.h"alignas(8) const unsigned char g_model[] = {0x20, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x00, 0x00,0x14, 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, 0x90, 0x00, 0x00, 0x00, 0xe8, 0x00, 0x00, 0x00,0xa4, 0x03, 0x00, 0x00, 0xb4, 0x03, 0x00, 0x00, 0x0c, 0x0a, 0x00, 0x00,0x03, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,0x00, 0x00, 0x0a, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x04, 0x00,0x0a, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00,0x38, 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, 0x98, 0xff, 0xff, 0xff,0x09, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00,0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x32, 0x00, 0x01, 0x00, 0x00, 0x00,0x04, 0x00, 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};
unsigned int sine_model_quantized_tflite_len = 2664;const int g_model_len = 2664;
然后就是编译三部曲:
idf.py set-target esp32
idf.py build
idf.py flash monitor
最后就能看到结果:
好吧,虽然还是很low,全部都只是照着弄,但是总算是跑通了。我有时候甚至觉得搭建环境算是成功了一半。。。接着有时间我会慢慢来分析分析的。
https://github.com/espressif/esp-tflite-micro/tree/master
https://github.com/tensorflow/tflite-micro
参考:
Windows 平台工具链的标准设置 - ESP32 - — ESP-IDF 编程指南 v5.2.2 文档
ESPFriends之ESP32模型部署训练历险记(一)_terserflowlite部署esp32-CSDN博客
【TinyML】用ESP32实现弱监督人体定位
最简单体验TinyML、TensorFlow Lite——ESP32跑机器学习(全代码)-CSDN博客
ESPFriends之ESP32模型部署训练历险记(一)_terserflowlite部署esp32-CSDN博客
Tensorflow Lite从入门到精通-CSDN博客