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APP_Framework/Framework/:update knowing framework

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1.fix some Kconfig file
2.add tensorflow-lite-for-mcu in knowing file
3.add mnist application,note the application cannot be used with RAM less than 500K.
4.the version need to separate application and OS(rtt),later by using add transform layer to solve it.
This commit is contained in:
chunyexixiaoyu
2021-07-15 10:07:02 +08:00
parent fcd14e038e
commit 38d2cb3c85
328 changed files with 130959 additions and 10 deletions
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6
APP_Framework/Applications/knowing_app/Kconfig
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@@ -1,7 +1,3 @@
menu "knowing app"
menuconfig APPLICATION_KNOWING
bool "Using knowing apps"
default n
source "$APP_DIR/Applications/knowing_app/mnist/Kconfig"
endmenu

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APP_Framework/Applications/knowing_app/SConscript Normal file
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import os
Import('RTT_ROOT')
from building import *
cwd = GetCurrentDir()
objs = []
list = os.listdir(cwd)
for d in list:
path = os.path.join(cwd, d)
if os.path.isfile(os.path.join(path, 'SConscript')):
objs = objs + SConscript(os.path.join(path, 'SConscript'))
Return('objs')

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APP_Framework/Applications/knowing_app/mnist/.gitignore vendored Normal file
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*.h5
*.tflite

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APP_Framework/Applications/knowing_app/mnist/K210 mnist .png Normal file
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APP_Framework/Applications/knowing_app/mnist/Kconfig Normal file
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config APP_MNIST
bool "enable apps/mnist"
depends on USING_TENSORFLOWLITEMICRO
default n

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APP_Framework/Applications/knowing_app/mnist/README.md Normal file
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@@ -0,0 +1,23 @@
# MNIST 说明
要使用本例程，MCU RAM必须至少500K左右，所以本例程目前在K210上面验证过，stm32f407 目前在rtt上原则上只能采取dlmodule加载的方式。
![K210 mnist ](E:\XIUOS_FRAMEWORK\xiuos\APP_Framework\Applications\knowing_app\mnist\K210 mnist .png)
## 使用
tools/mnist-train.py 训练生成 mnist 模型。
tools/mnist-inference.py 使用 mnist 模型进行推理。
tools/mnist-c-model.py 将 mnist 模型转换成 C 的数组保存在 model.h 中。
tools/mnist-c-digit.py 将 mnist 数据集中的某个数字转成数组保存在 digit.h 中。
## 参考资料
https://tensorflow.google.cn/lite/performance/post_training_quantization
https://tensorflow.google.cn/lite/performance/post_training_integer_quant
https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/micro/examples/hello_world/train/train_hello_world_model.ipynb

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APP_Framework/Applications/knowing_app/mnist/SConscript Normal file
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from building import *
cwd = GetCurrentDir()
src = Glob('*.c') + Glob('*.cpp')
CPPPATH = [cwd]
group = DefineGroup('Applications', src, depend = ['APP_MNIST'], LOCAL_CPPPATH = CPPPATH)
Return('group')

31
APP_Framework/Applications/knowing_app/mnist/digit.h Normal file
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const float mnist_digit[] = {
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.33, 0.73, 0.62, 0.59, 0.24, 0.14, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.87, 1.00, 1.00, 1.00, 1.00, 0.95, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.78, 0.67, 0.20, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.26, 0.45, 0.28, 0.45, 0.64, 0.89, 1.00, 0.88, 1.00, 1.00, 1.00, 0.98, 0.90, 1.00, 1.00, 0.55, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.07, 0.26, 0.05, 0.26, 0.26, 0.26, 0.23, 0.08, 0.93, 1.00, 0.42, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.33, 0.99, 0.82, 0.07, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.09, 0.91, 1.00, 0.33, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.51, 1.00, 0.93, 0.17, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.23, 0.98, 1.00, 0.24, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.52, 1.00, 0.73, 0.02, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.04, 0.80, 0.97, 0.23, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.49, 1.00, 0.71, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.29, 0.98, 0.94, 0.22, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.07, 0.87, 1.00, 0.65, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.01, 0.80, 1.00, 0.86, 0.14, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.15, 1.00, 1.00, 0.30, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.12, 0.88, 1.00, 0.45, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.52, 1.00, 1.00, 0.20, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.24, 0.95, 1.00, 1.00, 0.20, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.47, 1.00, 1.00, 0.86, 0.16, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.47, 1.00, 0.81, 0.07, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00,
0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00
};
const int mnist_label = 7;

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APP_Framework/Applications/knowing_app/mnist/main.cpp Normal file
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#include <rtthread.h>
#include <stdio.h>
#include "tensorflow/lite/micro/all_ops_resolver.h"
#include "tensorflow/lite/micro/micro_error_reporter.h"
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"
#include "digit.h"
#include "model.h"
namespace {
tflite::ErrorReporter* error_reporter = nullptr;
const tflite::Model* model = nullptr;
tflite::MicroInterpreter* interpreter = nullptr;
TfLiteTensor* input = nullptr;
TfLiteTensor* output = nullptr;
constexpr int kTensorArenaSize = 110 * 1024;
uint8_t *tensor_arena = nullptr;
//uint8_t tensor_arena[kTensorArenaSize];
}
extern "C" void mnist_app() {
tflite::MicroErrorReporter micro_error_reporter;
error_reporter = &micro_error_reporter;
model = tflite::GetModel(mnist_model);
if (model->version() != TFLITE_SCHEMA_VERSION) {
TF_LITE_REPORT_ERROR(error_reporter,
"Model provided is schema version %d not equal "
"to supported version %d.",
model->version(), TFLITE_SCHEMA_VERSION);
return;
}
tensor_arena = (uint8_t *)rt_malloc(kTensorArenaSize);
if (tensor_arena == nullptr) {
TF_LITE_REPORT_ERROR(error_reporter, "malloc for tensor_arena failed");
return;
}
tflite::AllOpsResolver resolver;
tflite::MicroInterpreter static_interpreter(
model, resolver, tensor_arena, kTensorArenaSize, error_reporter);
interpreter = &static_interpreter;
// Allocate memory from the tensor_arena for the model's tensors.
TfLiteStatus allocate_status = interpreter->AllocateTensors();
if (allocate_status != kTfLiteOk) {
TF_LITE_REPORT_ERROR(error_reporter, "AllocateTensors() failed");
return;
}
input = interpreter->input(0);
output = interpreter->output(0);
printf("------- Input Digit -------\n");
for (int i = 0; i < 28; i++) {
for (int j = 0; j < 28; j++) {
if (mnist_digit[i*28+j] > 0.3)
printf("#");
else
printf(".");
}
printf("\n");
}
for (int i = 0; i < 28*28; i++) {
input->data.f[i] = mnist_digit[i];
}
TfLiteStatus invoke_status = interpreter->Invoke();
if (invoke_status != kTfLiteOk) {
TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed on x_val\n");
return;
}
// Read the predicted y value from the model's output tensor
float max = 0.0;
int index;
for (int i = 0; i < 10; i++) {
if(output->data.f[i]>max){
max = output->data.f[i];
index = i;
}
}
printf("------- Output Result -------\n");
printf("result is %d\n", index);
}
extern "C" {
MSH_CMD_EXPORT(mnist_app, run mnist app);
}

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APP_Framework/Applications/knowing_app/mnist/model.h Normal file
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APP_Framework/Applications/knowing_app/mnist/tools/mnist-c-digit.py Normal file
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#!/usr/bin/env python3
import tensorflow as tf
print("TensorFlow version %s" % (tf.__version__))
def show(image):
for i in range(28):
for j in range(28):
if image[i][j] > 0.3:
print('#', end = '')
else:
print('.', end = '')
print()
digit_file_path = 'digit.h'
digit_content = '''const float mnist_digit[] = {
%s
};
const int mnist_label = %d;
'''
if __name__ == '__main__':
mnist = tf.keras.datasets.mnist
(_, _), (test_images, test_labels) = mnist.load_data()
index = 0
shape = 28
image = test_images[index].astype('float32')/255
label = test_labels[index]
print('label: %d' % label)
#show(image)
digit_data = (',\n ').join([ (', ').join([ '%.2f' % image[row][col] for col in range(shape)]) for row in range(shape)])
digit_file = open(digit_file_path, 'w')
digit_file.write(digit_content % (digit_data, label))
digit_file.close()

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APP_Framework/Applications/knowing_app/mnist/tools/mnist-c-model.py Normal file
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#!/usr/bin/env python3
#tflite_file_path = 'mnist-default-quan.tflite'
tflite_file_path = 'mnist.tflite'
model_file_path = 'model.h'
tflite_file = open(tflite_file_path, 'rb')
tflite_data = tflite_file.read()
tflite_file.close()
tflite_array = [ '0x%02x' % byte for byte in tflite_data ]
model_content = '''unsigned char mnist_model[] = {
%s
};
unsigned int mnist_model_len = %d;
'''
# 12 bytes in a line, the same with xxd
bytes_of_line = 12
model_data = (',\n ').join([ (', ').join(tflite_array[i:i+bytes_of_line]) for i in range(0, len(tflite_array), bytes_of_line) ])
model_file = open(model_file_path, 'w')
model_file.write(model_content % (model_data, len(tflite_array)))
model_file.close()

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APP_Framework/Applications/knowing_app/mnist/tools/mnist-inference.py Normal file
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#!/usr/bin/env python3
import tensorflow as tf
print("TensorFlow version %s" % (tf.__version__))
MODEL_NAME_H5 = 'mnist.h5'
MODEL_NAME_TFLITE = 'mnist.tflite'
DEFAULT_QUAN_MODEL_NAME_TFLITE = 'mnist-default-quan.tflite'
FULL_QUAN_MODEL_NAME_TFLITE = 'mnist-full-quan.tflite'
def show(image):
for i in range(28):
for j in range(28):
if image[i][j][0] > 0.3:
print('#', end = '')
else:
print(' ', end = '')
print()
if __name__ == '__main__':
mnist = tf.keras.datasets.mnist
(_, _), (test_images, test_labels) = mnist.load_data()
test_images = test_images.reshape(10000, 28, 28, 1)
index = 0
input_image = test_images[index].astype('float32')/255
target_label = test_labels[index]
interpreter = tf.lite.Interpreter(model_path = DEFAULT_QUAN_MODEL_NAME_TFLITE)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()[0]
output_details = interpreter.get_output_details()[0]
interpreter.set_tensor(input_details['index'], [input_image])
interpreter.invoke()
output = interpreter.get_tensor(output_details['index'])[0]
show(input_image)
print('target label: %d, predict label: %d' % (target_label, output.argmax()))

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APP_Framework/Applications/knowing_app/mnist/tools/mnist-train.py Normal file
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#!/usr/bin/env python3
import os
import tensorflow as tf
print("TensorFlow version %s" % (tf.__version__))
MODEL_NAME_H5 = 'mnist.h5'
MODEL_NAME_TFLITE = 'mnist.tflite'
DEFAULT_QUAN_MODEL_NAME_TFLITE = 'mnist-default-quan.tflite'
FULL_QUAN_MODEL_NAME_TFLITE = 'mnist-full-quan.tflite'
def build_model(model_name):
print('\n>>> load mnist dataset')
mnist = tf.keras.datasets.mnist
(train_images, train_labels),(test_images, test_labels) = mnist.load_data()
print("train images shape: ", train_images.shape)
print("train labels shape: ", train_labels.shape)
print("test images shape: ", test_images.shape)
print("test labels shape: ", test_labels.shape)
# transform label to categorical, like: 2 -> [0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
print('\n>>> transform label to categorical')
train_labels = tf.keras.utils.to_categorical(train_labels)
test_labels = tf.keras.utils.to_categorical(test_labels)
print("train labels shape: ", train_labels.shape)
print("test labels shape: ", test_labels.shape)
# transform color like: [0, 255] -> 0.xxx
print('\n>>> transform image color into float32')
train_images = train_images.astype('float32') / 255
test_images = test_images.astype('float32') / 255
# reshape image like: (60000, 28, 28) -> (60000, 28, 28, 1)
print('\n>>> reshape image with color channel')
train_images = train_images.reshape((60000, 28, 28, 1))
test_images = test_images.reshape((10000, 28, 28, 1))
print("train images shape: ", train_images.shape)
print("test images shape: ", test_images.shape)
print('\n>>> build model')
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32, (3, 3), activation=tf.nn.relu, input_shape=(28, 28, 1)),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(64, (3, 3), activation=tf.nn.relu),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(64, (3, 3), activation=tf.nn.relu),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(64, activation=tf.nn.relu),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
print('\n>>> train the model')
early_stopping = tf.keras.callbacks.EarlyStopping(
monitor='loss', min_delta=0.0005, patience=3, verbose=1, mode='auto',
baseline=None, restore_best_weights=True
)
model.fit(train_images, train_labels, epochs=100, batch_size=64, callbacks=[early_stopping])
print('\n>>> evaluate the model')
test_loss, test_acc = model.evaluate(test_images, test_labels)
print("lost: %f, accuracy: %f" % (test_loss, test_acc))
print('\n>>> save the keras model as %s' % model_name)
model.save(model_name)
if __name__ == '__main__':
if not os.path.exists(MODEL_NAME_H5):
build_model(MODEL_NAME_H5)
if not os.path.exists(MODEL_NAME_TFLITE):
print('\n>>> save the tflite model as %s' % MODEL_NAME_TFLITE)
converter = tf.lite.TFLiteConverter.from_keras_model(tf.keras.models.load_model(MODEL_NAME_H5))
tflite_model = converter.convert()
with open(MODEL_NAME_TFLITE, "wb") as f:
f.write(tflite_model)
if not os.path.exists(DEFAULT_QUAN_MODEL_NAME_TFLITE):
print('\n>>> save the default quantized model as %s' % DEFAULT_QUAN_MODEL_NAME_TFLITE)
converter = tf.lite.TFLiteConverter.from_keras_model(tf.keras.models.load_model(MODEL_NAME_H5))
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()
with open(DEFAULT_QUAN_MODEL_NAME_TFLITE, "wb") as f:
f.write(tflite_model)
if not os.path.exists(FULL_QUAN_MODEL_NAME_TFLITE):
mnist = tf.keras.datasets.mnist
(train_images, _), (_, _) = mnist.load_data()
train_images = train_images.astype('float32') / 255
train_images = train_images.reshape((60000, 28, 28, 1))
def representative_data_gen():
for input_value in tf.data.Dataset.from_tensor_slices(train_images).batch(1).take(100):
yield [input_value]
print('\n>>> save the full quantized model as %s' % DEFAULT_QUAN_MODEL_NAME_TFLITE)
converter = tf.lite.TFLiteConverter.from_keras_model(tf.keras.models.load_model(MODEL_NAME_H5))
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_data_gen
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8
tflite_model = converter.convert()
with open(FULL_QUAN_MODEL_NAME_TFLITE, "wb") as f:
f.write(tflite_model)