Browse Source

APP_Framework/:fix some Kconfig file in Applications and Framework. and change know to knowing ,remove uncomfortable file(their location is wrong)

pull/1/head
chunyexixiaoyu 1 year ago
parent
commit
fcd14e038e
  1. 11
      APP_Framework/Applications/connection_app/Kconfig
  2. 2
      APP_Framework/Framework/Kconfig
  3. 2
      APP_Framework/Framework/know/tflite_mnist/.gitignore
  4. 4
      APP_Framework/Framework/know/tflite_mnist/Kconfig
  5. 8
      APP_Framework/Framework/know/tflite_mnist/Makefile
  6. 19
      APP_Framework/Framework/know/tflite_mnist/README.md
  7. 51
      APP_Framework/Framework/know/tflite_mnist/digit.h
  8. 112
      APP_Framework/Framework/know/tflite_mnist/mnistapp.cpp
  9. 30
      APP_Framework/Framework/know/tflite_mnist/mnistmain.c
  10. 31429
      APP_Framework/Framework/know/tflite_mnist/model.h
  11. 54
      APP_Framework/Framework/know/tflite_mnist/tools/mnist-c-digit.py
  12. 41
      APP_Framework/Framework/know/tflite_mnist/tools/mnist-c-model.py
  13. 58
      APP_Framework/Framework/know/tflite_mnist/tools/mnist-inference.py
  14. 127
      APP_Framework/Framework/know/tflite_mnist/tools/mnist-train.py
  15. 4
      APP_Framework/Framework/know/tflite_sin/Kconfig
  16. 11
      APP_Framework/Framework/know/tflite_sin/Makefile
  17. 19
      APP_Framework/Framework/know/tflite_sin/constants.cc
  18. 32
      APP_Framework/Framework/know/tflite_sin/constants.h
  19. 119
      APP_Framework/Framework/know/tflite_sin/main_functions.cc
  20. 37
      APP_Framework/Framework/know/tflite_sin/main_functions.h
  21. 237
      APP_Framework/Framework/know/tflite_sin/model.cc
  22. 31
      APP_Framework/Framework/know/tflite_sin/model.h
  23. 24
      APP_Framework/Framework/know/tflite_sin/output_handler.cc
  24. 26
      APP_Framework/Framework/know/tflite_sin/output_handler.h
  25. 33
      APP_Framework/Framework/know/tflite_sin/sinmain.c
  26. 3
      APP_Framework/Framework/knowing/Kconfig
  27. 0
      APP_Framework/Framework/knowing/Makefile
  28. 47
      Ubiquitous/RT_Thread/bsp/k210/.config
  29. 7
      Ubiquitous/RT_Thread/bsp/k210/Kconfig
  30. 27
      Ubiquitous/RT_Thread/bsp/k210/rtconfig.h
  31. 179
      Ubiquitous/RT_Thread/bsp/stm32f407-atk-coreboard/.config
  32. 9
      Ubiquitous/RT_Thread/bsp/stm32f407-atk-coreboard/Kconfig
  33. 123
      Ubiquitous/RT_Thread/bsp/stm32f407-atk-coreboard/rtconfig.h
  34. 2
      Ubiquitous/RT_Thread/bsp/stm32f407-atk-coreboard/rtconfig.py

11
APP_Framework/Applications/connection_app/Kconfig

@ -1,14 +1,3 @@
menu "connection app"
menuconfig APPLICATION_CONNECTION
bool "Using connection apps"
default n
menuconfig CONNECTION_COMMUNICATION_ZIGBEE
bool "enable zigbee demo"
default n
select CONFIG_CONNECTION_COMMUNICATION_ZIGBEE
if CONNECTION_COMMUNICATION_ZIGBEE
source "$KERNEL_DIR/framework/connection/Adapter/zigbee/Kconfig"
endif
endmenu

2
APP_Framework/Framework/Kconfig

@ -20,7 +20,7 @@ menu "Framework"
source "$APP_DIR/Framework/sensor/Kconfig"
source "$APP_DIR/Framework/connection/Kconfig"
source "$APP_DIR/Framework/know/Kconfig"
source "$APP_DIR/Framework/knowing/Kconfig"
source "$APP_DIR/Framework/control/Kconfig"

2
APP_Framework/Framework/know/tflite_mnist/.gitignore

@ -1,2 +0,0 @@
*.h5
*.tflite

4
APP_Framework/Framework/know/tflite_mnist/Kconfig

@ -1,4 +0,0 @@
menuconfig USING_TFLITE_MNIST
bool "mnist demo app for tflite micro"
depends on INTELLIGENT_TFLITE
default n

8
APP_Framework/Framework/know/tflite_mnist/Makefile

@ -1,8 +0,0 @@
ifeq ($(CONFIG_USING_TFLITE_MNIST),y)
SRC_FILES := \
mnistapp.cpp \
mnistmain.c
CPPPATHS += -I.
endif
include $(KERNEL_ROOT)/compiler.mk

19
APP_Framework/Framework/know/tflite_mnist/README.md

@ -1,19 +0,0 @@
# MNIST 说明
## 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

51
APP_Framework/Framework/know/tflite_mnist/digit.h

@ -1,51 +0,0 @@
/*
* Copyright (c) 2020 AIIT XUOS Lab
* XiOS is licensed under Mulan PSL v2.
* You can use this software according to the terms and conditions of the Mulan PSL v2.
* You may obtain a copy of Mulan PSL v2 at:
* http://license.coscl.org.cn/MulanPSL2
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
* EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
* MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
* See the Mulan PSL v2 for more details.
*/
/**
* @file: digit.h
* @brief: store digits in this file
* @version: 1.0
* @author: AIIT XUOS Lab
* @date: 2021/4/30
*
*/
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;

112
APP_Framework/Framework/know/tflite_mnist/mnistapp.cpp

@ -1,112 +0,0 @@
/*
* Copyright (c) 2020 AIIT XUOS Lab
* XiOS is licensed under Mulan PSL v2.
* You can use this software according to the terms and conditions of the Mulan PSL v2.
* You may obtain a copy of Mulan PSL v2 at:
* http://license.coscl.org.cn/MulanPSL2
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
* EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
* MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
* See the Mulan PSL v2 for more details.
*/
/**
* @file: mnistapp.cpp
* @brief: mnist function
* @version: 1.0
* @author: AIIT XUOS Lab
* @date: 2021/4/30
*
*/
#include <xiuos.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);
KPrintf("\n------- 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)
KPrintf("#");
else
KPrintf(".");
}
KPrintf("\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;
}
}
KPrintf("\n------- Output Result -------\n");
KPrintf("result is %d\n\n", index);
}

30
APP_Framework/Framework/know/tflite_mnist/mnistmain.c

@ -1,30 +0,0 @@
/*
* Copyright (c) 2020 AIIT XUOS Lab
* XiOS is licensed under Mulan PSL v2.
* You can use this software according to the terms and conditions of the Mulan PSL v2.
* You may obtain a copy of Mulan PSL v2 at:
* http://license.coscl.org.cn/MulanPSL2
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
* EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
* MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
* See the Mulan PSL v2 for more details.
*/
/**
* @file: mnistmain.c
* @brief: start mnist function
* @version: 1.0
* @author: AIIT XUOS Lab
* @date: 2021/4/30
*
*/
#include <xiuos.h>
void mnist_app(void);
int tfmnist(void) {
mnist_app();
}
SHELL_EXPORT_CMD(SHELL_CMD_PERMISSION(0)|SHELL_CMD_TYPE(SHELL_TYPE_CMD_FUNC)|SHELL_CMD_PARAM_NUM(0), tfmnist, tfmnist, run mnist demo of tflite);

31429
APP_Framework/Framework/know/tflite_mnist/model.h

File diff suppressed because it is too large

54
APP_Framework/Framework/know/tflite_mnist/tools/mnist-c-digit.py

@ -1,54 +0,0 @@
#!/usr/bin/env python3
# ==========================================================================================
# Copyright (c) 2020 AIIT XUOS Lab
# XiOS is licensed under Mulan PSL v2.
# You can use this software according to the terms and conditions of the Mulan PSL v2.
# You may obtain a copy of Mulan PSL v2 at:
# http://license.coscl.org.cn/MulanPSL2
# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
# See the Mulan PSL v2 for more details.
# @file: mnist-c-digit.py
# @brief: print image digit at command line
# @version: 1.0
# @author: AIIT XUOS Lab
# @date: 2021/4/30
# ==========================================================================================
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()

41
APP_Framework/Framework/know/tflite_mnist/tools/mnist-c-model.py

@ -1,41 +0,0 @@
#!/usr/bin/env python3
# ==========================================================================================
# Copyright (c) 2020 AIIT XUOS Lab
# XiOS is licensed under Mulan PSL v2.
# You can use this software according to the terms and conditions of the Mulan PSL v2.
# You may obtain a copy of Mulan PSL v2 at:
# http://license.coscl.org.cn/MulanPSL2
# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
# See the Mulan PSL v2 for more details.
# @file: mnist-c-model.py
# @brief: open file path and load model
# @version: 1.0
# @author: AIIT XUOS Lab
# @date: 2021/4/30
# ==========================================================================================
#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()

58
APP_Framework/Framework/know/tflite_mnist/tools/mnist-inference.py

@ -1,58 +0,0 @@
#!/usr/bin/env python3
# ==========================================================================================
# Copyright (c) 2020 AIIT XUOS Lab
# XiOS is licensed under Mulan PSL v2.
# You can use this software according to the terms and conditions of the Mulan PSL v2.
# You may obtain a copy of Mulan PSL v2 at:
# http://license.coscl.org.cn/MulanPSL2
# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
# See the Mulan PSL v2 for more details.
# @file: mnist-inference.py
# @brief: load data amd start model omferemce
# @version: 1.0
# @author: AIIT XUOS Lab
# @date: 2021/4/30
# ==========================================================================================
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()))

127
APP_Framework/Framework/know/tflite_mnist/tools/mnist-train.py

@ -1,127 +0,0 @@
#!/usr/bin/env python3
# ==========================================================================================
# Copyright (c) 2020 AIIT XUOS Lab
# XiOS is licensed under Mulan PSL v2.
# You can use this software according to the terms and conditions of the Mulan PSL v2.
# You may obtain a copy of Mulan PSL v2 at:
# http://license.coscl.org.cn/MulanPSL2
# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND,
# EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT,
# MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE.
# See the Mulan PSL v2 for more details.
# @file: mnist-train.py
# @brief: model training
# @version: 1.0
# @author: AIIT XUOS Lab
# @date: 2021/4/30
# ==========================================================================================
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)

4
APP_Framework/Framework/know/tflite_sin/Kconfig

@ -1,4 +0,0 @@
menuconfig USING_TFLITE_SIN
bool "sin(x) demo app for tflite micro"
depends on INTELLIGENT_TFLITE
default n

11
APP_Framework/Framework/know/tflite_sin/Makefile

@ -1,11 +0,0 @@
ifeq ($(CONFIG_USING_TFLITE_SIN),y)
SRC_FILES := \
sinmain.c \
main_functions.cc \
model.cc \
output_handler.cc \
constants.cc
CPPPATHS += -I.
endif
include $(KERNEL_ROOT)/compiler.mk

19
APP_Framework/Framework/know/tflite_sin/constants.cc

@ -1,19 +0,0 @@
/* 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 "constants.h"
// This is a small number so that it's easy to read the logs
const int kInferencesPerCycle = 20;

32
APP_Framework/Framework/know/tflite_sin/constants.h

@ -1,32 +0,0 @@
/* 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_CONSTANTS_H_
#define TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_CONSTANTS_H_
// This constant represents the range of x values our model was trained on,
// which is from 0 to (2 * Pi). We approximate Pi to avoid requiring additional
// libraries.
const float kXrange = 2.f * 3.14159265359f;
// This constant determines the number of inferences to perform across the range
// of x values defined above. Since each inference takes time, the higher this
// number, the more time it will take to run through the entire range. The value
// of this constant can be tuned so that one full cycle takes a desired amount
// of time. Since different devices take different amounts of time to perform
// inference, this value should be defined per-device.
extern const int kInferencesPerCycle;
#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_CONSTANTS_H_

119
APP_Framework/Framework/know/tflite_sin/main_functions.cc

@ -1,119 +0,0 @@
/* 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.
==============================================================================*/
#include "main_functions.h"
#include "tensorflow/lite/micro/all_ops_resolver.h"
#include "constants.h"
#include "model.h"
#include "output_handler.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"
// Globals, used for compatibility with Arduino-style sketches.
namespace {
tflite::ErrorReporter* error_reporter = nullptr;
const tflite::Model* model = nullptr;
tflite::MicroInterpreter* interpreter = nullptr;
TfLiteTensor* input = nullptr;
TfLiteTensor* output = nullptr;
int inference_count = 0;
constexpr int kTensorArenaSize = 10240;
uint8_t tensor_arena[kTensorArenaSize];
} // namespace
// The name of this function is important for Arduino compatibility.
void setup() {
// Set up logging. Google style is to avoid globals or statics because of
// lifetime uncertainty, but since this has a trivial destructor it's okay.
// NOLINTNEXTLINE(runtime-global-variables)
static tflite::MicroErrorReporter micro_error_reporter;
error_reporter = &micro_error_reporter;
// Map the model into a usable data structure. This doesn't involve any
// copying or parsing, it's a very lightweight operation.
model = tflite::GetModel(g_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;
}
// This pulls in all the operation implementations we need.
// NOLINTNEXTLINE(runtime-global-variables)
static tflite::AllOpsResolver resolver;
// Build an interpreter to run the model with.
static 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;
}
// Obtain pointers to the model's input and output tensors.
input = interpreter->input(0);
output = interpreter->output(0);
// Keep track of how many inferences we have performed.
inference_count = 0;
}
// The name of this function is important for Arduino compatibility.
void loop() {
// Calculate an x value to feed into the model. We compare the current
// inference_count to the number of inferences per cycle to determine
// our position within the range of possible x values the model was
// trained on, and use this to calculate a value.
float position = static_cast<float>(inference_count) /
static_cast<float>(kInferencesPerCycle);
float x = position * kXrange;
// Quantize the input from floating-point to integer
int8_t x_quantized = x / input->params.scale + input->params.zero_point;
// Place the quantized input in the model's input tensor
input->data.int8[0] = x_quantized;
// Run inference, and report any error
TfLiteStatus invoke_status = interpreter->Invoke();
if (invoke_status != kTfLiteOk) {
TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed on x: %f\n",
static_cast<double>(x));
return;
}
// Obtain the quantized output from model's output tensor
int8_t y_quantized = output->data.int8[0];
// Dequantize the output from integer to floating-point
float y = (y_quantized - output->params.zero_point) * output->params.scale;
// Output the results. A custom HandleOutput function can be implemented
// for each supported hardware target.
HandleOutput(error_reporter, x, y);
// Increment the inference_counter, and reset it if we have reached
// the total number per cycle
inference_count += 1;
if (inference_count >= kInferencesPerCycle) inference_count = 0;
}

37
APP_Framework/Framework/know/tflite_sin/main_functions.h

@ -1,37 +0,0 @@
/* 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_MAIN_FUNCTIONS_H_
#define TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_MAIN_FUNCTIONS_H_
// Expose a C friendly interface for main functions.
#ifdef __cplusplus
extern "C" {
#endif
// Initializes all data needed for the example. The name is important, and needs
// to be setup() for Arduino compatibility.
void setup();
// Runs one iteration of data gathering and inference. This should be called
// repeatedly from the application code. The name needs to be loop() for Arduino
// compatibility.
void loop();
#ifdef __cplusplus
}
#endif
#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_MAIN_FUNCTIONS_H_

237
APP_Framework/Framework/know/tflite_sin/model.cc

@ -1,237 +0,0 @@
/* 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.
#include "model.h"
// Keep model aligned to 8 bytes to guarantee aligned 64-bit accesses.
alignas(8) const unsigned char g_model[] = {
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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,
0x96, 0xff, 0xff, 0xff, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08,
0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x04, 0x00, 0x04, 0x00,
0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00,
0x03, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0xca, 0xff, 0xff, 0xff, 0x10, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x08, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00,
0xba, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x01, 0x01, 0x00, 0x00, 0x00,
0x08, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00,
0x05, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00,
0x16, 0x00, 0x00, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x0b, 0x00, 0x04, 0x00,
0x0e, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08,
0x18, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00,
0x08, 0x00, 0x07, 0x00, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01,
0x01, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x4c, 0x04, 0x00, 0x00,
0xd0, 0x03, 0x00, 0x00, 0x68, 0x03, 0x00, 0x00, 0x0c, 0x03, 0x00, 0x00,
0x98, 0x02, 0x00, 0x00, 0x24, 0x02, 0x00, 0x00, 0xb0, 0x01, 0x00, 0x00,
0x24, 0x01, 0x00, 0x00, 0x98, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
0xf0, 0xfb, 0xff, 0xff, 0x18, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00,
0x54, 0x00, 0x00, 0x00, 0x0a, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
0x6c, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff,
0x01, 0x00, 0x00, 0x00, 0xdc, 0xfb, 0xff, 0xff, 0x10, 0x00, 0x00, 0x00,
0x18, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x4a, 0xce, 0x0a, 0x3c, 0x01, 0x00, 0x00, 0x00,
0x34, 0x84, 0x85, 0x3f, 0x01, 0x00, 0x00, 0x00, 0xc5, 0x02, 0x8f, 0xbf,
0x1e, 0x00, 0x00, 0x00, 0x53, 0x74, 0x61, 0x74, 0x65, 0x66, 0x75, 0x6c,
0x50, 0x61, 0x72, 0x74, 0x69, 0x74, 0x69, 0x6f, 0x6e, 0x65, 0x64, 0x43,
0x61, 0x6c, 0x6c, 0x3a, 0x30, 0x5f, 0x69, 0x6e, 0x74, 0x38, 0x00, 0x00,
0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x80, 0xfc, 0xff, 0xff, 0x18, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00,
0x54, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
0x64, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff,
0x10, 0x00, 0x00, 0x00, 0x6c, 0xfc, 0xff, 0xff, 0x10, 0x00, 0x00, 0x00,
0x18, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x80, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff,
0x01, 0x00, 0x00, 0x00, 0x93, 0xd0, 0xc0, 0x3b, 0x01, 0x00, 0x00, 0x00,
0xc2, 0x0f, 0xc0, 0x3f, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x14, 0x00, 0x00, 0x00, 0x74, 0x66, 0x6c, 0x2e, 0x66, 0x75, 0x6c, 0x6c,
0x79, 0x5f, 0x63, 0x6f, 0x6e, 0x6e, 0x65, 0x63, 0x74, 0x65, 0x64, 0x31,
0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x10, 0x00, 0x00, 0x00, 0x08, 0xfd, 0xff, 0xff, 0x18, 0x00, 0x00, 0x00,
0x20, 0x00, 0x00, 0x00, 0x58, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x09, 0x64, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
0xff, 0xff, 0xff, 0xff, 0x10, 0x00, 0x00, 0x00, 0xf4, 0xfc, 0xff, 0xff,
0x10, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00,
0x24, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x80, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0xe0, 0xdb, 0x47, 0x3c, 0x01, 0x00, 0x00, 0x00, 0x04, 0x14, 0x47, 0x40,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00,
0x74, 0x66, 0x6c, 0x2e, 0x66, 0x75, 0x6c, 0x6c, 0x79, 0x5f, 0x63, 0x6f,
0x6e, 0x6e, 0x65, 0x63, 0x74, 0x65, 0x64, 0x00, 0x02, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x02, 0xfe, 0xff, 0xff,
0x14, 0x00, 0x00, 0x00, 0x48, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x09, 0x50, 0x00, 0x00, 0x00, 0x6c, 0xfd, 0xff, 0xff,
0x10, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00,
0x20, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0xfb, 0x4b, 0x0b, 0x3c,
0x01, 0x00, 0x00, 0x00, 0x40, 0x84, 0x4b, 0x3f, 0x01, 0x00, 0x00, 0x00,
0x63, 0x35, 0x8a, 0xbf, 0x0d, 0x00, 0x00, 0x00, 0x73, 0x74, 0x64, 0x2e,
0x63, 0x6f, 0x6e, 0x73, 0x74, 0x61, 0x6e, 0x74, 0x32, 0x00, 0x00, 0x00,
0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
0x72, 0xfe, 0xff, 0xff, 0x14, 0x00, 0x00, 0x00, 0x48, 0x00, 0x00, 0x00,
0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x50, 0x00, 0x00, 0x00,
0xdc, 0xfd, 0xff, 0xff, 0x10, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00,
0x1c, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x60, 0x01, 0x4f, 0x3c, 0x01, 0x00, 0x00, 0x00, 0x47, 0x6d, 0xb3, 0x3f,
0x01, 0x00, 0x00, 0x00, 0x5d, 0x63, 0xcd, 0xbf, 0x0d, 0x00, 0x00, 0x00,
0x73, 0x74, 0x64, 0x2e, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x61, 0x6e, 0x74,
0x31, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
0x10, 0x00, 0x00, 0x00, 0xe2, 0xfe, 0xff, 0xff, 0x14, 0x00, 0x00, 0x00,
0x48, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09,
0x50, 0x00, 0x00, 0x00, 0x4c, 0xfe, 0xff, 0xff, 0x10, 0x00, 0x00, 0x00,
0x18, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0xd5, 0x6b, 0x8a, 0x3b, 0x01, 0x00, 0x00, 0x00,
0xab, 0x49, 0x01, 0x3f, 0x01, 0x00, 0x00, 0x00, 0xfd, 0x56, 0x09, 0xbf,
0x0c, 0x00, 0x00, 0x00, 0x73, 0x74, 0x64, 0x2e, 0x63, 0x6f, 0x6e, 0x73,
0x74, 0x61, 0x6e, 0x74, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x52, 0xff, 0xff, 0xff,
0x14, 0x00, 0x00, 0x00, 0x34, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x02, 0x3c, 0x00, 0x00, 0x00, 0x44, 0xff, 0xff, 0xff,
0x08, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x28, 0xb3, 0xd9, 0x38, 0x0c, 0x00, 0x00, 0x00,
0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x32, 0x2f, 0x62, 0x69, 0x61, 0x73,
0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
0xaa, 0xff, 0xff, 0xff, 0x14, 0x00, 0x00, 0x00, 0x30, 0x00, 0x00, 0x00,
0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x38, 0x00, 0x00, 0x00,
0x9c, 0xff, 0xff, 0xff, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0xdd, 0x9b, 0x21, 0x39, 0x0c, 0x00, 0x00, 0x00,
0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x33, 0x2f, 0x62, 0x69, 0x61, 0x73,
0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,
0x00, 0x00, 0x0e, 0x00, 0x18, 0x00, 0x14, 0x00, 0x13, 0x00, 0x0c, 0x00,
0x08, 0x00, 0x04, 0x00, 0x0e, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00,
0x40, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02,
0x48, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x00, 0x00,
0x08, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00,
0x14, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0xf4, 0xd4, 0x51, 0x38, 0x0c, 0x00, 0x00, 0x00, 0x64, 0x65, 0x6e, 0x73,
0x65, 0x5f, 0x34, 0x2f, 0x62, 0x69, 0x61, 0x73, 0x00, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x14, 0x00, 0x1c, 0x00,
0x18, 0x00, 0x17, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x00, 0x00,
0x00, 0x00, 0x04, 0x00, 0x14, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00,
0x2c, 0x00, 0x00, 0x00, 0x64, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x09, 0x84, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,
0xff, 0xff, 0xff, 0xff, 0x01, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x14, 0x00,
0x10, 0x00, 0x0c, 0x00, 0x08, 0x00, 0x04, 0x00, 0x0c, 0x00, 0x00, 0x00,
0x10, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00,
0x24, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x80, 0xff, 0xff, 0xff,
0xff, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x5d, 0x4f, 0xc9, 0x3c, 0x01, 0x00, 0x00, 0x00, 0x0e, 0x86, 0xc8, 0x40,
0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00,
0x73, 0x65, 0x72, 0x76, 0x69, 0x6e, 0x67, 0x5f, 0x64, 0x65, 0x66, 0x61,
0x75, 0x6c, 0x74, 0x5f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x32, 0x5f,
0x69, 0x6e, 0x70, 0x75, 0x74, 0x3a, 0x30, 0x5f, 0x69, 0x6e, 0x74, 0x38,
0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,
0x01, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00,
0x24, 0x00, 0x00, 0x00, 0x04, 0x00, 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, 0x04, 0x00,
0x0c, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,
0x00, 0x00, 0x00, 0x09};
const int g_model_len = 2488;

31
APP_Framework/Framework/know/tflite_sin/model.h

@ -1,31 +0,0 @@
/* 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_

24
APP_Framework/Framework/know/tflite_sin/output_handler.cc

@ -1,24 +0,0 @@
/* 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"
void HandleOutput(tflite::ErrorReporter* error_reporter, float x_value,
float y_value) {
// Log the current X and Y values
TF_LITE_REPORT_ERROR(error_reporter, "x_value: %f, y_value: %f\n",
static_cast<double>(x_value),
static_cast<double>(y_value));
}

26
APP_Framework/Framework/know/tflite_sin/output_handler.h

@ -1,26 +0,0 @@
/* 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"
#include "tensorflow/lite/micro/micro_error_reporter.h"
// Called by the main loop to produce some output based on the x and y values
void HandleOutput(tflite::ErrorReporter* error_reporter, float x_value,
float y_value);
#endif // TENSORFLOW_LITE_MICRO_EXAMPLES_HELLO_WORLD_OUTPUT_HANDLER_H_

33
APP_Framework/Framework/know/tflite_sin/sinmain.c

@ -1,33 +0,0 @@
/* 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 <xiuos.h>
#include "main_functions.h"
// This is the default main used on systems that have the standard C entry
// point. Other devices (for example FreeRTOS or ESP32) that have different
// requirements for entry code (like an app_main function) should specialize
// this main.cc file in a target-specific subfolder.
int tfsin(void) {
setup();
int count = 10;
while (count--) {
loop();
}
}
SHELL_EXPORT_CMD(SHELL_CMD_PERMISSION(0)|SHELL_CMD_TYPE(SHELL_TYPE_CMD_FUNC)|SHELL_CMD_PARAM_NUM(0), tfsin, tfsin, run sin demo of tflite);

3
APP_Framework/Framework/know/Kconfig → APP_Framework/Framework/knowing/Kconfig

@ -3,6 +3,5 @@ menuconfig SUPPORT_KNOWING_FRAMEWORK
default y
if SUPPORT_KNOWING_FRAMEWORK
source "$APP_DIR/Framework/know/tflite_sin/Kconfig"
source "$APP_DIR/Framework/know/tflite_mnist/Kconfig"
endif

0
APP_Framework/Framework/know/Makefile → APP_Framework/Framework/knowing/Makefile

47
Ubiquitous/RT_Thread/bsp/k210/.config

@ -394,11 +394,58 @@ CONFIG_PKG_KENDRYTE_SDK_VERNUM=0x0055
# CONFIG_PKG_USING_RW007 is not set
# CONFIG_DRV_USING_OV2640 is not set
#
# APP_Framework
#
#
# Applications
#
#
# config stack size and priority of main task
#
CONFIG_MAIN_KTASK_STACK_SIZE=1024
#
# test app
#
# CONFIG_USER_TEST is not set
#
# connection app
#
#
# control app
#
# CONFIG_APPLICATION_CONTROL is not set
#
# knowing app
#
# CONFIG_APPLICATION_KNOWING is not set
#
# sensor app
#
# CONFIG_APPLICATION_SENSOR is not set
#
# Framework
#
CONFIG_TRANSFORM_LAYER_ATTRIUBUTE=y
CONFIG_ADD_XIUOS_FETURES=y
# CONFIG_ADD_NUTTX_FETURES is not set
# CONFIG_ADD_RTTHREAD_FETURES is not set
# CONFIG_SUPPORT_SENSOR_FRAMEWORK is not set
# CONFIG_SUPPORT_CONNECTION_FRAMEWORK is not set
CONFIG_SUPPORT_KNOWING_FRAMEWORK=y
# CONFIG_SUPPORT_CONTROL_FRAMEWORK is not set
#
# app lib
#
CONFIG_APP_SELECT_NEWLIB=y
# CONFIG_APP_SELECT_OTHER_LIB is not set
CONFIG___STACKSIZE__=4096

7
Ubiquitous/RT_Thread/bsp/k210/Kconfig

@ -24,12 +24,15 @@ config BOARD_K210_EVB
select RT_USING_USER_MAIN
default y
config APP_DIR
string
default "../../../../APP_Framework"
source "$RTT_DIR/Kconfig"
source "base-drivers/Kconfig"
source "kendryte-sdk/Kconfig"
source "$RT_Thread_DIR/drivers/Kconfig"
source "$ROOT_DIR/APP_Framework/Applications/Kconfig"
source "$ROOT_DIR/APP_Framework/Framework/Kconfig"
source "$ROOT_DIR/APP_Framework/Kconfig"
config __STACKSIZE__
int "stack size for interrupt"

27
Ubiquitous/RT_Thread/bsp/k210/rtconfig.h

@ -263,10 +263,37 @@
/* More Drivers */
/* APP_Framework */
/* Applications */
/* config stack size and priority of main task */
#define MAIN_KTASK_STACK_SIZE 1024
/* test app */
/* connection app */
/* control app */
/* knowing app */
/* sensor app */
/* Framework */
#define TRANSFORM_LAYER_ATTRIUBUTE
#define ADD_XIUOS_FETURES
#define SUPPORT_KNOWING_FRAMEWORK
/* app lib */
#define APP_SELECT_NEWLIB
#define __STACKSIZE__ 4096
#endif

179
Ubiquitous/RT_Thread/bsp/stm32f407-atk-coreboard/.config

@ -34,7 +34,7 @@ CONFIG_RT_TIMER_THREAD_STACK_SIZE=512
# CONFIG_RT_KSERVICE_USING_STDLIB is not set
# CONFIG_RT_KSERVICE_USING_TINY_SIZE is not set
CONFIG_RT_DEBUG=y
CONFIG_RT_DEBUG_COLOR=y
# CONFIG_RT_DEBUG_COLOR is not set
# CONFIG_RT_DEBUG_INIT_CONFIG is not set
# CONFIG_RT_DEBUG_THREAD_CONFIG is not set
# CONFIG_RT_DEBUG_SCHEDULER_CONFIG is not set
@ -60,11 +60,10 @@ CONFIG_RT_USING_MESSAGEQUEUE=y
# Memory Management
#
CONFIG_RT_USING_MEMPOOL=y
CONFIG_RT_USING_MEMHEAP=y
# CONFIG_RT_USING_MEMHEAP is not set
# CONFIG_RT_USING_NOHEAP is not set
# CONFIG_RT_USING_SMALL_MEM is not set
CONFIG_RT_USING_SMALL_MEM=y
# CONFIG_RT_USING_SLAB is not set
CONFIG_RT_USING_MEMHEAP_AS_HEAP=y
# CONFIG_RT_USING_USERHEAP is not set
# CONFIG_RT_USING_MEMTRACE is not set
CONFIG_RT_USING_HEAP=y
@ -77,7 +76,7 @@ CONFIG_RT_USING_DEVICE=y
# CONFIG_RT_USING_INTERRUPT_INFO is not set