developer/xiuos
1
0
Fork 0
forked from xuos/xiuos
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

add the NNOM (a Neural Network library specifically for microcontrollers)as a part of knowing framework add a example about nnom. update k210 yolov2 detect procedure from Tian_Chunyu

Browse Source 
it is OK
This commit is contained in:
xuedongliang
2022-02-24 16:22:37 +08:00
parent 3b5cdc402c a180b2a53b
commit 287cd1bd89
86 changed files with 16257 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
APP_Framework/Applications/knowing_app/Kconfig
View File

@@ -9,7 +9,8 @@ menu "knowing app"
source "$APP_DIR/Applications/knowing_app/iris_ml_demo/Kconfig"
source "$APP_DIR/Applications/knowing_app/k210_fft_test/Kconfig"
source "$APP_DIR/Applications/knowing_app/image_processing/Kconfig"
source "$APP_DIR/Applications/knowing_app/cmsis_5_demo/Kconfig"
source "$APP_DIR/Applications/knowing_app/cmsis_5_demo/Kconfig"
source "$APP_DIR/Applications/knowing_app/nnom_demo/Kconfig"
endif
endmenu

2
APP_Framework/Applications/knowing_app/cmsis_5_demo/Kconfig
View File

@@ -1,6 +1,6 @@
menuconfig USING_CMSIS_5_DEMOAPP
bool "CMSIS-5 demo app"
depends on USING_USING_CMSIS_5_NN
depends on USING_CMSIS_5_NN
default n
if USING_CMSIS_5_DEMOAPP

2
APP_Framework/Applications/knowing_app/cmsis_5_demo/cmsisnn-cifar10/SConscript
View File

@@ -13,6 +13,6 @@ path = [
cwd + '/demo'
]
group = DefineGroup('CMSISNN-cifar10', src, depend = ['USING_CMSIS_5_DEMOAPP'], CPPPATH = path)
group = DefineGroup('CMSISNN-cifar10', src, depend = ['USING_CMSIS_5_NN_DEMOAPP'], CPPPATH = path)
Return('group')

14
APP_Framework/Applications/knowing_app/nnom_demo/Kconfig Normal file
View File

@@ -0,0 +1,14 @@
menuconfig USING_NNOM_DEMOAPP
bool "NNOM demo app"
depends on USING_NNOM
default n
if USING_NNOM_DEMOAPP
config USING_NNOM_MNIST_DEMOAPP
bool "Using NNOM mnist demo app"
default n
endif

14
APP_Framework/Applications/knowing_app/nnom_demo/SConscript Normal file
View File

@@ -0,0 +1,14 @@
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')

16
APP_Framework/Applications/knowing_app/nnom_demo/mnist_nnom/README.md Normal file
View File

@@ -0,0 +1,16 @@
# NNoM Mnist-simple Example
This example is from [[NNoM](https://github.com/majianjia/nnom)/**[mnist-simple](https://github.com/majianjia/nnom/tree/master/examples/mnist-simple)**] and can be deployed on Arm CPUs and RISC-V CPUs. CMSIS-NN can be used to accelerate on Arm Cortex-M CPUs.
## Requirements:
- NNoM in Framework/knowing/nnom
- To use CMSIS-NN backend, select in menuconfig "APP_Framework->Framework->support knowing framework->NNoM->Select NNoM Backend"
## To run this demo:
- Run demo by type the command
```
mnist_nnom num
```

10
APP_Framework/Applications/knowing_app/nnom_demo/mnist_nnom/SConscript Normal file
View File

@@ -0,0 +1,10 @@
import os
from building import *
cwd = GetCurrentDir()
src = Glob('*.c')
path = [cwd]
group = DefineGroup('NNOM mnist application', src, depend = ['USING_NNOM_MNIST_DEMOAPP'], CPPPATH = path)
Return('group')

36
APP_Framework/Applications/knowing_app/nnom_demo/mnist_nnom/image.h Normal file
View File

@@ -0,0 +1,36 @@
#define IMG0 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 88, 126, 126, 126, 126, 127, 64, 56, 19, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 81, 126, 126, 126, 126, 126, 126, 126, 126, 109, 42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 28, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 89, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 61, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 83, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 126, 126, 127, 126, 114, 23, 0, 31, 89, 126, 126, 126, 127, 126, 59, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 100, 126, 126, 126, 111, 26, 0, 0, 0, 28, 116, 126, 126, 126, 126, 107, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 42, 126, 126, 126, 126, 83, 0, 0, 0, 0, 0, 37, 116, 126, 126, 126, 126, 42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 42, 126, 126, 126, 126, 37, 0, 0, 0, 0, 0, 0, 84, 126, 126, 126, 126, 49, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 81, 126, 126, 126, 126, 13, 0, 0, 0, 0, 0, 0, 84, 126, 126, 126, 126, 111, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 113, 126, 126, 126, 127, 13, 0, 0, 0, 0, 0, 0, 84, 126, 127, 126, 126, 112, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 89, 126, 126, 126, 126, 13, 0, 0, 0, 0, 0, 0, 61, 126, 126, 126, 126, 72, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 42, 126, 126, 126, 126, 68, 0, 0, 0, 0, 0, 0, 30, 126, 126, 126, 126, 111, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 42, 126, 126, 126, 126, 112, 29, 5, 0, 0, 5, 69, 112, 126, 126, 126, 126, 104, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 19, 110, 126, 126, 126, 126, 126, 88, 70, 70, 89, 126, 126, 126, 126, 126, 126, 42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 99, 126, 126, 127, 126, 126, 126, 126, 127, 126, 126, 126, 126, 127, 126, 98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 75, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 111, 26, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 77, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 114, 26, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 42, 118, 126, 126, 126, 126, 126, 126, 126, 124, 72, 23, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 56, 56, 64, 126, 126, 126, 70, 49, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
#define IMG0_LABLE 0
#define IMG1 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 60, 127, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 113, 126, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 126, 126, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 126, 107, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 127, 127, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 126, 98, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 126, 84, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 126, 84, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 40, 127, 84, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 126, 84, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 58, 126, 84, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 20, 126, 84, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 127, 84, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 126, 84, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 126, 98, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 126, 126, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 127, 127, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 117, 126, 26, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 85, 126, 26, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 85, 126, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
#define IMG1_LABLE 1
#define IMG2 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 19, 83, 87, 87, 47, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 78, 122, 121, 113, 113, 127, 61, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 29, 117, 106, 64, 16, 0, 0, 67, 112, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 64, 120, 75, 7, 0, 0, 0, 0, 0, 93, 73, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 52, 124, 60, 0, 0, 0, 0, 0, 0, 0, 29, 120, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 106, 111, 30, 0, 0, 0, 0, 0, 0, 0, 0, 12, 121, 43, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 80, 57, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 19, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 31, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 69, 86, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 33, 122, 72, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 54, 121, 114, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 28, 70, 112, 127, 125, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 101, 126, 126, 126, 127, 117, 63, 23, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 27, 127, 127, 127, 127, 127, 127, 127, 127, 100, 53, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 40, 27, 6, 6, 6, 29, 69, 92, 117, 127, 122, 78, 34, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 45, 94, 121, 126, 107, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 53, 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
#define IMG2_LABLE 2
#define IMG3 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 61, 120, 108, 108, 96, 37, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 127, 127, 127, 127, 127, 127, 40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 58, 70, 70, 115, 127, 127, 121, 51, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 105, 127, 127, 119, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 102, 127, 127, 117, 30, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 20, 116, 127, 127, 71, 28, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 44, 127, 127, 127, 52, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 82, 127, 127, 124, 85, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 22, 110, 127, 127, 98, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 17, 76, 127, 127, 94, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 73, 127, 124, 52, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 110, 127, 111, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 49, 127, 127, 75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12, 102, 127, 127, 31, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 69, 123, 127, 120, 63, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 44, 119, 127, 127, 127, 22, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 69, 103, 52, 70, 125, 127, 127, 100, 63, 21, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 75, 127, 127, 127, 127, 127, 127, 94, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 98, 127, 127, 127, 115, 88, 38, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 76, 110, 47, 19, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
#define IMG3_LABLE 3
#define IMG4 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 44, 103, 126, 0, 0, 0, 0, 0, 57, 126, 111, 19, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 38, 123, 126, 126, 63, 0, 0, 0, 0, 56, 126, 126, 42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 42, 126, 126, 126, 55, 0, 0, 0, 0, 56, 126, 126, 99, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 42, 126, 126, 126, 0, 0, 0, 0, 0, 19, 117, 126, 126, 56, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 42, 126, 126, 126, 0, 0, 0, 0, 0, 0, 73, 126, 126, 94, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 43, 126, 126, 126, 0, 0, 0, 0, 0, 0, 42, 126, 126, 126, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 42, 126, 126, 126, 0, 0, 0, 0, 0, 0, 42, 126, 126, 126, 81, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 13, 100, 126, 126, 126, 0, 0, 0, 0, 0, 0, 9, 104, 126, 126, 126, 13, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 56, 126, 126, 126, 126, 14, 38, 84, 84, 84, 54, 9, 90, 126, 126, 126, 61, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 56, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 107, 44, 126, 126, 126, 83, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 31, 120, 126, 126, 126, 127, 126, 126, 126, 126, 127, 126, 126, 126, 126, 127, 84, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 50, 126, 126, 126, 126, 88, 83, 75, 53, 126, 126, 126, 126, 126, 126, 83, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 27, 27, 27, 28, 3, 0, 0, 0, 28, 27, 27, 93, 126, 126, 116, 37, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 37, 126, 126, 126, 101, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 94, 126, 126, 126, 42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 127, 126, 126, 42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 126, 126, 126, 42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 126, 126, 126, 57, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 118, 126, 126, 104, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 79, 126, 42, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
#define IMG4_LABLE 4
#define IMG5 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 6, 9, 66, 95, 78, 83, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 12, 64, 95, 126, 126, 126, 126, 126, 123, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 27, 92, 126, 126, 126, 126, 126, 126, 115, 96, 36, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 36, 114, 126, 126, 126, 126, 126, 102, 67, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 126, 126, 126, 126, 100, 96, 38, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 64, 126, 126, 115, 69, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 94, 126, 126, 90, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12, 126, 126, 126, 90, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 82, 126, 126, 105, 54, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12, 124, 126, 126, 126, 126, 121, 94, 60, 61, 26, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 98, 126, 126, 126, 126, 126, 126, 126, 127, 126, 80, 6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 54, 54, 54, 54, 105, 114, 119, 126, 126, 126, 126, 87, 23, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 23, 51, 109, 126, 126, 126, 112, 58, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 50, 102, 119, 126, 126, 18, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 90, 126, 126, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 48, 117, 126, 126, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 29, 102, 102, 9, 20, 43, 76, 106, 126, 126, 114, 64, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 36, 126, 126, 110, 117, 126, 126, 126, 126, 91, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 104, 126, 126, 126, 126, 126, 126, 115, 20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 65, 65, 123, 95, 65, 28, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
#define IMG5_LABLE 5
#define IMG6 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 38, 56, 88, 45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 97, 121, 126, 103, 121, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 100, 126, 99, 27, 12, 99, 82, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 53, 126, 121, 96, 14, 0, 0, 14, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 53, 126, 126, 44, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 19, 72, 126, 126, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 65, 126, 121, 44, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 60, 123, 126, 57, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 53, 126, 121, 96, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 53, 126, 126, 107, 70, 23, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 85, 126, 127, 126, 126, 126, 56, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 121, 126, 126, 88, 93, 126, 118, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 83, 126, 126, 28, 3, 6, 93, 126, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 117, 126, 118, 0, 0, 0, 84, 126, 47, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 42, 126, 126, 56, 0, 0, 0, 84, 126, 70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 43, 126, 126, 87, 0, 0, 0, 84, 126, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 101, 126, 126, 81, 28, 9, 98, 126, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 44, 126, 126, 126, 116, 104, 126, 126, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 49, 111, 126, 126, 126, 116, 72, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 126, 126, 110, 18, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
#define IMG6_LABLE 6
#define IMG7 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 37, 52, 122, 126, 126, 127, 126, 78, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 98, 126, 126, 126, 126, 114, 103, 126, 115, 12, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35, 123, 126, 118, 105, 44, 26, 28, 123, 126, 19, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 47, 66, 4, 0, 0, 0, 0, 82, 126, 88, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 82, 126, 121, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 82, 126, 126, 37, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 82, 126, 106, 17, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 82, 126, 88, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 89, 126, 88, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 126, 126, 88, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 126, 126, 89, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 126, 126, 88, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 78, 126, 126, 40, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 104, 126, 126, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15, 110, 126, 126, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 52, 126, 126, 126, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 52, 126, 126, 126, 14, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 52, 126, 126, 104, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 52, 126, 124, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 52, 64, 48, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
#define IMG7_LABLE 7
#define IMG8 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 107, 126, 127, 106, 66, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10, 81, 126, 126, 126, 126, 126, 86, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 36, 126, 127, 65, 31, 51, 96, 126, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 116, 126, 86, 5, 0, 0, 35, 126, 51, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 51, 127, 126, 0, 0, 0, 0, 25, 126, 51, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 71, 126, 85, 0, 0, 0, 0, 46, 126, 51, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 101, 127, 45, 0, 0, 0, 20, 107, 126, 20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 20, 121, 116, 15, 0, 0, 20, 121, 126, 106, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 56, 126, 117, 15, 0, 0, 86, 126, 127, 45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35, 126, 126, 86, 10, 91, 126, 126, 126, 45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 91, 127, 126, 127, 126, 127, 86, 127, 126, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 106, 126, 126, 126, 65, 5, 65, 126, 61, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 56, 126, 127, 25, 0, 0, 25, 126, 101, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 116, 126, 86, 5, 0, 0, 25, 126, 111, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 127, 126, 0, 0, 0, 0, 25, 126, 101, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 20, 126, 126, 0, 0, 0, 0, 46, 126, 81, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 51, 127, 126, 0, 0, 5, 86, 127, 116, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 51, 126, 126, 20, 20, 86, 126, 106, 15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 20, 117, 126, 127, 126, 127, 86, 20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35, 126, 126, 106, 45, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
#define IMG8_LABLE 8
#define IMG9 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 21, 59, 109, 120, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 49, 116, 121, 127, 127, 127, 120, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 80, 127, 127, 114, 89, 96, 127, 127, 83, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 78, 127, 127, 70, 20, 0, 19, 117, 127, 127, 59, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 127, 127, 82, 1, 0, 0, 0, 36, 127, 127, 110, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 28, 127, 123, 25, 0, 0, 0, 0, 18, 127, 127, 127, 17, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 63, 127, 120, 0, 0, 0, 0, 0, 83, 127, 127, 127, 17, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 97, 127, 90, 0, 0, 0, 0, 0, 15, 127, 127, 127, 17, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 34, 124, 127, 75, 0, 0, 0, 0, 0, 14, 127, 127, 127, 17, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 102, 127, 126, 103, 36, 22, 0, 0, 10, 112, 127, 127, 17, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 41, 119, 127, 127, 127, 115, 93, 93, 86, 83, 127, 127, 17, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 40, 121, 127, 127, 127, 127, 127, 127, 127, 127, 127, 17, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 33, 55, 84, 124, 124, 127, 127, 127, 127, 127, 17, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 76, 93, 127, 127, 127, 51, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 75, 127, 127, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 13, 127, 127, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 127, 127, 85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 127, 127, 92, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 122, 127, 127, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 62, 127, 127, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
#define IMG9_LABLE 9
#define TOTAL_IMAGE 10
static const int8_t img[10][784] = {IMG0,IMG1,IMG2,IMG3,IMG4,IMG5,IMG6,IMG7,IMG8,IMG9};
static const int8_t label[10] = {IMG0_LABLE,IMG1_LABLE,IMG2_LABLE,IMG3_LABLE,IMG4_LABLE,IMG5_LABLE,IMG6_LABLE,IMG7_LABLE,IMG8_LABLE,IMG9_LABLE};

77
APP_Framework/Applications/knowing_app/nnom_demo/mnist_nnom/main.c Normal file
View File

@@ -0,0 +1,77 @@
/*
* Copyright (c) 2018-2020, Jianjia Ma
*
* SPDX-License-Identifier: Apache-2.0
*
* Change Logs:
* Date Author Notes
* 2019-03-29 Jianjia Ma first implementation
*/
#include <stdio.h>
#include <transform.h>
#include "nnom.h"
#include "image.h"
#include "weights.h"
nnom_model_t *model;
const char codeLib[] = "@B%8&WM#*oahkbdpqwmZO0QLCJUYXzcvunxrjft/\\|()1{}[]?-_+~<>i!lI;:,\"^`'. ";
void print_img(int8_t * buf)
{
for(int y = 0; y < 28; y++)
{
for (int x = 0; x < 28; x++)
{
int index = 69 / 127.0 * (127 - buf[y*28+x]);
if(index > 69) index =69;
if(index < 0) index = 0;
printf("%c",codeLib[index]);
printf("%c",codeLib[index]);
}
printf("\n");
}
}
// Do simple test using image in "image.h" with model created previously.
void mnist_nnom(int argc, char **argv)
{
model = nnom_model_create();
uint32_t tick, time;
uint32_t predic_label;
float prob;
int32_t index = atoi(argv[1]);
if (index >= TOTAL_IMAGE || argc != 2)
{
printf("Please input image number within %d\n", TOTAL_IMAGE - 1);
return;
}
printf("\nprediction start.. \n");
#ifdef __RT_THREAD_H__
tick = rt_tick_get();
#endif
memcpy(nnom_input_data, (int8_t *)&img[index][0], 784);
nnom_predict(model, &predic_label, &prob);
#ifdef __RT_THREAD_H__
time = rt_tick_get() - tick;
#endif
// print original image to console
print_img((int8_t *)&img[index][0]);
#ifdef __RT_THREAD_H__
printf("Time: %d tick\n", time);
#endif
printf("Truth label: %d\n", label[index]);
printf("Predicted label: %d\n", predic_label);
printf("Probability: %d%%\n", (int)(prob * 100));
}
#ifdef __RT_THREAD_H__
MSH_CMD_EXPORT(mnist_nnom, nnom mnist demo and image number should be followed);
#endif

166
APP_Framework/Applications/knowing_app/nnom_demo/mnist_nnom/mnist_simple.py Normal file
View File

@@ -0,0 +1,166 @@
'''
Copyright (c) 2018-2020
Jianjia Ma
majianjia@live.com
SPDX-License-Identifier: Apache-2.0
Change Logs:
Date Author Notes
2019-02-12 Jianjia Ma The first version
'''
import matplotlib.pyplot as plt
import sys
import os
nnscript = os.path.abspath('../../../Framework/knowing/nnom/scripts')
sys.path.append(nnscript)
from tensorflow.keras import *
from tensorflow.keras.datasets import mnist
from tensorflow.keras.layers import *
from tensorflow.keras.models import load_model, save_model
import tensorflow as tf
import numpy as np
from nnom import *
model_name = 'mnist_simple_trained_model.h5'
def image_to_cfile(data, label, num_of_image, file='image.h'):
with open(file, 'w') as f:
for i in range(num_of_image):
selected = np.random.randint(0, 1000) # select 10 out of 1000.
f.write('#define IMG%d {'% (i))
np.round(data[selected]).flatten().tofile(f, sep=", ", format="%d") # convert 0~1 to 0~127
f.write('} \n')
f.write('#define IMG%d_LABLE'% (i))
f.write(' %d \n \n' % label[selected])
f.write('#define TOTAL_IMAGE %d \n \n'%(num_of_image))
f.write('static const int8_t img[%d][%d] = {' % (num_of_image, data[0].flatten().shape[0]))
f.write('IMG0')
for i in range(num_of_image -1):
f.write(',IMG%d'%(i+1))
f.write('};\n\n')
f.write('static const int8_t label[%d] = {' % (num_of_image))
f.write('IMG0_LABLE')
for i in range(num_of_image -1):
f.write(',IMG%d_LABLE'%(i+1))
f.write('};\n\n')
def train(x_train, y_train, x_test, y_test, batch_size=64, epochs=100):
inputs = Input(shape=x_train.shape[1:])
x = Conv2D(12, kernel_size=(3, 3), strides=(1, 1), padding='same')(inputs)
x = ReLU()(x)
x = MaxPool2D((2,2),strides=(2,2), padding="same")(x)
x = Conv2D(24 ,kernel_size=(3,3), strides=(1,1), padding="same")(x)
x = ReLU()(x)
x = MaxPool2D((2,2),strides=(2,2), padding="same")(x)
x = Conv2D(48, kernel_size=(3,3), strides=(1,1), padding="same")(x)
x = ReLU()(x)
x = Dropout(0.2)(x)
x = MaxPool2D((2,2),strides=(2,2), padding="same")(x)
x = Flatten()(x)
x = Dense(96)(x)
x = Dropout(0.2)(x)
x = ReLU()(x)
x = Dense(10)(x)
predictions = Softmax()(x)
model = Model(inputs=inputs, outputs=predictions)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.summary()
history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=2,
validation_data=(x_test, y_test),
shuffle=True)
# free the session to avoid nesting naming while we load the best model after.
save_model(model, model_name)
del model
tf.keras.backend.clear_session()
return history
if __name__ == "__main__":
epochs = 2
num_classes = 10
# The data, split between train and test sets:
(x_train, y_train_num), (x_test, y_test_num) = mnist.load_data()
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# Convert class vectors to binary class matrices.
y_train = tf.keras.utils.to_categorical(y_train_num, num_classes)
y_test = tf.keras.utils.to_categorical(y_test_num, num_classes)
# reshape to 4 d becaue we build for 4d?
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2], 1)
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2], 1)
print('x_train shape:', x_train.shape)
# quantize the range to 0~255 -> 0~1
x_test = x_test/255
x_train = x_train/255
print("data range", x_test.min(), x_test.max())
# select a few image and write them to image.h
image_to_cfile(x_test*127, y_test_num, 10, file='image.h')
# train model, save the best accuracy model
history = train(x_train, y_train, x_test, y_test, batch_size=64, epochs=epochs)
# reload best model
model = load_model(model_name)
# evaluate
evaluate_model(model, x_test, y_test)
# save weight
generate_model(model, np.vstack((x_train, x_test)), name="weights.h")
# plot
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
plt.plot(range(0, epochs), acc, color='red', label='Training acc')
plt.plot(range(0, epochs), val_acc, color='green', label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()

420
APP_Framework/Applications/knowing_app/nnom_demo/mnist_nnom/weights.h Normal file
View File

File diff suppressed because one or more lines are too long