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update some documents and add kmodel files

This commit is contained in:
chunyexixiaoyu 2021-08-16 10:41:56 +08:00
parent 6ad0b192e8 78387f9b16
commit f6a0c0b0b7
13 changed files with 321 additions and 27 deletions
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35
APP_Framework/Applications/knowing_app/face_detect/README.md Normal file
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# Face detection demo
### A face object detection task demo. Running MobileNet-yolo on K210-based edge devices.
---
## Training
kmodel from [GitHub](https://github.com/kendryte/kendryte-standalone-demo/blob/develop/face_detect/detect.kmodel).
## Deployment
### compile and burn
Use `(scons --)menuconfig` in bsp folder *(Ubiquitous/RT_Thread/bsp/k210)*, open:
- More Drivers --> ov2640 driver
- Board Drivers Config --> Enable LCD on SPI0
- Board Drivers Config --> Enable SDCARD (spi1(ss0))
- Board Drivers Config --> Enable DVP(camera)
- RT-Thread Components --> POSIX layer and C standard library --> Enable pthreads APIs
- APP_Framework --> Framework --> support knowing framework --> kpu model postprocessing --> yolov2 region layer
- APP_Framework --> Applications --> knowing app --> enable apps/face detect
`scons -j(n)` to compile and burn in by *kflash*.
### json config and kmodel
Copy json config for deployment o SD card */kmodel*. Example config file is *detect.json* in this directory. Copy final kmodel to SD card */kmodel* either.
---
## Run
In serial terminal, `face_detect` to start a detection thread, `face_detect_delete` to stop it. Detection results can be found in output.

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APP_Framework/Applications/knowing_app/face_detect/detect.kmodel Normal file
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2
APP_Framework/Applications/knowing_app/face_detect/face_detect.c
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@ -182,6 +182,8 @@ void face_detect()
printf("open ov2640 fail !!");
return;
}
_ioctl_set_dvp_reso set_dvp_reso = {sensor_output_size[1], sensor_output_size[0]};
ioctl(g_fd, IOCTRL_CAMERA_SET_DVP_RESO, &set_dvp_reso);
showbuffer = (unsigned char *)malloc(sensor_output_size[0] * sensor_output_size[1] * 2);
if (NULL == showbuffer) {
close(g_fd);

167
APP_Framework/Applications/knowing_app/helmet_detect/README.md Normal file
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# Helmet detection demo
### A helmet and head without helmet object detection task demo. Running MobileNet-yolo on K210-based edge devices.
---
## Training
### Enviroment preparation
Model generated by [aXeleRate](https://forgeplus.trustie.net/projects/yangtuo250/aXeleRate) and converted to kmodel by [nncase](https://github.com/kendryte/nncase/tree/v0.1.0-rc5).
```shell
# master branch for MobileNetv1-yolov2 and unstable branch to test MobileNetv1(v2)-yolov2(v3)
git clone https://git.trustie.net/yangtuo250/aXeleRate.git (-b unstable)
cd aXeleRate
pip install -r requirments.txt && pip install -e .
```
### training config setting
Example [config](https://forgeplus.trustie.net/projects/yangtuo250/aXeleRate/tree/master/configs/detector.json), some hyper-parameters:
- architecture: backbone, MobileNet7_5 for default, MobileNet1_0(α = 1.0) and above cannot run on K210 because of OOM on feature map in master branch. For unstable branch MobileNetV2_1_0 is OK.
- input_size: fixed model input size, single integer for height equals to width, otherwise a list([height, width]).
- anchors: yolov2 anchor(for master) or anchor scaled to 1.0(for unstable), can be generate by [darknet](https://github.com/AlexeyAB/darknet).
- labels: labels of all classes.
- train(valid)_image(annot)_folder: path of images and annoations for training and validation.
- saved_folder: path for trainig result storage(models, checkpoints, logs ...).
Mine config for unstable:
```json
{
"model": {
"type": "Detector",
"architecture": "MobileNetV2_1_0",
"input_size": [
224,
320
],
"anchors": [
[
[
0.1043,
0.1560
],
[
0.0839,
0.3036
],
[
0.1109,
0.3923
],
[
0.1378,
0.5244
],
[
0.2049,
0.6673
]
]
],
"labels": [
"human"
],
"obj_thresh": 0.5,
"iou_thresh": 0.45,
"coord_scale": 1.0,
"class_scale": 0.0,
"object_scale": 5.0,
"no_object_scale": 3.0
},
"weights": {
"full": "",
"backend": ""
},
"train": {
"actual_epoch": 2000,
"train_image_folder": "mydata/human/Images/train",
"train_annot_folder": "mydata/human/Annotations/train",
"train_times": 2,
"valid_image_folder": "mydata/human/Images/val",
"valid_annot_folder": "mydata/human/Annotations/val",
"valid_times": 1,
"valid_metric": "precision",
"batch_size": 32,
"learning_rate": 2e-5,
"saved_folder": "mydata/human/results",
"first_trainable_layer": "",
"augmentation": true,
"is_only_detect": false,
"validation_freq": 5,
"quantize": false,
"class_weights": [1.0]
},
"converter": {
"type": [
"k210"
]
}
}
```
*(For more detailed config usage, please refer to original aXeleRate repo.)*
### data preparation
Please refer to [VOC format](https://towardsdatascience.com/coco-data-format-for-object-detection-a4c5eaf518c5), path as config above.
### train it!
```shell
python -m aXeleRate.train -c PATH_TO_YOUR_CONFIG
```
### model convert
Please refer to [nncase repo](https://github.com/kendryte/nncase/tree/v0.1.0-rc5).
---
## Deployment
### compile and burn
Use `(scons --)menuconfig` in bsp folder *(Ubiquitous/RT_Thread/bsp/k210)*, open:
- More Drivers --> ov2640 driver
- Board Drivers Config --> Enable LCD on SPI0
- Board Drivers Config --> Enable SDCARD (spi1(ss0))
- Board Drivers Config --> Enable DVP(camera)
- RT-Thread Components --> POSIX layer and C standard library --> Enable pthreads APIs
- APP_Framework --> Framework --> support knowing framework --> kpu model postprocessing --> yolov2 region layer
- APP_Framework --> Applications --> knowing app --> enable apps/helmet detect
`scons -j(n)` to compile and burn in by *kflash*.
### json config and kmodel
Copy json config for deployment o SD card */kmodel*. Example config file is *helmet.json* in this directory. Something to be modified:
- net_input_size: same as *input_size* in training config file, but array only.
- net_output_shape: final feature map size, can be found in **nncase** output.
- sensor_output_size: image height and width from camera.
- kmodel_size: kmodel size shown in file system.
- anchors: same as *anchor* in training config file(multi-dimention anchors flatten to 1 dim).
- labels: same as *label* in training config file.
- obj_thresh: array, object threshold of each label.
- nms_thresh: NMS threshold of boxes.
Copy final kmodel to SD card */kmodel* either.
---
## Run
In serial terminal, `helmet_detect` to start a detection thread, `helmet_detect_delete` to stop it. Detection results can be found in output.
---
## TODO
- [ ] Fix LCD real-time result display.
- [ ] Test more object detection backbone and algorithm(like yolox).

30
APP_Framework/Applications/knowing_app/helmet_detect/helmet.json
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@ -13,26 +13,26 @@
256
],
"anchors": [
1.0432,
1.0920,
0.8391,
2.1250,
1.1085,
2.7463,
1.3783,
3.6706,
2.0491,
4.6711
0.1384,
0.276,
0.308,
0.504,
0.5792,
0.8952,
1.072,
1.6184,
2.1128,
3.184
],
"kmodel_path": "/kmodel/helmet.kmodel",
"kmodel_size": 2714044,
"obj_thresh": [
0.85,
0.6
0.7,
0.9
],
"labels": [
"helmet",
"head"
"head",
"helmet"
],
"nms_thresh": 0.3
"nms_thresh": 0.45
}

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APP_Framework/Applications/knowing_app/helmet_detect/helmet_detect.c
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@ -182,6 +182,8 @@ void helmet_detect()
printf("open ov2640 fail !!");
return;
}
_ioctl_set_dvp_reso set_dvp_reso = {sensor_output_size[1], sensor_output_size[0]};
ioctl(g_fd, IOCTRL_CAMERA_SET_DVP_RESO, &set_dvp_reso);
showbuffer = (unsigned char *)malloc(sensor_output_size[0] * sensor_output_size[1] * 2);
if (NULL == showbuffer) {
close(g_fd);

5
APP_Framework/Applications/knowing_app/instrusion_detect/README.md Normal file
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# Instrusion detect demo
### A human object detection task demo. Running MobileNet-yolo on K210-based edge devices.
***Training, deployment and running, please see helmet_detect***

24
APP_Framework/Applications/knowing_app/instrusion_detect/human.json
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@ -13,24 +13,24 @@
320
],
"anchors": [
1.0432,
1.0920,
0.8391,
2.1250,
1.1085,
2.7463,
1.3783,
3.6706,
2.0491,
1.043,
1.092,
0.839,
2.1252,
1.109,
2.7461,
1.378,
3.6708,
2.049,
4.6711
],
"kmodel_path": "/kmodel/human.kmodel",
"kmodel_size": 1903016,
"kmodel_size": 2713236,
"obj_thresh": [
0.35
0.55
],
"labels": [
"human"
],
"nms_thresh": 0.3
"nms_thresh": 0.35
}

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APP_Framework/Applications/knowing_app/instrusion_detect/human.kmodel Normal file
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APP_Framework/Applications/knowing_app/instrusion_detect/instrusion_detect.c
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@ -182,6 +182,8 @@ void instrusion_detect()
printf("open ov2640 fail !!");
return;
}
_ioctl_set_dvp_reso set_dvp_reso = {sensor_output_size[1], sensor_output_size[0]};
ioctl(g_fd, IOCTRL_CAMERA_SET_DVP_RESO, &set_dvp_reso);
showbuffer = (unsigned char *)malloc(sensor_output_size[0] * sensor_output_size[1] * 2);
if (NULL == showbuffer) {
close(g_fd);

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APP_Framework/Applications/knowing_app/iris_ml_demo/README.md Normal file
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# Machine learning demo using iris dataset
### Classification task demo, tested on stm32f4 and k210-based edge devices. Training on iris dataset by *Decision Tree classifier*, *Support Vector Machine classifier* and *Logistic Regression classifier*.
---
## Training
Model generated by [Sklearn](https://scikit-learn.org/stable/) and converted to C language by [micromlgen](https://forgeplus.trustie.net/projects/yangtuo250/micromlgen).
### Enviroment preparation
```shell
pip install scikit-learn
git clone https://git.trustie.net/yangtuo250/micromlgen.git -b C
cd micromlgen && pip install -e .
```
### Train it!
```python
# load iris dataset
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
# train SVC classifier and convert
clf = SVC(kernel='linear', gamma=0.001).fit(X, y)
print(port(clf, cplusplus=False, platform=platforms.STM32F4))
# train logistic regression classifier and convert
clf = LogisticRegression(max_iter=1000).fit(X, y)
print(port(clf, cplusplus=False, platform=platforms.STM32F4))
# train decision tree classifier and convert
clf = DecisionTreeClassifier().fit(X, y)
print(port(clf, cplusplus=False, platform=platforms.STM32F4)
```
Copy each content generated by print to a single C language file.
---
## Deployment
### compile and burn
Use `(scons --)menuconfig` in *bsp folder(Ubiquitous/RT_Thread/bsp/k210(or stm32f407-atk-coreboard))*, open **APP_Framwork --> Applications --> knowing app --> enable apps/iris ml demo** to enable this app. `scons -j(n)` to compile and burn in by *st-flash(for ARM)* or *kflash(for k210)*.
### testing set
Copy *iris.csv* to SD card */csv/iris.csv*.
---
## Run
In serial terminal:
- `iris_SVC_predict` for SVC prediction
- `iris_DecisonTree_predict` for decision tree prediction
- `iris_LogisticRegression_predict` for logistic regression prediction
Example output:
```shell
data 1: 5.1000 3.5000 1.4000 0.2000 result: 0
data 2: 6.4000 3.2000 4.5000 1.5000 result: 1
data 3: 5.8000 2.7000 5.1000 1.9000 result: 2
data 4: 7.7000 3.8000 6.7000 2.2000 result: 2
data 5: 5.5000 2.6000 4.4000 1.2000 result: 1
data 6: 5.1000 3.8000 1.9000 0.4000 result: 0
data 7: 5.8000 2.7000 3.9000 1.2000 result: 1
```

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APP_Framework/Framework/knowing/kpu-postprocessing/yolov2/README.md Normal file
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# KPU(K210) YOLOv2 region layer
## Introduction
KPU(k210) accelerate most of CNN network layers, but do not support some of operators of YOLOv2 region layer. Such layers and operators will run on MCU instead.
YOLOv2 region layer accept feature map(shape w\*h\*c) and return final detection boxes.
## Usage
Use `(scons --)menuconfig` in bsp folder *(Ubiquitous/RT_Thread/bsp/k210)*, open *APP_Framework --> Framework --> support knowing framework --> kpu model postprocessing --> yolov2 region layer*.