add rknn conversion - install and use scripts, auto convert to rknn after AI training is done and put pt and rknn models to /azaion/models directory

.gitignore

@@ -4,4 +4,7 @@
 datasets/
 runs/
 models/
-*.pt
+*.pt
+*.onnx
+*.rknn
+*.mp4

README.md

@@ -32,7 +32,7 @@ Linux
     pip install --upgrade huggingface_hub
     pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
     pip install git+https://github.com/airockchip/ultralytics_yolov8.git
-    pip install albumentations onnx
+    pip install albumentations
 ```
 
 **3. Fix possible problems**

orangepi5/convert-install.sh

@@ -0,0 +1,33 @@
+mkdir rknn-convert
+cd rknn-convert
+
+# Install converter PT to ONNX
+git clone https://github.com/airockchip/ultralytics_yolov8
+cd ultralytics_yolov8
+sudo apt install python3.12-venv
+python3 -m venv env
+source env/bin/activate
+pip install .
+pip install onnx
+cp ultralytics/cfg/default.yaml ultralytics/cfg/default_backup.yaml
+sed -i -E "s/(model: ).+( #.+)/\1azaion.pt\2/" ultralytics/cfg/default.yaml
+cd ..
+deactivate
+
+# Install converter ONNX to RKNN
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
+chmod +x miniconda.sh
+bash miniconda.sh -b -p $HOME/miniconda
+
+source ~/miniconda/bin/activate
+conda create -n toolkit2 -y python=3.11
+conda activate toolkit2
+git clone https://github.com/rockchip-linux/rknn-toolkit2.git
+cd rknn-toolkit2/rknn-toolkit2/packages
+pip install -r requirements_cp311-1.6.0.txt
+pip install rknn_toolkit2-1.6.0+81f21f4d-cp311-cp311-linux_x86_64.whl
+pip install "numpy<2.0"
+cd ../../../
+git clone https://github.com/airockchip/rknn_model_zoo.git
+conda deactivate
+conda deactivate

orangepi5/convert.sh

@@ -0,0 +1,20 @@
+# Use converter PT to ONNX
+cd rknn-convert/ultralytics_yolov8/
+cp --verbose /azaion/models/azaion.pt .
+source env/bin/activate
+pip install onnx
+export PYTHONPATH=./
+python ./ultralytics/engine/exporter.py
+cp --verbose azaion.onnx ../
+cd ..
+deactivate
+
+# Use converter ONNX to RKNN
+
+source ~/miniconda/bin/activate
+conda activate toolkit2
+cd rknn_model_zoo/examples/yolov8/python
+python convert.py ../../../../azaion.onnx rk3588 i8
+cp --verbose ../model/yolov8.rknn /azaion/models/azaion.rknn
+conda deactivate
+conda deactivate

orangepi5/install-os.md

@@ -0,0 +1,5 @@
+1. Download latest release from here https://joshua-riek.github.io/ubuntu-rockchip-download/boards/orangepi-5.html
+   f.e. https://github.com/Joshua-Riek/ubuntu-rockchip/releases/download/v2.3.2/ubuntu-22.04-preinstalled-desktop-arm64-orangepi-5.img.xz
+   but look to the more recent version on ubuntu 22.04
+   
+2. Write the image to the microsd using https://bztsrc.gitlab.io/usbimager/ (sudo ./usbimager on linux) (or use BalenaEtcher)

preprocess-train.py

@@ -0,0 +1,6 @@
+from preprocessing import preprocess_annotations
+from train import train_dataset, convert2rknn
+
+preprocess_annotations()
+train_dataset('2024-10-01')
+convert2rknn()

preprocessing.py

@@ -116,30 +116,35 @@ def process_image(img_ann):
     ))
 
 
-def main():
+def preprocess_annotations():
     os.makedirs(processed_images_dir, exist_ok=True)
     os.makedirs(processed_labels_dir, exist_ok=True)
-    while True:
-        processed_images = set(f.name for f in os.scandir(processed_images_dir))
-        images = []
-        with os.scandir(data_images_dir) as imd:
-            for image_file in imd:
-                if image_file.is_file() and image_file.name not in processed_images:
-                    images.append(image_file)
 
-        for image_file in images:
-            try:
-                image_path = os.path.join(data_images_dir, image_file.name)
-                labels_path = os.path.join(data_labels_dir, f'{Path(image_path).stem}.txt')
-                image = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
-                process_image(ImageLabel(
-                    image_path=image_path,
-                    image=image,
-                    labels_path=labels_path,
-                    labels=read_labels(labels_path)
-                ))
-            except Exception as e:
-                print(f'Error appeared {e}')
+    processed_images = set(f.name for f in os.scandir(processed_images_dir))
+    images = []
+    with os.scandir(data_images_dir) as imd:
+        for image_file in imd:
+            if image_file.is_file() and image_file.name not in processed_images:
+                images.append(image_file)
+
+    for image_file in images:
+        try:
+            image_path = os.path.join(data_images_dir, image_file.name)
+            labels_path = os.path.join(data_labels_dir, f'{Path(image_path).stem}.txt')
+            image = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
+            process_image(ImageLabel(
+                image_path=image_path,
+                image=image,
+                labels_path=labels_path,
+                labels=read_labels(labels_path)
+            ))
+        except Exception as e:
+            print(f'Error appeared {e}')
+
+
+def main():
+    while True:
+        preprocess_annotations()
         print('All processed, waiting for 2 minutes...')
         time.sleep(120)
 

train.py

@@ -1,4 +1,6 @@
+import os
 import random
+import subprocess
 from os import path, replace, remove, listdir, makedirs, scandir
 from os.path import abspath
 import shutil
@@ -127,7 +129,7 @@ def get_latest_model():
         dir_model_path = path.join(models_dir, d, 'weights', 'best.pt')
         return {'date': dir_date, 'path': dir_model_path}
 
-    dates = [convert(d) for d in listdir(models_dir)]
+    dates = [convert(d) for d in next(os.walk(models_dir))[1]]
     sorted_dates = list(sorted(dates, key=lambda x: x['date']))
     if len(sorted_dates) == 0:
         return None, None
@@ -135,21 +137,36 @@ def get_latest_model():
     return last_model['date'], last_model['path']
 
 
-if __name__ == '__main__':
+def train_dataset(existing_date=None):
     latest_date, latest_model = get_latest_model()
-    # create_yaml()
-    # form_dataset(latest_date)
 
+    if existing_date is not None:
+        cur_folder = f'{prefix}{existing_date}'
+        cur_dataset = path.join(datasets_dir, f'{prefix}{existing_date}')
+    else:
+        form_dataset(latest_date)
+        cur_folder = today_folder
+        cur_dataset = today_dataset
 
     model_name = latest_model if latest_model is not None and path.isfile(latest_model) else 'yolov8m.yaml'
     print(f'Initial model: {model_name}')
     model = YOLO(model_name)
-    
-    cur_folder = path.join(datasets_dir, f'{prefix}2024-09-19')
 
-    # cur_folder = today_dataset
-    yaml = abspath(path.join(cur_folder, 'data.yaml'))
-    results = model.train(data=yaml, epochs=100, batch=58, imgsz=640)
+    yaml = abspath(path.join(cur_dataset, 'data.yaml'))
+    results = model.train(data=yaml, epochs=100, batch=57, imgsz=640, save_period=1)
 
-    shutil.copytree(results.save_dir, path.join(models_dir, today_folder))
+    model_dir = path.join(models_dir, cur_folder)
+    shutil.copytree(results.save_dir, model_dir)
+
+    shutil.copy(path.join(model_dir, 'weights', 'best.pt'), path.join(models_dir, f'{prefix[:-1]}.pt'))
     shutil.rmtree('runs')
+
+
+def convert2rknn():
+    subprocess.call(['bash', 'convert.sh'], cwd="./orangepi5")
+    pass
+
+
+if __name__ == '__main__':
+    train_dataset()
+    convert2rknn()