small refactoring

simplify learning, improve readme

README.md

@@ -1,8 +1,20 @@
-1. Install dependencies first
+1. Install necessary libs and drivers
+```
+sudo apt install nvidia-driver-535
+
+wget https://developer.download.nvidia.com/compute/cudnn/9.2.0/local_installers/cudnn-local-repo-ubuntu2204-9.2.0_1.0-1_amd64.deb
+sudo dpkg -i cudnn-local-repo-ubuntu2204-9.2.0_1.0-1_amd64.deb
+
+sudo cp /var/cudnn-local-repo-ubuntu2204-9.2.0/cudnn-*-keyring.gpg /usr/share/keyrings/
+sudo apt-get update
+sudo apt-get -y install cudnn nvidia-cuda-toolkit -y
+nvcc --version
+```
+2. Install dependencies
 ```
     python -m pip install --upgrade pip
     pip install --upgrade huggingface_hub
     pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
     pip install -q git+https://github.com/THU-MIG/yolov10.git
     pip install albumentations
-```
+```

train.py

@@ -1,21 +1,27 @@
-import os
+from os import path, replace, remove, listdir, makedirs
+from os.path import abspath
 import shutil
 from datetime import datetime
 from pathlib import Path
-
 from ultralytics import YOLOv10
 from constants import current_images_dir, current_labels_dir, annotation_classes, prefix
 
-today_dataset = os.path.join('datasets', f'{prefix}{datetime.now():%Y-%m-%d}')
-yaml_path = os.path.join(today_dataset, 'data.yaml')
+
+def get_yaml_path(date_str):
+    date_dataset = path.join('datasets', f'{prefix}{date_str}')
+    date_yaml_path = abspath(path.join(date_dataset, 'data.yaml'))
+    return date_dataset, date_yaml_path
+
+
+today_dataset, today_yaml = get_yaml_path(f'{datetime.now():%Y-%m-%d}')
 train_set = 70
 valid_set = 20
 test_set = 10
 
 
 def form_dataset():
-    os.makedirs(today_dataset, exist_ok=True)
-    images = os.listdir(current_images_dir)
+    makedirs(today_dataset, exist_ok=True)
+    images = listdir(current_images_dir)
 
     train_size = int(len(images) * train_set / 100.0)
     valid_size = int(len(images) * valid_set / 100.0)
@@ -28,19 +34,19 @@ def form_dataset():
 
 
 def move_annotations(images, folder):
-    destination_images = os.path.join(today_dataset, folder, 'images')
-    os.makedirs(destination_images, exist_ok=True)
-    destination_labels = os.path.join(today_dataset, folder, 'labels')
-    os.makedirs(destination_labels, exist_ok=True)
+    destination_images = path.join(today_dataset, folder, 'images')
+    makedirs(destination_images, exist_ok=True)
+    destination_labels = path.join(today_dataset, folder, 'labels')
+    makedirs(destination_labels, exist_ok=True)
     for image_name in images:
-        image_path = os.path.join(current_images_dir, image_name)
+        image_path = path.join(current_images_dir, image_name)
         label_name = f'{Path(image_name).stem}.txt'
-        label_path = os.path.join(current_labels_dir, label_name)
-        if not os.path.exists(label_path):
-            os.remove(image_path)
+        label_path = path.join(current_labels_dir, label_name)
+        if not path.exists(label_path):
+            remove(image_path)
         else:
-            os.replace(image_path, os.path.join(destination_images, image_name))
-            os.replace(label_path, os.path.join(destination_labels, label_name))
+            replace(image_path, path.join(destination_images, image_name))
+            replace(label_path, path.join(destination_labels, label_name))
 
 
 def create_yaml():
@@ -48,53 +54,41 @@ def create_yaml():
     for c in annotation_classes:
         lines.append(f'- {annotation_classes[c].name}')
     lines.append(f'nc: {len(annotation_classes)}')
-    main_dir = f'../../{prefix}{datetime.now():%Y-%m-%d}'
 
-    lines.append(f'test: {main_dir}/test/images')
-    lines.append(f'train: {main_dir}/train/images')
-    lines.append(f'val: {main_dir}/valid/images')
+    lines.append(f'test: test/images')
+    lines.append(f'train: train/images')
+    lines.append(f'val: valid/images')
     lines.append('')
 
-    with open(yaml_path, 'w', encoding='utf-8') as f:
+    with open(today_yaml, 'w', encoding='utf-8') as f:
         f.writelines([f'{line}\n' for line in lines])
 
 
-def get_recent_model():
-    cur_model = None
-    cur_date = None
-    for d in os.listdir('datasets'):
-        date_str = d.replace(prefix, '')
-        if date_str == 'current' or date_str == f'{datetime.now():%Y-%m-%d}':
-            continue
-        date = datetime.strptime(date_str, '%Y-%m-%d')
-        for file in os.listdir(os.path.join('datasets', d)):
-            if file.endswith('pt') and (cur_date is None or cur_date < date):
-                cur_model = os.path.join('datasets', d, file)
-                cur_date = date
-    return cur_model
-
-
 def revert_to_current(date):
     def revert_dir(src_dir, dest_dir):
-        for file in os.listdir(src_dir):
-            s = os.path.join(src_dir, file)
-            d = os.path.join(dest_dir, file)
-            os.replace(s, d)
-    date_dataset = os.path.join('datasets', f'{prefix}{date}')
-    current_dataset = os.path.join('datasets', f'{prefix}current')
+        for file in listdir(src_dir):
+            s = path.join(src_dir, file)
+            d = path.join(dest_dir, file)
+            replace(s, d)
+    date_dataset = path.join('datasets', f'{prefix}{date}')
+    current_dataset = path.join('datasets', f'{prefix}current')
     for subset in ['test', 'train', 'valid']:
-        revert_dir(os.path.join(date_dataset, subset, 'images'), os.path.join(current_dataset, 'images'))
-        revert_dir(os.path.join(date_dataset, subset, 'labels'), os.path.join(current_dataset, 'labels'))
+        revert_dir(path.join(date_dataset, subset, 'images'), path.join(current_dataset, 'images'))
+        revert_dir(path.join(date_dataset, subset, 'labels'), path.join(current_dataset, 'labels'))
     shutil.rmtree(date_dataset)
 
 
 if __name__ == '__main__':
-    # revert_to_current('2024-06-06')
     # form_dataset()
     # create_yaml()
 
-    model = get_recent_model() or 'yolov10x.yaml'
-    model = YOLOv10(model=model, task='detect').to('cuda')
+    model = YOLOv10('datasets/zombobase-latest.pt' or 'yolov10x.yaml')
+    _, yaml = get_yaml_path('2024-06-09')
+    results = model.train(data=yaml, epochs=2, batch=10, imgsz=640)
+
+    print(results)
+    res_model = path.join(results['save_dir'], '/weights/best.pt')
+    print(res_model)
+    shutil.copy(res_model, 'datasets/zombobase-latest2.pt')
 
-    results = model.train(data=yaml_path, epochs=200, imgsz=1280, save=True, cache=True)
     pass