mirror of
https://github.com/azaion/ai-training.git
synced 2026-04-22 06:56:34 +00:00
Alex Bezdieniezhnykh
117 lines
4.0 KiB
Python
117 lines
4.0 KiB
Python
from os import path, replace, remove, listdir, makedirs
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from os.path import abspath
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import shutil
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from datetime import datetime
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from pathlib import Path
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from ultralytics import YOLO
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from constants import processed_images_dir, processed_labels_dir, annotation_classes, prefix, date_format, datasets_dir, models_dir
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latest_model = path.join(models_dir, f'{prefix}latest.pt')
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today_folder = f'{prefix}{datetime.now():{date_format}}'
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today_dataset = path.join(datasets_dir, today_folder)
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train_set = 70
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valid_set = 20
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test_set = 10
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def form_dataset():
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makedirs(today_dataset, exist_ok=True)
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images = listdir(processed_images_dir)
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train_size = int(len(images) * train_set / 100.0)
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valid_size = int(len(images) * valid_set / 100.0)
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move_annotations(images[:train_size], 'train')
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move_annotations(images[train_size:train_size + valid_size], 'valid')
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move_annotations(images[train_size + valid_size:], 'test')
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create_yaml()
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def move_annotations(images, folder):
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destination_images = path.join(today_dataset, folder, 'images')
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makedirs(destination_images, exist_ok=True)
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destination_labels = path.join(today_dataset, folder, 'labels')
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makedirs(destination_labels, exist_ok=True)
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for image_name in images:
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image_path = path.join(processed_images_dir, image_name)
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label_name = f'{Path(image_name).stem}.txt'
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label_path = path.join(processed_labels_dir, label_name)
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if not check_label(label_path):
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remove(image_path)
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else:
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replace(image_path, path.join(destination_images, image_name))
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replace(label_path, path.join(destination_labels, label_name))
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def check_label(label_path):
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lines_edited = False
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if not path.exists(label_path):
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return False
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with open(label_path, 'r') as f:
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lines = f.readlines()
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for line in lines:
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for val in line.split(' ')[1:]:
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if float(val) > 1:
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lines.remove(line)
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lines_edited = True
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if len(lines) == 0:
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return False
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if not lines_edited:
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return True
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with open(label_path, 'w') as label_write:
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label_write.writelines(lines)
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label_write.close()
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return True
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def create_yaml():
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lines = ['names:']
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for c in annotation_classes:
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lines.append(f'- {annotation_classes[c].name}')
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lines.append(f'nc: {len(annotation_classes)}')
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lines.append(f'test: test/images')
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lines.append(f'train: train/images')
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lines.append(f'val: valid/images')
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lines.append('')
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today_yaml = abspath(path.join(today_dataset, 'data.yaml'))
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with open(today_yaml, 'w', encoding='utf-8') as f:
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f.writelines([f'{line}\n' for line in lines])
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def revert_to_processed_data(date):
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def revert_dir(src_dir, dest_dir):
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for file in listdir(src_dir):
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s = path.join(src_dir, file)
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d = path.join(dest_dir, file)
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replace(s, d)
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date_dataset = path.join(datasets_dir, f'{prefix}{date}')
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makedirs(processed_images_dir, exist_ok=True)
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makedirs(processed_labels_dir, exist_ok=True)
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for subset in ['test', 'train', 'valid']:
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revert_dir(path.join(date_dataset, subset, 'images'), processed_images_dir)
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revert_dir(path.join(date_dataset, subset, 'labels'), processed_labels_dir)
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shutil.rmtree(date_dataset)
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if __name__ == '__main__':
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# form_dataset()
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model_name = latest_model if path.isfile(latest_model) else 'yolov8m.yaml'
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print(f'Initial model: {model_name}')
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model = YOLO(model_name)
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# cur_folder = path.join(datasets_dir, f'{prefix}2024-06-18')
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cur_folder = today_dataset
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yaml = abspath(path.join(cur_folder, 'data.yaml'))
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results = model.train(data=yaml, epochs=100, batch=55, imgsz=640, save_period=1)
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shutil.copy(f'{results.save_dir}/weights/best.pt', latest_model)
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shutil.copytree(results.save_dir, path.join(models_dir, cur_folder))
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shutil.rmtree('runs')
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shutil.rmtree(path.join(models_dir, f'{prefix}latest'))
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