mirror of
https://github.com/azaion/ai-training.git
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zxsanny
248 lines
8.5 KiB
Python
248 lines
8.5 KiB
Python
import concurrent.futures
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import os
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import random
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import shutil
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import subprocess
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from datetime import datetime
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from os import path, replace, listdir, makedirs, scandir
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from os.path import abspath
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from pathlib import Path
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from time import sleep
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import yaml
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from ultralytics import YOLO
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import constants
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from api_client import ApiCredentials, ApiClient
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from cdn_manager import CDNCredentials, CDNManager
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from constants import (processed_images_dir,
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processed_labels_dir,
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prefix, date_format,
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datasets_dir, models_dir,
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corrupted_images_dir, corrupted_labels_dir, sample_dir)
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from dto.annotationClass import AnnotationClass
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from inference.onnx_engine import OnnxEngine
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from security import Security
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from utils import Dotdict
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from exports import export_tensorrt, upload_model, export_onnx
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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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old_images_percentage = 75
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DEFAULT_CLASS_NUM = 80
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total_files_copied = 0
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def form_dataset(from_date: datetime):
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makedirs(today_dataset, exist_ok=True)
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images = []
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old_images = []
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with scandir(processed_images_dir) as imd:
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for image_file in imd:
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if not image_file.is_file():
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continue
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mod_time = datetime.fromtimestamp(image_file.stat().st_mtime).replace(hour=0, minute=0, second=0, microsecond=0)
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if from_date is None:
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images.append(image_file)
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elif mod_time > from_date:
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images.append(image_file)
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else: # gather old images as well in order to avoid overfitting on the only new data.
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old_images.append(image_file)
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random.shuffle(old_images)
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old_images_size = int(len(old_images) * old_images_percentage / 100.0)
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print(f'Got {len(images)} new images and {old_images_size} of old images (to prevent overfitting). Shuffling them...')
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images.extend(old_images[:old_images_size])
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random.shuffle(images)
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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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copy_annotations(images[:train_size], 'train')
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copy_annotations(images[train_size:train_size + valid_size], 'valid')
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copy_annotations(images[train_size + valid_size:], 'test')
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def copy_annotations(images, folder):
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global total_files_copied
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total_files_copied = 0
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def copy_image(image):
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global total_files_copied
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total_files_copied += 1
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label_name = f'{Path(image.path).stem}.txt'
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label_path = path.join(processed_labels_dir, label_name)
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if check_label(label_path):
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shutil.copy(image.path, path.join(destination_images, image.name))
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shutil.copy(label_path, path.join(destination_labels, label_name))
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else:
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shutil.copy(image.path, path.join(corrupted_images_dir, image.name))
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shutil.copy(label_path, path.join(corrupted_labels_dir, label_name))
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print(f'Label {label_path} is corrupted! Copy with its image to the corrupted directory ({corrupted_labels_dir})')
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if total_files_copied % 1000 == 0:
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print(f'{total_files_copied} copied...')
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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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makedirs(corrupted_images_dir, exist_ok=True)
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makedirs(corrupted_labels_dir, exist_ok=True)
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copied = 0
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print(f'Copying annotations to {destination_images} and {destination_labels} folders:')
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with concurrent.futures.ThreadPoolExecutor() as executor:
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executor.map(copy_image, images)
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print(f'Copied all {copied} annotations to {destination_images} and {destination_labels} folders')
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def check_label(label_path):
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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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return False
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return True
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def create_yaml():
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print('creating yaml...')
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lines = ['names:']
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annotation_classes = AnnotationClass.read_json()
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for i in range(DEFAULT_CLASS_NUM):
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if i in annotation_classes:
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lines.append(f'- {annotation_classes[i].name}')
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else:
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lines.append(f'- Class-{i + 1}')
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lines.append(f'nc: {DEFAULT_CLASS_NUM}')
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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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def get_latest_model():
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def convert(d: str):
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if not d.startswith(prefix):
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return None
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dir_date = datetime.strptime(d.replace(prefix, ''), '%Y-%m-%d')
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dir_model_path = path.join(models_dir, d, 'weights', 'best.pt')
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return {'date': dir_date, 'path': dir_model_path}
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dates = [convert(d) for d in next(os.walk(models_dir))[1]]
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dates = list(filter(lambda x : x is not None, dates))
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sorted_dates = list(sorted(dates, key=lambda x: x['date'] ))
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if len(sorted_dates) == 0:
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return None, None
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last_model = sorted_dates[-1]
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return last_model['date'], last_model['path']
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def resume_training(last_pt_path):
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model = YOLO(last_pt_path)
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results = model.train(data=yaml,
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resume=True,
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epochs=120,
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batch=11,
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imgsz=1280,
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save_period=1,
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workers=24)
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def train_dataset(existing_date=None, from_scratch=False):
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latest_date, latest_model = get_latest_model() if not from_scratch else None, None
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if existing_date is not None:
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cur_folder = f'{prefix}{existing_date}'
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cur_dataset = path.join(datasets_dir, f'{prefix}{existing_date}')
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else:
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# if from_scratch and Path(today_dataset).exists():
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# shutil.rmtree(today_dataset)
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# form_dataset(latest_date)
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# create_yaml()
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cur_folder = today_folder
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cur_dataset = today_dataset
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model_name = latest_model if latest_model is not None and path.isfile(latest_model) and not from_scratch else 'yolo11m.yaml'
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print(f'Initial model: {model_name}')
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model = YOLO(model_name)
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yaml = abspath(path.join(cur_dataset, 'data.yaml'))
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results = model.train(data=yaml,
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epochs=120,
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batch=11,
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imgsz=1280,
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save_period=1,
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workers=24)
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model_dir = path.join(models_dir, cur_folder)
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shutil.copytree(results.save_dir, model_dir)
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model_path = path.join(models_dir, f'{prefix[:-1]}.pt')
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shutil.copy(path.join(model_dir, 'weights', 'best.pt'), model_path)
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shutil.rmtree('runs')
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return model_path
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def convert2rknn():
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subprocess.call(['bash', 'convert.sh'], cwd="./orangepi5")
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latest_date, latest_model = get_latest_model()
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model = YOLO(latest_model)
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model.export(format="onnx")
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pass
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def validate(model_path):
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model = YOLO(model_path)
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metrics = model.val()
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pass
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if __name__ == '__main__':
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model_path = train_dataset(from_scratch=True)
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# validate(path.join('runs', 'detect', 'train7', 'weights', 'best.pt'))
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# form_data_sample(500)
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# convert2rknn()
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api_client = ApiClient()
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onnx_path = export_onnx('azaion.pt')
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with open(onnx_path, 'rb') as binary_file:
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onnx_bytes = binary_file.read()
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key = Security.get_model_encryption_key()
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api_client.upload_big_small_resource(onnx_bytes, onnx_path, constants.MODELS_FOLDER, key)
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