270 lines
9.1 KiB
Python
270 lines
9.1 KiB
Python
# AI 학습 — YOLOv8 재학습 및 모델 저장
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import multiprocessing
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import os
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import random
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import shutil
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from utils.path_helper import get_path
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import yaml
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from PyQt5.QtCore import QThread, pyqtSignal
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class Trainer:
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def __init__(self):
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self.model = None
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self.is_training = False
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# ------------------------------------------------------------------ #
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def prepare_dataset(self, image_folder: str) -> str:
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dataset_dir = get_path("ai", "dataset")
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if os.path.exists(dataset_dir):
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shutil.rmtree(dataset_dir)
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for split in ("train", "val"):
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os.makedirs(os.path.join(dataset_dir, "images", split), exist_ok=True)
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os.makedirs(os.path.join(dataset_dir, "labels", split), exist_ok=True)
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pairs = []
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for f in os.listdir(image_folder):
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if f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp")):
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img_path = os.path.join(image_folder, f)
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txt_path = os.path.join(
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image_folder, os.path.splitext(f)[0] + ".txt"
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)
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if os.path.exists(txt_path):
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pairs.append((img_path, txt_path))
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random.shuffle(pairs)
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split_idx = int(len(pairs) * 0.8)
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train_pairs = pairs[:split_idx]
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val_pairs = pairs[split_idx:]
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for img, lbl in train_pairs:
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shutil.copy(img, os.path.join(dataset_dir, "images", "train"))
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shutil.copy(lbl, os.path.join(dataset_dir, "labels", "train"))
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for img, lbl in val_pairs:
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shutil.copy(img, os.path.join(dataset_dir, "images", "val"))
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shutil.copy(lbl, os.path.join(dataset_dir, "labels", "val"))
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yaml_content = {
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"path": dataset_dir,
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"train": "images/train",
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"val": "images/val",
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"nc": 4,
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"names": ["스크래치", "이물", "흑점", "변형"],
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}
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yaml_path = os.path.join(dataset_dir, "data.yaml")
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with open(yaml_path, "w", encoding="utf-8") as fh:
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yaml.dump(yaml_content, fh, allow_unicode=True)
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return yaml_path
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# ------------------------------------------------------------------ #
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def train(
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self,
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image_folder: str,
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epochs: int,
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batch: int,
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save_path: str,
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log_callback=None,
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progress_callback=None,
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):
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self.is_training = True
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try:
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if log_callback:
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log_callback("데이터셋 준비 중...")
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yaml_path = self.prepare_dataset(image_folder)
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if log_callback:
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log_callback(f"데이터셋 준비 완료: {yaml_path}")
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if log_callback:
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log_callback("YOLOv8 모델 로드 중...")
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from ultralytics import YOLO # 지연 로딩 — 앱 시작 시 torch DLL 오류 방지
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self.model = YOLO("yolov8n.pt")
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if log_callback:
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log_callback(f"학습 시작 (epoch={epochs}, batch={batch})")
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def _on_epoch_end(trainer):
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ep = trainer.epoch + 1
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try:
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loss_val = float(trainer.loss)
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loss_str = f"{loss_val:.4f}"
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except Exception:
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loss_str = "?"
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if log_callback:
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log_callback(f"Epoch {ep}/{epochs} loss={loss_str}")
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if progress_callback:
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progress_callback(int(ep / epochs * 100))
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self.model.add_callback("on_train_epoch_end", _on_epoch_end)
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self.model.train(
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data=yaml_path,
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epochs=epochs,
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batch=batch,
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imgsz=640,
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project=get_path("ai", "runs"),
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name="train",
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exist_ok=True,
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verbose=True,
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workers=0, # disable DataLoader multiprocessing inside subprocess
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amp=False, # AMP check also spawns a subprocess on Windows
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plots=False, # matplotlib can interfere with Qt event loop
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)
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# best.pt 복사
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best_pt = get_path("ai", "runs", "train", "weights", "best.pt")
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if os.path.exists(best_pt):
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os.makedirs(os.path.dirname(save_path), exist_ok=True)
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shutil.copy(best_pt, save_path)
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if log_callback:
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log_callback(f"모델 저장 완료: {save_path}")
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if progress_callback:
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progress_callback(100)
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if log_callback:
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log_callback("학습 완료!")
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except BaseException as e:
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import traceback
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if log_callback:
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try:
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log_callback(f"학습 오류: {e}")
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log_callback(traceback.format_exc())
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except Exception:
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pass
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finally:
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self.is_training = False
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# ------------------------------------------------------------------ #
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def stop(self):
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self.is_training = False
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# ====================================================================== #
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# Subprocess entry point — defined at module level so it is picklable.
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# ultralytics training can call sys.exit() internally; running it in a
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# separate process completely isolates the Qt application from that.
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# ====================================================================== #
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def _train_subprocess_main(queue, image_folder, epochs, batch, save_path):
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"""Entry point for the isolated training subprocess."""
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try:
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trainer = Trainer()
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def _log(msg):
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try:
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queue.put(("log", msg))
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except Exception:
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pass
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def _progress(pct):
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try:
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queue.put(("progress", int(pct)))
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except Exception:
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pass
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trainer.train(
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image_folder=image_folder,
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epochs=epochs,
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batch=batch,
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save_path=save_path,
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log_callback=_log,
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progress_callback=_progress,
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)
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queue.put(("done", True))
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except BaseException as e:
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import traceback
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try:
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queue.put(("log", f"학습 오류: {e}"))
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queue.put(("log", traceback.format_exc()))
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except Exception:
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pass
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try:
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queue.put(("done", False))
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except Exception:
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pass
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# ====================================================================== #
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class TrainWorker(QThread):
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log_signal = pyqtSignal(str)
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progress_signal = pyqtSignal(int)
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finished_signal = pyqtSignal(bool)
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def __init__(self, trainer, image_folder, epochs, batch, save_path):
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super().__init__()
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self.trainer = trainer
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self.image_folder = image_folder
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self.epochs = epochs
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self.batch = batch
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self.save_path = save_path
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self._proc = None # training subprocess handle
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def run(self):
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# Spawn an isolated subprocess so that any sys.exit() call inside
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# ultralytics does not reach PyQt5's QThread handler and trigger
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# QApplication.exit() in the main process.
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ctx = multiprocessing.get_context("spawn")
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q = ctx.Queue()
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self._proc = ctx.Process(
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target=_train_subprocess_main,
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args=(q, self.image_folder, self.epochs, self.batch, self.save_path),
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daemon=True,
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)
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self._proc.start()
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success = False
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while True:
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proc_alive = self._proc.is_alive()
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try:
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msg_type, msg_data = q.get(timeout=0.3)
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except Exception:
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# queue.Empty — check if subprocess died unexpectedly
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if not proc_alive:
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# Give one last chance to read remaining messages
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while True:
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try:
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msg_type, msg_data = q.get_nowait()
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except Exception:
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break
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if msg_type == "log":
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self.log_signal.emit(str(msg_data))
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elif msg_type == "progress":
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self.progress_signal.emit(int(msg_data))
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elif msg_type == "done":
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success = bool(msg_data)
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break
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continue
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if msg_type == "log":
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self.log_signal.emit(str(msg_data))
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elif msg_type == "progress":
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self.progress_signal.emit(int(msg_data))
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elif msg_type == "done":
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success = bool(msg_data)
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break
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self._proc.join(timeout=30)
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if self._proc.is_alive():
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self._proc.terminate()
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self._proc.join(timeout=5)
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self.finished_signal.emit(success)
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def stop_subprocess(self):
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"""Call from the main thread to forcefully stop training."""
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if self._proc and self._proc.is_alive():
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self._proc.terminate()
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