(self, save_name, save_path)
| 242 | 'eval/precision': precision, 'eval/recall': recall, 'eval/F1': F1, 'eval/AUC': AUC} |
| 243 | |
| 244 | def save_model(self, save_name, save_path): |
| 245 | if self.it < 1000000: |
| 246 | return |
| 247 | save_filename = os.path.join(save_path, save_name) |
| 248 | # copy EMA parameters to ema_model for saving with model as temp |
| 249 | self.model.eval() |
| 250 | self.ema.apply_shadow() |
| 251 | ema_model = self.model.state_dict() |
| 252 | self.ema.restore() |
| 253 | self.model.train() |
| 254 | |
| 255 | torch.save({'model': self.model.state_dict(), |
| 256 | 'optimizer': self.optimizer.state_dict(), |
| 257 | 'scheduler': self.scheduler.state_dict(), |
| 258 | 'it': self.it + 1, |
| 259 | 'ema_model': ema_model}, |
| 260 | save_filename) |
| 261 | |
| 262 | self.print_fn(f"model saved: {save_filename}") |
| 263 | |
| 264 | def load_model(self, load_path): |
| 265 | checkpoint = torch.load(load_path) |
no test coverage detected