| from typing import Any |
|
|
| from pytorch_lightning import Callback, Trainer, LightningModule |
| from pytorch_lightning.utilities import rank_zero_only |
| from pytorch_lightning.utilities.parsing import AttributeDict |
|
|
|
|
| class ParamsLog(Callback): |
| """Log the number of parameters of the model |
| """ |
| def __init__(self, total_params_log: bool = True, trainable_params_log: bool = True, |
| non_trainable_params_log: bool = True): |
| super().__init__() |
| self._log_stats = AttributeDict( |
| { |
| 'total_params_log': total_params_log, |
| 'trainable_params_log': trainable_params_log, |
| 'non_trainable_params_log': non_trainable_params_log, |
| } |
| ) |
|
|
| @rank_zero_only |
| def on_fit_start(self, trainer: Trainer, pl_module: LightningModule) -> None: |
| logs = {} |
| if self._log_stats.total_params_log: |
| logs["model/params_total"] = sum(p.numel() for p in pl_module.parameters()) |
| if self._log_stats.trainable_params_log: |
| logs["model/params_trainable"] = sum(p.numel() for p in pl_module.parameters() |
| if p.requires_grad) |
| if self._log_stats.non_trainable_params_log: |
| logs["model/params_not_trainable"] = sum(p.numel() for p in pl_module.parameters() |
| if not p.requires_grad) |
| if trainer.logger is not None: |
| trainer.logger.log_hyperparams(logs) |
|
|
