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