Source code for pytorch_lightning.callbacks.model_summary

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Model Summary

Generates a summary of all layers in a :class:`~pytorch_lightning.core.module.LightningModule`.

The string representation of this summary prints a table with columns containing
the name, type and number of parameters for each layer.

import logging
from typing import List, Tuple, Union

import pytorch_lightning as pl
from pytorch_lightning.callbacks.callback import Callback
from pytorch_lightning.utilities.model_summary import DeepSpeedSummary
from pytorch_lightning.utilities.model_summary import ModelSummary as Summary
from pytorch_lightning.utilities.model_summary import summarize
from pytorch_lightning.utilities.model_summary.model_summary import _format_summary_table

log = logging.getLogger(__name__)

[docs]class ModelSummary(Callback): r""" Generates a summary of all layers in a :class:`~pytorch_lightning.core.module.LightningModule`. Args: max_depth: The maximum depth of layer nesting that the summary will include. A value of 0 turns the layer summary off. Example:: >>> from pytorch_lightning import Trainer >>> from pytorch_lightning.callbacks import ModelSummary >>> trainer = Trainer(callbacks=[ModelSummary(max_depth=1)]) """ def __init__(self, max_depth: int = 1) -> None: self._max_depth: int = max_depth
[docs] def on_fit_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None: if not self._max_depth: return None model_summary = self._summary(trainer, pl_module) summary_data = model_summary._get_summary_data() total_parameters = model_summary.total_parameters trainable_parameters = model_summary.trainable_parameters model_size = model_summary.model_size if trainer.is_global_zero: self.summarize(summary_data, total_parameters, trainable_parameters, model_size)
def _summary(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> Union[DeepSpeedSummary, Summary]: from pytorch_lightning.strategies.deepspeed import DeepSpeedStrategy if isinstance(trainer.strategy, DeepSpeedStrategy) and trainer.strategy.zero_stage_3: return DeepSpeedSummary(pl_module, max_depth=self._max_depth) return summarize(pl_module, max_depth=self._max_depth) @staticmethod def summarize( summary_data: List[Tuple[str, List[str]]], total_parameters: int, trainable_parameters: int, model_size: float, ) -> None: summary_table = _format_summary_table(total_parameters, trainable_parameters, model_size, *summary_data)"\n" + summary_table)

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