Source code for pytorch_lightning.callbacks.model_summary

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Model Summary
=============

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

import pytorch_lightning as pl
from pytorch_lightning.callbacks.base import Callback
from pytorch_lightning.utilities.model_summary import _format_summary_table, summarize

log = logging.getLogger(__name__)


class ModelSummary(Callback):
    r"""
    Generates a summary of all layers in a :class:`~pytorch_lightning.core.lightning.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

    def on_fit_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        if not self._max_depth:
            return None

        model_summary = summarize(pl_module, max_depth=self._max_depth)
        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)

    @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)
        log.info("\n" + summary_table)