Source code for pytorch_lightning.callbacks.rich_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.
from typing import List, Tuple
from pytorch_lightning.callbacks import ModelSummary
from pytorch_lightning.utilities.imports import _RICH_AVAILABLE
from pytorch_lightning.utilities.model_summary import get_human_readable_count
if _RICH_AVAILABLE:
from rich import get_console
from rich.table import Table
[docs]class RichModelSummary(ModelSummary):
r"""
Generates a summary of all layers in a :class:`~pytorch_lightning.core.module.LightningModule`
with `rich text formatting <https://github.com/Textualize/rich>`_.
Install it with pip:
.. code-block:: bash
pip install rich
.. code-block:: python
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import RichModelSummary
trainer = Trainer(callbacks=RichModelSummary())
You could also enable ``RichModelSummary`` using the :class:`~pytorch_lightning.callbacks.RichProgressBar`
.. code-block:: python
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import RichProgressBar
trainer = Trainer(callbacks=RichProgressBar())
Args:
max_depth: The maximum depth of layer nesting that the summary will include. A value of 0 turns the
layer summary off.
Raises:
ModuleNotFoundError:
If required `rich` package is not installed on the device.
"""
def __init__(self, max_depth: int = 1) -> None:
if not _RICH_AVAILABLE:
raise ModuleNotFoundError(
"`RichModelSummary` requires `rich` to be installed. Install it by running `pip install -U rich`."
)
super().__init__(max_depth)
@staticmethod
def summarize(
summary_data: List[Tuple[str, List[str]]],
total_parameters: int,
trainable_parameters: int,
model_size: float,
) -> None:
console = get_console()
table = Table(header_style="bold magenta")
table.add_column(" ", style="dim")
table.add_column("Name", justify="left", no_wrap=True)
table.add_column("Type")
table.add_column("Params", justify="right")
column_names = list(zip(*summary_data))[0]
for column_name in ["In sizes", "Out sizes"]:
if column_name in column_names:
table.add_column(column_name, justify="right", style="white")
rows = list(zip(*(arr[1] for arr in summary_data)))
for row in rows:
table.add_row(*row)
console.print(table)
parameters = []
for param in [trainable_parameters, total_parameters - trainable_parameters, total_parameters, model_size]:
parameters.append("{:<{}}".format(get_human_readable_count(int(param)), 10))
grid = Table.grid(expand=True)
grid.add_column()
grid.add_column()
grid.add_row(f"[bold]Trainable params[/]: {parameters[0]}")
grid.add_row(f"[bold]Non-trainable params[/]: {parameters[1]}")
grid.add_row(f"[bold]Total params[/]: {parameters[2]}")
grid.add_row(f"[bold]Total estimated model params size (MB)[/]: {parameters[3]}")
console.print(grid)