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"""Load PyTorch Lightning Loggers."""
# Copyright (C) 2020 Intel Corporation
#
# 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.
import logging
import os
from typing import Iterable, List, Union
from omegaconf.dictconfig import DictConfig
from omegaconf.listconfig import ListConfig
from pytorch_lightning.loggers import CSVLogger, LightningLoggerBase
from .tensorboard import AnomalibTensorBoardLogger
from .wandb import AnomalibWandbLogger
__all__ = [
"AnomalibTensorBoardLogger",
"AnomalibWandbLogger",
"configure_logger",
"get_experiment_logger",
]
AVAILABLE_LOGGERS = ["tensorboard", "wandb", "csv"]
class UnknownLogger(Exception):
"""This is raised when the logger option in `config.yaml` file is set incorrectly."""
def configure_logger(level: Union[int, str] = logging.INFO):
"""Get console logger by name.
Args:
level (Union[int, str], optional): Logger Level. Defaults to logging.INFO.
Returns:
Logger: The expected logger.
"""
if isinstance(level, str):
level = logging.getLevelName(level)
format_string = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
logging.basicConfig(format=format_string, level=level)
# Set Pytorch Lightning logs to have a the consistent formatting with anomalib.
for handler in logging.getLogger("pytorch_lightning").handlers:
handler.setFormatter(logging.Formatter(format_string))
handler.setLevel(level)
def get_experiment_logger(
config: Union[DictConfig, ListConfig]
) -> Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool]:
"""Return a logger based on the choice of logger in the config file.
Args:
config (DictConfig): config.yaml file for the corresponding anomalib model.
Raises:
ValueError: for any logger types apart from false and tensorboard
Returns:
Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool]: Logger
"""
if config.project.logger in [None, False]:
return False
logger_list: List[LightningLoggerBase] = []
if isinstance(config.project.logger, str):
config.project.logger = [config.project.logger]
for logger in config.project.logger:
if logger == "tensorboard":
logger_list.append(
AnomalibTensorBoardLogger(
name="Tensorboard Logs",
save_dir=os.path.join(config.project.path, "logs"),
)
)
elif logger == "wandb":
wandb_logdir = os.path.join(config.project.path, "logs")
os.makedirs(wandb_logdir, exist_ok=True)
logger_list.append(
AnomalibWandbLogger(
project=config.dataset.name,
name=f"{config.dataset.category} {config.model.name}",
save_dir=wandb_logdir,
)
)
elif logger == "csv":
logger_list.append(CSVLogger(save_dir=os.path.join(config.project.path, "logs")))
else:
raise UnknownLogger(
f"Unknown logger type: {config.project.logger}. "
f"Available loggers are: {AVAILABLE_LOGGERS}.\n"
f"To enable the logger, set `project.logger` to `true` or use one of available loggers in config.yaml\n"
f"To disable the logger, set `project.logger` to `false`."
)
return logger_list
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