Source code for pytorch_lightning.loggers.csv_logs
# 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.
"""
CSV logger
----------
CSV logger for basic experiment logging that does not require opening ports
"""
import csv
import logging
import os
from argparse import Namespace
from typing import Any, Dict, Optional, Union
import torch
from pytorch_lightning.core.saving import save_hparams_to_yaml
from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_experiment
from pytorch_lightning.utilities import rank_zero_warn
from pytorch_lightning.utilities.distributed import rank_zero_only
log = logging.getLogger(__name__)
[docs]class ExperimentWriter:
r"""
Experiment writer for CSVLogger.
Currently supports to log hyperparameters and metrics in YAML and CSV
format, respectively.
Args:
log_dir: Directory for the experiment logs
"""
NAME_HPARAMS_FILE = "hparams.yaml"
NAME_METRICS_FILE = "metrics.csv"
def __init__(self, log_dir: str) -> None:
self.hparams = {}
self.metrics = []
self.log_dir = log_dir
if os.path.exists(self.log_dir) and os.listdir(self.log_dir):
rank_zero_warn(
f"Experiment logs directory {self.log_dir} exists and is not empty."
" Previous log files in this directory will be deleted when the new ones are saved!"
)
os.makedirs(self.log_dir, exist_ok=True)
self.metrics_file_path = os.path.join(self.log_dir, self.NAME_METRICS_FILE)
[docs] def log_hparams(self, params: Dict[str, Any]) -> None:
"""Record hparams."""
self.hparams.update(params)
[docs] def log_metrics(self, metrics_dict: Dict[str, float], step: Optional[int] = None) -> None:
"""Record metrics."""
def _handle_value(value):
if isinstance(value, torch.Tensor):
return value.item()
return value
if step is None:
step = len(self.metrics)
metrics = {k: _handle_value(v) for k, v in metrics_dict.items()}
metrics["step"] = step
self.metrics.append(metrics)
[docs] def save(self) -> None:
"""Save recorded hparams and metrics into files."""
hparams_file = os.path.join(self.log_dir, self.NAME_HPARAMS_FILE)
save_hparams_to_yaml(hparams_file, self.hparams)
if not self.metrics:
return
last_m = {}
for m in self.metrics:
last_m.update(m)
metrics_keys = list(last_m.keys())
with open(self.metrics_file_path, "w", newline="") as f:
# Don't assign the writer to self.
# Keeps an open reference and prevents pickling otherwise
writer = csv.DictWriter(f, fieldnames=metrics_keys)
writer.writeheader()
writer.writerows(self.metrics)
[docs]class CSVLogger(LightningLoggerBase):
r"""
Log to local file system in yaml and CSV format.
Logs are saved to ``os.path.join(save_dir, name, version)``.
Example:
>>> from pytorch_lightning import Trainer
>>> from pytorch_lightning.loggers import CSVLogger
>>> logger = CSVLogger("logs", name="my_exp_name")
>>> trainer = Trainer(logger=logger)
Args:
save_dir: Save directory
name: Experiment name. Defaults to ``'default'``.
version: Experiment version. If version is not specified the logger inspects the save
directory for existing versions, then automatically assigns the next available version.
prefix: A string to put at the beginning of metric keys.
flush_logs_every_n_steps: How often to flush logs to disk (defaults to every 100 steps).
"""
LOGGER_JOIN_CHAR = "-"
def __init__(
self,
save_dir: str,
name: Optional[str] = "default",
version: Optional[Union[int, str]] = None,
prefix: str = "",
flush_logs_every_n_steps: int = 100,
):
super().__init__()
self._save_dir = save_dir
self._name = name or ""
self._version = version
self._prefix = prefix
self._experiment = None
self._flush_logs_every_n_steps = flush_logs_every_n_steps
@property
def root_dir(self) -> str:
"""Parent directory for all checkpoint subdirectories.
If the experiment name parameter is ``None`` or the empty string, no experiment subdirectory is used and the
checkpoint will be saved in "save_dir/version_dir"
"""
if not self.name:
return self.save_dir
return os.path.join(self.save_dir, self.name)
@property
def log_dir(self) -> str:
"""The log directory for this run.
By default, it is named ``'version_${self.version}'`` but it can be overridden by passing a string value for the
constructor's version parameter instead of ``None`` or an int.
"""
# create a pseudo standard path
version = self.version if isinstance(self.version, str) else f"version_{self.version}"
log_dir = os.path.join(self.root_dir, version)
return log_dir
@property
def save_dir(self) -> Optional[str]:
"""The current directory where logs are saved.
Returns:
The path to current directory where logs are saved.
"""
return self._save_dir
@property
@rank_zero_experiment
def experiment(self) -> ExperimentWriter:
r"""
Actual ExperimentWriter object. To use ExperimentWriter features in your
:class:`~pytorch_lightning.core.lightning.LightningModule` do the following.
Example::
self.logger.experiment.some_experiment_writer_function()
"""
if self._experiment:
return self._experiment
os.makedirs(self.root_dir, exist_ok=True)
self._experiment = ExperimentWriter(log_dir=self.log_dir)
return self._experiment
[docs] @rank_zero_only
def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
params = self._convert_params(params)
self.experiment.log_hparams(params)
[docs] @rank_zero_only
def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
metrics = self._add_prefix(metrics)
self.experiment.log_metrics(metrics, step)
if step is not None and (step + 1) % self._flush_logs_every_n_steps == 0:
self.save()
@property
def name(self) -> str:
"""Gets the name of the experiment.
Returns:
The name of the experiment.
"""
return self._name
@property
def version(self) -> int:
"""Gets the version of the experiment.
Returns:
The version of the experiment if it is specified, else the next version.
"""
if self._version is None:
self._version = self._get_next_version()
return self._version
def _get_next_version(self):
root_dir = os.path.join(self._save_dir, self.name)
if not os.path.isdir(root_dir):
log.warning("Missing logger folder: %s", root_dir)
return 0
existing_versions = []
for d in os.listdir(root_dir):
if os.path.isdir(os.path.join(root_dir, d)) and d.startswith("version_"):
existing_versions.append(int(d.split("_")[1]))
if len(existing_versions) == 0:
return 0
return max(existing_versions) + 1
