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* Added basic file logger #1803 * fixup! Added basic file logger #1803 * fixup! Added basic file logger #1803 * fixup! Added basic file logger #1803 * fixup! Added basic file logger #1803 * fixup! Added basic file logger #1803 * csv * Apply suggestions from code review * tests * tests * tests * miss * docs Co-authored-by: xmotli02 <[email protected]> Co-authored-by: Jirka Borovec <[email protected]> Co-authored-by: Jirka Borovec <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,204 @@ | ||
| """ | ||
| CSV logger | ||
| ---------- | ||
| CSV logger for basic experiment logging that does not require opening ports | ||
| """ | ||
| import io | ||
| import os | ||
| import csv | ||
| import torch | ||
| from argparse import Namespace | ||
| from typing import Optional, Dict, Any, Union | ||
|
|
||
| from pytorch_lightning import _logger as log | ||
| from pytorch_lightning.core.saving import save_hparams_to_yaml | ||
| from pytorch_lightning.loggers.base import LightningLoggerBase | ||
| from pytorch_lightning.utilities.distributed import rank_zero_warn, rank_zero_only | ||
|
|
||
|
|
||
| class ExperimentWriter(object): | ||
| 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' | ||
|
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| def __init__(self, log_dir: str) -> None: | ||
| self.hparams = {} | ||
| self.metrics = [] | ||
|
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| self.log_dir = log_dir | ||
| if os.path.exists(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) | ||
|
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||
| self.metrics_file_path = os.path.join(self.log_dir, self.NAME_METRICS_FILE) | ||
|
|
||
| def log_hparams(self, params: Dict[str, Any]) -> None: | ||
| """Record hparams""" | ||
| self.hparams.update(params) | ||
|
|
||
| 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) | ||
|
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||
| metrics = {k: _handle_value(v) for k, v in metrics_dict.items()} | ||
| metrics['step'] = step | ||
| self.metrics.append(metrics) | ||
|
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||
| 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()) | ||
|
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||
| with io.open(self.metrics_file_path, 'w', newline='') as f: | ||
| self.writer = csv.DictWriter(f, fieldnames=metrics_keys) | ||
| self.writer.writeheader() | ||
| self.writer.writerows(self.metrics) | ||
|
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||
|
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| 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. | ||
| """ | ||
|
|
||
| def __init__(self, | ||
| save_dir: str, | ||
| name: Optional[str] = "default", | ||
| version: Optional[Union[int, str]] = None): | ||
|
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| super().__init__() | ||
| self._save_dir = save_dir | ||
| self._name = name or '' | ||
| self._version = version | ||
| self._experiment = None | ||
|
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||
| @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) | ||
|
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| @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 ala test-tube | ||
| 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]: | ||
| return self._save_dir | ||
|
|
||
| @property | ||
| 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 | ||
|
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||
| os.makedirs(self.root_dir, exist_ok=True) | ||
| self._experiment = ExperimentWriter(log_dir=self.log_dir) | ||
| return self._experiment | ||
|
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||
| @rank_zero_only | ||
| def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None: | ||
| params = self._convert_params(params) | ||
| self.experiment.log_hparams(params) | ||
|
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||
| @rank_zero_only | ||
| def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None: | ||
| self.experiment.log_metrics(metrics, step) | ||
|
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||
| @rank_zero_only | ||
| def save(self) -> None: | ||
| super().save() | ||
| self.experiment.save() | ||
|
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| @rank_zero_only | ||
| def finalize(self, status: str) -> None: | ||
| self.save() | ||
|
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| @property | ||
| def name(self) -> str: | ||
| return self._name | ||
|
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||
| @property | ||
| def version(self) -> int: | ||
| if self._version is None: | ||
| self._version = self._get_next_version() | ||
| return self._version | ||
|
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||
| def _get_next_version(self): | ||
| root_dir = os.path.join(self._save_dir, self.name) | ||
|
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| if not os.path.isdir(root_dir): | ||
| log.warning('Missing logger folder: %s', root_dir) | ||
| return 0 | ||
|
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| 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 |
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