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remove redundant typing
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use logging
fix typing and pep8
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bmartinn committed Mar 11, 2020
1 parent b6ab8b9 commit 05ce77b
Showing 1 changed file with 34 additions and 40 deletions.
74 changes: 34 additions & 40 deletions pytorch_lightning/loggers/trains.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,42 +24,38 @@ def any_lightning_module_function_or_hook(...):
"""

import logging
from argparse import Namespace
from logging import getLogger
from pathlib import Path
from typing import Any, Dict, Optional, Union

import PIL
import numpy as np
import pandas as pd
import PIL
import torch

try:
import trains
except ImportError:
raise ImportError('Missing TRAINS package.')
raise ImportError('You want to use `TRAINS` logger which is not installed yet,'
' install it with `pip install trains`.')

from .base import LightningLoggerBase, rank_zero_only

logger = getLogger(__name__)


class TrainsLogger(LightningLoggerBase):
"""Logs using TRAINS
Args:
project_name (Optional[str], optional): The name of the experiment's project.
Defaults to None.
task_name (Optional[str], optional): The name of the experiment. Defaults to None.
task_type (str, optional): The name of the experiment. Defaults to 'training'.
reuse_last_task_id (bool, optional): Start with the previously used task id.
Defaults to True.
output_uri (Optional[str], optional): Default location for output models. Defaults to None.
auto_connect_arg_parser (bool, optional): Automatically grab the ArgParser
project_name: The name of the experiment's project. Defaults to None.
task_name: The name of the experiment. Defaults to None.
task_type: The name of the experiment. Defaults to 'training'.
reuse_last_task_id: Start with the previously used task id. Defaults to True.
output_uri: Default location for output models. Defaults to None.
auto_connect_arg_parser: Automatically grab the ArgParser
and connect it with the task. Defaults to True.
auto_connect_frameworks (bool, optional): If True, automatically patch to trains backend.
Defaults to True.
auto_resource_monitoring (bool, optional): If true, machine vitals will be
auto_connect_frameworks: If True, automatically patch to trains backend. Defaults to True.
auto_resource_monitoring: If true, machine vitals will be
sent along side the task scalars. Defaults to True.
"""

Expand Down Expand Up @@ -89,7 +85,7 @@ def experiment(self) -> trains.Task:
return self._trains

@property
def id(self) -> str:
def id(self) -> Union[str, None]:
if not self._trains:
return None
return self._trains.id
Expand All @@ -99,7 +95,7 @@ def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
"""Log hyperparameters (numeric values) in TRAINS experiments
Args:
params (Union[Dict[str, Any], Namespace]):
params:
The hyperparameters that passed through the model.
"""
if not self._trains:
Expand All @@ -117,12 +113,11 @@ def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) ->
This method will be called by Trainer.
Args:
metrics (Dict[str, float]):
metrics:
The dictionary of the metrics.
If the key contains "/", it will be split by the delimiter,
then the elements will be logged as "title" and "series" respectively.
step (Optional[int], optional): Step number at which the metrics should be recorded.
Defaults to None.
step: Step number at which the metrics should be recorded. Defaults to None.
"""
if not self._trains:
return None
Expand All @@ -132,7 +127,7 @@ def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) ->

for k, v in metrics.items():
if isinstance(v, str):
logger.warning("Discarding metric with string value {}={}".format(k, v))
logging.warning("Discarding metric with string value {}={}".format(k, v))
continue
if isinstance(v, torch.Tensor):
v = v.item()
Expand All @@ -151,11 +146,10 @@ def log_metric(self, title: str, series: str, value: float, step: Optional[int]
This method will be called by the users.
Args:
title (str): The title of the graph to log, e.g. loss, accuracy.
series (str): The series name in the graph, e.g. classification, localization.
value (float): The value to log.
step (Optional[int], optional): Step number at which the metrics should be recorded.
Defaults to None.
title: The title of the graph to log, e.g. loss, accuracy.
series: The series name in the graph, e.g. classification, localization.
value: The value to log.
step: Step number at which the metrics should be recorded. Defaults to None.
"""
if not self._trains:
return None
Expand All @@ -173,7 +167,7 @@ def log_text(self, text: str) -> None:
"""Log console text data in TRAINS experiment
Args:
text (str): The value of the log (data-point).
text: The value of the log (data-point).
"""
if not self._trains:
return None
Expand All @@ -188,16 +182,16 @@ def log_image(
"""Log Debug image in TRAINS experiment
Args:
title (str): The title of the debug image, i.e. "failed", "passed".
series (str): The series name of the debug image, i.e. "Image 0", "Image 1".
image (Union[str, np.ndarray, PIL.Image.Image, torch.Tensor]):
title: The title of the debug image, i.e. "failed", "passed".
series: The series name of the debug image, i.e. "Image 0", "Image 1".
image:
Debug image to log. Can be one of the following types:
Torch, Numpy, PIL image, path to image file (str)
If Numpy or Torch, the image is assume to be the following:
shape: CHW
color space: RGB
value range: [0., 1.] (float) or [0, 255] (uint8)
step (Optional[int], optional):
step:
Step number at which the metrics should be recorded. Defaults to None.
"""
if not self._trains:
Expand Down Expand Up @@ -225,19 +219,19 @@ def log_artifact(
"""Save an artifact (file/object) in TRAINS experiment storage.
Args:
name (str): Artifact name. Notice! it will override previous artifact
name: Artifact name. Notice! it will override previous artifact
if name already exists
artifact (Any): Artifact object to upload. Currently supports:
artifact: Artifact object to upload. Currently supports:
- string / pathlib2.Path are treated as path to artifact file to upload
If wildcard or a folder is passed, zip file containing the
local files will be created and uploaded
- dict will be stored as .json file and uploaded
- pandas.DataFrame will be stored as .csv.gz (compressed CSV file) and uploaded
- numpy.ndarray will be stored as .npz and uploaded
- PIL.Image will be stored to .png file and uploaded
metadata (Optional[Dict[str, Any]], optional):
metadata:
Simple key/value dictionary to store on the artifact. Defaults to None.
delete_after_upload (bool, optional):
delete_after_upload:
If True local artifact will be deleted (only applies if artifact_object is a
local file). Defaults to False.
"""
Expand All @@ -260,23 +254,23 @@ def finalize(self, status: str) -> None:
self._trains = None

@property
def name(self) -> str:
def name(self) -> Union[str, None]:
if not self._trains:
return None
return self._trains.name

@property
def version(self) -> str:
def version(self) -> Union[str, None]:
if not self._trains:
return None
return self._trains.id

def __getstate__(self) -> str:
def __getstate__(self) -> Union[str, None]:
if not self._trains:
return None
return self._trains.id

def __setstate__(self, state: str) -> str:
def __setstate__(self, state: str) -> None:
self._rank = 0
self._trains = None
if state:
Expand Down

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