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import random
import warnings
from importlib.util import find_spec
from typing import Callable
import numpy as np
import torch
from omegaconf import DictConfig
from .logger import RankedLogger
from .rich_utils import enforce_tags, print_config_tree
log = RankedLogger(__name__, rank_zero_only=True)
def extras(cfg: DictConfig) -> None:
"""Applies optional utilities before the task is started.
Utilities:
- Ignoring python warnings
- Setting tags from command line
- Rich config printing
"""
# return if no `extras` config
if not cfg.get("extras"):
log.warning("Extras config not found! <cfg.extras=null>")
return
# disable python warnings
if cfg.extras.get("ignore_warnings"):
log.info("Disabling python warnings! <cfg.extras.ignore_warnings=True>")
warnings.filterwarnings("ignore")
# prompt user to input tags from command line if none are provided in the config
if cfg.extras.get("enforce_tags"):
log.info("Enforcing tags! <cfg.extras.enforce_tags=True>")
enforce_tags(cfg, save_to_file=True)
# pretty print config tree using Rich library
if cfg.extras.get("print_config"):
log.info("Printing config tree with Rich! <cfg.extras.print_config=True>")
print_config_tree(cfg, resolve=True, save_to_file=True)
def task_wrapper(task_func: Callable) -> Callable:
"""Optional decorator that controls the failure behavior when executing the task function.
This wrapper can be used to:
- make sure loggers are closed even if the task function raises an exception (prevents multirun failure)
- save the exception to a `.log` file
- mark the run as failed with a dedicated file in the `logs/` folder (so we can find and rerun it later)
- etc. (adjust depending on your needs)
Example:
```
@utils.task_wrapper
def train(cfg: DictConfig) -> Tuple[dict, dict]:
...
return metric_dict, object_dict
```
""" # noqa: E501
def wrap(cfg: DictConfig):
# execute the task
try:
metric_dict, object_dict = task_func(cfg=cfg)
# things to do if exception occurs
except Exception as ex:
# save exception to `.log` file
log.exception("")
# some hyperparameter combinations might be invalid or
# cause out-of-memory errors so when using hparam search
# plugins like Optuna, you might want to disable
# raising the below exception to avoid multirun failure
raise ex
# things to always do after either success or exception
finally:
# display output dir path in terminal
log.info(f"Output dir: {cfg.paths.run_dir}")
# always close wandb run (even if exception occurs so multirun won't fail)
if find_spec("wandb"): # check if wandb is installed
import wandb
if wandb.run:
log.info("Closing wandb!")
wandb.finish()
return metric_dict, object_dict
return wrap
def get_metric_value(metric_dict: dict, metric_name: str) -> float:
"""Safely retrieves value of the metric logged in LightningModule."""
if not metric_name:
log.info("Metric name is None! Skipping metric value retrieval...")
return None
if metric_name not in metric_dict:
raise Exception(
f"Metric value not found! <metric_name={metric_name}>\n"
"Make sure metric name logged in LightningModule is correct!\n"
"Make sure `optimized_metric` name in `hparams_search` config is correct!"
)
metric_value = metric_dict[metric_name].item()
log.info(f"Retrieved metric value! <{metric_name}={metric_value}>")
return metric_value
def set_seed(seed: int):
if seed < 0:
seed = -seed
if seed > (1 << 31):
seed = 1 << 31
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
if torch.backends.cudnn.is_available():
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
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