66 lines
2.0 KiB
Python

import os
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Literal
from torch.utils.tensorboard import SummaryWriter
from tianshou.utils import TensorboardLogger, WandbLogger
TLogger = TensorboardLogger | WandbLogger
@dataclass
class Logger:
logger: TLogger
log_path: str
class LoggerFactory(ABC):
@abstractmethod
def create_logger(self, log_name: str, run_id: int | None, config_dict: dict) -> Logger:
pass
class DefaultLoggerFactory(LoggerFactory):
def __init__(
self,
log_dir: str = "log",
logger_type: Literal["tensorboard", "wandb"] = "tensorboard",
wandb_project: str | None = None,
):
if logger_type == "wandb" and wandb_project is None:
raise ValueError("Must provide 'wand_project'")
self.log_dir = log_dir
self.logger_type = logger_type
self.wandb_project = wandb_project
def create_logger(self, log_name: str, run_id: str | None, config_dict: dict) -> Logger:
writer = SummaryWriter(self.log_dir)
writer.add_text(
"args",
str(
dict(
log_dir=self.log_dir,
logger_type=self.logger_type,
wandb_project=self.wandb_project,
),
),
)
if self.logger_type == "wandb":
logger = WandbLogger(
save_interval=1,
name=log_name.replace(os.path.sep, "__"),
run_id=run_id,
config=config_dict,
project=self.wandb_project,
)
logger.load(writer)
elif self.logger_type == "tensorboard":
logger = TensorboardLogger(writer)
else:
raise ValueError(f"Unknown logger type '{self.logger_type}'")
log_path = os.path.join(self.log_dir, log_name)
os.makedirs(log_path, exist_ok=True)
return Logger(logger=logger, log_path=log_path)