Source code for compressai_trainer.config.engine

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from __future__ import annotations

from typing import Any, Dict, cast

import aim
from catalyst import dl
from omegaconf import DictConfig, OmegaConf

from compressai_trainer.registry.catalyst import CALLBACKS, RUNNERS
from compressai_trainer.typing.catalyst import TCallback, TRunner
from compressai_trainer.utils.catalyst.loggers import AimLogger

PRIMARY_LOGGER = "aim"

LOGGERS = {
    "csv": dl.CSVLogger,
    "tensorboard": dl.TensorboardLogger,
}


[docs]def create_callback(conf: DictConfig) -> TCallback:
    kwargs = OmegaConf.to_container(conf, resolve=True)
    kwargs = cast(Dict[str, Any], kwargs)
    del kwargs["type"]
    callback = CALLBACKS[conf.type](**kwargs)
    return callback


[docs]def create_logger(conf: DictConfig, logger_type: str) -> dl.ILogger:
    if logger_type == "aim":
        logger = AimLogger(
            experiment=conf.exp.name,
            run_hash=conf.env.aim.run_hash,
            repo=aim.Repo(
                conf.env.aim.repo,
                init=not aim.Repo.exists(conf.env.aim.repo),
            ),
            **conf.engine.loggers.aim,
        )
        conf.env.aim.run_hash = logger.run.hash
        return logger
    assert conf.env[PRIMARY_LOGGER].run_hash is not None
    if logger_type == "mlflow":
        return dl.MLflowLogger(
            experiment=conf.exp.name,
            **conf.engine.loggers.mlflow,
        )
    if logger_type in LOGGERS:
        return LOGGERS[logger_type](
            **conf.engine.loggers[logger_type],
        )
    raise ValueError(f"Unknown logger type: {logger_type}")


[docs]def create_runner(conf: DictConfig) -> TRunner:
    kwargs = OmegaConf.to_container(conf, resolve=True)
    kwargs = cast(Dict[str, Any], kwargs)
    del kwargs["type"]
    runner = RUNNERS[conf.type](**kwargs)
    return runner


[docs]def configure_engine(conf: DictConfig) -> dict[str, Any]:
    logger_types = [
        PRIMARY_LOGGER,
        *[k for k in conf.engine.loggers.keys() if k != PRIMARY_LOGGER],
    ]
    d = {
        "loggers": {t: create_logger(conf, t) for t in logger_types},
        "callbacks": [create_callback(cb_conf) for cb_conf in conf.engine.callbacks],
        "hparams": OmegaConf.to_container(conf, resolve=True),
    }
    return {**d["hparams"]["engine"], **d}