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Source code for ignite.handlers

from typing import Any, Callable, Optional, Union

from ignite.engine import Engine
from ignite.engine.events import Events
from ignite.handlers.checkpoint import Checkpoint, DiskSaver, ModelCheckpoint
from ignite.handlers.early_stopping import EarlyStopping
from ignite.handlers.ema_handler import EMAHandler
from ignite.handlers.lr_finder import FastaiLRFinder
from ignite.handlers.param_scheduler import (
    BaseParamScheduler,
    ConcatScheduler,
    CosineAnnealingScheduler,
    CyclicalScheduler,
    LinearCyclicalScheduler,
    LRScheduler,
    ParamGroupScheduler,
    ParamScheduler,
    PiecewiseLinear,
    create_lr_scheduler_with_warmup,
)
from ignite.handlers.state_param_scheduler import (
    ExpStateScheduler,
    LambdaStateScheduler,
    MultiStepStateScheduler,
    PiecewiseLinearStateScheduler,
    StateParamScheduler,
    StepStateScheduler,
)
from ignite.handlers.stores import EpochOutputStore
from ignite.handlers.terminate_on_nan import TerminateOnNan
from ignite.handlers.time_limit import TimeLimit
from ignite.handlers.time_profilers import BasicTimeProfiler, HandlersTimeProfiler
from ignite.handlers.timing import Timer

__all__ = [
    "ModelCheckpoint",
    "Checkpoint",
    "DiskSaver",
    "Timer",
    "EarlyStopping",
    "TerminateOnNan",
    "global_step_from_engine",
    "TimeLimit",
    "EpochOutputStore",
    "ConcatScheduler",
    "CosineAnnealingScheduler",
    "LinearCyclicalScheduler",
    "LRScheduler",
    "ParamGroupScheduler",
    "ParamScheduler",
    "PiecewiseLinear",
    "CyclicalScheduler",
    "create_lr_scheduler_with_warmup",
    "FastaiLRFinder",
    "EMAHandler",
    "BasicTimeProfiler",
    "HandlersTimeProfiler",
    "BaseParamScheduler",
    "StateParamScheduler",
    "LambdaStateScheduler",
    "PiecewiseLinearStateScheduler",
    "ExpStateScheduler",
    "StepStateScheduler",
    "MultiStepStateScheduler",
]


[docs]def global_step_from_engine(engine: Engine, custom_event_name: Optional[Events] = None) -> Callable: """Helper method to setup `global_step_transform` function using another engine. This can be helpful for logging trainer epoch/iteration while output handler is attached to an evaluator. Args: engine: engine which state is used to provide the global step custom_event_name: registered event name. Optional argument, event name to use. Returns: global step based on provided engine """ def wrapper(_: Any, event_name: Events) -> int: if custom_event_name is not None: event_name = custom_event_name return engine.state.get_event_attrib_value(event_name) return wrapper

© Copyright 2022, PyTorch-Ignite Contributors. Last updated on 10/03/2022, 1:28:46 PM.

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