from typing import Any, Dict, Generator, Iterable, List, Mapping, Union
from collections import OrderedDict
import os
import torch
from torch import nn, optim
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import DataLoader
from catalyst.callbacks.batch_overfit import BatchOverfitCallback
from catalyst.callbacks.checkpoint import CheckpointCallback, ICheckpointCallback
from catalyst.callbacks.criterion import CriterionCallback, ICriterionCallback
from catalyst.callbacks.misc import CheckRunCallback, TimerCallback, TqdmCallback
from catalyst.callbacks.optimizer import IOptimizerCallback, OptimizerCallback
from catalyst.callbacks.scheduler import ISchedulerCallback, SchedulerCallback
from catalyst.core.callback import Callback
from catalyst.core.logger import ILogger
from catalyst.core.misc import callback_isinstance, sort_callbacks_by_order
from catalyst.core.runner import IRunner
from catalyst.core.trial import ITrial
from catalyst.data.loader import ILoaderWrapper
from catalyst.engines import IEngine
from catalyst.loggers.console import ConsoleLogger
from catalyst.loggers.csv import CSVLogger
from catalyst.loggers.tensorboard import TensorboardLogger
from catalyst.runners.supervised import ISupervisedRunner
from catalyst.typing import (
Criterion,
Model,
Optimizer,
RunnerCriterion,
RunnerModel,
RunnerOptimizer,
RunnerScheduler,
Scheduler,
)
from catalyst.utils.data import get_loaders_from_params
from catalyst.utils.misc import maybe_recursive_call, set_global_seed
from catalyst.utils.torch import get_available_engine
def _process_loaders(
loaders: "OrderedDict[str, DataLoader]", initial_seed: int
) -> "OrderedDict[str, DataLoader]":
if not isinstance(loaders[list(loaders.keys())[0]], (DataLoader, ILoaderWrapper)):
loaders = get_loaders_from_params(initial_seed=initial_seed, **loaders)
return loaders
[docs]class Runner(IRunner):
"""Single-stage deep learning Runner with user-friendly API.
Args:
*args:
**kwargs:
"""
[docs] def __init__(self, *args, **kwargs):
"""Init."""
super().__init__(*args, **kwargs)
# the core
self._trial: ITrial = None
self._engine: IEngine = self.engine
self._model: RunnerModel = self.model
# the data
self._loaders: Dict[str, DataLoader] = None
# the components
self._criterion: RunnerCriterion = None
self._optimizer: RunnerOptimizer = None
self._scheduler: RunnerScheduler = None
# the callbacks
self._callbacks: Dict[str, Callback] = {}
# the loggers
self._loggers: Dict[str, ILogger] = {}
# extra
self._seed = 42
self._hparams: Dict = None
self._stage: str = "stage"
self._num_epochs: int = 1
# model selection
self._logdir = None
self._valid_loader = None
self._valid_metric = None
self._minimize_valid_metric = None
# extras
self._verbose = False
self._timeit = False
self._check = False
self._overfit = False
self._load_best_on_end = False
@property
def seed(self) -> int:
"""Experiment's initial seed value."""
return self._seed
@property
def name(self) -> str:
"""Returns run name."""
return "experiment" if self._trial is None else f"experiment_{self._trial.number}"
@property
def hparams(self) -> Dict:
"""Returns hyperparameters."""
if self._hparams is not None:
return self._hparams
elif self._trial is not None:
return self._trial.params
else:
return {}
@property
def stages(self) -> Iterable[str]:
"""Experiment's stage names (array with one value)."""
return [self._stage]
[docs] def get_stage_len(self, stage: str) -> int:
"""Returns the stage length in epochs for a given stage."""
return self._num_epochs
[docs] def get_trial(self) -> ITrial:
"""Returns the trial for a run."""
return self._trial
[docs] def get_engine(self) -> IEngine:
"""Returns the engine for a run."""
return self._engine or get_available_engine()
[docs] def get_loggers(self) -> Dict[str, ILogger]:
"""Returns the logger for a run."""
loggers = self._loggers or {}
is_logger_exists = lambda logger_fn: any(
isinstance(x, logger_fn) for x in loggers.values()
)
if not is_logger_exists(ConsoleLogger):
loggers["_console"] = ConsoleLogger()
if self._logdir is not None and not is_logger_exists(CSVLogger):
loggers["_csv"] = CSVLogger(logdir=self._logdir, use_logdir_postfix=True)
if self._logdir is not None and not is_logger_exists(TensorboardLogger):
loggers["_tensorboard"] = TensorboardLogger(
logdir=self._logdir, use_logdir_postfix=True
)
return loggers
[docs] def get_loaders(self, stage: str) -> "OrderedDict[str, DataLoader]":
"""Returns the loaders for a given stage."""
self._loaders = _process_loaders(loaders=self._loaders, initial_seed=self.seed)
return self._loaders
[docs] def get_model(self, stage: str) -> Model:
"""Returns the model for a given stage."""
model = (
self._model()
if callable(self._model) and not isinstance(self._model, nn.Module)
else self._model
)
return model
[docs] def get_criterion(self, stage: str) -> Criterion:
"""Returns the criterion for a given stage."""
return (
self._criterion()
if callable(self._criterion) and not isinstance(self._criterion, nn.Module)
else self._criterion
)
[docs] def get_optimizer(self, stage: str, model: Model) -> Optimizer:
"""Returns the optimizer for a given stage."""
return (
self._optimizer(model)
if callable(self._optimizer) and not isinstance(self._optimizer, optim.Optimizer)
else self._optimizer
)
[docs] def get_scheduler(self, stage: str, optimizer: Optimizer) -> Scheduler:
"""Returns the scheduler for a given stage."""
return (
self._scheduler(optimizer)
if callable(self._scheduler)
and not isinstance(
self._scheduler,
(optim.lr_scheduler.ReduceLROnPlateau, optim.lr_scheduler._LRScheduler),
)
else self._scheduler
)
[docs] def get_callbacks(self, stage: str) -> "OrderedDict[str, Callback]":
"""Returns the callbacks for a given stage."""
callbacks = sort_callbacks_by_order(self._callbacks)
is_callback_exists = lambda callback_fn: any(
callback_isinstance(x, callback_fn) for x in callbacks.values()
)
if self._verbose and not is_callback_exists(TqdmCallback):
callbacks["_verbose"] = TqdmCallback()
if self._timeit and not is_callback_exists(TimerCallback):
callbacks["_timer"] = TimerCallback()
if self._check and not is_callback_exists(CheckRunCallback):
callbacks["_check"] = CheckRunCallback()
if self._overfit and not is_callback_exists(BatchOverfitCallback):
callbacks["_overfit"] = BatchOverfitCallback()
if self._logdir is not None and not is_callback_exists(ICheckpointCallback):
callbacks["_checkpoint"] = CheckpointCallback(
logdir=os.path.join(self._logdir, "checkpoints"),
loader_key=self._valid_loader,
metric_key=self._valid_metric,
minimize=self._minimize_valid_metric,
)
return callbacks
[docs] def train(
self,
*,
# the data
loaders: "OrderedDict[str, DataLoader]",
# the core
model: Model,
engine: Union["IEngine", str] = None,
trial: ITrial = None,
# the components
criterion: Criterion = None,
optimizer: Optimizer = None,
scheduler: Scheduler = None,
# the callbacks
callbacks: "Union[List[Callback], OrderedDict[str, Callback]]" = None,
# the loggers
loggers: "Dict[str, ILogger]" = None,
# experiment info
seed: int = 42,
hparams: Dict[str, Any] = None,
# stage info
num_epochs: int = 1,
# extra info (callbacks info)
logdir: str = None,
valid_loader: str = None,
valid_metric: str = None,
minimize_valid_metric: bool = True,
verbose: bool = False,
timeit: bool = False,
check: bool = False,
overfit: bool = False,
load_best_on_end: bool = False,
# engine extra params,
fp16: bool = False,
amp: bool = False,
apex: bool = False,
ddp: bool = False,
) -> None:
"""
Starts the train stage of the model.
Args:
loaders: dictionary with one or several ``torch.utils.data.DataLoader``
for training, validation or inference
model: model to train
engine: engine to use for model training
trial: trial to use during model training
criterion: criterion function for training
optimizer: optimizer for training
scheduler: scheduler for training
callbacks: list or dictionary with Catalyst callbacks
loggers: dictionary with Catalyst loggers
seed: experiment's initial seed value
hparams: hyperparameters for the run
num_epochs: number of training epochs
logdir: path to output directory
valid_loader: loader name used to calculate
the metrics and save the checkpoints. For example,
you can pass `train` and then
the metrics will be taken from `train` loader.
valid_metric: the key to the name of the metric
by which the checkpoints will be selected.
minimize_valid_metric: flag to indicate whether
the ``valid_metric`` should be minimized or not (default: True).
verbose: if `True`, it displays the status of the training to the console.
timeit: if True, computes the execution time
of training process and displays it to the console.
check: if True, then only checks that pipeline is working
(3 epochs only with 3 batches per loader)
overfit: if True, then takes only one batch per loader
for model overfitting, for advance usage please check
``BatchOverfitCallback``
load_best_on_end: if True, Runner will load
best checkpoint state (model, optimizer, etc)
according to validation metrics. Requires specified ``logdir``.
fp16: boolean flag to use half-precision training (AMP > APEX)
amp: boolean flag to use amp half-precision
apex: boolean flag to use apex half-precision
ddp: if `True` will start training in distributed mode.
Note: Works only with python scripts. No jupyter support.
"""
# experiment setup
self._engine = engine or get_available_engine(fp16=fp16, ddp=ddp, amp=amp, apex=apex)
self._trial = trial
self._loggers = loggers
# the data
self._loaders = loaders
# the components
self._model = model
self._criterion = criterion
self._optimizer = optimizer
self._scheduler = scheduler
# the callbacks
self._callbacks = callbacks
# extra
self._stage = "train"
self._seed = seed
self._hparams = hparams
self._num_epochs = num_epochs
self._logdir = logdir
self._valid_loader = valid_loader
self._valid_metric = valid_metric
self._minimize_valid_metric = minimize_valid_metric
self._verbose = verbose
self._timeit = timeit
self._check = check
self._overfit = overfit
self._load_best_on_end = load_best_on_end
# run
self.run()
[docs] @torch.no_grad()
def predict_batch(self, batch: Mapping[str, Any], **kwargs) -> Mapping[str, Any]:
"""
Run model inference on specified data batch.
Args:
batch: dictionary with data batches from DataLoader.
**kwargs: additional kwargs to pass to the model
Returns:
Mapping: model output dictionary
Raises:
NotImplementedError: if not implemented yet
"""
raise NotImplementedError("Please implement `runner.predict_batch` method")
return None # noqa: WPS427
[docs] @torch.no_grad()
def predict_loader(
self,
*,
loader: DataLoader,
model: Model = None,
engine: Union["IEngine", str] = None,
seed: int = 42,
# engine extra params,
fp16: bool = False,
amp: bool = False,
apex: bool = False,
ddp: bool = False,
) -> Generator:
"""
Runs model inference on PyTorch DataLoader and returns
python generator with model predictions from `runner.predict_batch`.
Args:
loader: loader to predict
model: model to use for prediction
engine: engine to use for prediction
seed: random seed to use before prediction
fp16: boolean flag to use half-precision training (AMP > APEX)
amp: boolean flag to use amp half-precision
apex: boolean flag to use apex half-precision
ddp: if `True` will start training in distributed mode.
Note: Works only with python scripts. No jupyter support.
Yields:
bathes with model predictions
"""
self._engine = engine or get_available_engine(fp16=fp16, ddp=ddp, amp=amp, apex=apex)
if model is not None:
self.model = model
assert self.model is not None
# if resume is not None:
# checkpoint = load_checkpoint(resume)
# unpack_checkpoint(checkpoint, model=self.model)
self.model = self.engine.sync_device(self.model)
maybe_recursive_call(self.model, "train", mode=False)
set_global_seed(seed)
for batch in loader:
yield self.predict_batch(batch)
[docs]class SupervisedRunner(ISupervisedRunner, Runner):
"""Runner for experiments with supervised model.
Args:
model: Torch model instance
engine: IEngine instance
input_key: key in ``runner.batch`` dict mapping for model input
output_key: key for ``runner.batch`` to store model output
target_key: key in ``runner.batch`` dict mapping for target
loss_key: key for ``runner.batch_metrics`` to store criterion loss output
"""
[docs] def __init__(
self,
model: RunnerModel = None,
engine: IEngine = None,
input_key: Any = "features",
output_key: Any = "logits",
target_key: str = "targets",
loss_key: str = "loss",
):
"""Init."""
ISupervisedRunner.__init__(
self,
input_key=input_key,
output_key=output_key,
target_key=target_key,
loss_key=loss_key,
)
Runner.__init__(self, model=model, engine=engine)
[docs] @torch.no_grad()
def predict_batch(self, batch: Mapping[str, Any], **kwargs) -> Mapping[str, Any]:
"""
Run model inference on specified data batch.
.. warning::
You should not override this method. If you need specific model
call, override forward() method
Args:
batch: dictionary with data batch from DataLoader.
**kwargs: additional kwargs to pass to the model
Returns:
Mapping[str, Any]: model output dictionary
"""
batch = self._process_batch(batch)
batch = self.engine.sync_device(tensor_or_module=batch)
output = self.forward(batch, **kwargs)
return output
[docs] def get_callbacks(self, stage: str) -> "OrderedDict[str, Callback]":
"""Prepares the callbacks for selected stage.
Args:
stage: stage name
Returns:
dictionary with stage callbacks
"""
callbacks = super().get_callbacks(stage=stage)
is_callback_exists = lambda callback_fn: any(
callback_isinstance(x, callback_fn) for x in callbacks.values()
)
if isinstance(self._criterion, Criterion) and not is_callback_exists(ICriterionCallback):
callbacks["_criterion"] = CriterionCallback(
input_key=self._output_key, target_key=self._target_key, metric_key=self._loss_key,
)
if isinstance(self._optimizer, Optimizer) and not is_callback_exists(IOptimizerCallback):
callbacks["_optimizer"] = OptimizerCallback(metric_key=self._loss_key)
if isinstance(self._scheduler, (Scheduler, ReduceLROnPlateau)) and not is_callback_exists(
ISchedulerCallback
):
callbacks["_scheduler"] = SchedulerCallback(
loader_key=self._valid_loader, metric_key=self._valid_metric
)
return callbacks
__all__ = ["Runner", "SupervisedRunner"]