Source code for catalyst.callbacks.criterion
from catalyst.callbacks.metrics.functional_metric import FunctionalMetricCallback
from catalyst.core.callback import Callback
from catalyst.core.runner import IRunner
from catalyst.utils.misc import get_attr
class ICriterionCallback(Callback):
"""Criterion callback interface, abstraction over criterion step."""
pass
[docs]class CriterionCallback(FunctionalMetricCallback, ICriterionCallback):
"""Criterion callback, abstraction over criterion step.
Args:
input_key: input key to use for metric calculation, specifies our `y_pred`
target_key: output key to use for metric calculation, specifies our `y_true`
metric_key: key to store computed metric in ``runner.batch_metrics`` dictionary
criterion_key: A key to take a criterion in case
there are several of them, and they are in a dictionary format.
Examples:
.. code-block:: python
import torch
from torch.utils.data import DataLoader, TensorDataset
from catalyst import dl
# sample data
num_users, num_features, num_items = int(1e4), int(1e1), 10
X = torch.rand(num_users, num_features)
y = (torch.rand(num_users, num_items) > 0.5).to(torch.float32)
# pytorch loaders
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, num_workers=1)
loaders = {"train": loader, "valid": loader}
# model, criterion, optimizer, scheduler
model = torch.nn.Linear(num_features, num_items)
criterion = torch.nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters())
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, [2])
# model training
runner = dl.SupervisedRunner(
input_key="features", output_key="logits", target_key="targets", loss_key="loss"
)
runner.train(
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
loaders=loaders,
num_epochs=3,
verbose=True,
callbacks=[
dl.BatchTransformCallback(
transform=torch.sigmoid,
scope="on_batch_end",
input_key="logits",
output_key="scores"
),
dl.CriterionCallback(
input_key="logits", target_key="targets", metric_key="loss"
),
dl.AUCCallback(input_key="scores", target_key="targets"),
dl.HitrateCallback(
input_key="scores", target_key="targets", topk_args=(1, 3, 5)
),
dl.MRRCallback(input_key="scores", target_key="targets", topk_args=(1, 3, 5)),
dl.MAPCallback(input_key="scores", target_key="targets", topk_args=(1, 3, 5)),
dl.NDCGCallback(input_key="scores", target_key="targets", topk_args=(1, 3, 5)),
dl.OptimizerCallback(metric_key="loss"),
dl.SchedulerCallback(),
dl.CheckpointCallback(
logdir="./logs", loader_key="valid", metric_key="loss", minimize=True
),
]
)
.. note::
Please follow the `minimal examples`_ sections for more use cases.
.. _`minimal examples`: https://github.com/catalyst-team/catalyst#minimal-examples
"""
def __init__(
self,
input_key: str,
target_key: str,
metric_key: str,
criterion_key: str = None,
prefix: str = None,
suffix: str = None,
):
"""Init."""
super().__init__(
input_key=input_key,
target_key=target_key,
metric_fn=self._metric_fn,
metric_key=metric_key,
compute_on_call=True,
log_on_batch=True,
prefix=prefix,
suffix=suffix,
)
self.criterion_key = criterion_key
self.criterion = None
def _metric_fn(self, *args, **kwargs):
return self.criterion(*args, **kwargs)
def on_stage_start(self, runner: "IRunner"):
"""Checks that the current stage has correct criterion.
Args:
runner: current runner
"""
self.criterion = get_attr(runner, key="criterion", inner_key=self.criterion_key)
assert self.criterion is not None
__all__ = ["ICriterionCallback", "CriterionCallback"]