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import warnings
from typing import Callable, cast, List, Optional, Tuple, Union
import torch
import ignite.distributed as idist
from ignite.exceptions import NotComputableError
from ignite.metrics.metric import Metric, reinit__is_reduced
__all__ = ["EpochMetric"]
class EpochMetric(Metric):
"""Class for metrics that should be computed on the entire output history of a model.
Model's output and targets are restricted to be of shape ``(batch_size, n_targets)``. Output
datatype should be `float32`. Target datatype should be `long` for classification and `float` for regression.
.. warning::
Current implementation stores all input data (output and target) in as tensors before computing a metric.
This can potentially lead to a memory error if the input data is larger than available RAM.
In distributed configuration, all stored data (output and target) is mutually collected across all processes
using all gather collective operation. This can potentially lead to a memory error.
Compute method executes ``compute_fn`` on zero rank process only and final result is broadcasted to
all processes.
- ``update`` must receive output of the form ``(y_pred, y)``.
Args:
compute_fn: a callable which receives two tensors as the `predictions` and `targets`
and returns a scalar. Input tensors will be on specified ``device`` (see arg below).
output_transform: a callable that is used to transform the
:class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
form expected by the metric. This can be useful if, for example, you have a multi-output model and
you want to compute the metric with respect to one of the outputs.
check_compute_fn: if True, ``compute_fn`` is run on the first batch of data to ensure there are no
issues. If issues exist, user is warned that there might be an issue with the ``compute_fn``.
Default, True.
device: optional device specification for internal storage.
Example:
For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`.
.. include:: defaults.rst
:start-after: :orphan:
.. testcode::
def mse_fn(y_preds, y_targets):
return torch.mean(((y_preds - y_targets.type_as(y_preds)) ** 2)).item()
metric = EpochMetric(mse_fn)
metric.attach(default_evaluator, "mse")
y_true = torch.tensor([0, 1, 2, 3, 4, 5])
y_pred = y_true * 0.75
state = default_evaluator.run([[y_pred, y_true]])
print(state.metrics["mse"])
.. testoutput::
0.5729...
Warnings:
EpochMetricWarning: User is warned that there are issues with ``compute_fn`` on a batch of data processed.
To disable the warning, set ``check_compute_fn=False``.
.. versionchanged:: 0.5.1
``skip_unrolling`` argument is added.
"""
_state_dict_all_req_keys = ("_predictions", "_targets")
def __init__(
self,
compute_fn: Callable[[torch.Tensor, torch.Tensor], float],
output_transform: Callable = lambda x: x,
check_compute_fn: bool = True,
device: Union[str, torch.device] = torch.device("cpu"),
skip_unrolling: bool = False,
) -> None:
if not callable(compute_fn):
raise TypeError("Argument compute_fn should be callable.")
self.compute_fn = compute_fn
self._check_compute_fn = check_compute_fn
super(EpochMetric, self).__init__(
output_transform=output_transform, device=device, skip_unrolling=skip_unrolling
)
@reinit__is_reduced
def reset(self) -> None:
self._predictions: List[torch.Tensor] = []
self._targets: List[torch.Tensor] = []
self._result: Optional[float] = None
def _check_shape(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None:
y_pred, y = output
if y_pred.ndimension() not in (1, 2):
raise ValueError("Predictions should be of shape (batch_size, n_targets) or (batch_size, ).")
if y.ndimension() not in (1, 2):
raise ValueError("Targets should be of shape (batch_size, n_targets) or (batch_size, ).")
def _check_type(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None:
y_pred, y = output
if len(self._predictions) < 1:
return
dtype_preds = self._predictions[-1].dtype
if dtype_preds != y_pred.dtype:
raise ValueError(
f"Incoherent types between input y_pred and stored predictions: {dtype_preds} vs {y_pred.dtype}"
)
dtype_targets = self._targets[-1].dtype
if dtype_targets != y.dtype:
raise ValueError(f"Incoherent types between input y and stored targets: {dtype_targets} vs {y.dtype}")
@reinit__is_reduced
def update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None:
self._check_shape(output)
y_pred, y = output[0].detach(), output[1].detach()
if y_pred.ndimension() == 2 and y_pred.shape[1] == 1:
y_pred = y_pred.squeeze(dim=-1)
if y.ndimension() == 2 and y.shape[1] == 1:
y = y.squeeze(dim=-1)
y_pred = y_pred.clone().to(self._device)
y = y.clone().to(self._device)
self._check_type((y_pred, y))
self._predictions.append(y_pred)
self._targets.append(y)
# Check once the signature and execution of compute_fn
if len(self._predictions) == 1 and self._check_compute_fn:
try:
self.compute_fn(self._predictions[0], self._targets[0])
except Exception as e:
warnings.warn(f"Probably, there can be a problem with `compute_fn`:\n {e}.", EpochMetricWarning)
def compute(self) -> float:
if len(self._predictions) < 1 or len(self._targets) < 1:
raise NotComputableError("EpochMetric must have at least one example before it can be computed.")
if self._result is None:
_prediction_tensor = torch.cat(self._predictions, dim=0)
_target_tensor = torch.cat(self._targets, dim=0)
ws = idist.get_world_size()
if ws > 1:
# All gather across all processes
_prediction_tensor = cast(torch.Tensor, idist.all_gather(_prediction_tensor))
_target_tensor = cast(torch.Tensor, idist.all_gather(_target_tensor))
self._result = 0.0
if idist.get_rank() == 0:
# Run compute_fn on zero rank only
self._result = self.compute_fn(_prediction_tensor, _target_tensor)
if ws > 1:
# broadcast result to all processes
self._result = cast(float, idist.broadcast(self._result, src=0))
return self._result
class EpochMetricWarning(UserWarning):
pass
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