Source code for ignite.metrics.loss

from __future__ import division

from ignite.exceptions import NotComputableError
from ignite.metrics import Metric
from ignite.metrics.metric import sync_all_reduce, reinit__is_reduced

[docs]class Loss(Metric): """ Calculates the average loss according to the passed loss_fn. Args: loss_fn (callable): a callable taking a prediction tensor, a target tensor, optionally other arguments, and returns the average loss over all observations in the batch. output_transform (callable): a callable that is used to transform the :class:`~ignite.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. The output is expected to be a tuple `(prediction, target)` or (prediction, target, kwargs) where kwargs is a dictionary of extra keywords arguments. If extra keywords arguments are provided they are passed to `loss_fn`. batch_size (callable): a callable taking a target tensor that returns the first dimension size (usually the batch size). device (str of torch.device, optional): device specification in case of distributed computation usage. In most of the cases, it can be defined as "cuda:local_rank" or "cuda" if already set `torch.cuda.set_device(local_rank)`. By default, if a distributed process group is initialized and available, device is set to `cuda`. """ _required_output_keys = None def __init__(self, loss_fn, output_transform=lambda x: x, batch_size=lambda x: len(x), device=None): super(Loss, self).__init__(output_transform, device=device) self._loss_fn = loss_fn self._batch_size = batch_size @reinit__is_reduced def reset(self): self._sum = 0 self._num_examples = 0 @reinit__is_reduced def update(self, output): if len(output) == 2: y_pred, y = output kwargs = {} else: y_pred, y, kwargs = output average_loss = self._loss_fn(y_pred, y, **kwargs) if len(average_loss.shape) != 0: raise ValueError('loss_fn did not return the average loss.') N = self._batch_size(y) self._sum += average_loss.item() * N self._num_examples += N @sync_all_reduce("_sum", "_num_examples") def compute(self): if self._num_examples == 0: raise NotComputableError( 'Loss must have at least one example before it can be computed.') return self._sum / self._num_examples

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