Source code for torchbearer.metrics.default

Base metrics are the base classes which represent the metrics supplied with torchbearer. They all use the
:func:`.default_for_key` decorator so that they can be accessed in the call to :class:`.torchbearer.Model` via the
following strings:

- '`acc`' or '`accuracy`': The :class:`.DefaultAccuracy` metric
- '`binary_acc`' or '`binary_accuracy`': The :class:`.BinaryAccuracy` metric
- '`cat_acc`' or '`cat_accuracy`': The :class:`.CategoricalAccuracy` metric
- '`top_5_acc`' or '`top_5_accuracy`': The :class:`.TopKCategoricalAccuracy` metric
- '`top_10_acc`' or '`top_10_accuracy`': The :class:`.TopKCategoricalAccuracy` metric with k=10
- '`mse`': The :class:`.MeanSquaredError` metric
- '`loss`': The :class:`.Loss` metric
- '`epoch`': The :class:`.Epoch` metric
- '`lr`': The :class:`.LR` metric
- '`roc_auc`' or '`roc_auc_score`': The :class:`.RocAucScore` metric
import torch.nn as nn
import torch.nn.functional as F

import torchbearer
from torchbearer.metrics import default_for_key, Metric, CategoricalAccuracy, MeanSquaredError, BinaryAccuracy

    __loss_map__ = {
        # NN
        nn.CrossEntropyLoss.__name__: CategoricalAccuracy,
        nn.NLLLoss.__name__: CategoricalAccuracy,
        nn.MSELoss.__name__: MeanSquaredError,
        nn.BCELoss.__name__: BinaryAccuracy,
        nn.BCEWithLogitsLoss.__name__:  BinaryAccuracy,
        # Functional
        F.cross_entropy.__name__: CategoricalAccuracy,
        F.nll_loss.__name__: CategoricalAccuracy,
        F.mse_loss.__name__: MeanSquaredError,
        F.binary_cross_entropy.__name__: BinaryAccuracy,
        F.binary_cross_entropy_with_logits.__name__: BinaryAccuracy
except AttributeError:  # Thrown when building the docs with mocked pytorch
    __loss_map__ = {}

[docs]@default_for_key('accuracy') @default_for_key('acc') class DefaultAccuracy(Metric): """The default accuracy metric loads in a different accuracy metric depending on the loss function or criterion in use at the start of training. Default for keys: `acc`, `accuracy`. The following bindings are in place for both nn and functional variants: - cross entropy loss -> :class:`.CategoricalAccuracy` [DEFAULT] - nll loss -> :class:`.CategoricalAccuracy` - mse loss -> :class:`.MeanSquaredError` - bce loss -> :class:`.BinaryAccuracy` - bce loss with logits -> :class:`.BinaryAccuracy` """ def __init__(self): super(DefaultAccuracy, self).__init__('placeholder') # Don't set yet, wait for reset self.metric = CategoricalAccuracy() # Default to CategoricalAccuracy = self._loaded = False self._train = True
[docs] def train(self): self._train = True return self.metric.train()
[docs] def eval(self, data_key=None): self._train = False return self.metric.eval(data_key=data_key)
[docs] def process(self, *args): return self.metric.process(*args)
[docs] def process_final(self, *args): return self.metric.process_final(*args)
[docs] def reset(self, state): if not self._loaded: criterion = state[torchbearer.CRITERION] name = None if hasattr(criterion, '__name__'): name = criterion.__name__ elif hasattr(criterion, '__class__'): name = criterion.__class__.__name__ if name is not None and name in __loss_map__: self.metric = __loss_map__[name]() = if self._train: self.metric.train() else: self.metric.eval(data_key=state[torchbearer.DATA]) self._loaded = True return self.metric.reset(state)