# Because nn.Module has the abstract method _forward_unimplemented
# pylint: disable=abstract-method
import torch.nn as nn
from .metrics_registering import register_batch_metric, register_batch_metric_function
class BatchMetric(nn.Module):
def __init__(self, reduction: str = 'mean'):
super().__init__()
REDUCTIONS = ['none', 'mean', 'sum']
if reduction not in REDUCTIONS:
raise ValueError(f"Reduction is not in {REDUCTIONS}")
self.reduction = reduction
[docs]class Accuracy(BatchMetric):
r"""
This metric computes the accuracy using a similar interface to
:class:`~torch.nn.CrossEntropyLoss`.
Args:
ignore_index (int): Specifies a target value that is ignored and does not contribute
to the accuracy. (Default value = -100)
reduction (string, optional): Specifies the reduction to apply to the output:
``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
``'mean'``: the sum of the output will be divided by the number of
elements in the output, ``'sum'``: the output will be summed.
Possible string name in :class:`batch_metrics argument <poutyne.Model>`:
- ``'acc'``
- ``'accuracy'``
Keys in :class:`logs<poutyne.Callback>` dictionary of callbacks:
- Train: ``'acc'``
- Validation: ``'val_acc'``
Shape:
- Input: :math:`(N, C)` where `C = number of classes`, or
:math:`(N, C, d_1, d_2, ..., d_K)` with :math:`K \geq 1` in the case of
`K`-dimensional accuracy.
- Target: :math:`(N)` where each value is :math:`0 \leq \text{targets}[i] \leq C-1`, or
:math:`(N, d_1, d_2, ..., d_K)` with :math:`K \geq 1` in the case of
K-dimensional accuracy.
- Output: The accuracy.
"""
def __init__(self, *, ignore_index: int = -100, reduction: str = 'mean'):
super().__init__(reduction)
self.__name__ = 'acc'
self.ignore_index = ignore_index
def forward(self, y_pred, y_true):
return acc(y_pred, y_true, ignore_index=self.ignore_index, reduction=self.reduction)
[docs]@register_batch_metric('acc', 'accuracy')
def acc(y_pred, y_true, *, ignore_index=-100, reduction='mean'):
"""
Computes the accuracy.
This is a functional version of :class:`~poutyne.Accuracy`.
See :class:`~poutyne.Accuracy` for details.
"""
y_pred = y_pred.argmax(1)
weights = (y_true != ignore_index).float()
num_labels = weights.sum()
acc_pred = (y_pred == y_true).float() * weights
if reduction in ['mean', 'sum']:
acc_pred = acc_pred.sum()
if reduction == 'mean':
acc_pred = acc_pred / num_labels
return acc_pred * 100
[docs]class BinaryAccuracy(BatchMetric):
r"""
This metric computes the accuracy using a similar interface to
:class:`~torch.nn.BCEWithLogitsLoss`.
Args:
threshold (float): the threshold for class :math:`1`. Default value is ``0.``, that is a
probability of ``sigmoid(0.) = 0.5``.
reduction (string, optional): Specifies the reduction to apply to the output:
``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
``'mean'``: the sum of the output will be divided by the number of
elements in the output, ``'sum'``: the output will be summed.
Possible string name in :class:`batch_metrics argument <poutyne.Model>`:
- ``'bin_acc'``
- ``'binary_acc'``
- ``'binary_accuracy'``
Keys in :class:`logs<poutyne.Callback>` dictionary of callbacks:
- Train: ``'bin_acc'``
- Validation: ``'val_bin_acc'``
Shape:
- Input: :math:`(N, *)` where :math:`*` means, any number of additional
dimensions
- Target: :math:`(N, *)`, same shape as the input
- Output: The binary accuracy.
"""
def __init__(self, *, threshold: float = 0., reduction: str = 'mean'):
super().__init__(reduction)
self.__name__ = 'bin_acc'
self.threshold = threshold
def forward(self, y_pred, y_true):
return bin_acc(y_pred, y_true, threshold=self.threshold, reduction=self.reduction)
[docs]@register_batch_metric('binacc', 'binaryacc', 'binaryaccuracy')
def bin_acc(y_pred, y_true, *, threshold=0., reduction='mean'):
"""
Computes the binary accuracy.
This is a functional version of :class:`~poutyne.BinaryAccuracy`.
See :class:`~poutyne.BinaryAccuracy` for details.
"""
y_pred = (y_pred > threshold).float()
acc_pred = (y_pred == y_true).float()
if reduction == 'mean':
acc_pred = acc_pred.mean()
elif reduction == 'sum':
acc_pred = acc_pred.sum()
return acc_pred * 100
[docs]class TopKAccuracy(BatchMetric):
r"""
This metric computes the top-k accuracy using a similar interface to
:class:`~torch.nn.CrossEntropyLoss`.
Args:
k (int): Specifies the value of ``k`` in the top-k accuracy.
ignore_index (int): Specifies a target value that is ignored and does not contribute
to the top-k accuracy. (Default value = -100)
reduction (string, optional): Specifies the reduction to apply to the output:
``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
``'mean'``: the sum of the output will be divided by the number of
elements in the output, ``'sum'``: the output will be summed.
Possible string name in :class:`batch_metrics argument <poutyne.Model>`:
- ``'top{k}'``
- ``'top{k}_acc'``
- ``'top{k}_accuracy'``
for ``{k}`` from 1 to 10, 20, 30, ..., 100.
Keys in :class:`logs<poutyne.Callback>` dictionary of callbacks:
- Train: ``'top{k}'``
- Validation: ``'val_top{k}'``
where ``{k}`` is replaced by the value of parameter ``k``.
Shape:
- Input: :math:`(N, C)` where `C = number of classes`, or
:math:`(N, C, d_1, d_2, ..., d_K)` with :math:`K \geq 1` in the case of
`K`-dimensional top-k accuracy.
- Target: :math:`(N)` where each value is :math:`0 \leq \text{targets}[i] \leq C-1`, or
:math:`(N, d_1, d_2, ..., d_K)` with :math:`K \geq 1` in the case of
K-dimensional top-k accuracy.
- Output: The top-k accuracy.
"""
def __init__(self, k: int, *, ignore_index: int = -100, reduction: str = 'mean'):
super().__init__(reduction)
self.__name__ = f'top{k}'
self.k = k
self.ignore_index = ignore_index
def forward(self, y_pred, y_true):
return topk(y_pred, y_true, self.k, ignore_index=self.ignore_index, reduction=self.reduction)
[docs]def topk(y_pred, y_true, k, *, ignore_index=-100, reduction='mean'):
"""
Computes the top-k accuracy.
This is a functional version of :class:`~poutyne.TopKAccuracy`.
See :class:`~poutyne.TopKAccuracy` for details.
"""
topk_pred = y_pred.topk(k, dim=1)[1]
weights = (y_true != ignore_index).float()
num_labels = weights.sum()
topk_acc = (y_true.unsqueeze(1) == topk_pred).any(1).float() * weights
if reduction in ['mean', 'sum']:
topk_acc = topk_acc.sum()
if reduction == 'mean':
topk_acc = topk_acc / num_labels
return topk_acc * 100
@register_batch_metric('top1', 'top1acc', 'top1accuracy')
def top1(y_pred, y_true, **kwargs):
return acc(y_pred, y_true, **kwargs)
for k_value in range(2, 11):
register_batch_metric_function(TopKAccuracy(k_value),
[f'top{k_value}', f'top{k_value}acc', f'top{k_value}accuracy'])
del k_value
for k_value in range(20, 110, 10):
register_batch_metric_function(TopKAccuracy(k_value),
[f'top{k_value}', f'top{k_value}acc', f'top{k_value}accuracy'])
del k_value