''' loss factory '''
from typing import Optional
from mindspore import Tensor
from .cross_entropy_smooth import CrossEntropySmooth
from .binary_cross_entropy_smooth import BinaryCrossEntropySmooth
__all__ = ["create_loss"]
[文档]def create_loss(
name: str = 'CE',
weight: Optional[Tensor] = None,
reduction: str = 'mean',
label_smoothing: float = 0.,
aux_factor: float = 0.):
r"""Creates loss function
Args:
name (str): loss name, : 'CE' for cross_entropy. 'BCE': binary cross entropy. Default: 'CE'.
weight (Tensor): Class weight. Shape [C]. A rescaling weight applied to the loss of each batch element.
Data type must be float16 or float32.
reduction: Apply specific reduction method to the output: 'mean' or 'sum'. Default: 'mean'.
label_smoothing: Label smoothing factor, a regularization tool used to prevent the model
from overfitting when calculating Loss. The value range is [0.0, 1.0]. Default: 0.0.
aux_factor (float): Auxiliary loss factor. Set aux_fuactor > 0.0 if the model has auxilary logit outputs (i.e., deep supervision), like inception_v3. Default: 0.0.
Inputs:
- logits (Tensor or Tuple of Tensor): Input logits. Shape [N, C], where N is # samples, C is # classes.
Tuple of two input logits are supported in order (main_logits, aux_logits) for auxilary loss used in networks like inception_v3.
where `C = number of classes`. Data type must be float16 or float32.
- labels (Tensor): Ground truth labels. Shape: [N] or [N, C].
(1) shape (N), sparse labels representing the class indinces. Must be int type,
(2) shape [N, C], dense labels representing the ground truth class probability values, or the one-hot labels. Must be float type.
If the loss type is BCE, the shape of labels must be [N, C].
Returns:
Loss function to compute the loss between the input logits and labels.
"""
name = name.lower()
if name == 'ce':
loss = CrossEntropySmooth(smoothing=label_smoothing, aux_factor=aux_factor, reduction=reduction, weight=weight)
elif name == 'bce':
loss = BinaryCrossEntropySmooth(smoothing=label_smoothing, aux_factor=aux_factor, reduction=reduction, weight=weight, pos_weight=None)
else:
raise NotImplementedError
return loss