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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.ops import sigmoid_focal_loss as _sigmoid_focal_loss
from mmdet.registry import MODELS
from .accuracy import accuracy
from .utils import weight_reduce_loss
# This method is only for debugging
def py_sigmoid_focal_loss(pred,
target,
weight=None,
gamma=2.0,
alpha=0.25,
reduction='mean',
avg_factor=None):
"""PyTorch version of `Focal Loss <https://arxiv.org/abs/1708.02002>`_.
Args:
pred (torch.Tensor): The prediction with shape (N, C), C is the
number of classes
target (torch.Tensor): The learning label of the prediction.
weight (torch.Tensor, optional): Sample-wise loss weight.
gamma (float, optional): The gamma for calculating the modulating
factor. Defaults to 2.0.
alpha (float, optional): A balanced form for Focal Loss.
Defaults to 0.25.
reduction (str, optional): The method used to reduce the loss into
a scalar. Defaults to 'mean'.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
"""
pred_sigmoid = pred.sigmoid()
target = target.type_as(pred)
# Actually, pt here denotes (1 - pt) in the Focal Loss paper
pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target)
# Thus it's pt.pow(gamma) rather than (1 - pt).pow(gamma)
focal_weight = (alpha * target + (1 - alpha) *
(1 - target)) * pt.pow(gamma)
loss = F.binary_cross_entropy_with_logits(
pred, target, reduction='none') * focal_weight
if weight is not None:
if weight.shape != loss.shape:
if weight.size(0) == loss.size(0):
# For most cases, weight is of shape (num_priors, ),
# which means it does not have the second axis num_class
weight = weight.view(-1, 1)
else:
# Sometimes, weight per anchor per class is also needed. e.g.
# in FSAF. But it may be flattened of shape
# (num_priors x num_class, ), while loss is still of shape
# (num_priors, num_class).
assert weight.numel() == loss.numel()
weight = weight.view(loss.size(0), -1)
assert weight.ndim == loss.ndim
loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
return loss
def py_focal_loss_with_prob(pred,
target,
weight=None,
gamma=2.0,
alpha=0.25,
reduction='mean',
avg_factor=None):
"""PyTorch version of `Focal Loss <https://arxiv.org/abs/1708.02002>`_.
Different from `py_sigmoid_focal_loss`, this function accepts probability
as input.
Args:
pred (torch.Tensor): The prediction probability with shape (N, C),
C is the number of classes.
target (torch.Tensor): The learning label of the prediction.
The target shape support (N,C) or (N,), (N,C) means one-hot form.
weight (torch.Tensor, optional): Sample-wise loss weight.
gamma (float, optional): The gamma for calculating the modulating
factor. Defaults to 2.0.
alpha (float, optional): A balanced form for Focal Loss.
Defaults to 0.25.
reduction (str, optional): The method used to reduce the loss into
a scalar. Defaults to 'mean'.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
"""
if pred.dim() != target.dim():
num_classes = pred.size(1)
target = F.one_hot(target, num_classes=num_classes + 1)
target = target[:, :num_classes]
target = target.type_as(pred)
pt = (1 - pred) * target + pred * (1 - target)
focal_weight = (alpha * target + (1 - alpha) *
(1 - target)) * pt.pow(gamma)
loss = F.binary_cross_entropy(
pred, target, reduction='none') * focal_weight
if weight is not None:
if weight.shape != loss.shape:
if weight.size(0) == loss.size(0):
# For most cases, weight is of shape (num_priors, ),
# which means it does not have the second axis num_class
weight = weight.view(-1, 1)
else:
# Sometimes, weight per anchor per class is also needed. e.g.
# in FSAF. But it may be flattened of shape
# (num_priors x num_class, ), while loss is still of shape
# (num_priors, num_class).
assert weight.numel() == loss.numel()
weight = weight.view(loss.size(0), -1)
assert weight.ndim == loss.ndim
loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
return loss
def sigmoid_focal_loss(pred,
target,
weight=None,
gamma=2.0,
alpha=0.25,
reduction='mean',
avg_factor=None):
r"""A wrapper of cuda version `Focal Loss
<https://arxiv.org/abs/1708.02002>`_.
Args:
pred (torch.Tensor): The prediction with shape (N, C), C is the number
of classes.
target (torch.Tensor): The learning label of the prediction.
weight (torch.Tensor, optional): Sample-wise loss weight.
gamma (float, optional): The gamma for calculating the modulating
factor. Defaults to 2.0.
alpha (float, optional): A balanced form for Focal Loss.
Defaults to 0.25.
reduction (str, optional): The method used to reduce the loss into
a scalar. Defaults to 'mean'. Options are "none", "mean" and "sum".
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
"""
# Function.apply does not accept keyword arguments, so the decorator
# "weighted_loss" is not applicable
loss = _sigmoid_focal_loss(pred.contiguous(), target.contiguous(), gamma,
alpha, None, 'none')
if weight is not None:
if weight.shape != loss.shape:
if weight.size(0) == loss.size(0):
# For most cases, weight is of shape (num_priors, ),
# which means it does not have the second axis num_class
weight = weight.view(-1, 1)
else:
# Sometimes, weight per anchor per class is also needed. e.g.
# in FSAF. But it may be flattened of shape
# (num_priors x num_class, ), while loss is still of shape
# (num_priors, num_class).
assert weight.numel() == loss.numel()
weight = weight.view(loss.size(0), -1)
assert weight.ndim == loss.ndim
loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
return loss
@MODELS.register_module()
class FocalLoss(nn.Module):
def __init__(self,
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
reduction='mean',
loss_weight=1.0,
activated=False):
"""`Focal Loss <https://arxiv.org/abs/1708.02002>`_
Args:
use_sigmoid (bool, optional): Whether to the prediction is
used for sigmoid or softmax. Defaults to True.
gamma (float, optional): The gamma for calculating the modulating
factor. Defaults to 2.0.
alpha (float, optional): A balanced form for Focal Loss.
Defaults to 0.25.
reduction (str, optional): The method used to reduce the loss into
a scalar. Defaults to 'mean'. Options are "none", "mean" and
"sum".
loss_weight (float, optional): Weight of loss. Defaults to 1.0.
activated (bool, optional): Whether the input is activated.
If True, it means the input has been activated and can be
treated as probabilities. Else, it should be treated as logits.
Defaults to False.
"""
super(FocalLoss, self).__init__()
assert use_sigmoid is True, 'Only sigmoid focal loss supported now.'
self.use_sigmoid = use_sigmoid
self.gamma = gamma
self.alpha = alpha
self.reduction = reduction
self.loss_weight = loss_weight
self.activated = activated
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None):
"""Forward function.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning label of the prediction.
The target shape support (N,C) or (N,), (N,C) means
one-hot form.
weight (torch.Tensor, optional): The weight of loss for each
prediction. Defaults to None.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
reduction_override (str, optional): The reduction method used to
override the original reduction method of the loss.
Options are "none", "mean" and "sum".
Returns:
torch.Tensor: The calculated loss
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
if self.use_sigmoid:
if self.activated:
calculate_loss_func = py_focal_loss_with_prob
else:
if pred.dim() == target.dim():
# this means that target is already in One-Hot form.
calculate_loss_func = py_sigmoid_focal_loss
elif torch.cuda.is_available() and pred.is_cuda:
calculate_loss_func = sigmoid_focal_loss
else:
num_classes = pred.size(1)
target = F.one_hot(target, num_classes=num_classes + 1)
target = target[:, :num_classes]
calculate_loss_func = py_sigmoid_focal_loss
loss_cls = self.loss_weight * calculate_loss_func(
pred,
target,
weight,
gamma=self.gamma,
alpha=self.alpha,
reduction=reduction,
avg_factor=avg_factor)
else:
raise NotImplementedError
return loss_cls
@MODELS.register_module()
class FocalCustomLoss(nn.Module):
def __init__(self,
use_sigmoid=True,
num_classes=-1,
gamma=2.0,
alpha=0.25,
reduction='mean',
loss_weight=1.0,
activated=False):
"""`Focal Loss for V3Det <https://arxiv.org/abs/1708.02002>`_
Args:
use_sigmoid (bool, optional): Whether to the prediction is
used for sigmoid or softmax. Defaults to True.
num_classes (int): Number of classes to classify.
gamma (float, optional): The gamma for calculating the modulating
factor. Defaults to 2.0.
alpha (float, optional): A balanced form for Focal Loss.
Defaults to 0.25.
reduction (str, optional): The method used to reduce the loss into
a scalar. Defaults to 'mean'. Options are "none", "mean" and
"sum".
loss_weight (float, optional): Weight of loss. Defaults to 1.0.
activated (bool, optional): Whether the input is activated.
If True, it means the input has been activated and can be
treated as probabilities. Else, it should be treated as logits.
Defaults to False.
"""
super(FocalCustomLoss, self).__init__()
assert use_sigmoid is True, 'Only sigmoid focal loss supported now.'
self.use_sigmoid = use_sigmoid
self.num_classes = num_classes
self.gamma = gamma
self.alpha = alpha
self.reduction = reduction
self.loss_weight = loss_weight
self.activated = activated
assert self.num_classes != -1
# custom output channels of the classifier
self.custom_cls_channels = True
# custom activation of cls_score
self.custom_activation = True
# custom accuracy of the classsifier
self.custom_accuracy = True
def get_cls_channels(self, num_classes):
assert num_classes == self.num_classes
return num_classes
def get_activation(self, cls_score):
fine_cls_score = cls_score[:, :self.num_classes]
score_classes = fine_cls_score.sigmoid()
return score_classes
def get_accuracy(self, cls_score, labels):
fine_cls_score = cls_score[:, :self.num_classes]
pos_inds = labels < self.num_classes
acc_classes = accuracy(fine_cls_score[pos_inds], labels[pos_inds])
acc = dict()
acc['acc_classes'] = acc_classes
return acc
def forward(self,
pred,
target,
weight=None,
avg_factor=None,
reduction_override=None):
"""Forward function.
Args:
pred (torch.Tensor): The prediction.
target (torch.Tensor): The learning label of the prediction.
weight (torch.Tensor, optional): The weight of loss for each
prediction. Defaults to None.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
reduction_override (str, optional): The reduction method used to
override the original reduction method of the loss.
Options are "none", "mean" and "sum".
Returns:
torch.Tensor: The calculated loss
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
if self.use_sigmoid:
num_classes = pred.size(1)
target = F.one_hot(target, num_classes=num_classes + 1)
target = target[:, :num_classes]
calculate_loss_func = py_sigmoid_focal_loss
loss_cls = self.loss_weight * calculate_loss_func(
pred,
target,
weight,
gamma=self.gamma,
alpha=self.alpha,
reduction=reduction,
avg_factor=avg_factor)
else:
raise NotImplementedError
return loss_cls
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