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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.nn.functional as F
import paddle.nn as nn
from ppdet.core.workspace import register
__all__ = ['FocalLoss', 'Weighted_FocalLoss']
@register
class FocalLoss(nn.Layer):
"""A wrapper around paddle.nn.functional.sigmoid_focal_loss.
Args:
use_sigmoid (bool): currently only support use_sigmoid=True
alpha (float): parameter alpha in Focal Loss
gamma (float): parameter gamma in Focal Loss
loss_weight (float): final loss will be multiplied by this
"""
def __init__(self,
use_sigmoid=True,
alpha=0.25,
gamma=2.0,
loss_weight=1.0):
super(FocalLoss, self).__init__()
assert use_sigmoid == True, \
'Focal Loss only supports sigmoid at the moment'
self.use_sigmoid = use_sigmoid
self.alpha = alpha
self.gamma = gamma
self.loss_weight = loss_weight
def forward(self, pred, target, reduction='none'):
"""forward function.
Args:
pred (Tensor): logits of class prediction, of shape (N, num_classes)
target (Tensor): target class label, of shape (N, )
reduction (str): the way to reduce loss, one of (none, sum, mean)
"""
num_classes = pred.shape[1]
target = F.one_hot(target, num_classes+1).cast(pred.dtype)
target = target[:, :-1].detach()
loss = F.sigmoid_focal_loss(
pred, target, alpha=self.alpha, gamma=self.gamma,
reduction=reduction)
return loss * self.loss_weight
@register
class Weighted_FocalLoss(FocalLoss):
"""A wrapper around paddle.nn.functional.sigmoid_focal_loss.
Args:
use_sigmoid (bool): currently only support use_sigmoid=True
alpha (float): parameter alpha in Focal Loss
gamma (float): parameter gamma in Focal Loss
loss_weight (float): final loss will be multiplied by this
"""
def __init__(self,
use_sigmoid=True,
alpha=0.25,
gamma=2.0,
loss_weight=1.0,
reduction="mean"):
super(FocalLoss, self).__init__()
assert use_sigmoid == True, \
'Focal Loss only supports sigmoid at the moment'
self.use_sigmoid = use_sigmoid
self.alpha = alpha
self.gamma = gamma
self.loss_weight = loss_weight
self.reduction = reduction
def forward(self, pred, target, weight=None, avg_factor=None, reduction_override=None):
"""forward function.
Args:
pred (Tensor): logits of class prediction, of shape (N, num_classes)
target (Tensor): target class label, of shape (N, )
reduction (str): the way to reduce loss, one of (none, sum, mean)
"""
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
num_classes = pred.shape[1]
target = F.one_hot(target, num_classes + 1).astype(pred.dtype)
target = target[:, :-1].detach()
loss = F.sigmoid_focal_loss(
pred, target, alpha=self.alpha, gamma=self.gamma,
reduction='none')
if weight is not None:
if weight.shape != loss.shape:
if weight.shape[0] == loss.shape[0]:
# For most cases, weight is of shape (num_priors, ),
# which means it does not have the second axis num_class
weight = weight.reshape((-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.reshape((loss.shape[0], -1))
assert weight.ndim == loss.ndim
loss = loss * weight
# if avg_factor is not specified, just reduce the loss
if avg_factor is None:
if reduction == 'mean':
loss = loss.mean()
elif reduction == 'sum':
loss = loss.sum()
else:
# if reduction is mean, then average the loss by avg_factor
if reduction == 'mean':
# Avoid causing ZeroDivisionError when avg_factor is 0.0,
# i.e., all labels of an image belong to ignore index.
eps = 1e-10
loss = loss.sum() / (avg_factor + eps)
# if reduction is 'none', then do nothing, otherwise raise an error
elif reduction != 'none':
raise ValueError('avg_factor can not be used with reduction="sum"')
return loss * self.loss_weight
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