You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
63 lines
2.2 KiB
63 lines
2.2 KiB
# Copyright (c) 2022 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 paddlers_slim.models.ppdet.core.workspace import register |
|
|
|
__all__ = ['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
|
|
|