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smooth_l1_loss.py
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smooth_l1_loss.py
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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 as nn
import paddle.nn.functional as F
from ppdet.core.workspace import register
__all__ = ['SmoothL1Loss']
@register
class SmoothL1Loss(nn.Layer):
"""Smooth L1 Loss.
Args:
beta (float): controls smooth region, it becomes L1 Loss when beta=0.0
loss_weight (float): the final loss will be multiplied by this
"""
def __init__(self,
beta=1.0,
loss_weight=1.0):
super(SmoothL1Loss, self).__init__()
assert beta >= 0
self.beta = beta
self.loss_weight = loss_weight
def forward(self, pred, target, reduction='none'):
"""forward function, based on fvcore.
Args:
pred (Tensor): prediction tensor
target (Tensor): target tensor, pred.shape must be the same as target.shape
reduction (str): the way to reduce loss, one of (none, sum, mean)
"""
assert reduction in ('none', 'sum', 'mean')
target = target.detach()
if self.beta < 1e-5:
loss = paddle.abs(pred - target)
else:
n = paddle.abs(pred - target)
cond = n < self.beta
loss = paddle.where(cond, 0.5 * n ** 2 / self.beta, n - 0.5 * self.beta)
if reduction == 'mean':
loss = loss.mean() if loss.size > 0 else 0.0 * loss.sum()
elif reduction == 'sum':
loss = loss.sum()
return loss * self.loss_weight