44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
#copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
|
|
#
|
|
#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.
|
|
|
|
import paddle
|
|
import paddle.nn as nn
|
|
import paddle.nn.functional as F
|
|
|
|
from paddle.nn import L1Loss
|
|
from paddle.nn import MSELoss as L2Loss
|
|
from paddle.nn import SmoothL1Loss
|
|
|
|
|
|
class DistanceLoss(nn.Layer):
|
|
"""
|
|
DistanceLoss:
|
|
mode: loss mode
|
|
"""
|
|
|
|
def __init__(self, mode="l2", **kargs):
|
|
super().__init__()
|
|
assert mode in ["l1", "l2", "smooth_l1"]
|
|
if mode == "l1":
|
|
self.loss_func = nn.L1Loss(**kargs)
|
|
elif mode == "l2":
|
|
self.loss_func = nn.MSELoss(**kargs)
|
|
elif mode == "smooth_l1":
|
|
self.loss_func = nn.SmoothL1Loss(**kargs)
|
|
self.mode = mode
|
|
|
|
def forward(self, x, y):
|
|
loss = self.loss_func(x, y)
|
|
return {"loss_{}".format(self.mode): loss}
|