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#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 CELoss(nn.Layer):
def __init__(self, epsilon=None):
super().__init__()
if epsilon is not None and (epsilon <= 0 or epsilon >= 1):
epsilon = None
self.epsilon = epsilon
def _labelsmoothing(self, target, class_num):
if target.shape[-1] != class_num:
one_hot_target = F.one_hot(target, class_num)
else:
one_hot_target = target
soft_target = F.label_smooth(one_hot_target, epsilon=self.epsilon)
soft_target = paddle.reshape(soft_target, shape=[-1, class_num])
return soft_target
def forward(self, x, label):
loss_dict = {}
if self.epsilon is not None:
class_num = x.shape[-1]
label = self._labelsmoothing(label, class_num)
x = -F.log_softmax(x, axis=-1)
loss = paddle.sum(x * label, axis=-1)
else:
if label.shape[-1] == x.shape[-1]:
label = F.softmax(label, axis=-1)
soft_label = True
else:
soft_label = False
loss = F.cross_entropy(x, label=label, soft_label=soft_label)
return loss
class KLJSLoss(object):
def __init__(self, mode='kl'):
assert mode in ['kl', 'js', 'KL', 'JS'
], "mode can only be one of ['kl', 'KL', 'js', 'JS']"
self.mode = mode
def __call__(self, p1, p2, reduction="mean", eps=1e-5):
if self.mode.lower() == 'kl':
loss = paddle.multiply(p2,
paddle.log((p2 + eps) / (p1 + eps) + eps))
loss += paddle.multiply(p1,
paddle.log((p1 + eps) / (p2 + eps) + eps))
loss *= 0.5
elif self.mode.lower() == "js":
loss = paddle.multiply(
p2, paddle.log((2 * p2 + eps) / (p1 + p2 + eps) + eps))
loss += paddle.multiply(
p1, paddle.log((2 * p1 + eps) / (p1 + p2 + eps) + eps))
loss *= 0.5
else:
raise ValueError(
"The mode.lower() if KLJSLoss should be one of ['kl', 'js']")
if reduction == "mean":
loss = paddle.mean(loss, axis=[1, 2])
elif reduction == "none" or reduction is None:
return loss
else:
loss = paddle.sum(loss, axis=[1, 2])
return loss
class DMLLoss(nn.Layer):
"""
DMLLoss
"""
def __init__(self, act=None, use_log=False):
super().__init__()
if act is not None:
assert act in ["softmax", "sigmoid"]
if act == "softmax":
self.act = nn.Softmax(axis=-1)
elif act == "sigmoid":
self.act = nn.Sigmoid()
else:
self.act = None
self.use_log = use_log
self.jskl_loss = KLJSLoss(mode="kl")
def _kldiv(self, x, target):
eps = 1.0e-10
loss = target * (paddle.log(target + eps) - x)
# batch mean loss
loss = paddle.sum(loss) / loss.shape[0]
return loss
def forward(self, out1, out2):
if self.act is not None:
out1 = self.act(out1) + 1e-10
out2 = self.act(out2) + 1e-10
if self.use_log:
# for recognition distillation, log is needed for feature map
log_out1 = paddle.log(out1)
log_out2 = paddle.log(out2)
loss = (
self._kldiv(log_out1, out2) + self._kldiv(log_out2, out1)) / 2.0
else:
# for detection distillation log is not needed
loss = self.jskl_loss(out1, out2)
return loss
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)
def forward(self, x, y):
return self.loss_func(x, y)
class LossFromOutput(nn.Layer):
def __init__(self, key='loss', reduction='none'):
super().__init__()
self.key = key
self.reduction = reduction
def forward(self, predicts, batch):
loss = predicts
if self.key is not None and isinstance(predicts, dict):
loss = loss[self.key]
if self.reduction == 'mean':
loss = paddle.mean(loss)
elif self.reduction == 'sum':
loss = paddle.sum(loss)
return {'loss': loss}
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