129 lines
4.0 KiB
Python
129 lines
4.0 KiB
Python
#copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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#
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#Licensed under the Apache License, Version 2.0 (the "License");
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#you may not use this file except in compliance with the License.
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#You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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#Unless required by applicable law or agreed to in writing, software
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#distributed under the License is distributed on an "AS IS" BASIS,
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#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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#See the License for the specific language governing permissions and
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#limitations under the License.
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.nn import L1Loss
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from paddle.nn import MSELoss as L2Loss
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from paddle.nn import SmoothL1Loss
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class CELoss(nn.Layer):
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def __init__(self, epsilon=None):
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super().__init__()
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if epsilon is not None and (epsilon <= 0 or epsilon >= 1):
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epsilon = None
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self.epsilon = epsilon
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def _labelsmoothing(self, target, class_num):
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if target.shape[-1] != class_num:
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one_hot_target = F.one_hot(target, class_num)
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else:
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one_hot_target = target
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soft_target = F.label_smooth(one_hot_target, epsilon=self.epsilon)
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soft_target = paddle.reshape(soft_target, shape=[-1, class_num])
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return soft_target
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def forward(self, x, label):
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loss_dict = {}
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if self.epsilon is not None:
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class_num = x.shape[-1]
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label = self._labelsmoothing(label, class_num)
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x = -F.log_softmax(x, axis=-1)
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loss = paddle.sum(x * label, axis=-1)
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else:
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if label.shape[-1] == x.shape[-1]:
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label = F.softmax(label, axis=-1)
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soft_label = True
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else:
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soft_label = False
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loss = F.cross_entropy(x, label=label, soft_label=soft_label)
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return loss
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class KLJSLoss(object):
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def __init__(self, mode='kl'):
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assert mode in ['kl', 'js', 'KL', 'JS'], "mode can only be one of ['kl', 'js', 'KL', 'JS']"
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self.mode = mode
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def __call__(self, p1, p2, reduction="mean"):
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loss = paddle.multiply(p2, paddle.log( (p2+1e-5)/(p1+1e-5) + 1e-5))
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if self.mode.lower() == "js":
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loss += paddle.multiply(p1, paddle.log((p1+1e-5)/(p2+1e-5) + 1e-5))
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loss *= 0.5
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if reduction == "mean":
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loss = paddle.mean(loss, axis=[1,2])
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elif reduction=="none" or reduction is None:
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return loss
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else:
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loss = paddle.sum(loss, axis=[1,2])
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return loss
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class DMLLoss(nn.Layer):
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"""
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DMLLoss
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"""
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def __init__(self, act=None):
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super().__init__()
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if act is not None:
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assert act in ["softmax", "sigmoid"]
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if act == "softmax":
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self.act = nn.Softmax(axis=-1)
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elif act == "sigmoid":
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self.act = nn.Sigmoid()
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else:
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self.act = None
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self.jskl_loss = KLJSLoss(mode="js")
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def forward(self, out1, out2):
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if self.act is not None:
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out1 = self.act(out1)
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out2 = self.act(out2)
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if len(out1.shape) < 2:
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log_out1 = paddle.log(out1)
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log_out2 = paddle.log(out2)
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loss = (F.kl_div(
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log_out1, out2, reduction='batchmean') + F.kl_div(
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log_out2, out1, reduction='batchmean')) / 2.0
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else:
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loss = self.jskl_loss(out1, out2)
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return loss
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class DistanceLoss(nn.Layer):
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"""
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DistanceLoss:
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mode: loss mode
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"""
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def __init__(self, mode="l2", **kargs):
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super().__init__()
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assert mode in ["l1", "l2", "smooth_l1"]
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if mode == "l1":
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self.loss_func = nn.L1Loss(**kargs)
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elif mode == "l2":
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self.loss_func = nn.MSELoss(**kargs)
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elif mode == "smooth_l1":
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self.loss_func = nn.SmoothL1Loss(**kargs)
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def forward(self, x, y):
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return self.loss_func(x, y)
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