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68 lines
2.2 KiB
68 lines
2.2 KiB
3 years ago
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# 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 warnings
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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 ppcls.utils import logger
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class CELoss(nn.Layer):
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"""
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Cross entropy loss
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"""
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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 len(target.shape) == 1 or 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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if isinstance(x, dict):
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x = x["logits"]
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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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loss = loss.mean()
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return {"CELoss": loss}
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class MixCELoss(object):
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def __init__(self, *args, **kwargs):
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msg = "\"MixCELos\" is deprecated, please use \"CELoss\" instead."
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logger.error(DeprecationWarning(msg))
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raise DeprecationWarning(msg)
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