# 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 warnings import paddle import paddle.nn as nn import paddle.nn.functional as F from ppcls.utils import logger class CELoss(nn.Layer): """ Cross entropy loss """ 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 len(target.shape) == 1 or 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): if isinstance(x, dict): x = x["logits"] 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]: soft_label = True else: soft_label = False loss = F.cross_entropy(x, label=label, soft_label=soft_label) loss = loss.mean() return {"CELoss": loss} class MixCELoss(object): def __init__(self, *args, **kwargs): msg = "\"MixCELos\" is deprecated, please use \"CELoss\" instead." logger.error(DeprecationWarning(msg)) raise DeprecationWarning(msg)