| # copyright (c) 2022 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. | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| from paddle import nn | |
| from paddle.nn import functional as F | |
| class PRENHead(nn.Layer): | |
| def __init__(self, in_channels, out_channels, **kwargs): | |
| super(PRENHead, self).__init__() | |
| self.linear = nn.Linear(in_channels, out_channels) | |
| def forward(self, x, targets=None): | |
| predicts = self.linear(x) | |
| if not self.training: | |
| predicts = F.softmax(predicts, axis=2) | |
| return predicts | |