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| # copyright (c) 2020 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 | |
| import math | |
| import paddle | |
| from paddle import nn, ParamAttr | |
| import paddle.nn.functional as F | |
| class ClsHead(nn.Layer): | |
| """ | |
| Class orientation | |
| Args: | |
| params(dict): super parameters for build Class network | |
| """ | |
| def __init__(self, in_channels, class_dim, **kwargs): | |
| super(ClsHead, self).__init__() | |
| self.pool = nn.AdaptiveAvgPool2D(1) | |
| stdv = 1.0 / math.sqrt(in_channels * 1.0) | |
| self.fc = nn.Linear( | |
| in_channels, | |
| class_dim, | |
| weight_attr=ParamAttr( | |
| name="fc_0.w_0", | |
| initializer=nn.initializer.Uniform(-stdv, stdv)), | |
| bias_attr=ParamAttr(name="fc_0.b_0"), ) | |
| def forward(self, x, targets=None): | |
| x = self.pool(x) | |
| x = paddle.reshape(x, shape=[x.shape[0], x.shape[1]]) | |
| x = self.fc(x) | |
| if not self.training: | |
| x = F.softmax(x, axis=1) | |
| return x | |