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| # 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. | |
| """ | |
| This code is refer from: | |
| https://github.com/whai362/PSENet/blob/python3/models/head/psenet_head.py | |
| """ | |
| from paddle import nn | |
| class PSEHead(nn.Layer): | |
| def __init__(self, in_channels, hidden_dim=256, out_channels=7, **kwargs): | |
| super(PSEHead, self).__init__() | |
| self.conv1 = nn.Conv2D( | |
| in_channels, hidden_dim, kernel_size=3, stride=1, padding=1) | |
| self.bn1 = nn.BatchNorm2D(hidden_dim) | |
| self.relu1 = nn.ReLU() | |
| self.conv2 = nn.Conv2D( | |
| hidden_dim, out_channels, kernel_size=1, stride=1, padding=0) | |
| def forward(self, x, **kwargs): | |
| out = self.conv1(x) | |
| out = self.relu1(self.bn1(out)) | |
| out = self.conv2(out) | |
| return {'maps': out} | |