# 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} | |