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| # copyright (c) 2019 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 | |
| import paddle.nn.functional as F | |
| from paddle import ParamAttr | |
| import math | |
| from paddle.nn.initializer import TruncatedNormal, Constant, Normal | |
| ones_ = Constant(value=1.) | |
| zeros_ = Constant(value=0.) | |
| class CT_Head(nn.Layer): | |
| def __init__(self, | |
| in_channels, | |
| hidden_dim, | |
| num_classes, | |
| loss_kernel=None, | |
| loss_loc=None): | |
| super(CT_Head, 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, num_classes, kernel_size=1, stride=1, padding=0) | |
| for m in self.sublayers(): | |
| if isinstance(m, nn.Conv2D): | |
| n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels | |
| normal_ = Normal(mean=0.0, std=math.sqrt(2. / n)) | |
| normal_(m.weight) | |
| elif isinstance(m, nn.BatchNorm2D): | |
| zeros_(m.bias) | |
| ones_(m.weight) | |
| def _upsample(self, x, scale=1): | |
| return F.upsample(x, scale_factor=scale, mode='bilinear') | |
| def forward(self, f, targets=None): | |
| out = self.conv1(f) | |
| out = self.relu1(self.bn1(out)) | |
| out = self.conv2(out) | |
| if self.training: | |
| out = self._upsample(out, scale=4) | |
| return {'maps': out} | |
| else: | |
| score = F.sigmoid(out[:, 0, :, :]) | |
| return {'maps': out, 'score': score} | |