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70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import paddle
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from paddle import nn
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import paddle.nn.functional as F
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from paddle import ParamAttr
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import math
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from paddle.nn.initializer import TruncatedNormal, Constant, Normal
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ones_ = Constant(value=1.)
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zeros_ = Constant(value=0.)
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class CT_Head(nn.Layer):
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def __init__(self,
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in_channels,
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hidden_dim,
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num_classes,
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loss_kernel=None,
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loss_loc=None):
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super(CT_Head, self).__init__()
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self.conv1 = nn.Conv2D(
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in_channels, hidden_dim, kernel_size=3, stride=1, padding=1)
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self.bn1 = nn.BatchNorm2D(hidden_dim)
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self.relu1 = nn.ReLU()
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self.conv2 = nn.Conv2D(
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hidden_dim, num_classes, kernel_size=1, stride=1, padding=0)
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for m in self.sublayers():
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if isinstance(m, nn.Conv2D):
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n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
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normal_ = Normal(mean=0.0, std=math.sqrt(2. / n))
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normal_(m.weight)
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elif isinstance(m, nn.BatchNorm2D):
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zeros_(m.bias)
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ones_(m.weight)
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def _upsample(self, x, scale=1):
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return F.upsample(x, scale_factor=scale, mode='bilinear')
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def forward(self, f, targets=None):
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out = self.conv1(f)
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out = self.relu1(self.bn1(out))
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out = self.conv2(out)
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if self.training:
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out = self._upsample(out, scale=4)
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return {'maps': out}
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else:
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score = F.sigmoid(out[:, 0, :, :])
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return {'maps': out, 'score': score}
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