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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}
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