def conv3d_bn_non(in_planes,
out_planes,
kernel_size=(3, 3, 3),
stride=1,
dilation=(1, 1, 1),
padding=(1, 1, 1),
bias=False):
return nn.Sequential(
conv3d_pad(in_planes, out_planes, kernel_size, stride, dilation,
padding, bias), SynchronizedBatchNorm3d(out_planes))
def __init__(self,
channel,
channel_reduction=4,
spatial_reduction=4,
z_reduction=1):
super(SELayer, self).__init__()
self.pool_size = (z_reduction, spatial_reduction, spatial_reduction)
self.se = nn.Sequential(
nn.AvgPool3d(kernel_size=self.pool_size, stride=self.pool_size),
nn.Conv3d(channel, channel // channel_reduction, kernel_size=1),
SynchronizedBatchNorm3d(channel // channel_reduction),
nn.ELU(inplace=True),
nn.Conv3d(channel // channel_reduction, channel, kernel_size=1),
SynchronizedBatchNorm3d(channel),
nn.Sigmoid(),
nn.Upsample(scale_factor=self.pool_size,
mode='trilinear',
align_corners=False),
)