def __init__(self, inplanes, planes, stride=1, reduction=16):
super(CifarSEBasicBlock, self).__init__()
with self.init_scope():
self.conv1 = L.Convolution2D(inplanes,
planes,
ksize=3,
stride=stride,
pad=1,
nobias=True)
self.bn1 = L.BatchNormalization(planes)
self.conv2 = L.Convolution2D(planes,
planes,
ksize=3,
stride=1,
pad=1,
nobias=True)
self.bn2 = L.BatchNormalization(planes)
self.se = SELayer(planes, reduction)
if inplanes != planes:
self.downsample = Sequential(
L.Convolution2D(inplanes,
planes,
ksize=1,
stride=stride,
nobias=True), L.BatchNormalization(planes))
else:
self.downsample = lambda x: x
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
groups=1,
base_width=64,
dilation=1,
norm_layer=None,
*,
reduction=16):
super(SEBottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.se = SELayer(planes * 4, reduction)
self.downsample = downsample
self.stride = stride
def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=16):
super(SEBasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes, 1)
self.bn2 = nn.BatchNorm2d(planes)
self.se = SELayer(planes, reduction)
self.downsample = downsample
self.stride = stride
def __init__(self, inplanes, planes, stride=1, reduction=16):
super(CifarSEBasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.se = SELayer(planes, reduction)
if inplanes != planes:
self.downsample = nn.Sequential(nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes))
else:
self.downsample = lambda x: x
self.stride = stride
def __init__(self, i):
super(selayer, self).__init__()
ks = [3, 3, 3, 3, 3, 3, 2]
ps = [1, 1, 1, 1, 1, 1, 0]
ss = [1, 1, 1, 1, 1, 1, 1]
nm = [64, 128, 256, 256, 512, 512, 512]
nIn = nc if i == 0 else nm[i - 1]
nOut = nm[i]
self.conv = nn.Conv2d(nIn, nOut, ks[i], ss[i], ps[i])
self.bn = nn.BatchNorm2d(nOut)
self.relu = nn.ReLU(inplace=True)
self.se = SELayer(nOut, 16)
class Bottleneck(nn.Module):
expansion = 4
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
with_se=False):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
#add by zc
self.with_se = with_se
self.se_module = SELayer(512)
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
if self.with_se:
out = self.se_module.forward(out)
out += residual
out = self.relu(out)
return out
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
with_se=False):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
#add by zc
self.with_se = with_se
self.se_module = SELayer(512)
def __init__(self, in_planes, planes, stride=1, reduction=16):
super(SEBottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(self.expansion*planes)
self.relu = nn.ReLU(inplace=True)
self.se = SELayer(planes*self.expansion, reduction)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion*planes)
)
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
groups=1,
base_width=64,
dilation=1,
norm_layer=None,
*,
reduction=16):
super(SEBasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.PReLU = nn.PReLU(inplace=True)
self.conv2 = conv3x3(planes, planes, 1)
self.bn2 = nn.BatchNorm2d(planes)
self.se = SELayer(planes, reduction)
self.downsample = downsample
self.stride = stride
def __init__(self, num_classes, aux_logits=True, transform_input=False):
super(SEInception3, self).__init__()
model = Inception3(num_classes=num_classes,
aux_logits=aux_logits,
transform_input=transform_input)
model.Mixed_5b.add_module("SELayer", SELayer(192))
model.Mixed_5c.add_module("SELayer", SELayer(256))
model.Mixed_5d.add_module("SELayer", SELayer(288))
model.Mixed_6a.add_module("SELayer", SELayer(288))
model.Mixed_6b.add_module("SELayer", SELayer(768))
model.Mixed_6c.add_module("SELayer", SELayer(768))
model.Mixed_6d.add_module("SELayer", SELayer(768))
model.Mixed_6e.add_module("SELayer", SELayer(768))
if aux_logits:
model.AuxLogits.add_module("SELayer", SELayer(768))
model.Mixed_7a.add_module("SELayer", SELayer(768))
model.Mixed_7b.add_module("SELayer", SELayer(1280))
model.Mixed_7c.add_module("SELayer", SELayer(2048))
self.model = model
