def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
downstride=2,
groups=1,
base_width=64,
dilation=1):
super(Bottleneck, self).__init__()
width = int(planes * (base_width / 64.)) * groups
self.conv1 = conv1x1(inplanes, width)
self.bn1 = nn.BatchNorm2d(width)
self.conv2 = conv3x3(width, width, stride, groups, dilation)
self.bn2 = nn.BatchNorm2d(width)
self.conv3 = conv1x1(width, planes * self.expansion)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.ca = ChannelAttention(planes * 4)
self.sa = SpatialAttention()
if downsample is True:
self.downsample = nn.Sequential(
conv1x1(inplanes, planes * self.expansion * 2, downstride),
nn.BatchNorm2d(planes * self.expansion * 2),
)
else:
self.downsample = None
def __init__(self, inplanes, planes, baseWidth=4, cardinality=32, stride=1, downsample=None, downstride=2):
""" Constructor
Args:
inplanes: input channel dimensionality
planes: output channel dimensionality
baseWidth: base width.
cardinality: num of convolution groups.
stride: conv stride. Replaces pooling layer.
"""
super(Bottleneck, self).__init__()
D = int(math.floor(planes * (baseWidth / 64)))
C = cardinality
self.conv1 = nn.Conv2d(inplanes, D*C, kernel_size=1, stride=1, padding=0, bias=False)
self.bn1 = nn.BatchNorm2d(D*C)
self.conv2 = nn.Conv2d(D*C, D*C, kernel_size=3, stride=stride, padding=1, groups=C, bias=False)
self.bn2 = nn.BatchNorm2d(D*C)
self.conv3 = nn.Conv2d(D*C, planes * 4, kernel_size=1, stride=1, padding=0, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.ca = ChannelAttention(planes * 4)
self.sa_fm = SpatialAttention()
if downsample is True:
self.downsample = nn.Sequential(
conv1x1(inplanes, planes * self.expansion*2, downstride),
nn.BatchNorm2d(planes * self.expansion*2),
)
else:
self.downsample = None