def __init__(self, inplanes, planes, stride=1, cbam=None, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, padding=1, stride=stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, stride=1)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
if cbam is not None:
self.cbam = CBAMLayer(planes)
else:
self.cbam = None
def __init__(self, inplanes, planes, stride=1, cbam=None, downsample=None):
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 * Bottleneck.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * Bottleneck.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
if cbam is not None:
self.cbam = CBAMLayer(planes * Bottleneck.expansion)
else:
self.cbam = None