def __init__(self, in_planes, planes, num_partition, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = BibdConv2d(in_planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
groups=num_partition)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = BibdConv2d(planes,
planes,
kernel_size=3,
stride=1,
padding=1,
groups=num_partition)
self.bn2 = nn.BatchNorm2d(planes)
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, in_planes, planes, stride=1):
super(BBottleneck, self).__init__()
self.conv1 = BibdConv2d(in_planes, planes, kernel_size=1)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = BibdConv2d(planes, planes, kernel_size=3, stride=stride, padding=1)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = BibdConv2d(planes, self.expansion*planes, kernel_size=1)
self.bn3 = nn.BatchNorm2d(self.expansion*planes)
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)
)