def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(in_planes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes)
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),
nn.BatchNorm2d(self.expansion * planes)
)
def __init__(self, num_classes):
super(ResNet18, self).__init__()
self.in_planes = 16
self.conv1 = conv3x3(1, 16)
self.bn1 = nn.BatchNorm2d(16)
self.layer1 = self._make_layer(BasicBlock, 16, 2, stride=1)
self.layer2 = self._make_layer(BasicBlock, 32, 2, stride=2)
self.layer3 = self._make_layer(BasicBlock, 64, 2, stride=2)
self.linear = nn.Linear(64 * BasicBlock.expansion, num_classes)