def __init__(self, oc, use_bn=False):
super().__init__()
self.conv1x1 = conv(oc, oc, 1)
if use_bn:
self.bblock = nn.Sequential(conv(oc, oc, 3), nn.BatchNorm2d(oc), relu(), conv(oc, oc, 3, bias=False))
else:
self.bblock = nn.Sequential(conv(oc, oc, 3), relu(), conv(oc, oc, 3, bias=False)) # Basic block
def __init__(self, fc, ic, oc):
super().__init__()
nc = ic + oc
self.reduce = nn.Sequential(conv(fc, oc, 1), relu(), conv(oc, oc, 1))
self.transform = nn.Sequential(conv(nc, nc, 3),
relu(), conv(nc, nc, 3), relu(),
conv(nc, oc, 3), relu())
def __init__(self, oc, deepest):
super().__init__()
self.convreluconv = nn.Sequential(conv(2 * oc, oc, 1), relu(), conv(oc, oc, 1))
self.deepest = deepest