def __init__(self, in_channels, base_width, kernel_size=7):
super(L0SX, self).__init__()
self.in_channels = in_channels
self.out_channels = base_width
self.pool = Pool()
self.conv = conv.DilatedConvELU2D(self.in_channels, self.out_channels,
kernel_size)
def __init__(self, base_width, kernel_size=3):
super(L2SA, self).__init__()
self.in_channels = (4 + 3) * base_width
self.out_channels = 3 * base_width
self.cat = shape.Concatenate()
self.pool = Pool()
self.conv = conv.DilatedConvELU2D(self.in_channels, self.out_channels,
kernel_size)
def __init__(self, base_width, kernel_size=5):
super(L1SY, self).__init__()
self.in_channels = (3 + 2 + 1) * base_width
self.out_channels = 2 * base_width
self.cat = shape.Concatenate()
self.upsample = Upsample()
self.conv = conv.DilatedConvELU2D(self.in_channels, self.out_channels,
kernel_size)
def __init__(self, base_width, kernel_size=5):
super(L1SX, self).__init__()
self.in_channels = 1 * base_width
self.out_channels = 2 * base_width
self.upsample = Upsample()
self.pool = Pool()
self.conv = conv.DilatedConvELU2D(self.in_channels, self.out_channels,
kernel_size)
