- def __init__(self, nb_classes, ks = 2, nc = 32):
- super(NetToy1dWithDilation, self).__init__()
- self.conv0 = nn.Conv1d(1, nc, kernel_size = 1)
- self.pad1 = ((ks-1) * 2, 0)
- self.conv1 = nn.Conv1d(nc, nc, kernel_size = ks, dilation = 2)
- self.pad2 = ((ks-1) * 4, 0)
- self.conv2 = nn.Conv1d(nc, nc, kernel_size = ks, dilation = 4)
- self.pad3 = ((ks-1) * 8, 0)
- self.conv3 = nn.Conv1d(nc, nc, kernel_size = ks, dilation = 8)
- self.pad4 = ((ks-1) * 16, 0)
- self.conv4 = nn.Conv1d(nc, nc, kernel_size = ks, dilation = 16)
- self.conv5 = nn.Conv1d(nc, nb_classes, kernel_size = 1)
+ def __init__(self, nb_classes, ks=2, nc=32):
+ super().__init__()
+ self.conv0 = nn.Conv1d(1, nc, kernel_size=1)
+ self.pad1 = ((ks - 1) * 2, 0)
+ self.conv1 = nn.Conv1d(nc, nc, kernel_size=ks, dilation=2)
+ self.pad2 = ((ks - 1) * 4, 0)
+ self.conv2 = nn.Conv1d(nc, nc, kernel_size=ks, dilation=4)
+ self.pad3 = ((ks - 1) * 8, 0)
+ self.conv3 = nn.Conv1d(nc, nc, kernel_size=ks, dilation=8)
+ self.pad4 = ((ks - 1) * 16, 0)
+ self.conv4 = nn.Conv1d(nc, nc, kernel_size=ks, dilation=16)
+ self.conv5 = nn.Conv1d(nc, nb_classes, kernel_size=1)