def __init__(self, in_channels, vote_per_seed=1, gt_per_seed=3, conv_channels=(16, 16), conv_cfg=dict(type='Conv1d'), norm_cfg=dict(type='BN1d'), norm_feats=True, vote_loss=None):
    super().__init__()
    self.in_channels = in_channels
    self.vote_per_seed = vote_per_seed
    self.gt_per_seed = gt_per_seed
    self.norm_feats = norm_feats
    self.vote_loss = build_loss(vote_loss)
    prev_channels = in_channels
    vote_conv_list = list()
    for k in range(len(conv_channels)):
        vote_conv_list.append(ConvModule(prev_channels, conv_channels[k], 1, padding=0, conv_cfg=conv_cfg, norm_cfg=norm_cfg, bias=True, inplace=True))
        prev_channels = conv_channels[k]
    self.vote_conv = nn.Sequential(*vote_conv_list)
    out_channel = ((3 + in_channels) * self.vote_per_seed)
    self.conv_out = nn.Conv1d(prev_channels, out_channel, 1)
def __init__(self, in_channels, vote_per_seed=1, gt_per_seed=3, conv_channels=(16, 16), conv_cfg=dict(type='Conv1d'), norm_cfg=dict(type='BN1d'), norm_feats=True, vote_loss=None):
    super().__init__()
    self.in_channels = in_channels
    self.vote_per_seed = vote_per_seed
    self.gt_per_seed = gt_per_seed
    self.norm_feats = norm_feats
    self.vote_loss = build_loss(vote_loss)
    prev_channels = in_channels
    vote_conv_list = list()
    for k in range(len(conv_channels)):
        vote_conv_list.append(ConvModule(prev_channels, conv_channels[k], 1, padding=0, conv_cfg=conv_cfg, norm_cfg=norm_cfg, bias=True, inplace=True))
        prev_channels = conv_channels[k]
    self.vote_conv = nn.Sequential(*vote_conv_list)
    out_channel = ((3 + in_channels) * self.vote_per_seed)
    self.conv_out = nn.Conv1d(prev_channels, out_channel, 1)