def __init__(self, in_channels, out_channels, kernel_size, stride, padding, act_cfg=dict(type='ReLU'), norm_cfg=dict(type='BN')):
    super().__init__()
    self.in_channels = in_channels
    self.out_channels = out_channels
    self.kernel_size = kernel_size
    self.stride = stride
    self.padding = padding
    self.act_cfg = act_cfg
    self.norm_cfg = norm_cfg
    self.net = nn.Sequential(build_activation_layer(self.act_cfg), nn.Conv2d(self.in_channels, self.out_channels, self.kernel_size, self.stride, self.padding, bias=False), build_norm_layer(self.norm_cfg, self.out_channels)[1])
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, act_cfg=dict(type='ReLU'), norm_cfg=dict(type='BN')):
    super().__init__()
    self.in_channels = in_channels
    self.out_channels = out_channels
    self.kernel_size = kernel_size
    self.stride = stride
    self.padding = padding
    self.act_cfg = act_cfg
    self.norm_cfg = norm_cfg
    self.net = nn.Sequential(build_activation_layer(self.act_cfg), nn.Conv2d(self.in_channels, self.out_channels, self.kernel_size, self.stride, self.padding, bias=False), build_norm_layer(self.norm_cfg, self.out_channels)[1])