Umamba (https://arxiv.org/pdf/2401.04722.pdf) 里输入就用最正常的Pytorch格式就行，即 （B, C, H, W）,

class MambaLayer(nn.Module):
    def __init__(self, dim, d_state = 16, d_conv = 4, expand = 2):
        super().__init__()
        self.dim = dim
        self.norm = nn.LayerNorm(dim)
        self.mamba = Mamba(
                d_model=dim, # Model dimension d_model
                d_state=d_state,  # SSM state expansion factor
                d_conv=d_conv,    # Local convolution width
                expand=expand,    # Block expansion factor
        )
    
    @autocast(enabled=False)
    def forward(self, x):
        if x.dtype == torch.float16:
            x = x.type(torch.float32)
        B, C = x.shape[:2]
        assert C == self.dim
        n_tokens = x.shape[2:].numel()
        img_dims = x.shape[2:]
        x_flat = x.reshape(B, C, n_tokens).transpose(-1, -2)
        x_norm = self.norm(x_flat)
        x_mamba = self.mamba(x_norm)
        out = x_mamba.transpose(-1, -2).reshape(B, C, *img_dims)

        return out

训练或推理时迭代的x， 是(B, C, H ,W), B 为batch size, C是channel dim, 比如浅层或许为1， 3， 深层512, H, W 为当前图像长宽。 

初始化MambaLayer时候， dim对应的是当前channel dim, 其实就是特征深度或者说厚度，

即 mamba_exmaple = MambaLayer(C).

我们再看看mamba本身库里， 

    def forward(self, hidden_states, inference_params=None):
        """
        hidden_states: (B, L, D)
        Returns: same shape as hidden_states
        """
        batch, seqlen, dim = hidden_states.shape

hidden_states是输入x, 在上面第一块代码里即为x_norm, x_norm的维度是 所谓（B，L, D）， 这里有符号的不同， 这里的D其实就是上面C， 即为特征厚度，或说特征通道数。 L是HxW， 即把图像拉长成一维数据。