pfcfuse/ConvSSM.py

import time
import math
from functools import partial
from typing import Optional, Callable

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from einops import rearrange, repeat
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
# try:
#     from mamba_ssm.ops.selective_scan_interface import selective_scan_fn, selective_scan_ref
# except:
#     pass

# an alternative for mamba_ssm (in which causal_conv1d is needed)
try:
    from mamba_ssm.ops.selective_scan_interface import selective_scan_fn, selective_scan_ref

    # from mamba_ssm.selective_scan import selective_scan_fn as selective_scan_fn_v1
    # from selective_scan import selective_scan_ref as selective_scan_ref_v1
except:
    pass

DropPath.__repr__ = lambda self: f"timm.DropPath({self.drop_prob})"


"""
CNN在远程建模能力方面的局限性使它们无法有效地提取图像中的特征，而Transformers则受到其二次计算复杂性的阻碍。最近的研究表明，以Mamba为代表的状态空间模型（SSM）可以在保持线性计算复杂度的同时有效地模拟长程相互作用。
我们介绍了一个新颖的Conv-SSM模块。Conv-SSM将卷积层的局部特征提取能力与SSM捕获长程依赖性的能力相结合。
"""


def flops_selective_scan_ref(B=1, L=256, D=768, N=16, with_D=True, with_Z=False, with_Group=True, with_complex=False):
    """
    u: r(B D L)
    delta: r(B D L)
    A: r(D N)
    B: r(B N L)
    C: r(B N L)
    D: r(D)
    z: r(B D L)
    delta_bias: r(D), fp32

    ignores:
        [.float(), +, .softplus, .shape, new_zeros, repeat, stack, to(dtype), silu]
    """
    import numpy as np

    # fvcore.nn.jit_handles
    def get_flops_einsum(input_shapes, equation):
        np_arrs = [np.zeros(s) for s in input_shapes]
        optim = np.einsum_path(equation, *np_arrs, optimize="optimal")[1]
        for line in optim.split("\n"):
            if "optimized flop" in line.lower():
                # divided by 2 because we count MAC (multiply-add counted as one flop)
                flop = float(np.floor(float(line.split(":")[-1]) / 2))
                return flop

    assert not with_complex

    flops = 0  # below code flops = 0
    if False:
        ...
        """
        dtype_in = u.dtype
        u = u.float()
        delta = delta.float()
        if delta_bias is not None:
            delta = delta + delta_bias[..., None].float()
        if delta_softplus:
            delta = F.softplus(delta)
        batch, dim, dstate = u.shape[0], A.shape[0], A.shape[1]
        is_variable_B = B.dim() >= 3
        is_variable_C = C.dim() >= 3
        if A.is_complex():
            if is_variable_B:
                B = torch.view_as_complex(rearrange(B.float(), "... (L two) -> ... L two", two=2))
            if is_variable_C:
                C = torch.view_as_complex(rearrange(C.float(), "... (L two) -> ... L two", two=2))
        else:
            B = B.float()
            C = C.float()
        x = A.new_zeros((batch, dim, dstate))
        ys = []
        """

    flops += get_flops_einsum([[B, D, L], [D, N]], "bdl,dn->bdln")
    if with_Group:
        flops += get_flops_einsum([[B, D, L], [B, N, L], [B, D, L]], "bdl,bnl,bdl->bdln")
    else:
        flops += get_flops_einsum([[B, D, L], [B, D, N, L], [B, D, L]], "bdl,bdnl,bdl->bdln")
    if False:
        ...
        """
        deltaA = torch.exp(torch.einsum('bdl,dn->bdln', delta, A))
        if not is_variable_B:
            deltaB_u = torch.einsum('bdl,dn,bdl->bdln', delta, B, u)
        else:
            if B.dim() == 3:
                deltaB_u = torch.einsum('bdl,bnl,bdl->bdln', delta, B, u)
            else:
                B = repeat(B, "B G N L -> B (G H) N L", H=dim // B.shape[1])
                deltaB_u = torch.einsum('bdl,bdnl,bdl->bdln', delta, B, u)
        if is_variable_C and C.dim() == 4:
            C = repeat(C, "B G N L -> B (G H) N L", H=dim // C.shape[1])
        last_state = None
        """

    in_for_flops = B * D * N
    if with_Group:
        in_for_flops += get_flops_einsum([[B, D, N], [B, D, N]], "bdn,bdn->bd")
    else:
        in_for_flops += get_flops_einsum([[B, D, N], [B, N]], "bdn,bn->bd")
    flops += L * in_for_flops
    if False:
        ...
        """
        for i in range(u.shape[2]):
            x = deltaA[:, :, i] * x + deltaB_u[:, :, i]
            if not is_variable_C:
                y = torch.einsum('bdn,dn->bd', x, C)
            else:
                if C.dim() == 3:
                    y = torch.einsum('bdn,bn->bd', x, C[:, :, i])
                else:
                    y = torch.einsum('bdn,bdn->bd', x, C[:, :, :, i])
            if i == u.shape[2] - 1:
                last_state = x
            if y.is_complex():
                y = y.real * 2
            ys.append(y)
        y = torch.stack(ys, dim=2) # (batch dim L)
        """

    if with_D:
        flops += B * D * L
    if with_Z:
        flops += B * D * L
    if False:
        ...
        """
        out = y if D is None else y + u * rearrange(D, "d -> d 1")
        if z is not None:
            out = out * F.silu(z)
        out = out.to(dtype=dtype_in)
        """

    return flops


class PatchEmbed2D(nn.Module):
    r""" Image to Patch Embedding
    Args:
        patch_size (int): Patch token size. Default: 4.
        in_chans (int): Number of input image channels. Default: 3.
        embed_dim (int): Number of linear projection output channels. Default: 96.
        norm_layer (nn.Module, optional): Normalization layer. Default: None
    """

    def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None, **kwargs):
        super().__init__()
        if isinstance(patch_size, int):
            patch_size = (patch_size, patch_size)
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        if norm_layer is not None:
            self.norm = norm_layer(embed_dim)
        else:
            self.norm = None

    def forward(self, x):
        x = self.proj(x).permute(0, 2, 3, 1)
        if self.norm is not None:
            x = self.norm(x)
        return x


class PatchMerging2D(nn.Module):
    r""" Patch Merging Layer.
    Args:
        input_resolution (tuple[int]): Resolution of input feature.
        dim (int): Number of input channels.
        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
    """

    def __init__(self, dim, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
        self.norm = norm_layer(4 * dim)

    def forward(self, x):
        B, H, W, C = x.shape

        SHAPE_FIX = [-1, -1]
        if (W % 2 != 0) or (H % 2 != 0):
            print(f"Warning, x.shape {x.shape} is not match even ===========", flush=True)
            SHAPE_FIX[0] = H // 2
            SHAPE_FIX[1] = W // 2

        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C

        if SHAPE_FIX[0] > 0:
            x0 = x0[:, :SHAPE_FIX[0], :SHAPE_FIX[1], :]
            x1 = x1[:, :SHAPE_FIX[0], :SHAPE_FIX[1], :]
            x2 = x2[:, :SHAPE_FIX[0], :SHAPE_FIX[1], :]
            x3 = x3[:, :SHAPE_FIX[0], :SHAPE_FIX[1], :]

        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
        x = x.view(B, H // 2, W // 2, 4 * C)  # B H/2*W/2 4*C

        x = self.norm(x)
        x = self.reduction(x)

        return x


class PatchExpand2D(nn.Module):
    def __init__(self, dim, dim_scale=2, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim * 2
        self.dim_scale = dim_scale
        self.expand = nn.Linear(self.dim, dim_scale * self.dim, bias=False)
        self.norm = norm_layer(self.dim // dim_scale)

    def forward(self, x):
        B, H, W, C = x.shape
        x = self.expand(x)

        x = rearrange(x, 'b h w (p1 p2 c)-> b (h p1) (w p2) c', p1=self.dim_scale, p2=self.dim_scale,
                      c=C // self.dim_scale)
        x = self.norm(x)

        return x


class Final_PatchExpand2D(nn.Module):
    def __init__(self, dim, dim_scale=4, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.dim_scale = dim_scale
        self.expand = nn.Linear(self.dim, dim_scale * self.dim, bias=False)
        self.norm = norm_layer(self.dim // dim_scale)

    def forward(self, x):
        B, H, W, C = x.shape
        x = self.expand(x)

        x = rearrange(x, 'b h w (p1 p2 c)-> b (h p1) (w p2) c', p1=self.dim_scale, p2=self.dim_scale,
                      c=C // self.dim_scale)
        x = self.norm(x)

        return x


class SS2D(nn.Module): # SS2D（State Space Model 2D）类，它是实现状态空间模型（SSM）在视觉任务中应用的核心部分。这个类将状态空间模型的长程依赖性建模能力与卷积操作结合起来，旨在捕获图像中的长程相互作用，同时保持对局部特征的敏感度。
    def __init__(
            self,
            d_model,
            d_state=16,
            # d_state="auto", # 20240109
            d_conv=3,
            expand=2,
            dt_rank="auto",
            dt_min=0.001,
            dt_max=0.1,
            dt_init="random",
            dt_scale=1.0,
            dt_init_floor=1e-4,
            dropout=0.,
            conv_bias=True,
            bias=False,
            device=None,
            dtype=None,
            **kwargs,
    ):
        factory_kwargs = {"device": device, "dtype": dtype}
        super().__init__()
        self.d_model = d_model
        self.d_state = d_state
        # self.d_state = math.ceil(self.d_model / 6) if d_state == "auto" else d_model # 20240109
        self.d_conv = d_conv
        self.expand = expand
        self.d_inner = int(self.expand * self.d_model)
        self.dt_rank = math.ceil(self.d_model / 16) if dt_rank == "auto" else dt_rank

        self.in_proj = nn.Linear(self.d_model, self.d_inner * 2, bias=bias, **factory_kwargs)
        self.conv2d = nn.Conv2d(
            in_channels=self.d_inner,
            out_channels=self.d_inner,
            groups=self.d_inner,
            bias=conv_bias,
            kernel_size=d_conv,
            padding=(d_conv - 1) // 2,
            **factory_kwargs,
        )
        self.act = nn.SiLU()

        self.x_proj = (
            nn.Linear(self.d_inner, (self.dt_rank + self.d_state * 2), bias=False, **factory_kwargs),
            nn.Linear(self.d_inner, (self.dt_rank + self.d_state * 2), bias=False, **factory_kwargs),
            nn.Linear(self.d_inner, (self.dt_rank + self.d_state * 2), bias=False, **factory_kwargs),
            nn.Linear(self.d_inner, (self.dt_rank + self.d_state * 2), bias=False, **factory_kwargs),
        )
        self.x_proj_weight = nn.Parameter(torch.stack([t.weight for t in self.x_proj], dim=0))  # (K=4, N, inner)
        del self.x_proj

        self.dt_projs = (
            self.dt_init(self.dt_rank, self.d_inner, dt_scale, dt_init, dt_min, dt_max, dt_init_floor,
                         **factory_kwargs),
            self.dt_init(self.dt_rank, self.d_inner, dt_scale, dt_init, dt_min, dt_max, dt_init_floor,
                         **factory_kwargs),
            self.dt_init(self.dt_rank, self.d_inner, dt_scale, dt_init, dt_min, dt_max, dt_init_floor,
                         **factory_kwargs),
            self.dt_init(self.dt_rank, self.d_inner, dt_scale, dt_init, dt_min, dt_max, dt_init_floor,
                         **factory_kwargs),
        )
        self.dt_projs_weight = nn.Parameter(torch.stack([t.weight for t in self.dt_projs], dim=0))  # (K=4, inner, rank)
        self.dt_projs_bias = nn.Parameter(torch.stack([t.bias for t in self.dt_projs], dim=0))  # (K=4, inner)
        del self.dt_projs

        self.A_logs = self.A_log_init(self.d_state, self.d_inner, copies=4, merge=True)  # (K=4, D, N)
        self.Ds = self.D_init(self.d_inner, copies=4, merge=True)  # (K=4, D, N)

        self.selective_scan = selective_scan_fn
        self.forward_core = self.forward_corev0

        self.out_norm = nn.LayerNorm(self.d_inner)
        self.out_proj = nn.Linear(self.d_inner, self.d_model, bias=bias, **factory_kwargs)
        self.dropout = nn.Dropout(dropout) if dropout > 0. else None

    @staticmethod
    def dt_init(dt_rank, d_inner, dt_scale=1.0, dt_init="random", dt_min=0.001, dt_max=0.1, dt_init_floor=1e-4,
                **factory_kwargs):
        dt_proj = nn.Linear(dt_rank, d_inner, bias=True, **factory_kwargs)

        # Initialize special dt projection to preserve variance at initialization
        dt_init_std = dt_rank ** -0.5 * dt_scale
        if dt_init == "constant":
            nn.init.constant_(dt_proj.weight, dt_init_std)
        elif dt_init == "random":
            nn.init.uniform_(dt_proj.weight, -dt_init_std, dt_init_std)
        else:
            raise NotImplementedError

        # Initialize dt bias so that F.softplus(dt_bias) is between dt_min and dt_max
        dt = torch.exp(
            torch.rand(d_inner, **factory_kwargs) * (math.log(dt_max) - math.log(dt_min))
            + math.log(dt_min)
        ).clamp(min=dt_init_floor)
        # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
        inv_dt = dt + torch.log(-torch.expm1(-dt))
        with torch.no_grad():
            dt_proj.bias.copy_(inv_dt)
        # Our initialization would set all Linear.bias to zero, need to mark this one as _no_reinit
        dt_proj.bias._no_reinit = True

        return dt_proj

    @staticmethod
    def A_log_init(d_state, d_inner, copies=1, device=None, merge=True):
        # S4D real initialization
        A = repeat(
            torch.arange(1, d_state + 1, dtype=torch.float32, device=device),
            "n -> d n",
            d=d_inner,
        ).contiguous()
        A_log = torch.log(A)  # Keep A_log in fp32
        if copies > 1:
            A_log = repeat(A_log, "d n -> r d n", r=copies)
            if merge:
                A_log = A_log.flatten(0, 1)
        A_log = nn.Parameter(A_log)
        A_log._no_weight_decay = True
        return A_log

    @staticmethod
    def D_init(d_inner, copies=1, device=None, merge=True):
        # D "skip" parameter
        D = torch.ones(d_inner, device=device)
        if copies > 1:
            D = repeat(D, "n1 -> r n1", r=copies)
            if merge:
                D = D.flatten(0, 1)
        D = nn.Parameter(D)  # Keep in fp32
        D._no_weight_decay = True
        return D

    def forward_corev0(self, x: torch.Tensor):
        self.selective_scan = selective_scan_fn

        B, C, H, W = x.shape
        L = H * W
        K = 4

        x_hwwh = torch.stack([x.view(B, -1, L), torch.transpose(x, dim0=2, dim1=3).contiguous().view(B, -1, L)],
                             dim=1).view(B, 2, -1, L)
        xs = torch.cat([x_hwwh, torch.flip(x_hwwh, dims=[-1])], dim=1)  # (b, k, d, l)

        x_dbl = torch.einsum("b k d l, k c d -> b k c l", xs.view(B, K, -1, L), self.x_proj_weight)
        # x_dbl = x_dbl + self.x_proj_bias.view(1, K, -1, 1)
        dts, Bs, Cs = torch.split(x_dbl, [self.dt_rank, self.d_state, self.d_state], dim=2)
        dts = torch.einsum("b k r l, k d r -> b k d l", dts.view(B, K, -1, L), self.dt_projs_weight)
        # dts = dts + self.dt_projs_bias.view(1, K, -1, 1)

        xs = xs.float().view(B, -1, L)  # (b, k * d, l)
        dts = dts.contiguous().float().view(B, -1, L)  # (b, k * d, l)
        Bs = Bs.float().view(B, K, -1, L)  # (b, k, d_state, l)
        Cs = Cs.float().view(B, K, -1, L)  # (b, k, d_state, l)
        Ds = self.Ds.float().view(-1)  # (k * d)
        As = -torch.exp(self.A_logs.float()).view(-1, self.d_state)  # (k * d, d_state)
        dt_projs_bias = self.dt_projs_bias.float().view(-1)  # (k * d)

        out_y = self.selective_scan(
            xs, dts,
            As, Bs, Cs, Ds, z=None,
            delta_bias=dt_projs_bias,
            delta_softplus=True,
            return_last_state=False,
        ).view(B, K, -1, L)
        assert out_y.dtype == torch.float

        inv_y = torch.flip(out_y[:, 2:4], dims=[-1]).view(B, 2, -1, L)
        wh_y = torch.transpose(out_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)
        invwh_y = torch.transpose(inv_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)

        return out_y[:, 0], inv_y[:, 0], wh_y, invwh_y

    # an alternative to forward_corev1
    def forward_corev1(self, x: torch.Tensor):
        self.selective_scan = selective_scan_fn

        B, C, H, W = x.shape
        L = H * W
        K = 4

        x_hwwh = torch.stack([x.view(B, -1, L), torch.transpose(x, dim0=2, dim1=3).contiguous().view(B, -1, L)],
                             dim=1).view(B, 2, -1, L)
        xs = torch.cat([x_hwwh, torch.flip(x_hwwh, dims=[-1])], dim=1)  # (b, k, d, l)

        x_dbl = torch.einsum("b k d l, k c d -> b k c l", xs.view(B, K, -1, L), self.x_proj_weight)
        # x_dbl = x_dbl + self.x_proj_bias.view(1, K, -1, 1)
        dts, Bs, Cs = torch.split(x_dbl, [self.dt_rank, self.d_state, self.d_state], dim=2)
        dts = torch.einsum("b k r l, k d r -> b k d l", dts.view(B, K, -1, L), self.dt_projs_weight)
        # dts = dts + self.dt_projs_bias.view(1, K, -1, 1)

        xs = xs.float().view(B, -1, L)  # (b, k * d, l)
        dts = dts.contiguous().float().view(B, -1, L)  # (b, k * d, l)
        Bs = Bs.float().view(B, K, -1, L)  # (b, k, d_state, l)
        Cs = Cs.float().view(B, K, -1, L)  # (b, k, d_state, l)
        Ds = self.Ds.float().view(-1)  # (k * d)
        As = -torch.exp(self.A_logs.float()).view(-1, self.d_state)  # (k * d, d_state)
        dt_projs_bias = self.dt_projs_bias.float().view(-1)  # (k * d)

        out_y = self.selective_scan(
            xs, dts,
            As, Bs, Cs, Ds,
            delta_bias=dt_projs_bias,
            delta_softplus=True,
        ).view(B, K, -1, L)
        assert out_y.dtype == torch.float

        inv_y = torch.flip(out_y[:, 2:4], dims=[-1]).view(B, 2, -1, L)
        wh_y = torch.transpose(out_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)
        invwh_y = torch.transpose(inv_y[:, 1].view(B, -1, W, H), dim0=2, dim1=3).contiguous().view(B, -1, L)

        return out_y[:, 0], inv_y[:, 0], wh_y, invwh_y

    def forward(self, x: torch.Tensor, **kwargs):
        B, H, W, C = x.shape

        xz = self.in_proj(x)
        x, z = xz.chunk(2, dim=-1)  # (b, h, w, d)

        x = x.permute(0, 3, 1, 2).contiguous()
        x = self.act(self.conv2d(x))  # (b, d, h, w)
        y1, y2, y3, y4 = self.forward_core(x)
        assert y1.dtype == torch.float32
        y = y1 + y2 + y3 + y4
        y = torch.transpose(y, dim0=1, dim1=2).contiguous().view(B, H, W, -1)
        y = self.out_norm(y)
        y = y * F.silu(z)
        out = self.out_proj(y)
        if self.dropout is not None:
            out = self.dropout(out)
        return out


class ConvSSM(nn.Module):
    """
    这个类组合了自注意力机制和卷积操作，旨在融合自注意力的全局感知能力和卷积的局部特征提取能力。
    输入特征被分成两部分，一部分通过SS2D自注意力模块处理，另一部分通过一系列卷积层处理。处理后的两部分再次合并，并通过最终的卷积层生成输出特征。
    """
    def __init__(
            self,
            hidden_dim: int = 0,
            drop_path: float = 0,
            norm_layer: Callable[..., torch.nn.Module] = partial(nn.LayerNorm, eps=1e-6),
            attn_drop_rate: float = 0,
            d_state: int = 16,
            **kwargs,
    ):
        super().__init__()
        self.ln_1 = norm_layer(hidden_dim // 2)
        self.self_attention = SS2D(d_model=hidden_dim // 2, dropout=attn_drop_rate, d_state=d_state, **kwargs)
        self.drop_path = DropPath(drop_path)

        self.conv33conv33conv11 = nn.Sequential(
            nn.Conv2d(in_channels=hidden_dim // 2, out_channels=hidden_dim // 2, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(hidden_dim // 2),
            nn.ReLU(),
            nn.Conv2d(in_channels=hidden_dim // 2, out_channels=hidden_dim // 2, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(hidden_dim // 2),
            nn.ReLU(),
            nn.Conv2d(in_channels=hidden_dim // 2, out_channels=hidden_dim // 2, kernel_size=1, stride=1)
        )
        self.finalconv11 = nn.Conv2d(in_channels=hidden_dim, out_channels=hidden_dim, kernel_size=1, stride=1)

    # def forward(self, input: torch.Tensor):
    #
    #
    #     """
    #     <img src="http://42.192.130.83:9000/picgo/imgs/image-20240502123357829.png" alt="image-202http://42.192.130.83:9000/picgo/imgs/image-20240502123357829.png
    #
    #     """
    #
    #     # 将输入图像在最后一个维度上切分成两半 left 4 32 32 64 right 4 32 32 64
    #     input_left, input_right = input.chunk(2, dim=-1)
    #     # 应用自注意力机制，并经过drop_path和ln_1（层归一化）处理input_right
    #     # self.self_attention为SS2D模块，它实现了自注意力机制
    #     x = input_right + self.drop_path(self.self_attention(self.ln_1(input_right))) # 4 32 32 64
    #     # 将x的维度置换回与input_left匹配，以便后续合并
    #     x = x.permute(0, 3, 1, 2).contiguous() # 4 64 32 32
    #
    #     # 对input_left进行维度置换，以适配后续卷积操作的需要
    #     input_left = input_left.permute(0, 3, 1, 2).contiguous() # 4 64 32 32
    #     # 应用特定结构的卷积操作 conv33conv33conv11
    #     input_left = self.conv33conv33conv11(input_left) # 4 64 32 32
    #
    #     output = torch.cat((input_left, x), dim=1) # 4 128 32 32
    #     output = self.finalconv11(output).permute(0, 2, 3, 1).contiguous() # 4 32 32 128
    #     return output + input
    def forward(self, input: torch.Tensor):


        """
        <img src="http://42.192.130.83:9000/picgo/imgs/image-20240502123357829.png" alt="image-202http://42.192.130.83:9000/picgo/imgs/image-20240502123357829.png

        """
        input = input.permute(0, 2, 3, 1)
        print(input.is_cuda)
        # 将输入图像在最后一个维度上切分成两半 left 4 32 32 64 right 4 32 32 64
        input_left, input_right = input.chunk(2, dim=-1)
        # 应用自注意力机制，并经过drop_path和ln_1（层归一化）处理input_right
        # self.self_attention为SS2D模块，它实现了自注意力机制
        x = input_right + self.drop_path(self.self_attention(self.ln_1(input_right))) # 4 32 32 64
        # 将x的维度置换回与input_left匹配，以便后续合并
        x = x.permute(0, 3, 1, 2).contiguous() # 4 64 32 32

        # 对input_left进行维度置换，以适配后续卷积操作的需要
        input_left = input_left.permute(0, 3, 1, 2).contiguous() # 4 64 32 32
        # 应用特定结构的卷积操作 conv33conv33conv11
        input_left = self.conv33conv33conv11(input_left) # 4 64 32 32

        output = torch.cat((input_left, x), dim=1) # 4 128 32 32
        output = self.finalconv11(output).permute(0, 2, 3, 1).contiguous() # 4 32 32 128

        return (output + input).permute(0, 3, 1, 2)


if __name__ == '__main__':
    # 初始化ConvSSM模块，hidden_dim为128
    # block = ConvSSM(hidden_dim=128)
    block = ConvSSM(hidden_dim=4)

    # 生成随机输入张量，尺寸为[批次大小, 高度, 宽度, 通道数]
    # 这里批次大小为4，高度和宽度为32，通道数为128（符合hidden_dim的大小）
    # input_tensor = torch.rand(4, 32, 32, 128)
    # input_tensor = torch.rand(2, 1, 256, 128)
    input_tensor = torch.rand(2, 4, 256, 256).cuda()


    # 前向传递输入张量通过ConvSSM模块
    output = block(input_tensor)

    # 打印输入和输出张量的尺寸
    print("Input tensor size:", input_tensor.size())
    print("Output tensor size:", output.size())