在import math
import logging
from functools import partial
from collections import OrderedDict
from copy import deepcopy

import torch
import torch.nn as nn
import torch.nn.functional as F

from timm.models.layers import to_2tuple

from lib.models.layers.patch_embed import PatchEmbed, PatchEmbed_event, xcorr_depthwise
from .utils import combine_tokens, recover_tokens
from .vit import VisionTransformer
from ..layers.attn_blocks import CEBlock
import random
import numpy as np
# from .muti_cou import Counter_Guide_Enhanced_with_MultiHead, Cross_Attention
from .cross_module import Counter_Guide_Enhanced, Cross_Attention

_logger = logging.getLogger(__name__)
                            

class VisionTransformerCE(VisionTransformer):
    """ Vision Transformer with candidate elimination (CE) module

    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
        - https://arxiv.org/abs/2010.11929

    Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`
        - https://arxiv.org/abs/2012.12877
    """
    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
                 num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None,
                 act_layer=None, weight_init='',
                 ce_loc=None, ce_keep_ratio=None):
        super().__init__()
        if isinstance(img_size, tuple):
            self.img_size = img_size
        else:
            self.img_size = to_2tuple(img_size)
        self.patch_size = patch_size
        self.in_chans = in_chans

        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
        self.num_tokens = 2 if distilled else 1
        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
        act_layer = act_layer or nn.GELU

        self.patch_embed = embed_layer(
            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
        num_patches = self.patch_embed.num_patches

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None
        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_rate)
        self.pos_embed_event = PatchEmbed_event(in_chans=32, embed_dim=768, kernel_size=4, stride=4)

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
        blocks = []
        ce_index = 0
        self.ce_loc = ce_loc
        for i in range(depth):
            ce_keep_ratio_i = 1.0
            if ce_loc is not None and i in ce_loc:
                ce_keep_ratio_i = ce_keep_ratio[ce_index]
                ce_index += 1

            blocks.append(
                CEBlock(
                    dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,
                    attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer,
                    keep_ratio_search=ce_keep_ratio_i)
            )

        self.blocks = nn.Sequential(*blocks)
        self.norm = norm_layer(embed_dim)

        self.init_weights(weight_init)
    
        # 添加交互模块counter_guide-此处对模块已经做了更新
        self.counter_guide = Counter_Guide_Enhanced(768, 768)
        # self.counter_guide = Counter_Guide_Enhanced_with_MultiHead(768, 768, 12)
    
        # self.cross_attention = Cross_Attention(768, 768)

    # 新增随机遮蔽特征操作
    # def masking_fea(self,z, event_z, x, event_x, ratio=0.8):
    #     b,nz,c = z.shape
    #     b,nez,c = event_z.shape
    #     b,nx,c = x.shape
    #     b,nex,c = event_x.shape

    #     assert(nz == nez)
    #     assert(nx == nex)
    #     lenz_out = int(nz*ratio)
    #     lenx_out = int(nx*ratio)

    #     mask_nz = torch.rand(b,nz).float()
    #     mask_ez = torch.rand(b,nez).float()
    #     mask_nx = torch.rand(b,nx).float()
    #     mask_ex = torch.rand(b,nex).float()
    #     mask_nz = mask_nz>0.4
    #     mask_ez = mask_ez>0.4
    #     mask_ez = ~mask_nz + mask_ez
    #     mask_nz_idx = mask_nz.float().sort(1,descending=True)[-1].to(device = z.device)
    #     mask_ez_idx = mask_ez.float().sort(1,descending=True)[-1].to(device = z.device)
    #     mask_nx = mask_nx>0.4
    #     mask_ex = mask_ex>0.4
    #     mask_ex = ~mask_nx + mask_ex
    #     mask_nx_idx = mask_nx.float().sort(1,descending=True)[-1].to(device = z.device)
    #     mask_ex_idx = mask_ex.float().sort(1,descending=True)[-1].to(device = z.device)

    #     masked_z = torch.gather(z, 1, mask_nz_idx[:,:lenz_out,None].repeat([1,1,c]))
    #     masked_ez = torch.gather(event_z, 1, mask_ez_idx[:,:lenz_out,None].repeat([1,1,c]))
    #     masked_x = torch.gather(x, 1, mask_nx_idx[:,:lenx_out,None].repeat([1,1,c]))
    #     masked_ex = torch.gather(event_x, 1, mask_ex_idx[:,:lenx_out,None].repeat([1,1,c]))
    #     return masked_z, masked_ez, masked_x, masked_ex,{'x1':mask_nx_idx[:,:lenx_out],'x0':mask_nx_idx[:,lenx_out:],
    #                                                     'ex1':mask_ex_idx[:,:lenx_out],'ex0':mask_ex_idx[:,lenx_out:], }
    
    # 随机遮蔽特征操作完成
    # def forward_features(self, z, x, event_z, event_x,
    #                      mask_z=None, mask_x=None,
    #                      ce_template_mask=None, ce_keep_rate=None,
    #                      return_last_attn=False, Track=False
    #                      ):
    def forward_features(self, z, x, event_z, event_x,
                        mask_z=None, mask_x=None,
                        ce_template_mask=None, ce_keep_rate=None,
                        return_last_attn=False
                        ):
        # 分支1 处理流程
        B, H, W = x.shape[0], x.shape[2], x.shape[3]
        x = self.patch_embed(x) 
        z = self.patch_embed(z) 
        z += self.pos_embed_z
        x += self.pos_embed_x   
        
        # event_x = self.pos_embed_event(event_x)  
        # event_z = self.pos_embed_event(event_z) 
        # event_x += self.pos_embed_x
        # event_z += self.pos_embed_z
        # if Track == False:
        #     masked_z, masked_event_z, masked_x, masked_event_x, token_idx = self.masking_fea(z, event_z, x, event_x, ratio=0.98)

        # z = masked_z
        # x = masked_x
        # event_x = masked_event_x
        # event_z = masked_event_z  

        if mask_z is not None and mask_x is not None:
            mask_z = F.interpolate(mask_z[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0]
            mask_z = mask_z.flatten(1).unsqueeze(-1)

            mask_x = F.interpolate(mask_x[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0]
            mask_x = mask_x.flatten(1).unsqueeze(-1)

            mask_x = combine_tokens(mask_z, mask_x, mode=self.cat_mode)
            mask_x = mask_x.squeeze(-1)

        if self.add_cls_token:
            cls_tokens = self.cls_token.expand(B, -1, -1)
            cls_tokens = cls_tokens + self.cls_pos_embed
        if self.add_sep_seg:
            x += self.search_segment_pos_embed
            z += self.template_segment_pos_embed
        x = combine_tokens(z, x, mode=self.cat_mode)
        if self.add_cls_token:
            x = torch.cat([cls_tokens, x], dim=1)
        x = self.pos_drop(x)

        lens_z = self.pos_embed_z.shape[1]
        lens_x = self.pos_embed_x.shape[1]
        global_index_t = torch.linspace(0, lens_z - 1, lens_z).to(x.device)
        global_index_t = global_index_t.repeat(B, 1)
        global_index_s = torch.linspace(0, lens_x - 1, lens_x).to(x.device)
        global_index_s = global_index_s.repeat(B, 1)
        removed_indexes_s = []

        # # 分支2 处理流程
        event_x = self.pos_embed_event(event_x)  
        event_z = self.pos_embed_event(event_z) 
        event_x += self.pos_embed_x
        event_z += self.pos_embed_z
        event_x = combine_tokens(event_z, event_x, mode=self.cat_mode)

        if self.add_cls_token:
            event_x = torch.cat([cls_tokens, event_x], dim=1)

        lens_z = self.pos_embed_z.shape[1]
        lens_x = self.pos_embed_x.shape[1]

        global_index_t1 = torch.linspace(0, lens_z - 1, lens_z).to(event_x.device)
        global_index_t1 = global_index_t1.repeat(B, 1)
        global_index_s1 = torch.linspace(0, lens_x - 1, lens_x).to(event_x.device)
        global_index_s1 = global_index_s1.repeat(B, 1)

        removed_indexes_s1 = []

        out_attn = []
        out_attn1 = []
        for i, blk in enumerate(self.blocks):
            # 第一个分支处理
            x, global_index_t, global_index_s, removed_index_s, attn = \
                blk(x, global_index_t, global_index_s, mask_x, ce_template_mask, ce_keep_rate)
            # 第二个分支处理
            event_x, global_index_t1, global_index_s1, removed_index_s1, attn1 = \
                blk(event_x, global_index_t1, global_index_s1, mask_x, ce_template_mask, ce_keep_rate)


            if self.ce_loc is not None and i in self.ce_loc:
                removed_indexes_s.append(removed_index_s)
                removed_indexes_s1.append(removed_index_s1)

            out_attn.append(attn)
            out_attn1.append(attn1)
            
            if i == 0 :
                # 新增原始特征（在counter_guide之前的特征，便于后续的loss计算）
                A_original_x = x.clone() # 保存原始x特征 （RGB）
                A_original_event_x = event_x.clone() # 保存原始event特征 （event）

                # 引入counter_guide进行特征增强 此处RGB模态特征作为主特征,event_x作为补充特征
                enhanced_x, enhanced_event_x = self.counter_guide(x, event_x)

                # 保存经过counter_guide增强的特征
                A_enhanced_x = enhanced_x  # 保存增强后的特征
                A_enhanced_event_x = enhanced_event_x  # 保存增强后的特征
                # 将增强后的特征与原特征相加，这里类似残差操作
                x = x + enhanced_x
                event_x = event_x + enhanced_event_x

        out_attn_idx = random.choice(np.arange(len(out_attn)))
        out_attn = out_attn[out_attn_idx]

        out_attn_idx1 = random.choice(np.arange(len(out_attn1)))
        out_attn1 = out_attn1[out_attn_idx1]

        # 应用LayerNorm归一化处理
        x = self.norm(x) 
        event_x = self.norm(event_x)

        x_cat = torch.cat([event_x, x], dim=1) 
        x = x_cat # cat仅仅是做 rgb 和 event voxel拼接，数据增强采用的随机遮蔽特征策略。
        # x_cat = self.cross_attention(event_x, x)
        # x = x_cat


        # aux_dict = {
        #     "attn": out_attn,
        #     "attn1": out_attn1,
        #     "removed_indexes_s": removed_indexes_s,  # used for visualization
        #     'removed_indexes_s1': removed_indexes_s1,
        # }
        aux_dict = {
            "attn": out_attn,
            "attn1": out_attn1,
            "removed_indexes_s": removed_indexes_s,  # used for visualization
            'removed_indexes_s1': removed_indexes_s1,
        }
        return x, aux_dict 

    # def forward(self, z, x, event_z, event_x,
    #             ce_template_mask=None, ce_keep_rate=None,
    #             tnc_keep_rate=None,
    #             return_last_attn=False, Track=False):
    def forward(self, z, x, event_z, event_x,
                ce_template_mask=None, ce_keep_rate=None,
                tnc_keep_rate=None,
                return_last_attn=False):
        x, aux_dict = self.forward_features(z, x, event_z, event_x, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate)
        # x, aux_dict = self.forward_features(z, x, event_z, event_x, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate,Track=Track)

        return x, aux_dict


def _create_vision_transformer(pretrained=False, **kwargs):
    model = VisionTransformerCE(**kwargs)
    
    if pretrained:
        if 'npz' in pretrained:
            model.load_pretrained(pretrained, prefix='')
        else:
            checkpoint = torch.load(pretrained, map_location="cpu")
            missing_keys, unexpected_keys = model.load_state_dict(checkpoint["model"], strict=False)
            print('Load pretrained model from: ' + pretrained)

    return model


def vit_base_patch16_224_ce(pretrained=False, **kwargs):
    """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
    """
    model_kwargs = dict(
        patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
    model = _create_vision_transformer(pretrained=pretrained, **model_kwargs)
    return model


def vit_large_patch16_224_ce(pretrained=False, **kwargs):
    """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
    """
    model_kwargs = dict(
        patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
    model = _create_vision_transformer(pretrained=pretrained, **model_kwargs)
    return model

中，将分支2，即event数据分支的模型换为：import torch
import torch.nn as nn
from spikingjelly.clock_driven.neuron import MultiStepLIFNode
from spikingjelly.clock_driven import layer, surrogate
from timm.models.layers import to_2tuple, trunc_normal_, DropPath
from timm.models.registry import register_model
from timm.models.vision_transformer import _cfg
from einops.layers.torch import Rearrange
import torch.nn.functional as F
from functools import partial

__all__ = ['spikformer']

class MLP(nn.Module):
    def __init__(self, in_features, hidden_features=None, out_features=None, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1_conv = nn.Conv1d(in_features, hidden_features, kernel_size=1, stride=1)
        self.fc1_bn = nn.BatchNorm1d(hidden_features)
        self.fc1_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')

        self.fc2_conv = nn.Conv1d(hidden_features, out_features, kernel_size=1, stride=1)
        self.fc2_bn = nn.BatchNorm1d(out_features)
        self.fc2_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
        self.c_hidden = hidden_features
        self.c_output = out_features
    def forward(self, x):
        T,B,C,N = x.shape
        x = self.fc1_conv(x.flatten(0,1))
        x = self.fc1_bn(x).reshape(T,B,self.c_hidden,N).contiguous()
        x = self.fc1_lif(x)

        x = self.fc2_conv(x.flatten(0,1))
        x = self.fc2_bn(x).reshape(T,B,C,N).contiguous()
        x = self.fc2_lif(x)
        return x

class SSA(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
        super().__init__()
        assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
        self.dim = dim
        self.num_heads = num_heads
        self.scale = 0.25

        self.q_conv = nn.Conv1d(dim, dim, kernel_size=1, stride=1,bias=False)

        self.q_bn = nn.BatchNorm1d(dim)
        self.q_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')

        self.k_conv = nn.Conv1d(dim, dim, kernel_size=1, stride=1,bias=False)
        self.k_bn = nn.BatchNorm1d(dim)
        self.k_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')

        self.v_conv = nn.Conv1d(dim, dim, kernel_size=1, stride=1,bias=False)
        self.v_bn = nn.BatchNorm1d(dim)
        self.v_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')

        self.attn_drop = nn.Dropout(0.2)
        self.res_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
        self.attn_lif = MultiStepLIFNode(tau=2.0, v_threshold=0.5, detach_reset=True, backend='cupy')

        self.proj_conv = nn.Conv1d(dim, dim, kernel_size=1, stride=1)
        self.proj_bn = nn.BatchNorm1d(dim)
        self.proj_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')

    def forward(self, x):
        T, B, C, N = x.shape
        x_for_qkv = x.flatten(0, 1)
        q_conv_out = self.q_conv(x_for_qkv)
        q_conv_out = self.q_bn(q_conv_out).reshape(T,B,C,N).contiguous()
        q_conv_out = self.q_lif(q_conv_out)
        q = q_conv_out.transpose(-1, -2).reshape(T, B, N, self.num_heads, C//self.num_heads).permute(0, 1, 3, 2, 4).contiguous()

        k_conv_out = self.k_conv(x_for_qkv)
        k_conv_out = self.k_bn(k_conv_out).reshape(T,B,C,N).contiguous()
        k_conv_out = self.k_lif(k_conv_out)
        k = k_conv_out.transpose(-1, -2).reshape(T, B, N, self.num_heads, C//self.num_heads).permute(0, 1, 3, 2, 4).contiguous()

        v_conv_out = self.v_conv(x_for_qkv)
        v_conv_out = self.v_bn(v_conv_out).reshape(T,B,C,N).contiguous()
        v_conv_out = self.v_lif(v_conv_out)
        v = v_conv_out.transpose(-1, -2).reshape(T, B, N, self.num_heads, C//self.num_heads).permute(0, 1, 3, 2, 4).contiguous()

        attn = (q @ k.transpose(-2, -1))
        x = (attn @ v) * self.scale

        x = x.transpose(3, 4).reshape(T, B, C, N).contiguous()
        x = self.attn_lif(x)
        x = x.flatten(0,1)
        x = self.proj_lif(self.proj_bn(self.proj_conv(x)).reshape(T,B,C,N))

        return x


class Block(nn.Module):
    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, sr_ratio=1):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = SSA(dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
                              attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, drop=drop)

    def forward(self, x):
        x = x + self.attn(x)
        x = x + (self.mlp(x))
        return x


class SPS(nn.Module):
    def __init__(self, img_size_h=128, img_size_w=128, patch_size=4, in_channels=2, embed_dims=256):
        super().__init__()
        self.image_size = [img_size_h, img_size_w]
        patch_size = to_2tuple(patch_size)
        self.patch_size = patch_size
        self.C = in_channels
        self.H, self.W = self.image_size[0] // patch_size[0], self.image_size[1] // patch_size[1]
        self.num_patches = self.H * self.W
        self.proj_conv = nn.Conv2d(in_channels, embed_dims//8, kernel_size=3, stride=1, padding=1, bias=False)
        self.proj_bn = nn.BatchNorm2d(embed_dims//8)
        self.proj_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
        self.maxpool = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)

        self.proj_conv1 = nn.Conv2d(embed_dims//8, embed_dims//4, kernel_size=3, stride=1, padding=1, bias=False)
        self.proj_bn1 = nn.BatchNorm2d(embed_dims//4)
        self.proj_lif1 = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
        self.maxpool1 = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)

        self.proj_conv2 = nn.Conv2d(embed_dims//4, embed_dims//2, kernel_size=3, stride=1, padding=1, bias=False)
        self.proj_bn2 = nn.BatchNorm2d(embed_dims//2)
        self.proj_lif2 = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
        self.maxpool2 = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)

        self.proj_conv3 = nn.Conv2d(embed_dims//2, embed_dims, kernel_size=3, stride=1, padding=1, bias=False)
        self.proj_bn3 = nn.BatchNorm2d(embed_dims)
        self.proj_lif3 = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
        self.maxpool3 = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)

        self.rpe_conv = nn.Conv2d(embed_dims, embed_dims, kernel_size=3, stride=1, padding=1, bias=False)
        self.rpe_bn = nn.BatchNorm2d(embed_dims)
        self.rpe_lif = MultiStepLIFNode(tau=2.0, detach_reset=True, backend='cupy')
    def forward(self, x):
        T, B, C, H, W = x.shape
        x = self.proj_conv(x.flatten(0, 1)) # have some fire value
        x = self.proj_bn(x).reshape(T,B,-1,H,W).contiguous()
        x = self.proj_lif(x).flatten(0,1).contiguous()
        x = self.maxpool(x)

        x = self.proj_conv1(x)
        x = self.proj_bn1(x).reshape(T, B, -1, 64, 64).contiguous()
        x = self.proj_lif1(x).flatten(0, 1).contiguous()
        x = self.maxpool1(x)

        x = self.proj_conv2(x)
        x = self.proj_bn2(x).reshape(T, B, -1, 32, 32).contiguous()
        x = self.proj_lif2(x).flatten(0, 1).contiguous()
        x = self.maxpool2(x)

        x = self.proj_conv3(x)
        x = self.proj_bn3(x).reshape(T, B, -1, 16, 16).contiguous()
        x = self.proj_lif3(x).flatten(0, 1).contiguous()
        x = self.maxpool3(x)

        x_rpe = self.rpe_bn(self.rpe_conv(x)).reshape(T,B,-1,H//16,W//16).contiguous()
        x_rpe = self.rpe_lif(x_rpe).flatten(0,1)
        x = x + x_rpe
        x = x.reshape(T, B, -1, (H//16)*(H//16)).contiguous()

        return x

class Spikformer(nn.Module):
    def __init__(self,
                 img_size_h=128, img_size_w=128, patch_size=16, in_channels=2, num_classes=11,
                 embed_dims=[64, 128, 256], num_heads=[1, 2, 4], mlp_ratios=[4, 4, 4], qkv_bias=False, qk_scale=None,
                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
                 depths=[6, 8, 6], sr_ratios=[8, 4, 2]
                 ):
        super().__init__()
        self.num_classes = num_classes
        self.depths = depths

        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depths)]  # stochastic depth decay rule

        patch_embed = SPS(img_size_h=img_size_h,
                                 img_size_w=img_size_w,
                                 patch_size=patch_size,
                                 in_channels=in_channels,
                                 embed_dims=embed_dims)
        num_patches = patch_embed.num_patches
        pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dims))
        block = nn.ModuleList([Block(
            dim=embed_dims, num_heads=num_heads, mlp_ratio=mlp_ratios, qkv_bias=qkv_bias,
            qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[j],
            norm_layer=norm_layer, sr_ratio=sr_ratios)
            for j in range(depths)])

        setattr(self, f"patch_embed", patch_embed)
        setattr(self, f"pos_embed", pos_embed)
        setattr(self, f"block", block)

        self.norm = norm_layer(embed_dims)
        # classification head 这里不需要脉冲，因为输入的是在T时长平均发射值
        self.head = nn.Linear(embed_dims, num_classes) if num_classes > 0 else nn.Identity()

        pos_embed = getattr(self, f"pos_embed")
        trunc_normal_(pos_embed, std=.02)
        self.apply(self._init_weights)

    @torch.jit.ignore
    def _get_pos_embed(self, pos_embed, patch_embed, H, W):
        if H * W == self.patch_embed1.num_patches:
            return pos_embed
        else:
            return F.interpolate(
                pos_embed.reshape(1, patch_embed.H, patch_embed.W, -1).permute(0, 3, 1, 2),
                size=(H, W), mode="bilinear").reshape(1, -1, H * W).permute(0, 2, 1)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    def forward_features(self, x):

        block = getattr(self, f"block")
        patch_embed = getattr(self, f"patch_embed")
        x = patch_embed(x)
        for blk in block:
            x = blk(x)
        return x.mean(3)

    def forward(self, x):
        x = x.permute(1, 0, 2, 3, 4)  # [T, N, 2, *, *]
        x = self.forward_features(x)
        x = self.head(x.mean(0))
        return x


@register_model
def spikformer(pretrained=False, **kwargs):
    model = Spikformer(
        patch_size=16, embed_dims=256, num_heads=16, mlp_ratios=4,
        in_channels=2, num_classes=10, qkv_bias=False,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=2, sr_ratios=1,
        **kwargs
    )
    model.default_cfg = _cfg()
    return model