对比：① 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

_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)


    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:], }
    
    def masking_fea_rgb(self, z, x, ratio=0.8):
        b, nz, c = z.shape
        b, nx, c = x.shape

        lenz_out = int(nz * ratio)
        lenx_out = int(nx * ratio)

        # 生成遮罩
        mask_nz = torch.rand(b, nz).float() > 0.4
        mask_nx = torch.rand(b, nx).float() > 0.4

        # 获取遮罩后的索引
        mask_nz_idx = mask_nz.float().sort(1, descending=True)[-1].to(device=z.device)
        mask_nx_idx = mask_nx.float().sort(1, descending=True)[-1].to(device=x.device)
        
        # 应用遮罩并且重构数据
        masked_z = torch.gather(z, 1, mask_nz_idx[:, :lenz_out, None].repeat(1, 1, c))
        masked_x = torch.gather(x, 1, mask_nx_idx[:, :lenx_out, None].repeat(1, 1, c))

        return masked_z, masked_x, {'z1': mask_nz_idx[:, :lenz_out], 'z0': mask_nz_idx[:, lenz_out:],
                                    'x1': mask_nx_idx[:, :lenx_out], 'x0': mask_nx_idx[:, lenx_out:]}    
    
    def masking_fea_event(self,event_z,event_x,ratio=0.8):
        b,nez,c = event_z.shape
        b,nex,c = event_x.shape
        lenz_out = int(nez*ratio)
        lenx_out = int(nex*ratio)
        mask_ez = torch.rand(b,nez).float() > 0.4
        mask_ex = torch.rand(b,nex).float() > 0.4
        mask_ez_idx = mask_ez.float().sort(1,descending=True)[-1].to(device = event_z.device)
        mask_ex_idx = mask_ex.float().sort(1, descending=True)[-1].to(device=event_x.device)
        masked_ez = torch.gather(event_z, 1, mask_ez_idx[:,:lenz_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_ez, masked_ex , {'ez1': mask_ez_idx[:, :lenz_out], 'ez0': mask_ez_idx[:, lenz_out:],
                                       'ex1': mask_ex_idx[:, :lenx_out], 'ex0': mask_ex_idx[:, lenx_out:]}   



    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
        # 数据加载或预处理部分 ###########################
        z, event_z, x, event_x,_=self.masking_fea(z,event_z,x,event_x,ratio=0.95)
        ################################################
        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 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 = 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 = []

        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)


        # 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。


        aux_dict = {
            "attn": attn,
            "attn1": 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):

        x, aux_dict = self.forward_features(z, x, event_z, event_x, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate,)

        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
②  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

_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)


    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:], }
    
    def masking_fea_rgb(self, z, x, ratio=0.8):
        b, nz, c = z.shape
        b, nx, c = x.shape

        lenz_out = int(nz * ratio)
        lenx_out = int(nx * ratio)

        # 生成遮罩
        mask_nz = torch.rand(b, nz).float() > 0.4
        mask_nx = torch.rand(b, nx).float() > 0.4

        # 获取遮罩后的索引
        mask_nz_idx = mask_nz.float().sort(1, descending=True)[-1].to(device=z.device)
        mask_nx_idx = mask_nx.float().sort(1, descending=True)[-1].to(device=x.device)
        
        # 应用遮罩并且重构数据
        masked_z = torch.gather(z, 1, mask_nz_idx[:, :lenz_out, None].repeat(1, 1, c))
        masked_x = torch.gather(x, 1, mask_nx_idx[:, :lenx_out, None].repeat(1, 1, c))

        return masked_z, masked_x, {'z1': mask_nz_idx[:, :lenz_out], 'z0': mask_nz_idx[:, lenz_out:],
                                    'x1': mask_nx_idx[:, :lenx_out], 'x0': mask_nx_idx[:, lenx_out:]}    
    
    def masking_fea_event(self,event_z,event_x,ratio=0.8):
        b,nez,c = event_z.shape
        b,nex,c = event_x.shape
        lenz_out = int(nez*ratio)
        lenx_out = int(nex*ratio)
        mask_ez = torch.rand(b,nez).float() > 0.4
        mask_ex = torch.rand(b,nex).float() > 0.4
        mask_ez_idx = mask_ez.float().sort(1,descending=True)[-1].to(device = event_z.device)
        mask_ex_idx = mask_ex.float().sort(1, descending=True)[-1].to(device=event_x.device)
        masked_ez = torch.gather(event_z, 1, mask_ez_idx[:,:lenz_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_ez, masked_ex , {'ez1': mask_ez_idx[:, :lenz_out], 'ez0': mask_ez_idx[:, lenz_out:],
                                       'ex1': mask_ex_idx[:, :lenx_out], 'ex0': mask_ex_idx[:, lenx_out:]}   



    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
        #################################
        masked_z, masked_event_z, masked_x, masked_event_x, token_idx = self.masking_fea(z, event_z, x, event_x, ratio=0.95)
        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 = 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 = []

        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)


        # 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。


        aux_dict = {
            "attn": attn,
            "attn1": 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):

        x, aux_dict = self.forward_features(z, x, event_z, event_x, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate,)

        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
③ 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

_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)


    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:], }
    
    def masking_fea_rgb(self, z, x, ratio=0.8):
        b, nz, c = z.shape
        b, nx, c = x.shape

        lenz_out = int(nz * ratio)
        lenx_out = int(nx * ratio)

        # 生成遮罩
        mask_nz = torch.rand(b, nz).float() > 0.4
        mask_nx = torch.rand(b, nx).float() > 0.4

        # 获取遮罩后的索引
        mask_nz_idx = mask_nz.float().sort(1, descending=True)[-1].to(device=z.device)
        mask_nx_idx = mask_nx.float().sort(1, descending=True)[-1].to(device=x.device)
        
        # 应用遮罩并且重构数据
        masked_z = torch.gather(z, 1, mask_nz_idx[:, :lenz_out, None].repeat(1, 1, c))
        masked_x = torch.gather(x, 1, mask_nx_idx[:, :lenx_out, None].repeat(1, 1, c))

        return masked_z, masked_x, {'z1': mask_nz_idx[:, :lenz_out], 'z0': mask_nz_idx[:, lenz_out:],
                                    'x1': mask_nx_idx[:, :lenx_out], 'x0': mask_nx_idx[:, lenx_out:]}    
    
    def masking_fea_event(self,event_z,event_x,ratio=0.8):
        b,nez,c = event_z.shape
        b,nex,c = event_x.shape
        lenz_out = int(nez*ratio)
        lenx_out = int(nex*ratio)
        mask_ez = torch.rand(b,nez).float() > 0.4
        mask_ex = torch.rand(b,nex).float() > 0.4
        mask_ez_idx = mask_ez.float().sort(1,descending=True)[-1].to(device = event_z.device)
        mask_ex_idx = mask_ex.float().sort(1, descending=True)[-1].to(device=event_x.device)
        masked_ez = torch.gather(event_z, 1, mask_ez_idx[:,:lenz_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_ez, masked_ex , {'ez1': mask_ez_idx[:, :lenz_out], 'ez0': mask_ez_idx[:, lenz_out:],
                                       'ex1': mask_ex_idx[:, :lenx_out], 'ex0': mask_ex_idx[:, lenx_out:]}   



    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 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 = 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 = []

        for i, blk in enumerate(self.blocks):
            ######################## 每隔一层应用遮罩处理 ###########################
            if i % 2 == 0:
                z, event_z, x, event_x, _ = self.masking_fea(z,event_z,x,event_x,ratio=0.95)  
            # 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)


        # 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。


        aux_dict = {
            "attn": attn,
            "attn1": 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):

        x, aux_dict = self.forward_features(z, x, event_z, event_x, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate,)

        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
中关于masking_fea的部分，说出他们的共性和特性。