模型是：# 将 4输入分开，构建新的相同模态结合的2输入，2分支
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
from .new_counter_guide import Counter_Guide
from .ad_counter_guide import Counter_Guide_Enhanced
from ..mamba_fetrack.models_mamba import VisionMamba, vim_small_patch16_224_bimambav2_final_pool_mean_abs_pos_embed_with_midclstok_div2
from ..mamba_fetrack.mamba_cross import CrossMamba
_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
        self.counter_guide = Counter_Guide_Enhanced(768, 768)
    
    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        

        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 = []

        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)

            # 在第1层增加counter_guide模块,验证早期融合效果
            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
                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
                x = x + enhanced_x
                event_x = event_x + enhanced_event_x
        
        # 恢复特征tokens ###############
        if removed_indexes_s and removed_indexes_s[0] is not None:
            removed_indexes_cat = torch.cat(removed_indexes_s, dim=1)
            pruned_lens_x = lens_x - global_index_s.shape[1]
            pad_x = torch.zeros([B, pruned_lens_x, x.shape[2]], device=x.device)
            x = torch.cat([x, pad_x], dim=1)
            index_all = torch.cat([global_index_s, removed_indexes_cat], dim=1)
            C = x.shape[-1]
            x = torch.zeros_like(x).scatter(dim=1, index=index_all.unsqueeze(-1).expand(B, -1, C).to(torch.int64), src=x)

        if removed_indexes_s1 and removed_indexes_s1[0] is not None:
            removed_indexes_cat1 = torch.cat(removed_indexes_s1, dim=1)
            pruned_lens_x1 = lens_x - global_index_s1.shape[1]
            pad_x1 = torch.zeros([B, pruned_lens_x1, event_x.shape[2]], device=event_x.device)
            event_x = torch.cat([event_x, pad_x1], dim=1)
            index_all1 = torch.cat([global_index_s1, removed_indexes_cat1], dim=1)
            C = event_x.shape[-1]
            event_x = torch.zeros_like(event_x).scatter_(dim=1, index=index_all1.unsqueeze(-1).expand(B, -1, C).to(torch.int64), src=event_x)
        #################################
        
        
        # 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
        aux_dict = {
            "attn": attn,
            'attn1': attn1,
            "removed_indexes_s": removed_indexes_s,  # used for visualization
            'removed_indexes_s1': removed_indexes_s1,
            # 在aux_dict中返回原始特征和增强特征
            'A_original_x': A_original_x,
            'A_original_event_x': A_original_event_x,
            'A_enhanced_x': A_enhanced_x,
            'A_enhanced_event_x': A_enhanced_event_x,
        }
        
        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 ，counter_guide是import torch
import torch.nn as nn
import torch.nn.functional as F

# 为结合vit中的attention优势,设计了新的交叉注意力
class Cross_Attention(nn.Module):
    def __init__(self, input_channels, output_channels):
        super(Cross_Attention, self).__init__()
        self.query = nn.Linear(input_channels, output_channels)
        self.key = nn.Linear(input_channels, output_channels)
        self.value = nn.Linear(input_channels, output_channels)
        self.scale = output_channels ** -0.5  # 缩放因子，防止softmax的梯度消失/爆炸
    
    def forward(self, assistant, present):
        # 生成查询、键和值
        q = self.query(assistant)
        k = self.key(present)
        v = self.value(present)
        
        # 计算注意力分数
        attention_scores = torch.bmm(q, k.transpose(1, 2)) * self.scale
        attention = F.softmax(attention_scores, dim=-1)
        
        # 应用注意力机制
        attended_features = torch.bmm(attention, v)
        return attended_features

class Multi_Context(nn.Module):
    def __init__(self, input_channels, output_channels):  # 修正了def init为def init
        super(Multi_Context, self).__init__()
        self.linear1 = nn.Linear(input_channels, output_channels)
        self.linear2 = nn.Linear(input_channels, output_channels)
        self.linear3 = nn.Linear(input_channels, output_channels)
        self.linear_final = nn.Linear(output_channels * 3, output_channels)

    def forward(self, x):
        x1 = F.relu(self.linear1(x))
        x2 = F.relu(self.linear2(x))
        x3 = F.relu(self.linear3(x))
        x = torch.cat([x1, x2, x3], dim=-1)  # Concatenate along the feature dimension
        x = self.linear_final(x)
        return x

class Adaptive_Weight(nn.Module):
    def __init__(self, input_channels):  # 修正了def init为def init
        super(Adaptive_Weight, self).__init__()
        self.fc1 = nn.Linear(input_channels, input_channels // 4)
        self.fc2 = nn.Linear(input_channels // 4, input_channels)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x_avg = torch.mean(x, dim=1)  # Global Average Pooling along the ‘sequence length’ dimension
        weight = F.relu(self.fc1(x_avg))
        weight = self.fc2(weight)
        weight = self.sigmoid(weight).unsqueeze(1)
        out = x * weight
        return out

class Counter_Guide_Enhanced(nn.Module):
    def __init__(self, input_channels, output_channels):
        super(Counter_Guide_Enhanced, self).__init__()
        self.mc = Multi_Context(input_channels, output_channels)
        self.cross_attention = Cross_Attention(output_channels, output_channels)
        # 结合自适应
        self.adaptive_weight = Adaptive_Weight(output_channels)
    
    def forward(self, x, event_x):
        # 首先使用Multi-Context增强事件特征
        # enhanced_event_x = self.mc(event_x)

        # 使用Cross-Attention进行特征交互,(x和event_x互相增强)
        enhanced_x = self.cross_attention(x, event_x)
        enhanced_event_x_via_x = self.cross_attention(event_x, enhanced_x)
        
        # 进一步通过adaptive weight调整增强的特征权重
        adaptive_enhanced_x = self.adaptive_weight(enhanced_x)
        adaptive_enhanced_event_x = self.adaptive_weight(enhanced_event_x_via_x)
        
        return adaptive_enhanced_x, adaptive_enhanced_event_x
，损失函数是：
from . import BaseActor
from lib.utils.misc import NestedTensor
from lib.utils.box_ops import box_cxcywh_to_xyxy, box_xywh_to_xyxy
import torch
from lib.utils.merge import merge_template_search
from ...utils.heapmap_utils import generate_heatmap
from ...utils.ce_utils import generate_mask_cond, adjust_keep_rate
#
import torch.nn.functional as F

class CEUTrackActor(BaseActor):
    """ Actor for training CEUTrack models """

    def __init__(self, net, objective, loss_weight, settings, cfg=None):
        super().__init__(net, objective)
        self.loss_weight = loss_weight
        self.settings = settings
        self.bs = self.settings.batchsize  # batch size
        self.cfg = cfg

    def __call__(self, data):
        """
        args:
            data - The input data, should contain the fields 'template', 'search', 'gt_bbox'.
            template_images: (N_t, batch, 3, H, W)
            search_images: (N_s, batch, 3, H, W)
        returns:
            loss    - the training loss
            status  -  dict containing detailed losses
        """
        # forward pass
        out_dict = self.forward_pass(data)

        # compute losses
        loss, status = self.compute_losses(out_dict, data)

        return loss, status

    def forward_pass(self, data):
        # currently only support 1 template and 1 search region
        assert len(data['template_images']) == 1
        assert len(data['search_images']) == 1
        assert len(data['template_event']) == 1
        assert len(data['search_event']) == 1

        template_list = []
        for i in range(self.settings.num_template):
            template_img_i = data['template_images'][i].view(-1,
                                                             *data['template_images'].shape[2:])  # (batch, 3, 128, 128)
            # template_att_i = data['template_att'][i].view(-1, *data['template_att'].shape[2:])  # (batch, 128, 128)
            template_list.append(template_img_i)

        search_img = data['search_images'][0].view(-1, *data['search_images'].shape[2:])  # (batch, 3, 320, 320)
        # search_att = data['search_att'][0].view(-1, *data['search_att'].shape[2:])  # (batch, 320, 320)

        template_event = data['template_event'][0].view(-1, *data['template_event'].shape[2:])
        search_event = data['search_event'][0].view(-1, *data['search_event'].shape[2:])

        box_mask_z = None
        ce_keep_rate = None
        if self.cfg.MODEL.BACKBONE.CE_LOC:
            box_mask_z = generate_mask_cond(self.cfg, template_list[0].shape[0], template_list[0].device,
                                            data['template_anno'][0])

            ce_start_epoch = self.cfg.TRAIN.CE_START_EPOCH
            ce_warm_epoch = self.cfg.TRAIN.CE_WARM_EPOCH
            ce_keep_rate = adjust_keep_rate(data['epoch'], warmup_epochs=ce_start_epoch,
                                                total_epochs=ce_start_epoch + ce_warm_epoch,
                                                ITERS_PER_EPOCH=1,
                                                base_keep_rate=self.cfg.MODEL.BACKBONE.CE_KEEP_RATIO[0])

        if len(template_list) == 1:
            template_list = template_list[0]

        out_dict = self.net(template=template_list,
                            search=search_img,
                            event_template=template_event,
                            event_search=search_event,
                            ce_template_mask=box_mask_z,
                            ce_keep_rate=ce_keep_rate,
                            return_last_attn=False)

        return out_dict

    def compute_losses(self, pred_dict, gt_dict, return_status=True):
        # gt gaussian map
        gt_bbox = gt_dict['search_anno'][-1]  # (Ns, batch, 4) (x1,y1,w,h) -> (batch, 4)
        gt_gaussian_maps = generate_heatmap(gt_dict['search_anno'], self.cfg.DATA.SEARCH.SIZE, self.cfg.MODEL.BACKBONE.STRIDE)
        gt_gaussian_maps = gt_gaussian_maps[-1].unsqueeze(1)

        # Get boxes
        pred_boxes = pred_dict['pred_boxes']
        if torch.isnan(pred_boxes).any():
            raise ValueError("Network outputs is NAN! Stop Training")
        num_queries = pred_boxes.size(1)
        pred_boxes_vec = box_cxcywh_to_xyxy(pred_boxes).view(-1, 4)  # (B,N,4) --> (BN,4) (x1,y1,x2,y2)
        gt_boxes_vec = box_xywh_to_xyxy(gt_bbox)[:, None, :].repeat((1, num_queries, 1)).view(-1, 4).clamp(min=0.0,
                                                                                                           max=1.0)  # (B,4) --> (B,1,4) --> (B,N,4)
        # compute giou and iou
        try:
            giou_loss, iou = self.objective['giou'](pred_boxes_vec, gt_boxes_vec)  # (BN,4) (BN,4)
        except:
            giou_loss, iou = torch.tensor(0.0).cuda(), torch.tensor(0.0).cuda()

        # 新增 compute ciou and iou
        # try:
        #     ciou_loss, iou = self.objective['ciou'](pred_boxes_vec, gt_boxes_vec)  # (BN,4) (BN,4)
        # except:
        #     ciou_loss, iou = torch.tensor(0.0).cuda(), torch.tensor(0.0).cuda()
        ###############################
        # compute l1 loss
        l1_loss = self.objective['l1'](pred_boxes_vec, gt_boxes_vec)  # (BN,4) (BN,4)
        # compute location loss
        if 'score_map' in pred_dict:
            location_loss = self.objective['focal'](pred_dict['score_map'], gt_gaussian_maps)
        else:
            location_loss = torch.tensor(0.0, device=l1_loss.device)

        # #######
        # # 计算特征一致性损失
        # 目的是：减少特征扰动，通过约束增强特征和原始特征的一致性，降低增强过程中的特征扰动；
        #        保持特征稳定，确保特征在增强或其他处理模块中的变化稳定，保持输入输出特征的一致性
        #        提高跟踪精度，减少不必要的特征变动，提高特征的稳定和跟踪精度。
        feature_consistency_loss = F.mse_loss(pred_dict['A_enhanced_x'], pred_dict['A_original_x']) + F.mse_loss(pred_dict['A_enhanced_event_x'], pred_dict['A_original_event_x'])

        # weighted sum
        loss = self.loss_weight['giou'] * giou_loss + self.loss_weight['l1'] * l1_loss + self.loss_weight['focal'] * location_loss # + 0.5 * feature_consistency_loss
        if return_status:
            # status for log
            mean_iou = iou.detach().mean()
            status = {"Loss/total": loss.item(),
                      "Loss/giou": giou_loss.item(),
                      "Loss/l1": l1_loss.item(),
                      "Loss/location": location_loss.item(),
                    #   'Loss/feature_consistency': feature_consistency_loss.item(),
                      "IoU": mean_iou.item()}
            return loss, status
        else:
            return loss
，目标任务是目标跟踪，当前模型中存在的问题是，模型的SR指标低，现在期望设计新的损失约束counter_guide。