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
from .cross_module import Counter_Guide_Enhanced, Cross_Attention
from .UEP import UEPModule
_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-sota模块
        self.counter_guide = Counter_Guide_Enhanced(768, 768)
        # 新增 UEP实例化
        self.uep_module = UEPModule(num_patches, 
                                    embed_dim, 
                                    embed_dim // 4, 
                                    self.img_size)
        
    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):
            # 第一个分支处理
            # 新增 uep 模块
            x = self.uep_module(x)
            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
            # 新增 uep 模块
            event_x = self.uep_module(event_x)
            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)
            
            if i == 0 :
                enhanced_x, enhanced_event_x = self.counter_guide(x, event_x)
                x = x + enhanced_x
                event_x = event_x + enhanced_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, # used for visualization
        }
        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 和 ② import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import to_2tuple

class UEPModule(nn.Module):
    def __init__(self, num_patches, embed_dim, hidden_dim, img_size):
        super(UEPModule, self).__init__()
        self.num_patches = num_patches
        self.embed_dim = embed_dim
        self.hidden_dim = hidden_dim
        self.img_size = to_2tuple(img_size)
        self.patch_size = int((self.img_size[0] * self.img_size[1]) ** 0.5 / num_patches ** 0.5)
        # Recalculate H and W
        self.H = self.img_size[0] // self.patch_size
        self.W = self.img_size[1] // self.patch_size
        
        # 1x1 Conv for Low-dimensional Projection
        self.conv1 = nn.Conv2d(embed_dim, hidden_dim, kernel_size=1)
        
        # Parallel 1x1 Conv and 1x1 DW-Conv
        self.conv2 = nn.Conv2d(hidden_dim, hidden_dim, kernel_size=1)
        self.dw_conv = nn.Conv2d(hidden_dim, hidden_dim, kernel_size=1, groups=hidden_dim)  # Assuming DW-Conv is depthwise
        
        # Additional 1x1 Conv before Activation
        self.additional_conv = nn.Conv2d(hidden_dim, hidden_dim, kernel_size=1)
        
        # 1x1 Conv for High-dimensional Projection
        self.conv3 = nn.Conv2d(hidden_dim, embed_dim, kernel_size=1)
        
        # Activation
        self.gelu = nn.GELU()
    
    def forward(self, x):
        # Reshape (B,N,D) → (B,C,H,W)
        B, N, D = x.shape
        assert N == self.num_patches and D == self.embed_dim, 'Input shape does not match model configuration'
        x = x.permute(0, 2, 1).view(B, D, self.H, self.W)
        
        # Low-dimensional projection
        identity = x
        x = self.conv1(x)
        
        # Element-wise adding of parallel convolutions
        conv2_out = self.conv2(x)
        dw_conv_out = self.dw_conv(x)
        x = conv2_out + dw_conv_out
        
        # Additional 1x1 Conv and GELU
        x = self.additional_conv(x)
        x = self.gelu(x)
        
        # High-dimensional projection
        x = self.conv3(x)
        x = x + identity # Add identity after high-dimensional projection
        
        # Reshape back to (B,N,D)
        x = x.view(B, self.embed_dim, -1).permute(0, 2, 1)
        return x
③ import numpy as np
import torch.nn as nn

from timm.models.layers import to_2tuple
import torch
import torch.nn.functional as F

class PatchEmbed(nn.Module):
    """ 2D Image to Patch Embedding
    """

    def __init__(self, img_size=256, patch_size=16, in_chans=3, embed_dim=768, norm_layer=False, flatten=True):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) # 224 / 16 = 14
        self.num_patches = self.grid_size[0] * self.grid_size[1]
        self.flatten = flatten

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
        self.norm = nn.LayerNorm(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        # allow different input size
        # B, C, H, W = x.shape
        # _assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).")
        # _assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).")
        x = self.proj(x)
        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC
        x = self.norm(x)
        return x


class PatchEmbed_event(nn.Module):
    def __init__(self, in_chans=320, embed_dim=768, kernel_size=5, stride=1, flatten=True, norm_layer=False):
        super().__init__()
        self.pos_embedding = nn.Conv1d(in_channels=3, out_channels=16, kernel_size=1, stride=1)
        self.in_chans = in_chans
        self.flatten = flatten
        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=kernel_size, stride=stride)
        # self.proj2 = nn.Conv2d(embed_dim, embed_dim, kernel_size=1, stride=1)
        self.norm = nn.LayerNorm(embed_dim) if norm_layer else nn.Identity()
        # self.attn = SelfAttn(768, 4, 3072, 0.1, 'relu')

    def forward(self, x):
        # allow different input size
        x = x.type(torch.cuda.FloatTensor)
        xyz = self.pos_embedding(x.squeeze(dim=1)[:, :3, :])
        xyz = F.relu(xyz)
        x = torch.cat([xyz, x.squeeze(dim=1)[:, 3:, :]], dim=1)
        B, N, C = x.shape        # 1 1 19 10000
        H = W = int(np.sqrt(N*C//self.in_chans))
        x = x.reshape(B, self.in_chans, H, W)       #  B 19 100 100
        x = self.proj(x)        # B 768 16 16
        # x = self.proj2(x)        # B 768 16 16

        if self.flatten:
            x = x.flatten(2).transpose(1, 2)  # BCHW -> BNC  768*256
        # x = self.attn(src1=x, pos_src1=pos_embed)
        x = self.norm(x)
        return x

def xcorr_depthwise(x, kernel):  # x, event_x
    """depthwise cross correlation
    """
    batch = kernel.size(0)
    channel = kernel.size(2)
    H = W = int(np.sqrt(x.size(1)))
    x = x.reshape(1, batch*channel, H, W)
    kernel = kernel.reshape(batch*channel, 1, H, W)
    corr_weight = F.conv2d(x, kernel, groups=batch*channel)
    # out = out.reshape(batch, H*W, channel)
    out = x * corr_weight
    return out.reshape(batch, H*W, channel)
，运行之后报错：Training crashed at epoch 1
Traceback for the error!
Traceback (most recent call last):
  File "/data2/local_userdata/heyuting/CEU2/CEUTrack/lib/train/../../lib/train/trainers/base_trainer.py", line 85, in train
    self.train_epoch()
  File "/data2/local_userdata/heyuting/CEU2/CEUTrack/lib/train/../../lib/train/trainers/ltr_trainer.py", line 133, in train_epoch
    self.cycle_dataset(loader)
  File "/data2/local_userdata/heyuting/CEU2/CEUTrack/lib/train/../../lib/train/trainers/ltr_trainer.py", line 86, in cycle_dataset
    loss, stats = self.actor(data)
  File "/data2/local_userdata/heyuting/CEU2/CEUTrack/lib/train/../../lib/train/actors/ceutrack.py", line 285, in __call__
    out_dict = self.forward_pass(data)
  File "/data2/local_userdata/heyuting/CEU2/CEUTrack/lib/train/../../lib/train/actors/ceutrack.py", line 334, in forward_pass
    return_last_attn=False)
  File "/home/heyuting/anaconda3/envs/ceu/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/heyuting/anaconda3/envs/ceu/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 1008, in forward
    output = self._run_ddp_forward(*inputs, **kwargs)
  File "/home/heyuting/anaconda3/envs/ceu/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 969, in _run_ddp_forward
    return module_to_run(*inputs[0], **kwargs[0])
  File "/home/heyuting/anaconda3/envs/ceu/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/data2/local_userdata/heyuting/CEU2/CEUTrack/lib/train/../../lib/models/ceutrack/ceutrack.py", line 54, in forward
    return_last_attn=return_last_attn)
  File "/home/heyuting/anaconda3/envs/ceu/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/data2/local_userdata/heyuting/CEU2/CEUTrack/lib/train/../../lib/models/ceutrack/vit_ce.py", line 194, in forward
    x, aux_dict = self.forward_features(z, x, event_z, event_x, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate)
  File "/data2/local_userdata/heyuting/CEU2/CEUTrack/lib/train/../../lib/models/ceutrack/vit_ce.py", line 158, in forward_features
    x = self.uep_module(x)
  File "/home/heyuting/anaconda3/envs/ceu/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/data2/local_userdata/heyuting/CEU2/CEUTrack/lib/train/../../lib/models/ceutrack/UEP.py", line 37, in forward
    assert N == self.num_patches and D == self.embed_dim, 'Input shape does not match model configuration'
AssertionError: Input shape does not match model configuration

Restarting training from last epoch ...
Finished training!