在# – coding: utf-8 --**
# GCN model

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_geometric.transforms as T
from itertools import permutations
from torch_geometric.nn import GMMConv,global_mean_pool
import os
import sys
import pdb
from models import torchvision_resnet
from models import crossreplacetransformer
from models.crossreplacetransformer import CrossReplaceTransformerLayer,CrossReplaceTransformer
from timm.models.layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_


import cv2
import numpy as np
import torch
from tqdm import tqdm
from pytorch_grad_cam import GradCAM, ScoreCAM, GradCAMPlusPlus, AblationCAM, XGradCAM, EigenCAM, EigenGradCAM, LayerCAM, FullGrad
from pytorch_grad_cam import GuidedBackpropReLUModel
from pytorch_grad_cam.utils.image import show_cam_on_image, preprocess_image


curPath = os.path.abspath(os.path.dirname(file))
rootPath = os.path.split(curPath)[0]
sys.path.append(rootPath)

class CustomGRU(nn.Module):
def init(self, input_size, hidden_size, num_layers, dropout_rate):
super(CustomGRU, self).init()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.gru = nn.GRU(input_size, hidden_size, num_layers, batch_first=True)
self.dropout = nn.Dropout(dropout_rate)

def forward(self, x):
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)
out, _ = self.gru(x, h0)
out = self.dropout(out)
return out

class Attention(nn.Module):
# fused_attn: Final[bool]

def init(
self,
dim,
num_heads=6,
qkv_bias=False,
qk_norm=False,
attn_drop=0.,
proj_drop=0.,
norm_layer=nn.LayerNorm,
):
super().init()
assert dim % num_heads == 0, ‘dim should be divisible by num_heads’
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim ** -0.5
self.fused_attn = False

self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)

def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
q, k = self.q_norm(q), self.k_norm(k)

if self.fused_attn:
x = F.scaled_dot_product_attention(
q, k, v,
dropout_p=self.attn_drop.p,
)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v

x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x

class ResidualBlock(torch.nn.Module):
def init(self, in_channel, out_channel):
super(ResidualBlock, self).init()
self.left_conv1 = GMMConv(in_channel, out_channel, dim=3, kernel_size=5)
self.left_bn1 = torch.nn.SyncBatchNorm(out_channel)
self.left_conv2 = GMMConv(out_channel, out_channel, dim=3, kernel_size=5)
self.left_bn2 = torch.nn.SyncBatchNorm(out_channel)

self.shortcut_conv = GMMConv(in_channel, out_channel, dim=3, kernel_size=1)
self.shortcut_bn = torch.nn.SyncBatchNorm(out_channel)

def forward(self, data):
data.x = F.elu(self.left_bn2(
self.left_conv2(F.elu(self.left_bn1(self.left_conv1(data.x, data.edge_index, data.edge_attr))),
data.edge_index, data.edge_attr)) + self.shortcut_bn(
self.shortcut_conv(data.x, data.edge_index, data.edge_attr)))

return data

class PatchEmbed(nn.Module):
“”" 2D Image to Patch Embedding
“”“
def init(self, img_size=8, patch_size=4, in_chans=512, embed_dim=768, norm_layer=None, flatten=True):
super().init()
img_size = (img_size,img_size)
patch_size = (patch_size,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])
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 = norm_layer(embed_dim) if norm_layer else nn.Identity()

def forward(self, x):
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 LayerScale(nn.Module):
def init(self, dim, init_values=1e-5, inplace=False):
super().init()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))

def forward(self, x):
return x.mul_(self.gamma) if self.inplace else x * self.gamma

class ST_Transformer_block(torch.nn.Module):
def init(self,dim=768,num_heads=4,mlp_ratio=4,attn_drop=0.,drop=0.,drop_path=0.,
qkv_bias=False,init_values=None):
super(ST_Transformer_block, self).init()
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.mlp1 = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=nn.GELU, drop=drop)
self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()

def forward(self,input):
temporal_form = input + self.drop_path1(self.ls1(self.attn(self.norm1(input))))
temporal_form = self.norm2(temporal_form)
temporal_form_out = temporal_form + self.drop_path2(self.ls2(self.mlp1(temporal_form)))#torch.Size([4, 9, 512])

return temporal_form_out

class Net(torch.nn.Module):
def init(self,pretrained=None, batchNorm=True, output_layers=None, init_std=0.05,
dim=24, clip_len=8, num_heads=6, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., init_values=None, drop_path=0.,embed_dim=768):
super(Net, self).init()
self.patch_emd = PatchEmbed(img_size=8, patch_size=4, in_chans=512, embed_dim=embed_dim, norm_layer=None, flatten=True)
self.drop = drop
drop_rate = 0.
self.ST_Transformer_block_1 = ST_Transformer_block(
dim=dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
attn_drop=attn_drop,
drop=drop,
qkv_bias=qkv_bias,
init_values=init_values,
drop_path=drop_path)
self.ST_Transformer_block_2 = ST_Transformer_block(
dim=dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
attn_drop=attn_drop,
drop=drop,
qkv_bias=qkv_bias,
init_values=init_values,
drop_path=drop_path)
self.ST_Transformer_block_3 = ST_Transformer_block(
dim=dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
attn_drop=attn_drop,
drop=drop,
qkv_bias=qkv_bias,
init_values=init_values,
drop_path=drop_path)
self.ST_Transformer_block_4 = ST_Transformer_block(
dim=dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
attn_drop=attn_drop,
drop=drop,
qkv_bias=qkv_bias,
init_values=init_values,
drop_path=drop_path)
self.ST_Transformer_block_5 = ST_Transformer_block(
dim=dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
attn_drop=attn_drop,
drop=drop,
qkv_bias=qkv_bias,
init_values=init_values,
drop_path=drop_path)

self.clip_len = clip_len
self.batchNorm = batchNorm

self.resnet18_feature_extractor = torchvision_resnet.resnet18(pretrained = True)

self.base_layer = CrossReplaceTransformerLayer(d_model=768, nhead=8, theta=(0.025,0.031))

# 创建 CrossReplaceTransformer
num_layers = 2
replace_start = 1 # 从第2层开始进行替换操作
replace_end = 2 # 在第3层结束替换操作
self.cross_replace_transformer = CrossReplaceTransformer(self.base_layer, num_layers, replace_start, replace_end)


self.pos_drop = nn.Dropout(p=drop_rate)

self.pos_embed = nn.Parameter(torch.zeros([1, self.clip_len4, embed_dim],requires_grad=True))
self.cls_token = nn.Parameter(torch.randn([1, 1, embed_dim], requires_grad=True))
#voxel#
self.conv_0 = GMMConv(16, 64, dim=3, kernel_size=5)
self.bn_0 = torch.nn.SyncBatchNorm(64)


self.block1 = ResidualBlock(64,128)
self.block2 = ResidualBlock(128,256)

self.block3 = ResidualBlock(256,512)
#self.block4 = ResidualBlock(400,512)
self.bottle_neck = nn.Parameter(torch.randn([1, 32, 768], requires_grad=True))
self.fc0 = torch.nn.Linear(768,768)
self.bn0 = torch.nn.SyncBatchNorm(768)
self.fc1 = torch.nn.Linear(768,512)
self.bn = torch.nn.SyncBatchNorm(512)
self.drop_out = torch.nn.Dropout()
self.fc2 = torch.nn.Linear(512, 114)
self.embed_adjust = nn.Linear(512, 768)
self.gru = CustomGRU(input_size=768, hidden_size=768, num_layers=1, dropout_rate=0.2)
def forward(self,data_voxel,data_frame):

##################################################################################
data_frame = data_frame.permute(0,2,1,3,4)
B,C,N,H,W=data_frame.shape # B 3 8 240 240)
res_img_F = F.interpolate(data_frame, size = [self.clip_len, 240, 240], mode=‘trilinear’) #([b, 3,8, 240, 240])

res_img = res_img_F.permute(0,2,1,3,4).reshape(self.clip_lenB,C,240,240) #([bt, 3, h, w]) 5 D -> 4 D
img_with_feature = self.resnet18_feature_extractor(res_img) #[bn,512,8,8]
###############get feature map by resnet#########################################
patched_data = self.patch_emd(img_with_feature) # [bn N C][64,4,768]
###############get pathed token by path embeding##################################
input_former_data_ori = patched_data.reshape(B,-1,patched_data.shape[2]) #[b,Nn,C][8,32,768]

############### Former Block#####################################################
input_former_data = self.pos_drop(input_former_data_ori + self.pos_embed) #

former_b3_out = self.ST_Transformer_block_1(input_former_data)

################# GCN Block ######################################################
#breakpoint()
data_voxel.x = F.elu(self.bn_0(self.conv_0(data_voxel.x, data_voxel.edge_index, data_voxel.edge_attr)))
#breakpoint()
data_voxel = self.block1(data_voxel)
data_voxel = self.block2(data_voxel)
data_voxel = self.block3(data_voxel)
# data_voxel = self.block4(data_voxel)

#################################交换模块##################################################
# 对于 data_frame
src1 = former_b3_out
src1 = src1.transpose(0, 1)
# 对于 data_voxel
graph_sizes = data_voxel.ptr[1:] - data_voxel.ptr[:-1]
graphs = torch.split(data_voxel.x, graph_sizes.tolist())
# 对每个图的节点特征进行处理
src2_list = []
for graph in graphs:
graph_mean = graph.mean(dim=0)
src2_list.append(graph_mean)

src2 = torch.stack(src2_list, dim=0)
src2 = self.embed_adjust(src2)
src2 = src2.unsqueeze(1)
src2 = src2.expand(-1, 32, -1)
src2 = src2.transpose(0, 1)

x1, x2 = self.cross_replace_transformer(src1, src2)

# 将 former_b3_out 换回为 x1
x1 = x1.transpose(0, 1)#torch.Size([2, 32, 768])
#######################bottle_nc###########################################

bottle_nc = self.bottle_neck.expand_as(x1)#torch.Size([2, 32, 768])
fusion_input = torch.cat((x1, bottle_nc), 1)
bn_fusion_out = self.ST_Transformer_block_4(fusion_input) #torch.Size([2, 64, 768])
# 将 data_voxel.x 换回为 x2
x2 = x2.transpose(0, 1)
x2 = torch.mean(x2, dim=1) #[2,768]
x_input = self.bn0(x2) #[2,768]

################ get mean vector of GCN Block’s output##############################
x_input = torch.unsqueeze(x_input,1)
bn_fused = bn_fusion_out[:,32:,:] #torch.Size([2, 32, 768])
bn_input_gcn = torch.cat((bn_fused,x_input),1) #torch.Size([2, 33, 768])

bn_fusion_out_2 = self.ST_Transformer_block_5(bn_input_gcn) #torch.Size([2, 33, 768])
former_fused = bn_fusion_out[:,:32,:] #torch.Size([2, 32, 768])
gcn_fused = bn_fusion_out_2[:,:32,:] #torch.Size([2, 32, 768])
x_fused = torch.cat((gcn_fused,former_fused),1) #torch.Size([2, 64, 768])

############################### GRU #############################################

x_fused_split = [bn_fusion_out[:,:32,:], bn_fusion_out[:,32:,:], gcn_fused]

perms = list(permutations(x_fused_split, 3))[:5]
gru_inputs = torch.stack([torch.cat(perm, dim=1) for perm in perms])

gru_inputs = gru_inputs.view(-1, 96, 768) #torch.Size([5, 8, 96, 768])
gru_output = self.gru(gru_inputs)
gru_output = gru_output.view(5, -1, 96, 768)
gru_output_mean = gru_output.mean(dim=0)
x_fused = gru_output_mean

x = torch.mean(x_fused,1) # [b,768]
################cat vector of dual branch and classification ##################################################
x = self.fc1(x)
x = F.elu(x)
x = self.bn(x)
x = self.drop_out(x)
x = self.fc2(x)

return F.log_softmax(x, dim=1)
中调用了CrossReplaceTransformerLayer和CrossReplaceTransformer，现在只说明调用它们的方法，位置以及目的。
调用CrossReplaceTransformerLayer和CrossReplaceTransformer的目的是在某些Transformer编码层之间引入交替替换操作，以增强不同输入序列之间的交互，可能提高模型的性能和泛化能力。下面是具体调用它们的说明：

### CrossReplaceTransformerLayer 的调用位置和作用

CrossReplaceTransformerLayer在Net类的构造函数（init）中创建并初始化：

- 位置:

self.base_layer = CrossReplaceTransformerLayer(d_model=768, nhead=8, theta=(0.025,0.031))


- 目的:
它定义了一个基础的Transformer编码层，其中包含自注意力块、前馈块和交替替换块。该层将在CrossReplaceTransformer中作为基础层进行复制。

### CrossReplaceTransformer 的调用位置和作用

CrossReplaceTransformer在Net类的构造函数（init）中被创建并初始化：

- 位置:

num_layers = 2
replace_start = 1 # 从第2层开始进行替换操作
replace_end = 2 # 在第3层结束替换操作
self.cross_replace_transformer = CrossReplaceTransformer(self.base_layer, num_layers, replace_start, replace_end)


- 目的:
它创建了一个包含2层的交替替换Transformer模型，这个模型在指定的层（即第2和第3层）之间进行交替替换操作。

### 交替替换模块插入位置和目的

- 位置:
在Net类的前向传播函数（forward）中，两次使用self.cross_replace_transformer进行调用并插入交替替换模块，分别是：

1. 对frame数据：

src1 = former_b3_out
src1 = src1.transpose(0, 1)


2. 对voxel数据：

graph_sizes = data_voxel.ptr[1:] - data_voxel.ptr[:-1]
graphs = torch.split(data_voxel.x, graph_sizes.tolist())
src2_list = []
for graph in graphs:
graph_mean = graph.mean(dim=0)
src2_list.append(graph_mean)

src2 = torch.stack(src2_list, dim=0)
src2 = self.embed_adjust(src2)
src2 = src2.unsqueeze(1)
src2 = src2.expand(-1, 32, -1)
src2 = src2.transpose(0, 1)


- 调用和替换操作:

x1, x2 = self.cross_replace_transformer(src1, src2)


- 目的:
通过交替替换操作，旨在将frame数据的注意力权重和voxel数据的全局特征进行相互替换，以便让模型捕捉到两种不同类型数据之间的互动和互补特征，更好地理解复杂场景中的信息。

### 总结

- 目的:
CrossReplaceTransformerLayer和CrossReplaceTransformer用于实现一种增强机制，通过使用交替替换模块，使得模型能利用来自两个不同源的数据的特征交互。这种方法增加了模型的表达能力，潜在地提升其性能和泛化能力。

- 调用位置:
这些类在Net模型的初始化过程中被实例化，并在前向传播过程中被调用以处理输入的frame数据和voxel数据，实现交替替换操作。那么在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
from .new_counter_guide import Counter_Guide
from .ad_counter_guide import Counter_Guide_Enhanced
_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 = <br/> 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 = <br/> 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
# 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中是否可以将CrossReplaceTransformerLayer,CrossReplaceTransformer集成进去？说出集成位置以及目的。
可以通过借鉴Net模型中使用 CrossReplaceTransformerLayer 和 CrossReplaceTransformer 的方法，将这些模块集成到 VisionTransformerCE 中。下面是详细的集成位置和目的说明：

### 步骤总览
1. 定义CrossReplaceTransformerLayer和CrossReplaceTransformer层：
- 在模型的初始化方法__init__中定义CrossReplaceTransformerLayer和CrossReplaceTransformer层。
2. 在前向传播（forward）过程中应用这些层：
- 在合适的位置调用CrossReplaceTransformerLayer和CrossReplaceTransformer，以便在两个分支之间进行特征交互。

### 详细步骤

#### 1. 在__init__方法中定义CrossReplaceTransformerLayer和CrossReplaceTransformer层

首先，我们需要在模型的初始化方法中实例化 CrossReplaceTransformerLayer 和 CrossReplaceTransformer。

from models.crossreplacetransformer import CrossReplaceTransformerLayer, CrossReplaceTransformer  # Assuming these are defined similarly as in your Net class

class VisionTransformerCE(VisionTransformer):
    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()
        …
        # 添加 CrossReplaceTransformerLayer 和 CrossReplaceTransformer
        self.base_layer = CrossReplaceTransformerLayer(d_model=embed_dim, nhead=num_heads, theta=(0.025, 0.031))
        num_layers = 2
        replace_start = 1  # 从第2层开始进行替换操作
        replace_end = 2  # 在第3层结束替换操作
        self.cross_replace_transformer = CrossReplaceTransformer(self.base_layer, num_layers, replace_start, replace_end)
        …


#### 2. 在forward_features方法中应用这些层

在合适的位置调用 self.cross_replace_transformer，进行交替替换操作。假设我们要在x 和 event_x 之间进行交替替换。

class VisionTransformerCE(VisionTransformer):
    …
    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):
        …
        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 = <br/>                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 = <br/>                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 :
                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）
                enhanced_x, enhanced_event_x = self.counter_guide(x, event_x)
                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
                x = x + enhanced_x
                event_x = event_x + enhanced_event_x

            # 在指定层之间进行交替替换操作
            # 可以在0层和1层之间进行替换，示例如下：
            if i == 1:
                src1 = x.transpose(0, 1)
                src2 = event_x.transpose(0, 1)
                x, event_x = self.cross_replace_transformer(src1, src2)
                x = x.transpose(0, 1)
                event_x = event_x.transpose(0, 1)
        …


### 目的
- 目的:
通过引入 CrossReplaceTransformerLayer 和 CrossReplaceTransformer，在不同来源数据（如frame和voxel）的Transformer编码层之间进行交替替换操作，促使模型更好地捕获这些数据之间的交互信息。

- 详细目的:
- 在 VisionTransformerCE 的特征提取过程中，对不同来源的特征进行交替替换，使模型能够并行处理和融合这些特征，提高模型的鲁棒性和性能。

### 总结
通过以上步骤，可以将 CrossReplaceTransformerLayer 和 CrossReplaceTransformer 集成到 VisionTransformerCE 中，从而在不同数据来源之间进行有效的特征交互和融合。那么现在在vit_ce模型的基础上，将 CrossReplaceTransformerLayer 和 CrossReplaceTransformer 添加进去，如何同counter_guide_enhanced一并实现特征增强？