将# 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
# 集成CrossMamba模块进行不同模态特征交互
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)
# 初始化CrossMamba模块
self.cross_mamba = CrossMamba(dim=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 :
# 引入counter_guide进行特征增强
enhanced_x, enhanced_event_x = self.counter_guide(x, event_x)
# 将增强后的特征与原特征相加
x = x + enhanced_x
event_x = event_x + enhanced_event_x
##############
lens_x_new = global_index_s.shape[1]
lens_z_new = global_index_t.shape[1]
z = x[:, :lens_z_new2]
x = x[:, lens_z_new2:]
lens_eventx_new = global_index_s1.shape[1]
lens_eventz_new = global_index_t1.shape[1]
event_z = event_x[:, :lens_eventx_new2]
event_x = event_x[:, lens_eventx_new*2:]
## 恢复原始的 token 顺序 #################################
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 - lens_x_new
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)
# 恢复原始顺序
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)
x = recover_tokens(x, lens_z_new, lens_x, mode=self.cat_mode)

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 - lens_eventx_new
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)
# 恢复原始顺序
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)
event_x = recover_tokens(event_x, lens_eventz_new, lens_x, mode=self.cat_mode)
################################################################

## 引入Crossmamba进行事件和图像特征融合,不采用concatenate
x = self.cross_mamba(x, event_x)
# 应用LayerNorm归一化处理
x = self.norm(x)

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中的x = recover_tokens(x, lens_z_new, lens_x, mode=self.cat_mode)和event_x = recover_tokens(event_x, lens_eventz_new, lens_x, mode=self.cat_mode)的lens_z_new，lens_eventz_new进行检查。