当前模型：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_module1 = UEPModule(256,
embed_dim,
embed_dim // 4,
self.img_size)
self.uep_module2 = UEPModule(64,
embed_dim,
embed_dim // 4,
self.img_size)
self.uep_module3 = UEPModule(320,
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)
x = self.uep_module1(x)
z = self.uep_module2(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.uep_module1(event_x)
event_z = self.uep_module2(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 = <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 :
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，其中rgb分支读取的aps目录下的文件，event分支读取的是dvs目录下的文件，那么上文中的fusion目录下的文件，是否可以替换或者作为新的输入源？其中生成fusion目录文件的代码是：import os
import cv2
from PIL import Image
import matplotlib.pyplot as plt
import time


data_path = r'/data2/local_userdata/heyuting/datasets/COESOT/train'
save_path = r'/data2/local_userdata/heyuting/datasets/COESOT/train'
if not os.path.exists(save_path):
    os.mkdir(save_path)

filenames = sorted([f for f in os.listdir(data_path) if os.path.isdir(os.path.join(data_path, f))])


def is_directory_empty(directory_path):
    return len(os.listdir(directory_path)) == 0


file_count = len(filenames)

for count, filename in enumerate(filenames, start=1):
    print(f'[{count} / {file_count}]开始处理：', filename)
    aps_sequence_path = os.path.join(data_path, filename, filename + "_aps")
    print('aps_sequence_path:',aps_sequence_path)
    dvs_sequence_path = os.path.join(data_path, filename, filename + "_dvs")
    print('dvs_sequence_path:',dvs_sequence_path)
    aps_count = len(os.listdir(aps_sequence_path))

    # fusion_count = len(os.listdir(os.path.join(save_path, filename + '_fusion', filename + "_fusion_aps")))
    # fusion_count = len(os.listdir(os.path.join(save_path, filename, filename + "_fusion_aps")))
    
    if not os.path.exists(os.path.join(save_path, filename, filename + "_fusion" )):
        os.makedirs(os.path.join(save_path, filename, filename + "_fusion"))
    fusion_count = len(os.listdir(os.path.join(save_path, filename, filename + "_fusion")))
    if aps_count == fusion_count:
        print(f"{aps_count}={fusion_count} ==>> Skip this video : {filename}")
        continue
    
    
    # if not is_directory_empty(os.path.join(save_path, filename + '_fusion', filename + "_fusion_aps")):
    #     print(f"==>> Skip this video : {filename}")
    #     continue

    aps_count = len(os.listdir(aps_sequence_path))

    for frame_id in range(0, aps_count):
        if os.path.exists(os.path.join(aps_sequence_path, 'frame{:04}.png'.format(frame_id))):
            rgb_frame_path = os.path.join(aps_sequence_path, 'frame{:04}.png'.format(frame_id))
        else:
            rgb_frame_path = os.path.join(aps_sequence_path, 'frame{:04}.bmp'.format(frame_id))

        if os.path.exists(os.path.join(dvs_sequence_path, 'frame{:04}.png'.format(frame_id))):
            event_frame_path = os.path.join(dvs_sequence_path, 'frame{:04}.png'.format(frame_id))
        else:
            event_frame_path = os.path.join(dvs_sequence_path, 'frame{:04}.bmp'.format(frame_id))

        fusion_save_path = os.path.join(save_path, filename, filename + "_fusion", 'frame{:04}.bmp'.format(frame_id))

        rgb_frame = Image.open(rgb_frame_path).convert("RGBA")
        event_frame = Image.open(event_frame_path).convert("RGBA")
        # rgb_frame = Image.open(rgb_frame_path)
        # event_frame = Image.open(event_frame_path)
        data = event_frame.getdata()

        new_data = []
        for item in data:
            # 如果像素点不是白色，将其保留
            if item[:3] != (255, 255, 255):
                new_data.append(item)
            else:
                # 如果是白色像素点，设置为透明
                new_data.append((255, 255, 255, 0))

        event_frame.putdata(new_data)

        fusion_frame = Image.new("RGBA", rgb_frame.size)

        for x in range(rgb_frame.width):
            for y in range(rgb_frame.height):
                bg_pixel = rgb_frame.getpixel((x, y))
                fg_pixel = event_frame.getpixel((x, y))

                # 如果前景图像对应像素点是透明的，则使用背景图像的颜色
                if fg_pixel[3] == 0:
                    fusion_frame.putpixel((x, y), bg_pixel)
                else:
                    # 混合前景和背景颜色
                    blended_pixel = (
                        int((fg_pixel[0] + bg_pixel[0]) / 2),
                        int((fg_pixel[1] + bg_pixel[1]) / 2),
                        int((fg_pixel[2] + bg_pixel[2]) / 2),
                        255
                    )
                    fusion_frame.putpixel((x, y), blended_pixel)

        # fusion_frame = Image.blend(rgb_frame, event_frame, alpha=0.2)
        # fusion_frame.show()

        fusion_frame.save(fusion_save_path)

        print(f'[{count} / {file_count}] :{filename}','save success', 'frame{:04}.bmp'.format(frame_id))