USER
现在解读：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)
self.counter_guide = Counter_Guide_Enhanced(768, 768)
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
):
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 = []
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):
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)
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
lens_x_new = global_index_s.shape[1]
lens_z_new = global_index_t.shape[1]
z = x[:, :lens_z_new]
x = x[:, lens_z_new:]
lens_eventx_new = global_index_s1.shape[1]
lens_eventz_new = global_index_t1.shape[1]
event_z = event_x[:, :lens_eventz_new]
event_x = event_x[:, lens_eventz_new:]
residual_rgb_f = 0
x = self.cross_mamba(x, residual_rgb_f, event_x)
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
ASSISTANT
这个代码片段定义了一个基于Vision Transformer（ViT）的目标跟踪模型，并集成了多个模块，包括特征融合和交互模块CrossMamba。这进一步增强了ViT的功能，使其不仅可以处理视觉特征，还可以结合多种模态数据（例如事件数据）。以下是代码的具体解读：
### 依赖库和模块导入
```python
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__)
```
首先，代码导入了所需的各种库和模块，包括PyTorch库、ViT实现、不同的特征嵌入模块以及特征交互模块CrossMamba。
### VisionTransformerCE 类定义
```python
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__()
# 初始化基本参数和嵌入层
self.num_features = self.embed_dim = embed_dim
self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
# 初始化位置嵌入和分类token
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, self.patch_embed.num_patches + 1, embed_dim))
# Dropout
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)
# 初始化CEBlock和相关参数
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
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)
# 初始化权重
self.init_weights(weight_init)
# 初始化额外的模块
self.counter_guide = Counter_Guide_Enhanced(768, 768)
self.cross_mamba = CrossMamba(dim=768)
```
在这个类初始化部分，定义了一些基本参数，如图像大小、嵌入维度、层数、注意力头数等。通过嵌入层（PatchEmbed）将输入图像转换为嵌入特征，并初始化位置嵌入和分类token。使用多个CEBlock构建Transformer的编码器部分，并添加了两个增强模块：Counter_Guide_Enhanced和CrossMamba。
### forward_features 方法
```python
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):
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 = []
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):
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)
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
lens_x_new = global_index_s.shape[1]
lens_z_new = global_index_t.shape[1]
z = x[:, :lens_z_new]
x = x[:, lens_z_new:]
lens_eventx_new = global_index_s1.shape[1]
lens_eventz_new = global_index_t1.shape[1]
event_z = event_x[:, :lens_eventz_new]
event_x = event_x[:, lens_eventz_new:]
residual_rgb_f = 0
x = self.cross_mamba(x, residual_rgb_f, event_x)
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
```
此方法首先对输入z和x进行嵌入和位置编码处理，并进行适当的遮罩处理。如果添加了分类token和分割嵌入，就对位置嵌入后的特征进行合并操作。
在主循环中，逐层通过CEBlock块对特征进行处理。此外，还对事件数据进行相似处理。在第一个CEBlock块之后，通过Counter_Guide_Enhanced模块增强特征。最终，通过CrossMamba模块融合x和event_x特征，并进行归一化处理，结束特征处理流程。
### forward 方法
```python
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
```
该方法调用`forward_features`方法进行特征提取和融合，并返回处理后的特征和辅助字典（aux_dict），其中包含注意力信息和删除索引信息，用于进一步的分析和可视化。
### _create_vision_transformer 函数
```python
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
```
该函数用于创建并初始化`VisionTransformerCE`模型。如果加载预训练权重，则根据预训练文件类型（npz或其他）进行相应的加载处理。
### vit_base_patch16_224_ce 函数
```python
def vit_base_patch16_224_ce(pretrained=False, **kwargs):
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
```
这是一个工厂函数，用于创建ViT-Base模型，并利用`_create_vision_transformer`函数初始化相关参数和权重。
### 总结
该代码实现了一个基于ViT的增强目标跟踪模型，通过多个模块（如CEBlock、Counter_Guide_Enhanced和CrossMamba）增强和融合多模态特征，提高模型的目标跟踪性能。