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
import random
import numpy as np
from .DCM import Counter_Guide_Enhanced

_logger = logging.getLogger(name)


class StarReLU(nn.Module):
“”“
StarReLU: s * relu(x) ** 2 + b
“””
def init(self, scale_value=1.0, bias_value=0.0,
scale_learnable=True, bias_learnable=True,
mode=None, inplace=False):
super().init()
self.inplace = inplace
self.relu = nn.ReLU(inplace=inplace)
self.scale = nn.Parameter(scale_value * torch.ones(1),
requires_grad=scale_learnable)
self.bias = nn.Parameter(bias_value * torch.ones(1),
requires_grad=bias_learnable)
def forward(self, x):
return self.scale * self.relu(x)**2 + self.bias

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-此处对模块已经做了更新
self.counter_guide = Counter_Guide_Enhanced(768, 768)

# 新增随机遮蔽特征操作
def masking_fea(self,z, event_z, x, event_x, ratio=0.8):
b,nz,c = z.shape
b,nez,c = event_z.shape
b,nx,c = x.shape
b,nex,c = event_x.shape

assert(nz == nez)
assert(nx == nex)
lenz_out = int(nzratio)
lenx_out = int(nx*ratio)

mask_nz = torch.rand(b,nz).float()
mask_ez = torch.rand(b,nez).float()
mask_nx = torch.rand(b,nx).float()
mask_ex = torch.rand(b,nex).float()
mask_nz = mask_nz>0.4
mask_ez = mask_ez>0.4
mask_ez = ~mask_nz + mask_ez
mask_nz_idx = mask_nz.float().sort(1,descending=True)[-1].to(device = z.device)
mask_ez_idx = mask_ez.float().sort(1,descending=True)[-1].to(device = z.device)
mask_nx = mask_nx>0.4
mask_ex = mask_ex>0.4
mask_ex = ~mask_nx + mask_ex
mask_nx_idx = mask_nx.float().sort(1,descending=True)[-1].to(device = z.device)
mask_ex_idx = mask_ex.float().sort(1,descending=True)[-1].to(device = z.device)

masked_z = torch.gather(z, 1, mask_nz_idx[:,:lenz_out,None].repeat([1,1,c]))
masked_ez = torch.gather(event_z, 1, mask_ez_idx[:,:lenz_out,None].repeat([1,1,c]))
masked_x = torch.gather(x, 1, mask_nx_idx[:,:lenx_out,None].repeat([1,1,c]))
masked_ex = torch.gather(event_x, 1, mask_ex_idx[:,:lenx_out,None].repeat([1,1,c]))
return masked_z, masked_ez, masked_x, masked_ex,{‘x1’:mask_nx_idx[:,:lenx_out],‘x0’:mask_nx_idx[:,lenx_out:],
‘ex1’:mask_ex_idx[:,:lenx_out],‘ex0’:mask_ex_idx[:,lenx_out:], }

# 随机遮蔽特征操作完成
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, Track=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 Track == False:
masked_z, masked_event_z, masked_x, masked_event_x, token_idx = self.masking_fea(z, event_z, x, event_x, ratio=0.98)

z = masked_z
x = masked_x
event_x = masked_event_x
event_z = masked_event_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 = []

out_attn = []
out_attn1 = []
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)

out_attn.append(attn)
out_attn1.append(attn1)

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 此处RGB模态特征作为主特征,event_x作为补充特征
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，这里类似残差操作
x = x + enhanced_x
event_x = event_x + enhanced_event_x

out_attn_idx = random.choice(np.arange(len(out_attn)))
out_attn = out_attn[out_attn_idx]

out_attn_idx1 = random.choice(np.arange(len(out_attn1)))
out_attn1 = out_attn1[out_attn_idx1]

# 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 # cat仅仅是做 rgb 和 event voxel拼接，数据增强采用的随机遮蔽特征策略。


aux_dict = {
“attn”: out_attn,
“attn1”: out_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, Track=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,Track=Track)

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，有GELU和StarReLU,是否可以用StarReLU替换掉CELU？替换方式是什么？