在此模型：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

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):
“””
Args:
img_size (int, tuple): input image size
patch_size (int, tuple): patch size
in_chans (int): number of input channels
num_classes (int): number of classes for classification head
embed_dim (int): embedding dimension
depth (int): depth of transformer
num_heads (int): number of attention heads
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
qkv_bias (bool): enable bias for qkv if True
representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
distilled (bool): model includes a distillation token and head as in DeiT models
drop_rate (float): dropout rate
attn_drop_rate (float): attention dropout rate
drop_path_rate (float): stochastic depth rate
embed_layer (nn.Module): patch embedding layer
norm_layer: (nn.Module): normalization layer
weight_init: (str): weight init scheme
“”"
# super().init()
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)
# self.pos_embed_event = PatchEmbed_event(in_chans=32, embed_dim=768, kernel_size=4, stride=4)
# self.pos_embed_event_z = PatchEmbed_event(in_chans=32, embed_dim=768, kernel_size=3, stride=1)
# attn = CrossAttn(768, 4, 3072, 0.1, ‘relu’)
# self.cross_attn = Iter_attn(attn, 2)

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)
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(nxratio)

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:], }

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
):
B, H, W = x.shape[0], x.shape[2], x.shape[3]
# print(‘shape of event_z before projection:{}, event_x:{}’.format(event_z.shape, event_x.shape))
event_z = self.pos_embed_event(event_z) # [:,:,:,:1000]
event_x = self.pos_embed_event(event_x) # B 768 1024
x = self.patch_embed(x)
z = self.patch_embed(z)
# print(‘shape of event_z:{}, event_x:{}, x:{}, z:{}’.format(event_z.shape,event_x.shape,x.shape,z.shape ))
event_z += self.pos_embed_z
event_x += self.pos_embed_x
z += self.pos_embed_z
x += self.pos_embed_x

# attention mask handling # B, H, W
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
if Track == False:
z, event_z, x, event_x, token_idx = self.masking_fea(z, event_z, x, event_x, ratio=0.9)
x = combine_tokens(z, event_z, x, event_x, mode=self.cat_mode) # 64+64+256+256=640
# x = combine_tokens(z, x, event_z, event_x, mode=self.cat_mode) # 64+64+256+256=640
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]
lens_z = z.shape[1]
lens_x = 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 = []
out_attn = []
for i, blk in enumerate(self.blocks):
# out_global_s.append(global_index_s)
# out_global_t.append(global_index_t)
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)

if self.ce_loc is not None and i in self.ce_loc:
removed_indexes_s.append(removed_index_s)
out_attn.append(attn)
# print(‘shape of attn:{}, lens_z:{}, lens_x:{}’.format(attn.shape, lens_z, lens_x))


out_attn_idx = random.choice(np.arange(len(out_attn)))
out_attn = out_attn[out_attn_idx]
x = self.norm(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_new*2:]
if Track == False:
idx1 = token_idx[‘x1’]
idx0 = token_idx[‘x0’]
idex1 = token_idx[‘ex1’]
idex0 = token_idx[‘ex0’]
ex = x[:,idex1.shape[1]:]
x = x[:,:idex1.shape[1]]
# 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 = idx0.shape[1]
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([idx1, idx0], dim=1)
# recover original token order
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)
ex = torch.cat([ex, pad_x], dim=1)
index_all = torch.cat([idex1, idex0], dim=1)
# recover original token order
C = ex.shape[-1]
ex = torch.zeros_like(ex).scatter_(dim=1, index=index_all.unsqueeze(-1).expand(B, -1, C).to(torch.int64), src=ex)
x = torch.cat([x,ex],dim=1)
x = recover_tokens(x, lens_z_new, lens_x, mode=self.cat_mode)
event_x = x[:, lens_x:] # RGB head
x = x[:, :lens_x] # RGB head
x = torch.cat([event_x, x], dim=1)

aux_dict = {
# “attn”: attn,
“attn”: out_attn,
“removed_indexes_s”: removed_indexes_s, # 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,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maksing_fea以及tokens恢复的代码分别是什么？写出来