目录
环境配置与脚本编写
前向传播过程
网络结构
环境配置与脚本编写
按照官网执行并没有顺利完成，将yaml文件中的 pip 项 手动安装的
conda create -n artrack python=3.9
# 启动该环境，并跳转到项目主目录路径下
astor==0.8.1 configparser==5.2.0
data==0.4 docker-pycreds==0.4.0 easydict==1.9 einops==0.4.1 formulaic==0.5.2 funcsigs==1.0.2 future==0.18.2
gitdb==4.0.9 gitpython==3.1.27 interface-meta==1.3.0 iopath==0.1.9 jpeg4py==0.1.4 jsonpatch==1.32 jsonpointer==2.3 latex==0.7.0
libarchive-c==2.9 linearmodels==4.29 lmdb==1.3.0 loguru==0.6.0 mat73==0.59 memory-profiler==0.60.0 msgpack==1.0.2 ninja==1.11.1
opencv-python==4.5.5.64 pathtools==0.1.2 promise==2.3 property-cached==1.6.4 protobuf==3.20.0 pycocotools==2.0.4 pyhdfe==0.1.2
ruamel-yaml-conda==0.15.100 sentry-sdk==1.5.8 setproctitle==1.2.2 setuptools-scm==7.1.0 shapely==1.8.1.post1 shortuuid==1.0.8
shutilwhich==1.1.0 smmap==5.0.0 tables==3.6.1 tempdir==0.7.1 tensorboardx==2.5.1 thop==0.1.0.post2207010342 tikzplotlib==0.10.1
timm==0.5.4 tomli==2.0.1 torch==1.11.0 torchfile==0.1.0 visdom==0.1.8.9 wandb==0.12.11 webcolors==1.12 yaspin==2.1.0里面的默认路径需要改写
python tracking/create_default_local_file.py --workspace_dir . --data_dir ./data --save_dir ./outpu官网下载训练好的模型，创建路径，将模型放在该路径下
ARTrack-main/output/checkpoints/train/artrack_seq/artrack_seq_256_full/ARTrackSeq_ep0060.pth.tar创建encoder的预训练模型路径，并把预训练模型放入这里，在yaml文件中进行更改，并且源脚本文件 artrack_seq.py中也需要更改
mkdir pretrained_model
#
mae_pretrain_vit_base.pth 文件名
# artrack_seq_256_full.yaml 中用绝对路径改写
PRETRAIN_PTH: "/root/data/zjx/Code-subject/ARTrack/ARTrack-main/pretrained_models"
# 同时将artrack_seq.py --100 中的
load_from = cfg.MODEL.PRETRAIN_PTH
# 改为
load_from = cfg.MODEL.PRETRAIN_PTH +'/' + cfg.MODEL.PRETRAIN_FILE
#同时将 artrack_seq.py -- 103 中的
missing_keys, unexpected_keys = model.load_state_dict(checkpoint["net"], strict=False)
# 改为
missing_keys, unexpected_keys = model.load_state_dict(checkpoint["model"], strict=False)代码中没有实现 run video 的脚本，这里需要自定义一个脚本实现
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import random
import argparse
import multiprocessing
import cv2
import torch
import torch.nn as nn
import numpy as np
from glob import glob
from lib.test.evaluation.tracker import Tracker
import sys
prj_path = os.path.join(os.path.dirname(__file__), '..')
if prj_path not in sys.path:
sys.path.append(prj_path)
torch.set_num_threads(1)
parser = argparse.ArgumentParser(description='Run tracker on sequence or dataset.')
parser.add_argument('tracker_name', type=str, help='Name of tracking method.')
parser.add_argument('tracker_param', type=str, help='Name of config file.')
parser.add_argument('--runid', type=int, default=None, help='The run id.')
parser.add_argument('--video_path', type=str, default='None', help='Name of dataset (otb, nfs, uav, tpl, vot, tn, gott, gotv, lasot).')
parser.add_argument('--sequence', type=str, default=None, help='Sequence number or name.')
parser.add_argument('--debug', type=int, default=0, help='Debug level.')
parser.add_argument('--threads', type=int, default=0, help='Number of threads.')
parser.add_argument('--num_gpus', type=int, default=8)
args = parser.parse_args()
def main(): # 这里已经是图片了
colors = [random.randint(0, 255) for _ in range(3)]
print('[INFO] Loading the model')
# load config
trackers = Tracker(args.tracker_name, args.tracker_param, None, args.runid)
try:
worker_name = multiprocessing.current_process().name
worker_id = int(worker_name[worker_name.find('-') + 1:]) - 1
gpu_id = worker_id % args.num_gpus
torch.cuda.set_device(gpu_id)
except:
pass
trackers.run_video(args.video_path, None, None, None, False)
if __name__=='__main__':
main()
执行
python tracking/run_video.py artrack_seq artrack_seq_256_full --video_path /root/data/zjx/Code-subject/OSTrack-main/experiments/video/soccer1.avi 前向传播过程
裁剪模板区域和OSTrack代码一样，初始化的时候，为需要保留的N帧的bbox的坐标信息创建了一个buffer--self.store_result，初始化时全为 init bbox，N的值此时设置为7
for i in range(self.save_all - 1):
self.store_result.append(info['init_bbox'].copy())搜索区域的裁剪和OSTrack的一样。将之前帧的坐标进行变换， 以前一帧预测的坐标为参考点计算相对坐标，因为当前帧的裁剪的搜索区域的就是以上一帧预测的bbox为中心进行裁剪的，所以搜索区域的中心实则是前一帧预测的bbox的中心。只不过前一帧预测的bbox为原img的尺度，而搜索区域为crop size上的尺度，因此，只需要将计算原img尺度上的也就是之前帧的预测的坐标与前一帧预测的坐标的相对坐标，再乘以resize factor就可以将相对坐标转换到crop size 的尺度下。并且，前一帧的预测的bbox转换实则移到了搜索区域的中心点，也就是 （crop_size/2, crop_ size/2)。
转换后除以 crop size 进行了归一化，不过这里有可能会 小于0 或者 大于 1，因为坐标变换可能会超出边界。接下来将xywh转换成 xyxy 形式，并筛选只保留（-0.5，1.5）区间的。然后对坐标进行量化。加上0.5 为了防止 出现负数，最终将bbox量化到 2*（bins-1）之间。最终，包含时空上下文信息的坐标输入为
seqs_out = seqs_out.unsqueeze(0) # （1，28）将 模板 和 搜索区域送入 ViT backbone中进行特征提取，这个过程中一共 16倍 下采样。然后将 提取的 sequence patch、以及位置编码、外加之前转换后的之前帧的bbox的信息 送入 接下来的Transformer中。
首先进入一个 encoder，在FeatureFusionEncoder类中进行一些预处理，主要的基本模块是 FeatureFusion 模块。这个encoder的主要过程如下所示，最终返回 z 和 x 一样shape的特征 patch。
接下来 ，将 之前帧的 bbox 坐标序列以及开始标志拼接在一起，作为decoder的输入 sequence。因为只需要预测bbox的坐标，所以不需要额外的结束标志，输出的序列长度直接为4即可。
1、 将输入的sequence 进行词汇嵌入，词向量的长度是crop img 下采样得到的特征patch的分辨率
2、 将初始输入tgt、模板特征、搜索特征、patch z的位置编码、 x patch的位置编码、identity高斯截断分布、高斯截断分布、查询嵌入、输入序列的掩码 送入decoderdecoder主要由TargetQueryDecoderLayer层组成。该模块的前向过程如下所示，一共有6层
最终输出和 tgt shape一样的token sequence。得到的输出的shape为（1，length，768），这个length为tgt的长度，随sequence的预测而逐渐增加。接下来，
1、 拿出得到的 query的 最后一个单词嵌入，并与词向量的权重矩阵进行矩阵乘法，得到与每个位置量化后的相关联的预测值。
2、 取softmax，得到关于量化后的坐标的概率分布。
3、 采用argmax sampleing，也就是看最大概率的位置。
4、 将当前预测的量化后的坐标加入到 tgt当中，执行循环。
5、 最终得到预测的bbox的量化坐标。
得到网络的输出预测后，
1、 bbox坐标反量化
2、 xyxy 转为 xywh 中心点加长宽
3、 尺度返回到原img， 转成 xywh， 左顶点加长宽
4、 平滑处理，去掉bbox超出图片的部分
5、 对于之前保存的坐标信息，将最靠前的弹出去，在最靠后的也就是前一帧的坐标加入当前预测的。好比出栈入栈操作。
网络结构
ARTrackSeq(
(backbone): VisionTransformer(
(patch_embed): PatchEmbed(
(proj): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))
(norm): Identity()
)
(pos_drop): Dropout(p=0.0, inplace=False)
(blocks): Sequential(
(0): Block(
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(drop_path): Identity()
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(1): Block(
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(drop_path): DropPath()
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(2): Block(
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(drop_path): DropPath()
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(3): Block(
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(drop_path): DropPath()
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(4): Block(
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(drop_path): DropPath()
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(5): Block(
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(drop_path): DropPath()
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(6): Block(
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(drop_path): DropPath()
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(7): Block(
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(drop_path): DropPath()
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(8): Block(
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(drop_path): DropPath()
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(9): Block(
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(drop_path): DropPath()
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(10): Block(
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(drop_path): DropPath()
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
(11): Block(
(norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(attn): Attention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.0, inplace=False)
)
(drop_path): DropPath()
(norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
)
)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
)
(pix_head): Pix2Track(
(word_embeddings): Embedding(802, 768, padding_idx=800, max_norm=1)
(position_embeddings): Embedding(5, 768)
(prev_position_embeddings): Embedding(28, 768)
(encoder): FeatureFusionEncoder(
(layers): ModuleList(
(0): FeatureFusion(
(z_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(x_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(z_self_attn): SelfAttention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.1, inplace=False)
)
(x_self_attn): SelfAttention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.1, inplace=False)
)
(z_norm2_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(z_norm2_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(x_norm2_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(x_norm2_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(z_x_cross_attention): CrossAttention(
(q): Linear(in_features=768, out_features=768, bias=True)
(kv): Linear(in_features=768, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.1, inplace=False)
)
(x_z_cross_attention): CrossAttention(
(q): Linear(in_features=768, out_features=768, bias=True)
(kv): Linear(in_features=768, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.1, inplace=False)
)
(z_norm3): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(x_norm3): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(z_mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop): Dropout(p=0.1, inplace=False)
)
(x_mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop): Dropout(p=0.1, inplace=False)
)
(drop_path): Identity()
)
(1): FeatureFusion(
(z_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(x_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(z_self_attn): SelfAttention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.1, inplace=False)
)
(x_self_attn): SelfAttention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.1, inplace=False)
)
(z_norm2_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(z_norm2_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(x_norm2_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(x_norm2_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(z_x_cross_attention): CrossAttention(
(q): Linear(in_features=768, out_features=768, bias=True)
(kv): Linear(in_features=768, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.1, inplace=False)
)
(x_z_cross_attention): CrossAttention(
(q): Linear(in_features=768, out_features=768, bias=True)
(kv): Linear(in_features=768, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.1, inplace=False)
)
(z_norm3): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(x_norm3): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(z_mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop): Dropout(p=0.1, inplace=False)
)
(x_mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop): Dropout(p=0.1, inplace=False)
)
(drop_path): Identity()
)
(2): FeatureFusion(
(z_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(x_norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(z_self_attn): SelfAttention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.1, inplace=False)
)
(x_self_attn): SelfAttention(
(qkv): Linear(in_features=768, out_features=2304, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.1, inplace=False)
)
(z_norm2_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(z_norm2_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(x_norm2_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(x_norm2_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(z_x_cross_attention): CrossAttention(
(q): Linear(in_features=768, out_features=768, bias=True)
(kv): Linear(in_features=768, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.1, inplace=False)
)
(x_z_cross_attention): CrossAttention(
(q): Linear(in_features=768, out_features=768, bias=True)
(kv): Linear(in_features=768, out_features=1536, bias=True)
(attn_drop): Dropout(p=0.0, inplace=False)
(proj): Linear(in_features=768, out_features=768, bias=True)
(proj_drop): Dropout(p=0.1, inplace=False)
)
(z_norm3): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(x_norm3): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(z_mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop): Dropout(p=0.1, inplace=False)
)
(x_mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop): Dropout(p=0.1, inplace=False)
)
(drop_path): Identity()
)
)
(z_pos_enc): Untied2DPositionalEncoder(
(pos): Learned2DPositionalEncoder()
(norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(pos_q_linear): Linear(in_features=768, out_features=768, bias=True)
(pos_k_linear): Linear(in_features=768, out_features=768, bias=True)
)
(x_pos_enc): Untied2DPositionalEncoder(
(pos): Learned2DPositionalEncoder()
(norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(pos_q_linear): Linear(in_features=768, out_features=768, bias=True)
(pos_k_linear): Linear(in_features=768, out_features=768, bias=True)
)
(z_rel_pos_bias_table): RelativePosition2DEncoder()
(x_rel_pos_bias_table): RelativePosition2DEncoder()
(z_x_rel_pos_bias_table): RelativePosition2DEncoder()
(x_z_rel_pos_bias_table): RelativePosition2DEncoder()
)
(decoder): TargetQueryDecoderBlock(
(layers): ModuleList(
(0): TargetQueryDecoderLayer(
(norm_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(self_attn1): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(norm_2_query): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(norm_2_memory): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(norm_3): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlpz): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop): Dropout(p=0.1, inplace=False)
)
(drop_path): Identity()
)
(1): TargetQueryDecoderLayer(
(norm_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(self_attn1): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(norm_2_query): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(norm_2_memory): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(norm_3): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlpz): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop): Dropout(p=0.1, inplace=False)
)
(drop_path): Identity()
)
(2): TargetQueryDecoderLayer(
(norm_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(self_attn1): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(norm_2_query): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(norm_2_memory): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(norm_3): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlpz): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop): Dropout(p=0.1, inplace=False)
)
(drop_path): Identity()
)
(3): TargetQueryDecoderLayer(
(norm_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(self_attn1): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(norm_2_query): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(norm_2_memory): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(norm_3): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlpz): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop): Dropout(p=0.1, inplace=False)
)
(drop_path): Identity()
)
(4): TargetQueryDecoderLayer(
(norm_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(self_attn1): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(norm_2_query): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(norm_2_memory): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(norm_3): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlpz): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop): Dropout(p=0.1, inplace=False)
)
(drop_path): Identity()
)
(5): TargetQueryDecoderLayer(
(norm_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(self_attn1): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(norm_2_query): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(norm_2_memory): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(multihead_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
)
(norm_3): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(mlpz): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop): Dropout(p=0.1, inplace=False)
)
(drop_path): Identity()
)
)
(norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
)
)
![[论文阅读] Revisiting Feature Propagation and Aggregation in Polyp Segmentation](https://img-blog.csdnimg.cn/direct/6c68dad46b844d08bc38d0d1bd327404.jpeg#pic_center)
