VSCode

1 Install

Download ubuntu version from   https://code.visualstudio.com/Download

sudo dpkg -i  xxx.deb

2 Shortcuts

Show Command: Ctrl+Shift+P
Open Files:   Ctrl+O
Open Folder: Ctrl+K, Ctrl+O

3 Extensions

Install Python 
Install Code Runner
Install Rainbow Brackets 
Install Markdown preview enhanced

5 Select Indentation

6 Font Size

Bigger: Ctrl+ '+'  
Smaller: Ctrl+ '-'

7 Theme Style

files-->preferences-->color theme
https://www.cnblogs.com/qianguyihao/archive/2019/04/18/10732375.html

8 Local Debugging

Select Python interpreter'virtual environment
Debug: F5 
Step over: F10 
Step into: F11 
Run Coder: Alt+Ctrl+N

9 Configuration SSH

Linux:
sudo apt-get install openssh-server
sudo apt-get install openssh-client

cd /home/username/
ssh-keygen -t rsa -P ""
cd ~/.ssh 
ssh-copy-id   reomte-host-name@romote-host-ip

Note: windows system can not find 'ssh-copy-id' command, you should open the xxx.pub file (/C/xxx/xxx/.ssh/xxx.pub) of your windows computer, then copy the **contents** of the file to the  authorized_keys file of the remote host manually. If your remote host does not have authorized_keys, you should manually create it before performing the copy operation:
    cd ~/.ssh (Remote host)
    touch authorized_keys
    vim  authorized_keys

Configure the name of the remote host to relize password-free login
cd ~/.ssh 
touch config
vim config 

Then add tehe following content to the config file:
Host server1(The nick name of the remote host)    
    HostName ip address(The ip address of remote host)    
    User root (The user name of remote host)    
    port 22 (The port of remote host)

10 Remote Debugging

Install Remote development
Display Image:
Install Remote x11 (https://blog.csdn.net/zb12138/article/details/107160825/)

Parallel Train

1 Single GPU

1 Set the visible GPU number

Method 1
CUDA_VISIBLE_DEVICES=0,1  python xxx.py 

Method 2
vim xxx.py 
os.environ['CUDA_VISIBLE_DEVICES']='0,1'
	
2 Put the model, data, and loss function on the GPU device 

Method 1
model=model.cuda() 
Loss=Loss.cuda()

Method 2 
device=torch.device('cuda:{}'.format(args.gpu_id))
model=model.to(device)

2 DataParallel (Multi GPU)

model = torch.nn.DataParallel(model.cuda())

3 DistributedDataParallel (Multi GPU)

1 Run in terminal

python -m torch.distributed.launch --nproc_per_node=NUM_GPUS  ./bin/dist_train.py 

2 Parse parameters

parser.add_argument("--local_rank", type=int,default=0)

    2 nodes, 2 GPUs for per node
    rank=0,1,2,3 
    node1: local_rank= 0 or 1 (rank%node_num)
    mode2: local_rank= 0 or 1 
	
3 Initialize communication method		  

	torch.distributed.init_process_group(backend='nccl', init_method='env://')

4 Set the GPU number that the current process needs to use
	torch.cuda.set_device(args.local_rank)
	
5 Generate  corresponding data labels for each  proceess
	
	train_sampler=torch.utils.data.distributed.DistributedSampler(train_data)
	train_loader=torch.utils.data.DataLoader(train_data,batch_size=batch_size,shuffle=F
alse,num_workers=2, pin_memory=True, sampler=train_sampler)

Before each epoch, the shuffle effect is achieved by calling the following commands:
	train_sampler.set_epoch(epoch) 
	
6 Calculate the  loss, summarize the information of each process

	Example:	

    def reduce_tesnor(tensor):
        # sum the tensor data across all machines
        dist.all_reduce(rt. op=dist.reduce_op.SUM)
        return rt

     output=model(input)

     loss=Loss(output,label)

     log_loss = reduce_tensor(loss.clone().detach_())

     torch.cuda.synchronize()  # wait every process finish above transmission

     loss_total += log_loss.item()   
     
7 Avoid conflicts when writing log files or print
	if args.local_rank==0:
		print('xxxx')
		log.info('xxxx')

SiamFC

1 SiamFC-VID

1 Train 
	python ./bin/my_train.py

2 Test and Evaluate  with got10k-toolkit
	python ./bin/my_test.py

3 Evaluate
	python ./bin/my_eval.py

2 SiamFC-GOT

1 Train 
	python ./bin/my_train.py

2 Test and Evaluate with got10k-toolkit
	python ./bin/my_test.py

3 Batch Test   
	./bin/cmd_test.sh

4 Evaluate 
	python ./bin/my_eval.py

5 Hyperparameter
	python ./bin/hp_search.py

SiamRPN

1 SiamRPN (YTB&VID)

1 Generate training set
	python ./bin/create_dataset_ytbid.py

2 Generate Lmdb file
	python ./bin/create_lmdb.py

3 Train
	python ./bin/my_train.py

4 Test and Evaluate
	python ./bin/my_test.py

2 SiamRPN-GOT

1 Train
	python ./bin/my_train.py

2 Test and  Evaluate
	python ./bin/my_test.py

3 Batch Test
	./bin/cmd_test.sh

4 Hyperparameter
	python ./bin/hp_search.py

5 DDP Train 
	./bin/cmd_dist_train.sh

3 SiamRPNpp-UP

Note that you should first build region by run the follow command: 
	python setup.py build_ext —-inplace
	
1 Train
	python ./bin/my_train.py

2 Test 
	python ./bin/my_test.py

3 Batch Test
	./bin/cmd_test.sh

4 Batch Evaluate
	./bin/cmd_eval.sh

5 Demo
	python ./bin/my_demo.py

6 Hyperparameter
	python ./bin/hp_search.py

7 DDP Train 
	./bin/cmd_dist_train.sh

DaSiamRPN

1 DaSiamRPN

1 Test 
	python ./bin/my_test.py

2 DaSiamRPN-GOT

1 Train
	python ./bin/my_train.py

2 Test 
	python ./bin/my_test.py

3 Batch Test
	./bin/cmd_test.sh

4 Hyperparameter
	python ./bin/hp_search.py

3 SiamRPNpp-DA

Note that you should first build region by run the follow command: 
python setup.py build_ext —-inplace

1 Train
	python ./bin/my_train.py

2 Test 
	python ./bin/my_test.py

3 Batch Test
	./bin/cmd_test.sh

4 Batch Evaluate
	./bin/cmd_eval.sh

5 Demo
	python ./bin/my_demo.py

6 Hyperparameter
	python ./bin/hp_search.py

7 DDP Train 
	./bin/cmd_dist_train.sh

UpdateNet

1 UpdateNet-FC

1 Generate training set
	python ./updatenet/create_template.py

2 Train UpdateNet (Note thae you should change the stage value )
	python ./updatenet/train_upd.py

3 Test UpdateNet (Note that you should set udpatenet path and stage value)
	python ./bin/my_test.py

2 UpdateNet-DA

1 Generate training set
	python ./updatenet/create_template.py

2 Train UpdateNet (Note that you should change the stage value 
)
	python ./updatenet/train_upd.py

3 Test UpdateNet (Note that you should set udpatenet path and stage value)
	python ./bin/my_test.py

3 UpdateNet-UP

1 Generate training set
	python ./updatenet/create_template.py
	
2 Train UpdateNet
	python ./updatenet/train_upd.py

3 Test UpdateNet
	python ./bin/my_test.py

4 UpdateNet-DW

Note that you should first build region by run the follow command: 
python setup.py build_ext —-inplace

1 Generate training set
	python ./updatenet/create_template.py

2 Train UpdateNet
Note that you should change the stage value 
	python ./updatenet/train_upd.py 

3 Test UpdateNet (Note that you should set udpatenet path and stage value)
	python ./bin/my_test.py

SiamDW

1 SiamDW-FC

1 Train
	python ./bin/my_train.py

2 Teat and Evaluate
	python ./bin/my_test.py

3 Batch Test
	python ./bin/cmd_test.py

3 Hyperparameters
	python ./bin/hp_search.py

2 SiamDW-UP

1 Train
	python ./bin/my_train.py

2 Teat and Evaluate
	python ./bin/my_test.py

3 Batch Test
	python ./bin/cmd_test.py

3 Hyperparameters
	python ./bin/hp_search.py

SiamRPNpp

1 SiamRPNpp-DW

Note that you should first build region by run the follow command: 
python setup.py build_ext —-inplace

1 Train
	python ./bin/my_train.py

2 Test 
	python ./bin/my_test.py

3 Batch Test
	./bin/cmd_test.sh

4 Batch Evaluate
	./bin/cmd_eval.sh

5 Demo
	python ./bin/my_demo.py

6 Hyperparameter
	python ./bin/hp_search.py

7 DDP Train 
	./bin/cmd_dist_train.sh

2 SiamRPNpp-ResNet

SiamFCpp

1 SiamFCpp-GOT

1 first, you should run compile.sh 
	sh ./compile.sh

2 Train

	python ./bin/my_train.py

3 Test

	python ./bin/my_test.py

Experiments

    OTB 2015 
    "success_score": 0.6289266117015362,
    "precision_score": 0.830571693318284,
    "success_rate": 0.7891486658050533,
    "speed_fps": 84.38537836344958

    official
    "success_score": 0.6797143434600249,  
    "precision_score": 0.8841645010368359,
    "success_rate": 0.8551268591684209,
    "speed_fps": 144.9084986738754

2 SiamFCpp-GoogleNet

Experiments

Trackers		SiamFC	SiamRPN	SiamRPN	DaSiamRPN	DaSiamRPN	SiamRPNpp	SiamRPNpp	SiamRPNpp	SiamRPNpp	SiamFCpp	SiamFCpp
Train Set		GOT	official	GOT	official	official	official	GOT	GOT	GOT	GOT	official
Backbone		Group	AlexNet	AlexNet	AlexNet	DA	DW	DW	UP	DA	AlexNet	AlexNet
FPS		85	>120	>120	>120	>120	>120	>120	>120	>120	>120	>120

OTB100	AUC	0.589	0.637	0.603	0.655	0.646	0.648	0.623	0.619	0.634	0.629	0.680
	DP	0.794	0.851	0.820	0.880	0.859	0.853	0.837	0.823	0.846	0.830	0.884

UAV123	AUC	0.504	0.527		0.586	0.604	0.578					0.623
	DP	0.702	0.748		0.796	0.801	0.769					0.781

UAV20L	AUC	0.410	0.454			0.524	0.530					0.516
	DP	0.566	0.617			0.691	0.695					0.613

DTB70	AUC	0.487				0.554	0.588					0.639
	DP	0.735				0.766	0.797					0.826

UAVDT	AUC	0.451				0.593	0.566					0.632
	DP	0.710				0.836	0.793					0.846

VisDrone-Train	AUC	0.510				0.547	0.572					0.588
	DP	0.698				0.722	0.764					0.784

VOT2016	A	0.538	0.56		0.61	0.625	0.618	0.582			0.612	0.626
	R	0.424	0.26		0.22	0.224	0.238	0.266			0.266	0.144
	E	0.262	0.344		0.411	0.439	0.393	0.372			0.357	0.460
	Lost	91				48	51	57			57	31

VOT2018	A	0.501	0.49		0.56	0.586	0.576	0.563	0.555/0.562	0.557	0.584	0.577
	R	0.534	0.46		0.34	0.276	0.290	0.375	0.384/0.398	0.412	0.342	0.183
	E	0.223	0.244		0.326	0.383	0.352	0.300	0.292/0.292	0.275	0.308	0.385
	Lost	114				59	62	80	82/85	88	73	39

Files

Tutorial.md

Latest commit

History

Tutorial.md

File metadata and controls

VSCode

1 Install

2 Shortcuts

3 Extensions

5 Select Indentation

6 Font Size

7 Theme Style

8 Local Debugging

9 Configuration SSH

10 Remote Debugging

Parallel Train

1 Single GPU

2 DataParallel (Multi GPU)

3 DistributedDataParallel (Multi GPU)

SiamFC

1 SiamFC-VID

2 SiamFC-GOT

SiamRPN

1 SiamRPN (YTB&VID)

2 SiamRPN-GOT

3 SiamRPNpp-UP

DaSiamRPN

1 DaSiamRPN

2 DaSiamRPN-GOT

3 SiamRPNpp-DA

UpdateNet

1 UpdateNet-FC

2 UpdateNet-DA

3 UpdateNet-UP

4 UpdateNet-DW

SiamDW

1 SiamDW-FC

2 SiamDW-UP

SiamRPNpp

1 SiamRPNpp-DW

2 SiamRPNpp-ResNet

SiamFCpp

1 SiamFCpp-GOT

2 SiamFCpp-GoogleNet

Experiments