├── lib ├── __init__.py ├── config │ ├── __init__.py │ ├── .DS_Store │ └── ostrack │ │ ├── .DS_Store │ │ └── config.py ├── test │ ├── __init__.py │ ├── tracker │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── ostrack.cpython-38.pyc │ │ │ ├── vis_utils.cpython-38.pyc │ │ │ ├── basetracker.cpython-38.pyc │ │ │ └── data_utils.cpython-38.pyc │ │ ├── vis_utils.py │ │ ├── data_utils.py │ │ └── basetracker.py │ ├── analysis │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── __init__.cpython-37.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── plot_results.cpython-37.pyc │ │ │ ├── plot_results.cpython-38.pyc │ │ │ ├── extract_results.cpython-37.pyc │ │ │ └── extract_results.cpython-38.pyc │ ├── parameter │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── ostrack.cpython-38.pyc │ │ │ └── __init__.cpython-38.pyc │ │ └── ostrack.py │ ├── .DS_Store │ ├── utils │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── hann.cpython-38.pyc │ │ │ ├── params.cpython-37.pyc │ │ │ ├── params.cpython-38.pyc │ │ │ ├── __init__.cpython-37.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── _init_paths.cpython-38.pyc │ │ │ ├── load_text.cpython-37.pyc │ │ │ └── load_text.cpython-38.pyc │ │ ├── _init_paths.py │ │ ├── params.py │ │ ├── transform_trackingnet.py │ │ ├── load_text.py │ │ ├── transform_got10k.py │ │ └── hann.py │ └── evaluation │ │ ├── .DS_Store │ │ ├── __init__.py │ │ ├── tc128dataset.py │ │ ├── tnl2kdataset.py │ │ ├── tc128cedataset.py │ │ ├── got10kdataset.py │ │ ├── datasets.py │ │ ├── trackingnetdataset.py │ │ ├── itbdataset.py │ │ └── environment.py ├── vis │ ├── __init__.py │ ├── __pycache__ │ │ ├── utils.cpython-38.pyc │ │ ├── __init__.cpython-38.pyc │ │ ├── plotting.cpython-38.pyc │ │ └── visdom_cus.cpython-38.pyc │ ├── utils.py │ └── plotting.py ├── models │ ├── layers │ │ ├── __init__.py │ │ ├── .DS_Store │ │ ├── frozen_bn.py │ │ ├── rpe.py │ │ ├── attn.py │ │ └── attn_blocks.py │ ├── __init__.py │ ├── ostrack │ │ ├── __init__.py │ │ ├── .DS_Store │ │ └── utils.py │ └── .DS_Store ├── train │ ├── __init__.py │ ├── .DS_Store │ ├── admin │ │ ├── .DS_Store │ │ ├── __init__.py │ │ ├── settings.py │ │ ├── multigpu.py │ │ ├── tensorboard.py │ │ ├── stats.py │ │ └── environment.py │ ├── data │ │ ├── .DS_Store │ │ ├── __pycache__ │ │ │ ├── loader.cpython-37.pyc │ │ │ ├── loader.cpython-38.pyc │ │ │ ├── __init__.cpython-37.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── sampler.cpython-38.pyc │ │ │ ├── processing.cpython-38.pyc │ │ │ ├── transforms.cpython-38.pyc │ │ │ ├── image_loader.cpython-37.pyc │ │ │ ├── image_loader.cpython-38.pyc │ │ │ ├── sampler_datr.cpython-38.pyc │ │ │ ├── sampler_imix.cpython-38.pyc │ │ │ ├── wandb_logger.cpython-38.pyc │ │ │ ├── processing_datr.cpython-38.pyc │ │ │ ├── processing_imix.cpython-38.pyc │ │ │ └── processing_utils.cpython-38.pyc │ │ ├── __init__.py │ │ ├── wandb_logger.py │ │ ├── bounding_box_utils.py │ │ └── image_loader.py │ ├── actors │ │ ├── .DS_Store │ │ ├── __init__.py │ │ └── base_actor.py │ ├── trainers │ │ ├── .DS_Store │ │ └── __init__.py │ ├── dataset │ │ ├── __pycache__ │ │ │ ├── coco.cpython-38.pyc │ │ │ ├── lasot.cpython-38.pyc │ │ │ ├── __init__.cpython-38.pyc │ │ │ ├── coco_seq.cpython-38.pyc │ │ │ ├── got10k.cpython-38.pyc │ │ │ ├── COCO_tool.cpython-38.pyc │ │ │ ├── lasot_lmdb.cpython-38.pyc │ │ │ ├── coco_seq_lmdb.cpython-38.pyc │ │ │ ├── got10k_lmdb.cpython-38.pyc │ │ │ ├── imagenetvid.cpython-38.pyc │ │ │ ├── tracking_net.cpython-38.pyc │ │ │ ├── base_image_dataset.cpython-38.pyc │ │ │ ├── base_video_dataset.cpython-38.pyc │ │ │ ├── imagenetvid_lmdb.cpython-38.pyc │ │ │ └── tracking_net_lmdb.cpython-38.pyc │ │ ├── __init__.py │ │ ├── base_image_dataset.py │ │ ├── base_video_dataset.py │ │ └── imagenetvid_lmdb.py │ ├── _init_paths.py │ ├── data_specs │ │ └── README.md │ ├── train_script.py │ ├── train_script_datr.py │ ├── train_script_distill.py │ └── run_training.py ├── utils │ ├── __init__.py │ ├── __pycache__ │ │ ├── merge.cpython-38.pyc │ │ ├── misc.cpython-38.pyc │ │ ├── tensor.cpython-37.pyc │ │ ├── tensor.cpython-38.pyc │ │ ├── __init__.cpython-37.pyc │ │ ├── __init__.cpython-38.pyc │ │ ├── box_ops.cpython-38.pyc │ │ ├── ce_utils.cpython-38.pyc │ │ ├── focal_loss.cpython-38.pyc │ │ ├── lmdb_utils.cpython-37.pyc │ │ ├── lmdb_utils.cpython-38.pyc │ │ └── heapmap_utils.cpython-38.pyc │ ├── merge.py │ ├── variable_hook.py │ ├── lmdb_utils.py │ ├── focal_loss.py │ ├── box_ops.py │ ├── ce_utils.py │ └── heapmap_utils.py └── .DS_Store ├── .idea ├── .name ├── .gitignore ├── inspectionProfiles │ ├── profiles_settings.xml │ └── Project_Default.xml ├── misc.xml ├── modules.xml ├── DATr-code.iml └── deployment.xml ├── .DS_Store ├── framework.png ├── results.png ├── experiments.png ├── experiments ├── .DS_Store └── ostrack │ ├── .DS_Store │ ├── datr_vitb_384_mae_ce_32x4_ep300.yaml │ └── datr_vitb_256_mae_ce_32x4_ep300.yaml ├── tracking ├── __pycache__ │ ├── test.cpython-38.pyc │ ├── _init_paths.cpython-37.pyc │ └── _init_paths.cpython-38.pyc ├── _init_paths.py ├── convert_transt.py ├── analysis_results_ITP.py ├── create_default_local_file.py ├── analysis_results.py ├── video_demo.py ├── test.py ├── test_exp.py ├── pre_read_datasets.py ├── analysis_results.ipynb ├── train.py └── profile_model.py ├── eval.sh ├── LICENSE ├── README.md ├── install.sh └── ostrack_cuda113_env.yaml /lib/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /.idea/.name: -------------------------------------------------------------------------------- 1 | README.md -------------------------------------------------------------------------------- /lib/config/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /lib/test/__init__.py: 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https://raw.githubusercontent.com/zj5559/DATr/HEAD/lib/train/dataset/__pycache__/tracking_net.cpython-38.pyc -------------------------------------------------------------------------------- /lib/test/analysis/__pycache__/extract_results.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zj5559/DATr/HEAD/lib/test/analysis/__pycache__/extract_results.cpython-37.pyc -------------------------------------------------------------------------------- /lib/test/analysis/__pycache__/extract_results.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zj5559/DATr/HEAD/lib/test/analysis/__pycache__/extract_results.cpython-38.pyc -------------------------------------------------------------------------------- /lib/train/data/__init__.py: -------------------------------------------------------------------------------- 1 | from .loader import LTRLoader 2 | from .image_loader import jpeg4py_loader, opencv_loader, jpeg4py_loader_w_failsafe, default_image_loader 3 | -------------------------------------------------------------------------------- /lib/train/dataset/__pycache__/base_image_dataset.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zj5559/DATr/HEAD/lib/train/dataset/__pycache__/base_image_dataset.cpython-38.pyc -------------------------------------------------------------------------------- /lib/train/dataset/__pycache__/base_video_dataset.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zj5559/DATr/HEAD/lib/train/dataset/__pycache__/base_video_dataset.cpython-38.pyc -------------------------------------------------------------------------------- /lib/train/dataset/__pycache__/imagenetvid_lmdb.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zj5559/DATr/HEAD/lib/train/dataset/__pycache__/imagenetvid_lmdb.cpython-38.pyc -------------------------------------------------------------------------------- /lib/train/dataset/__pycache__/tracking_net_lmdb.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zj5559/DATr/HEAD/lib/train/dataset/__pycache__/tracking_net_lmdb.cpython-38.pyc -------------------------------------------------------------------------------- /lib/vis/utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | 4 | 5 | def numpy_to_torch(a: np.ndarray): 6 | return torch.from_numpy(a).float().permute(2, 0, 1).unsqueeze(0) -------------------------------------------------------------------------------- /lib/train/admin/__init__.py: -------------------------------------------------------------------------------- 1 | from .environment import env_settings, create_default_local_file_ITP_train 2 | from .stats import AverageMeter, StatValue 3 | from .tensorboard import TensorboardWriter 4 | -------------------------------------------------------------------------------- /lib/test/evaluation/__init__.py: -------------------------------------------------------------------------------- 1 | from .data import Sequence 2 | from .tracker import Tracker, trackerlist 3 | from .datasets import get_dataset 4 | from .environment import create_default_local_file_ITP_test -------------------------------------------------------------------------------- /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # Default ignored files 2 | /shelf/ 3 | /workspace.xml 4 | # Editor-based HTTP Client requests 5 | /httpRequests/ 6 | # Datasource local storage ignored files 7 | /dataSources/ 8 | /dataSources.local.xml 9 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /eval.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # training models with our datr 4 | config='datr_vitb_256_mae_ce_32x4_ep300' 5 | python tracking/train.py --script ostrack --config ${config} --save_dir /path/of/model --mode multiple --nproc_per_node 4 --datr 1 6 | python tracking/test.py ostrack ${config} --dataset lasot_extension_subset --threads 4 --num_gpus 4 7 | 8 | 9 | -------------------------------------------------------------------------------- /lib/train/admin/settings.py: -------------------------------------------------------------------------------- 1 | from lib.train.admin.environment import env_settings 2 | 3 | 4 | class Settings: 5 | """ Training settings, e.g. the paths to datasets and networks.""" 6 | def __init__(self): 7 | self.set_default() 8 | 9 | def set_default(self): 10 | self.env = env_settings() 11 | self.use_gpu = True 12 | 13 | 14 | -------------------------------------------------------------------------------- /tracking/_init_paths.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | 5 | import os.path as osp 6 | import sys 7 | 8 | 9 | def add_path(path): 10 | if path not in sys.path: 11 | sys.path.insert(0, path) 12 | 13 | 14 | this_dir = osp.dirname(__file__) 15 | 16 | prj_path = osp.join(this_dir, '..') 17 | add_path(prj_path) 18 | -------------------------------------------------------------------------------- /lib/train/_init_paths.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | 5 | import os.path as osp 6 | import sys 7 | 8 | 9 | def add_path(path): 10 | if path not in sys.path: 11 | sys.path.insert(0, path) 12 | 13 | 14 | this_dir = osp.dirname(__file__) 15 | 16 | prj_path = osp.join(this_dir, '../..') 17 | add_path(prj_path) 18 | -------------------------------------------------------------------------------- /lib/test/utils/_init_paths.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | 5 | import os.path as osp 6 | import sys 7 | 8 | 9 | def add_path(path): 10 | if path not in sys.path: 11 | sys.path.insert(0, path) 12 | 13 | 14 | this_dir = osp.dirname(__file__) 15 | 16 | prj_path = osp.join(this_dir, '..', '..', '..') 17 | add_path(prj_path) 18 | -------------------------------------------------------------------------------- /lib/train/dataset/__init__.py: -------------------------------------------------------------------------------- 1 | from .lasot import Lasot 2 | from .got10k import Got10k 3 | from .tracking_net import TrackingNet 4 | from .imagenetvid import ImagenetVID 5 | from .coco import MSCOCO 6 | from .coco_seq import MSCOCOSeq 7 | from .got10k_lmdb import Got10k_lmdb 8 | from .lasot_lmdb import Lasot_lmdb 9 | from .imagenetvid_lmdb import ImagenetVID_lmdb 10 | from .coco_seq_lmdb import MSCOCOSeq_lmdb 11 | from .tracking_net_lmdb import TrackingNet_lmdb 12 | -------------------------------------------------------------------------------- /lib/train/admin/multigpu.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | # Here we use DistributedDataParallel(DDP) rather than DataParallel(DP) for multiple GPUs training 3 | 4 | 5 | def is_multi_gpu(net): 6 | return isinstance(net, (MultiGPU, nn.parallel.distributed.DistributedDataParallel)) 7 | 8 | 9 | class MultiGPU(nn.parallel.distributed.DistributedDataParallel): 10 | def __getattr__(self, item): 11 | try: 12 | return super().__getattr__(item) 13 | except: 14 | pass 15 | return getattr(self.module, item) 16 | -------------------------------------------------------------------------------- /.idea/DATr-code.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 12 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/Project_Default.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 15 | -------------------------------------------------------------------------------- /tracking/convert_transt.py: -------------------------------------------------------------------------------- 1 | import _init_paths 2 | import os 3 | from lib.test.evaluation import get_dataset 4 | import shutil 5 | 6 | trackers = [] 7 | # dataset_name = 'uav' 8 | dataset_name = 'nfs' 9 | 10 | 11 | root_dir = "/data/sda/v-yanbi/iccv21/STARK_Latest/Stark" 12 | base_dir = os.path.join(root_dir, "test/tracking_results/TransT_N2") 13 | dataset = get_dataset(dataset_name) 14 | for x in dataset: 15 | seq_name = x.name 16 | file_name = "%s.txt" % (seq_name.replace("nfs_", "")) 17 | file_path = os.path.join(base_dir, file_name) 18 | file_path_new = os.path.join(base_dir, "%s.txt" % seq_name) 19 | if os.path.exists(file_path): 20 | shutil.move(file_path, file_path_new) 21 | 22 | -------------------------------------------------------------------------------- /lib/train/data_specs/README.md: -------------------------------------------------------------------------------- 1 | # README 2 | 3 | ## Description for different text files 4 | GOT10K 5 | - got10k_train_full_split.txt: the complete GOT-10K training set. (9335 videos) 6 | - got10k_train_split.txt: part of videos from the GOT-10K training set 7 | - got10k_val_split.txt: another part of videos from the GOT-10K training set 8 | - got10k_vot_exclude.txt: 1k videos that are forbidden from "using to train models then testing on VOT" (as required by [VOT Challenge](https://www.votchallenge.net/vot2020/participation.html)) 9 | - got10k_vot_train_split.txt: part of videos from the "VOT-permitted" GOT-10K training set 10 | - got10k_vot_val_split.txt: another part of videos from the "VOT-permitted" GOT-10K training set 11 | 12 | LaSOT 13 | - lasot_train_split.txt: the complete LaSOT training set 14 | 15 | TrackingNnet 16 | - trackingnet_classmap.txt: The map from the sequence name to the target class for the TrackingNet -------------------------------------------------------------------------------- /tracking/analysis_results_ITP.py: -------------------------------------------------------------------------------- 1 | import _init_paths 2 | import argparse 3 | from lib.test.analysis.plot_results import print_results 4 | from lib.test.evaluation import get_dataset, trackerlist 5 | 6 | 7 | def parse_args(): 8 | """ 9 | args for evaluation. 10 | """ 11 | parser = argparse.ArgumentParser(description='Parse args for training') 12 | # for train 13 | parser.add_argument('--script', type=str, help='training script name') 14 | parser.add_argument('--config', type=str, default='baseline', help='yaml configure file name') 15 | 16 | args = parser.parse_args() 17 | 18 | return args 19 | 20 | 21 | if __name__ == "__main__": 22 | args = parse_args() 23 | trackers = [] 24 | trackers.extend(trackerlist(args.script, args.config, "None", None, args.config)) 25 | 26 | dataset = get_dataset('lasot') 27 | 28 | print_results(trackers, dataset, 'LaSOT', merge_results=True, plot_types=('success', 'prec', 'norm_prec')) -------------------------------------------------------------------------------- /tracking/create_default_local_file.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import _init_paths 4 | from lib.train.admin import create_default_local_file_ITP_train 5 | from lib.test.evaluation import create_default_local_file_ITP_test 6 | 7 | 8 | def parse_args(): 9 | parser = argparse.ArgumentParser(description='Create default local file on ITP or PAI') 10 | parser.add_argument("--workspace_dir", type=str, required=True) # workspace dir 11 | parser.add_argument("--data_dir", type=str, required=True) 12 | parser.add_argument("--save_dir", type=str, required=True) 13 | args = parser.parse_args() 14 | return args 15 | 16 | 17 | if __name__ == "__main__": 18 | args = parse_args() 19 | workspace_dir = os.path.realpath(args.workspace_dir) 20 | data_dir = os.path.realpath(args.data_dir) 21 | save_dir = os.path.realpath(args.save_dir) 22 | create_default_local_file_ITP_train(workspace_dir, data_dir) 23 | create_default_local_file_ITP_test(workspace_dir, data_dir, save_dir) 24 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Botao Ye 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /lib/test/parameter/ostrack.py: -------------------------------------------------------------------------------- 1 | from lib.test.utils import TrackerParams 2 | import os 3 | from lib.test.evaluation.environment import env_settings 4 | from lib.config.ostrack.config import cfg, update_config_from_file 5 | 6 | 7 | def parameters(yaml_name: str): 8 | params = TrackerParams() 9 | prj_dir = env_settings().prj_dir 10 | save_dir = env_settings().save_dir 11 | # update default config from yaml file 12 | yaml_file = os.path.join(prj_dir, 'experiments/ostrack/%s.yaml' % yaml_name) 13 | update_config_from_file(yaml_file) 14 | params.cfg = cfg 15 | print("test config: ", cfg) 16 | 17 | # template and search region 18 | params.template_factor = cfg.TEST.TEMPLATE_FACTOR 19 | params.template_size = cfg.TEST.TEMPLATE_SIZE 20 | params.search_factor = cfg.TEST.SEARCH_FACTOR 21 | params.search_size = cfg.TEST.SEARCH_SIZE 22 | 23 | # Network checkpoint path 24 | params.checkpoint = os.path.join(save_dir, "checkpoints/train/ostrack/%s/OSTrack_ep%04d.pth.tar" % 25 | (yaml_name, cfg.TEST.EPOCH)) 26 | 27 | # whether to save boxes from all queries 28 | params.save_all_boxes = False 29 | 30 | return params 31 | -------------------------------------------------------------------------------- /lib/train/data/wandb_logger.py: -------------------------------------------------------------------------------- 1 | from collections import OrderedDict 2 | 3 | try: 4 | import wandb 5 | except ImportError: 6 | raise ImportError( 7 | 'Please run "pip install wandb" to install wandb') 8 | 9 | 10 | class WandbWriter: 11 | def __init__(self, exp_name, cfg, output_dir, cur_step=0, step_interval=0): 12 | self.wandb = wandb 13 | self.step = cur_step 14 | self.interval = step_interval 15 | wandb.init(project="tracking", name=exp_name, config=cfg, dir=output_dir) 16 | 17 | def write_log(self, stats: OrderedDict, epoch=-1): 18 | self.step += 1 19 | for loader_name, loader_stats in stats.items(): 20 | if loader_stats is None: 21 | continue 22 | 23 | log_dict = {} 24 | for var_name, val in loader_stats.items(): 25 | if hasattr(val, 'avg'): 26 | log_dict.update({loader_name + '/' + var_name: val.avg}) 27 | else: 28 | log_dict.update({loader_name + '/' + var_name: val.val}) 29 | 30 | if epoch >= 0: 31 | log_dict.update({loader_name + '/epoch': epoch}) 32 | 33 | self.wandb.log(log_dict, step=self.step*self.interval) 34 | -------------------------------------------------------------------------------- /lib/train/admin/tensorboard.py: -------------------------------------------------------------------------------- 1 | import os 2 | from collections import OrderedDict 3 | try: 4 | from torch.utils.tensorboard import SummaryWriter 5 | except: 6 | print('WARNING: You are using tensorboardX instead sis you have a too old pytorch version.') 7 | from tensorboardX import SummaryWriter 8 | 9 | 10 | class TensorboardWriter: 11 | def __init__(self, directory, loader_names): 12 | self.directory = directory 13 | self.writer = OrderedDict({name: SummaryWriter(os.path.join(self.directory, name)) for name in loader_names}) 14 | 15 | def write_info(self, script_name, description): 16 | tb_info_writer = SummaryWriter(os.path.join(self.directory, 'info')) 17 | tb_info_writer.add_text('Script_name', script_name) 18 | tb_info_writer.add_text('Description', description) 19 | tb_info_writer.close() 20 | 21 | def write_epoch(self, stats: OrderedDict, epoch: int, ind=-1): 22 | for loader_name, loader_stats in stats.items(): 23 | if loader_stats is None: 24 | continue 25 | for var_name, val in loader_stats.items(): 26 | if hasattr(val, 'history') and getattr(val, 'has_new_data', True): 27 | self.writer[loader_name].add_scalar(var_name, val.history[ind], epoch) -------------------------------------------------------------------------------- /tracking/analysis_results.py: -------------------------------------------------------------------------------- 1 | import _init_paths 2 | import matplotlib.pyplot as plt 3 | plt.rcParams['figure.figsize'] = [8, 8] 4 | 5 | from lib.test.analysis.plot_results import plot_results, print_results, print_per_sequence_results 6 | from lib.test.evaluation import get_dataset, trackerlist 7 | 8 | trackers = [] 9 | dataset_name = 'lasot_extension_subset' 10 | # dataset_name = 'lasot' 11 | 12 | """ostrack""" 13 | trackers.extend(trackerlist(name='ostrack', parameter_name='datr_vitb_256_mae_ce_32x4_ep300', dataset_name=dataset_name, 14 | run_ids=None, display_name='datr_vitb_256')) 15 | 16 | 17 | trackers.extend(trackerlist(name='ostrack', parameter_name='vitb_256_mae_ce_32x4_ep300', dataset_name=dataset_name, 18 | run_ids=None, display_name='vitb_256')) 19 | 20 | 21 | 22 | dataset = get_dataset(dataset_name) 23 | # dataset = get_dataset('otb', 'nfs', 'uav', 'tc128ce') 24 | # plot_results(trackers, dataset, 'OTB2015', merge_results=True, plot_types=('success', 'norm_prec'), 25 | # skip_missing_seq=False, force_evaluation=True, plot_bin_gap=0.05) 26 | print_results(trackers, dataset, dataset_name, merge_results=True,seq_eval=False, plot_types=('success', 'norm_prec', 'prec')) 27 | # print_results(trackers, dataset, 'UNO', merge_results=True, plot_types=('success', 'prec')) 28 | -------------------------------------------------------------------------------- /lib/utils/merge.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | def merge_template_search(inp_list, return_search=False, return_template=False): 5 | """NOTICE: search region related features must be in the last place""" 6 | seq_dict = {"feat": torch.cat([x["feat"] for x in inp_list], dim=0), 7 | "mask": torch.cat([x["mask"] for x in inp_list], dim=1), 8 | "pos": torch.cat([x["pos"] for x in inp_list], dim=0)} 9 | if return_search: 10 | x = inp_list[-1] 11 | seq_dict.update({"feat_x": x["feat"], "mask_x": x["mask"], "pos_x": x["pos"]}) 12 | if return_template: 13 | z = inp_list[0] 14 | seq_dict.update({"feat_z": z["feat"], "mask_z": z["mask"], "pos_z": z["pos"]}) 15 | return seq_dict 16 | 17 | 18 | def get_qkv(inp_list): 19 | """The 1st element of the inp_list is about the template, 20 | the 2nd (the last) element is about the search region""" 21 | dict_x = inp_list[-1] 22 | dict_c = {"feat": torch.cat([x["feat"] for x in inp_list], dim=0), 23 | "mask": torch.cat([x["mask"] for x in inp_list], dim=1), 24 | "pos": torch.cat([x["pos"] for x in inp_list], dim=0)} # concatenated dict 25 | q = dict_x["feat"] + dict_x["pos"] 26 | k = dict_c["feat"] + dict_c["pos"] 27 | v = dict_c["feat"] 28 | key_padding_mask = dict_c["mask"] 29 | return q, k, v, key_padding_mask 30 | -------------------------------------------------------------------------------- /lib/test/utils/params.py: -------------------------------------------------------------------------------- 1 | from lib.utils import TensorList 2 | import random 3 | 4 | 5 | class TrackerParams: 6 | """Class for tracker parameters.""" 7 | def set_default_values(self, default_vals: dict): 8 | for name, val in default_vals.items(): 9 | if not hasattr(self, name): 10 | setattr(self, name, val) 11 | 12 | def get(self, name: str, *default): 13 | """Get a parameter value with the given name. If it does not exists, it return the default value given as a 14 | second argument or returns an error if no default value is given.""" 15 | if len(default) > 1: 16 | raise ValueError('Can only give one default value.') 17 | 18 | if not default: 19 | return getattr(self, name) 20 | 21 | return getattr(self, name, default[0]) 22 | 23 | def has(self, name: str): 24 | """Check if there exist a parameter with the given name.""" 25 | return hasattr(self, name) 26 | 27 | 28 | class FeatureParams: 29 | """Class for feature specific parameters""" 30 | def __init__(self, *args, **kwargs): 31 | if len(args) > 0: 32 | raise ValueError 33 | 34 | for name, val in kwargs.items(): 35 | if isinstance(val, list): 36 | setattr(self, name, TensorList(val)) 37 | else: 38 | setattr(self, name, val) 39 | 40 | 41 | def Choice(*args): 42 | """Can be used to sample random parameter values.""" 43 | return random.choice(args) 44 | -------------------------------------------------------------------------------- /lib/train/actors/base_actor.py: -------------------------------------------------------------------------------- 1 | from lib.utils import TensorDict 2 | 3 | 4 | class BaseActor: 5 | """ Base class for actor. The actor class handles the passing of the data through the network 6 | and calculation the loss""" 7 | def __init__(self, net, objective): 8 | """ 9 | args: 10 | net - The network to train 11 | objective - The loss function 12 | """ 13 | self.net = net 14 | self.objective = objective 15 | 16 | def __call__(self, data: TensorDict): 17 | """ Called in each training iteration. Should pass in input data through the network, calculate the loss, and 18 | return the training stats for the input data 19 | args: 20 | data - A TensorDict containing all the necessary data blocks. 21 | 22 | returns: 23 | loss - loss for the input data 24 | stats - a dict containing detailed losses 25 | """ 26 | raise NotImplementedError 27 | 28 | def to(self, device): 29 | """ Move the network to device 30 | args: 31 | device - device to use. 'cpu' or 'cuda' 32 | """ 33 | self.net.to(device) 34 | 35 | def train(self, mode=True): 36 | """ Set whether the network is in train mode. 37 | args: 38 | mode (True) - Bool specifying whether in training mode. 39 | """ 40 | self.net.train(mode) 41 | 42 | def eval(self): 43 | """ Set network to eval mode""" 44 | self.train(False) -------------------------------------------------------------------------------- /lib/test/utils/transform_trackingnet.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import os 3 | import shutil 4 | import argparse 5 | import _init_paths 6 | from lib.test.evaluation.environment import env_settings 7 | 8 | 9 | def transform_trackingnet(tracker_name, cfg_name): 10 | env = env_settings() 11 | result_dir = env.results_path 12 | src_dir = os.path.join(result_dir, "%s/%s/trackingnet/" % (tracker_name, cfg_name)) 13 | dest_dir = os.path.join(result_dir, "%s/%s/trackingnet_submit/" % (tracker_name, cfg_name)) 14 | if not os.path.exists(dest_dir): 15 | os.makedirs(dest_dir) 16 | items = os.listdir(src_dir) 17 | for item in items: 18 | if "all" in item: 19 | continue 20 | if "time" not in item: 21 | src_path = os.path.join(src_dir, item) 22 | dest_path = os.path.join(dest_dir, item) 23 | bbox_arr = np.loadtxt(src_path, dtype=np.int, delimiter='\t') 24 | np.savetxt(dest_path, bbox_arr, fmt='%d', delimiter=',') 25 | # make zip archive 26 | shutil.make_archive(src_dir, "zip", src_dir) 27 | shutil.make_archive(dest_dir, "zip", dest_dir) 28 | # Remove the original files 29 | shutil.rmtree(src_dir) 30 | shutil.rmtree(dest_dir) 31 | 32 | 33 | if __name__ == "__main__": 34 | parser = argparse.ArgumentParser(description='transform trackingnet results.') 35 | parser.add_argument('--tracker_name', type=str, help='Name of tracking method.') 36 | parser.add_argument('--cfg_name', type=str, help='Name of config file.') 37 | 38 | args = parser.parse_args() 39 | transform_trackingnet(args.tracker_name, args.cfg_name) 40 | -------------------------------------------------------------------------------- /tracking/video_demo.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import argparse 4 | 5 | prj_path = os.path.join(os.path.dirname(__file__), '..') 6 | if prj_path not in sys.path: 7 | sys.path.append(prj_path) 8 | 9 | from lib.test.evaluation import Tracker 10 | 11 | 12 | def run_video(tracker_name, tracker_param, videofile, optional_box=None, debug=None, save_results=False): 13 | """Run the tracker on your webcam. 14 | args: 15 | tracker_name: Name of tracking method. 16 | tracker_param: Name of parameter file. 17 | debug: Debug level. 18 | """ 19 | tracker = Tracker(tracker_name, tracker_param, "video") 20 | tracker.run_video(videofilepath=videofile, optional_box=optional_box, debug=debug, save_results=save_results) 21 | 22 | 23 | def main(): 24 | parser = argparse.ArgumentParser(description='Run the tracker on your webcam.') 25 | parser.add_argument('tracker_name', type=str, help='Name of tracking method.') 26 | parser.add_argument('tracker_param', type=str, help='Name of parameter file.') 27 | parser.add_argument('videofile', type=str, help='path to a video file.') 28 | parser.add_argument('--optional_box', type=float, default=None, nargs="+", help='optional_box with format x y w h.') 29 | parser.add_argument('--debug', type=int, default=0, help='Debug level.') 30 | parser.add_argument('--save_results', dest='save_results', action='store_true', help='Save bounding boxes') 31 | parser.set_defaults(save_results=False) 32 | 33 | args = parser.parse_args() 34 | 35 | run_video(args.tracker_name, args.tracker_param, args.videofile, args.optional_box, args.debug, args.save_results) 36 | 37 | 38 | if __name__ == '__main__': 39 | main() 40 | -------------------------------------------------------------------------------- /lib/utils/variable_hook.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from bytecode import Bytecode, Instr 3 | 4 | 5 | class get_local(object): 6 | cache = {} 7 | is_activate = False 8 | 9 | def __init__(self, varname): 10 | self.varname = varname 11 | 12 | def __call__(self, func): 13 | if not type(self).is_activate: 14 | return func 15 | 16 | type(self).cache[func.__qualname__] = [] 17 | c = Bytecode.from_code(func.__code__) 18 | extra_code = [ 19 | Instr('STORE_FAST', '_res'), 20 | Instr('LOAD_FAST', self.varname), 21 | Instr('STORE_FAST', '_value'), 22 | Instr('LOAD_FAST', '_res'), 23 | Instr('LOAD_FAST', '_value'), 24 | Instr('BUILD_TUPLE', 2), 25 | Instr('STORE_FAST', '_result_tuple'), 26 | Instr('LOAD_FAST', '_result_tuple'), 27 | ] 28 | c[-1:-1] = extra_code 29 | func.__code__ = c.to_code() 30 | 31 | def wrapper(*args, **kwargs): 32 | res, values = func(*args, **kwargs) 33 | if isinstance(values, torch.Tensor): 34 | type(self).cache[func.__qualname__].append(values.detach().cpu().numpy()) 35 | elif isinstance(values, list): # list of Tensor 36 | type(self).cache[func.__qualname__].append([value.detach().cpu().numpy() for value in values]) 37 | else: 38 | raise NotImplementedError 39 | return res 40 | 41 | return wrapper 42 | 43 | @classmethod 44 | def clear(cls): 45 | for key in cls.cache.keys(): 46 | cls.cache[key] = [] 47 | 48 | @classmethod 49 | def activate(cls): 50 | cls.is_activate = True 51 | -------------------------------------------------------------------------------- /lib/models/layers/frozen_bn.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | class FrozenBatchNorm2d(torch.nn.Module): 5 | """ 6 | BatchNorm2d where the batch statistics and the affine parameters are fixed. 7 | 8 | Copy-paste from torchvision.misc.ops with added eps before rqsrt, 9 | without which any other models than torchvision.models.resnet[18,34,50,101] 10 | produce nans. 11 | """ 12 | 13 | def __init__(self, n): 14 | super(FrozenBatchNorm2d, self).__init__() 15 | self.register_buffer("weight", torch.ones(n)) 16 | self.register_buffer("bias", torch.zeros(n)) 17 | self.register_buffer("running_mean", torch.zeros(n)) 18 | self.register_buffer("running_var", torch.ones(n)) 19 | 20 | def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, 21 | missing_keys, unexpected_keys, error_msgs): 22 | num_batches_tracked_key = prefix + 'num_batches_tracked' 23 | if num_batches_tracked_key in state_dict: 24 | del state_dict[num_batches_tracked_key] 25 | 26 | super(FrozenBatchNorm2d, self)._load_from_state_dict( 27 | state_dict, prefix, local_metadata, strict, 28 | missing_keys, unexpected_keys, error_msgs) 29 | 30 | def forward(self, x): 31 | # move reshapes to the beginning 32 | # to make it fuser-friendly 33 | w = self.weight.reshape(1, -1, 1, 1) 34 | b = self.bias.reshape(1, -1, 1, 1) 35 | rv = self.running_var.reshape(1, -1, 1, 1) 36 | rm = self.running_mean.reshape(1, -1, 1, 1) 37 | eps = 1e-5 38 | scale = w * (rv + eps).rsqrt() # rsqrt(x): 1/sqrt(x), r: reciprocal 39 | bias = b - rm * scale 40 | return x * scale + bias 41 | -------------------------------------------------------------------------------- /lib/test/utils/load_text.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | 4 | 5 | def load_text_numpy(path, delimiter, dtype): 6 | if isinstance(delimiter, (tuple, list)): 7 | for d in delimiter: 8 | try: 9 | ground_truth_rect = np.loadtxt(path, delimiter=d, dtype=dtype) 10 | return ground_truth_rect 11 | except: 12 | pass 13 | 14 | raise Exception('Could not read file {}'.format(path)) 15 | else: 16 | ground_truth_rect = np.loadtxt(path, delimiter=delimiter, dtype=dtype) 17 | return ground_truth_rect 18 | 19 | 20 | def load_text_pandas(path, delimiter, dtype): 21 | if isinstance(delimiter, (tuple, list)): 22 | for d in delimiter: 23 | try: 24 | ground_truth_rect = pd.read_csv(path, delimiter=d, header=None, dtype=dtype, na_filter=False, 25 | low_memory=False).values 26 | return ground_truth_rect 27 | except Exception as e: 28 | pass 29 | 30 | raise Exception('Could not read file {}'.format(path)) 31 | else: 32 | ground_truth_rect = pd.read_csv(path, delimiter=delimiter, header=None, dtype=dtype, na_filter=False, 33 | low_memory=False).values 34 | return ground_truth_rect 35 | 36 | 37 | def load_text(path, delimiter=' ', dtype=np.float32, backend='numpy'): 38 | if backend == 'numpy': 39 | return load_text_numpy(path, delimiter, dtype) 40 | elif backend == 'pandas': 41 | return load_text_pandas(path, delimiter, dtype) 42 | 43 | 44 | def load_str(path): 45 | with open(path, "r") as f: 46 | text_str = f.readline().strip().lower() 47 | return text_str 48 | -------------------------------------------------------------------------------- /lib/utils/lmdb_utils.py: -------------------------------------------------------------------------------- 1 | import lmdb 2 | import numpy as np 3 | import cv2 4 | import json 5 | 6 | LMDB_ENVS = dict() 7 | LMDB_HANDLES = dict() 8 | LMDB_FILELISTS = dict() 9 | 10 | 11 | def get_lmdb_handle(name): 12 | global LMDB_HANDLES, LMDB_FILELISTS 13 | item = LMDB_HANDLES.get(name, None) 14 | if item is None: 15 | env = lmdb.open(name, readonly=True, lock=False, readahead=False, meminit=False) 16 | LMDB_ENVS[name] = env 17 | item = env.begin(write=False) 18 | LMDB_HANDLES[name] = item 19 | 20 | return item 21 | 22 | 23 | def decode_img(lmdb_fname, key_name): 24 | handle = get_lmdb_handle(lmdb_fname) 25 | binfile = handle.get(key_name.encode()) 26 | if binfile is None: 27 | print("Illegal data detected. %s %s" % (lmdb_fname, key_name)) 28 | s = np.frombuffer(binfile, np.uint8) 29 | x = cv2.cvtColor(cv2.imdecode(s, cv2.IMREAD_COLOR), cv2.COLOR_BGR2RGB) 30 | return x 31 | 32 | 33 | def decode_str(lmdb_fname, key_name): 34 | handle = get_lmdb_handle(lmdb_fname) 35 | binfile = handle.get(key_name.encode()) 36 | string = binfile.decode() 37 | return string 38 | 39 | 40 | def decode_json(lmdb_fname, key_name): 41 | return json.loads(decode_str(lmdb_fname, key_name)) 42 | 43 | 44 | if __name__ == "__main__": 45 | lmdb_fname = "/data/sda/v-yanbi/iccv21/LittleBoy_clean/data/got10k_lmdb" 46 | '''Decode image''' 47 | # key_name = "test/GOT-10k_Test_000001/00000001.jpg" 48 | # img = decode_img(lmdb_fname, key_name) 49 | # cv2.imwrite("001.jpg", img) 50 | '''Decode str''' 51 | # key_name = "test/list.txt" 52 | # key_name = "train/GOT-10k_Train_000001/groundtruth.txt" 53 | key_name = "train/GOT-10k_Train_000001/absence.label" 54 | str_ = decode_str(lmdb_fname, key_name) 55 | print(str_) 56 | -------------------------------------------------------------------------------- /lib/train/admin/stats.py: -------------------------------------------------------------------------------- 1 | 2 | 3 | class StatValue: 4 | def __init__(self): 5 | self.clear() 6 | 7 | def reset(self): 8 | self.val = 0 9 | 10 | def clear(self): 11 | self.reset() 12 | self.history = [] 13 | 14 | def update(self, val): 15 | self.val = val 16 | self.history.append(self.val) 17 | 18 | 19 | class AverageMeter(object): 20 | """Computes and stores the average and current value""" 21 | def __init__(self): 22 | self.clear() 23 | self.has_new_data = False 24 | 25 | def reset(self): 26 | self.avg = 0 27 | self.val = 0 28 | self.sum = 0 29 | self.count = 0 30 | 31 | def clear(self): 32 | self.reset() 33 | self.history = [] 34 | 35 | def update(self, val, n=1): 36 | self.val = val 37 | self.sum += val * n 38 | self.count += n 39 | self.avg = self.sum / self.count 40 | 41 | def new_epoch(self): 42 | if self.count > 0: 43 | self.history.append(self.avg) 44 | self.reset() 45 | self.has_new_data = True 46 | else: 47 | self.has_new_data = False 48 | 49 | 50 | def topk_accuracy(output, target, topk=(1,)): 51 | """Computes the precision@k for the specified values of k""" 52 | single_input = not isinstance(topk, (tuple, list)) 53 | if single_input: 54 | topk = (topk,) 55 | 56 | maxk = max(topk) 57 | batch_size = target.size(0) 58 | 59 | _, pred = output.topk(maxk, 1, True, True) 60 | pred = pred.t() 61 | correct = pred.eq(target.view(1, -1).expand_as(pred)) 62 | 63 | res = [] 64 | for k in topk: 65 | correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)[0] 66 | res.append(correct_k * 100.0 / batch_size) 67 | 68 | if single_input: 69 | return res[0] 70 | 71 | return res 72 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # DATr 2 | PyTorch implementation of "Leveraging the Power of Data Augmentation for Transformer-based Tracking" (WACV2024). 3 | 4 | Please find the paper [here](https://arxiv.org/pdf/2309.08264.pdf). 5 | 6 | ## Introduction 7 | In this paper, we perform systematic experiments to explore the impact of General Data Augmentations (GDA) on transformer trackers, including the pure transformer tracker and the hybrid CNN-Transformer tracker. Results below show GDAs have limited effects on SOTA trackers. 8 | ![DATR figure](experiments.png) 9 | 10 | Then, We propose two Data Augmentation methods based on challenges faced by Transformer-based trackers, DATr for short. They improve trackers from perspectives of adaptability to different scales, flexibility to boundary targets, and robustness to interference, respectively. 11 | ![DATR figure](framework.png) 12 | 13 | Extensive experiments on different baseline trackers and benchmarks demonstrate the effectiveness and generalization of our DATr, especially for sequences with challenges and unseen classes. 14 | ![DATR figure](results.png) 15 | 16 | ## Installation 17 | The environment installation and training configurations (like project path, pretrained models) are similar to the baseline trackers, e.g., OSTrack, please refer to [OSTrack](https://github.com/botaoye/OSTrack). 18 | 19 | ## Training and Testing 20 | Please see eval.sh to find the commands for training and testing. 21 | 22 | ## Models and Results 23 | Models and results can be found [here](https://drive.google.com/drive/folders/19-jBvfFVZxPcvZmy6ZXwtyW6NCnQ6bjY?usp=share_link). 24 | 25 | ## Acknowledgments 26 | Our work is mainly implemented on three different Transformer trackers, i.e., [OSTrack](https://github.com/botaoye/OSTrack), [MixFormer](https://github.com/MCG-NJU/MixFormer), and [STARK](https://github.com/MasterBin-IIAU/Stark-1). Thanks for these concise and effective SOT frameworks. 27 | -------------------------------------------------------------------------------- /lib/test/evaluation/tc128dataset.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList 3 | import os 4 | import glob 5 | import six 6 | 7 | 8 | class TC128Dataset(BaseDataset): 9 | """ 10 | TC-128 Dataset 11 | modified from the implementation in got10k-toolkit (https://github.com/got-10k/toolkit) 12 | """ 13 | def __init__(self): 14 | super().__init__() 15 | self.base_path = self.env_settings.tc128_path 16 | self.anno_files = sorted(glob.glob( 17 | os.path.join(self.base_path, '*/*_gt.txt'))) 18 | self.seq_dirs = [os.path.dirname(f) for f in self.anno_files] 19 | self.seq_names = [os.path.basename(d) for d in self.seq_dirs] 20 | # valid frame range for each sequence 21 | self.range_files = [glob.glob(os.path.join(d, '*_frames.txt'))[0] for d in self.seq_dirs] 22 | 23 | def get_sequence_list(self): 24 | return SequenceList([self._construct_sequence(s) for s in self.seq_names]) 25 | 26 | def _construct_sequence(self, sequence_name): 27 | if isinstance(sequence_name, six.string_types): 28 | if not sequence_name in self.seq_names: 29 | raise Exception('Sequence {} not found.'.format(sequence_name)) 30 | index = self.seq_names.index(sequence_name) 31 | # load valid frame range 32 | frames = np.loadtxt(self.range_files[index], dtype=int, delimiter=',') 33 | img_files = [os.path.join(self.seq_dirs[index], 'img/%04d.jpg' % f) for f in range(frames[0], frames[1] + 1)] 34 | 35 | # load annotations 36 | anno = np.loadtxt(self.anno_files[index], delimiter=',') 37 | assert len(img_files) == len(anno) 38 | assert anno.shape[1] == 4 39 | 40 | # return img_files, anno 41 | return Sequence(sequence_name, img_files, 'tc128', anno.reshape(-1, 4)) 42 | 43 | def __len__(self): 44 | return len(self.seq_names) 45 | -------------------------------------------------------------------------------- /lib/test/evaluation/tnl2kdataset.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | import numpy as np 4 | from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList 5 | from lib.test.utils.load_text import load_text, load_str 6 | 7 | ############ 8 | # current 00000492.png of test_015_Sord_video_Q01_done is damaged and replaced by a copy of 00000491.png 9 | ############ 10 | 11 | 12 | class TNL2kDataset(BaseDataset): 13 | """ 14 | TNL2k test set 15 | """ 16 | def __init__(self): 17 | super().__init__() 18 | self.base_path = self.env_settings.tnl2k_path 19 | self.sequence_list = self._get_sequence_list() 20 | 21 | def get_sequence_list(self): 22 | return SequenceList([self._construct_sequence(s) for s in self.sequence_list]) 23 | 24 | def _construct_sequence(self, sequence_name): 25 | # class_name = sequence_name.split('-')[0] 26 | anno_path = '{}/{}/groundtruth.txt'.format(self.base_path, sequence_name) 27 | 28 | ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64) 29 | 30 | text_dsp_path = '{}/{}/language.txt'.format(self.base_path, sequence_name) 31 | text_dsp = load_str(text_dsp_path) 32 | 33 | frames_path = '{}/{}/imgs'.format(self.base_path, sequence_name) 34 | frames_list = [f for f in os.listdir(frames_path)] 35 | frames_list = sorted(frames_list) 36 | frames_list = ['{}/{}'.format(frames_path, frame_i) for frame_i in frames_list] 37 | 38 | # target_class = class_name 39 | return Sequence(sequence_name, frames_list, 'tnl2k', ground_truth_rect.reshape(-1, 4), text_dsp=text_dsp) 40 | 41 | def __len__(self): 42 | return len(self.sequence_list) 43 | 44 | def _get_sequence_list(self): 45 | sequence_list = [] 46 | for seq in os.listdir(self.base_path): 47 | if os.path.isdir(os.path.join(self.base_path, seq)): 48 | sequence_list.append(seq) 49 | 50 | return sequence_list 51 | -------------------------------------------------------------------------------- /experiments/ostrack/datr_vitb_384_mae_ce_32x4_ep300.yaml: -------------------------------------------------------------------------------- 1 | DA: 2 | border_prob: 0.05 3 | sfactor: [2.0,6.0] 4 | imix: True 5 | imix_epoch: 1 6 | imix_interval: 11 7 | imix_occrate: 0.5 8 | imix_reverse_prob: 0.0 9 | mix_type: 1 10 | norm_type: 'global' 11 | DATA: 12 | MAX_SAMPLE_INTERVAL: 200 13 | MEAN: 14 | - 0.485 15 | - 0.456 16 | - 0.406 17 | SEARCH: 18 | CENTER_JITTER: 4.5 19 | FACTOR: 5.0 20 | SCALE_JITTER: 0.5 21 | SIZE: 384 22 | STD: 23 | - 0.229 24 | - 0.224 25 | - 0.225 26 | TEMPLATE: 27 | CENTER_JITTER: 0 28 | FACTOR: 2.0 29 | SCALE_JITTER: 0 30 | SIZE: 192 31 | # TRAIN: 32 | # DATASETS_NAME: 33 | # - GOT10K_train_full 34 | # DATASETS_RATIO: 35 | # - 1 36 | # SAMPLE_PER_EPOCH: 60000 37 | 38 | TRAIN: 39 | DATASETS_NAME: 40 | - LASOT 41 | - GOT10K_vottrain 42 | - COCO17 43 | - TRACKINGNET 44 | DATASETS_RATIO: 45 | - 1 46 | - 1 47 | - 1 48 | - 1 49 | SAMPLE_PER_EPOCH: 60000 50 | VAL: 51 | DATASETS_NAME: 52 | - GOT10K_votval 53 | DATASETS_RATIO: 54 | - 1 55 | SAMPLE_PER_EPOCH: 10000 56 | MODEL: 57 | PRETRAIN_FILE: "mae_pretrain_vit_base.pth" 58 | EXTRA_MERGER: False 59 | RETURN_INTER: False 60 | BACKBONE: 61 | TYPE: vit_base_patch16_224_ce 62 | STRIDE: 16 63 | CE_LOC: [3, 6, 9] 64 | CE_KEEP_RATIO: [0.7, 0.7, 0.7] 65 | CE_TEMPLATE_RANGE: 'CTR_POINT' # choose between ALL, CTR_POINT, CTR_REC, GT_BOX 66 | HEAD: 67 | TYPE: CENTER 68 | NUM_CHANNELS: 256 69 | TRAIN: 70 | BACKBONE_MULTIPLIER: 0.1 71 | DROP_PATH_RATE: 0.1 72 | BATCH_SIZE: 32 73 | EPOCH: 300 74 | GIOU_WEIGHT: 2.0 75 | L1_WEIGHT: 5.0 76 | GRAD_CLIP_NORM: 0.1 77 | LR: 0.0004 78 | LR_DROP_EPOCH: 240 79 | NUM_WORKER: 10 80 | OPTIMIZER: ADAMW 81 | PRINT_INTERVAL: 50 82 | SCHEDULER: 83 | TYPE: step 84 | DECAY_RATE: 0.1 85 | VAL_EPOCH_INTERVAL: 20 86 | WEIGHT_DECAY: 0.0001 87 | AMP: False 88 | TEST: 89 | EPOCH: 300 90 | SEARCH_FACTOR: 5.0 91 | SEARCH_SIZE: 384 92 | TEMPLATE_FACTOR: 2.0 93 | TEMPLATE_SIZE: 192 -------------------------------------------------------------------------------- /lib/test/evaluation/tc128cedataset.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList 3 | import os 4 | import glob 5 | import six 6 | 7 | 8 | class TC128CEDataset(BaseDataset): 9 | """ 10 | TC-128 Dataset (78 newly added sequences) 11 | modified from the implementation in got10k-toolkit (https://github.com/got-10k/toolkit) 12 | """ 13 | def __init__(self): 14 | super().__init__() 15 | self.base_path = self.env_settings.tc128_path 16 | self.anno_files = sorted(glob.glob( 17 | os.path.join(self.base_path, '*/*_gt.txt'))) 18 | """filter the newly added sequences (_ce)""" 19 | self.anno_files = [s for s in self.anno_files if "_ce" in s] 20 | self.seq_dirs = [os.path.dirname(f) for f in self.anno_files] 21 | self.seq_names = [os.path.basename(d) for d in self.seq_dirs] 22 | # valid frame range for each sequence 23 | self.range_files = [glob.glob(os.path.join(d, '*_frames.txt'))[0] for d in self.seq_dirs] 24 | 25 | def get_sequence_list(self): 26 | return SequenceList([self._construct_sequence(s) for s in self.seq_names]) 27 | 28 | def _construct_sequence(self, sequence_name): 29 | if isinstance(sequence_name, six.string_types): 30 | if not sequence_name in self.seq_names: 31 | raise Exception('Sequence {} not found.'.format(sequence_name)) 32 | index = self.seq_names.index(sequence_name) 33 | # load valid frame range 34 | frames = np.loadtxt(self.range_files[index], dtype=int, delimiter=',') 35 | img_files = [os.path.join(self.seq_dirs[index], 'img/%04d.jpg' % f) for f in range(frames[0], frames[1] + 1)] 36 | 37 | # load annotations 38 | anno = np.loadtxt(self.anno_files[index], delimiter=',') 39 | assert len(img_files) == len(anno) 40 | assert anno.shape[1] == 4 41 | 42 | # return img_files, anno 43 | return Sequence(sequence_name, img_files, 'tc128', anno.reshape(-1, 4)) 44 | 45 | def __len__(self): 46 | return len(self.seq_names) 47 | -------------------------------------------------------------------------------- /.idea/deployment.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 50 | -------------------------------------------------------------------------------- /lib/test/tracker/vis_utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | ############## used for visulize eliminated tokens ################# 5 | def get_keep_indices(decisions): 6 | keep_indices = [] 7 | for i in range(3): 8 | if i == 0: 9 | keep_indices.append(decisions[i]) 10 | else: 11 | keep_indices.append(keep_indices[-1][decisions[i]]) 12 | return keep_indices 13 | 14 | 15 | def gen_masked_tokens(tokens, indices, alpha=0.2): 16 | # indices = [i for i in range(196) if i not in indices] 17 | indices = indices[0].astype(int) 18 | tokens = tokens.copy() 19 | tokens[indices] = alpha * tokens[indices] + (1 - alpha) * 255 20 | return tokens 21 | 22 | 23 | def recover_image(tokens, H, W, Hp, Wp, patch_size): 24 | # image: (C, 196, 16, 16) 25 | image = tokens.reshape(Hp, Wp, patch_size, patch_size, 3).swapaxes(1, 2).reshape(H, W, 3) 26 | return image 27 | 28 | 29 | def pad_img(img): 30 | height, width, channels = img.shape 31 | im_bg = np.ones((height, width + 8, channels)) * 255 32 | im_bg[0:height, 0:width, :] = img 33 | return im_bg 34 | 35 | 36 | def gen_visualization(image, mask_indices, patch_size=16): 37 | # image [224, 224, 3] 38 | # mask_indices, list of masked token indices 39 | 40 | # mask mask_indices need to cat 41 | # mask_indices = mask_indices[::-1] 42 | num_stages = len(mask_indices) 43 | for i in range(1, num_stages): 44 | mask_indices[i] = np.concatenate([mask_indices[i-1], mask_indices[i]], axis=1) 45 | 46 | # keep_indices = get_keep_indices(decisions) 47 | image = np.asarray(image) 48 | H, W, C = image.shape 49 | Hp, Wp = H // patch_size, W // patch_size 50 | image_tokens = image.reshape(Hp, patch_size, Wp, patch_size, 3).swapaxes(1, 2).reshape(Hp * Wp, patch_size, patch_size, 3) 51 | 52 | stages = [ 53 | recover_image(gen_masked_tokens(image_tokens, mask_indices[i]), H, W, Hp, Wp, patch_size) 54 | for i in range(num_stages) 55 | ] 56 | imgs = [image] + stages 57 | imgs = [pad_img(img) for img in imgs] 58 | viz = np.concatenate(imgs, axis=1) 59 | return viz 60 | -------------------------------------------------------------------------------- /lib/test/utils/transform_got10k.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import os 3 | import shutil 4 | import argparse 5 | import _init_paths 6 | from lib.test.evaluation.environment import env_settings 7 | 8 | 9 | def transform_got10k(tracker_name, cfg_name): 10 | env = env_settings() 11 | result_dir = env.results_path 12 | src_dir = os.path.join(result_dir, "%s/%s/got10k/" % (tracker_name, cfg_name)) 13 | dest_dir = os.path.join(result_dir, "%s/%s/got10k_submit/" % (tracker_name, cfg_name)) 14 | if not os.path.exists(dest_dir): 15 | os.makedirs(dest_dir) 16 | items = os.listdir(src_dir) 17 | for item in items: 18 | if "all" in item: 19 | continue 20 | src_path = os.path.join(src_dir, item) 21 | if "time" not in item: 22 | seq_name = item.replace(".txt", '') 23 | seq_dir = os.path.join(dest_dir, seq_name) 24 | if not os.path.exists(seq_dir): 25 | os.makedirs(seq_dir) 26 | new_item = item.replace(".txt", '_001.txt') 27 | dest_path = os.path.join(seq_dir, new_item) 28 | bbox_arr = np.loadtxt(src_path, dtype=np.int, delimiter='\t') 29 | np.savetxt(dest_path, bbox_arr, fmt='%d', delimiter=',') 30 | else: 31 | seq_name = item.replace("_time.txt", '') 32 | seq_dir = os.path.join(dest_dir, seq_name) 33 | if not os.path.exists(seq_dir): 34 | os.makedirs(seq_dir) 35 | dest_path = os.path.join(seq_dir, item) 36 | os.system("cp %s %s" % (src_path, dest_path)) 37 | # make zip archive 38 | shutil.make_archive(src_dir, "zip", src_dir) 39 | shutil.make_archive(dest_dir, "zip", dest_dir) 40 | # Remove the original files 41 | shutil.rmtree(src_dir) 42 | shutil.rmtree(dest_dir) 43 | 44 | 45 | if __name__ == "__main__": 46 | parser = argparse.ArgumentParser(description='transform got10k results.') 47 | parser.add_argument('--tracker_name', type=str, help='Name of tracking method.') 48 | parser.add_argument('--cfg_name', type=str, help='Name of config file.') 49 | 50 | args = parser.parse_args() 51 | transform_got10k(args.tracker_name, args.cfg_name) 52 | 53 | -------------------------------------------------------------------------------- /experiments/ostrack/datr_vitb_256_mae_ce_32x4_ep300.yaml: -------------------------------------------------------------------------------- 1 | DA: 2 | border_prob: 0.05 3 | sfactor: [2.0,6.0] 4 | imix: True 5 | imix_epoch: 1 6 | imix_interval: 11 7 | imix_occrate: 0.5 8 | imix_reverse_prob: 0.0 9 | mix_type: 1 10 | norm_type: 'global' 11 | DATA: 12 | MAX_SAMPLE_INTERVAL: 200 13 | MEAN: 14 | - 0.485 15 | - 0.456 16 | - 0.406 17 | SEARCH: 18 | CENTER_JITTER: 3 19 | FACTOR: 4.0 20 | SCALE_JITTER: 0.25 21 | SIZE: 256 22 | NUMBER: 1 23 | STD: 24 | - 0.229 25 | - 0.224 26 | - 0.225 27 | TEMPLATE: 28 | CENTER_JITTER: 0 29 | FACTOR: 2.0 30 | SCALE_JITTER: 0 31 | SIZE: 128 32 | # TRAIN: 33 | # DATASETS_NAME: 34 | # - GOT10K_train_full 35 | # DATASETS_RATIO: 36 | # - 1 37 | # SAMPLE_PER_EPOCH: 60000 38 | 39 | TRAIN: 40 | DATASETS_NAME: 41 | - LASOT 42 | - GOT10K_vottrain 43 | - COCO17 44 | - TRACKINGNET 45 | DATASETS_RATIO: 46 | - 1 47 | - 1 48 | - 1 49 | - 1 50 | SAMPLE_PER_EPOCH: 60000 51 | VAL: 52 | DATASETS_NAME: 53 | - GOT10K_votval 54 | DATASETS_RATIO: 55 | - 1 56 | SAMPLE_PER_EPOCH: 10000 57 | MODEL: 58 | PRETRAIN_FILE: "mae_pretrain_vit_base.pth" 59 | EXTRA_MERGER: False 60 | RETURN_INTER: False 61 | BACKBONE: 62 | TYPE: vit_base_patch16_224_ce 63 | STRIDE: 16 64 | CE_LOC: [3, 6, 9] 65 | CE_KEEP_RATIO: [0.7, 0.7, 0.7] 66 | CE_TEMPLATE_RANGE: 'CTR_POINT' # choose between ALL, CTR_POINT, CTR_REC, GT_BOX 67 | HEAD: 68 | TYPE: CENTER 69 | NUM_CHANNELS: 256 70 | TRAIN: 71 | BACKBONE_MULTIPLIER: 0.1 72 | DROP_PATH_RATE: 0.1 73 | CE_START_EPOCH: 20 # candidate elimination start epoch 74 | CE_WARM_EPOCH: 80 # candidate elimination warm up epoch 75 | BATCH_SIZE: 32 76 | EPOCH: 300 77 | GIOU_WEIGHT: 2.0 78 | L1_WEIGHT: 5.0 79 | GRAD_CLIP_NORM: 0.1 80 | LR: 0.0004 81 | LR_DROP_EPOCH: 240 82 | NUM_WORKER: 10 83 | OPTIMIZER: ADAMW 84 | PRINT_INTERVAL: 50 85 | SCHEDULER: 86 | TYPE: step 87 | DECAY_RATE: 0.1 88 | VAL_EPOCH_INTERVAL: 20 89 | WEIGHT_DECAY: 0.0001 90 | AMP: False 91 | TEST: 92 | EPOCH: 300 93 | SEARCH_FACTOR: 4.0 94 | SEARCH_SIZE: 256 95 | TEMPLATE_FACTOR: 2.0 96 | TEMPLATE_SIZE: 128 -------------------------------------------------------------------------------- /lib/test/tracker/data_utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | from lib.utils.misc import NestedTensor 4 | 5 | 6 | class Preprocessor(object): 7 | def __init__(self): 8 | self.mean = torch.tensor([0.485, 0.456, 0.406]).view((1, 3, 1, 1)).cuda() 9 | self.std = torch.tensor([0.229, 0.224, 0.225]).view((1, 3, 1, 1)).cuda() 10 | 11 | def process(self, img_arr: np.ndarray, amask_arr: np.ndarray): 12 | # Deal with the image patch 13 | img_tensor = torch.tensor(img_arr).cuda().float().permute((2,0,1)).unsqueeze(dim=0) 14 | img_tensor_norm = ((img_tensor / 255.0) - self.mean) / self.std # (1,3,H,W) 15 | # Deal with the attention mask 16 | amask_tensor = torch.from_numpy(amask_arr).to(torch.bool).cuda().unsqueeze(dim=0) # (1,H,W) 17 | return NestedTensor(img_tensor_norm, amask_tensor) 18 | 19 | 20 | class PreprocessorX(object): 21 | def __init__(self): 22 | self.mean = torch.tensor([0.485, 0.456, 0.406]).view((1, 3, 1, 1)).cuda() 23 | self.std = torch.tensor([0.229, 0.224, 0.225]).view((1, 3, 1, 1)).cuda() 24 | 25 | def process(self, img_arr: np.ndarray, amask_arr: np.ndarray): 26 | # Deal with the image patch 27 | img_tensor = torch.tensor(img_arr).cuda().float().permute((2,0,1)).unsqueeze(dim=0) 28 | img_tensor_norm = ((img_tensor / 255.0) - self.mean) / self.std # (1,3,H,W) 29 | # Deal with the attention mask 30 | amask_tensor = torch.from_numpy(amask_arr).to(torch.bool).cuda().unsqueeze(dim=0) # (1,H,W) 31 | return img_tensor_norm, amask_tensor 32 | 33 | 34 | class PreprocessorX_onnx(object): 35 | def __init__(self): 36 | self.mean = np.array([0.485, 0.456, 0.406]).reshape((1, 3, 1, 1)) 37 | self.std = np.array([0.229, 0.224, 0.225]).reshape((1, 3, 1, 1)) 38 | 39 | def process(self, img_arr: np.ndarray, amask_arr: np.ndarray): 40 | """img_arr: (H,W,3), amask_arr: (H,W)""" 41 | # Deal with the image patch 42 | img_arr_4d = img_arr[np.newaxis, :, :, :].transpose(0, 3, 1, 2) 43 | img_arr_4d = (img_arr_4d / 255.0 - self.mean) / self.std # (1, 3, H, W) 44 | # Deal with the attention mask 45 | amask_arr_3d = amask_arr[np.newaxis, :, :] # (1,H,W) 46 | return img_arr_4d.astype(np.float32), amask_arr_3d.astype(np.bool) 47 | -------------------------------------------------------------------------------- /tracking/test.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import argparse 4 | 5 | prj_path = os.path.join(os.path.dirname(__file__), '..') 6 | if prj_path not in sys.path: 7 | sys.path.append(prj_path) 8 | 9 | from lib.test.evaluation import get_dataset 10 | from lib.test.evaluation.running import run_dataset 11 | from lib.test.evaluation.tracker import Tracker 12 | 13 | 14 | def run_tracker(tracker_name, tracker_param, run_id=None, dataset_name='otb', sequence=None, debug=0, threads=0, 15 | num_gpus=8): 16 | """Run tracker on sequence or dataset. 17 | args: 18 | tracker_name: Name of tracking method. 19 | tracker_param: Name of parameter file. 20 | run_id: The run id. 21 | dataset_name: Name of dataset (otb, nfs, uav, tpl, vot, tn, gott, gotv, lasot). 22 | sequence: Sequence number or name. 23 | debug: Debug level. 24 | threads: Number of threads. 25 | """ 26 | 27 | dataset = get_dataset(dataset_name) 28 | 29 | if sequence is not None: 30 | dataset = [dataset[sequence]] 31 | 32 | trackers = [Tracker(tracker_name, tracker_param, dataset_name, run_id)] 33 | 34 | run_dataset(dataset, trackers, debug, threads, num_gpus=num_gpus) 35 | 36 | 37 | def main(): 38 | parser = argparse.ArgumentParser(description='Run tracker on sequence or dataset.') 39 | parser.add_argument('tracker_name', type=str, help='Name of tracking method.') 40 | parser.add_argument('tracker_param', type=str, help='Name of config file.') 41 | parser.add_argument('--runid', type=int, default=None, help='The run id.') 42 | parser.add_argument('--dataset_name', type=str, default='otb', help='Name of dataset (otb, nfs, uav, tpl, vot, tn, gott, gotv, lasot).') 43 | parser.add_argument('--sequence', type=str, default=None, help='Sequence number or name.') 44 | parser.add_argument('--debug', type=int, default=0, help='Debug level.') 45 | parser.add_argument('--threads', type=int, default=0, help='Number of threads.') 46 | parser.add_argument('--num_gpus', type=int, default=8) 47 | 48 | args = parser.parse_args() 49 | 50 | try: 51 | seq_name = int(args.sequence) 52 | except: 53 | seq_name = args.sequence 54 | 55 | run_tracker(args.tracker_name, args.tracker_param, args.runid, args.dataset_name, seq_name, args.debug, 56 | args.threads, num_gpus=args.num_gpus) 57 | 58 | 59 | if __name__ == '__main__': 60 | main() 61 | -------------------------------------------------------------------------------- /lib/test/evaluation/got10kdataset.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList 3 | from lib.test.utils.load_text import load_text 4 | import os 5 | 6 | 7 | class GOT10KDataset(BaseDataset): 8 | """ GOT-10k dataset. 9 | 10 | Publication: 11 | GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild 12 | Lianghua Huang, Xin Zhao, and Kaiqi Huang 13 | arXiv:1810.11981, 2018 14 | https://arxiv.org/pdf/1810.11981.pdf 15 | 16 | Download dataset from http://got-10k.aitestunion.com/downloads 17 | """ 18 | def __init__(self, split): 19 | super().__init__() 20 | # Split can be test, val, or ltrval (a validation split consisting of videos from the official train set) 21 | if split == 'test' or split == 'val': 22 | self.base_path = os.path.join(self.env_settings.got10k_path, split) 23 | else: 24 | self.base_path = os.path.join(self.env_settings.got10k_path, 'train') 25 | 26 | self.sequence_list = self._get_sequence_list(split) 27 | self.split = split 28 | 29 | def get_sequence_list(self): 30 | return SequenceList([self._construct_sequence(s) for s in self.sequence_list]) 31 | 32 | def _construct_sequence(self, sequence_name): 33 | anno_path = '{}/{}/groundtruth.txt'.format(self.base_path, sequence_name) 34 | 35 | ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64) 36 | 37 | frames_path = '{}/{}'.format(self.base_path, sequence_name) 38 | frame_list = [frame for frame in os.listdir(frames_path) if frame.endswith(".jpg")] 39 | frame_list.sort(key=lambda f: int(f[:-4])) 40 | frames_list = [os.path.join(frames_path, frame) for frame in frame_list] 41 | 42 | return Sequence(sequence_name, frames_list, 'got10k', ground_truth_rect.reshape(-1, 4)) 43 | 44 | def __len__(self): 45 | return len(self.sequence_list) 46 | 47 | def _get_sequence_list(self, split): 48 | with open('{}/list.txt'.format(self.base_path)) as f: 49 | sequence_list = f.read().splitlines() 50 | 51 | if split == 'ltrval': 52 | with open('{}/got10k_val_split.txt'.format(self.env_settings.dataspec_path)) as f: 53 | seq_ids = f.read().splitlines() 54 | 55 | sequence_list = [sequence_list[int(x)] for x in seq_ids] 56 | return sequence_list 57 | -------------------------------------------------------------------------------- /tracking/test_exp.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import argparse 4 | 5 | prj_path = os.path.join(os.path.dirname(__file__), '..') 6 | if prj_path not in sys.path: 7 | sys.path.append(prj_path) 8 | 9 | from lib.test.evaluation import get_dataset 10 | from lib.test.evaluation.running import run_dataset 11 | from lib.test.evaluation.tracker import Tracker 12 | 13 | 14 | def run_tracker(tracker_name, tracker_param, run_id=None, dataset_name='otb', sequence=None, debug=0, threads=0, 15 | num_gpus=8): 16 | """Run tracker on sequence or dataset. 17 | args: 18 | tracker_name: Name of tracking method. 19 | tracker_param: Name of parameter file. 20 | run_id: The run id. 21 | dataset_name: Name of dataset (otb, nfs, uav, tpl, vot, tn, gott, gotv, lasot). 22 | sequence: Sequence number or name. 23 | debug: Debug level. 24 | threads: Number of threads. 25 | """ 26 | 27 | dataset = get_dataset(*dataset_name) 28 | 29 | if sequence is not None: 30 | dataset = [dataset[sequence]] 31 | 32 | trackers = [Tracker(tracker_name, tracker_param, dataset_name, run_id)] 33 | 34 | run_dataset(dataset, trackers, debug, threads, num_gpus=num_gpus) 35 | 36 | 37 | def main(): 38 | parser = argparse.ArgumentParser(description='Run tracker on sequence or dataset.') 39 | parser.add_argument('tracker_name', type=str, help='Name of tracking method.') 40 | parser.add_argument('tracker_param', type=str, help='Name of config file.') 41 | parser.add_argument('--runid', type=int, default=None, help='The run id.') 42 | parser.add_argument('--dataset_name', type=str, default='otb', help='Name of dataset (otb, nfs, uav, tpl, vot, tn, gott, gotv, lasot).') 43 | parser.add_argument('--sequence', type=str, default=None, help='Sequence number or name.') 44 | parser.add_argument('--debug', type=int, default=0, help='Debug level.') 45 | parser.add_argument('--threads', type=int, default=0, help='Number of threads.') 46 | parser.add_argument('--num_gpus', type=int, default=8) 47 | 48 | args = parser.parse_args() 49 | 50 | try: 51 | seq_name = int(args.sequence) 52 | except: 53 | seq_name = args.sequence 54 | 55 | args.dataset_name = ['trackingnet', 'got10k_test', 'lasot'] 56 | 57 | run_tracker(args.tracker_name, args.tracker_param, args.runid, args.dataset_name, seq_name, args.debug, 58 | args.threads, num_gpus=args.num_gpus) 59 | 60 | 61 | if __name__ == '__main__': 62 | main() 63 | -------------------------------------------------------------------------------- /lib/test/evaluation/datasets.py: -------------------------------------------------------------------------------- 1 | from collections import namedtuple 2 | import importlib 3 | from lib.test.evaluation.data import SequenceList 4 | 5 | DatasetInfo = namedtuple('DatasetInfo', ['module', 'class_name', 'kwargs']) 6 | 7 | pt = "lib.test.evaluation.%sdataset" # Useful abbreviations to reduce the clutter 8 | 9 | dataset_dict = dict( 10 | otb=DatasetInfo(module=pt % "otb", class_name="OTBDataset", kwargs=dict()), 11 | nfs=DatasetInfo(module=pt % "nfs", class_name="NFSDataset", kwargs=dict()), 12 | uav=DatasetInfo(module=pt % "uav", class_name="UAVDataset", kwargs=dict()), 13 | tc128=DatasetInfo(module=pt % "tc128", class_name="TC128Dataset", kwargs=dict()), 14 | tc128ce=DatasetInfo(module=pt % "tc128ce", class_name="TC128CEDataset", kwargs=dict()), 15 | trackingnet=DatasetInfo(module=pt % "trackingnet", class_name="TrackingNetDataset", kwargs=dict()), 16 | got10k_test=DatasetInfo(module=pt % "got10k", class_name="GOT10KDataset", kwargs=dict(split='test')), 17 | got10k_val=DatasetInfo(module=pt % "got10k", class_name="GOT10KDataset", kwargs=dict(split='val')), 18 | got10k_ltrval=DatasetInfo(module=pt % "got10k", class_name="GOT10KDataset", kwargs=dict(split='ltrval')), 19 | lasot=DatasetInfo(module=pt % "lasot", class_name="LaSOTDataset", kwargs=dict()), 20 | lasot_lmdb=DatasetInfo(module=pt % "lasot_lmdb", class_name="LaSOTlmdbDataset", kwargs=dict()), 21 | 22 | vot18=DatasetInfo(module=pt % "vot", class_name="VOTDataset", kwargs=dict()), 23 | vot22=DatasetInfo(module=pt % "vot", class_name="VOTDataset", kwargs=dict(year=22)), 24 | itb=DatasetInfo(module=pt % "itb", class_name="ITBDataset", kwargs=dict()), 25 | tnl2k=DatasetInfo(module=pt % "tnl2k", class_name="TNL2kDataset", kwargs=dict()), 26 | lasot_extension_subset=DatasetInfo(module=pt % "lasotextensionsubset", class_name="LaSOTExtensionSubsetDataset", 27 | kwargs=dict()), 28 | ) 29 | 30 | 31 | def load_dataset(name: str): 32 | """ Import and load a single dataset.""" 33 | name = name.lower() 34 | dset_info = dataset_dict.get(name) 35 | if dset_info is None: 36 | raise ValueError('Unknown dataset \'%s\'' % name) 37 | 38 | m = importlib.import_module(dset_info.module) 39 | dataset = getattr(m, dset_info.class_name)(**dset_info.kwargs) # Call the constructor 40 | return dataset.get_sequence_list() 41 | 42 | 43 | def get_dataset(*args): 44 | """ Get a single or set of datasets.""" 45 | dset = SequenceList() 46 | for name in args: 47 | dset.extend(load_dataset(name)) 48 | return dset -------------------------------------------------------------------------------- /lib/test/evaluation/trackingnetdataset.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList 3 | import os 4 | from lib.test.utils.load_text import load_text 5 | 6 | 7 | class TrackingNetDataset(BaseDataset): 8 | """ TrackingNet test set. 9 | 10 | Publication: 11 | TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild. 12 | Matthias Mueller,Adel Bibi, Silvio Giancola, Salman Al-Subaihi and Bernard Ghanem 13 | ECCV, 2018 14 | https://ivul.kaust.edu.sa/Documents/Publications/2018/TrackingNet%20A%20Large%20Scale%20Dataset%20and%20Benchmark%20for%20Object%20Tracking%20in%20the%20Wild.pdf 15 | 16 | Download the dataset using the toolkit https://github.com/SilvioGiancola/TrackingNet-devkit. 17 | """ 18 | def __init__(self): 19 | super().__init__() 20 | self.base_path = self.env_settings.trackingnet_path 21 | 22 | sets = 'TEST' 23 | if not isinstance(sets, (list, tuple)): 24 | if sets == 'TEST': 25 | sets = ['TEST'] 26 | elif sets == 'TRAIN': 27 | sets = ['TRAIN_{}'.format(i) for i in range(5)] 28 | 29 | self.sequence_list = self._list_sequences(self.base_path, sets) 30 | 31 | def get_sequence_list(self): 32 | return SequenceList([self._construct_sequence(set, seq_name) for set, seq_name in self.sequence_list]) 33 | 34 | def _construct_sequence(self, set, sequence_name): 35 | anno_path = '{}/{}/anno/{}.txt'.format(self.base_path, set, sequence_name) 36 | 37 | ground_truth_rect = load_text(str(anno_path), delimiter=',', dtype=np.float64, backend='numpy') 38 | 39 | frames_path = '{}/{}/frames/{}'.format(self.base_path, set, sequence_name) 40 | frame_list = [frame for frame in os.listdir(frames_path) if frame.endswith(".jpg")] 41 | frame_list.sort(key=lambda f: int(f[:-4])) 42 | frames_list = [os.path.join(frames_path, frame) for frame in frame_list] 43 | 44 | return Sequence(sequence_name, frames_list, 'trackingnet', ground_truth_rect.reshape(-1, 4)) 45 | 46 | def __len__(self): 47 | return len(self.sequence_list) 48 | 49 | def _list_sequences(self, root, set_ids): 50 | sequence_list = [] 51 | 52 | for s in set_ids: 53 | anno_dir = os.path.join(root, s, "anno") 54 | sequences_cur_set = [(s, os.path.splitext(f)[0]) for f in os.listdir(anno_dir) if f.endswith('.txt')] 55 | 56 | sequence_list += sequences_cur_set 57 | 58 | return sequence_list 59 | -------------------------------------------------------------------------------- /lib/utils/focal_loss.py: -------------------------------------------------------------------------------- 1 | from abc import ABC 2 | 3 | import torch 4 | import torch.nn as nn 5 | import torch.nn.functional as F 6 | 7 | 8 | class FocalLoss(nn.Module, ABC): 9 | def __init__(self, alpha=2, beta=4): 10 | super(FocalLoss, self).__init__() 11 | self.alpha = alpha 12 | self.beta = beta 13 | 14 | def forward(self, prediction, target): 15 | positive_index = target.eq(1).float() 16 | negative_index = target.lt(1).float() 17 | 18 | negative_weights = torch.pow(1 - target, self.beta) 19 | # clamp min value is set to 1e-12 to maintain the numerical stability 20 | prediction = torch.clamp(prediction, 1e-12) 21 | 22 | positive_loss = torch.log(prediction) * torch.pow(1 - prediction, self.alpha) * positive_index 23 | negative_loss = torch.log(1 - prediction) * torch.pow(prediction, 24 | self.alpha) * negative_weights * negative_index 25 | 26 | num_positive = positive_index.float().sum() 27 | positive_loss = positive_loss.sum() 28 | negative_loss = negative_loss.sum() 29 | 30 | if num_positive == 0: 31 | loss = -negative_loss 32 | else: 33 | loss = -(positive_loss + negative_loss) / num_positive 34 | 35 | return loss 36 | 37 | 38 | class LBHinge(nn.Module): 39 | """Loss that uses a 'hinge' on the lower bound. 40 | This means that for samples with a label value smaller than the threshold, the loss is zero if the prediction is 41 | also smaller than that threshold. 42 | args: 43 | error_matric: What base loss to use (MSE by default). 44 | threshold: Threshold to use for the hinge. 45 | clip: Clip the loss if it is above this value. 46 | """ 47 | def __init__(self, error_metric=nn.MSELoss(), threshold=None, clip=None): 48 | super().__init__() 49 | self.error_metric = error_metric 50 | self.threshold = threshold if threshold is not None else -100 51 | self.clip = clip 52 | 53 | def forward(self, prediction, label, target_bb=None): 54 | negative_mask = (label < self.threshold).float() 55 | positive_mask = (1.0 - negative_mask) 56 | 57 | prediction = negative_mask * F.relu(prediction) + positive_mask * prediction 58 | 59 | loss = self.error_metric(prediction, positive_mask * label) 60 | 61 | if self.clip is not None: 62 | loss = torch.min(loss, torch.tensor([self.clip], device=loss.device)) 63 | return loss -------------------------------------------------------------------------------- /install.sh: -------------------------------------------------------------------------------- 1 | echo "****************** Installing pytorch ******************" 2 | conda install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cudatoolkit=10.2 -c pytorch 3 | 4 | echo "" 5 | echo "" 6 | echo "****************** Installing yaml ******************" 7 | pip install PyYAML 8 | 9 | echo "" 10 | echo "" 11 | echo "****************** Installing easydict ******************" 12 | pip install easydict 13 | 14 | echo "" 15 | echo "" 16 | echo "****************** Installing cython ******************" 17 | pip install cython 18 | 19 | echo "" 20 | echo "" 21 | echo "****************** Installing opencv-python ******************" 22 | pip install opencv-python 23 | 24 | echo "" 25 | echo "" 26 | echo "****************** Installing pandas ******************" 27 | pip install pandas 28 | 29 | echo "" 30 | echo "" 31 | echo "****************** Installing tqdm ******************" 32 | conda install -y tqdm 33 | 34 | echo "" 35 | echo "" 36 | echo "****************** Installing coco toolkit ******************" 37 | pip install pycocotools 38 | 39 | echo "" 40 | echo "" 41 | echo "****************** Installing jpeg4py python wrapper ******************" 42 | pip install jpeg4py 43 | 44 | echo "" 45 | echo "" 46 | echo "****************** Installing tensorboard ******************" 47 | pip install tb-nightly 48 | 49 | echo "" 50 | echo "" 51 | echo "****************** Installing tikzplotlib ******************" 52 | pip install tikzplotlib 53 | 54 | echo "" 55 | echo "" 56 | echo "****************** Installing thop tool for FLOPs and Params computing ******************" 57 | pip install thop-0.0.31.post2005241907 58 | 59 | echo "" 60 | echo "" 61 | echo "****************** Installing colorama ******************" 62 | pip install colorama 63 | 64 | echo "" 65 | echo "" 66 | echo "****************** Installing lmdb ******************" 67 | pip install lmdb 68 | 69 | echo "" 70 | echo "" 71 | echo "****************** Installing scipy ******************" 72 | pip install scipy 73 | 74 | echo "" 75 | echo "" 76 | echo "****************** Installing visdom ******************" 77 | pip install visdom 78 | 79 | 80 | echo "" 81 | echo "" 82 | echo "****************** Installing tensorboardX ******************" 83 | pip install tensorboardX 84 | 85 | 86 | echo "" 87 | echo "" 88 | echo "****************** Downgrade setuptools ******************" 89 | pip install setuptools==59.5.0 90 | 91 | 92 | echo "" 93 | echo "" 94 | echo "****************** Installing wandb ******************" 95 | pip install wandb 96 | 97 | echo "" 98 | echo "" 99 | echo "****************** Installing timm ******************" 100 | pip install timm 101 | 102 | echo "" 103 | echo "" 104 | echo "****************** Installation complete! ******************" 105 | -------------------------------------------------------------------------------- /lib/train/dataset/base_image_dataset.py: -------------------------------------------------------------------------------- 1 | import torch.utils.data 2 | from lib.train.data.image_loader import jpeg4py_loader 3 | 4 | 5 | class BaseImageDataset(torch.utils.data.Dataset): 6 | """ Base class for image datasets """ 7 | 8 | def __init__(self, name, root, image_loader=jpeg4py_loader): 9 | """ 10 | args: 11 | root - The root path to the dataset 12 | image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) 13 | is used by default. 14 | """ 15 | self.name = name 16 | self.root = root 17 | self.image_loader = image_loader 18 | 19 | self.image_list = [] # Contains the list of sequences. 20 | self.class_list = [] 21 | 22 | def __len__(self): 23 | """ Returns size of the dataset 24 | returns: 25 | int - number of samples in the dataset 26 | """ 27 | return self.get_num_images() 28 | 29 | def __getitem__(self, index): 30 | """ Not to be used! Check get_frames() instead. 31 | """ 32 | return None 33 | 34 | def get_name(self): 35 | """ Name of the dataset 36 | 37 | returns: 38 | string - Name of the dataset 39 | """ 40 | raise NotImplementedError 41 | 42 | def get_num_images(self): 43 | """ Number of sequences in a dataset 44 | 45 | returns: 46 | int - number of sequences in the dataset.""" 47 | return len(self.image_list) 48 | 49 | def has_class_info(self): 50 | return False 51 | 52 | def get_class_name(self, image_id): 53 | return None 54 | 55 | def get_num_classes(self): 56 | return len(self.class_list) 57 | 58 | def get_class_list(self): 59 | return self.class_list 60 | 61 | def get_images_in_class(self, class_name): 62 | raise NotImplementedError 63 | 64 | def has_segmentation_info(self): 65 | return False 66 | 67 | def get_image_info(self, seq_id): 68 | """ Returns information about a particular image, 69 | 70 | args: 71 | seq_id - index of the image 72 | 73 | returns: 74 | Dict 75 | """ 76 | raise NotImplementedError 77 | 78 | def get_image(self, image_id, anno=None): 79 | """ Get a image 80 | 81 | args: 82 | image_id - index of image 83 | anno(None) - The annotation for the sequence (see get_sequence_info). If None, they will be loaded. 84 | 85 | returns: 86 | image - 87 | anno - 88 | dict - A dict containing meta information about the sequence, e.g. class of the target object. 89 | 90 | """ 91 | raise NotImplementedError 92 | 93 | -------------------------------------------------------------------------------- /tracking/pre_read_datasets.py: -------------------------------------------------------------------------------- 1 | import _init_paths 2 | import multiprocessing as mp 3 | import argparse 4 | import os 5 | from lib.utils.lmdb_utils import decode_str 6 | import time 7 | import json 8 | 9 | 10 | def parse_args(): 11 | """ 12 | args for training. 13 | """ 14 | parser = argparse.ArgumentParser(description='Parse args for training') 15 | parser.add_argument('--data_dir', type=str, help='directory where lmdb data is located') 16 | parser.add_argument('--dataset_str', type=str, help="which datasets to use") 17 | args = parser.parse_args() 18 | 19 | return args 20 | 21 | 22 | def get_trknet_dict(trknet_dir): 23 | with open(os.path.join(trknet_dir, "seq_list.json"), "r") as f: 24 | seq_list = json.loads(f.read()) 25 | res_dict = {} 26 | set_idx_pre = -1 27 | for set_idx, seq_name in seq_list: 28 | if set_idx != set_idx_pre: 29 | res_dict[set_idx] = "anno/%s.txt" % seq_name 30 | set_idx_pre = set_idx 31 | return res_dict 32 | 33 | 34 | def target(lmdb_dir, key_name): 35 | _ = decode_str(lmdb_dir, key_name) 36 | 37 | 38 | if __name__ == "__main__": 39 | args = parse_args() 40 | data_dir = args.data_dir 41 | dataset_str = args.dataset_str 42 | key_dict = {"got10k_lmdb": "train/list.txt", 43 | "lasot_lmdb": "LaSOTBenchmark.json", 44 | "coco_lmdb": "annotations/instances_train2017.json", 45 | "vid_lmdb": "cache.json"} 46 | print("Ready to pre load datasets") 47 | start = time.time() 48 | ps = [] 49 | datasets = [] 50 | if 'g' in dataset_str: 51 | datasets.append("got10k_lmdb") 52 | if 'l' in dataset_str: 53 | datasets.append("lasot_lmdb") 54 | if 'c' in dataset_str: 55 | datasets.append("coco_lmdb") 56 | if 'v' in dataset_str: 57 | datasets.append("vid_lmdb") 58 | for dataset in datasets: 59 | lmdb_dir = os.path.join(data_dir, dataset) 60 | p = mp.Process(target=target, args=(lmdb_dir, key_dict[dataset])) 61 | print("add %s %s to job queue" % (lmdb_dir, key_dict[dataset])) 62 | ps.append(p) 63 | # deal with trackingnet 64 | if 't' in dataset_str: 65 | trknet_dict = get_trknet_dict(os.path.join(data_dir, "trackingnet_lmdb")) 66 | for set_idx, seq_path in trknet_dict.items(): 67 | lmdb_dir = os.path.join(data_dir, "trackingnet_lmdb", "TRAIN_%d_lmdb" % set_idx) 68 | p = mp.Process(target=target, args=(lmdb_dir, seq_path)) 69 | print("add %s %s to job queue" % (lmdb_dir, seq_path)) 70 | ps.append(p) 71 | for p in ps: 72 | p.start() 73 | for p in ps: 74 | p.join() 75 | 76 | print("Pre read over") 77 | end = time.time() 78 | hour = (end - start) / 3600 79 | print("it takes %.2f hours to pre-read data" % hour) 80 | -------------------------------------------------------------------------------- /tracking/analysis_results.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "outputs": [], 7 | "source": [ 8 | "%load_ext autoreload\n", 9 | "%autoreload 2\n", 10 | "%matplotlib inline\n", 11 | "import os\n", 12 | "import sys\n", 13 | "import matplotlib.pyplot as plt\n", 14 | "plt.rcParams['figure.figsize'] = [14, 8]\n", 15 | "\n", 16 | "sys.path.append('/home/yebotao/OSTrack')\n", 17 | "from lib.test.analysis.plot_results import plot_results, print_results, print_per_sequence_results, print_results_per_video\n", 18 | "from lib.test.evaluation import get_dataset, trackerlist" 19 | ], 20 | "metadata": { 21 | "collapsed": false, 22 | "pycharm": { 23 | "name": "#%%\n" 24 | } 25 | } 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": null, 30 | "outputs": [], 31 | "source": [ 32 | "dataset_name = 'lasot'\n", 33 | "\n", 34 | "trackers = []\n", 35 | "trackers.extend(trackerlist(name='ostrack', parameter_name='vitb_256_mae_ce_32x4_ep300', dataset_name=dataset_name,\n", 36 | " run_ids=None, display_name='OSTrack256'))\n", 37 | "trackers.extend(trackerlist(name='ostrack', parameter_name='vitb_384_mae_ce_32x4_ep300', dataset_name=dataset_name,\n", 38 | " run_ids=None, display_name='OSTrack384'))\n", 39 | "\n", 40 | "dataset = get_dataset(dataset_name)\n", 41 | "# plot_results(trackers, dataset, dataset_name, merge_results=True, plot_types=('success', 'prec'),\n", 42 | "# skip_missing_seq=False, force_evaluation=True, plot_bin_gap=0.05, exclude_invalid_frames=False)\n", 43 | "print_results(trackers, dataset, dataset_name, merge_results=True, plot_types=('success', 'prec', 'norm_prec'))\n", 44 | "# print_results_per_video(trackers, dataset, dataset_name, merge_results=True, plot_types=('success', 'prec', 'norm_prec'),\n", 45 | "# per_video=True, force_evaluation=True)\n", 46 | "# print_per_sequence_results(trackers, dataset, dataset_name, merge_results=True, plot_types=('success', 'prec', 'norm_prec'))" 47 | ], 48 | "metadata": { 49 | "collapsed": false, 50 | "pycharm": { 51 | "name": "#%%\n" 52 | } 53 | } 54 | } 55 | ], 56 | "metadata": { 57 | "kernelspec": { 58 | "display_name": "Python 3", 59 | "language": "python", 60 | "name": "python3" 61 | }, 62 | "language_info": { 63 | "codemirror_mode": { 64 | "name": "ipython", 65 | "version": 2 66 | }, 67 | "file_extension": ".py", 68 | "mimetype": "text/x-python", 69 | "name": "python", 70 | "nbconvert_exporter": "python", 71 | "pygments_lexer": "ipython2", 72 | "version": "2.7.6" 73 | } 74 | }, 75 | "nbformat": 4, 76 | "nbformat_minor": 0 77 | } -------------------------------------------------------------------------------- /lib/utils/box_ops.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torchvision.ops.boxes import box_area 3 | import numpy as np 4 | 5 | 6 | def box_cxcywh_to_xyxy(x): 7 | x_c, y_c, w, h = x.unbind(-1) 8 | b = [(x_c - 0.5 * w), (y_c - 0.5 * h), 9 | (x_c + 0.5 * w), (y_c + 0.5 * h)] 10 | return torch.stack(b, dim=-1) 11 | 12 | 13 | def box_xywh_to_xyxy(x): 14 | x1, y1, w, h = x.unbind(-1) 15 | b = [x1, y1, x1 + w, y1 + h] 16 | return torch.stack(b, dim=-1) 17 | 18 | 19 | def box_xyxy_to_xywh(x): 20 | x1, y1, x2, y2 = x.unbind(-1) 21 | b = [x1, y1, x2 - x1, y2 - y1] 22 | return torch.stack(b, dim=-1) 23 | 24 | 25 | def box_xyxy_to_cxcywh(x): 26 | x0, y0, x1, y1 = x.unbind(-1) 27 | b = [(x0 + x1) / 2, (y0 + y1) / 2, 28 | (x1 - x0), (y1 - y0)] 29 | return torch.stack(b, dim=-1) 30 | 31 | 32 | # modified from torchvision to also return the union 33 | '''Note that this function only supports shape (N,4)''' 34 | 35 | 36 | def box_iou(boxes1, boxes2): 37 | """ 38 | 39 | :param boxes1: (N, 4) (x1,y1,x2,y2) 40 | :param boxes2: (N, 4) (x1,y1,x2,y2) 41 | :return: 42 | """ 43 | area1 = box_area(boxes1) # (N,) 44 | area2 = box_area(boxes2) # (N,) 45 | 46 | lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # (N,2) 47 | rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # (N,2) 48 | 49 | wh = (rb - lt).clamp(min=0) # (N,2) 50 | inter = wh[:, 0] * wh[:, 1] # (N,) 51 | 52 | union = area1 + area2 - inter 53 | 54 | iou = inter / union 55 | return iou, union 56 | 57 | 58 | '''Note that this implementation is different from DETR's''' 59 | 60 | 61 | def generalized_box_iou(boxes1, boxes2): 62 | """ 63 | Generalized IoU from https://giou.stanford.edu/ 64 | 65 | The boxes should be in [x0, y0, x1, y1] format 66 | 67 | boxes1: (N, 4) 68 | boxes2: (N, 4) 69 | """ 70 | # degenerate boxes gives inf / nan results 71 | # so do an early check 72 | # try: 73 | assert (boxes1[:, 2:] >= boxes1[:, :2]).all() 74 | assert (boxes2[:, 2:] >= boxes2[:, :2]).all() 75 | iou, union = box_iou(boxes1, boxes2) # (N,) 76 | 77 | lt = torch.min(boxes1[:, :2], boxes2[:, :2]) 78 | rb = torch.max(boxes1[:, 2:], boxes2[:, 2:]) 79 | 80 | wh = (rb - lt).clamp(min=0) # (N,2) 81 | area = wh[:, 0] * wh[:, 1] # (N,) 82 | 83 | return iou - (area - union) / area, iou 84 | 85 | 86 | def giou_loss(boxes1, boxes2): 87 | """ 88 | 89 | :param boxes1: (N, 4) (x1,y1,x2,y2) 90 | :param boxes2: (N, 4) (x1,y1,x2,y2) 91 | :return: 92 | """ 93 | giou, iou = generalized_box_iou(boxes1, boxes2) 94 | return (1 - giou).mean(), iou 95 | 96 | 97 | def clip_box(box: list, H, W, margin=0): 98 | x1, y1, w, h = box 99 | x2, y2 = x1 + w, y1 + h 100 | x1 = min(max(0, x1), W-margin) 101 | x2 = min(max(margin, x2), W) 102 | y1 = min(max(0, y1), H-margin) 103 | y2 = min(max(margin, y2), H) 104 | w = max(margin, x2-x1) 105 | h = max(margin, y2-y1) 106 | return [x1, y1, w, h] 107 | -------------------------------------------------------------------------------- /lib/train/data/bounding_box_utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | def rect_to_rel(bb, sz_norm=None): 5 | """Convert standard rectangular parametrization of the bounding box [x, y, w, h] 6 | to relative parametrization [cx/sw, cy/sh, log(w), log(h)], where [cx, cy] is the center coordinate. 7 | args: 8 | bb - N x 4 tensor of boxes. 9 | sz_norm - [N] x 2 tensor of value of [sw, sh] (optional). sw=w and sh=h if not given. 10 | """ 11 | 12 | c = bb[...,:2] + 0.5 * bb[...,2:] 13 | if sz_norm is None: 14 | c_rel = c / bb[...,2:] 15 | else: 16 | c_rel = c / sz_norm 17 | sz_rel = torch.log(bb[...,2:]) 18 | return torch.cat((c_rel, sz_rel), dim=-1) 19 | 20 | 21 | def rel_to_rect(bb, sz_norm=None): 22 | """Inverts the effect of rect_to_rel. See above.""" 23 | 24 | sz = torch.exp(bb[...,2:]) 25 | if sz_norm is None: 26 | c = bb[...,:2] * sz 27 | else: 28 | c = bb[...,:2] * sz_norm 29 | tl = c - 0.5 * sz 30 | return torch.cat((tl, sz), dim=-1) 31 | 32 | 33 | def masks_to_bboxes(mask, fmt='c'): 34 | 35 | """ Convert a mask tensor to one or more bounding boxes. 36 | Note: This function is a bit new, make sure it does what it says. /Andreas 37 | :param mask: Tensor of masks, shape = (..., H, W) 38 | :param fmt: bbox layout. 'c' => "center + size" or (x_center, y_center, width, height) 39 | 't' => "top left + size" or (x_left, y_top, width, height) 40 | 'v' => "vertices" or (x_left, y_top, x_right, y_bottom) 41 | :return: tensor containing a batch of bounding boxes, shape = (..., 4) 42 | """ 43 | batch_shape = mask.shape[:-2] 44 | mask = mask.reshape((-1, *mask.shape[-2:])) 45 | bboxes = [] 46 | 47 | for m in mask: 48 | mx = m.sum(dim=-2).nonzero() 49 | my = m.sum(dim=-1).nonzero() 50 | bb = [mx.min(), my.min(), mx.max(), my.max()] if (len(mx) > 0 and len(my) > 0) else [0, 0, 0, 0] 51 | bboxes.append(bb) 52 | 53 | bboxes = torch.tensor(bboxes, dtype=torch.float32, device=mask.device) 54 | bboxes = bboxes.reshape(batch_shape + (4,)) 55 | 56 | if fmt == 'v': 57 | return bboxes 58 | 59 | x1 = bboxes[..., :2] 60 | s = bboxes[..., 2:] - x1 + 1 61 | 62 | if fmt == 'c': 63 | return torch.cat((x1 + 0.5 * s, s), dim=-1) 64 | elif fmt == 't': 65 | return torch.cat((x1, s), dim=-1) 66 | 67 | raise ValueError("Undefined bounding box layout '%s'" % fmt) 68 | 69 | 70 | def masks_to_bboxes_multi(mask, ids, fmt='c'): 71 | assert mask.dim() == 2 72 | bboxes = [] 73 | 74 | for id in ids: 75 | mx = (mask == id).sum(dim=-2).nonzero() 76 | my = (mask == id).float().sum(dim=-1).nonzero() 77 | bb = [mx.min(), my.min(), mx.max(), my.max()] if (len(mx) > 0 and len(my) > 0) else [0, 0, 0, 0] 78 | 79 | bb = torch.tensor(bb, dtype=torch.float32, device=mask.device) 80 | 81 | x1 = bb[:2] 82 | s = bb[2:] - x1 + 1 83 | 84 | if fmt == 'v': 85 | pass 86 | elif fmt == 'c': 87 | bb = torch.cat((x1 + 0.5 * s, s), dim=-1) 88 | elif fmt == 't': 89 | bb = torch.cat((x1, s), dim=-1) 90 | else: 91 | raise ValueError("Undefined bounding box layout '%s'" % fmt) 92 | bboxes.append(bb) 93 | 94 | return bboxes 95 | -------------------------------------------------------------------------------- /lib/test/evaluation/itbdataset.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from lib.test.evaluation.data import Sequence, BaseDataset, SequenceList 3 | from lib.test.utils.load_text import load_text 4 | import os 5 | 6 | 7 | class ITBDataset(BaseDataset): 8 | """ NUS-PRO dataset 9 | """ 10 | 11 | def __init__(self): 12 | super().__init__() 13 | self.base_path = self.env_settings.itb_path 14 | self.sequence_info_list = self._get_sequence_info_list(self.base_path) 15 | 16 | def get_sequence_list(self): 17 | return SequenceList([self._construct_sequence(s) for s in self.sequence_info_list]) 18 | 19 | def _construct_sequence(self, sequence_info): 20 | sequence_path = sequence_info['path'] 21 | nz = sequence_info['nz'] 22 | ext = sequence_info['ext'] 23 | start_frame = sequence_info['startFrame'] 24 | end_frame = sequence_info['endFrame'] 25 | 26 | init_omit = 0 27 | if 'initOmit' in sequence_info: 28 | init_omit = sequence_info['initOmit'] 29 | 30 | frames = ['{base_path}/{sequence_path}/{frame:0{nz}}.{ext}'.format(base_path=self.base_path, 31 | sequence_path=sequence_path, frame=frame_num, 32 | nz=nz, ext=ext) for frame_num in 33 | range(start_frame + init_omit, end_frame + 1)] 34 | 35 | anno_path = '{}/{}'.format(self.base_path, sequence_info['anno_path']) 36 | 37 | # NOTE: NUS has some weird annos which panda cannot handle 38 | ground_truth_rect = load_text(str(anno_path), delimiter=(',', None), dtype=np.float64, backend='numpy') 39 | return Sequence(sequence_info['name'], frames, 'otb', ground_truth_rect[init_omit:, :], 40 | object_class=sequence_info['object_class']) 41 | 42 | def __len__(self): 43 | return len(self.sequence_info_list) 44 | 45 | def get_fileNames(self, rootdir): 46 | fs = [] 47 | fs_all = [] 48 | for root, dirs, files in os.walk(rootdir, topdown=True): 49 | files.sort() 50 | files.sort(key=len) 51 | if files is not None: 52 | for name in files: 53 | _, ending = os.path.splitext(name) 54 | if ending == ".jpg": 55 | _, root_ = os.path.split(root) 56 | fs.append(os.path.join(root_, name)) 57 | fs_all.append(os.path.join(root, name)) 58 | 59 | return fs_all, fs 60 | 61 | def _get_sequence_info_list(self, base_path): 62 | sequence_info_list = [] 63 | for scene in os.listdir(base_path): 64 | if '.' in scene: 65 | continue 66 | videos = os.listdir(os.path.join(base_path, scene)) 67 | for video in videos: 68 | _, fs = self.get_fileNames(os.path.join(base_path, scene, video)) 69 | video_tmp = {"name": video, "path": scene + '/' + video, "startFrame": 1, "endFrame": len(fs), 70 | "nz": len(fs[0].split('/')[-1].split('.')[0]), "ext": "jpg", 71 | "anno_path": scene + '/' + video + "/groundtruth.txt", 72 | "object_class": "unknown"} 73 | sequence_info_list.append(video_tmp) 74 | 75 | return sequence_info_list # sequence_info_list_50 # 76 | -------------------------------------------------------------------------------- /lib/utils/ce_utils.py: -------------------------------------------------------------------------------- 1 | import math 2 | 3 | import torch 4 | import torch.nn.functional as F 5 | 6 | 7 | def generate_bbox_mask(bbox_mask, bbox): 8 | b, h, w = bbox_mask.shape 9 | for i in range(b): 10 | bbox_i = bbox[i].cpu().tolist() 11 | bbox_mask[i, int(bbox_i[1]):int(bbox_i[1] + bbox_i[3] - 1), int(bbox_i[0]):int(bbox_i[0] + bbox_i[2] - 1)] = 1 12 | return bbox_mask 13 | 14 | 15 | def generate_mask_cond(cfg, bs, device, gt_bbox): 16 | template_size = cfg.DATA.TEMPLATE.SIZE 17 | stride = cfg.MODEL.BACKBONE.STRIDE 18 | template_feat_size = template_size // stride 19 | 20 | if cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE == 'ALL': 21 | box_mask_z = None 22 | elif cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE == 'CTR_POINT': 23 | if template_feat_size == 8: 24 | index = slice(3, 4) 25 | elif template_feat_size == 12: 26 | index = slice(5, 6) 27 | elif template_feat_size == 7: 28 | index = slice(3, 4) 29 | elif template_feat_size == 14: 30 | index = slice(6, 7) 31 | else: 32 | raise NotImplementedError 33 | box_mask_z = torch.zeros([bs, template_feat_size, template_feat_size], device=device) 34 | box_mask_z[:, index, index] = 1 35 | box_mask_z = box_mask_z.flatten(1).to(torch.bool) 36 | elif cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE == 'CTR_REC': 37 | # use fixed 4x4 region, 3:5 for 8x8 38 | # use fixed 4x4 region 5:6 for 12x12 39 | if template_feat_size == 8: 40 | index = slice(3, 5) 41 | elif template_feat_size == 12: 42 | index = slice(5, 7) 43 | elif template_feat_size == 7: 44 | index = slice(3, 4) 45 | else: 46 | raise NotImplementedError 47 | box_mask_z = torch.zeros([bs, template_feat_size, template_feat_size], device=device) 48 | box_mask_z[:, index, index] = 1 49 | box_mask_z = box_mask_z.flatten(1).to(torch.bool) 50 | 51 | elif cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE == 'GT_BOX': 52 | box_mask_z = torch.zeros([bs, template_size, template_size], device=device) 53 | # box_mask_z_ori = data['template_seg'][0].view(-1, 1, *data['template_seg'].shape[2:]) # (batch, 1, 128, 128) 54 | box_mask_z = generate_bbox_mask(box_mask_z, gt_bbox * template_size).unsqueeze(1).to( 55 | torch.float) # (batch, 1, 128, 128) 56 | # box_mask_z_vis = box_mask_z.cpu().numpy() 57 | box_mask_z = F.interpolate(box_mask_z, scale_factor=1. / cfg.MODEL.BACKBONE.STRIDE, mode='bilinear', 58 | align_corners=False) 59 | box_mask_z = box_mask_z.flatten(1).to(torch.bool) 60 | # box_mask_z_vis = box_mask_z[:, 0, ...].cpu().numpy() 61 | # gaussian_maps_vis = generate_heatmap(data['template_anno'], self.cfg.DATA.TEMPLATE.SIZE, self.cfg.MODEL.STRIDE)[0].cpu().numpy() 62 | else: 63 | raise NotImplementedError 64 | 65 | return box_mask_z 66 | 67 | 68 | def adjust_keep_rate(epoch, warmup_epochs, total_epochs, ITERS_PER_EPOCH, base_keep_rate=0.5, max_keep_rate=1, iters=-1): 69 | if epoch < warmup_epochs: 70 | return 1 71 | if epoch >= total_epochs: 72 | return base_keep_rate 73 | if iters == -1: 74 | iters = epoch * ITERS_PER_EPOCH 75 | total_iters = ITERS_PER_EPOCH * (total_epochs - warmup_epochs) 76 | iters = iters - ITERS_PER_EPOCH * warmup_epochs 77 | keep_rate = base_keep_rate + (max_keep_rate - base_keep_rate) \ 78 | * (math.cos(iters / total_iters * math.pi) + 1) * 0.5 79 | 80 | return keep_rate 81 | -------------------------------------------------------------------------------- /lib/train/dataset/base_video_dataset.py: -------------------------------------------------------------------------------- 1 | import torch.utils.data 2 | # 2021.1.5 use jpeg4py_loader_w_failsafe as default 3 | from lib.train.data.image_loader import jpeg4py_loader_w_failsafe 4 | 5 | 6 | class BaseVideoDataset(torch.utils.data.Dataset): 7 | """ Base class for video datasets """ 8 | 9 | def __init__(self, name, root, image_loader=jpeg4py_loader_w_failsafe): 10 | """ 11 | args: 12 | root - The root path to the dataset 13 | image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py) 14 | is used by default. 15 | """ 16 | self.name = name 17 | self.root = root 18 | self.image_loader = image_loader 19 | 20 | self.sequence_list = [] # Contains the list of sequences. 21 | self.class_list = [] 22 | 23 | def __len__(self): 24 | """ Returns size of the dataset 25 | returns: 26 | int - number of samples in the dataset 27 | """ 28 | return self.get_num_sequences() 29 | 30 | def __getitem__(self, index): 31 | """ Not to be used! Check get_frames() instead. 32 | """ 33 | return None 34 | 35 | def is_video_sequence(self): 36 | """ Returns whether the dataset is a video dataset or an image dataset 37 | 38 | returns: 39 | bool - True if a video dataset 40 | """ 41 | return True 42 | 43 | def is_synthetic_video_dataset(self): 44 | """ Returns whether the dataset contains real videos or synthetic 45 | 46 | returns: 47 | bool - True if a video dataset 48 | """ 49 | return False 50 | 51 | def get_name(self): 52 | """ Name of the dataset 53 | 54 | returns: 55 | string - Name of the dataset 56 | """ 57 | raise NotImplementedError 58 | 59 | def get_num_sequences(self): 60 | """ Number of sequences in a dataset 61 | 62 | returns: 63 | int - number of sequences in the dataset.""" 64 | return len(self.sequence_list) 65 | 66 | def has_class_info(self): 67 | return False 68 | 69 | def has_occlusion_info(self): 70 | return False 71 | 72 | def get_num_classes(self): 73 | return len(self.class_list) 74 | 75 | def get_class_list(self): 76 | return self.class_list 77 | 78 | def get_sequences_in_class(self, class_name): 79 | raise NotImplementedError 80 | 81 | def has_segmentation_info(self): 82 | return False 83 | 84 | def get_sequence_info(self, seq_id): 85 | """ Returns information about a particular sequences, 86 | 87 | args: 88 | seq_id - index of the sequence 89 | 90 | returns: 91 | Dict 92 | """ 93 | raise NotImplementedError 94 | 95 | def get_frames(self, seq_id, frame_ids, anno=None): 96 | """ Get a set of frames from a particular sequence 97 | 98 | args: 99 | seq_id - index of sequence 100 | frame_ids - a list of frame numbers 101 | anno(None) - The annotation for the sequence (see get_sequence_info). If None, they will be loaded. 102 | 103 | returns: 104 | list - List of frames corresponding to frame_ids 105 | list - List of dicts for each frame 106 | dict - A dict containing meta information about the sequence, e.g. class of the target object. 107 | 108 | """ 109 | raise NotImplementedError 110 | 111 | -------------------------------------------------------------------------------- /lib/train/data/image_loader.py: -------------------------------------------------------------------------------- 1 | import jpeg4py 2 | import cv2 as cv 3 | from PIL import Image 4 | import numpy as np 5 | 6 | davis_palette = np.repeat(np.expand_dims(np.arange(0,256), 1), 3, 1).astype(np.uint8) 7 | davis_palette[:22, :] = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], 8 | [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128], 9 | [64, 0, 0], [191, 0, 0], [64, 128, 0], [191, 128, 0], 10 | [64, 0, 128], [191, 0, 128], [64, 128, 128], [191, 128, 128], 11 | [0, 64, 0], [128, 64, 0], [0, 191, 0], [128, 191, 0], 12 | [0, 64, 128], [128, 64, 128]] 13 | 14 | 15 | def default_image_loader(path): 16 | """The default image loader, reads the image from the given path. It first tries to use the jpeg4py_loader, 17 | but reverts to the opencv_loader if the former is not available.""" 18 | if default_image_loader.use_jpeg4py is None: 19 | # Try using jpeg4py 20 | im = jpeg4py_loader(path) 21 | if im is None: 22 | default_image_loader.use_jpeg4py = False 23 | print('Using opencv_loader instead.') 24 | else: 25 | default_image_loader.use_jpeg4py = True 26 | return im 27 | if default_image_loader.use_jpeg4py: 28 | return jpeg4py_loader(path) 29 | return opencv_loader(path) 30 | 31 | default_image_loader.use_jpeg4py = None 32 | 33 | 34 | def jpeg4py_loader(path): 35 | """ Image reading using jpeg4py https://github.com/ajkxyz/jpeg4py""" 36 | try: 37 | return jpeg4py.JPEG(path).decode() 38 | except Exception as e: 39 | print('ERROR: Could not read image "{}"'.format(path)) 40 | print(e) 41 | return None 42 | 43 | 44 | def opencv_loader(path): 45 | """ Read image using opencv's imread function and returns it in rgb format""" 46 | try: 47 | im = cv.imread(path, cv.IMREAD_COLOR) 48 | 49 | # convert to rgb and return 50 | return cv.cvtColor(im, cv.COLOR_BGR2RGB) 51 | except Exception as e: 52 | print('ERROR: Could not read image "{}"'.format(path)) 53 | print(e) 54 | return None 55 | 56 | 57 | def jpeg4py_loader_w_failsafe(path): 58 | """ Image reading using jpeg4py https://github.com/ajkxyz/jpeg4py""" 59 | try: 60 | return jpeg4py.JPEG(path).decode() 61 | except: 62 | try: 63 | im = cv.imread(path, cv.IMREAD_COLOR) 64 | 65 | # convert to rgb and return 66 | return cv.cvtColor(im, cv.COLOR_BGR2RGB) 67 | except Exception as e: 68 | print('ERROR: Could not read image "{}"'.format(path)) 69 | print(e) 70 | return None 71 | 72 | 73 | def opencv_seg_loader(path): 74 | """ Read segmentation annotation using opencv's imread function""" 75 | try: 76 | return cv.imread(path) 77 | except Exception as e: 78 | print('ERROR: Could not read image "{}"'.format(path)) 79 | print(e) 80 | return None 81 | 82 | 83 | def imread_indexed(filename): 84 | """ Load indexed image with given filename. Used to read segmentation annotations.""" 85 | 86 | im = Image.open(filename) 87 | 88 | annotation = np.atleast_3d(im)[...,0] 89 | return annotation 90 | 91 | 92 | def imwrite_indexed(filename, array, color_palette=None): 93 | """ Save indexed image as png. Used to save segmentation annotation.""" 94 | 95 | if color_palette is None: 96 | color_palette = davis_palette 97 | 98 | if np.atleast_3d(array).shape[2] != 1: 99 | raise Exception("Saving indexed PNGs requires 2D array.") 100 | 101 | im = Image.fromarray(array) 102 | im.putpalette(color_palette.ravel()) 103 | im.save(filename, format='PNG') -------------------------------------------------------------------------------- /lib/train/train_script.py: -------------------------------------------------------------------------------- 1 | import os 2 | # loss function related 3 | from lib.utils.box_ops import giou_loss 4 | from torch.nn.functional import l1_loss 5 | from torch.nn import BCEWithLogitsLoss 6 | # train pipeline related 7 | from lib.train.trainers import LTRTrainer 8 | # distributed training related 9 | from torch.nn.parallel import DistributedDataParallel as DDP 10 | # some more advanced functions 11 | from .base_functions import * 12 | # network related 13 | from lib.models.ostrack import build_ostrack 14 | # forward propagation related 15 | from lib.train.actors import OSTrackActor 16 | # for import modules 17 | import importlib 18 | 19 | from ..utils.focal_loss import FocalLoss 20 | 21 | 22 | def run(settings): 23 | settings.description = 'Training script for STARK-S, STARK-ST stage1, and STARK-ST stage2' 24 | 25 | # update the default configs with config file 26 | if not os.path.exists(settings.cfg_file): 27 | raise ValueError("%s doesn't exist." % settings.cfg_file) 28 | config_module = importlib.import_module("lib.config.%s.config" % settings.script_name) 29 | cfg = config_module.cfg 30 | config_module.update_config_from_file(settings.cfg_file) 31 | if settings.local_rank in [-1, 0]: 32 | print("New configuration is shown below.") 33 | for key in cfg.keys(): 34 | print("%s configuration:" % key, cfg[key]) 35 | print('\n') 36 | 37 | # update settings based on cfg 38 | update_settings(settings, cfg) 39 | 40 | # Record the training log 41 | log_dir = os.path.join(settings.save_dir, 'logs') 42 | if settings.local_rank in [-1, 0]: 43 | if not os.path.exists(log_dir): 44 | os.makedirs(log_dir) 45 | settings.log_file = os.path.join(log_dir, "%s-%s.log" % (settings.script_name, settings.config_name)) 46 | 47 | # Build dataloaders 48 | loader_train, loader_val = build_dataloaders(cfg, settings) 49 | 50 | if "RepVGG" in cfg.MODEL.BACKBONE.TYPE or "swin" in cfg.MODEL.BACKBONE.TYPE or "LightTrack" in cfg.MODEL.BACKBONE.TYPE: 51 | cfg.ckpt_dir = settings.save_dir 52 | 53 | # Create network 54 | if settings.script_name == "ostrack": 55 | net = build_ostrack(cfg) 56 | else: 57 | raise ValueError("illegal script name") 58 | 59 | # wrap networks to distributed one 60 | net.cuda() 61 | if settings.local_rank != -1: 62 | # net = torch.nn.SyncBatchNorm.convert_sync_batchnorm(net) # add syncBN converter 63 | net = DDP(net, device_ids=[settings.local_rank], find_unused_parameters=True) 64 | settings.device = torch.device("cuda:%d" % settings.local_rank) 65 | else: 66 | settings.device = torch.device("cuda:0") 67 | settings.deep_sup = getattr(cfg.TRAIN, "DEEP_SUPERVISION", False) 68 | settings.distill = getattr(cfg.TRAIN, "DISTILL", False) 69 | settings.distill_loss_type = getattr(cfg.TRAIN, "DISTILL_LOSS_TYPE", "KL") 70 | # Loss functions and Actors 71 | if settings.script_name == "ostrack": 72 | focal_loss = FocalLoss() 73 | objective = {'giou': giou_loss, 'l1': l1_loss, 'focal': focal_loss, 'cls': BCEWithLogitsLoss()} 74 | loss_weight = {'giou': cfg.TRAIN.GIOU_WEIGHT, 'l1': cfg.TRAIN.L1_WEIGHT, 'focal': 1., 'cls': 1.0} 75 | actor = OSTrackActor(net=net, objective=objective, loss_weight=loss_weight, settings=settings, cfg=cfg) 76 | else: 77 | raise ValueError("illegal script name") 78 | 79 | # if cfg.TRAIN.DEEP_SUPERVISION: 80 | # raise ValueError("Deep supervision is not supported now.") 81 | 82 | # Optimizer, parameters, and learning rates 83 | optimizer, lr_scheduler = get_optimizer_scheduler(net, cfg) 84 | use_amp = getattr(cfg.TRAIN, "AMP", False) 85 | trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler, use_amp=use_amp) 86 | 87 | # train process 88 | trainer.train(cfg.TRAIN.EPOCH, load_latest=True, fail_safe=True) 89 | -------------------------------------------------------------------------------- /lib/test/tracker/basetracker.py: -------------------------------------------------------------------------------- 1 | import time 2 | 3 | import torch 4 | from _collections import OrderedDict 5 | 6 | from lib.train.data.processing_utils import transform_image_to_crop 7 | from lib.vis.visdom_cus import Visdom 8 | 9 | 10 | class BaseTracker: 11 | """Base class for all trackers.""" 12 | 13 | def __init__(self, params): 14 | self.params = params 15 | self.visdom = None 16 | 17 | def predicts_segmentation_mask(self): 18 | return False 19 | 20 | def initialize(self, image, info: dict) -> dict: 21 | """Overload this function in your tracker. This should initialize the model.""" 22 | raise NotImplementedError 23 | 24 | def track(self, image, info: dict = None) -> dict: 25 | """Overload this function in your tracker. This should track in the frame and update the model.""" 26 | raise NotImplementedError 27 | 28 | def visdom_draw_tracking(self, image, box, segmentation=None): 29 | if isinstance(box, OrderedDict): 30 | box = [v for k, v in box.items()] 31 | else: 32 | box = (box,) 33 | if segmentation is None: 34 | self.visdom.register((image, *box), 'Tracking', 1, 'Tracking') 35 | else: 36 | self.visdom.register((image, *box, segmentation), 'Tracking', 1, 'Tracking') 37 | 38 | def transform_bbox_to_crop(self, box_in, resize_factor, device, box_extract=None, crop_type='template'): 39 | # box_in: list [x1, y1, w, h], not normalized 40 | # box_extract: same as box_in 41 | # out bbox: Torch.tensor [1, 1, 4], x1y1wh, normalized 42 | if crop_type == 'template': 43 | crop_sz = torch.Tensor([self.params.template_size, self.params.template_size]) 44 | elif crop_type == 'search': 45 | crop_sz = torch.Tensor([self.params.search_size, self.params.search_size]) 46 | else: 47 | raise NotImplementedError 48 | 49 | box_in = torch.tensor(box_in) 50 | if box_extract is None: 51 | box_extract = box_in 52 | else: 53 | box_extract = torch.tensor(box_extract) 54 | template_bbox = transform_image_to_crop(box_in, box_extract, resize_factor, crop_sz, normalize=True) 55 | template_bbox = template_bbox.view(1, 1, 4).to(device) 56 | 57 | return template_bbox 58 | 59 | def _init_visdom(self, visdom_info, debug): 60 | visdom_info = {} if visdom_info is None else visdom_info 61 | self.pause_mode = False 62 | self.step = False 63 | self.next_seq = False 64 | if debug > 0 and visdom_info.get('use_visdom', True): 65 | try: 66 | self.visdom = Visdom(debug, {'handler': self._visdom_ui_handler, 'win_id': 'Tracking'}, 67 | visdom_info=visdom_info) 68 | 69 | # # Show help 70 | # help_text = 'You can pause/unpause the tracker by pressing ''space'' with the ''Tracking'' window ' \ 71 | # 'selected. During paused mode, you can track for one frame by pressing the right arrow key.' \ 72 | # 'To enable/disable plotting of a data block, tick/untick the corresponding entry in ' \ 73 | # 'block list.' 74 | # self.visdom.register(help_text, 'text', 1, 'Help') 75 | except: 76 | time.sleep(0.5) 77 | print('!!! WARNING: Visdom could not start, so using matplotlib visualization instead !!!\n' 78 | '!!! Start Visdom in a separate terminal window by typing \'visdom\' !!!') 79 | 80 | def _visdom_ui_handler(self, data): 81 | if data['event_type'] == 'KeyPress': 82 | if data['key'] == ' ': 83 | self.pause_mode = not self.pause_mode 84 | 85 | elif data['key'] == 'ArrowRight' and self.pause_mode: 86 | self.step = True 87 | 88 | elif data['key'] == 'n': 89 | self.next_seq = True 90 | -------------------------------------------------------------------------------- /lib/train/train_script_datr.py: -------------------------------------------------------------------------------- 1 | import os 2 | # loss function related 3 | from lib.utils.box_ops import giou_loss 4 | from torch.nn.functional import l1_loss 5 | from torch.nn import BCEWithLogitsLoss 6 | # train pipeline related 7 | from lib.train.trainers import LTRTrainer 8 | # distributed training related 9 | from torch.nn.parallel import DistributedDataParallel as DDP 10 | # some more advanced functions 11 | from .base_functions import * 12 | # network related 13 | from lib.models.ostrack import build_ostrack 14 | # forward propagation related 15 | from lib.train.actors import OSTrackActor 16 | # for import modules 17 | import importlib 18 | 19 | from ..utils.focal_loss import FocalLoss 20 | 21 | 22 | def run(settings): 23 | settings.description = 'Training script for STARK-S, STARK-ST stage1, and STARK-ST stage2' 24 | 25 | # update the default configs with config file 26 | if not os.path.exists(settings.cfg_file): 27 | raise ValueError("%s doesn't exist." % settings.cfg_file) 28 | config_module = importlib.import_module("lib.config.%s.config" % settings.script_name) 29 | cfg = config_module.cfg 30 | config_module.update_config_from_file(settings.cfg_file) 31 | if settings.local_rank in [-1, 0]: 32 | print("New configuration is shown below.") 33 | for key in cfg.keys(): 34 | print("%s configuration:" % key, cfg[key]) 35 | print('\n') 36 | 37 | # update settings based on cfg 38 | update_settings(settings, cfg) 39 | 40 | # Record the training log 41 | log_dir = os.path.join(settings.save_dir, 'logs') 42 | if settings.local_rank in [-1, 0]: 43 | if not os.path.exists(log_dir): 44 | os.makedirs(log_dir) 45 | settings.log_file = os.path.join(log_dir, "%s-%s.log" % (settings.script_name, settings.config_name)) 46 | 47 | # Build dataloaders 48 | loader_train, loader_val = build_dataloaders_datr(cfg, settings) 49 | 50 | if "RepVGG" in cfg.MODEL.BACKBONE.TYPE or "swin" in cfg.MODEL.BACKBONE.TYPE or "LightTrack" in cfg.MODEL.BACKBONE.TYPE: 51 | cfg.ckpt_dir = settings.save_dir 52 | 53 | # Create network 54 | if settings.script_name == "ostrack": 55 | net = build_ostrack(cfg) 56 | else: 57 | raise ValueError("illegal script name") 58 | 59 | # wrap networks to distributed one 60 | net.cuda() 61 | if settings.local_rank != -1: 62 | # net = torch.nn.SyncBatchNorm.convert_sync_batchnorm(net) # add syncBN converter 63 | net = DDP(net, device_ids=[settings.local_rank], find_unused_parameters=True) 64 | settings.device = torch.device("cuda:%d" % settings.local_rank) 65 | else: 66 | settings.device = torch.device("cuda:0") 67 | settings.deep_sup = getattr(cfg.TRAIN, "DEEP_SUPERVISION", False) 68 | settings.distill = getattr(cfg.TRAIN, "DISTILL", False) 69 | settings.distill_loss_type = getattr(cfg.TRAIN, "DISTILL_LOSS_TYPE", "KL") 70 | # Loss functions and Actors 71 | if settings.script_name == "ostrack": 72 | focal_loss = FocalLoss() 73 | objective = {'giou': giou_loss, 'l1': l1_loss, 'focal': focal_loss, 'cls': BCEWithLogitsLoss()} 74 | loss_weight = {'giou': cfg.TRAIN.GIOU_WEIGHT, 'l1': cfg.TRAIN.L1_WEIGHT, 'focal': 1., 'cls': 1.0} 75 | actor = OSTrackActor(net=net, objective=objective, loss_weight=loss_weight, settings=settings, cfg=cfg) 76 | else: 77 | raise ValueError("illegal script name") 78 | 79 | # if cfg.TRAIN.DEEP_SUPERVISION: 80 | # raise ValueError("Deep supervision is not supported now.") 81 | 82 | # Optimizer, parameters, and learning rates 83 | optimizer, lr_scheduler = get_optimizer_scheduler(net, cfg) 84 | use_amp = getattr(cfg.TRAIN, "AMP", False) 85 | trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler=lr_scheduler, 86 | imix=cfg.DA.imix,imix_epoch=cfg.DA.imix_epoch,imix_interval=cfg.DA.imix_interval,use_amp=use_amp) 87 | 88 | # train process 89 | trainer.train(cfg.TRAIN.EPOCH, load_latest=True, fail_safe=True) 90 | -------------------------------------------------------------------------------- /ostrack_cuda113_env.yaml: -------------------------------------------------------------------------------- 1 | name: false 2 | channels: 3 | - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch 4 | - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge 5 | - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main 6 | - defaults 7 | dependencies: 8 | - _libgcc_mutex=0.1=main 9 | - _openmp_mutex=4.5=1_gnu 10 | - blas=1.0=mkl 11 | - bzip2=1.0.8=h7f98852_4 12 | - ca-certificates=2022.3.29=h06a4308_0 13 | - certifi=2021.10.8=py38h578d9bd_2 14 | - colorama=0.4.4=pyhd3eb1b0_0 15 | - cudatoolkit=11.3.1=h2bc3f7f_2 16 | - ffmpeg=4.3=hf484d3e_0 17 | - freetype=2.10.4=h0708190_1 18 | - gmp=6.2.1=h58526e2_0 19 | - gnutls=3.6.13=h85f3911_1 20 | - intel-openmp=2021.4.0=h06a4308_3561 21 | - jpeg=9d=h7f8727e_0 22 | - lame=3.100=h7f98852_1001 23 | - ld_impl_linux-64=2.35.1=h7274673_9 24 | - libffi=3.3=he6710b0_2 25 | - libgcc-ng=9.3.0=h5101ec6_17 26 | - libgomp=9.3.0=h5101ec6_17 27 | - libiconv=1.16=h516909a_0 28 | - libpng=1.6.37=h21135ba_2 29 | - libstdcxx-ng=9.3.0=hd4cf53a_17 30 | - libtiff=4.0.10=hc3755c2_1005 31 | - libuv=1.40.0=h7b6447c_0 32 | - lz4-c=1.9.3=h9c3ff4c_1 33 | - mkl=2021.4.0=h06a4308_640 34 | - mkl-service=2.4.0=py38h497a2fe_0 35 | - mkl_fft=1.3.1=py38hd3c417c_0 36 | - mkl_random=1.2.2=py38h1abd341_0 37 | - ncurses=6.3=h7f8727e_2 38 | - nettle=3.6=he412f7d_0 39 | - numpy=1.21.2=py38h20f2e39_0 40 | - numpy-base=1.21.2=py38h79a1101_0 41 | - olefile=0.46=pyh9f0ad1d_1 42 | - openh264=2.1.1=h780b84a_0 43 | - openssl=1.1.1n=h7f8727e_0 44 | - pillow=6.2.1=py38h6b7be26_0 45 | - pip=21.2.4=py38h06a4308_0 46 | - python=3.8.13=h12debd9_0 47 | - python_abi=3.8=2_cp38 48 | - pytorch=1.10.0=py3.8_cuda11.3_cudnn8.2.0_0 49 | - pytorch-mutex=1.0=cuda 50 | - readline=8.1.2=h7f8727e_1 51 | - setuptools=58.0.4=py38h06a4308_0 52 | - six=1.16.0=pyh6c4a22f_0 53 | - sqlite=3.38.2=hc218d9a_0 54 | - tk=8.6.11=h1ccaba5_0 55 | - torchaudio=0.10.0=py38_cu113 56 | - torchvision=0.11.0=py38_cu113 57 | - tqdm=4.63.0=pyhd3eb1b0_0 58 | - typing_extensions=4.1.1=pyha770c72_0 59 | - wheel=0.37.1=pyhd3eb1b0_0 60 | - xz=5.2.5=h7b6447c_0 61 | - zlib=1.2.11=h7f8727e_4 62 | - zstd=1.4.9=ha95c52a_0 63 | - pip: 64 | - absl-py==1.0.0 65 | - cachetools==5.0.0 66 | - cffi==1.15.0 67 | - charset-normalizer==2.0.12 68 | - click==8.1.2 69 | - cycler==0.11.0 70 | - cython==0.29.28 71 | - docker-pycreds==0.4.0 72 | - easydict==1.9 73 | - fonttools==4.31.2 74 | - gitdb==4.0.9 75 | - gitpython==3.1.27 76 | - google-auth==2.6.2 77 | - google-auth-oauthlib==0.4.6 78 | - grpcio==1.45.0 79 | - idna==3.3 80 | - importlib-metadata==4.11.3 81 | - jpeg4py==0.1.4 82 | - jsonpatch==1.32 83 | - jsonpointer==2.2 84 | - kiwisolver==1.4.2 85 | - lmdb==1.3.0 86 | - markdown==3.3.6 87 | - matplotlib==3.5.1 88 | - oauthlib==3.2.0 89 | - opencv-python==4.5.5.64 90 | - packaging==21.3 91 | - pandas==1.4.2 92 | - pathtools==0.1.2 93 | - promise==2.3 94 | - protobuf==3.20.0 95 | - psutil==5.9.0 96 | - pyasn1==0.4.8 97 | - pyasn1-modules==0.2.8 98 | - pycocotools==2.0.4 99 | - pycparser==2.21 100 | - pyparsing==3.0.7 101 | - python-dateutil==2.8.2 102 | - pytz==2022.1 103 | - pyyaml==6.0 104 | - pyzmq==22.3.0 105 | - requests==2.27.1 106 | - requests-oauthlib==1.3.1 107 | - rsa==4.8 108 | - scipy==1.8.0 109 | - sentry-sdk==1.5.8 110 | - setproctitle==1.2.2 111 | - shortuuid==1.0.8 112 | - smmap==5.0.0 113 | - tb-nightly==2.9.0a20220403 114 | - tensorboard-data-server==0.6.1 115 | - tensorboard-plugin-wit==1.8.1 116 | - termcolor==1.1.0 117 | - thop==0.0.31-2005241907 118 | - tikzplotlib==0.10.1 119 | - timm==0.5.4 120 | - torchfile==0.1.0 121 | - tornado==6.1 122 | - urllib3==1.26.9 123 | - visdom==0.1.8.9 124 | - wandb==0.12.11 125 | - webcolors==1.11.1 126 | - websocket-client==1.3.2 127 | - werkzeug==2.1.1 128 | - yaspin==2.1.0 129 | - zipp==3.7.0 130 | prefix: /public/yebotao/conda_envs/ostrack 131 | -------------------------------------------------------------------------------- /lib/train/dataset/imagenetvid_lmdb.py: -------------------------------------------------------------------------------- 1 | import os 2 | from .base_video_dataset import BaseVideoDataset 3 | from lib.train.data import jpeg4py_loader 4 | import torch 5 | from collections import OrderedDict 6 | from lib.train.admin import env_settings 7 | from lib.utils.lmdb_utils import decode_img, decode_json 8 | 9 | 10 | def get_target_to_image_ratio(seq): 11 | anno = torch.Tensor(seq['anno']) 12 | img_sz = torch.Tensor(seq['image_size']) 13 | return (anno[0, 2:4].prod() / (img_sz.prod())).sqrt() 14 | 15 | 16 | class ImagenetVID_lmdb(BaseVideoDataset): 17 | """ Imagenet VID dataset. 18 | 19 | Publication: 20 | ImageNet Large Scale Visual Recognition Challenge 21 | Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, 22 | Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei 23 | IJCV, 2015 24 | https://arxiv.org/pdf/1409.0575.pdf 25 | 26 | Download the dataset from http://image-net.org/ 27 | """ 28 | def __init__(self, root=None, image_loader=jpeg4py_loader, min_length=0, max_target_area=1): 29 | """ 30 | args: 31 | root - path to the imagenet vid dataset. 32 | image_loader (default_image_loader) - The function to read the images. If installed, 33 | jpeg4py (https://github.com/ajkxyz/jpeg4py) is used by default. Else, 34 | opencv's imread is used. 35 | min_length - Minimum allowed sequence length. 36 | max_target_area - max allowed ratio between target area and image area. Can be used to filter out targets 37 | which cover complete image. 38 | """ 39 | root = env_settings().imagenet_dir if root is None else root 40 | super().__init__("imagenetvid_lmdb", root, image_loader) 41 | 42 | sequence_list_dict = decode_json(root, "cache.json") 43 | self.sequence_list = sequence_list_dict 44 | 45 | # Filter the sequences based on min_length and max_target_area in the first frame 46 | self.sequence_list = [x for x in self.sequence_list if len(x['anno']) >= min_length and 47 | get_target_to_image_ratio(x) < max_target_area] 48 | 49 | def get_name(self): 50 | return 'imagenetvid_lmdb' 51 | 52 | def get_num_sequences(self): 53 | return len(self.sequence_list) 54 | 55 | def get_sequence_info(self, seq_id): 56 | bb_anno = torch.Tensor(self.sequence_list[seq_id]['anno']) 57 | valid = (bb_anno[:, 2] > 0) & (bb_anno[:, 3] > 0) 58 | visible = torch.ByteTensor(self.sequence_list[seq_id]['target_visible']) & valid.byte() 59 | return {'bbox': bb_anno, 'valid': valid, 'visible': visible} 60 | 61 | def _get_frame(self, sequence, frame_id): 62 | set_name = 'ILSVRC2015_VID_train_{:04d}'.format(sequence['set_id']) 63 | vid_name = 'ILSVRC2015_train_{:08d}'.format(sequence['vid_id']) 64 | frame_number = frame_id + sequence['start_frame'] 65 | frame_path = os.path.join('Data', 'VID', 'train', set_name, vid_name, 66 | '{:06d}.JPEG'.format(frame_number)) 67 | return decode_img(self.root, frame_path) 68 | 69 | def get_frames(self, seq_id, frame_ids, anno=None): 70 | sequence = self.sequence_list[seq_id] 71 | 72 | frame_list = [self._get_frame(sequence, f) for f in frame_ids] 73 | 74 | if anno is None: 75 | anno = self.get_sequence_info(seq_id) 76 | 77 | # Create anno dict 78 | anno_frames = {} 79 | for key, value in anno.items(): 80 | anno_frames[key] = [value[f_id, ...].clone() for f_id in frame_ids] 81 | 82 | # added the class info to the meta info 83 | object_meta = OrderedDict({'object_class': sequence['class_name'], 84 | 'motion_class': None, 85 | 'major_class': None, 86 | 'root_class': None, 87 | 'motion_adverb': None}) 88 | 89 | return frame_list, anno_frames, object_meta 90 | 91 | -------------------------------------------------------------------------------- /lib/models/ostrack/utils.py: -------------------------------------------------------------------------------- 1 | import math 2 | 3 | import torch 4 | import torch.nn.functional as F 5 | 6 | 7 | def combine_tokens(template_tokens, search_tokens, mode='direct', return_res=False): 8 | # [B, HW, C] 9 | len_t = template_tokens.shape[1] 10 | len_s = search_tokens.shape[1] 11 | 12 | if mode == 'direct': 13 | merged_feature = torch.cat((template_tokens, search_tokens), dim=1) 14 | elif mode == 'template_central': 15 | central_pivot = len_s // 2 16 | first_half = search_tokens[:, :central_pivot, :] 17 | second_half = search_tokens[:, central_pivot:, :] 18 | merged_feature = torch.cat((first_half, template_tokens, second_half), dim=1) 19 | elif mode == 'partition': 20 | feat_size_s = int(math.sqrt(len_s)) 21 | feat_size_t = int(math.sqrt(len_t)) 22 | window_size = math.ceil(feat_size_t / 2.) 23 | # pad feature maps to multiples of window size 24 | B, _, C = template_tokens.shape 25 | H = W = feat_size_t 26 | template_tokens = template_tokens.view(B, H, W, C) 27 | pad_l = pad_b = pad_r = 0 28 | # pad_r = (window_size - W % window_size) % window_size 29 | pad_t = (window_size - H % window_size) % window_size 30 | template_tokens = F.pad(template_tokens, (0, 0, pad_l, pad_r, pad_t, pad_b)) 31 | _, Hp, Wp, _ = template_tokens.shape 32 | template_tokens = template_tokens.view(B, Hp // window_size, window_size, W, C) 33 | template_tokens = torch.cat([template_tokens[:, 0, ...], template_tokens[:, 1, ...]], dim=2) 34 | _, Hc, Wc, _ = template_tokens.shape 35 | template_tokens = template_tokens.view(B, -1, C) 36 | merged_feature = torch.cat([template_tokens, search_tokens], dim=1) 37 | 38 | # calculate new h and w, which may be useful for SwinT or others 39 | merged_h, merged_w = feat_size_s + Hc, feat_size_s 40 | if return_res: 41 | return merged_feature, merged_h, merged_w 42 | 43 | else: 44 | raise NotImplementedError 45 | 46 | return merged_feature 47 | 48 | 49 | def recover_tokens(merged_tokens, len_template_token, len_search_token, mode='direct'): 50 | if mode == 'direct': 51 | recovered_tokens = merged_tokens 52 | elif mode == 'template_central': 53 | central_pivot = len_search_token // 2 54 | len_remain = len_search_token - central_pivot 55 | len_half_and_t = central_pivot + len_template_token 56 | 57 | first_half = merged_tokens[:, :central_pivot, :] 58 | second_half = merged_tokens[:, -len_remain:, :] 59 | template_tokens = merged_tokens[:, central_pivot:len_half_and_t, :] 60 | 61 | recovered_tokens = torch.cat((template_tokens, first_half, second_half), dim=1) 62 | elif mode == 'partition': 63 | recovered_tokens = merged_tokens 64 | else: 65 | raise NotImplementedError 66 | 67 | return recovered_tokens 68 | 69 | 70 | def window_partition(x, window_size: int): 71 | """ 72 | Args: 73 | x: (B, H, W, C) 74 | window_size (int): window size 75 | 76 | Returns: 77 | windows: (num_windows*B, window_size, window_size, C) 78 | """ 79 | B, H, W, C = x.shape 80 | x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) 81 | windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) 82 | return windows 83 | 84 | 85 | def window_reverse(windows, window_size: int, H: int, W: int): 86 | """ 87 | Args: 88 | windows: (num_windows*B, window_size, window_size, C) 89 | window_size (int): Window size 90 | H (int): Height of image 91 | W (int): Width of image 92 | 93 | Returns: 94 | x: (B, H, W, C) 95 | """ 96 | B = int(windows.shape[0] / (H * W / window_size / window_size)) 97 | x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) 98 | x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) 99 | return x 100 | -------------------------------------------------------------------------------- /lib/test/utils/hann.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import math 3 | import torch.nn.functional as F 4 | 5 | 6 | def hann1d(sz: int, centered = True) -> torch.Tensor: 7 | """1D cosine window.""" 8 | if centered: 9 | return 0.5 * (1 - torch.cos((2 * math.pi / (sz + 1)) * torch.arange(1, sz + 1).float())) 10 | w = 0.5 * (1 + torch.cos((2 * math.pi / (sz + 2)) * torch.arange(0, sz//2 + 1).float())) 11 | return torch.cat([w, w[1:sz-sz//2].flip((0,))]) 12 | 13 | 14 | def hann2d(sz: torch.Tensor, centered = True) -> torch.Tensor: 15 | """2D cosine window.""" 16 | return hann1d(sz[0].item(), centered).reshape(1, 1, -1, 1) * hann1d(sz[1].item(), centered).reshape(1, 1, 1, -1) 17 | 18 | 19 | def hann2d_bias(sz: torch.Tensor, ctr_point: torch.Tensor, centered = True) -> torch.Tensor: 20 | """2D cosine window.""" 21 | distance = torch.stack([ctr_point, sz-ctr_point], dim=0) 22 | max_distance, _ = distance.max(dim=0) 23 | 24 | hann1d_x = hann1d(max_distance[0].item() * 2, centered) 25 | hann1d_x = hann1d_x[max_distance[0] - distance[0, 0]: max_distance[0] + distance[1, 0]] 26 | hann1d_y = hann1d(max_distance[1].item() * 2, centered) 27 | hann1d_y = hann1d_y[max_distance[1] - distance[0, 1]: max_distance[1] + distance[1, 1]] 28 | 29 | return hann1d_y.reshape(1, 1, -1, 1) * hann1d_x.reshape(1, 1, 1, -1) 30 | 31 | 32 | 33 | def hann2d_clipped(sz: torch.Tensor, effective_sz: torch.Tensor, centered = True) -> torch.Tensor: 34 | """1D clipped cosine window.""" 35 | 36 | # Ensure that the difference is even 37 | effective_sz += (effective_sz - sz) % 2 38 | effective_window = hann1d(effective_sz[0].item(), True).reshape(1, 1, -1, 1) * hann1d(effective_sz[1].item(), True).reshape(1, 1, 1, -1) 39 | 40 | pad = (sz - effective_sz) // 2 41 | 42 | window = F.pad(effective_window, (pad[1].item(), pad[1].item(), pad[0].item(), pad[0].item()), 'replicate') 43 | 44 | if centered: 45 | return window 46 | else: 47 | mid = (sz / 2).int() 48 | window_shift_lr = torch.cat((window[:, :, :, mid[1]:], window[:, :, :, :mid[1]]), 3) 49 | return torch.cat((window_shift_lr[:, :, mid[0]:, :], window_shift_lr[:, :, :mid[0], :]), 2) 50 | 51 | 52 | def gauss_fourier(sz: int, sigma: float, half: bool = False) -> torch.Tensor: 53 | if half: 54 | k = torch.arange(0, int(sz/2+1)) 55 | else: 56 | k = torch.arange(-int((sz-1)/2), int(sz/2+1)) 57 | return (math.sqrt(2*math.pi) * sigma / sz) * torch.exp(-2 * (math.pi * sigma * k.float() / sz)**2) 58 | 59 | 60 | def gauss_spatial(sz, sigma, center=0, end_pad=0): 61 | k = torch.arange(-(sz-1)/2, (sz+1)/2+end_pad) 62 | return torch.exp(-1.0/(2*sigma**2) * (k - center)**2) 63 | 64 | 65 | def label_function(sz: torch.Tensor, sigma: torch.Tensor): 66 | return gauss_fourier(sz[0].item(), sigma[0].item()).reshape(1, 1, -1, 1) * gauss_fourier(sz[1].item(), sigma[1].item(), True).reshape(1, 1, 1, -1) 67 | 68 | def label_function_spatial(sz: torch.Tensor, sigma: torch.Tensor, center: torch.Tensor = torch.zeros(2), end_pad: torch.Tensor = torch.zeros(2)): 69 | """The origin is in the middle of the image.""" 70 | return gauss_spatial(sz[0].item(), sigma[0].item(), center[0], end_pad[0].item()).reshape(1, 1, -1, 1) * \ 71 | gauss_spatial(sz[1].item(), sigma[1].item(), center[1], end_pad[1].item()).reshape(1, 1, 1, -1) 72 | 73 | 74 | def cubic_spline_fourier(f, a): 75 | """The continuous Fourier transform of a cubic spline kernel.""" 76 | 77 | bf = (6*(1 - torch.cos(2 * math.pi * f)) + 3*a*(1 - torch.cos(4 * math.pi * f)) 78 | - (6 + 8*a)*math.pi*f*torch.sin(2 * math.pi * f) - 2*a*math.pi*f*torch.sin(4 * math.pi * f)) \ 79 | / (4 * math.pi**4 * f**4) 80 | 81 | bf[f == 0] = 1 82 | 83 | return bf 84 | 85 | def max2d(a: torch.Tensor) -> (torch.Tensor, torch.Tensor): 86 | """Computes maximum and argmax in the last two dimensions.""" 87 | 88 | max_val_row, argmax_row = torch.max(a, dim=-2) 89 | max_val, argmax_col = torch.max(max_val_row, dim=-1) 90 | argmax_row = argmax_row.view(argmax_col.numel(),-1)[torch.arange(argmax_col.numel()), argmax_col.view(-1)] 91 | argmax_row = argmax_row.reshape(argmax_col.shape) 92 | argmax = torch.cat((argmax_row.unsqueeze(-1), argmax_col.unsqueeze(-1)), -1) 93 | return max_val, argmax 94 | -------------------------------------------------------------------------------- /tracking/train.py: -------------------------------------------------------------------------------- 1 | import os 2 | import argparse 3 | import random 4 | 5 | 6 | def parse_args(): 7 | """ 8 | args for training. 9 | """ 10 | parser = argparse.ArgumentParser(description='Parse args for training') 11 | # for train 12 | parser.add_argument('--script', type=str, help='training script name') 13 | parser.add_argument('--config', type=str, default='baseline', help='yaml configure file name') 14 | parser.add_argument('--save_dir', type=str, help='root directory to save checkpoints, logs, and tensorboard') 15 | parser.add_argument('--seed', type=int, default=42, help='seed for random numbers') 16 | parser.add_argument('--mode', type=str, choices=["single", "multiple", "multi_node"], default="multiple", 17 | help="train on single gpu or multiple gpus") 18 | parser.add_argument('--nproc_per_node', type=int, help="number of GPUs per node") # specify when mode is multiple 19 | parser.add_argument('--use_lmdb', type=int, choices=[0, 1], default=0) # whether datasets are in lmdb format 20 | parser.add_argument('--script_prv', type=str, help='training script name') 21 | parser.add_argument('--config_prv', type=str, default='baseline', help='yaml configure file name') 22 | parser.add_argument('--use_wandb', type=int, choices=[0, 1], default=0) # whether to use wandb 23 | # for knowledge distillation 24 | parser.add_argument('--distill', type=int, choices=[0, 1], default=0) # whether to use knowledge distillation 25 | parser.add_argument('--datr', type=int, choices=[0, 1,2], default=0) # whether to use our data augmentation datr 26 | parser.add_argument('--script_teacher', type=str, help='teacher script name') 27 | parser.add_argument('--config_teacher', type=str, help='teacher yaml configure file name') 28 | 29 | # for multiple machines 30 | parser.add_argument('--rank', type=int, help='Rank of the current process.') 31 | parser.add_argument('--world-size', type=int, help='Number of processes participating in the job.') 32 | parser.add_argument('--ip', type=str, default='127.0.0.1', help='IP of the current rank 0.') 33 | parser.add_argument('--port', type=int, default='20000', help='Port of the current rank 0.') 34 | 35 | args = parser.parse_args() 36 | 37 | return args 38 | 39 | 40 | def main(): 41 | args = parse_args() 42 | if args.mode == "single": 43 | train_cmd = "python lib/train/run_training.py --script %s --config %s --save_dir %s --seed %d --use_lmdb %d " \ 44 | "--script_prv %s --config_prv %s --distill %d --datr %d --script_teacher %s --config_teacher %s --use_wandb %d"\ 45 | % (args.script, args.config, args.save_dir,args.seed, args.use_lmdb, args.script_prv, args.config_prv, 46 | args.distill,args.datr, args.script_teacher, args.config_teacher, args.use_wandb) 47 | elif args.mode == "multiple": 48 | train_cmd = "python -m torch.distributed.launch --nproc_per_node %d --master_port %d lib/train/run_training.py " \ 49 | "--script %s --config %s --save_dir %s --seed %d --use_lmdb %d --script_prv %s --config_prv %s --use_wandb %d " \ 50 | "--distill %d --datr %d --script_teacher %s --config_teacher %s" \ 51 | % (args.nproc_per_node, random.randint(10000, 50000), args.script, args.config, args.save_dir,args.seed, args.use_lmdb, args.script_prv, args.config_prv, args.use_wandb, 52 | args.distill,args.datr, args.script_teacher, args.config_teacher) 53 | elif args.mode == "multi_node": 54 | train_cmd = "python -m torch.distributed.launch --nproc_per_node %d --master_addr %s --master_port %d --nnodes %d --node_rank %d lib/train/run_training.py " \ 55 | "--script %s --config %s --save_dir %s --use_lmdb %d --script_prv %s --config_prv %s --use_wandb %d " \ 56 | "--distill %d --datr %d --script_teacher %s --config_teacher %s" \ 57 | % (args.nproc_per_node, args.ip, args.port, args.world_size, args.rank, args.script, args.config, args.save_dir, args.use_lmdb, args.script_prv, args.config_prv, args.use_wandb, 58 | args.distill,args.datr, args.script_teacher, args.config_teacher) 59 | else: 60 | raise ValueError("mode should be 'single' or 'multiple'.") 61 | os.system(train_cmd) 62 | 63 | 64 | if __name__ == "__main__": 65 | main() 66 | -------------------------------------------------------------------------------- /lib/models/layers/rpe.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from timm.models.layers import trunc_normal_ 4 | 5 | 6 | def generate_2d_relative_positional_encoding_index(z_shape, x_shape): 7 | ''' 8 | z_shape: (z_h, z_w) 9 | x_shape: (x_h, x_w) 10 | ''' 11 | z_2d_index_h, z_2d_index_w = torch.meshgrid(torch.arange(z_shape[0]), torch.arange(z_shape[1])) 12 | x_2d_index_h, x_2d_index_w = torch.meshgrid(torch.arange(x_shape[0]), torch.arange(x_shape[1])) 13 | 14 | z_2d_index_h = z_2d_index_h.flatten(0) 15 | z_2d_index_w = z_2d_index_w.flatten(0) 16 | x_2d_index_h = x_2d_index_h.flatten(0) 17 | x_2d_index_w = x_2d_index_w.flatten(0) 18 | 19 | diff_h = z_2d_index_h[:, None] - x_2d_index_h[None, :] 20 | diff_w = z_2d_index_w[:, None] - x_2d_index_w[None, :] 21 | 22 | diff = torch.stack((diff_h, diff_w), dim=-1) 23 | _, indices = torch.unique(diff.view(-1, 2), return_inverse=True, dim=0) 24 | return indices.view(z_shape[0] * z_shape[1], x_shape[0] * x_shape[1]) 25 | 26 | 27 | def generate_2d_concatenated_self_attention_relative_positional_encoding_index(z_shape, x_shape): 28 | ''' 29 | z_shape: (z_h, z_w) 30 | x_shape: (x_h, x_w) 31 | ''' 32 | z_2d_index_h, z_2d_index_w = torch.meshgrid(torch.arange(z_shape[0]), torch.arange(z_shape[1])) 33 | x_2d_index_h, x_2d_index_w = torch.meshgrid(torch.arange(x_shape[0]), torch.arange(x_shape[1])) 34 | 35 | z_2d_index_h = z_2d_index_h.flatten(0) 36 | z_2d_index_w = z_2d_index_w.flatten(0) 37 | x_2d_index_h = x_2d_index_h.flatten(0) 38 | x_2d_index_w = x_2d_index_w.flatten(0) 39 | 40 | concatenated_2d_index_h = torch.cat((z_2d_index_h, x_2d_index_h)) 41 | concatenated_2d_index_w = torch.cat((z_2d_index_w, x_2d_index_w)) 42 | 43 | diff_h = concatenated_2d_index_h[:, None] - concatenated_2d_index_h[None, :] 44 | diff_w = concatenated_2d_index_w[:, None] - concatenated_2d_index_w[None, :] 45 | 46 | z_len = z_shape[0] * z_shape[1] 47 | x_len = x_shape[0] * x_shape[1] 48 | a = torch.empty((z_len + x_len), dtype=torch.int64) 49 | a[:z_len] = 0 50 | a[z_len:] = 1 51 | b=a[:, None].repeat(1, z_len + x_len) 52 | c=a[None, :].repeat(z_len + x_len, 1) 53 | 54 | diff = torch.stack((diff_h, diff_w, b, c), dim=-1) 55 | _, indices = torch.unique(diff.view((z_len + x_len) * (z_len + x_len), 4), return_inverse=True, dim=0) 56 | return indices.view((z_len + x_len), (z_len + x_len)) 57 | 58 | 59 | def generate_2d_concatenated_cross_attention_relative_positional_encoding_index(z_shape, x_shape): 60 | ''' 61 | z_shape: (z_h, z_w) 62 | x_shape: (x_h, x_w) 63 | ''' 64 | z_2d_index_h, z_2d_index_w = torch.meshgrid(torch.arange(z_shape[0]), torch.arange(z_shape[1])) 65 | x_2d_index_h, x_2d_index_w = torch.meshgrid(torch.arange(x_shape[0]), torch.arange(x_shape[1])) 66 | 67 | z_2d_index_h = z_2d_index_h.flatten(0) 68 | z_2d_index_w = z_2d_index_w.flatten(0) 69 | x_2d_index_h = x_2d_index_h.flatten(0) 70 | x_2d_index_w = x_2d_index_w.flatten(0) 71 | 72 | concatenated_2d_index_h = torch.cat((z_2d_index_h, x_2d_index_h)) 73 | concatenated_2d_index_w = torch.cat((z_2d_index_w, x_2d_index_w)) 74 | 75 | diff_h = x_2d_index_h[:, None] - concatenated_2d_index_h[None, :] 76 | diff_w = x_2d_index_w[:, None] - concatenated_2d_index_w[None, :] 77 | 78 | z_len = z_shape[0] * z_shape[1] 79 | x_len = x_shape[0] * x_shape[1] 80 | 81 | a = torch.empty(z_len + x_len, dtype=torch.int64) 82 | a[: z_len] = 0 83 | a[z_len:] = 1 84 | c = a[None, :].repeat(x_len, 1) 85 | 86 | diff = torch.stack((diff_h, diff_w, c), dim=-1) 87 | _, indices = torch.unique(diff.view(x_len * (z_len + x_len), 3), return_inverse=True, dim=0) 88 | return indices.view(x_len, (z_len + x_len)) 89 | 90 | 91 | class RelativePosition2DEncoder(nn.Module): 92 | def __init__(self, num_heads, embed_size): 93 | super(RelativePosition2DEncoder, self).__init__() 94 | self.relative_position_bias_table = nn.Parameter(torch.empty((num_heads, embed_size))) 95 | trunc_normal_(self.relative_position_bias_table, std=0.02) 96 | 97 | def forward(self, attn_rpe_index): 98 | ''' 99 | Args: 100 | attn_rpe_index (torch.Tensor): (*), any shape containing indices, max(attn_rpe_index) < embed_size 101 | Returns: 102 | torch.Tensor: (1, num_heads, *) 103 | ''' 104 | return self.relative_position_bias_table[:, attn_rpe_index].unsqueeze(0) 105 | -------------------------------------------------------------------------------- /lib/train/admin/environment.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | import os 3 | from collections import OrderedDict 4 | 5 | 6 | def create_default_local_file(): 7 | path = os.path.join(os.path.dirname(__file__), 'local.py') 8 | 9 | empty_str = '\'\'' 10 | default_settings = OrderedDict({ 11 | 'workspace_dir': empty_str, 12 | 'tensorboard_dir': 'self.workspace_dir + \'/tensorboard/\'', 13 | 'pretrained_networks': 'self.workspace_dir + \'/pretrained_networks/\'', 14 | 'lasot_dir': empty_str, 15 | 'got10k_dir': empty_str, 16 | 'trackingnet_dir': empty_str, 17 | 'coco_dir': empty_str, 18 | 'lvis_dir': empty_str, 19 | 'sbd_dir': empty_str, 20 | 'imagenet_dir': empty_str, 21 | 'imagenetdet_dir': empty_str, 22 | 'ecssd_dir': empty_str, 23 | 'hkuis_dir': empty_str, 24 | 'msra10k_dir': empty_str, 25 | 'davis_dir': empty_str, 26 | 'youtubevos_dir': empty_str}) 27 | 28 | comment = {'workspace_dir': 'Base directory for saving network checkpoints.', 29 | 'tensorboard_dir': 'Directory for tensorboard files.'} 30 | 31 | with open(path, 'w') as f: 32 | f.write('class EnvironmentSettings:\n') 33 | f.write(' def __init__(self):\n') 34 | 35 | for attr, attr_val in default_settings.items(): 36 | comment_str = None 37 | if attr in comment: 38 | comment_str = comment[attr] 39 | if comment_str is None: 40 | f.write(' self.{} = {}\n'.format(attr, attr_val)) 41 | else: 42 | f.write(' self.{} = {} # {}\n'.format(attr, attr_val, comment_str)) 43 | 44 | 45 | def create_default_local_file_ITP_train(workspace_dir, data_dir): 46 | path = os.path.join(os.path.dirname(__file__), 'local.py') 47 | 48 | empty_str = '\'\'' 49 | default_settings = OrderedDict({ 50 | 'workspace_dir': workspace_dir, 51 | 'tensorboard_dir': os.path.join(workspace_dir, 'tensorboard'), # Directory for tensorboard files. 52 | 'pretrained_networks': os.path.join(workspace_dir, 'pretrained_networks'), 53 | 'lasot_dir': os.path.join(data_dir, 'lasot'), 54 | 'got10k_dir': os.path.join(data_dir, 'got10k/train'), 55 | 'got10k_val_dir': os.path.join(data_dir, 'got10k/val'), 56 | 'lasot_lmdb_dir': os.path.join(data_dir, 'lasot_lmdb'), 57 | 'got10k_lmdb_dir': os.path.join(data_dir, 'got10k_lmdb'), 58 | 'trackingnet_dir': os.path.join(data_dir, 'trackingnet'), 59 | 'trackingnet_lmdb_dir': os.path.join(data_dir, 'trackingnet_lmdb'), 60 | 'coco_dir': os.path.join(data_dir, 'coco'), 61 | 'coco_lmdb_dir': os.path.join(data_dir, 'coco_lmdb'), 62 | 'lvis_dir': empty_str, 63 | 'sbd_dir': empty_str, 64 | 'imagenet_dir': os.path.join(data_dir, 'vid'), 65 | 'imagenet_lmdb_dir': os.path.join(data_dir, 'vid_lmdb'), 66 | 'imagenetdet_dir': empty_str, 67 | 'ecssd_dir': empty_str, 68 | 'hkuis_dir': empty_str, 69 | 'msra10k_dir': empty_str, 70 | 'davis_dir': empty_str, 71 | 'youtubevos_dir': empty_str}) 72 | 73 | comment = {'workspace_dir': 'Base directory for saving network checkpoints.', 74 | 'tensorboard_dir': 'Directory for tensorboard files.'} 75 | 76 | with open(path, 'w') as f: 77 | f.write('class EnvironmentSettings:\n') 78 | f.write(' def __init__(self):\n') 79 | 80 | for attr, attr_val in default_settings.items(): 81 | comment_str = None 82 | if attr in comment: 83 | comment_str = comment[attr] 84 | if comment_str is None: 85 | if attr_val == empty_str: 86 | f.write(' self.{} = {}\n'.format(attr, attr_val)) 87 | else: 88 | f.write(' self.{} = \'{}\'\n'.format(attr, attr_val)) 89 | else: 90 | f.write(' self.{} = \'{}\' # {}\n'.format(attr, attr_val, comment_str)) 91 | 92 | 93 | def env_settings(): 94 | env_module_name = 'lib.train.admin.local' 95 | try: 96 | env_module = importlib.import_module(env_module_name) 97 | return env_module.EnvironmentSettings() 98 | except: 99 | env_file = os.path.join(os.path.dirname(__file__), 'local.py') 100 | 101 | create_default_local_file() 102 | raise RuntimeError('YOU HAVE NOT SETUP YOUR local.py!!!\n Go to "{}" and set all the paths you need. Then try to run again.'.format(env_file)) 103 | -------------------------------------------------------------------------------- /tracking/profile_model.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | 4 | prj_path = os.path.join(os.path.dirname(__file__), '..') 5 | if prj_path not in sys.path: 6 | sys.path.append(prj_path) 7 | 8 | import argparse 9 | import torch 10 | from lib.utils.misc import NestedTensor 11 | from thop import profile 12 | from thop.utils import clever_format 13 | import time 14 | import importlib 15 | 16 | 17 | def parse_args(): 18 | """ 19 | args for training. 20 | """ 21 | parser = argparse.ArgumentParser(description='Parse args for training') 22 | # for train 23 | parser.add_argument('--script', type=str, default='ostrack', choices=['ostrack'], 24 | help='training script name') 25 | parser.add_argument('--config', type=str, default='vitb_256_mae_ce_32x4_ep300', help='yaml configure file name') 26 | args = parser.parse_args() 27 | 28 | return args 29 | 30 | 31 | def evaluate_vit(model, template, search): 32 | '''Speed Test''' 33 | macs1, params1 = profile(model, inputs=(template, search), 34 | custom_ops=None, verbose=False) 35 | macs, params = clever_format([macs1, params1], "%.3f") 36 | print('overall macs is ', macs) 37 | print('overall params is ', params) 38 | 39 | T_w = 500 40 | T_t = 1000 41 | print("testing speed ...") 42 | torch.cuda.synchronize() 43 | with torch.no_grad(): 44 | # overall 45 | for i in range(T_w): 46 | _ = model(template, search) 47 | start = time.time() 48 | for i in range(T_t): 49 | _ = model(template, search) 50 | torch.cuda.synchronize() 51 | end = time.time() 52 | avg_lat = (end - start) / T_t 53 | print("The average overall latency is %.2f ms" % (avg_lat * 1000)) 54 | print("FPS is %.2f fps" % (1. / avg_lat)) 55 | # for i in range(T_w): 56 | # _ = model(template, search) 57 | # start = time.time() 58 | # for i in range(T_t): 59 | # _ = model(template, search) 60 | # end = time.time() 61 | # avg_lat = (end - start) / T_t 62 | # print("The average backbone latency is %.2f ms" % (avg_lat * 1000)) 63 | 64 | 65 | def evaluate_vit_separate(model, template, search): 66 | '''Speed Test''' 67 | T_w = 50 68 | T_t = 1000 69 | print("testing speed ...") 70 | z = model.forward_backbone(template, image_type='template') 71 | x = model.forward_backbone(search, image_type='search') 72 | with torch.no_grad(): 73 | # overall 74 | for i in range(T_w): 75 | _ = model.forward_backbone(search, image_type='search') 76 | _ = model.forward_cat(z, x) 77 | start = time.time() 78 | for i in range(T_t): 79 | _ = model.forward_backbone(search, image_type='search') 80 | _ = model.forward_cat(z, x) 81 | end = time.time() 82 | avg_lat = (end - start) / T_t 83 | print("The average overall latency is %.2f ms" % (avg_lat * 1000)) 84 | 85 | 86 | def get_data(bs, sz): 87 | img_patch = torch.randn(bs, 3, sz, sz) 88 | att_mask = torch.rand(bs, sz, sz) > 0.5 89 | return NestedTensor(img_patch, att_mask) 90 | 91 | 92 | if __name__ == "__main__": 93 | device = "cuda:0" 94 | torch.cuda.set_device(device) 95 | # Compute the Flops and Params of our STARK-S model 96 | args = parse_args() 97 | '''update cfg''' 98 | yaml_fname = 'experiments/%s/%s.yaml' % (args.script, args.config) 99 | config_module = importlib.import_module('lib.config.%s.config' % args.script) 100 | cfg = config_module.cfg 101 | config_module.update_config_from_file(yaml_fname) 102 | '''set some values''' 103 | bs = 1 104 | z_sz = cfg.TEST.TEMPLATE_SIZE 105 | x_sz = cfg.TEST.SEARCH_SIZE 106 | 107 | if args.script == "ostrack": 108 | model_module = importlib.import_module('lib.models') 109 | model_constructor = model_module.build_ostrack 110 | model = model_constructor(cfg, training=False) 111 | # get the template and search 112 | template = torch.randn(bs, 3, z_sz, z_sz) 113 | search = torch.randn(bs, 3, x_sz, x_sz) 114 | # transfer to device 115 | model = model.to(device) 116 | template = template.to(device) 117 | search = search.to(device) 118 | 119 | merge_layer = cfg.MODEL.BACKBONE.MERGE_LAYER 120 | if merge_layer <= 0: 121 | evaluate_vit(model, template, search) 122 | else: 123 | evaluate_vit_separate(model, template, search) 124 | 125 | else: 126 | raise NotImplementedError 127 | -------------------------------------------------------------------------------- /lib/train/train_script_distill.py: -------------------------------------------------------------------------------- 1 | import os 2 | # loss function related 3 | from lib.utils.box_ops import giou_loss 4 | from torch.nn.functional import l1_loss 5 | from torch.nn import BCEWithLogitsLoss 6 | # train pipeline related 7 | from lib.train.trainers import LTRTrainer 8 | # distributed training related 9 | from torch.nn.parallel import DistributedDataParallel as DDP 10 | # some more advanced functions 11 | from .base_functions import * 12 | # network related 13 | from lib.models.stark import build_starks, build_starkst 14 | from lib.models.stark import build_stark_lightning_x_trt 15 | # forward propagation related 16 | from lib.train.actors import STARKLightningXtrtdistillActor 17 | # for import modules 18 | import importlib 19 | 20 | 21 | def build_network(script_name, cfg): 22 | # Create network 23 | if script_name == "stark_s": 24 | net = build_starks(cfg) 25 | elif script_name == "stark_st1" or script_name == "stark_st2": 26 | net = build_starkst(cfg) 27 | elif script_name == "stark_lightning_X_trt": 28 | net = build_stark_lightning_x_trt(cfg, phase="train") 29 | else: 30 | raise ValueError("illegal script name") 31 | return net 32 | 33 | 34 | def run(settings): 35 | settings.description = 'Training script for STARK-S, STARK-ST stage1, and STARK-ST stage2' 36 | 37 | # update the default configs with config file 38 | if not os.path.exists(settings.cfg_file): 39 | raise ValueError("%s doesn't exist." % settings.cfg_file) 40 | config_module = importlib.import_module("lib.config.%s.config" % settings.script_name) 41 | cfg = config_module.cfg 42 | config_module.update_config_from_file(settings.cfg_file) 43 | if settings.local_rank in [-1, 0]: 44 | print("New configuration is shown below.") 45 | for key in cfg.keys(): 46 | print("%s configuration:" % key, cfg[key]) 47 | print('\n') 48 | 49 | # update the default teacher configs with teacher config file 50 | if not os.path.exists(settings.cfg_file_teacher): 51 | raise ValueError("%s doesn't exist." % settings.cfg_file_teacher) 52 | config_module_teacher = importlib.import_module("lib.config.%s.config" % settings.script_teacher) 53 | cfg_teacher = config_module_teacher.cfg 54 | config_module_teacher.update_config_from_file(settings.cfg_file_teacher) 55 | if settings.local_rank in [-1, 0]: 56 | print("New teacher configuration is shown below.") 57 | for key in cfg_teacher.keys(): 58 | print("%s configuration:" % key, cfg_teacher[key]) 59 | print('\n') 60 | 61 | # update settings based on cfg 62 | update_settings(settings, cfg) 63 | 64 | # Record the training log 65 | log_dir = os.path.join(settings.save_dir, 'logs') 66 | if settings.local_rank in [-1, 0]: 67 | if not os.path.exists(log_dir): 68 | os.makedirs(log_dir) 69 | settings.log_file = os.path.join(log_dir, "%s-%s.log" % (settings.script_name, settings.config_name)) 70 | 71 | # Build dataloaders 72 | loader_train, loader_val = build_dataloaders(cfg, settings) 73 | 74 | if "RepVGG" in cfg.MODEL.BACKBONE.TYPE or "swin" in cfg.MODEL.BACKBONE.TYPE: 75 | cfg.ckpt_dir = settings.save_dir 76 | """turn on the distillation mode""" 77 | cfg.TRAIN.DISTILL = True 78 | cfg_teacher.TRAIN.DISTILL = True 79 | net = build_network(settings.script_name, cfg) 80 | net_teacher = build_network(settings.script_teacher, cfg_teacher) 81 | 82 | # wrap networks to distributed one 83 | net.cuda() 84 | net_teacher.cuda() 85 | net_teacher.eval() 86 | 87 | if settings.local_rank != -1: 88 | net = DDP(net, device_ids=[settings.local_rank], find_unused_parameters=True) 89 | net_teacher = DDP(net_teacher, device_ids=[settings.local_rank], find_unused_parameters=True) 90 | settings.device = torch.device("cuda:%d" % settings.local_rank) 91 | else: 92 | settings.device = torch.device("cuda:0") 93 | # settings.deep_sup = getattr(cfg.TRAIN, "DEEP_SUPERVISION", False) 94 | # settings.distill = getattr(cfg.TRAIN, "DISTILL", False) 95 | settings.distill_loss_type = getattr(cfg.TRAIN, "DISTILL_LOSS_TYPE", "L1") 96 | # Loss functions and Actors 97 | if settings.script_name == "stark_lightning_X_trt": 98 | objective = {'giou': giou_loss, 'l1': l1_loss} 99 | loss_weight = {'giou': cfg.TRAIN.GIOU_WEIGHT, 'l1': cfg.TRAIN.L1_WEIGHT} 100 | actor = STARKLightningXtrtdistillActor(net=net, objective=objective, loss_weight=loss_weight, settings=settings, 101 | net_teacher=net_teacher) 102 | else: 103 | raise ValueError("illegal script name") 104 | 105 | # Optimizer, parameters, and learning rates 106 | optimizer, lr_scheduler = get_optimizer_scheduler(net, cfg) 107 | use_amp = getattr(cfg.TRAIN, "AMP", False) 108 | trainer = LTRTrainer(actor, [loader_train, loader_val], optimizer, settings, lr_scheduler, use_amp=use_amp) 109 | 110 | # train process 111 | trainer.train(cfg.TRAIN.EPOCH, load_latest=True, fail_safe=True, distill=True) 112 | -------------------------------------------------------------------------------- /lib/config/ostrack/config.py: -------------------------------------------------------------------------------- 1 | from easydict import EasyDict as edict 2 | import yaml 3 | 4 | """ 5 | Add default config for OSTrack. 6 | """ 7 | cfg = edict() 8 | 9 | #data augmentation 10 | cfg.DA=edict() 11 | cfg.DA.border_prob=0.0 12 | cfg.DA.sfactor=[4,4] 13 | cfg.DA.imix=False 14 | cfg.DA.imix_occrate=0.5 15 | cfg.DA.imix_reverse_prob=0.3 16 | cfg.DA.imix_epoch=40 17 | cfg.DA.imix_interval=1 18 | cfg.DA.mix_type=1 19 | cfg.DA.norm_type='channel' 20 | cfg.DA.update_bbox=False 21 | 22 | cfg.CUTMIX=edict() 23 | cfg.CUTMIX.prob=0.0 24 | cfg.CUTMIX.occrate=0.5 25 | # MODEL 26 | cfg.MODEL = edict() 27 | cfg.MODEL.PRETRAIN_FILE = "mae_pretrain_vit_base.pth" 28 | cfg.MODEL.EXTRA_MERGER = False 29 | 30 | cfg.MODEL.RETURN_INTER = False 31 | cfg.MODEL.RETURN_STAGES = [] 32 | 33 | # MODEL.BACKBONE 34 | cfg.MODEL.BACKBONE = edict() 35 | cfg.MODEL.BACKBONE.TYPE = "vit_base_patch16_224" 36 | cfg.MODEL.BACKBONE.STRIDE = 16 37 | cfg.MODEL.BACKBONE.MID_PE = False 38 | cfg.MODEL.BACKBONE.SEP_SEG = False 39 | cfg.MODEL.BACKBONE.CAT_MODE = 'direct' 40 | cfg.MODEL.BACKBONE.MERGE_LAYER = 0 41 | cfg.MODEL.BACKBONE.ADD_CLS_TOKEN = False 42 | cfg.MODEL.BACKBONE.CLS_TOKEN_USE_MODE = 'ignore' 43 | 44 | cfg.MODEL.BACKBONE.CE_LOC = [] 45 | cfg.MODEL.BACKBONE.CE_KEEP_RATIO = [] 46 | cfg.MODEL.BACKBONE.CE_TEMPLATE_RANGE = 'ALL' # choose between ALL, CTR_POINT, CTR_REC, GT_BOX 47 | 48 | # MODEL.HEAD 49 | cfg.MODEL.HEAD = edict() 50 | cfg.MODEL.HEAD.TYPE = "CENTER" 51 | cfg.MODEL.HEAD.NUM_CHANNELS = 256 52 | 53 | # TRAIN 54 | cfg.TRAIN = edict() 55 | cfg.TRAIN.LR = 0.0001 56 | cfg.TRAIN.WEIGHT_DECAY = 0.0001 57 | cfg.TRAIN.EPOCH = 500 58 | cfg.TRAIN.LR_DROP_EPOCH = 400 59 | cfg.TRAIN.BATCH_SIZE = 16 60 | cfg.TRAIN.NUM_WORKER = 8 61 | cfg.TRAIN.OPTIMIZER = "ADAMW" 62 | cfg.TRAIN.BACKBONE_MULTIPLIER = 0.1 63 | cfg.TRAIN.GIOU_WEIGHT = 2.0 64 | cfg.TRAIN.L1_WEIGHT = 5.0 65 | cfg.TRAIN.FREEZE_LAYERS = [0, ] 66 | cfg.TRAIN.PRINT_INTERVAL = 50 67 | cfg.TRAIN.VAL_EPOCH_INTERVAL = 20 68 | cfg.TRAIN.GRAD_CLIP_NORM = 0.1 69 | cfg.TRAIN.AMP = False 70 | 71 | cfg.TRAIN.CE_START_EPOCH = 20 # candidate elimination start epoch 72 | cfg.TRAIN.CE_WARM_EPOCH = 80 # candidate elimination warm up epoch 73 | cfg.TRAIN.DROP_PATH_RATE = 0.1 # drop path rate for ViT backbone 74 | 75 | # TRAIN.SCHEDULER 76 | cfg.TRAIN.SCHEDULER = edict() 77 | cfg.TRAIN.SCHEDULER.TYPE = "step" 78 | cfg.TRAIN.SCHEDULER.DECAY_RATE = 0.1 79 | 80 | # DATA 81 | cfg.DATA = edict() 82 | cfg.DATA.SAMPLER_MODE = "causal" # sampling methods 83 | cfg.DATA.DATA_FRACTION = None # None means all data, could also use 1 here 84 | cfg.DATA.MEAN = [0.485, 0.456, 0.406] 85 | cfg.DATA.STD = [0.229, 0.224, 0.225] 86 | cfg.DATA.MAX_SAMPLE_INTERVAL = 200 87 | # DATA.TRAIN 88 | cfg.DATA.TRAIN = edict() 89 | cfg.DATA.TRAIN.DATASETS_NAME = ["LASOT", "GOT10K_vottrain"] 90 | cfg.DATA.TRAIN.DATASETS_RATIO = [1, 1] 91 | cfg.DATA.TRAIN.SAMPLE_PER_EPOCH = 60000 92 | # DATA.VAL 93 | cfg.DATA.VAL = edict() 94 | cfg.DATA.VAL.DATASETS_NAME = ["GOT10K_votval"] 95 | cfg.DATA.VAL.DATASETS_RATIO = [1] 96 | cfg.DATA.VAL.SAMPLE_PER_EPOCH = 10000 97 | # DATA.SEARCH 98 | cfg.DATA.SEARCH = edict() 99 | cfg.DATA.SEARCH.SIZE = 320 100 | cfg.DATA.SEARCH.FACTOR = 5.0 101 | cfg.DATA.SEARCH.CENTER_JITTER = 4.5 102 | cfg.DATA.SEARCH.SCALE_JITTER = 0.5 103 | cfg.DATA.SEARCH.NUMBER = 1 104 | # DATA.TEMPLATE 105 | cfg.DATA.TEMPLATE = edict() 106 | cfg.DATA.TEMPLATE.NUMBER = 1 107 | cfg.DATA.TEMPLATE.SIZE = 128 108 | cfg.DATA.TEMPLATE.FACTOR = 2.0 109 | cfg.DATA.TEMPLATE.CENTER_JITTER = 0 110 | cfg.DATA.TEMPLATE.SCALE_JITTER = 0 111 | 112 | # TEST 113 | cfg.TEST = edict() 114 | cfg.TEST.TEMPLATE_FACTOR = 2.0 115 | cfg.TEST.TEMPLATE_SIZE = 128 116 | cfg.TEST.SEARCH_FACTOR = 5.0 117 | cfg.TEST.SEARCH_SIZE = 320 118 | cfg.TEST.EPOCH = 500 119 | 120 | 121 | def _edict2dict(dest_dict, src_edict): 122 | if isinstance(dest_dict, dict) and isinstance(src_edict, dict): 123 | for k, v in src_edict.items(): 124 | if not isinstance(v, edict): 125 | dest_dict[k] = v 126 | else: 127 | dest_dict[k] = {} 128 | _edict2dict(dest_dict[k], v) 129 | else: 130 | return 131 | 132 | 133 | def gen_config(config_file): 134 | cfg_dict = {} 135 | _edict2dict(cfg_dict, cfg) 136 | with open(config_file, 'w') as f: 137 | yaml.dump(cfg_dict, f, default_flow_style=False) 138 | 139 | 140 | def _update_config(base_cfg, exp_cfg): 141 | if isinstance(base_cfg, dict) and isinstance(exp_cfg, edict): 142 | for k, v in exp_cfg.items(): 143 | if k in base_cfg: 144 | if not isinstance(v, dict): 145 | base_cfg[k] = v 146 | else: 147 | _update_config(base_cfg[k], v) 148 | else: 149 | raise ValueError("{} not exist in config.py".format(k)) 150 | else: 151 | return 152 | 153 | 154 | def update_config_from_file(filename, base_cfg=None): 155 | exp_config = None 156 | with open(filename) as f: 157 | exp_config = edict(yaml.safe_load(f)) 158 | if base_cfg is not None: 159 | _update_config(base_cfg, exp_config) 160 | else: 161 | _update_config(cfg, exp_config) 162 | -------------------------------------------------------------------------------- /lib/models/layers/attn.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.nn.functional as F 4 | from timm.models.layers import trunc_normal_ 5 | 6 | from lib.models.layers.rpe import generate_2d_concatenated_self_attention_relative_positional_encoding_index 7 | 8 | 9 | class Attention(nn.Module): 10 | def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0., 11 | rpe=False, z_size=7, x_size=14): 12 | super().__init__() 13 | self.num_heads = num_heads 14 | head_dim = dim // num_heads 15 | self.scale = head_dim ** -0.5 16 | 17 | self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) 18 | self.attn_drop = nn.Dropout(attn_drop) 19 | self.proj = nn.Linear(dim, dim) 20 | self.proj_drop = nn.Dropout(proj_drop) 21 | 22 | self.rpe =rpe 23 | if self.rpe: 24 | relative_position_index = \ 25 | generate_2d_concatenated_self_attention_relative_positional_encoding_index([z_size, z_size], 26 | [x_size, x_size]) 27 | self.register_buffer("relative_position_index", relative_position_index) 28 | # define a parameter table of relative position bias 29 | self.relative_position_bias_table = nn.Parameter(torch.empty((num_heads, 30 | relative_position_index.max() + 1))) 31 | trunc_normal_(self.relative_position_bias_table, std=0.02) 32 | 33 | def forward(self, x, mask=None, return_attention=False): 34 | # x: B, N, C 35 | # mask: [B, N, ] torch.bool 36 | B, N, C = x.shape 37 | qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) 38 | q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple) 39 | 40 | attn = (q @ k.transpose(-2, -1)) * self.scale 41 | 42 | if self.rpe: 43 | relative_position_bias = self.relative_position_bias_table[:, self.relative_position_index].unsqueeze(0) 44 | attn += relative_position_bias 45 | 46 | if mask is not None: 47 | attn = attn.masked_fill(mask.unsqueeze(1).unsqueeze(2), float('-inf'),) 48 | 49 | attn = attn.softmax(dim=-1) 50 | attn = self.attn_drop(attn) 51 | 52 | x = (attn @ v).transpose(1, 2).reshape(B, N, C) 53 | x = self.proj(x) 54 | x = self.proj_drop(x) 55 | 56 | if return_attention: 57 | return x, attn 58 | else: 59 | return x 60 | 61 | 62 | class Attention_talking_head(nn.Module): 63 | # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py 64 | # with slight modifications to add Talking Heads Attention (https://arxiv.org/pdf/2003.02436v1.pdf) 65 | def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., 66 | rpe=True, z_size=7, x_size=14): 67 | super().__init__() 68 | 69 | self.num_heads = num_heads 70 | 71 | head_dim = dim // num_heads 72 | 73 | self.scale = qk_scale or head_dim ** -0.5 74 | 75 | self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) 76 | self.attn_drop = nn.Dropout(attn_drop) 77 | 78 | self.proj = nn.Linear(dim, dim) 79 | 80 | self.proj_l = nn.Linear(num_heads, num_heads) 81 | self.proj_w = nn.Linear(num_heads, num_heads) 82 | 83 | self.proj_drop = nn.Dropout(proj_drop) 84 | 85 | self.rpe = rpe 86 | if self.rpe: 87 | relative_position_index = \ 88 | generate_2d_concatenated_self_attention_relative_positional_encoding_index([z_size, z_size], 89 | [x_size, x_size]) 90 | self.register_buffer("relative_position_index", relative_position_index) 91 | # define a parameter table of relative position bias 92 | self.relative_position_bias_table = nn.Parameter(torch.empty((num_heads, 93 | relative_position_index.max() + 1))) 94 | trunc_normal_(self.relative_position_bias_table, std=0.02) 95 | 96 | def forward(self, x, mask=None): 97 | B, N, C = x.shape 98 | qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) 99 | q, k, v = qkv[0] * self.scale, qkv[1], qkv[2] 100 | 101 | attn = (q @ k.transpose(-2, -1)) 102 | 103 | if self.rpe: 104 | relative_position_bias = self.relative_position_bias_table[:, self.relative_position_index].unsqueeze(0) 105 | attn += relative_position_bias 106 | 107 | if mask is not None: 108 | attn = attn.masked_fill(mask.unsqueeze(1).unsqueeze(2), 109 | float('-inf'),) 110 | 111 | attn = self.proj_l(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) 112 | 113 | attn = attn.softmax(dim=-1) 114 | 115 | attn = self.proj_w(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) 116 | attn = self.attn_drop(attn) 117 | 118 | x = (attn @ v).transpose(1, 2).reshape(B, N, C) 119 | x = self.proj(x) 120 | x = self.proj_drop(x) 121 | return x -------------------------------------------------------------------------------- /lib/test/evaluation/environment.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | import os 3 | 4 | 5 | class EnvSettings: 6 | def __init__(self): 7 | test_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) 8 | 9 | self.results_path = '{}/tracking_results/'.format(test_path) 10 | self.segmentation_path = '{}/segmentation_results/'.format(test_path) 11 | self.network_path = '{}/networks/'.format(test_path) 12 | self.result_plot_path = '{}/result_plots/'.format(test_path) 13 | self.otb_path = '' 14 | self.nfs_path = '' 15 | self.uav_path = '' 16 | self.tpl_path = '' 17 | self.vot_path = '' 18 | self.got10k_path = '' 19 | self.lasot_path = '' 20 | self.trackingnet_path = '' 21 | self.davis_dir = '' 22 | self.youtubevos_dir = '' 23 | 24 | self.got_packed_results_path = '' 25 | self.got_reports_path = '' 26 | self.tn_packed_results_path = '' 27 | 28 | 29 | def create_default_local_file(): 30 | comment = {'results_path': 'Where to store tracking results', 31 | 'network_path': 'Where tracking networks are stored.'} 32 | 33 | path = os.path.join(os.path.dirname(__file__), 'local.py') 34 | with open(path, 'w') as f: 35 | settings = EnvSettings() 36 | 37 | f.write('from test.evaluation.environment import EnvSettings\n\n') 38 | f.write('def local_env_settings():\n') 39 | f.write(' settings = EnvSettings()\n\n') 40 | f.write(' # Set your local paths here.\n\n') 41 | 42 | for attr in dir(settings): 43 | comment_str = None 44 | if attr in comment: 45 | comment_str = comment[attr] 46 | attr_val = getattr(settings, attr) 47 | if not attr.startswith('__') and not callable(attr_val): 48 | if comment_str is None: 49 | f.write(' settings.{} = \'{}\'\n'.format(attr, attr_val)) 50 | else: 51 | f.write(' settings.{} = \'{}\' # {}\n'.format(attr, attr_val, comment_str)) 52 | f.write('\n return settings\n\n') 53 | 54 | 55 | class EnvSettings_ITP: 56 | def __init__(self, workspace_dir, data_dir, save_dir): 57 | self.prj_dir = workspace_dir 58 | self.save_dir = save_dir 59 | self.results_path = os.path.join(save_dir, 'test/tracking_results') 60 | self.segmentation_path = os.path.join(save_dir, 'test/segmentation_results') 61 | self.network_path = os.path.join(save_dir, 'test/networks') 62 | self.result_plot_path = os.path.join(save_dir, 'test/result_plots') 63 | self.otb_path = os.path.join(data_dir, 'otb') 64 | self.nfs_path = os.path.join(data_dir, 'nfs') 65 | self.uav_path = os.path.join(data_dir, 'uav') 66 | self.tc128_path = os.path.join(data_dir, 'TC128') 67 | self.tpl_path = '' 68 | self.vot_path = os.path.join(data_dir, 'VOT2019') 69 | self.got10k_path = os.path.join(data_dir, 'got10k') 70 | self.got10k_lmdb_path = os.path.join(data_dir, 'got10k_lmdb') 71 | self.lasot_path = os.path.join(data_dir, 'lasot') 72 | self.lasot_lmdb_path = os.path.join(data_dir, 'lasot_lmdb') 73 | self.trackingnet_path = os.path.join(data_dir, 'trackingnet') 74 | self.vot18_path = os.path.join(data_dir, 'vot2018') 75 | self.vot22_path = os.path.join(data_dir, 'vot2022') 76 | self.itb_path = os.path.join(data_dir, 'itb') 77 | self.tnl2k_path = os.path.join(data_dir, 'tnl2k') 78 | self.lasot_extension_subset_path_path = os.path.join(data_dir, 'lasot_extension_subset') 79 | self.davis_dir = '' 80 | self.youtubevos_dir = '' 81 | 82 | self.got_packed_results_path = '' 83 | self.got_reports_path = '' 84 | self.tn_packed_results_path = '' 85 | 86 | 87 | def create_default_local_file_ITP_test(workspace_dir, data_dir, save_dir): 88 | comment = {'results_path': 'Where to store tracking results', 89 | 'network_path': 'Where tracking networks are stored.'} 90 | 91 | path = os.path.join(os.path.dirname(__file__), 'local.py') 92 | with open(path, 'w') as f: 93 | settings = EnvSettings_ITP(workspace_dir, data_dir, save_dir) 94 | 95 | f.write('from lib.test.evaluation.environment import EnvSettings\n\n') 96 | f.write('def local_env_settings():\n') 97 | f.write(' settings = EnvSettings()\n\n') 98 | f.write(' # Set your local paths here.\n\n') 99 | 100 | for attr in dir(settings): 101 | comment_str = None 102 | if attr in comment: 103 | comment_str = comment[attr] 104 | attr_val = getattr(settings, attr) 105 | if not attr.startswith('__') and not callable(attr_val): 106 | if comment_str is None: 107 | f.write(' settings.{} = \'{}\'\n'.format(attr, attr_val)) 108 | else: 109 | f.write(' settings.{} = \'{}\' # {}\n'.format(attr, attr_val, comment_str)) 110 | f.write('\n return settings\n\n') 111 | 112 | 113 | def env_settings(): 114 | env_module_name = 'lib.test.evaluation.local' 115 | try: 116 | env_module = importlib.import_module(env_module_name) 117 | return env_module.local_env_settings() 118 | except: 119 | env_file = os.path.join(os.path.dirname(__file__), 'local.py') 120 | 121 | # Create a default file 122 | create_default_local_file() 123 | raise RuntimeError('YOU HAVE NOT SETUP YOUR local.py!!!\n Go to "{}" and set all the paths you need. ' 124 | 'Then try to run again.'.format(env_file)) -------------------------------------------------------------------------------- /lib/train/run_training.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import argparse 4 | import importlib 5 | import cv2 as cv 6 | import torch.backends.cudnn 7 | import torch.distributed as dist 8 | 9 | import random 10 | import numpy as np 11 | torch.backends.cudnn.benchmark = False 12 | 13 | import _init_paths 14 | import lib.train.admin.settings as ws_settings 15 | 16 | 17 | def init_seeds(seed): 18 | print('seed:',seed) 19 | random.seed(seed) 20 | np.random.seed(seed) 21 | torch.manual_seed(seed) 22 | torch.cuda.manual_seed(seed) 23 | torch.backends.cudnn.deterministic = True 24 | torch.backends.cudnn.benchmark = False 25 | 26 | 27 | def run_training(script_name, config_name, cudnn_benchmark=True, local_rank=-1, save_dir=None,base_seed=None, 28 | use_lmdb=False, script_name_prv=None, config_name_prv=None, use_wandb=False, 29 | distill=None,datr=None, script_teacher=None, config_teacher=None): 30 | """Run the train script. 31 | args: 32 | script_name: Name of emperiment in the "experiments/" folder. 33 | config_name: Name of the yaml file in the "experiments/". 34 | cudnn_benchmark: Use cudnn benchmark or not (default is True). 35 | """ 36 | if save_dir is None: 37 | print("save_dir dir is not given. Use the default dir instead.") 38 | # This is needed to avoid strange crashes related to opencv 39 | cv.setNumThreads(0) 40 | 41 | torch.backends.cudnn.benchmark = cudnn_benchmark 42 | 43 | print('script_name: {}.py config_name: {}.yaml'.format(script_name, config_name)) 44 | 45 | '''2021.1.5 set seed for different process''' 46 | if base_seed is not None: 47 | if local_rank != -1: 48 | init_seeds(base_seed + local_rank) 49 | else: 50 | init_seeds(base_seed) 51 | 52 | settings = ws_settings.Settings() 53 | settings.script_name = script_name 54 | settings.config_name = config_name 55 | settings.project_path = 'train/{}/{}'.format(script_name, config_name) 56 | if script_name_prv is not None and config_name_prv is not None: 57 | settings.project_path_prv = 'train/{}/{}'.format(script_name_prv, config_name_prv) 58 | settings.local_rank = local_rank 59 | settings.save_dir = os.path.abspath(save_dir) 60 | settings.use_lmdb = use_lmdb 61 | prj_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")) 62 | settings.cfg_file = os.path.join(prj_dir, 'experiments/%s/%s.yaml' % (script_name, config_name)) 63 | settings.use_wandb = use_wandb 64 | if datr==1: 65 | expr_module = importlib.import_module('lib.train.train_script_datr') 66 | elif distill: 67 | settings.distill = distill 68 | settings.script_teacher = script_teacher 69 | settings.config_teacher = config_teacher 70 | if script_teacher is not None and config_teacher is not None: 71 | settings.project_path_teacher = 'train/{}/{}'.format(script_teacher, config_teacher) 72 | settings.cfg_file_teacher = os.path.join(prj_dir, 'experiments/%s/%s.yaml' % (script_teacher, config_teacher)) 73 | expr_module = importlib.import_module('lib.train.train_script_distill') 74 | else: 75 | expr_module = importlib.import_module('lib.train.train_script') 76 | expr_func = getattr(expr_module, 'run') 77 | expr_func(settings) 78 | 79 | 80 | def main(): 81 | parser = argparse.ArgumentParser(description='Run a train scripts in train_settings.') 82 | parser.add_argument('--script', type=str, required=True, help='Name of the train script.') 83 | parser.add_argument('--config', type=str, required=True, help="Name of the config file.") 84 | parser.add_argument('--cudnn_benchmark', type=bool, default=True, help='Set cudnn benchmark on (1) or off (0) (default is on).') 85 | parser.add_argument('--local_rank', default=-1, type=int, help='node rank for distributed training') 86 | parser.add_argument('--save_dir', type=str, help='the directory to save checkpoints and logs') 87 | parser.add_argument('--seed', type=int, default=42, help='seed for random numbers') 88 | parser.add_argument('--use_lmdb', type=int, choices=[0, 1], default=0) # whether datasets are in lmdb format 89 | parser.add_argument('--script_prv', type=str, default=None, help='Name of the train script of previous model.') 90 | parser.add_argument('--config_prv', type=str, default=None, help="Name of the config file of previous model.") 91 | parser.add_argument('--use_wandb', type=int, choices=[0, 1], default=0) # whether to use wandb 92 | # for knowledge distillation 93 | parser.add_argument('--distill', type=int, choices=[0, 1], default=0) # whether to use knowledge distillation 94 | parser.add_argument('--datr', type=int, choices=[0, 1,2], default=0) # whether to use our data augmentation datr 95 | parser.add_argument('--script_teacher', type=str, help='teacher script name') 96 | parser.add_argument('--config_teacher', type=str, help='teacher yaml configure file name') 97 | 98 | args = parser.parse_args() 99 | if args.local_rank != -1: 100 | dist.init_process_group(backend='nccl') 101 | torch.cuda.set_device(args.local_rank) 102 | else: 103 | torch.cuda.set_device(0) 104 | run_training(args.script, args.config, cudnn_benchmark=args.cudnn_benchmark, 105 | local_rank=args.local_rank, save_dir=args.save_dir, base_seed=args.seed, 106 | use_lmdb=args.use_lmdb, script_name_prv=args.script_prv, config_name_prv=args.config_prv, 107 | use_wandb=args.use_wandb, 108 | distill=args.distill, datr=args.datr,script_teacher=args.script_teacher, config_teacher=args.config_teacher) 109 | 110 | 111 | if __name__ == '__main__': 112 | main() 113 | -------------------------------------------------------------------------------- /lib/vis/plotting.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import numpy as np 3 | import torch 4 | import cv2 5 | 6 | 7 | def draw_figure(fig): 8 | fig.canvas.draw() 9 | fig.canvas.flush_events() 10 | plt.pause(0.001) 11 | 12 | 13 | def show_tensor(a: torch.Tensor, fig_num = None, title = None, range=(None, None), ax=None): 14 | """Display a 2D tensor. 15 | args: 16 | fig_num: Figure number. 17 | title: Title of figure. 18 | """ 19 | a_np = a.squeeze().cpu().clone().detach().numpy() 20 | if a_np.ndim == 3: 21 | a_np = np.transpose(a_np, (1, 2, 0)) 22 | 23 | if ax is None: 24 | fig = plt.figure(fig_num) 25 | plt.tight_layout() 26 | plt.cla() 27 | plt.imshow(a_np, vmin=range[0], vmax=range[1]) 28 | plt.axis('off') 29 | plt.axis('equal') 30 | if title is not None: 31 | plt.title(title) 32 | draw_figure(fig) 33 | else: 34 | ax.cla() 35 | ax.imshow(a_np, vmin=range[0], vmax=range[1]) 36 | ax.set_axis_off() 37 | ax.axis('equal') 38 | if title is not None: 39 | ax.set_title(title) 40 | draw_figure(plt.gcf()) 41 | 42 | 43 | def plot_graph(a: torch.Tensor, fig_num = None, title = None): 44 | """Plot graph. Data is a 1D tensor. 45 | args: 46 | fig_num: Figure number. 47 | title: Title of figure. 48 | """ 49 | a_np = a.squeeze().cpu().clone().detach().numpy() 50 | if a_np.ndim > 1: 51 | raise ValueError 52 | fig = plt.figure(fig_num) 53 | # plt.tight_layout() 54 | plt.cla() 55 | plt.plot(a_np) 56 | if title is not None: 57 | plt.title(title) 58 | draw_figure(fig) 59 | 60 | 61 | def show_image_with_boxes(im, boxes, iou_pred=None, disp_ids=None): 62 | im_np = im.clone().cpu().squeeze().numpy() 63 | im_np = np.ascontiguousarray(im_np.transpose(1, 2, 0).astype(np.uint8)) 64 | 65 | boxes = boxes.view(-1, 4).cpu().numpy().round().astype(int) 66 | 67 | # Draw proposals 68 | for i_ in range(boxes.shape[0]): 69 | if disp_ids is None or disp_ids[i_]: 70 | bb = boxes[i_, :] 71 | disp_color = (i_*38 % 256, (255 - i_*97) % 256, (123 + i_*66) % 256) 72 | cv2.rectangle(im_np, (bb[0], bb[1]), (bb[0] + bb[2], bb[1] + bb[3]), 73 | disp_color, 1) 74 | 75 | if iou_pred is not None: 76 | text_pos = (bb[0], bb[1] - 5) 77 | cv2.putText(im_np, 'ID={} IOU = {:3.2f}'.format(i_, iou_pred[i_]), text_pos, 78 | cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1, bottomLeftOrigin=False) 79 | 80 | im_tensor = torch.from_numpy(im_np.transpose(2, 0, 1)).float() 81 | 82 | return im_tensor 83 | 84 | 85 | 86 | def _pascal_color_map(N=256, normalized=False): 87 | """ 88 | Python implementation of the color map function for the PASCAL VOC data set. 89 | Official Matlab version can be found in the PASCAL VOC devkit 90 | http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html#devkit 91 | """ 92 | 93 | def bitget(byteval, idx): 94 | return (byteval & (1 << idx)) != 0 95 | 96 | dtype = 'float32' if normalized else 'uint8' 97 | cmap = np.zeros((N, 3), dtype=dtype) 98 | for i in range(N): 99 | r = g = b = 0 100 | c = i 101 | for j in range(8): 102 | r = r | (bitget(c, 0) << 7 - j) 103 | g = g | (bitget(c, 1) << 7 - j) 104 | b = b | (bitget(c, 2) << 7 - j) 105 | c = c >> 3 106 | 107 | cmap[i] = np.array([r, g, b]) 108 | 109 | cmap = cmap / 255 if normalized else cmap 110 | return cmap 111 | 112 | 113 | def overlay_mask(im, ann, alpha=0.5, colors=None, contour_thickness=None): 114 | """ Overlay mask over image. 115 | Source: https://github.com/albertomontesg/davis-interactive/blob/master/davisinteractive/utils/visualization.py 116 | This function allows you to overlay a mask over an image with some 117 | transparency. 118 | # Arguments 119 | im: Numpy Array. Array with the image. The shape must be (H, W, 3) and 120 | the pixels must be represented as `np.uint8` data type. 121 | ann: Numpy Array. Array with the mask. The shape must be (H, W) and the 122 | values must be intergers 123 | alpha: Float. Proportion of alpha to apply at the overlaid mask. 124 | colors: Numpy Array. Optional custom colormap. It must have shape (N, 3) 125 | being N the maximum number of colors to represent. 126 | contour_thickness: Integer. Thickness of each object index contour draw 127 | over the overlay. This function requires to have installed the 128 | package `opencv-python`. 129 | # Returns 130 | Numpy Array: Image of the overlay with shape (H, W, 3) and data type 131 | `np.uint8`. 132 | """ 133 | im, ann = np.asarray(im, dtype=np.uint8), np.asarray(ann, dtype=np.int) 134 | if im.shape[:-1] != ann.shape: 135 | raise ValueError('First two dimensions of `im` and `ann` must match') 136 | if im.shape[-1] != 3: 137 | raise ValueError('im must have three channels at the 3 dimension') 138 | 139 | colors = colors or _pascal_color_map() 140 | colors = np.asarray(colors, dtype=np.uint8) 141 | 142 | mask = colors[ann] 143 | fg = im * alpha + (1 - alpha) * mask 144 | 145 | img = im.copy() 146 | img[ann > 0] = fg[ann > 0] 147 | 148 | if contour_thickness: # pragma: no cover 149 | import cv2 150 | for obj_id in np.unique(ann[ann > 0]): 151 | contours = cv2.findContours((ann == obj_id).astype( 152 | np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:] 153 | cv2.drawContours(img, contours[0], -1, colors[obj_id].tolist(), 154 | contour_thickness) 155 | return img 156 | -------------------------------------------------------------------------------- /lib/utils/heapmap_utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | 4 | 5 | def generate_heatmap(bboxes, patch_size=320, stride=16): 6 | """ 7 | Generate ground truth heatmap same as CenterNet 8 | Args: 9 | bboxes (torch.Tensor): shape of [num_search, bs, 4] 10 | 11 | Returns: 12 | gaussian_maps: list of generated heatmap 13 | 14 | """ 15 | gaussian_maps = [] 16 | heatmap_size = patch_size // stride 17 | for single_patch_bboxes in bboxes: 18 | bs = single_patch_bboxes.shape[0] 19 | gt_scoremap = torch.zeros(bs, heatmap_size, heatmap_size) 20 | classes = torch.arange(bs).to(torch.long) 21 | bbox = single_patch_bboxes * heatmap_size 22 | wh = bbox[:, 2:] 23 | centers_int = (bbox[:, :2] + wh / 2).round() 24 | CenterNetHeatMap.generate_score_map(gt_scoremap, classes, wh, centers_int, 0.7) 25 | gaussian_maps.append(gt_scoremap.to(bbox.device)) 26 | return gaussian_maps 27 | 28 | 29 | class CenterNetHeatMap(object): 30 | @staticmethod 31 | def generate_score_map(fmap, gt_class, gt_wh, centers_int, min_overlap): 32 | radius = CenterNetHeatMap.get_gaussian_radius(gt_wh, min_overlap) 33 | radius = torch.clamp_min(radius, 0) 34 | radius = radius.type(torch.int).cpu().numpy() 35 | for i in range(gt_class.shape[0]): 36 | channel_index = gt_class[i] 37 | CenterNetHeatMap.draw_gaussian(fmap[channel_index], centers_int[i], radius[i]) 38 | 39 | @staticmethod 40 | def get_gaussian_radius(box_size, min_overlap): 41 | """ 42 | copyed from CornerNet 43 | box_size (w, h), it could be a torch.Tensor, numpy.ndarray, list or tuple 44 | notice: we are using a bug-version, please refer to fix bug version in CornerNet 45 | """ 46 | # box_tensor = torch.Tensor(box_size) 47 | box_tensor = box_size 48 | width, height = box_tensor[..., 0], box_tensor[..., 1] 49 | 50 | a1 = 1 51 | b1 = height + width 52 | c1 = width * height * (1 - min_overlap) / (1 + min_overlap) 53 | sq1 = torch.sqrt(b1 ** 2 - 4 * a1 * c1) 54 | r1 = (b1 + sq1) / 2 55 | 56 | a2 = 4 57 | b2 = 2 * (height + width) 58 | c2 = (1 - min_overlap) * width * height 59 | sq2 = torch.sqrt(b2 ** 2 - 4 * a2 * c2) 60 | r2 = (b2 + sq2) / 2 61 | 62 | a3 = 4 * min_overlap 63 | b3 = -2 * min_overlap * (height + width) 64 | c3 = (min_overlap - 1) * width * height 65 | sq3 = torch.sqrt(b3 ** 2 - 4 * a3 * c3) 66 | r3 = (b3 + sq3) / 2 67 | 68 | return torch.min(r1, torch.min(r2, r3)) 69 | 70 | @staticmethod 71 | def gaussian2D(radius, sigma=1): 72 | # m, n = [(s - 1.) / 2. for s in shape] 73 | m, n = radius 74 | y, x = np.ogrid[-m: m + 1, -n: n + 1] 75 | 76 | gauss = np.exp(-(x * x + y * y) / (2 * sigma * sigma)) 77 | gauss[gauss < np.finfo(gauss.dtype).eps * gauss.max()] = 0 78 | return gauss 79 | 80 | @staticmethod 81 | def draw_gaussian(fmap, center, radius, k=1): 82 | diameter = 2 * radius + 1 83 | gaussian = CenterNetHeatMap.gaussian2D((radius, radius), sigma=diameter / 6) 84 | gaussian = torch.Tensor(gaussian) 85 | x, y = int(center[0]), int(center[1]) 86 | height, width = fmap.shape[:2] 87 | 88 | left, right = min(x, radius), min(width - x, radius + 1) 89 | top, bottom = min(y, radius), min(height - y, radius + 1) 90 | 91 | masked_fmap = fmap[y - top: y + bottom, x - left: x + right] 92 | masked_gaussian = gaussian[radius - top: radius + bottom, radius - left: radius + right] 93 | if min(masked_gaussian.shape) > 0 and min(masked_fmap.shape) > 0: 94 | masked_fmap = torch.max(masked_fmap, masked_gaussian * k) 95 | fmap[y - top: y + bottom, x - left: x + right] = masked_fmap 96 | # return fmap 97 | 98 | 99 | def compute_grids(features, strides): 100 | """ 101 | grids regret to the input image size 102 | """ 103 | grids = [] 104 | for level, feature in enumerate(features): 105 | h, w = feature.size()[-2:] 106 | shifts_x = torch.arange( 107 | 0, w * strides[level], 108 | step=strides[level], 109 | dtype=torch.float32, device=feature.device) 110 | shifts_y = torch.arange( 111 | 0, h * strides[level], 112 | step=strides[level], 113 | dtype=torch.float32, device=feature.device) 114 | shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x) 115 | shift_x = shift_x.reshape(-1) 116 | shift_y = shift_y.reshape(-1) 117 | grids_per_level = torch.stack((shift_x, shift_y), dim=1) + \ 118 | strides[level] // 2 119 | grids.append(grids_per_level) 120 | return grids 121 | 122 | 123 | def get_center3x3(locations, centers, strides, range=3): 124 | ''' 125 | Inputs: 126 | locations: M x 2 127 | centers: N x 2 128 | strides: M 129 | ''' 130 | range = (range - 1) / 2 131 | M, N = locations.shape[0], centers.shape[0] 132 | locations_expanded = locations.view(M, 1, 2).expand(M, N, 2) # M x N x 2 133 | centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2 134 | strides_expanded = strides.view(M, 1, 1).expand(M, N, 2) # M x N 135 | centers_discret = ((centers_expanded / strides_expanded).int() * strides_expanded).float() + \ 136 | strides_expanded / 2 # M x N x 2 137 | dist_x = (locations_expanded[:, :, 0] - centers_discret[:, :, 0]).abs() 138 | dist_y = (locations_expanded[:, :, 1] - centers_discret[:, :, 1]).abs() 139 | return (dist_x <= strides_expanded[:, :, 0] * range) & \ 140 | (dist_y <= strides_expanded[:, :, 0] * range) 141 | 142 | 143 | def get_pred(score_map_ctr, size_map, offset_map, feat_size): 144 | max_score, idx = torch.max(score_map_ctr.flatten(1), dim=1, keepdim=True) 145 | 146 | idx = idx.unsqueeze(1).expand(idx.shape[0], 2, 1) 147 | size = size_map.flatten(2).gather(dim=2, index=idx).squeeze(-1) 148 | offset = offset_map.flatten(2).gather(dim=2, index=idx).squeeze(-1) 149 | 150 | return size * feat_size, offset 151 | -------------------------------------------------------------------------------- /lib/models/layers/attn_blocks.py: -------------------------------------------------------------------------------- 1 | import math 2 | import torch 3 | import torch.nn as nn 4 | from timm.models.layers import Mlp, DropPath, trunc_normal_, lecun_normal_ 5 | 6 | from lib.models.layers.attn import Attention 7 | 8 | 9 | def candidate_elimination(attn: torch.Tensor, tokens: torch.Tensor, lens_t: int, keep_ratio: float, global_index: torch.Tensor, box_mask_z: torch.Tensor): 10 | """ 11 | Eliminate potential background candidates for computation reduction and noise cancellation. 12 | Args: 13 | attn (torch.Tensor): [B, num_heads, L_t + L_s, L_t + L_s], attention weights 14 | tokens (torch.Tensor): [B, L_t + L_s, C], template and search region tokens 15 | lens_t (int): length of template 16 | keep_ratio (float): keep ratio of search region tokens (candidates) 17 | global_index (torch.Tensor): global index of search region tokens 18 | box_mask_z (torch.Tensor): template mask used to accumulate attention weights 19 | 20 | Returns: 21 | tokens_new (torch.Tensor): tokens after candidate elimination 22 | keep_index (torch.Tensor): indices of kept search region tokens 23 | removed_index (torch.Tensor): indices of removed search region tokens 24 | """ 25 | lens_s = attn.shape[-1] - lens_t 26 | bs, hn, _, _ = attn.shape 27 | 28 | lens_keep = math.ceil(keep_ratio * lens_s) 29 | if lens_keep == lens_s: 30 | return tokens, global_index, None 31 | 32 | attn_t = attn[:, :, :lens_t, lens_t:] 33 | 34 | if box_mask_z is not None: 35 | box_mask_z = box_mask_z.unsqueeze(1).unsqueeze(-1).expand(-1, attn_t.shape[1], -1, attn_t.shape[-1]) 36 | # attn_t = attn_t[:, :, box_mask_z, :] 37 | attn_t = attn_t[box_mask_z] 38 | attn_t = attn_t.view(bs, hn, -1, lens_s) 39 | attn_t = attn_t.mean(dim=2).mean(dim=1) # B, H, L-T, L_s --> B, L_s 40 | 41 | # attn_t = [attn_t[i, :, box_mask_z[i, :], :] for i in range(attn_t.size(0))] 42 | # attn_t = [attn_t[i].mean(dim=1).mean(dim=0) for i in range(len(attn_t))] 43 | # attn_t = torch.stack(attn_t, dim=0) 44 | else: 45 | attn_t = attn_t.mean(dim=2).mean(dim=1) # B, H, L-T, L_s --> B, L_s 46 | 47 | # use sort instead of topk, due to the speed issue 48 | # https://github.com/pytorch/pytorch/issues/22812 49 | sorted_attn, indices = torch.sort(attn_t, dim=1, descending=True) 50 | 51 | topk_attn, topk_idx = sorted_attn[:, :lens_keep], indices[:, :lens_keep] 52 | non_topk_attn, non_topk_idx = sorted_attn[:, lens_keep:], indices[:, lens_keep:] 53 | 54 | keep_index = global_index.gather(dim=1, index=topk_idx) 55 | removed_index = global_index.gather(dim=1, index=non_topk_idx) 56 | 57 | # separate template and search tokens 58 | tokens_t = tokens[:, :lens_t] 59 | tokens_s = tokens[:, lens_t:] 60 | 61 | # obtain the attentive and inattentive tokens 62 | B, L, C = tokens_s.shape 63 | # topk_idx_ = topk_idx.unsqueeze(-1).expand(B, lens_keep, C) 64 | attentive_tokens = tokens_s.gather(dim=1, index=topk_idx.unsqueeze(-1).expand(B, -1, C)) 65 | # inattentive_tokens = tokens_s.gather(dim=1, index=non_topk_idx.unsqueeze(-1).expand(B, -1, C)) 66 | 67 | # compute the weighted combination of inattentive tokens 68 | # fused_token = non_topk_attn @ inattentive_tokens 69 | 70 | # concatenate these tokens 71 | # tokens_new = torch.cat([tokens_t, attentive_tokens, fused_token], dim=0) 72 | tokens_new = torch.cat([tokens_t, attentive_tokens], dim=1) 73 | 74 | return tokens_new, keep_index, removed_index 75 | 76 | 77 | class CEBlock(nn.Module): 78 | 79 | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., 80 | drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, keep_ratio_search=1.0,): 81 | super().__init__() 82 | self.norm1 = norm_layer(dim) 83 | self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) 84 | # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here 85 | self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() 86 | self.norm2 = norm_layer(dim) 87 | mlp_hidden_dim = int(dim * mlp_ratio) 88 | self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) 89 | 90 | self.keep_ratio_search = keep_ratio_search 91 | 92 | def forward(self, x, global_index_template, global_index_search, mask=None, ce_template_mask=None, keep_ratio_search=None): 93 | x_attn, attn = self.attn(self.norm1(x), mask, True) 94 | x = x + self.drop_path(x_attn) 95 | lens_t = global_index_template.shape[1] 96 | 97 | removed_index_search = None 98 | if self.keep_ratio_search < 1 and (keep_ratio_search is None or keep_ratio_search < 1): 99 | keep_ratio_search = self.keep_ratio_search if keep_ratio_search is None else keep_ratio_search 100 | x, global_index_search, removed_index_search = candidate_elimination(attn, x, lens_t, keep_ratio_search, global_index_search, ce_template_mask) 101 | 102 | x = x + self.drop_path(self.mlp(self.norm2(x))) 103 | return x, global_index_template, global_index_search, removed_index_search, attn 104 | 105 | 106 | class Block(nn.Module): 107 | 108 | def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., 109 | drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): 110 | super().__init__() 111 | self.norm1 = norm_layer(dim) 112 | self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) 113 | # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here 114 | self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() 115 | self.norm2 = norm_layer(dim) 116 | mlp_hidden_dim = int(dim * mlp_ratio) 117 | self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) 118 | 119 | def forward(self, x, mask=None): 120 | x = x + self.drop_path(self.attn(self.norm1(x), mask)) 121 | x = x + self.drop_path(self.mlp(self.norm2(x))) 122 | return x 123 | --------------------------------------------------------------------------------