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View Code? Open in Web Editor NEWAn official implementation of Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers
License: MIT License
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FileNotFoundError: MO3TRDataset: [Errno 2] No such file or directory: 'MOT17/annotations/half-train-SDP_cocoformat.json'
Hi. Thanks very much for the good work.
I was trying to run >python run/train_track_nf.py and encountered this error
fatal: not a git repository (or any parent up to mount point /content)
Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).
2023-02-15 21:47:34,585 - mmtrack - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.8.10 (default, Nov 14 2022, 12:59:47) [GCC 9.4.0]
CUDA available: True
GPU 0: Tesla T4
CUDA_HOME: /usr/local/cuda
NVCC: Build cuda_11.6.r11.6/compiler.31057947_0
GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
PyTorch: 1.7.1
PyTorch compiling details: PyTorch built with:
- GCC 7.3
- C++ Version: 201402
- Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
- Intel(R) MKL-DNN v1.6.0 (Git Hash 5ef631a030a6f73131c77892041042805a06064f)
- OpenMP 201511 (a.k.a. OpenMP 4.5)
- NNPACK is enabled
- CPU capability usage: AVX2
- CUDA Runtime 10.2
- NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75
- CuDNN 7.6.5
- Magma 2.5.2
- Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,
TorchVision: 0.8.2
OpenCV: 4.6.0
MMCV: 1.4.2
MMCV Compiler: GCC 7.3
MMCV CUDA Compiler: 10.2
MMTracking: 0.8.0+
------------------------------------------------------------
2023-02-15 21:47:34,585 - mmtrack - INFO - Distributed training: False
2023-02-15 21:47:35,280 - mmtrack - INFO - Config:
total_epochs = 20
load_from = ''
fp_rate = 0.5
dup_rate = 0
fpdb_rate = 0.5
grad = 'separate'
bs = 1
num_workers = 0
frame_range = 3
num_ref_imgs = 5
noise = 0
root_work = '/storage/alan/workspace/mmStorage/mot/'
work_dir = '/storage/alan/workspace/mmStorage/mot/mo3tr_temphs_fr5_randseq'
img_scale = (800, 1440)
optimizer = dict(
type='AdamW',
lr=2e-05,
weight_decay=0.0001,
paramwise_cfg=dict(
custom_keys=dict(
backbone=dict(lr_mult=0.1),
sampling_offsets=dict(lr_mult=0.1),
reference_points=dict(lr_mult=0.1))))
optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
lr_config = dict(policy='step', step=[10])
runner = dict(type='EpochBasedRunner', max_epochs=20)
model = dict(
detector=dict(
type='YOLOX',
input_size=(800, 1440),
random_size_range=(18, 32),
random_size_interval=10,
backbone=dict(
type='CSPDarknet',
deepen_factor=1.33,
widen_factor=1.25,
frozen_stages=4),
neck=dict(
type='YOLOXPAFPN',
in_channels=[320, 640, 1280],
out_channels=320,
num_csp_blocks=4,
freeze=True),
bbox_head=dict(
type='Mo3trDetrHead',
num_query=300,
num_classes=1,
in_channels=320,
sync_cls_avg_factor=True,
with_box_refine=True,
as_two_stage=False,
transformer=dict(
type='MO3TRTransformer',
sa=False,
encoder=dict(
type='DetrTransformerEncoder',
num_layers=1,
transformerlayers=dict(
type='BaseTransformerLayer',
attn_cfgs=dict(
type='MultiScaleDeformableAttention',
embed_dims=320,
num_levels=3),
feedforward_channels=1024,
ffn_cfgs=dict(
type='FFN',
embed_dims=320,
feedforward_channels=1024,
num_fcs=2,
ffn_drop=0.0,
act_cfg=dict(type='ReLU', inplace=True)),
ffn_dropout=0.1,
operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
decoder=dict(
type='DeformableDetrTransformerDecoder',
num_layers=6,
return_intermediate=True,
transformerlayers=dict(
type='DetrTransformerDecoderLayer',
attn_cfgs=[
dict(
type='MultiheadAttention',
embed_dims=320,
num_heads=8,
dropout=0.1),
dict(
type='MultiScaleDeformableAttention',
embed_dims=320,
num_levels=3)
],
feedforward_channels=1024,
ffn_cfgs=dict(
type='FFN',
embed_dims=320,
feedforward_channels=1024,
num_fcs=2,
ffn_drop=0.0,
act_cfg=dict(type='ReLU', inplace=True)),
ffn_dropout=0.1,
operation_order=('self_attn', 'norm', 'cross_attn',
'norm', 'ffn', 'norm')))),
positional_encoding=dict(
type='SinePositionalEncoding',
num_feats=160,
normalize=True,
offset=-0.5),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=2.0),
loss_bbox=dict(type='L1Loss', loss_weight=5.0),
loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
train_cfg=dict(
assigner=dict(
type='HungarianAssignerMO3TR',
cls_cost=dict(type='FocalLossCost', weight=2.0),
reg_cost=dict(
type='BBoxL1Cost', weight=5.0, box_format='xywh'),
iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
test_cfg=dict(max_per_img=100)),
type='MO3TRnF',
tracker=dict(
type='Mo3trTracker',
init_track_thr=0.5,
prop_thr=0.5,
num_frames_retain=1),
fp_rate=0.5,
dup_rate=0,
noise=0,
fpdb_rate=0.5,
grad='separate')
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadMultiImagesFromFile', to_float32=True),
dict(type='SeqLoadAnnotations', with_bbox=True, with_track=True),
dict(
type='SeqResize',
img_scale=(800, 1440),
share_params=True,
keep_ratio=True,
bbox_clip_border=False),
dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),
dict(
type='SeqPad',
size_divisor=32,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='MatchInstancesMO3TR', skip_nomatch=True),
dict(
type='VideoCollect',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_match_indices',
'gt_instance_ids'
]),
dict(type='SeqDefaultFormatBundleMO3TR')
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(800, 1440),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
size_divisor=32,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='ImageToFloatTensor', keys=['img']),
dict(type='VideoCollect', keys=['img'])
])
]
data_root = 'MOT17/'
data = dict(
samples_per_gpu=1,
workers_per_gpu=0,
persistent_workers=False,
val=dict(
type='MO3TRDataset',
ann_file='MOT17/annotations/half-val-SDP_cocoformat.json',
img_prefix='MOT17/train',
ref_img_sampler=None,
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(800, 1440),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
size_divisor=32,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='ImageToFloatTensor', keys=['img']),
dict(type='VideoCollect', keys=['img'])
])
],
interpolate_tracks_cfg=dict(min_num_frames=5, max_num_frames=20)),
test=dict(
type='MO3TRDataset',
ann_file='MOT17/annotations/half-val-SDP_cocoformat.json',
img_prefix='MOT17/train',
ref_img_sampler=None,
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(800, 1440),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(
type='Pad',
size_divisor=32,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='ImageToFloatTensor', keys=['img']),
dict(type='VideoCollect', keys=['img'])
])
],
interpolate_tracks_cfg=dict(min_num_frames=5, max_num_frames=20)),
train=dict(
type='MO3TRDataset',
visibility_thr=-1,
ann_file='MOT17/annotations/half-train-SDP_cocoformat.json',
img_prefix='MOT17/train',
ref_img_sampler=dict(
num_ref_imgs=5,
frame_range=3,
filter_key_img=True,
method='uniform'),
pipeline=[
dict(type='LoadMultiImagesFromFile', to_float32=True),
dict(type='SeqLoadAnnotations', with_bbox=True, with_track=True),
dict(
type='SeqResize',
img_scale=(800, 1440),
share_params=True,
keep_ratio=True,
bbox_clip_border=False),
dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),
dict(
type='SeqPad',
size_divisor=32,
pad_val=dict(img=(114.0, 114.0, 114.0))),
dict(type='MatchInstancesMO3TR', skip_nomatch=True),
dict(
type='VideoCollect',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_match_indices',
'gt_instance_ids'
]),
dict(type='SeqDefaultFormatBundleMO3TR')
]))
checkpoint_config = dict(interval=1)
log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [
dict(type='SyncNormHook', num_last_epochs=15, interval=5, priority=48),
dict(
type='ExpMomentumEMAHook',
resume_from=None,
momentum=0.0001,
priority=49)
]
dist_params = dict(backend='nccl')
log_level = 'INFO'
resume_from = ''
workflow = [('train', 1)]
evaluation = dict(metric=['bbox', 'track'], interval=1)
search_metrics = ['MOTA', 'IDF1', 'FN', 'FP', 'IDs', 'MT', 'ML']
gpu_ids = range(0, 1)
2023-02-15 21:47:35,372 - mmtrack - INFO - Set random seed to 2015075619, deterministic: False
/usr/local/lib/python3.8/dist-packages/mmcv/ops/multi_scale_deform_attn.py:209: UserWarning: You'd better set embed_dims in MultiScaleDeformAttention to make the dimension of each attention head a power of 2 which is more efficient in our CUDA implementation.
warnings.warn(
2023-02-15 21:47:37,415 - mmtrack - INFO - initialize CSPDarknet with init_cfg {'type': 'Kaiming', 'layer': 'Conv2d', 'a': 2.23606797749979, 'distribution': 'uniform', 'mode': 'fan_in', 'nonlinearity': 'leaky_relu'}
2023-02-15 21:47:37,816 - mmtrack - INFO - initialize YOLOXPAFPN with init_cfg {'type': 'Kaiming', 'layer': 'Conv2d', 'a': 2.23606797749979, 'distribution': 'uniform', 'mode': 'fan_in', 'nonlinearity': 'leaky_relu'}
loading annotations into memory...
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/mmcv/utils/registry.py", line 52, in build_from_cfg
return obj_cls(**args)
File "/usr/local/lib/python3.8/dist-packages/mmtrack/datasets/mot_challenge_dataset.py", line 42, in __init__
super().__init__(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/mmtrack/datasets/coco_video_dataset.py", line 46, in __init__
super().__init__(*args, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/mmdet/datasets/custom.py", line 92, in __init__
self.data_infos = self.load_annotations(local_path)
File "/usr/local/lib/python3.8/dist-packages/mmtrack/datasets/coco_video_dataset.py", line 61, in load_annotations
data_infos = self.load_video_anns(ann_file)
File "/usr/local/lib/python3.8/dist-packages/mmtrack/datasets/coco_video_dataset.py", line 73, in load_video_anns
self.coco = CocoVID(ann_file)
File "/usr/local/lib/python3.8/dist-packages/mmtrack/datasets/parsers/coco_video_parser.py", line 22, in __init__
super(CocoVID, self).__init__(annotation_file=annotation_file)
File "/usr/local/lib/python3.8/dist-packages/mmdet/datasets/api_wrappers/coco_api.py", line 23, in __init__
super().__init__(annotation_file=annotation_file)
File "/usr/local/lib/python3.8/dist-packages/pycocotools/coco.py", line 81, in __init__
with open(annotation_file, 'r') as f:
FileNotFoundError: [Errno 2] No such file or directory: 'MOT17/annotations/half-train-SDP_cocoformat.json'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/content/drive/MyDrive/MO3TR-main/run/train_track_nf.py", line 187, in <module>
main()
File "/content/drive/MyDrive/MO3TR-main/run/train_track_nf.py", line 162, in main
datasets = [build_dataset(cfg.data.train)]
File "/usr/local/lib/python3.8/dist-packages/mmdet/datasets/builder.py", line 81, in build_dataset
dataset = build_from_cfg(cfg, DATASETS, default_args)
File "/usr/local/lib/python3.8/dist-packages/mmcv/utils/registry.py", line 55, in build_from_cfg
raise type(e)(f'{obj_cls.__name__}: {e}')
FileNotFoundError: MO3TRDataset: [Errno 2] No such file or directory: 'MOT17/annotations/half-train-SDP_cocoformat.json'
Please add inference script
please add inference script
To training and running the model on new dataset ?
training on custom data
Hello,
I have a question regarding training MO3TR on a custom dataset.
Training the tracker directly produces an empty tensor error since the detection scores are filtered out. In your paper you mentioned that you implemented a multi-stage training approach. Where you first train the detector for 300 epoch and then use it to train the tracker.
My question is how do i train a detector on a custom dataset and use those weights to train MO3TR.
thanks
Please add documentation
I'm having trouble reproducing this work. If you could add the following documentation, it would be greatly appreciated:
- What python version you are running
- Where to acquire the test dataset(s). The file structure in MOT17 did not match the one hardcoded in the example config.
- Full examples of how to recreate results shown in the paper.
Empty tensor? and Reproducibility?
Hi. I have two questions.
The first is that I encounter an error training (log below) when running python run/train_track_nf.py
Traceback (most recent call last):
File "run/train_track_nf.py", line 187, in <module>
main()
File "run/train_track_nf.py", line 183, in main
meta=meta)
File "/lhome/MO3TR/moter_venv/lib/python3.7/site-packages/mmtrack/apis/train.py", line 175, in train_model
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
File "/lhome/MO3TR/moter_venv/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 127, in run
epoch_runner(data_loaders[i], **kwargs)
File "/lhome/MO3TR/moter_venv/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 50, in train
self.run_iter(data_batch, train_mode=True, **kwargs)
File "/lhome/MO3TR/moter_venv/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 30, in run_iter
**kwargs)
File "/lhome/MO3TR/moter_venv/lib/python3.7/site-packages/mmcv/parallel/data_parallel.py", line 75, in train_step
return self.module.train_step(*inputs[0], **kwargs[0])
File "/lhome/MO3TR/moter_venv/lib/python3.7/site-packages/mmtrack/models/mot/mo3tr.py", line 375, in train_step
losses = self(**data)
File "/lhome/MO3TR/moter_venv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/lhome/MO3TR/moter_venv/lib/python3.7/site-packages/mmtrack/models/mot/mo3tr.py", line 370, in forward
return self.forward_train(**kwargs)
File "/lhome/MO3TR/moter_venv/lib/python3.7/site-packages/mmtrack/models/mot/mo3tr.py", line 387, in forward_train
track_prev = self.forward_prev_nf(all_frames, last_frame)
File "/lhome/MO3TR/moter_venv/lib/python3.7/site-packages/mmtrack/models/mot/mo3tr.py", line 460, in forward_prev_nf
prev_hs, prev_bboxes = self.temporal_model(prev_hs, prev_bboxes)
File "/lhome/MO3TR/moter_venv/lib/python3.7/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
result = self.forward(*input, **kwargs)
File "/lhome/MO3TR/moter_venv/lib/python3.7/site-packages/mmtrack/models/mot/mo3tr.py", line 490, in forward
prev_hs = torch.cat(([torch.cat([torch.zeros((self.seq_len - len(hs), 1, hs.shape[-1]), device=hs.device), hs]) for hs in prev_hs]), dim=1)
RuntimeError: There were no tensor arguments to this function (e.g., you passed an empty list of Tensors), but no fallback function is registered for schema aten::_cat. This usually means that this function requires a non-empty list of Tensors. Available functions are [CPU, CUDA, QuantizedCPU, BackendSelect, Named, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradPrivateUse1, AutogradPrivateUse2, AutogradPrivateUse3, Tracer, Autocast, Batched, VmapMode].
I looked into the code and found that in this section of mot3tr_tracker.py
# Select with threshold
det_scores = det_cls.sigmoid()[:, 0]
valid_det_idx = det_scores > self.init_track_thr
det_labels = torch.zeros_like(det_scores, dtype=torch.int32)
# Generate Valid Dets
valid_det_bboxes = det_bboxes[valid_det_idx]
ids = torch.arange(len(valid_det_bboxes), device=valid_det_bboxes.device)
valid_det_labels = det_labels[valid_det_idx]
valid_det_hs = det_hs[valid_det_idx]
valid_det_scores = det_scores[valid_det_idx]
valid_det_cls = det_cls[valid_det_idx]
valid_det_bboxes = torch.cat([valid_det_bboxes, valid_det_scores.unsqueeze(-1)], dim=-1)
self.max_track_id = len(valid_det_bboxes) - 1
self.update(ids=ids, bboxes=valid_det_bboxes, labels=valid_det_labels, frame_ids=frame_id, hs=valid_det_hs)
the valid_det_idx is all false. Hence the emtpy prev_hs. Can you have a look into this?
My second question is that is your code intended to reproduce the result? In your paper you trained in two stages, first 300 epochs on 3 datasets then 100 epochs on MOT. But in the config file cfg_mo3tr_nf_git.py the epoch number is 20 and trained only on MOT.
Many thanks.
ModuleNotFoundError: No module named 'mmcv._ext'
Hi. Thanks very much for the good work.
I was trying to run python run/train_track_nf.py and encountered this error
Traceback (most recent call last):
File "run/train_track_nf.py", line 14, in <module>
from mmdet.apis import set_random_seed
File "/lhome/MO3TR/moter_py37_venv/lib/python3.7/site-packages/mmdet/apis/__init__.py", line 2, in <module>
from .inference import (async_inference_detector, inference_detector,
File "/lhome/MO3TR/moter_py37_venv/lib/python3.7/site-packages/mmdet/apis/inference.py", line 7, in <module>
from mmcv.ops import RoIPool
File "/lhome/MO3TR/moter_py37_venv/lib/python3.7/site-packages/mmcv/ops/__init__.py", line 2, in <module>
from .assign_score_withk import assign_score_withk
File "/lhome/MO3TR/moter_py37_venv/lib/python3.7/site-packages/mmcv/ops/assign_score_withk.py", line 6, in <module>
'_ext', ['assign_score_withk_forward', 'assign_score_withk_backward'])
File "/lhome/MO3TR/moter_py37_venv/lib/python3.7/site-packages/mmcv/utils/ext_loader.py", line 13, in load_ext
ext = importlib.import_module('mmcv.' + name)
File "/usr/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
ModuleNotFoundError: No module named 'mmcv._ext'
I have installed the packages and ran bash setup as required.
My specs
- Ubuntu 20.04
- Python 3.7.5
- PyTorch 1.9.1+cu102
How to train the MO3TRTemp andBoxTransformer Module?
hello, I am interesting in your paper especially the MO3TRTemp andBoxTransformer Module. I read your code but I have some questions as follows:
- How to train the MO3TRTemp andBoxTransformer Module? End to End or separate with the whole model?
- How to set the loss function for these two module?
Hope to receive your reply. Thanks!
算力问题
一张rtx3090跑得动吗？
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