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ovtrack_r50.py
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model = dict(
type='OVTrack',
freeze_detector=True,
method='ovtrack-teta',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=True,
style='caffe',
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')
),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
norm_cfg=dict(type='SyncBN', requires_grad=True)),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
roi_head=dict(
type='OVTrackRoIHead',
finetune_track=True,
prompt_path='saved_models/pretrained_models/detpro_prompt.pt',
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=dict(
type='Shared4Conv1FCCliPBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=1203,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0),
ensemble=True,
with_cls=False,
norm_cfg=dict(type='SyncBN', requires_grad=True)),
track_head=dict(
type='QuasiDenseEmbedHead',
num_convs=4,
num_fcs=1,
embed_channels=256,
norm_cfg=dict(type='GN', num_groups=32),
loss_track=dict(type='MultiPosCrossEntropyLoss', loss_weight=0.25,
version="unbiased"),
loss_track_aux=dict(
type='L2Loss',
neg_pos_ub=3,
pos_margin=0,
neg_margin=0.1,
hard_mining=True,
loss_weight=1.0)),
),
train_cfg=dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
match_low_quality=True,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False),
embed=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.5,
match_low_quality=False,
ignore_iof_thr=-1),
sampler=dict(
type='CombinedSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=3,
add_gt_as_proposals=True,
pos_sampler=dict(type='InstanceBalancedPosSampler'),
neg_sampler=dict(type='RandomSampler')))
),
tracker=dict(
type='OVTracker',
init_score_thr=0.0001,
obj_score_thr=0.0001,
match_score_thr=0.5,
memo_frames=10,
momentum_embed=0.8,
momentum_obj_score=0.5,
match_metric='bisoftmax',
match_with_cosine=True,
contrastive_thr=0.5,
),
test_cfg = dict(
rpn=dict(
nms_pre=1000,
max_per_img=1000,
nms=dict(type='nms', iou_threshold=0.7),
min_bbox_size=0),
rcnn=dict(
score_thr=0.0001,
nms=dict(type='nms', iou_threshold=0.5, class_agnostic=True, split_thr=1000000),
max_per_img=50)
)
)
dataset_type = 'TaoDataset'
data_root = 'data/tao/'
img_scale = (800, 1333)
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='SeqMosaic', img_scale=img_scale, pad_val=114.0),
dict(
type='SeqRandomAffine'),
dict(type='SeqYOLOXHSVRandomAug'),
dict(type='SeqRandomFlip',share_params=False, flip_ratio=0.5),
dict(
type='SeqResize',
img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
share_params=False,
multiscale_mode='value',
keep_ratio=True),
dict(type='SeqNormalize', **img_norm_cfg),
dict(
type='SeqPad',
size_divisor=32,
# If the image is three-channel, the pad value needs
# to be set separately for each channel
),
dict(type='SeqFilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
dict(type='SeqDefaultFormatBundle'),
dict(
type='SeqCollect',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_match_indices'],
ref_prefix='ref'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
# dict(type='LoadImageFromFile',
# file_client_args=dict(
# img_db_path='data/tao/tao_val_imgs.hdf5',
# backend='hdf5',
# type='tao')),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='VideoCollect', keys=['img'])
])
]
## datasets settings
dataset_type = 'TaoDataset'
data = dict(
samples_per_gpu=2,
workers_per_gpu=3,
persistent_workers=True,
train=dict(
type='SeqMultiImageMixDataset',
dataset=dict(
type='ClassBalancedDataset',
oversample_thr=1e-3,
dataset=dict(
type=dataset_type,
classes='data/lvis/annotations/lvis_classes_v1.txt',
load_as_video=True,
ann_file='data/tao/annotations/ovtrack/ovtrack_lvis_generated_image_pairs_repeat_1.json',
key_img_sampler=dict(interval=1),
ref_img_sampler=dict(num_ref_imgs=1, scope=1, method='uniform', pesudo=True),
pipeline=[
dict(
type='LoadMultiImagesFromFile',
file_client_args=dict(
img_db_path='data/tao/ovtrack/ovtrack_lvis_generated_image_pairs_repeat_1.h5',
backend='hdf5',
type='lvis')),
dict(type='SeqLoadAnnotations', with_bbox=True, with_ins_id=True),
])
),
pipeline=train_pipeline),
val=dict(
type=dataset_type,
classes='data/lvis/annotations/lvis_classes_v1.txt',
ann_file='data/tao/annotations/validation_ours_v1.json',
img_prefix='data/tao/frames/',
ref_img_sampler=None,
pipeline=test_pipeline),
test=dict(
type=dataset_type,
classes='data/lvis/annotations/lvis_classes_v1.txt',
ann_file='data/tao/annotations/validation_ours_v1.json',
img_prefix='data/tao/frames/',
ref_img_sampler=None,
pipeline=test_pipeline)
)
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1000,
warmup_ratio=1.0 / 1000,
step=[3, 5])
total_epochs = 6
load_from = 'saved_models/pretrained_models/ovtrack_clip_distillation.pth'
evaluation = dict(metric=['track'], start=6, interval=1, resfile_path='/scratch/tmp/')
checkpoint_config = dict(interval=1, create_symlink=False)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
resume_from = None
workflow = [('train', 1)]
gpu_ids = range(0, 8)
find_unused_parameters = True