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cem_swinS_lvis.py
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cem_swinS_lvis.py
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# model settings
_base_ = '../_base_/qdtrack_faster_rcnn_r50_fpn.py'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa
model = dict(
type='TETer',
freeze_detector=False,
backbone=dict(
_delete_=True,
type='SwinTransformer',
embed_dims=96,
depths=[2, 2, 18, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.2,
patch_norm=True,
out_indices=(0, 1, 2, 3),
with_cp=False,
convert_weights=True,
init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
neck=dict(in_channels=[96, 192, 384, 768]),
roi_head=dict(
type='TETerRoIHead',
bbox_head=dict(num_classes=1230),
cem_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]),
cem_head=dict(
type='ClsExemplarHead',
num_convs=4,
num_fcs=3,
embed_channels=1230,
norm_cfg=dict(type='GN', num_groups=32),
loss_track=dict(type='UnbiasedSupConLoss',
temperature=0.07,
contrast_mode='all',
pos_normalize=True,
loss_weight=0.25)
, softmax_temp=-1),
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))
),
tracker=dict(
type='TETerTAO',
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,
),
train_cfg=dict(
cem=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=1,
neg_pos_ub=0,
add_gt_as_proposals=True,
pos_sampler=dict(type='InstanceBalancedPosSampler'),
neg_sampler=dict(type='RandomSampler'))
)
),
test_cfg=dict(
rcnn=dict(
score_thr=0.0001,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=300)
)
)
# dataset settings
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'),
# comment above line and comment out the lines below if use hdf5 file.
# dict(
# type='LoadMultiImagesFromFile',
# file_client_args=dict(
# img_db_path='data/lvis/train_imgs.hdf5',
# backend='hdf5',
# type='lvis')),
dict(type='SeqLoadAnnotations', with_bbox=True, with_ins_id=True),
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='SeqRandomFlip', share_params=False, flip_ratio=0.5),
dict(type='SeqNormalize', **img_norm_cfg),
dict(type='SeqPad', size_divisor=32),
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'])
])
]
## dataset settings
dataset_type = 'TaoDataset'
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
_delete_=True,
type='ClassBalancedDataset',
oversample_thr=1e-3,
dataset=dict(
type=dataset_type,
classes='data/lvis/annotations/lvis_classes.txt',
load_as_video=False,
ann_file='data/lvis/annotations/lvisv0.5+coco_train.json',
img_prefix='data/lvis/train2017/',
key_img_sampler=dict(interval=1),
ref_img_sampler=dict(num_ref_imgs=1, scope=1, method='uniform'),
pipeline=train_pipeline)
),
val=dict(
type=dataset_type,
classes='data/lvis/annotations/lvis_classes.txt',
ann_file='data/tao/annotations/validation_ours.json',
img_prefix='data/tao/frames/',
ref_img_sampler=None,
pipeline=test_pipeline),
test=dict(
type=dataset_type,
classes='data/lvis/annotations/lvis_classes.txt',
ann_file='data/tao/annotations/validation_ours.json',
img_prefix='data/tao/frames/',
ref_img_sampler=None,
pipeline=test_pipeline)
)
# optimizer
optimizer = dict(
# _delete_=True,
type='AdamW',
lr=0.0001,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg=dict(
custom_keys={
'absolute_pos_embed': dict(decay_mult=0.),
'relative_position_bias_table': dict(decay_mult=0.),
'norm': dict(decay_mult=0.)
}))
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1000,
warmup_ratio=0.001,
step=[27, 33])
runner = dict(type='EpochBasedRunner', max_epochs=36)
# checkpoint saving
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
])
# yapf:enable
# runtime settings
total_epochs = 36
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
evaluation = dict(metric=['bbox'], start=2, interval=2)