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swin_l_carafe_simota_focal_giou_iouhead_tower_dcn_coco_384_uvo_finetune.py
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swin_l_carafe_simota_focal_giou_iouhead_tower_dcn_coco_384_uvo_finetune.py
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tta_flip = True
tta_scale = [(667, 400), (833, 500), (1000, 600), (1067, 640), (1167, 700),
(1333, 800), (1500, 900), (1667, 1000), (1833, 1100),
(2000, 1200), (2167, 1300), (2333, 1400), (3000, 1800)]
scale_ranges = [(96, 10000), (96, 10000), (64, 10000), (64, 10000),
(64, 10000), (0, 10000), (0, 10000), (0, 10000), (0, 256),
(0, 256), (0, 192), (0, 192), (0, 96)]
_base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py'
pretrained = 'PATH/TO/YOUR/swin_large_patch4_window12_384_22k.pth'
model = dict(
backbone=dict(
_delete_=True,
type='SwinTransformer',
embed_dims=192,
depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48],
window_size=12,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.3,
patch_norm=True,
out_indices=(0, 1, 2, 3),
with_cp=False,
convert_weights=True,
load_like_mmseg=True,
init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
neck=dict(
type='FPN_CARAFE',
in_channels=[192, 384, 768, 1536],
out_channels=256,
num_outs=5,
start_level=0,
end_level=-1,
norm_cfg=None,
act_cfg=None,
order=('conv', 'norm', 'act'),
upsample_cfg=dict(
type='carafe',
up_kernel=5,
up_group=1,
encoder_kernel=3,
encoder_dilation=1,
compressed_channels=64)),
rpn_head=dict(
_delete_=True,
type='UVORPNHead',
num_stages=2,
stages=[
dict(
type='UVOStageCascadeRPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[1.0],
strides=[4, 8, 16, 32, 64]),
adapt_cfg=dict(type='dilation', dilation=3),
dcn_on_last_conv=True,
bridged_feature=True,
sampling=False,
with_cls=False,
cls_head='cls_head',
reg_decoded_bbox=False,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=(.0, .0, .0, .0),
target_stds=(0.1, 0.1, 0.2, 0.2)),
use_tower_convs=True,
loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
),
dict(
type='UVOStageCascadeRPNHead',
in_channels=256,
feat_channels=256,
adapt_cfg=dict(type='offset'),
dcn_on_last_conv=True,
bridged_feature=False,
sampling=False,
with_cls=True,
cls_head='cls_head',
reg_decoded_bbox=False,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=(.0, .0, .0, .0),
target_stds=(0.05, 0.05, 0.1, 0.1)),
use_tower_convs=True,
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
)
]),
train_cfg=dict(rpn=[
dict(
assigner=dict(type='RPN_SimOTAAssigner', center_radius=.25),
aux_assigner=dict(type='RPN_SimOTAAssigner', center_radius=.25, candidate_topk=20),
#assigner=dict(type='ATSSAssigner', topk=9),
#aux_assigner=dict(type='ATSSAssigner', topk=21),
aux_sampler=None,
allowed_border=-1,
pos_weight=-1,
debug=False),
dict(
assigner=dict(type='RPN_SimOTAAssigner', center_radius=.25),
aux_assigner=dict(type='RPN_SimOTAAssigner', center_radius=.25, candidate_topk=20),
#assigner=dict(type='ATSSAssigner', topk=9),
#aux_assigner=dict(type='ATSSAssigner', topk=21),
aux_sampler=None,
allowed_border=-1,
pos_weight=-1,
debug=False),
]),
test_cfg=dict(
rpn=dict(
scale_ranges=scale_ranges,
score_thr=0.00000001,
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.8),
min_bbox_size=0))
)
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_label=False),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=tta_scale,
flip=tta_flip,
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='Collect', keys=['img']),
])
]
dataset_type = 'CocoDataset'
data_root = 'data/uvo/'
uvo_frame_train=dict(
type='RepeatDataset',
times=3,
dataset=dict(
type=dataset_type,
data_root='data/uvo/',
ann_file='annotations/UVO_frame_train.json',
img_prefix='uvo_videos_sparse_frames/',
pipeline=train_pipeline,
)
)
uvo_frame_val=dict(
type='RepeatDataset',
times=3,
dataset=dict(
type=dataset_type,
data_root='data/uvo/',
ann_file='annotations/UVO_frame_val.json',
img_prefix='uvo_videos_sparse_frames/',
pipeline=train_pipeline,
)
)
uvo_dense_train=dict(
type='RepeatDataset',
times=1,
dataset=dict(
type=dataset_type,
data_root='data/uvo/',
ann_file='annotations/UVO_dense_train_coco_format.json',
img_prefix='uvo_videos_dense_frames/',
pipeline=train_pipeline,
)
)
uvo_dense_val=dict(
type='RepeatDataset',
times=1,
dataset=dict(
type=dataset_type,
data_root='data/uvo/',
ann_file='annotations/UVO_dense_val_coco_format.json',
img_prefix='uvo_videos_dense_frames/',
pipeline=train_pipeline,
)
)
dataset_type = 'CocoDataset'
data_root = 'data/uvo/'
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=[
uvo_frame_train,
uvo_frame_val,
uvo_dense_train,
uvo_dense_val,
],
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/UVO_frame_val.json',
img_prefix=data_root + 'uvo_videos_sparse_frames/',
),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/UVO_frame_val.json',
img_prefix=data_root + 'uvo_videos_sparse_frames/',
))
optimizer = dict(
_delete_=True,
type='AdamW',
lr=0.00001,
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.)
}))
load_from = 'PATH/TO/YOUR/PRETRAINED/COCO/MODEL'
lr_config = dict(warmup_iters=1000, step=[3, 5])
runner = dict(max_epochs=6)