-
Notifications
You must be signed in to change notification settings - Fork 132
/
mask_rcnn_vssm_fpn_coco_small_ms_3x.py
99 lines (91 loc) · 3.01 KB
/
mask_rcnn_vssm_fpn_coco_small_ms_3x.py
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
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
_base_ = [
'../swin/mask-rcnn_swin-t-p4-w7_fpn_1x_coco.py'
]
model = dict(
backbone=dict(
type='MM_VSSM',
out_indices=(0, 1, 2, 3),
pretrained="",
# copied from classification/configs/vssm/vssm_small_224.yaml
dims=96,
depths=(2, 2, 15, 2),
ssm_d_state=1,
ssm_dt_rank="auto",
ssm_ratio=2.0,
ssm_conv=3,
ssm_conv_bias=False,
forward_type="v05_noz", # v3_noz
mlp_ratio=4.0,
downsample_version="v3",
patchembed_version="v2",
drop_path_rate=0.3,
norm_layer="ln2d",
),
)
# train_dataloader = dict(batch_size=2) # as gpus=8
# augmentation strategy originates from DETR / Sparse RCNN
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='RandomFlip', prob=0.5),
dict(
type='RandomChoice',
transforms=[[
dict(
type='RandomChoiceResize',
scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
(608, 1333), (640, 1333), (672, 1333), (704, 1333),
(736, 1333), (768, 1333), (800, 1333)],
keep_ratio=True)
],
[
dict(
type='RandomChoiceResize',
scales=[(400, 1333), (500, 1333), (600, 1333)],
keep_ratio=True),
dict(
type='RandomCrop',
crop_type='absolute_range',
crop_size=(384, 600),
allow_negative_crop=True),
dict(
type='RandomChoiceResize',
scales=[(480, 1333), (512, 1333), (544, 1333),
(576, 1333), (608, 1333), (640, 1333),
(672, 1333), (704, 1333), (736, 1333),
(768, 1333), (800, 1333)],
keep_ratio=True)
]]),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
max_epochs = 36
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
end=1000),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[27, 33],
gamma=0.1)
]
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
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=dict(
_delete_=True,
type='AdamW',
lr=0.0001,
betas=(0.9, 0.999),
weight_decay=0.05))