-
Notifications
You must be signed in to change notification settings - Fork 2.6k
/
maskformer_r50-d32_8xb2-160k_ade20k-512x512.py
141 lines (140 loc) · 4.51 KB
/
maskformer_r50-d32_8xb2-160k_ade20k-512x512.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
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
_base_ = [
'../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
crop_size = (512, 512)
data_preprocessor = dict(
type='SegDataPreProcessor',
size=crop_size,
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_val=0,
seg_pad_val=255)
# model_cfg
num_classes = 150
model = dict(
type='EncoderDecoder',
data_preprocessor=data_preprocessor,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 1, 1),
strides=(1, 2, 2, 2),
norm_cfg=norm_cfg,
norm_eval=True,
style='pytorch',
contract_dilation=True,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
decode_head=dict(
type='MaskFormerHead',
in_channels=[256, 512, 1024,
2048], # input channels of pixel_decoder modules
feat_channels=256,
in_index=[0, 1, 2, 3],
num_classes=150,
out_channels=256,
num_queries=100,
pixel_decoder=dict(
type='mmdet.PixelDecoder',
norm_cfg=dict(type='GN', num_groups=32),
act_cfg=dict(type='ReLU')),
enforce_decoder_input_project=False,
positional_encoding=dict( # SinePositionalEncoding
num_feats=128, normalize=True),
transformer_decoder=dict( # DetrTransformerDecoder
return_intermediate=True,
num_layers=6,
layer_cfg=dict( # DetrTransformerDecoderLayer
self_attn_cfg=dict( # MultiheadAttention
embed_dims=256,
num_heads=8,
attn_drop=0.1,
proj_drop=0.1,
dropout_layer=None,
batch_first=True),
cross_attn_cfg=dict( # MultiheadAttention
embed_dims=256,
num_heads=8,
attn_drop=0.1,
proj_drop=0.1,
dropout_layer=None,
batch_first=True),
ffn_cfg=dict(
embed_dims=256,
feedforward_channels=2048,
num_fcs=2,
act_cfg=dict(type='ReLU', inplace=True),
ffn_drop=0.1,
dropout_layer=None,
add_identity=True)),
init_cfg=None),
loss_cls=dict(
type='mmdet.CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0,
reduction='mean',
class_weight=[1.0] * num_classes + [0.1]),
loss_mask=dict(
type='mmdet.FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
reduction='mean',
loss_weight=20.0),
loss_dice=dict(
type='mmdet.DiceLoss',
use_sigmoid=True,
activate=True,
reduction='mean',
naive_dice=True,
eps=1.0,
loss_weight=1.0),
train_cfg=dict(
assigner=dict(
type='mmdet.HungarianAssigner',
match_costs=[
dict(type='mmdet.ClassificationCost', weight=1.0),
dict(
type='mmdet.FocalLossCost',
weight=20.0,
binary_input=True),
dict(
type='mmdet.DiceCost',
weight=1.0,
pred_act=True,
eps=1.0)
]),
sampler=dict(type='mmdet.MaskPseudoSampler'))),
# training and testing settings
train_cfg=dict(),
test_cfg=dict(mode='whole'),
)
# optimizer
optimizer = dict(
type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.0001)
optim_wrapper = dict(
_delete_=True,
type='OptimWrapper',
optimizer=optimizer,
clip_grad=dict(max_norm=0.01, norm_type=2),
paramwise_cfg=dict(custom_keys={
'backbone': dict(lr_mult=0.1),
}))
# learning policy
param_scheduler = [
dict(
type='PolyLR',
eta_min=0,
power=0.9,
begin=0,
end=160000,
by_epoch=False)
]
# In MaskFormer implementation we use batch size 2 per GPU as default
train_dataloader = dict(batch_size=2, num_workers=2)
val_dataloader = dict(batch_size=1, num_workers=4)
test_dataloader = val_dataloader