Hugging Face's logo

Spaces:
LXT
/
OMG_Seg
Runtime error

App Files Community
4
OMG_Seg
File size: 2,939 Bytes 
b34d1d6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
# dataset settings
from mmcv.transforms import LoadImageFromFile, RandomResize
from mmengine.dataset import DefaultSampler

from mmdet.datasets import AspectRatioBatchSampler
from mmdet.datasets.transforms import LoadPanopticAnnotations, RandomFlip, RandomCrop, PackDetInputs, Resize
from mmdet.evaluation import CocoPanopticMetric

from mmdet.datasets.ade20k import ADE20KPanopticDataset


data_root = 'data/ade/'
backend_args = None
image_size = (1024, 1024)

train_pipeline = [
    dict(
        type=LoadImageFromFile,
        to_float32=True,
        backend_args=backend_args),
    dict(
        type=LoadPanopticAnnotations,
        with_bbox=True,
        with_mask=True,
        with_seg=True,
        backend_args=backend_args),
    dict(type=RandomFlip, prob=0.5),
    dict(
        type=RandomResize,
        resize_type=Resize,
        scale=image_size,
        ratio_range=(0.1, 2.0),
        keep_ratio=True,
    ),
    dict(
        type=RandomCrop,
        crop_size=image_size,
        crop_type='absolute',
        recompute_bbox=True,
        allow_negative_crop=True),
    dict(type=PackDetInputs)
]
train_dataloader = dict(
    batch_size=2,
    num_workers=2,
    persistent_workers=True,
    sampler=dict(type=DefaultSampler, shuffle=True),
    batch_sampler=dict(type=AspectRatioBatchSampler),
    dataset=dict(
        type=ADE20KPanopticDataset,
        data_root=data_root,
        ann_file='ADEChallengeData2016/ade20k_panoptic_train.json',
        data_prefix=dict(img='ADEChallengeData2016/images/training/',
                         seg='ADEChallengeData2016/ade20k_panoptic_train/'),
        filter_cfg=dict(filter_empty_gt=True, min_size=32),
        pipeline=train_pipeline,
        backend_args=backend_args
    )
)

test_pipeline = [
    dict(type=LoadImageFromFile, backend_args=backend_args),
    dict(type=Resize, scale=(2560, 640), keep_ratio=True),
    dict(type=LoadPanopticAnnotations, backend_args=backend_args),
    dict(
        type=PackDetInputs,
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor')
    )
]
val_dataloader = dict(
    batch_size=2,
    num_workers=2,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type=DefaultSampler, shuffle=False),
    dataset=dict(
        type=ADE20KPanopticDataset,
        data_root=data_root,
        ann_file='ADEChallengeData2016/ade20k_panoptic_val.json',
        data_prefix=dict(img='ADEChallengeData2016/images/validation/',
                         seg='ADEChallengeData2016/ade20k_panoptic_val/'),
        test_mode=True,
        pipeline=test_pipeline,
        backend_args=backend_args
    )
)
test_dataloader = val_dataloader

val_evaluator = dict(
    type=CocoPanopticMetric,
    ann_file=data_root + 'ADEChallengeData2016/ade20k_panoptic_val.json',
    seg_prefix=data_root + 'ADEChallengeData2016/ade20k_panoptic_val/',
    backend_args=backend_args
)
test_evaluator = val_evaluator