forked from mindspore-lab/mindocr
-
Notifications
You must be signed in to change notification settings - Fork 0
/
db_mobilenetv3_ppocrv3.yaml
158 lines (147 loc) · 3.96 KB
/
db_mobilenetv3_ppocrv3.yaml
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
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
system:
mode: 0 # 0 for graph mode, 1 for pynative mode in MindSpore
distribute: True
amp_level: 'O0'
seed: 42
log_interval: 10
val_while_train: True
drop_overflow_update: False
model:
type: det
transform: null
backbone:
name: det_mobilenet_v3_enhance
architecture: large
alpha: 0.5
disable_se: True
pretrained: False
neck:
name: RSEFPN
out_channels: 96
shortcut: True
head:
name: DBHeadEnhance
k: 50
bias: False
adaptive: True
pretrained: https://download-mindspore.osinfra.cn/toolkits/mindocr/dbnet/dbnet_mobilenetv3_ppocrv3-70d6018f.ckpt
postprocess:
name: DBPostprocess
box_type: quad # whether to output a polygon or a box
binary_thresh: 0.3 # binarization threshold
box_thresh: 0.9 # box score threshold 0.9
max_candidates: 1000
expand_ratio: 1.5 # coefficient for expanding predictions
metric:
name: DetMetric
main_indicator: f-score
loss:
name: DBLoss
eps: 1.0e-6
l1_scale: 10
bce_scale: 5
bce_replace: diceloss
scheduler:
scheduler: warmup_cosine_decay
lr: 0.001
min_lr: 0.0
num_epochs: 500
warmup_epochs: 2
decay_epochs: 498
optimizer:
opt: Adam
beta1: 0.9
beta2: 0.999
weight_decay: 5.0e-05
# only used for mixed precision training
loss_scaler:
type: dynamic
loss_scale: 512
scale_factor: 2
scale_window: 1000
train:
ckpt_save_dir: ./tmp_det
dataset_sink_mode: False
dataset:
type: DetDataset
dataset_root: dir/to/data/
data_dir: training/
label_file: train_det.txt
sample_ratio: 1.0
transform_pipeline:
- DecodeImage:
img_mode: RGB
to_float32: False
- DetLabelEncode:
- RandomColorAdjust:
brightness: 0.1255 # 32.0 / 255
saturation: 0.5
- RandomHorizontalFlip:
p: 0.5
- RandomRotate:
degrees: [ -10, 10 ]
expand_canvas: False
p: 1.0
- RandomScale:
scale_range: [ 0.5, 3.0 ]
p: 1.0
- RandomCropWithBBox:
max_tries: 10
min_crop_ratio: 0.1
crop_size: [ 960, 960 ]
p: 1.0
- ValidatePolygons:
- ShrinkBinaryMap:
min_text_size: 8
shrink_ratio: 0.4
- BorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- NormalizeImage:
bgr_to_rgb: False
is_hwc: True
mean: imagenet
std: imagenet
- ToCHWImage:
# the order of the dataloader list, matching the network input and the input labels for the loss function, and optional data for debug/visualize
output_columns: [ 'image', 'binary_map', 'mask', 'thresh_map', 'thresh_mask']
net_input_column_index: [0] # input indices for network forward func in output_columns
label_column_index: [1, 2, 3, 4] # input indices marked as label
loader:
shuffle: True
batch_size: 8
drop_remainder: False
num_workers: 10
eval:
ckpt_load_path: tmp_det/best.ckpt
dataset_sink_mode: False
dataset:
type: DetDataset
dataset_root: dir/to/data/
data_dir: validation/
label_file: val_det.txt
sample_ratio: 1.0
transform_pipeline:
- DecodeImage:
img_mode: RGB
to_float32: False
- DetLabelEncode:
- DetResize: # GridResize 32
limit_type: 'min'
limit_side_len: 736
- NormalizeImage:
bgr_to_rgb: True
is_hwc: True
mean: imagenet
std: imagenet
- ToCHWImage:
# the order of the dataloader list, matching the network input and the labels for evaluation
output_columns: [ 'image', 'polys', 'ignore_tags', 'shape_list' ]
net_input_column_index: [0] # input indices for network forward func in output_columns
label_column_index: [1, 2] # input indices marked as label
loader:
shuffle: False
batch_size: 1 # TODO: due to dynamic shape of polygons (num of boxes varies), BS has to be 1
drop_remainder: False
num_workers: 3