-
Notifications
You must be signed in to change notification settings - Fork 563
/
Copy pathyolov7_w-p6_syncbn_fast_8x16b-300e_coco.py
182 lines (167 loc) · 6.07 KB
/
yolov7_w-p6_syncbn_fast_8x16b-300e_coco.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
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
_base_ = './yolov7_l_syncbn_fast_8x16b-300e_coco.py'
# ========================modified parameters========================
# -----data related-----
img_scale = (1280, 1280) # height, width
num_classes = 80 # Number of classes for classification
# Config of batch shapes. Only on val
# It means not used if batch_shapes_cfg is None.
batch_shapes_cfg = dict(
img_size=img_scale[
0], # The image scale of padding should be divided by pad_size_divisor
size_divisor=64) # Additional paddings for pixel scale
tta_img_scales = [(1280, 1280), (1024, 1024), (1536, 1536)]
# -----model related-----
# Basic size of multi-scale prior box
anchors = [
[(19, 27), (44, 40), (38, 94)], # P3/8
[(96, 68), (86, 152), (180, 137)], # P4/16
[(140, 301), (303, 264), (238, 542)], # P5/32
[(436, 615), (739, 380), (925, 792)] # P6/64
]
strides = [8, 16, 32, 64] # Strides of multi-scale prior box
num_det_layers = 4 # # The number of model output scales
norm_cfg = dict(type='BN', momentum=0.03, eps=0.001)
# Data augmentation
max_translate_ratio = 0.2 # YOLOv5RandomAffine
scaling_ratio_range = (0.1, 2.0) # YOLOv5RandomAffine
mixup_prob = 0.15 # YOLOv5MixUp
randchoice_mosaic_prob = [0.8, 0.2]
mixup_alpha = 8.0 # YOLOv5MixUp
mixup_beta = 8.0 # YOLOv5MixUp
# -----train val related-----
loss_cls_weight = 0.3
loss_bbox_weight = 0.05
loss_obj_weight = 0.7
obj_level_weights = [4.0, 1.0, 0.25, 0.06]
simota_candidate_topk = 20
# The only difference between P6 and P5 in terms of
# hyperparameters is lr_factor
lr_factor = 0.2
# ===============================Unmodified in most cases====================
pre_transform = _base_.pre_transform
model = dict(
backbone=dict(arch='W', out_indices=(2, 3, 4, 5)),
neck=dict(
in_channels=[256, 512, 768, 1024],
out_channels=[128, 256, 384, 512],
use_maxpool_in_downsample=False,
use_repconv_outs=False),
bbox_head=dict(
head_module=dict(
type='YOLOv7p6HeadModule',
in_channels=[128, 256, 384, 512],
featmap_strides=strides,
norm_cfg=norm_cfg,
act_cfg=dict(type='SiLU', inplace=True)),
prior_generator=dict(base_sizes=anchors, strides=strides),
simota_candidate_topk=simota_candidate_topk, # note
# scaled based on number of detection layers
loss_cls=dict(loss_weight=loss_cls_weight *
(num_classes / 80 * 3 / num_det_layers)),
loss_bbox=dict(loss_weight=loss_bbox_weight * (3 / num_det_layers)),
loss_obj=dict(loss_weight=loss_obj_weight *
((img_scale[0] / 640)**2 * 3 / num_det_layers)),
obj_level_weights=obj_level_weights))
mosiac4_pipeline = [
dict(
type='Mosaic',
img_scale=img_scale,
pad_val=114.0,
pre_transform=pre_transform),
dict(
type='YOLOv5RandomAffine',
max_rotate_degree=0.0,
max_shear_degree=0.0,
max_translate_ratio=max_translate_ratio, # note
scaling_ratio_range=scaling_ratio_range, # note
# img_scale is (width, height)
border=(-img_scale[0] // 2, -img_scale[1] // 2),
border_val=(114, 114, 114)),
]
mosiac9_pipeline = [
dict(
type='Mosaic9',
img_scale=img_scale,
pad_val=114.0,
pre_transform=pre_transform),
dict(
type='YOLOv5RandomAffine',
max_rotate_degree=0.0,
max_shear_degree=0.0,
max_translate_ratio=max_translate_ratio, # note
scaling_ratio_range=scaling_ratio_range, # note
# img_scale is (width, height)
border=(-img_scale[0] // 2, -img_scale[1] // 2),
border_val=(114, 114, 114)),
]
randchoice_mosaic_pipeline = dict(
type='RandomChoice',
transforms=[mosiac4_pipeline, mosiac9_pipeline],
prob=randchoice_mosaic_prob)
train_pipeline = [
*pre_transform,
randchoice_mosaic_pipeline,
dict(
type='YOLOv5MixUp',
alpha=mixup_alpha, # note
beta=mixup_beta, # note
prob=mixup_prob,
pre_transform=[*pre_transform, randchoice_mosaic_pipeline]),
dict(type='YOLOv5HSVRandomAug'),
dict(type='mmdet.RandomFlip', prob=0.5),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip',
'flip_direction'))
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='YOLOv5KeepRatioResize', scale=img_scale),
dict(
type='LetterResize',
scale=img_scale,
allow_scale_up=False,
pad_val=dict(img=114)),
dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'pad_param'))
]
val_dataloader = dict(
dataset=dict(pipeline=test_pipeline, batch_shapes_cfg=batch_shapes_cfg))
test_dataloader = val_dataloader
default_hooks = dict(param_scheduler=dict(lr_factor=lr_factor))
# Config for Test Time Augmentation. (TTA)
_multiscale_resize_transforms = [
dict(
type='Compose',
transforms=[
dict(type='YOLOv5KeepRatioResize', scale=s),
dict(
type='LetterResize',
scale=s,
allow_scale_up=False,
pad_val=dict(img=114))
]) for s in tta_img_scales
]
tta_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(
type='TestTimeAug',
transforms=[
_multiscale_resize_transforms,
[
dict(type='mmdet.RandomFlip', prob=1.),
dict(type='mmdet.RandomFlip', prob=0.)
], [dict(type='mmdet.LoadAnnotations', with_bbox=True)],
[
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'pad_param', 'flip',
'flip_direction'))
]
])
]