forked from open-mmlab/OpenPCDet
-
Notifications
You must be signed in to change notification settings - Fork 0
/
pointrcnn.yaml
160 lines (133 loc) · 4.16 KB
/
pointrcnn.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
159
160
CLASS_NAMES: ['Car', 'Pedestrian', 'Cyclist']
DATA_CONFIG:
_BASE_CONFIG_: cfgs/dataset_configs/kitti_dataset.yaml
DATA_PROCESSOR:
- NAME: mask_points_and_boxes_outside_range
REMOVE_OUTSIDE_BOXES: True
- NAME: sample_points
NUM_POINTS: {
'train': 16384,
'test': 16384
}
- NAME: shuffle_points
SHUFFLE_ENABLED: {
'train': True,
'test': False
}
MODEL:
NAME: PointRCNN
BACKBONE_3D:
NAME: PointNet2MSG
SA_CONFIG:
NPOINTS: [4096, 1024, 256, 64]
RADIUS: [[0.1, 0.5], [0.5, 1.0], [1.0, 2.0], [2.0, 4.0]]
NSAMPLE: [[16, 32], [16, 32], [16, 32], [16, 32]]
MLPS: [[[16, 16, 32], [32, 32, 64]],
[[64, 64, 128], [64, 96, 128]],
[[128, 196, 256], [128, 196, 256]],
[[256, 256, 512], [256, 384, 512]]]
FP_MLPS: [[128, 128], [256, 256], [512, 512], [512, 512]]
POINT_HEAD:
NAME: PointHeadBox
CLS_FC: [256, 256]
REG_FC: [256, 256]
CLASS_AGNOSTIC: False
USE_POINT_FEATURES_BEFORE_FUSION: False
TARGET_CONFIG:
GT_EXTRA_WIDTH: [0.2, 0.2, 0.2]
BOX_CODER: PointResidualCoder
BOX_CODER_CONFIG: {
'use_mean_size': True,
'mean_size': [
[3.9, 1.6, 1.56],
[0.8, 0.6, 1.73],
[1.76, 0.6, 1.73]
]
}
LOSS_CONFIG:
LOSS_REG: WeightedSmoothL1Loss
LOSS_WEIGHTS: {
'point_cls_weight': 1.0,
'point_box_weight': 1.0,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
}
ROI_HEAD:
NAME: PointRCNNHead
CLASS_AGNOSTIC: True
ROI_POINT_POOL:
POOL_EXTRA_WIDTH: [0.0, 0.0, 0.0]
NUM_SAMPLED_POINTS: 512
DEPTH_NORMALIZER: 70.0
XYZ_UP_LAYER: [128, 128]
CLS_FC: [256, 256]
REG_FC: [256, 256]
DP_RATIO: 0.0
USE_BN: False
SA_CONFIG:
NPOINTS: [128, 32, -1]
RADIUS: [0.2, 0.4, 100]
NSAMPLE: [16, 16, 16]
MLPS: [[128, 128, 128],
[128, 128, 256],
[256, 256, 512]]
NMS_CONFIG:
TRAIN:
NMS_TYPE: nms_gpu
MULTI_CLASSES_NMS: False
NMS_PRE_MAXSIZE: 9000
NMS_POST_MAXSIZE: 512
NMS_THRESH: 0.8
TEST:
NMS_TYPE: nms_gpu
MULTI_CLASSES_NMS: False
NMS_PRE_MAXSIZE: 9000
NMS_POST_MAXSIZE: 100
NMS_THRESH: 0.85
TARGET_CONFIG:
BOX_CODER: ResidualCoder
ROI_PER_IMAGE: 128
FG_RATIO: 0.5
SAMPLE_ROI_BY_EACH_CLASS: True
CLS_SCORE_TYPE: cls
CLS_FG_THRESH: 0.6
CLS_BG_THRESH: 0.45
CLS_BG_THRESH_LO: 0.1
HARD_BG_RATIO: 0.8
REG_FG_THRESH: 0.55
LOSS_CONFIG:
CLS_LOSS: BinaryCrossEntropy
REG_LOSS: smooth-l1
CORNER_LOSS_REGULARIZATION: True
LOSS_WEIGHTS: {
'rcnn_cls_weight': 1.0,
'rcnn_reg_weight': 1.0,
'rcnn_corner_weight': 1.0,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
}
POST_PROCESSING:
RECALL_THRESH_LIST: [0.3, 0.5, 0.7]
SCORE_THRESH: 0.1
OUTPUT_RAW_SCORE: False
EVAL_METRIC: kitti
NMS_CONFIG:
MULTI_CLASSES_NMS: False
NMS_TYPE: nms_gpu
NMS_THRESH: 0.1
NMS_PRE_MAXSIZE: 4096
NMS_POST_MAXSIZE: 500
OPTIMIZATION:
BATCH_SIZE_PER_GPU: 2
NUM_EPOCHS: 80
OPTIMIZER: adam_onecycle
LR: 0.01
WEIGHT_DECAY: 0.01
MOMENTUM: 0.9
MOMS: [0.95, 0.85]
PCT_START: 0.4
DIV_FACTOR: 10
DECAY_STEP_LIST: [35, 45]
LR_DECAY: 0.1
LR_CLIP: 0.0000001
LR_WARMUP: False
WARMUP_EPOCH: 1
GRAD_NORM_CLIP: 10