-
Notifications
You must be signed in to change notification settings - Fork 3
/
S3DIS.sh
180 lines (174 loc) · 7.5 KB
/
S3DIS.sh
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
# S3DIS experiment scripts
# We provide 0.1% and 0.01% weakly setting experiment scripts
# 0.1% baseline
CUDA_VISIBLE_DEVICES=1 python launch.py ddp_train.py --config config/default.yaml \
GENERAL.exp_name 1e-3_percentage_baseline \
TRAINER.name TwoStreamTrainer \
MODEL.out_channels 13 \
DATA.name StanfordDataLoader \
DATA.dataset StanfordArea5Dataset \
DATA.voxel_size 0.05 \
DATA.batch_size 2 \
DATA.train_limit_numpoints 1000000 \
OPTIMIZER.lr 0.01 \
OPTIMIZER.weight_decay 0.001 \
SCHEDULER.name PolyLR \
TRAINER.epochs 180 \
EVALUATOR.iou_num_class 13 \
DATA.stanford3d_path ${YOUR_PATH_TO}/stanford_fully_supervised_preprocessed \
DATA.stanford3d_sampled_inds ${YOUR_PATH_TO}/stanford_fully_supervised_preprocessed/points/percentage0.001evenc \
DATA.sparse_label False \
DATA.two_stream True \
MODEL.two_stream_model_apply True \
TRAINER.two_stream_feats_key semantic_scores \
TRAINER.two_stream_loss_mode js_divergence_v2 \
TRAINER.two_stream_seg_both True \
TRAINER.two_stream_loss_weight 0.0 \
AUGMENTATION.use_color_jitter False
# 0.1% consistency baseline, consis weight 1
CUDA_VISIBLE_DEVICES=0 python launch.py ddp_train.py --config config/default.yaml \
GENERAL.exp_name 1e-3_percentage_consis_weight1 \
TRAINER.name TwoStreamTrainer \
MODEL.out_channels 13 \
DATA.name StanfordDataLoader \
DATA.dataset StanfordArea5Dataset \
DATA.voxel_size 0.05 \
DATA.batch_size 2 \
DATA.train_limit_numpoints 1000000 \
OPTIMIZER.lr 0.01 \
OPTIMIZER.weight_decay 0.001 \
SCHEDULER.name PolyLR \
TRAINER.epochs 180 \
EVALUATOR.iou_num_class 13 \
DATA.stanford3d_path ${YOUR_PATH_TO}/stanford_fully_supervised_preprocessed \
DATA.stanford3d_sampled_inds ${YOUR_PATH_TO}/stanford_fully_supervised_preprocessed/points/percentage0.001evenc \
DATA.sparse_label False \
DATA.two_stream True \
MODEL.two_stream_model_apply True \
TRAINER.two_stream_feats_key semantic_scores \
TRAINER.two_stream_loss_mode js_divergence_v2 \
TRAINER.two_stream_seg_both True \
TRAINER.two_stream_loss_weight 1.0 \
AUGMENTATION.use_color_jitter False
# 0.1% CPCM, consis weight 2, mask_mode grid, grid_size 4, mask two stream, mask weight 5
CUDA_VISIBLE_DEVICES=0 python launch.py ddp_train.py --config config/default.yaml \
GENERAL.exp_name 1e-3_percentage_consis_weight2_maskGrid075GridSize4_weight5 \
TRAINER.name TwoStreamTrainer \
MODEL.out_channels 13 \
DATA.name StanfordDataLoader \
DATA.dataset StanfordArea5Dataset \
DATA.voxel_size 0.05 \
DATA.batch_size 2 \
DATA.train_limit_numpoints 1000000 \
OPTIMIZER.lr 0.01 \
OPTIMIZER.weight_decay 0.001 \
SCHEDULER.name PolyLR \
TRAINER.epochs 180 \
EVALUATOR.iou_num_class 13 \
DATA.stanford3d_path ${YOUR_PATH_TO}/stanford_fully_supervised_preprocessed \
DATA.stanford3d_sampled_inds ${YOUR_PATH_TO}/stanford_fully_supervised_preprocessed/points/percentage0.001evenc \
DATA.sparse_label False \
DATA.two_stream True \
MODEL.two_stream_model_apply True \
TRAINER.two_stream_feats_key semantic_scores \
TRAINER.two_stream_loss_mode js_divergence_v2 \
TRAINER.two_stream_seg_both True \
TRAINER.two_stream_loss_weight 2.0 \
AUGMENTATION.use_color_jitter False \
TRAINER.two_stream_mask_grid_size 4 \
TRAINER.two_stream_loss_mask_mode js_divergence_v2 \
TRAINER.two_stream_mask_ratio 0.75 \
TRAINER.two_stream_mask_mode grid \
TRAINER.two_stream_mask_extra_stream True \
TRAINER.two_stream_mask_feats_key semantic_scores \
TRAINER.two_stream_mask_corr_loss True \
TRAINER.two_stream_mask_self_loss True \
TRAINER.two_stream_loss_mask_weight 5. \
TRAINER.two_stream_mask_loss_threshold -1.0 \
TRAINER.empty_cache_every 1
# 0.01% baseline
CUDA_VISIBLE_DEVICES=0 python launch.py ddp_train.py --config config/default.yaml \
GENERAL.exp_name 1e-4_percentage_baseline \
TRAINER.name TwoStreamTrainer \
MODEL.out_channels 13 \
DATA.name StanfordDataLoader \
DATA.dataset StanfordArea5Dataset \
DATA.voxel_size 0.05 \
DATA.batch_size 2 \
DATA.train_limit_numpoints 1000000 \
OPTIMIZER.lr 0.01 \
OPTIMIZER.weight_decay 0.001 \
SCHEDULER.name PolyLR \
TRAINER.epochs 180 \
EVALUATOR.iou_num_class 13 \
DATA.stanford3d_path ${YOUR_PATH_TO}/stanford_fully_supervised_preprocessed \
DATA.stanford3d_sampled_inds ${YOUR_PATH_TO}/stanford_fully_supervised_preprocessed/points/percentage0.0001evenc \
DATA.sparse_label False \
DATA.two_stream True \
MODEL.two_stream_model_apply True \
TRAINER.two_stream_feats_key semantic_scores \
TRAINER.two_stream_loss_mode js_divergence_v2 \
TRAINER.two_stream_seg_both True \
TRAINER.two_stream_loss_weight 0.0 \
AUGMENTATION.use_color_jitter False
# 0.01% consistency baseline, consis weight 5
CUDA_VISIBLE_DEVICES=0 python launch.py ddp_train.py --config config/default.yaml \
GENERAL.exp_name 1e-4_percentage_consis_weight5 \
TRAINER.name TwoStreamTrainer \
MODEL.out_channels 13 \
DATA.name StanfordDataLoader \
DATA.dataset StanfordArea5Dataset \
DATA.voxel_size 0.05 \
DATA.batch_size 2 \
DATA.train_limit_numpoints 1000000 \
OPTIMIZER.lr 0.01 \
OPTIMIZER.weight_decay 0.001 \
SCHEDULER.name PolyLR \
TRAINER.epochs 180 \
EVALUATOR.iou_num_class 13 \
DATA.stanford3d_path ${YOUR_PATH_TO}/stanford_fully_supervised_preprocessed \
DATA.stanford3d_sampled_inds ${YOUR_PATH_TO}/stanford_fully_supervised_preprocessed/points/percentage0.0001evenc \
DATA.sparse_label False \
DATA.two_stream True \
MODEL.two_stream_model_apply True \
TRAINER.two_stream_feats_key semantic_scores \
TRAINER.two_stream_loss_mode js_divergence_v2 \
TRAINER.two_stream_seg_both True \
TRAINER.two_stream_loss_weight 5.0 \
AUGMENTATION.use_color_jitter False
# 0.01% CPCM, consis weight 2, mask_mode grid, grid_size 8, mask two stream, mask weight 10
CUDA_VISIBLE_DEVICES=0 python launch.py ddp_train.py --config config/default.yaml \
GENERAL.exp_name 1e-4_percentage_consis_weight2_maskGrid075GridSize8_extraMaskStreamSelfCorr_weight10 \
TRAINER.name TwoStreamTrainer \
MODEL.out_channels 13 \
DATA.name StanfordDataLoader \
DATA.dataset StanfordArea5Dataset \
DATA.voxel_size 0.05 \
DATA.batch_size 2 \
DATA.train_limit_numpoints 1000000 \
OPTIMIZER.lr 0.01 \
OPTIMIZER.weight_decay 0.001 \
SCHEDULER.name PolyLR \
TRAINER.epochs 180 \
EVALUATOR.iou_num_class 13 \
DATA.stanford3d_path ${YOUR_PATH_TO}/stanford_fully_supervised_preprocessed \
DATA.stanford3d_sampled_inds ${YOUR_PATH_TO}/stanford_fully_supervised_preprocessed/points/percentage0.0001evenc \
DATA.sparse_label False \
DATA.two_stream True \
MODEL.two_stream_model_apply True \
TRAINER.two_stream_feats_key semantic_scores \
TRAINER.two_stream_loss_mode js_divergence_v2 \
TRAINER.two_stream_seg_both True \
TRAINER.two_stream_loss_weight 2.0 \
AUGMENTATION.use_color_jitter False \
TRAINER.two_stream_mask_grid_size 8 \
TRAINER.two_stream_loss_mask_mode js_divergence_v2 \
TRAINER.two_stream_mask_ratio 0.75 \
TRAINER.two_stream_mask_mode grid \
TRAINER.two_stream_mask_extra_stream True \
TRAINER.two_stream_mask_feats_key semantic_scores \
TRAINER.two_stream_mask_corr_loss True \
TRAINER.two_stream_mask_self_loss True \
TRAINER.two_stream_loss_mask_weight 10. \
TRAINER.two_stream_mask_loss_threshold -1. \
TRAINER.empty_cache_every 1