-
Notifications
You must be signed in to change notification settings - Fork 16
/
unitr+lss.yaml
317 lines (269 loc) · 8.9 KB
/
unitr+lss.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
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
CLASS_NAMES: ['car','truck', 'construction_vehicle', 'bus', 'trailer',
'barrier', 'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone']
DATA_CONFIG:
_BASE_CONFIG_: cfgs/dataset_configs/nuscenes_dataset.yaml
POINT_CLOUD_RANGE: [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
INFO_PATH: {
'train': [nuscenes_infos_10sweeps_train.pkl],
'test': [nuscenes_infos_10sweeps_val.pkl],
}
CAMERA_CONFIG:
USE_CAMERA: True
IMAGE:
FINAL_DIM: [256,704]
RESIZE_LIM_TRAIN: [0.38, 0.55]
RESIZE_LIM_TEST: [0.48, 0.48]
DATA_AUGMENTOR:
DISABLE_AUG_LIST: ['placeholder']
AUG_CONFIG_LIST:
- NAME: gt_sampling
IMG_AUG_TYPE: nuscenes
IMG_AUG_MIXUP: 0.7
DB_INFO_PATH:
- nuscenes_dbinfos_10sweeps_withvelo.pkl
PREPARE: {
filter_by_min_points: [
'car:5','truck:5', 'construction_vehicle:5', 'bus:5', 'trailer:5',
'barrier:5', 'motorcycle:5', 'bicycle:5', 'pedestrian:5', 'traffic_cone:5'
],
}
USE_SHARED_MEMORY: False # set it to True to speed up (it costs about 24+150GB shared memory)
DB_DATA_PATH:
- nuscenes_10sweeps_withvelo_lidar.npy
- nuscenes_10sweeps_withvelo_img.npy
SAMPLE_GROUPS: [
'car:2','truck:3', 'construction_vehicle:7', 'bus:4', 'trailer:6',
'barrier:2', 'motorcycle:6', 'bicycle:6', 'pedestrian:2', 'traffic_cone:2'
]
NUM_POINT_FEATURES: 5
DATABASE_WITH_FAKELIDAR: False
REMOVE_EXTRA_WIDTH: [0.0, 0.0, 0.0]
LIMIT_WHOLE_SCENE: True
# hf cfg
use_hf: False
fr_path: /private_dataset/nuscenes_pcdet/v1.0-trainval/gt_database_10sweeps_withvelo
fr_num: 54899
- NAME: random_world_flip
ALONG_AXIS_LIST: ['x', 'y']
- NAME: random_world_rotation
WORLD_ROT_ANGLE: [-0.78539816, 0.78539816]
- NAME: random_world_scaling
WORLD_SCALE_RANGE: [0.9, 1.1]
- NAME: random_world_translation
NOISE_TRANSLATE_STD: [0.5, 0.5, 0.5]
- NAME: imgaug
ROT_LIM: [-5.4, 5.4]
RAND_FLIP: True
DATA_PROCESSOR:
- NAME: mask_points_and_boxes_outside_range
REMOVE_OUTSIDE_BOXES: True
- NAME: shuffle_points
SHUFFLE_ENABLED: {
'train': True,
'test': True
}
- NAME: transform_points_to_voxels_placeholder
VOXEL_SIZE: [0.3, 0.3, 8.0]
- NAME: image_calibrate
- NAME: image_normalize
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
MODEL:
NAME: UniTR
MM_BACKBONE:
NAME: UniTR
PATCH_EMBED:
in_channels: 3
image_size: [256, 704]
embed_dims: 128
patch_size: 8
patch_norm: True
norm_cfg: {'type': 'LN'}
IMAGE_INPUT_LAYER:
sparse_shape: [32, 88, 1]
d_model: [128]
set_info: [[90, 4]]
window_shape: [[30, 30, 1]]
hybrid_factor: [1, 1, 1] # x, y, z
shifts_list: [[[0, 0, 0], [15, 15, 0]]]
input_image: True
LIDAR_INPUT_LAYER:
sparse_shape: [360, 360, 1]
d_model: [128]
set_info: [[90, 4]]
window_shape: [[30, 30, 1]]
hybrid_factor: [1, 1, 1] # x, y, z
shifts_list: [[[0, 0, 0], [15, 15, 0]]]
set_info: [[90, 4]]
d_model: [128]
nhead: [8]
dim_feedforward: [256]
dropout: 0.0
activation: gelu
checkpoint_blocks: [0,1,2,3] # here can save 50% CUDA memory with marginal speed drop
layer_cfg: {'use_bn': False, 'split_ffn': True, 'split_residual': True}
# fuse backbone config
FUSE_BACKBONE:
IMAGE2LIDAR:
block_start: 3
block_end: 4
point_cloud_range: [-54.0, -54.0, -10.0, 54.0, 54.0, 10.0]
voxel_size: [0.3,0.3,20.0]
sample_num: 20
image2lidar_layer:
sparse_shape: [360, 360, 1]
d_model: [128]
set_info: [[90, 1]]
window_shape: [[30, 30, 1]]
hybrid_factor: [1, 1, 1]
shifts_list: [[[0, 0, 0], [15, 15, 0]]]
expand_max_voxels: 10
LIDAR2IMAGE:
block_start: 1
block_end: 3
point_cloud_range: [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
voxel_size: [0.3,0.3,8.0]
sample_num: 1
lidar2image_layer:
sparse_shape: [96, 264, 6]
d_model: [128]
set_info: [[90, 2]]
window_shape: [[30, 30, 1]]
hybrid_factor: [1, 1, 1]
shifts_list: [[[0, 0, 0], [15, 15, 0]]]
expand_max_voxels: 30
out_indices: [0, 1, 2, 3]
NECK:
NAME: GeneralizedLSSFPN
IN_CHANNELS: [128, 128, 128, 128]
OUT_CHANNELS: 256
START_LEVEL: 0
END_LEVEL: -1
NUM_OUTS: 4
USE_BIAS: True
ALIGN_CORNERS: True
VTRANSFORM:
NAME: LSSTransform
IMAGE_SIZE: [256,704]
IN_CHANNEL: 256
OUT_CHANNEL: 80
FEATURE_SIZE: [32,88]
XBOUND: [-54.0, 54.0, 0.3]
YBOUND: [-54.0, 54.0, 0.3]
ZBOUND: [-10.0, 10.0, 20.0]
DBOUND: [1.0, 60.0, 0.5]
DOWNSAMPLE: 1
FUSER:
NAME: ConvFuser
IN_CHANNEL: 208
OUT_CHANNEL: 128
VFE:
NAME: DynPillarVFE
WITH_DISTANCE: False
USE_ABSLOTE_XYZ: True
USE_NORM: True
NUM_FILTERS: [ 128, 128 ]
MAP_TO_BEV:
NAME: PointPillarScatter3d
INPUT_SHAPE: [360, 360, 1]
NUM_BEV_FEATURES: 128
BACKBONE_2D:
NAME: BaseBEVResBackbone
LAYER_NUMS: [ 1, 2, 2, 2] #
LAYER_STRIDES: [1, 2, 2, 2]
NUM_FILTERS: [128, 128, 256, 256]
UPSAMPLE_STRIDES: [0.5, 1, 2, 4]
NUM_UPSAMPLE_FILTERS: [128, 128, 128, 128]
DENSE_HEAD:
CLASS_AGNOSTIC: False
NAME: TransFusionHead
QUERY_RADIUS: 20
QUERY_LOCAL: True
USE_BIAS_BEFORE_NORM: True
NUM_PROPOSALS: 200
HIDDEN_CHANNEL: 128
NUM_CLASSES: 10
NUM_HEADS: 8
NMS_KERNEL_SIZE: 3
FFN_CHANNEL: 256
DROPOUT: 0.1
BN_MOMENTUM: 0.1
ACTIVATION: relu
NUM_HM_CONV: 2
SEPARATE_HEAD_CFG:
HEAD_ORDER: ['center', 'height', 'dim', 'rot', 'vel','iou']
HEAD_DICT: {
'center': {'out_channels': 2, 'num_conv': 2},
'height': {'out_channels': 1, 'num_conv': 2},
'dim': {'out_channels': 3, 'num_conv': 2},
'rot': {'out_channels': 2, 'num_conv': 2},
'vel': {'out_channels': 2, 'num_conv': 2},
'iou': {'out_channels': 1, 'num_conv': 2}
}
TARGET_ASSIGNER_CONFIG:
FEATURE_MAP_STRIDE: 2
DATASET: nuScenes
GAUSSIAN_OVERLAP: 0.1
MIN_RADIUS: 2
HUNGARIAN_ASSIGNER:
cls_cost: {'gamma': 2.0, 'alpha': 0.25, 'weight': 0.15}
reg_cost: {'weight': 0.25}
iou_cost: {'weight': 0.25}
LOSS_CONFIG:
LOSS_WEIGHTS: {
'cls_weight': 1.0,
'bbox_weight': 0.25,
'hm_weight': 1.0,
'iou_weight': 0.5,
'iou_reg_weight': 0.5,
'code_weights': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2]
}
LOSS_CLS:
use_sigmoid: True
gamma: 2.0
alpha: 0.25
LOSS_IOU: True
LOSS_IOU_REG: True
POST_PROCESSING:
SCORE_THRESH: 0.0
POST_CENTER_RANGE: [-61.2, -61.2, -10.0, 61.2, 61.2, 10.0]
USE_IOU_TO_RECTIFY_SCORE: True
IOU_RECTIFIER: [0.5]
NMS_CONFIG:
NMS_TYPE: nms_gpu
NMS_THRESH: 0.2
NMS_PRE_MAXSIZE: 1000
NMS_POST_MAXSIZE: 100
SCORE_THRES: 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: True
NMS_TYPE: nms_gpu
NMS_THRESH: 0.2
NMS_PRE_MAXSIZE: 1000
NMS_POST_MAXSIZE: 83
OPTIMIZATION:
BATCH_SIZE_PER_GPU: 3
NUM_EPOCHS: 10
OPTIMIZER: adam_onecycle
LR: 0.003
WEIGHT_DECAY: 0.03
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
LOSS_SCALE_FP16: 32
HOOK:
DisableAugmentationHook:
DISABLE_AUG_LIST: ['gt_sampling']
NUM_LAST_EPOCHS: 2