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20260315_224809/20260315_224809.json
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{"lr": 0.0001, "data_time": 1.0969552993774414, "loss": 58.39009094238281, "loss_cls": 2.302908182144165, "loss_bbox": 7.731889247894287, "d0.loss_cls": 2.2785091400146484, "d0.loss_bbox": 7.366260528564453, "d1.loss_cls": 2.2856218814849854, "d1.loss_bbox": 7.410768032073975, "d2.loss_cls": 2.240107536315918, "d2.loss_bbox": 7.315112590789795, "d3.loss_cls": 2.2907700538635254, "d3.loss_bbox": 7.372400760650635, "d4.loss_cls": 2.3866851329803467, "d4.loss_bbox": 7.409055709838867, "time": 24.124476194381714, "epoch": 1, "iter": 1, "step": 1}
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{"NDS": 0.018478272642075605, "mAP": 0.00023853881585173065, "data_time": 0.6630229949951172, "time": 2.191067934036255, "step": 1}
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{"lr": 0.0001, "data_time": 10.68934166431427, "loss": 54.70582580566406, "loss_cls": 2.289698362350464, "loss_bbox": 7.151990175247192, "d0.loss_cls": 2.2585201263427734, "d0.loss_bbox": 6.771386623382568, "d1.loss_cls": 2.226685881614685, "d1.loss_bbox": 6.752256631851196, "d2.loss_cls": 2.1500898599624634, "d2.loss_bbox": 6.782212257385254, "d3.loss_cls": 2.218158483505249, "d3.loss_bbox": 6.89587140083313, "d4.loss_cls": 2.27994966506958, "d4.loss_bbox": 6.929007530212402, "time": 32.79427921772003, "epoch": 1, "iter": 2, "step": 2}
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{"NDS": 0.017786070826831646, "mAP": 0.0002481977075675005, "data_time": 0.7300244569778442, "time": 2.301406979560852, "step": 2}
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20260315_224809/config.py
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|
|
| 1 |
+
_dim_ = 256
|
| 2 |
+
_ffn_dim_ = 512
|
| 3 |
+
_num_levels_ = 1
|
| 4 |
+
_pos_dim_ = 128
|
| 5 |
+
auto_scale_lr = dict(base_batch_size=16, enable=False)
|
| 6 |
+
bev_h_ = 50
|
| 7 |
+
bev_w_ = 50
|
| 8 |
+
by_epoch = False
|
| 9 |
+
class_names = [
|
| 10 |
+
'car',
|
| 11 |
+
'truck',
|
| 12 |
+
'construction_vehicle',
|
| 13 |
+
'bus',
|
| 14 |
+
'trailer',
|
| 15 |
+
'barrier',
|
| 16 |
+
'motorcycle',
|
| 17 |
+
'bicycle',
|
| 18 |
+
'pedestrian',
|
| 19 |
+
'traffic_cone',
|
| 20 |
+
]
|
| 21 |
+
custom_hooks = [
|
| 22 |
+
dict(
|
| 23 |
+
by_epoch=False,
|
| 24 |
+
clean_local=False,
|
| 25 |
+
interval=1,
|
| 26 |
+
repo_id='5421Project',
|
| 27 |
+
type='CheckpointUploader'),
|
| 28 |
+
dict(repo_id='5421Project', resume_type='last', type='CheckpointResumer'),
|
| 29 |
+
]
|
| 30 |
+
data = dict(
|
| 31 |
+
nonshuffler_sampler=dict(type='DistributedSampler'),
|
| 32 |
+
samples_per_gpu=1,
|
| 33 |
+
shuffler_sampler=dict(type='DistributedGroupSampler'),
|
| 34 |
+
test=dict(
|
| 35 |
+
ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl',
|
| 36 |
+
bev_size=(
|
| 37 |
+
50,
|
| 38 |
+
50,
|
| 39 |
+
),
|
| 40 |
+
classes=[
|
| 41 |
+
'car',
|
| 42 |
+
'truck',
|
| 43 |
+
'construction_vehicle',
|
| 44 |
+
'bus',
|
| 45 |
+
'trailer',
|
| 46 |
+
'barrier',
|
| 47 |
+
'motorcycle',
|
| 48 |
+
'bicycle',
|
| 49 |
+
'pedestrian',
|
| 50 |
+
'traffic_cone',
|
| 51 |
+
],
|
| 52 |
+
data_root='data/nuscenes/v1.0-mini/',
|
| 53 |
+
frame=[
|
| 54 |
+
-3,
|
| 55 |
+
-2,
|
| 56 |
+
-1,
|
| 57 |
+
],
|
| 58 |
+
modality=dict(
|
| 59 |
+
use_camera=True,
|
| 60 |
+
use_external=False,
|
| 61 |
+
use_lidar=False,
|
| 62 |
+
use_map=False,
|
| 63 |
+
use_radar=False),
|
| 64 |
+
pipeline=[
|
| 65 |
+
dict(to_float32=True, type='LoadMultiViewImageFromFiles'),
|
| 66 |
+
dict(
|
| 67 |
+
mean=[
|
| 68 |
+
123.675,
|
| 69 |
+
116.28,
|
| 70 |
+
103.53,
|
| 71 |
+
],
|
| 72 |
+
std=[
|
| 73 |
+
58.395,
|
| 74 |
+
57.12,
|
| 75 |
+
57.375,
|
| 76 |
+
],
|
| 77 |
+
to_rgb=True,
|
| 78 |
+
type='NormalizeMultiviewImage'),
|
| 79 |
+
dict(
|
| 80 |
+
flip=False,
|
| 81 |
+
img_scale=(
|
| 82 |
+
800,
|
| 83 |
+
450,
|
| 84 |
+
),
|
| 85 |
+
pts_scale_ratio=[
|
| 86 |
+
1.0,
|
| 87 |
+
],
|
| 88 |
+
transforms=[
|
| 89 |
+
dict(
|
| 90 |
+
scales=[
|
| 91 |
+
0.5,
|
| 92 |
+
], type='RandomScaleImageMultiViewImage'),
|
| 93 |
+
dict(size_divisor=32, type='PadMultiViewImage'),
|
| 94 |
+
dict(
|
| 95 |
+
class_names=[
|
| 96 |
+
'car',
|
| 97 |
+
'truck',
|
| 98 |
+
'construction_vehicle',
|
| 99 |
+
'bus',
|
| 100 |
+
'trailer',
|
| 101 |
+
'barrier',
|
| 102 |
+
'motorcycle',
|
| 103 |
+
'bicycle',
|
| 104 |
+
'pedestrian',
|
| 105 |
+
'traffic_cone',
|
| 106 |
+
],
|
| 107 |
+
type='CustomDefaultFormatBundle3D'),
|
| 108 |
+
dict(keys=[
|
| 109 |
+
'img',
|
| 110 |
+
], type='CustomCollect3D'),
|
| 111 |
+
],
|
| 112 |
+
type='MultiScaleFlipAug3D'),
|
| 113 |
+
],
|
| 114 |
+
test_mode=True,
|
| 115 |
+
type='CustomNuScenesDataset'),
|
| 116 |
+
train=dict(
|
| 117 |
+
ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_train.pkl',
|
| 118 |
+
bev_size=(
|
| 119 |
+
50,
|
| 120 |
+
50,
|
| 121 |
+
),
|
| 122 |
+
box_type_3d='LiDAR',
|
| 123 |
+
classes=[
|
| 124 |
+
'car',
|
| 125 |
+
'truck',
|
| 126 |
+
'construction_vehicle',
|
| 127 |
+
'bus',
|
| 128 |
+
'trailer',
|
| 129 |
+
'barrier',
|
| 130 |
+
'motorcycle',
|
| 131 |
+
'bicycle',
|
| 132 |
+
'pedestrian',
|
| 133 |
+
'traffic_cone',
|
| 134 |
+
],
|
| 135 |
+
data_root='data/nuscenes/v1.0-mini/',
|
| 136 |
+
modality=dict(
|
| 137 |
+
use_camera=True,
|
| 138 |
+
use_external=False,
|
| 139 |
+
use_lidar=False,
|
| 140 |
+
use_map=False,
|
| 141 |
+
use_radar=False),
|
| 142 |
+
pipeline=[
|
| 143 |
+
dict(to_float32=True, type='LoadMultiViewImageFromFiles'),
|
| 144 |
+
dict(
|
| 145 |
+
type='LoadAnnotations3D',
|
| 146 |
+
with_bbox_3d=True,
|
| 147 |
+
with_label_3d=True),
|
| 148 |
+
dict(
|
| 149 |
+
point_cloud_range=[
|
| 150 |
+
-51.2,
|
| 151 |
+
-51.2,
|
| 152 |
+
-5.0,
|
| 153 |
+
51.2,
|
| 154 |
+
51.2,
|
| 155 |
+
3.0,
|
| 156 |
+
],
|
| 157 |
+
type='ObjectRangeFilter'),
|
| 158 |
+
dict(
|
| 159 |
+
classes=[
|
| 160 |
+
'car',
|
| 161 |
+
'truck',
|
| 162 |
+
'construction_vehicle',
|
| 163 |
+
'bus',
|
| 164 |
+
'trailer',
|
| 165 |
+
'barrier',
|
| 166 |
+
'motorcycle',
|
| 167 |
+
'bicycle',
|
| 168 |
+
'pedestrian',
|
| 169 |
+
'traffic_cone',
|
| 170 |
+
],
|
| 171 |
+
type='ObjectNameFilter'),
|
| 172 |
+
dict(type='PhotoMetricDistortionMultiViewImage'),
|
| 173 |
+
dict(
|
| 174 |
+
mean=[
|
| 175 |
+
123.675,
|
| 176 |
+
116.28,
|
| 177 |
+
103.53,
|
| 178 |
+
],
|
| 179 |
+
std=[
|
| 180 |
+
58.395,
|
| 181 |
+
57.12,
|
| 182 |
+
57.375,
|
| 183 |
+
],
|
| 184 |
+
to_rgb=True,
|
| 185 |
+
type='NormalizeMultiviewImage'),
|
| 186 |
+
dict(scales=[
|
| 187 |
+
0.5,
|
| 188 |
+
], type='RandomScaleImageMultiViewImage'),
|
| 189 |
+
dict(size_divisor=32, type='PadMultiViewImage'),
|
| 190 |
+
dict(
|
| 191 |
+
class_names=[
|
| 192 |
+
'car',
|
| 193 |
+
'truck',
|
| 194 |
+
'construction_vehicle',
|
| 195 |
+
'bus',
|
| 196 |
+
'trailer',
|
| 197 |
+
'barrier',
|
| 198 |
+
'motorcycle',
|
| 199 |
+
'bicycle',
|
| 200 |
+
'pedestrian',
|
| 201 |
+
'traffic_cone',
|
| 202 |
+
],
|
| 203 |
+
type='CustomDefaultFormatBundle3D'),
|
| 204 |
+
dict(
|
| 205 |
+
keys=[
|
| 206 |
+
'gt_bboxes_3d',
|
| 207 |
+
'gt_labels_3d',
|
| 208 |
+
'img',
|
| 209 |
+
],
|
| 210 |
+
type='CustomCollect3D'),
|
| 211 |
+
dict(type='TypeConverter'),
|
| 212 |
+
],
|
| 213 |
+
queue_length=4,
|
| 214 |
+
test_mode=False,
|
| 215 |
+
type='CustomNuScenesDataset',
|
| 216 |
+
use_valid_flag=True),
|
| 217 |
+
val=dict(
|
| 218 |
+
ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl',
|
| 219 |
+
bev_size=(
|
| 220 |
+
50,
|
| 221 |
+
50,
|
| 222 |
+
),
|
| 223 |
+
classes=[
|
| 224 |
+
'car',
|
| 225 |
+
'truck',
|
| 226 |
+
'construction_vehicle',
|
| 227 |
+
'bus',
|
| 228 |
+
'trailer',
|
| 229 |
+
'barrier',
|
| 230 |
+
'motorcycle',
|
| 231 |
+
'bicycle',
|
| 232 |
+
'pedestrian',
|
| 233 |
+
'traffic_cone',
|
| 234 |
+
],
|
| 235 |
+
data_root='data/nuscenes/v1.0-mini/',
|
| 236 |
+
frame=(),
|
| 237 |
+
frames=[
|
| 238 |
+
-3,
|
| 239 |
+
-2,
|
| 240 |
+
-1,
|
| 241 |
+
],
|
| 242 |
+
modality=dict(
|
| 243 |
+
use_camera=True,
|
| 244 |
+
use_external=False,
|
| 245 |
+
use_lidar=False,
|
| 246 |
+
use_map=False,
|
| 247 |
+
use_radar=False),
|
| 248 |
+
pipeline=[
|
| 249 |
+
dict(to_float32=True, type='LoadMultiViewImageFromFiles'),
|
| 250 |
+
dict(
|
| 251 |
+
mean=[
|
| 252 |
+
123.675,
|
| 253 |
+
116.28,
|
| 254 |
+
103.53,
|
| 255 |
+
],
|
| 256 |
+
std=[
|
| 257 |
+
58.395,
|
| 258 |
+
57.12,
|
| 259 |
+
57.375,
|
| 260 |
+
],
|
| 261 |
+
to_rgb=True,
|
| 262 |
+
type='NormalizeMultiviewImage'),
|
| 263 |
+
dict(
|
| 264 |
+
flip=False,
|
| 265 |
+
img_scale=(
|
| 266 |
+
800,
|
| 267 |
+
450,
|
| 268 |
+
),
|
| 269 |
+
pts_scale_ratio=[
|
| 270 |
+
1.0,
|
| 271 |
+
],
|
| 272 |
+
transforms=[
|
| 273 |
+
dict(
|
| 274 |
+
scales=[
|
| 275 |
+
0.5,
|
| 276 |
+
], type='RandomScaleImageMultiViewImage'),
|
| 277 |
+
dict(size_divisor=32, type='PadMultiViewImage'),
|
| 278 |
+
dict(
|
| 279 |
+
class_names=[
|
| 280 |
+
'car',
|
| 281 |
+
'truck',
|
| 282 |
+
'construction_vehicle',
|
| 283 |
+
'bus',
|
| 284 |
+
'trailer',
|
| 285 |
+
'barrier',
|
| 286 |
+
'motorcycle',
|
| 287 |
+
'bicycle',
|
| 288 |
+
'pedestrian',
|
| 289 |
+
'traffic_cone',
|
| 290 |
+
],
|
| 291 |
+
type='CustomDefaultFormatBundle3D'),
|
| 292 |
+
dict(keys=[
|
| 293 |
+
'img',
|
| 294 |
+
], type='CustomCollect3D'),
|
| 295 |
+
],
|
| 296 |
+
type='MultiScaleFlipAug3D'),
|
| 297 |
+
],
|
| 298 |
+
samples_per_gpu=1,
|
| 299 |
+
test_mode=True,
|
| 300 |
+
type='CustomNuScenesDataset'),
|
| 301 |
+
workers_per_gpu=4)
|
| 302 |
+
data_root = 'data/nuscenes/v1.0-mini/'
|
| 303 |
+
dataset_type = 'CustomNuScenesDataset'
|
| 304 |
+
decoder = dict(
|
| 305 |
+
num_layers=6,
|
| 306 |
+
return_intermediate=True,
|
| 307 |
+
transformerlayers=dict(
|
| 308 |
+
attn_cfgs=[
|
| 309 |
+
dict(
|
| 310 |
+
dropout=0.1,
|
| 311 |
+
embed_dims=256,
|
| 312 |
+
num_heads=8,
|
| 313 |
+
type='MultiheadAttention'),
|
| 314 |
+
dict(
|
| 315 |
+
embed_dims=256,
|
| 316 |
+
num_levels=1,
|
| 317 |
+
type='CustomMSDeformableAttention'),
|
| 318 |
+
],
|
| 319 |
+
ffn_cfgs=dict(
|
| 320 |
+
feedforward_channels=512, ffn_drop=0.1, num_fcs=2, type='FFN'),
|
| 321 |
+
operation_order=(
|
| 322 |
+
'self_attn',
|
| 323 |
+
'norm',
|
| 324 |
+
'cross_attn',
|
| 325 |
+
'norm',
|
| 326 |
+
'ffn',
|
| 327 |
+
'norm',
|
| 328 |
+
),
|
| 329 |
+
type='DetrTransformerDecoderLayer'),
|
| 330 |
+
type='DetectionTransformerDecoder')
|
| 331 |
+
default_hooks = dict(
|
| 332 |
+
checkpoint=dict(
|
| 333 |
+
by_epoch=False,
|
| 334 |
+
interval=1,
|
| 335 |
+
max_keep_ckpts=1,
|
| 336 |
+
save_best=[
|
| 337 |
+
'loss',
|
| 338 |
+
'mAP',
|
| 339 |
+
'NDS',
|
| 340 |
+
],
|
| 341 |
+
type='CheckpointHookV2'),
|
| 342 |
+
logger=dict(
|
| 343 |
+
interval=1,
|
| 344 |
+
interval_exp_name=1000,
|
| 345 |
+
log_metric_by_epoch=False,
|
| 346 |
+
type='LoggerHook'),
|
| 347 |
+
param_scheduler=dict(type='ParamSchedulerHook'),
|
| 348 |
+
runtime_info=dict(type='RuntimeInfoHook'),
|
| 349 |
+
sampler_seed=dict(type='DistSamplerSeedHook'),
|
| 350 |
+
timer=dict(type='IterTimerHook'))
|
| 351 |
+
encoder = dict(
|
| 352 |
+
num_layers=3,
|
| 353 |
+
num_points_in_pillar=8,
|
| 354 |
+
pc_range=[
|
| 355 |
+
-51.2,
|
| 356 |
+
-51.2,
|
| 357 |
+
-5.0,
|
| 358 |
+
51.2,
|
| 359 |
+
51.2,
|
| 360 |
+
3.0,
|
| 361 |
+
],
|
| 362 |
+
return_intermediate=False,
|
| 363 |
+
transformerlayers=dict(
|
| 364 |
+
attn_cfgs=[
|
| 365 |
+
dict(embed_dims=256, num_levels=1, type='TemporalSelfAttention'),
|
| 366 |
+
dict(
|
| 367 |
+
deformable_attention=dict(
|
| 368 |
+
embed_dims=256,
|
| 369 |
+
num_levels=1,
|
| 370 |
+
num_points=8,
|
| 371 |
+
type='MSDeformableAttention3D'),
|
| 372 |
+
embed_dims=256,
|
| 373 |
+
pc_range=[
|
| 374 |
+
-51.2,
|
| 375 |
+
-51.2,
|
| 376 |
+
-5.0,
|
| 377 |
+
51.2,
|
| 378 |
+
51.2,
|
| 379 |
+
3.0,
|
| 380 |
+
],
|
| 381 |
+
type='SpatialCrossAttention'),
|
| 382 |
+
],
|
| 383 |
+
ffn_cfgs=dict(
|
| 384 |
+
feedforward_channels=512, ffn_drop=0.1, num_fcs=2, type='FFN'),
|
| 385 |
+
operation_order=(
|
| 386 |
+
'self_attn',
|
| 387 |
+
'norm',
|
| 388 |
+
'cross_attn',
|
| 389 |
+
'norm',
|
| 390 |
+
'ffn',
|
| 391 |
+
'norm',
|
| 392 |
+
),
|
| 393 |
+
type='BEVFormerLayer'),
|
| 394 |
+
type='BEVFormerEncoder')
|
| 395 |
+
env_cfg = dict(dist_cfg=dict(backend='nccl'))
|
| 396 |
+
experiment_name = 'debug'
|
| 397 |
+
file_client_args = dict(backend='disk')
|
| 398 |
+
frames = [
|
| 399 |
+
-3,
|
| 400 |
+
-2,
|
| 401 |
+
-1,
|
| 402 |
+
]
|
| 403 |
+
gpu_ids = range(0, 1)
|
| 404 |
+
img_norm_cfg = dict(
|
| 405 |
+
mean=[
|
| 406 |
+
123.675,
|
| 407 |
+
116.28,
|
| 408 |
+
103.53,
|
| 409 |
+
],
|
| 410 |
+
std=[
|
| 411 |
+
58.395,
|
| 412 |
+
57.12,
|
| 413 |
+
57.375,
|
| 414 |
+
],
|
| 415 |
+
to_rgb=True)
|
| 416 |
+
input_modality = dict(
|
| 417 |
+
use_camera=True,
|
| 418 |
+
use_external=False,
|
| 419 |
+
use_lidar=False,
|
| 420 |
+
use_map=False,
|
| 421 |
+
use_radar=False)
|
| 422 |
+
interval = 1
|
| 423 |
+
launcher = 'none'
|
| 424 |
+
load_from = None
|
| 425 |
+
log_interval = 1
|
| 426 |
+
log_processor = dict(window_size=20)
|
| 427 |
+
lr_config = dict(
|
| 428 |
+
min_lr_ratio=0.001,
|
| 429 |
+
policy='CosineAnnealing',
|
| 430 |
+
warmup='linear',
|
| 431 |
+
warmup_iters=500,
|
| 432 |
+
warmup_ratio=0.3333333333333333)
|
| 433 |
+
max_epochs = 5
|
| 434 |
+
max_iters = 2
|
| 435 |
+
model = dict(
|
| 436 |
+
img_backbone=dict(
|
| 437 |
+
depth=50,
|
| 438 |
+
frozen_stages=1,
|
| 439 |
+
norm_cfg=dict(requires_grad=False, type='BN'),
|
| 440 |
+
norm_eval=True,
|
| 441 |
+
num_stages=4,
|
| 442 |
+
out_indices=(3, ),
|
| 443 |
+
style='pytorch',
|
| 444 |
+
type='ResNet'),
|
| 445 |
+
img_neck=dict(
|
| 446 |
+
add_extra_convs='on_output',
|
| 447 |
+
in_channels=[
|
| 448 |
+
2048,
|
| 449 |
+
],
|
| 450 |
+
num_outs=1,
|
| 451 |
+
out_channels=256,
|
| 452 |
+
relu_before_extra_convs=True,
|
| 453 |
+
start_level=0,
|
| 454 |
+
type='FPN'),
|
| 455 |
+
pretrained=dict(img='torchvision://resnet50'),
|
| 456 |
+
pts_bbox_head=dict(
|
| 457 |
+
as_two_stage=False,
|
| 458 |
+
bbox_coder=dict(
|
| 459 |
+
max_num=300,
|
| 460 |
+
num_classes=10,
|
| 461 |
+
pc_range=[
|
| 462 |
+
-51.2,
|
| 463 |
+
-51.2,
|
| 464 |
+
-5.0,
|
| 465 |
+
51.2,
|
| 466 |
+
51.2,
|
| 467 |
+
3.0,
|
| 468 |
+
],
|
| 469 |
+
post_center_range=[
|
| 470 |
+
-61.2,
|
| 471 |
+
-61.2,
|
| 472 |
+
-10.0,
|
| 473 |
+
61.2,
|
| 474 |
+
61.2,
|
| 475 |
+
10.0,
|
| 476 |
+
],
|
| 477 |
+
type='NMSFreeCoder',
|
| 478 |
+
voxel_size=[
|
| 479 |
+
0.2,
|
| 480 |
+
0.2,
|
| 481 |
+
8,
|
| 482 |
+
]),
|
| 483 |
+
bev_h=50,
|
| 484 |
+
bev_w=50,
|
| 485 |
+
in_channels=256,
|
| 486 |
+
loss_bbox=dict(loss_weight=0.5, type='L1Loss'),
|
| 487 |
+
loss_cls=dict(
|
| 488 |
+
alpha=0.25,
|
| 489 |
+
gamma=2.0,
|
| 490 |
+
loss_weight=2.0,
|
| 491 |
+
type='FocalLoss',
|
| 492 |
+
use_sigmoid=True),
|
| 493 |
+
loss_iou=dict(loss_weight=0.25, type='GIoULoss'),
|
| 494 |
+
num_classes=10,
|
| 495 |
+
num_query=900,
|
| 496 |
+
positional_encoding=dict(
|
| 497 |
+
col_num_embed=50,
|
| 498 |
+
num_feats=128,
|
| 499 |
+
row_num_embed=50,
|
| 500 |
+
type='LearnedPositionalEncoding'),
|
| 501 |
+
sync_cls_avg_factor=True,
|
| 502 |
+
transformer=dict(
|
| 503 |
+
decoder=dict(
|
| 504 |
+
num_layers=6,
|
| 505 |
+
return_intermediate=True,
|
| 506 |
+
transformerlayers=dict(
|
| 507 |
+
attn_cfgs=[
|
| 508 |
+
dict(
|
| 509 |
+
dropout=0.1,
|
| 510 |
+
embed_dims=256,
|
| 511 |
+
num_heads=8,
|
| 512 |
+
type='MultiheadAttention'),
|
| 513 |
+
dict(
|
| 514 |
+
embed_dims=256,
|
| 515 |
+
num_levels=1,
|
| 516 |
+
type='CustomMSDeformableAttention'),
|
| 517 |
+
],
|
| 518 |
+
ffn_cfgs=dict(
|
| 519 |
+
feedforward_channels=512,
|
| 520 |
+
ffn_drop=0.1,
|
| 521 |
+
num_fcs=2,
|
| 522 |
+
type='FFN'),
|
| 523 |
+
operation_order=(
|
| 524 |
+
'self_attn',
|
| 525 |
+
'norm',
|
| 526 |
+
'cross_attn',
|
| 527 |
+
'norm',
|
| 528 |
+
'ffn',
|
| 529 |
+
'norm',
|
| 530 |
+
),
|
| 531 |
+
type='DetrTransformerDecoderLayer'),
|
| 532 |
+
type='DetectionTransformerDecoder'),
|
| 533 |
+
embed_dims=256,
|
| 534 |
+
encoder=dict(
|
| 535 |
+
num_layers=3,
|
| 536 |
+
num_points_in_pillar=8,
|
| 537 |
+
pc_range=[
|
| 538 |
+
-51.2,
|
| 539 |
+
-51.2,
|
| 540 |
+
-5.0,
|
| 541 |
+
51.2,
|
| 542 |
+
51.2,
|
| 543 |
+
3.0,
|
| 544 |
+
],
|
| 545 |
+
return_intermediate=False,
|
| 546 |
+
transformerlayers=dict(
|
| 547 |
+
attn_cfgs=[
|
| 548 |
+
dict(
|
| 549 |
+
embed_dims=256,
|
| 550 |
+
num_levels=1,
|
| 551 |
+
type='TemporalSelfAttention'),
|
| 552 |
+
dict(
|
| 553 |
+
deformable_attention=dict(
|
| 554 |
+
embed_dims=256,
|
| 555 |
+
num_levels=1,
|
| 556 |
+
num_points=8,
|
| 557 |
+
type='MSDeformableAttention3D'),
|
| 558 |
+
embed_dims=256,
|
| 559 |
+
pc_range=[
|
| 560 |
+
-51.2,
|
| 561 |
+
-51.2,
|
| 562 |
+
-5.0,
|
| 563 |
+
51.2,
|
| 564 |
+
51.2,
|
| 565 |
+
3.0,
|
| 566 |
+
],
|
| 567 |
+
type='SpatialCrossAttention'),
|
| 568 |
+
],
|
| 569 |
+
ffn_cfgs=dict(
|
| 570 |
+
feedforward_channels=512,
|
| 571 |
+
ffn_drop=0.1,
|
| 572 |
+
num_fcs=2,
|
| 573 |
+
type='FFN'),
|
| 574 |
+
operation_order=(
|
| 575 |
+
'self_attn',
|
| 576 |
+
'norm',
|
| 577 |
+
'cross_attn',
|
| 578 |
+
'norm',
|
| 579 |
+
'ffn',
|
| 580 |
+
'norm',
|
| 581 |
+
),
|
| 582 |
+
type='BEVFormerLayer'),
|
| 583 |
+
type='BEVFormerEncoder'),
|
| 584 |
+
num_cams=6,
|
| 585 |
+
num_feature_levels=1,
|
| 586 |
+
rotate_prev_bev=True,
|
| 587 |
+
type='PerceptionTransformer',
|
| 588 |
+
use_can_bus=True,
|
| 589 |
+
use_shift=True),
|
| 590 |
+
type='BEVFormerHead',
|
| 591 |
+
with_box_refine=True),
|
| 592 |
+
train_cfg=dict(
|
| 593 |
+
pts=dict(
|
| 594 |
+
assigner=dict(
|
| 595 |
+
cls_cost=dict(type='FocalCost', weight=2.0),
|
| 596 |
+
iou_cost=dict(type='SmoothL1Cost', weight=0.25),
|
| 597 |
+
pc_range=[
|
| 598 |
+
-51.2,
|
| 599 |
+
-51.2,
|
| 600 |
+
-5.0,
|
| 601 |
+
51.2,
|
| 602 |
+
51.2,
|
| 603 |
+
3.0,
|
| 604 |
+
],
|
| 605 |
+
reg_cost=dict(type='BBox3DL1Cost', weight=0.25),
|
| 606 |
+
type='HungarianAssigner3D'),
|
| 607 |
+
grid_size=[
|
| 608 |
+
512,
|
| 609 |
+
512,
|
| 610 |
+
1,
|
| 611 |
+
],
|
| 612 |
+
out_size_factor=4,
|
| 613 |
+
point_cloud_range=[
|
| 614 |
+
-51.2,
|
| 615 |
+
-51.2,
|
| 616 |
+
-5.0,
|
| 617 |
+
51.2,
|
| 618 |
+
51.2,
|
| 619 |
+
3.0,
|
| 620 |
+
],
|
| 621 |
+
voxel_size=[
|
| 622 |
+
0.2,
|
| 623 |
+
0.2,
|
| 624 |
+
8,
|
| 625 |
+
])),
|
| 626 |
+
type='BEVFormerDetector',
|
| 627 |
+
use_grid_mask=True,
|
| 628 |
+
video_test_mode=True)
|
| 629 |
+
optim_wrapper = dict(
|
| 630 |
+
optimizer=dict(lr=0.0001, type='AdamW', weight_decay=0.01),
|
| 631 |
+
type='OptimWrapper')
|
| 632 |
+
optimizer = dict(lr=0.0001, type='AdamW', weight_decay=0.01)
|
| 633 |
+
param_scheduler = dict(
|
| 634 |
+
milestones=[
|
| 635 |
+
1,
|
| 636 |
+
2,
|
| 637 |
+
], type='MultiStepLR')
|
| 638 |
+
point_cloud_range = [
|
| 639 |
+
-51.2,
|
| 640 |
+
-51.2,
|
| 641 |
+
-5.0,
|
| 642 |
+
51.2,
|
| 643 |
+
51.2,
|
| 644 |
+
3.0,
|
| 645 |
+
]
|
| 646 |
+
pts_bbox_head = dict(
|
| 647 |
+
as_two_stage=False,
|
| 648 |
+
bbox_coder=dict(
|
| 649 |
+
max_num=300,
|
| 650 |
+
num_classes=10,
|
| 651 |
+
pc_range=[
|
| 652 |
+
-51.2,
|
| 653 |
+
-51.2,
|
| 654 |
+
-5.0,
|
| 655 |
+
51.2,
|
| 656 |
+
51.2,
|
| 657 |
+
3.0,
|
| 658 |
+
],
|
| 659 |
+
post_center_range=[
|
| 660 |
+
-61.2,
|
| 661 |
+
-61.2,
|
| 662 |
+
-10.0,
|
| 663 |
+
61.2,
|
| 664 |
+
61.2,
|
| 665 |
+
10.0,
|
| 666 |
+
],
|
| 667 |
+
type='NMSFreeCoder',
|
| 668 |
+
voxel_size=[
|
| 669 |
+
0.2,
|
| 670 |
+
0.2,
|
| 671 |
+
8,
|
| 672 |
+
]),
|
| 673 |
+
bev_h=50,
|
| 674 |
+
bev_w=50,
|
| 675 |
+
in_channels=256,
|
| 676 |
+
loss_bbox=dict(loss_weight=0.5, type='L1Loss'),
|
| 677 |
+
loss_cls=dict(
|
| 678 |
+
alpha=0.25,
|
| 679 |
+
gamma=2.0,
|
| 680 |
+
loss_weight=2.0,
|
| 681 |
+
type='FocalLoss',
|
| 682 |
+
use_sigmoid=True),
|
| 683 |
+
loss_iou=dict(loss_weight=0.25, type='GIoULoss'),
|
| 684 |
+
num_classes=10,
|
| 685 |
+
num_query=900,
|
| 686 |
+
positional_encoding=dict(
|
| 687 |
+
col_num_embed=50,
|
| 688 |
+
num_feats=128,
|
| 689 |
+
row_num_embed=50,
|
| 690 |
+
type='LearnedPositionalEncoding'),
|
| 691 |
+
sync_cls_avg_factor=True,
|
| 692 |
+
transformer=dict(
|
| 693 |
+
decoder=dict(
|
| 694 |
+
num_layers=6,
|
| 695 |
+
return_intermediate=True,
|
| 696 |
+
transformerlayers=dict(
|
| 697 |
+
attn_cfgs=[
|
| 698 |
+
dict(
|
| 699 |
+
dropout=0.1,
|
| 700 |
+
embed_dims=256,
|
| 701 |
+
num_heads=8,
|
| 702 |
+
type='MultiheadAttention'),
|
| 703 |
+
dict(
|
| 704 |
+
embed_dims=256,
|
| 705 |
+
num_levels=1,
|
| 706 |
+
type='CustomMSDeformableAttention'),
|
| 707 |
+
],
|
| 708 |
+
ffn_cfgs=dict(
|
| 709 |
+
feedforward_channels=512,
|
| 710 |
+
ffn_drop=0.1,
|
| 711 |
+
num_fcs=2,
|
| 712 |
+
type='FFN'),
|
| 713 |
+
operation_order=(
|
| 714 |
+
'self_attn',
|
| 715 |
+
'norm',
|
| 716 |
+
'cross_attn',
|
| 717 |
+
'norm',
|
| 718 |
+
'ffn',
|
| 719 |
+
'norm',
|
| 720 |
+
),
|
| 721 |
+
type='DetrTransformerDecoderLayer'),
|
| 722 |
+
type='DetectionTransformerDecoder'),
|
| 723 |
+
embed_dims=256,
|
| 724 |
+
encoder=dict(
|
| 725 |
+
num_layers=3,
|
| 726 |
+
num_points_in_pillar=8,
|
| 727 |
+
pc_range=[
|
| 728 |
+
-51.2,
|
| 729 |
+
-51.2,
|
| 730 |
+
-5.0,
|
| 731 |
+
51.2,
|
| 732 |
+
51.2,
|
| 733 |
+
3.0,
|
| 734 |
+
],
|
| 735 |
+
return_intermediate=False,
|
| 736 |
+
transformerlayers=dict(
|
| 737 |
+
attn_cfgs=[
|
| 738 |
+
dict(
|
| 739 |
+
embed_dims=256,
|
| 740 |
+
num_levels=1,
|
| 741 |
+
type='TemporalSelfAttention'),
|
| 742 |
+
dict(
|
| 743 |
+
deformable_attention=dict(
|
| 744 |
+
embed_dims=256,
|
| 745 |
+
num_levels=1,
|
| 746 |
+
num_points=8,
|
| 747 |
+
type='MSDeformableAttention3D'),
|
| 748 |
+
embed_dims=256,
|
| 749 |
+
pc_range=[
|
| 750 |
+
-51.2,
|
| 751 |
+
-51.2,
|
| 752 |
+
-5.0,
|
| 753 |
+
51.2,
|
| 754 |
+
51.2,
|
| 755 |
+
3.0,
|
| 756 |
+
],
|
| 757 |
+
type='SpatialCrossAttention'),
|
| 758 |
+
],
|
| 759 |
+
ffn_cfgs=dict(
|
| 760 |
+
feedforward_channels=512,
|
| 761 |
+
ffn_drop=0.1,
|
| 762 |
+
num_fcs=2,
|
| 763 |
+
type='FFN'),
|
| 764 |
+
operation_order=(
|
| 765 |
+
'self_attn',
|
| 766 |
+
'norm',
|
| 767 |
+
'cross_attn',
|
| 768 |
+
'norm',
|
| 769 |
+
'ffn',
|
| 770 |
+
'norm',
|
| 771 |
+
),
|
| 772 |
+
type='BEVFormerLayer'),
|
| 773 |
+
type='BEVFormerEncoder'),
|
| 774 |
+
num_cams=6,
|
| 775 |
+
num_feature_levels=1,
|
| 776 |
+
rotate_prev_bev=True,
|
| 777 |
+
type='PerceptionTransformer',
|
| 778 |
+
use_can_bus=True,
|
| 779 |
+
use_shift=True),
|
| 780 |
+
type='BEVFormerHead',
|
| 781 |
+
with_box_refine=True)
|
| 782 |
+
queue_length = 4
|
| 783 |
+
resume = False
|
| 784 |
+
scales = [
|
| 785 |
+
0.5,
|
| 786 |
+
]
|
| 787 |
+
test_cfg = dict(max_iters=1)
|
| 788 |
+
test_dataloader = dict(
|
| 789 |
+
batch_size=1,
|
| 790 |
+
collate_fn=dict(type='test_collate'),
|
| 791 |
+
dataset=dict(
|
| 792 |
+
ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl',
|
| 793 |
+
bev_size=(
|
| 794 |
+
50,
|
| 795 |
+
50,
|
| 796 |
+
),
|
| 797 |
+
classes=[
|
| 798 |
+
'car',
|
| 799 |
+
'truck',
|
| 800 |
+
'construction_vehicle',
|
| 801 |
+
'bus',
|
| 802 |
+
'trailer',
|
| 803 |
+
'barrier',
|
| 804 |
+
'motorcycle',
|
| 805 |
+
'bicycle',
|
| 806 |
+
'pedestrian',
|
| 807 |
+
'traffic_cone',
|
| 808 |
+
],
|
| 809 |
+
data_root='data/nuscenes/v1.0-mini/',
|
| 810 |
+
frame=[
|
| 811 |
+
-3,
|
| 812 |
+
-2,
|
| 813 |
+
-1,
|
| 814 |
+
],
|
| 815 |
+
modality=dict(
|
| 816 |
+
use_camera=True,
|
| 817 |
+
use_external=False,
|
| 818 |
+
use_lidar=False,
|
| 819 |
+
use_map=False,
|
| 820 |
+
use_radar=False),
|
| 821 |
+
pipeline=[
|
| 822 |
+
dict(to_float32=True, type='LoadMultiViewImageFromFiles'),
|
| 823 |
+
dict(
|
| 824 |
+
mean=[
|
| 825 |
+
123.675,
|
| 826 |
+
116.28,
|
| 827 |
+
103.53,
|
| 828 |
+
],
|
| 829 |
+
std=[
|
| 830 |
+
58.395,
|
| 831 |
+
57.12,
|
| 832 |
+
57.375,
|
| 833 |
+
],
|
| 834 |
+
to_rgb=True,
|
| 835 |
+
type='NormalizeMultiviewImage'),
|
| 836 |
+
dict(
|
| 837 |
+
flip=False,
|
| 838 |
+
img_scale=(
|
| 839 |
+
800,
|
| 840 |
+
450,
|
| 841 |
+
),
|
| 842 |
+
pts_scale_ratio=[
|
| 843 |
+
1.0,
|
| 844 |
+
],
|
| 845 |
+
transforms=[
|
| 846 |
+
dict(
|
| 847 |
+
scales=[
|
| 848 |
+
0.5,
|
| 849 |
+
], type='RandomScaleImageMultiViewImage'),
|
| 850 |
+
dict(size_divisor=32, type='PadMultiViewImage'),
|
| 851 |
+
dict(
|
| 852 |
+
class_names=[
|
| 853 |
+
'car',
|
| 854 |
+
'truck',
|
| 855 |
+
'construction_vehicle',
|
| 856 |
+
'bus',
|
| 857 |
+
'trailer',
|
| 858 |
+
'barrier',
|
| 859 |
+
'motorcycle',
|
| 860 |
+
'bicycle',
|
| 861 |
+
'pedestrian',
|
| 862 |
+
'traffic_cone',
|
| 863 |
+
],
|
| 864 |
+
type='CustomDefaultFormatBundle3D'),
|
| 865 |
+
dict(keys=[
|
| 866 |
+
'img',
|
| 867 |
+
], type='CustomCollect3D'),
|
| 868 |
+
],
|
| 869 |
+
type='MultiScaleFlipAug3D'),
|
| 870 |
+
],
|
| 871 |
+
test_mode=True,
|
| 872 |
+
type='CustomNuScenesDataset'),
|
| 873 |
+
num_workers=0,
|
| 874 |
+
sampler=dict(shuffle=True, type='DefaultSampler'))
|
| 875 |
+
test_evaluator = dict(metrics=[
|
| 876 |
+
dict(
|
| 877 |
+
ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl',
|
| 878 |
+
data_root='data/nuscenes/v1.0-mini/',
|
| 879 |
+
type='src.NuScenesMetric',
|
| 880 |
+
version='v1.0-mini'),
|
| 881 |
+
])
|
| 882 |
+
test_max_iters = 1
|
| 883 |
+
test_pipeline = [
|
| 884 |
+
dict(to_float32=True, type='LoadMultiViewImageFromFiles'),
|
| 885 |
+
dict(
|
| 886 |
+
mean=[
|
| 887 |
+
123.675,
|
| 888 |
+
116.28,
|
| 889 |
+
103.53,
|
| 890 |
+
],
|
| 891 |
+
std=[
|
| 892 |
+
58.395,
|
| 893 |
+
57.12,
|
| 894 |
+
57.375,
|
| 895 |
+
],
|
| 896 |
+
to_rgb=True,
|
| 897 |
+
type='NormalizeMultiviewImage'),
|
| 898 |
+
dict(
|
| 899 |
+
flip=False,
|
| 900 |
+
img_scale=(
|
| 901 |
+
800,
|
| 902 |
+
450,
|
| 903 |
+
),
|
| 904 |
+
pts_scale_ratio=[
|
| 905 |
+
1.0,
|
| 906 |
+
],
|
| 907 |
+
transforms=[
|
| 908 |
+
dict(scales=[
|
| 909 |
+
0.5,
|
| 910 |
+
], type='RandomScaleImageMultiViewImage'),
|
| 911 |
+
dict(size_divisor=32, type='PadMultiViewImage'),
|
| 912 |
+
dict(
|
| 913 |
+
class_names=[
|
| 914 |
+
'car',
|
| 915 |
+
'truck',
|
| 916 |
+
'construction_vehicle',
|
| 917 |
+
'bus',
|
| 918 |
+
'trailer',
|
| 919 |
+
'barrier',
|
| 920 |
+
'motorcycle',
|
| 921 |
+
'bicycle',
|
| 922 |
+
'pedestrian',
|
| 923 |
+
'traffic_cone',
|
| 924 |
+
],
|
| 925 |
+
type='CustomDefaultFormatBundle3D'),
|
| 926 |
+
dict(keys=[
|
| 927 |
+
'img',
|
| 928 |
+
], type='CustomCollect3D'),
|
| 929 |
+
],
|
| 930 |
+
type='MultiScaleFlipAug3D'),
|
| 931 |
+
]
|
| 932 |
+
train_cfg = dict(by_epoch=False, max_epochs=5, max_iters=2, val_interval=1)
|
| 933 |
+
train_dataloader = dict(
|
| 934 |
+
batch_size=1,
|
| 935 |
+
collate_fn=dict(type='train_collate'),
|
| 936 |
+
dataset=dict(
|
| 937 |
+
ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_train.pkl',
|
| 938 |
+
bev_size=(
|
| 939 |
+
50,
|
| 940 |
+
50,
|
| 941 |
+
),
|
| 942 |
+
box_type_3d='LiDAR',
|
| 943 |
+
classes=[
|
| 944 |
+
'car',
|
| 945 |
+
'truck',
|
| 946 |
+
'construction_vehicle',
|
| 947 |
+
'bus',
|
| 948 |
+
'trailer',
|
| 949 |
+
'barrier',
|
| 950 |
+
'motorcycle',
|
| 951 |
+
'bicycle',
|
| 952 |
+
'pedestrian',
|
| 953 |
+
'traffic_cone',
|
| 954 |
+
],
|
| 955 |
+
data_root='data/nuscenes/v1.0-mini/',
|
| 956 |
+
modality=dict(
|
| 957 |
+
use_camera=True,
|
| 958 |
+
use_external=False,
|
| 959 |
+
use_lidar=False,
|
| 960 |
+
use_map=False,
|
| 961 |
+
use_radar=False),
|
| 962 |
+
pipeline=[
|
| 963 |
+
dict(to_float32=True, type='LoadMultiViewImageFromFiles'),
|
| 964 |
+
dict(
|
| 965 |
+
type='LoadAnnotations3D',
|
| 966 |
+
with_bbox_3d=True,
|
| 967 |
+
with_label_3d=True),
|
| 968 |
+
dict(
|
| 969 |
+
point_cloud_range=[
|
| 970 |
+
-51.2,
|
| 971 |
+
-51.2,
|
| 972 |
+
-5.0,
|
| 973 |
+
51.2,
|
| 974 |
+
51.2,
|
| 975 |
+
3.0,
|
| 976 |
+
],
|
| 977 |
+
type='ObjectRangeFilter'),
|
| 978 |
+
dict(
|
| 979 |
+
classes=[
|
| 980 |
+
'car',
|
| 981 |
+
'truck',
|
| 982 |
+
'construction_vehicle',
|
| 983 |
+
'bus',
|
| 984 |
+
'trailer',
|
| 985 |
+
'barrier',
|
| 986 |
+
'motorcycle',
|
| 987 |
+
'bicycle',
|
| 988 |
+
'pedestrian',
|
| 989 |
+
'traffic_cone',
|
| 990 |
+
],
|
| 991 |
+
type='ObjectNameFilter'),
|
| 992 |
+
dict(type='PhotoMetricDistortionMultiViewImage'),
|
| 993 |
+
dict(
|
| 994 |
+
mean=[
|
| 995 |
+
123.675,
|
| 996 |
+
116.28,
|
| 997 |
+
103.53,
|
| 998 |
+
],
|
| 999 |
+
std=[
|
| 1000 |
+
58.395,
|
| 1001 |
+
57.12,
|
| 1002 |
+
57.375,
|
| 1003 |
+
],
|
| 1004 |
+
to_rgb=True,
|
| 1005 |
+
type='NormalizeMultiviewImage'),
|
| 1006 |
+
dict(scales=[
|
| 1007 |
+
0.5,
|
| 1008 |
+
], type='RandomScaleImageMultiViewImage'),
|
| 1009 |
+
dict(size_divisor=32, type='PadMultiViewImage'),
|
| 1010 |
+
dict(
|
| 1011 |
+
class_names=[
|
| 1012 |
+
'car',
|
| 1013 |
+
'truck',
|
| 1014 |
+
'construction_vehicle',
|
| 1015 |
+
'bus',
|
| 1016 |
+
'trailer',
|
| 1017 |
+
'barrier',
|
| 1018 |
+
'motorcycle',
|
| 1019 |
+
'bicycle',
|
| 1020 |
+
'pedestrian',
|
| 1021 |
+
'traffic_cone',
|
| 1022 |
+
],
|
| 1023 |
+
type='CustomDefaultFormatBundle3D'),
|
| 1024 |
+
dict(
|
| 1025 |
+
keys=[
|
| 1026 |
+
'gt_bboxes_3d',
|
| 1027 |
+
'gt_labels_3d',
|
| 1028 |
+
'img',
|
| 1029 |
+
],
|
| 1030 |
+
type='CustomCollect3D'),
|
| 1031 |
+
dict(type='TypeConverter'),
|
| 1032 |
+
],
|
| 1033 |
+
queue_length=4,
|
| 1034 |
+
test_mode=False,
|
| 1035 |
+
type='CustomNuScenesDataset',
|
| 1036 |
+
use_valid_flag=True),
|
| 1037 |
+
num_workers=0,
|
| 1038 |
+
sampler=dict(shuffle=True, type='DefaultSampler'))
|
| 1039 |
+
train_pipeline = [
|
| 1040 |
+
dict(to_float32=True, type='LoadMultiViewImageFromFiles'),
|
| 1041 |
+
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
|
| 1042 |
+
dict(
|
| 1043 |
+
point_cloud_range=[
|
| 1044 |
+
-51.2,
|
| 1045 |
+
-51.2,
|
| 1046 |
+
-5.0,
|
| 1047 |
+
51.2,
|
| 1048 |
+
51.2,
|
| 1049 |
+
3.0,
|
| 1050 |
+
],
|
| 1051 |
+
type='ObjectRangeFilter'),
|
| 1052 |
+
dict(
|
| 1053 |
+
classes=[
|
| 1054 |
+
'car',
|
| 1055 |
+
'truck',
|
| 1056 |
+
'construction_vehicle',
|
| 1057 |
+
'bus',
|
| 1058 |
+
'trailer',
|
| 1059 |
+
'barrier',
|
| 1060 |
+
'motorcycle',
|
| 1061 |
+
'bicycle',
|
| 1062 |
+
'pedestrian',
|
| 1063 |
+
'traffic_cone',
|
| 1064 |
+
],
|
| 1065 |
+
type='ObjectNameFilter'),
|
| 1066 |
+
dict(type='PhotoMetricDistortionMultiViewImage'),
|
| 1067 |
+
dict(
|
| 1068 |
+
mean=[
|
| 1069 |
+
123.675,
|
| 1070 |
+
116.28,
|
| 1071 |
+
103.53,
|
| 1072 |
+
],
|
| 1073 |
+
std=[
|
| 1074 |
+
58.395,
|
| 1075 |
+
57.12,
|
| 1076 |
+
57.375,
|
| 1077 |
+
],
|
| 1078 |
+
to_rgb=True,
|
| 1079 |
+
type='NormalizeMultiviewImage'),
|
| 1080 |
+
dict(scales=[
|
| 1081 |
+
0.5,
|
| 1082 |
+
], type='RandomScaleImageMultiViewImage'),
|
| 1083 |
+
dict(size_divisor=32, type='PadMultiViewImage'),
|
| 1084 |
+
dict(
|
| 1085 |
+
class_names=[
|
| 1086 |
+
'car',
|
| 1087 |
+
'truck',
|
| 1088 |
+
'construction_vehicle',
|
| 1089 |
+
'bus',
|
| 1090 |
+
'trailer',
|
| 1091 |
+
'barrier',
|
| 1092 |
+
'motorcycle',
|
| 1093 |
+
'bicycle',
|
| 1094 |
+
'pedestrian',
|
| 1095 |
+
'traffic_cone',
|
| 1096 |
+
],
|
| 1097 |
+
type='CustomDefaultFormatBundle3D'),
|
| 1098 |
+
dict(
|
| 1099 |
+
keys=[
|
| 1100 |
+
'gt_bboxes_3d',
|
| 1101 |
+
'gt_labels_3d',
|
| 1102 |
+
'img',
|
| 1103 |
+
], type='CustomCollect3D'),
|
| 1104 |
+
dict(type='TypeConverter'),
|
| 1105 |
+
]
|
| 1106 |
+
transformer = dict(
|
| 1107 |
+
decoder=dict(
|
| 1108 |
+
num_layers=6,
|
| 1109 |
+
return_intermediate=True,
|
| 1110 |
+
transformerlayers=dict(
|
| 1111 |
+
attn_cfgs=[
|
| 1112 |
+
dict(
|
| 1113 |
+
dropout=0.1,
|
| 1114 |
+
embed_dims=256,
|
| 1115 |
+
num_heads=8,
|
| 1116 |
+
type='MultiheadAttention'),
|
| 1117 |
+
dict(
|
| 1118 |
+
embed_dims=256,
|
| 1119 |
+
num_levels=1,
|
| 1120 |
+
type='CustomMSDeformableAttention'),
|
| 1121 |
+
],
|
| 1122 |
+
ffn_cfgs=dict(
|
| 1123 |
+
feedforward_channels=512, ffn_drop=0.1, num_fcs=2, type='FFN'),
|
| 1124 |
+
operation_order=(
|
| 1125 |
+
'self_attn',
|
| 1126 |
+
'norm',
|
| 1127 |
+
'cross_attn',
|
| 1128 |
+
'norm',
|
| 1129 |
+
'ffn',
|
| 1130 |
+
'norm',
|
| 1131 |
+
),
|
| 1132 |
+
type='DetrTransformerDecoderLayer'),
|
| 1133 |
+
type='DetectionTransformerDecoder'),
|
| 1134 |
+
embed_dims=256,
|
| 1135 |
+
encoder=dict(
|
| 1136 |
+
num_layers=3,
|
| 1137 |
+
num_points_in_pillar=8,
|
| 1138 |
+
pc_range=[
|
| 1139 |
+
-51.2,
|
| 1140 |
+
-51.2,
|
| 1141 |
+
-5.0,
|
| 1142 |
+
51.2,
|
| 1143 |
+
51.2,
|
| 1144 |
+
3.0,
|
| 1145 |
+
],
|
| 1146 |
+
return_intermediate=False,
|
| 1147 |
+
transformerlayers=dict(
|
| 1148 |
+
attn_cfgs=[
|
| 1149 |
+
dict(
|
| 1150 |
+
embed_dims=256, num_levels=1,
|
| 1151 |
+
type='TemporalSelfAttention'),
|
| 1152 |
+
dict(
|
| 1153 |
+
deformable_attention=dict(
|
| 1154 |
+
embed_dims=256,
|
| 1155 |
+
num_levels=1,
|
| 1156 |
+
num_points=8,
|
| 1157 |
+
type='MSDeformableAttention3D'),
|
| 1158 |
+
embed_dims=256,
|
| 1159 |
+
pc_range=[
|
| 1160 |
+
-51.2,
|
| 1161 |
+
-51.2,
|
| 1162 |
+
-5.0,
|
| 1163 |
+
51.2,
|
| 1164 |
+
51.2,
|
| 1165 |
+
3.0,
|
| 1166 |
+
],
|
| 1167 |
+
type='SpatialCrossAttention'),
|
| 1168 |
+
],
|
| 1169 |
+
ffn_cfgs=dict(
|
| 1170 |
+
feedforward_channels=512, ffn_drop=0.1, num_fcs=2, type='FFN'),
|
| 1171 |
+
operation_order=(
|
| 1172 |
+
'self_attn',
|
| 1173 |
+
'norm',
|
| 1174 |
+
'cross_attn',
|
| 1175 |
+
'norm',
|
| 1176 |
+
'ffn',
|
| 1177 |
+
'norm',
|
| 1178 |
+
),
|
| 1179 |
+
type='BEVFormerLayer'),
|
| 1180 |
+
type='BEVFormerEncoder'),
|
| 1181 |
+
num_cams=6,
|
| 1182 |
+
num_feature_levels=1,
|
| 1183 |
+
rotate_prev_bev=True,
|
| 1184 |
+
type='PerceptionTransformer',
|
| 1185 |
+
use_can_bus=True,
|
| 1186 |
+
use_shift=True)
|
| 1187 |
+
val_cfg = dict(max_iters=1)
|
| 1188 |
+
val_dataloader = dict(
|
| 1189 |
+
batch_size=1,
|
| 1190 |
+
collate_fn=dict(type='test_collate'),
|
| 1191 |
+
dataset=dict(
|
| 1192 |
+
ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl',
|
| 1193 |
+
bev_size=(
|
| 1194 |
+
50,
|
| 1195 |
+
50,
|
| 1196 |
+
),
|
| 1197 |
+
classes=[
|
| 1198 |
+
'car',
|
| 1199 |
+
'truck',
|
| 1200 |
+
'construction_vehicle',
|
| 1201 |
+
'bus',
|
| 1202 |
+
'trailer',
|
| 1203 |
+
'barrier',
|
| 1204 |
+
'motorcycle',
|
| 1205 |
+
'bicycle',
|
| 1206 |
+
'pedestrian',
|
| 1207 |
+
'traffic_cone',
|
| 1208 |
+
],
|
| 1209 |
+
data_root='data/nuscenes/v1.0-mini/',
|
| 1210 |
+
frame=(),
|
| 1211 |
+
frames=[
|
| 1212 |
+
-3,
|
| 1213 |
+
-2,
|
| 1214 |
+
-1,
|
| 1215 |
+
],
|
| 1216 |
+
modality=dict(
|
| 1217 |
+
use_camera=True,
|
| 1218 |
+
use_external=False,
|
| 1219 |
+
use_lidar=False,
|
| 1220 |
+
use_map=False,
|
| 1221 |
+
use_radar=False),
|
| 1222 |
+
pipeline=[
|
| 1223 |
+
dict(to_float32=True, type='LoadMultiViewImageFromFiles'),
|
| 1224 |
+
dict(
|
| 1225 |
+
mean=[
|
| 1226 |
+
123.675,
|
| 1227 |
+
116.28,
|
| 1228 |
+
103.53,
|
| 1229 |
+
],
|
| 1230 |
+
std=[
|
| 1231 |
+
58.395,
|
| 1232 |
+
57.12,
|
| 1233 |
+
57.375,
|
| 1234 |
+
],
|
| 1235 |
+
to_rgb=True,
|
| 1236 |
+
type='NormalizeMultiviewImage'),
|
| 1237 |
+
dict(
|
| 1238 |
+
flip=False,
|
| 1239 |
+
img_scale=(
|
| 1240 |
+
800,
|
| 1241 |
+
450,
|
| 1242 |
+
),
|
| 1243 |
+
pts_scale_ratio=[
|
| 1244 |
+
1.0,
|
| 1245 |
+
],
|
| 1246 |
+
transforms=[
|
| 1247 |
+
dict(
|
| 1248 |
+
scales=[
|
| 1249 |
+
0.5,
|
| 1250 |
+
], type='RandomScaleImageMultiViewImage'),
|
| 1251 |
+
dict(size_divisor=32, type='PadMultiViewImage'),
|
| 1252 |
+
dict(
|
| 1253 |
+
class_names=[
|
| 1254 |
+
'car',
|
| 1255 |
+
'truck',
|
| 1256 |
+
'construction_vehicle',
|
| 1257 |
+
'bus',
|
| 1258 |
+
'trailer',
|
| 1259 |
+
'barrier',
|
| 1260 |
+
'motorcycle',
|
| 1261 |
+
'bicycle',
|
| 1262 |
+
'pedestrian',
|
| 1263 |
+
'traffic_cone',
|
| 1264 |
+
],
|
| 1265 |
+
type='CustomDefaultFormatBundle3D'),
|
| 1266 |
+
dict(keys=[
|
| 1267 |
+
'img',
|
| 1268 |
+
], type='CustomCollect3D'),
|
| 1269 |
+
],
|
| 1270 |
+
type='MultiScaleFlipAug3D'),
|
| 1271 |
+
],
|
| 1272 |
+
samples_per_gpu=1,
|
| 1273 |
+
test_mode=True,
|
| 1274 |
+
type='CustomNuScenesDataset'),
|
| 1275 |
+
num_workers=0,
|
| 1276 |
+
sampler=dict(shuffle=True, type='DefaultSampler'))
|
| 1277 |
+
val_evaluator = dict(metrics=[
|
| 1278 |
+
dict(
|
| 1279 |
+
ann_file='data/nuscenes/v1.0-mini/nuscenes_infos_temporal_val.pkl',
|
| 1280 |
+
classes=[
|
| 1281 |
+
'car',
|
| 1282 |
+
'truck',
|
| 1283 |
+
'construction_vehicle',
|
| 1284 |
+
'bus',
|
| 1285 |
+
'trailer',
|
| 1286 |
+
'barrier',
|
| 1287 |
+
'motorcycle',
|
| 1288 |
+
'bicycle',
|
| 1289 |
+
'pedestrian',
|
| 1290 |
+
'traffic_cone',
|
| 1291 |
+
],
|
| 1292 |
+
data_root='data/nuscenes/v1.0-mini/',
|
| 1293 |
+
jsonfile_prefix='results',
|
| 1294 |
+
modality=dict(
|
| 1295 |
+
use_camera=True,
|
| 1296 |
+
use_external=False,
|
| 1297 |
+
use_lidar=False,
|
| 1298 |
+
use_map=False,
|
| 1299 |
+
use_radar=False),
|
| 1300 |
+
plot_every_run=True,
|
| 1301 |
+
plot_examples=1,
|
| 1302 |
+
type='src.NuScenesMetric',
|
| 1303 |
+
version='v1.0-mini'),
|
| 1304 |
+
])
|
| 1305 |
+
val_interval = 1
|
| 1306 |
+
val_max_iters = 1
|
| 1307 |
+
version = 'v1.0-mini'
|
| 1308 |
+
visualizer = dict(
|
| 1309 |
+
type='Visualizer',
|
| 1310 |
+
vis_backends=[
|
| 1311 |
+
dict(type='LocalVisBackend'),
|
| 1312 |
+
dict(type='TensorboardVisBackend'),
|
| 1313 |
+
])
|
| 1314 |
+
voxel_size = [
|
| 1315 |
+
0.2,
|
| 1316 |
+
0.2,
|
| 1317 |
+
8,
|
| 1318 |
+
]
|
| 1319 |
+
work_dir = 'experiment'
|
20260315_224809/events.out.tfevents.1773629289.Minhs-MacBook-Air.local.50677.0
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:35af3ee9f27095eb3711359ebb596af0f8f6cbe0475444f4dabe8f31fbc6589c
|
| 3 |
+
size 39662
|
20260315_224809/scalars.json
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{"lr": 0.0001, "data_time": 1.0969552993774414, "loss": 58.39009094238281, "loss_cls": 2.302908182144165, "loss_bbox": 7.731889247894287, "d0.loss_cls": 2.2785091400146484, "d0.loss_bbox": 7.366260528564453, "d1.loss_cls": 2.2856218814849854, "d1.loss_bbox": 7.410768032073975, "d2.loss_cls": 2.240107536315918, "d2.loss_bbox": 7.315112590789795, "d3.loss_cls": 2.2907700538635254, "d3.loss_bbox": 7.372400760650635, "d4.loss_cls": 2.3866851329803467, "d4.loss_bbox": 7.409055709838867, "time": 24.124476194381714, "epoch": 1, "iter": 1, "step": 1}
|
| 2 |
+
{"NDS": 0.018478272642075605, "mAP": 0.00023853881585173065, "data_time": 0.6630229949951172, "time": 2.191067934036255, "step": 1}
|
| 3 |
+
{"lr": 0.0001, "data_time": 10.68934166431427, "loss": 54.70582580566406, "loss_cls": 2.289698362350464, "loss_bbox": 7.151990175247192, "d0.loss_cls": 2.2585201263427734, "d0.loss_bbox": 6.771386623382568, "d1.loss_cls": 2.226685881614685, "d1.loss_bbox": 6.752256631851196, "d2.loss_cls": 2.1500898599624634, "d2.loss_bbox": 6.782212257385254, "d3.loss_cls": 2.218158483505249, "d3.loss_bbox": 6.89587140083313, "d4.loss_cls": 2.27994966506958, "d4.loss_bbox": 6.929007530212402, "time": 32.79427921772003, "epoch": 1, "iter": 2, "step": 2}
|
| 4 |
+
{"NDS": 0.017786070826831646, "mAP": 0.0002481977075675005, "data_time": 0.7300244569778442, "time": 2.301406979560852, "step": 2}
|