File size: 8,245 Bytes
34d1f8b |
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 |
_base_ = [
# 'mmdet3d::_base_/datasets/nus-3d.py',
'../../../configs/_base_/default_runtime.py'
]
custom_imports = dict(imports=['projects.DETR3D.detr3d'])
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
voxel_size = [0.2, 0.2, 8]
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], bgr_to_rgb=False)
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
input_modality = dict(
use_lidar=False,
use_camera=True,
use_radar=False,
use_map=False,
use_external=False)
# this means type='DETR3D' will be processed as 'mmdet3d.DETR3D'
default_scope = 'mmdet3d'
model = dict(
type='DETR3D',
use_grid_mask=True,
data_preprocessor=dict(
type='Det3DDataPreprocessor', **img_norm_cfg, pad_size_divisor=32),
img_backbone=dict(
type='mmdet.ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN2d', requires_grad=False),
norm_eval=True,
style='caffe',
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, False, True, True)),
img_neck=dict(
type='mmdet.FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_output',
num_outs=4,
relu_before_extra_convs=True),
pts_bbox_head=dict(
type='DETR3DHead',
num_query=900,
num_classes=10,
in_channels=256,
sync_cls_avg_factor=True,
with_box_refine=True,
as_two_stage=False,
transformer=dict(
type='Detr3DTransformer',
decoder=dict(
type='Detr3DTransformerDecoder',
num_layers=6,
return_intermediate=True,
transformerlayers=dict(
type='mmdet.DetrTransformerDecoderLayer',
attn_cfgs=[
dict(
type='MultiheadAttention', # mmcv.
embed_dims=256,
num_heads=8,
dropout=0.1),
dict(
type='Detr3DCrossAtten',
pc_range=point_cloud_range,
num_points=1,
embed_dims=256)
],
feedforward_channels=512,
ffn_dropout=0.1,
operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
'ffn', 'norm')))),
bbox_coder=dict(
type='NMSFreeCoder',
post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
pc_range=point_cloud_range,
max_num=300,
voxel_size=voxel_size,
num_classes=10),
positional_encoding=dict(
type='mmdet.SinePositionalEncoding',
num_feats=128,
normalize=True,
offset=-0.5),
loss_cls=dict(
type='mmdet.FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=2.0),
loss_bbox=dict(type='mmdet.L1Loss', loss_weight=0.25),
loss_iou=dict(type='mmdet.GIoULoss', loss_weight=0.0)),
# model training and testing settings
train_cfg=dict(
pts=dict(
grid_size=[512, 512, 1],
voxel_size=voxel_size,
point_cloud_range=point_cloud_range,
out_size_factor=4,
assigner=dict(
type='HungarianAssigner3D',
cls_cost=dict(type='mmdet.FocalLossCost', weight=2.0),
reg_cost=dict(type='BBox3DL1Cost', weight=0.25),
# ↓ Fake cost. This is just to get compatible with DETR head
iou_cost=dict(type='mmdet.IoUCost', weight=0.0),
pc_range=point_cloud_range))))
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
test_transforms = [
dict(
type='RandomResize3D',
scale=(1600, 900),
ratio_range=(1., 1.),
keep_ratio=True)
]
train_transforms = [dict(type='PhotoMetricDistortion3D')] + test_transforms
backend_args = None
train_pipeline = [
dict(
type='LoadMultiViewImageFromFiles',
to_float32=True,
num_views=6,
backend_args=backend_args),
dict(
type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
with_attr_label=False),
dict(type='MultiViewWrapper', transforms=train_transforms),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectNameFilter', classes=class_names),
dict(type='Pack3DDetInputs', keys=['img', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(
type='LoadMultiViewImageFromFiles',
to_float32=True,
num_views=6,
backend_args=backend_args),
dict(type='MultiViewWrapper', transforms=test_transforms),
dict(type='Pack3DDetInputs', keys=['img'])
]
metainfo = dict(classes=class_names)
data_prefix = dict(
pts='',
CAM_FRONT='samples/CAM_FRONT',
CAM_FRONT_LEFT='samples/CAM_FRONT_LEFT',
CAM_FRONT_RIGHT='samples/CAM_FRONT_RIGHT',
CAM_BACK='samples/CAM_BACK',
CAM_BACK_RIGHT='samples/CAM_BACK_RIGHT',
CAM_BACK_LEFT='samples/CAM_BACK_LEFT')
train_dataloader = dict(
batch_size=1,
num_workers=4,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='nuscenes_infos_train.pkl',
pipeline=train_pipeline,
load_type='frame_based',
metainfo=metainfo,
modality=input_modality,
test_mode=False,
data_prefix=data_prefix,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='LiDAR',
backend_args=backend_args))
val_dataloader = dict(
batch_size=1,
num_workers=4,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='nuscenes_infos_val.pkl',
load_type='frame_based',
pipeline=test_pipeline,
metainfo=metainfo,
modality=input_modality,
test_mode=True,
data_prefix=data_prefix,
box_type_3d='LiDAR',
backend_args=backend_args))
test_dataloader = val_dataloader
val_evaluator = dict(
type='NuScenesMetric',
data_root=data_root,
ann_file=data_root + 'nuscenes_infos_val.pkl',
metric='bbox',
backend_args=backend_args)
test_evaluator = val_evaluator
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=2e-4, weight_decay=0.01),
paramwise_cfg=dict(custom_keys={'img_backbone': dict(lr_mult=0.1)}),
clip_grad=dict(max_norm=35, norm_type=2),
)
# learning policy
param_scheduler = [
dict(
type='LinearLR',
start_factor=1.0 / 3,
by_epoch=False,
begin=0,
end=500),
dict(
type='CosineAnnealingLR',
by_epoch=True,
begin=0,
end=24,
T_max=24,
eta_min_ratio=1e-3)
]
total_epochs = 24
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=total_epochs, val_interval=2)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
default_hooks = dict(
checkpoint=dict(
type='CheckpointHook', interval=1, max_keep_ckpts=1, save_last=True))
load_from = 'ckpts/fcos3d.pth'
# setuptools 65 downgrades to 58.
# In mmlab-node we use setuptools 61 but occurs NO errors
vis_backends = [dict(type='TensorboardVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
|