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cmt_lidar_voxel0075_cbgs.py
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cmt_lidar_voxel0075_cbgs.py
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plugin=True
plugin_dir='projects/mmdet3d_plugin/'
point_cloud_range = [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
voxel_size = [0.075, 0.075, 0.2]
out_size_factor = 8
evaluation = dict(interval=20)
dataset_type = 'CustomNuScenesDataset'
data_root = 'data/nuscenes/'
input_modality = dict(
use_lidar=True,
use_camera=False,
use_radar=False,
use_map=False,
use_external=False)
train_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=[0, 1, 2, 3, 4],
),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
use_dim=[0, 1, 2, 3, 4],
),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(
type='ObjectSample',
db_sampler=dict(
data_root=None,
info_path=data_root + 'nuscenes_dbinfos_train.pkl',
rate=1.0,
prepare=dict(
filter_by_difficulty=[-1],
filter_by_min_points=dict(
car=5,
truck=5,
bus=5,
trailer=5,
construction_vehicle=5,
traffic_cone=5,
barrier=5,
motorcycle=5,
bicycle=5,
pedestrian=5)),
classes=class_names,
sample_groups=dict(
car=2,
truck=3,
construction_vehicle=7,
bus=4,
trailer=6,
barrier=2,
motorcycle=6,
bicycle=6,
pedestrian=2,
traffic_cone=2),
points_loader=dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=[0, 1, 2, 3, 4],
))),
dict(
type='GlobalRotScaleTrans',
rot_range=[-0.3925 * 2, 0.3925 * 2],
scale_ratio_range=[0.9, 1.1],
translation_std=[0.5, 0.5, 0.5]),
dict(
type='RandomFlip3D',
sync_2d=False,
flip_ratio_bev_horizontal=0.5,
flip_ratio_bev_vertical=0.5),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectNameFilter', classes=class_names),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'],
meta_keys=('filename', 'ori_shape', 'img_shape', 'lidar2img',
'depth2img', 'cam2img', 'pad_shape',
'scale_factor', 'flip', 'pcd_horizontal_flip',
'pcd_vertical_flip', 'box_mode_3d', 'box_type_3d',
'img_norm_cfg', 'pcd_trans', 'sample_idx',
'pcd_scale_factor', 'pcd_rotation', 'pts_filename',
'transformation_3d_flow', 'rot_degree',
'gt_bboxes_3d', 'gt_labels_3d'))
]
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=[0, 1, 2, 3, 4],
),
dict(
type='LoadPointsFromMultiSweeps',
sweeps_num=10,
use_dim=[0, 1, 2, 3, 4],
),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1.0, 1.0],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=6,
train=dict(
type='CBGSDataset',
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + '/nuscenes_infos_train.pkl',
load_interval=1,
pipeline=train_pipeline,
classes=class_names,
modality=input_modality,
test_mode=False,
box_type_3d='LiDAR')),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + '/nuscenes_infos_val.pkl',
load_interval=1,
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
test_mode=True,
box_type_3d='LiDAR'),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + '/nuscenes_infos_val.pkl',
load_interval=1,
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
test_mode=True,
box_type_3d='LiDAR'))
model = dict(
type='CmtDetector',
pts_voxel_layer=dict(
num_point_features=5,
max_num_points=10,
voxel_size=voxel_size,
max_voxels=(120000, 160000),
point_cloud_range=point_cloud_range),
pts_voxel_encoder=dict(
type='HardSimpleVFE',
num_features=5,
),
pts_middle_encoder=dict(
type='SparseEncoder',
in_channels=5,
sparse_shape=[41, 1440, 1440],
output_channels=128,
order=('conv', 'norm', 'act'),
encoder_channels=((16, 16, 32), (32, 32, 64), (64, 64, 128), (128, 128)),
encoder_paddings=((0, 0, 1), (0, 0, 1), (0, 0, [0, 1, 1]), (0, 0)),
block_type='basicblock'),
pts_backbone=dict(
type='SECOND',
in_channels=256,
out_channels=[128, 256],
layer_nums=[5, 5],
layer_strides=[1, 2],
norm_cfg=dict(type='BN', eps=0.001, momentum=0.01),
conv_cfg=dict(type='Conv2d', bias=False)),
pts_neck=dict(
type='SECONDFPN',
in_channels=[128, 256],
out_channels=[256, 256],
upsample_strides=[1, 2],
norm_cfg=dict(type='BN', eps=0.001, momentum=0.01),
upsample_cfg=dict(type='deconv', bias=False),
use_conv_for_no_stride=True),
pts_bbox_head=dict(
type='CmtLidarHead',
in_channels=512,
hidden_dim=256,
downsample_scale=8,
common_heads=dict(center=(2, 2), height=(1, 2), dim=(3, 2), rot=(2, 2), vel=(2, 2)),
tasks=[
dict(num_class=10, class_names=[
'car', 'truck', 'construction_vehicle',
'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle',
'pedestrian', 'traffic_cone'
]),
],
bbox_coder=dict(
type='MultiTaskBBoxCoder',
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),
separate_head=dict(
type='SeparateTaskHead', init_bias=-2.19, final_kernel=3),
transformer=dict(
type='CmtLidarTransformer',
decoder=dict(
type='PETRTransformerDecoder',
return_intermediate=True,
num_layers=6,
transformerlayers=dict(
type='PETRTransformerDecoderLayer',
attn_cfgs=[
dict(
type='MultiheadAttention',
embed_dims=256,
num_heads=8,
dropout=0.1),
dict(
type='PETRMultiheadFlashAttention',
embed_dims=256,
num_heads=8,
dropout=0.1),
],
ffn_cfgs=dict(
type='FFN',
embed_dims=256,
feedforward_channels=1024,
num_fcs=2,
ffn_drop=0.,
act_cfg=dict(type='ReLU', inplace=True),
),
feedforward_channels=1024, #unused
operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
'ffn', 'norm')),
)),
loss_cls=dict(type='FocalLoss', use_sigmoid=True, gamma=2, alpha=0.25, reduction='mean', loss_weight=2.0),
loss_bbox=dict(type='L1Loss', reduction='mean', loss_weight=0.25),
loss_heatmap=dict(type='GaussianFocalLoss', reduction='mean', loss_weight=1.0),
),
train_cfg=dict(
pts=dict(
dataset='nuScenes',
assigner=dict(
type='HungarianAssigner3D',
cls_cost=dict(type='FocalLossCost', weight=2.0),
reg_cost=dict(type='BBox3DL1Cost', weight=0.25),
iou_cost=dict(type='IoUCost', weight=0.0), # Fake cost. This is just to make it compatible with DETR head.
pc_range=point_cloud_range,
code_weights=[2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2],
),
pos_weight=-1,
gaussian_overlap=0.1,
min_radius=2,
grid_size=[1440, 1440, 40], # [x_len, y_len, 1]
voxel_size=voxel_size,
out_size_factor=out_size_factor,
code_weights=[2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2],
point_cloud_range=point_cloud_range)),
test_cfg=dict(
pts=dict(
dataset='nuScenes',
grid_size=[1440, 1440, 40],
out_size_factor=out_size_factor,
pc_range=point_cloud_range[0:2],
voxel_size=voxel_size[:2],
nms_type=None,
nms_thr=0.2,
use_rotate_nms=True,
max_num=200
)))
optimizer = dict(type='AdamW', lr=0.0001, weight_decay=0.01) # for 8gpu * 2sample_per_gpu
optimizer_config = dict(
type='CustomFp16OptimizerHook',
loss_scale='dynamic',
grad_clip=dict(max_norm=35, norm_type=2),
custom_fp16=dict(pts_voxel_encoder=False, pts_middle_encoder=False, pts_bbox_head=False))
lr_config = dict(
policy='cyclic',
target_ratio=(6, 0.0001),
cyclic_times=1,
step_ratio_up=0.4)
momentum_config = dict(
policy='cyclic',
target_ratio=(0.8947368421052632, 1),
cyclic_times=1,
step_ratio_up=0.4)
total_epochs = 20
checkpoint_config = dict(interval=1)
log_config = dict(
interval=50,
hooks=[dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = None
load_from = None
resume_from = None
workflow = [('train', 1)]
gpu_ids = range(0, 8)