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bevdet-r50-4d-depth-cbgs.py
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bevdet-r50-4d-depth-cbgs.py
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# Copyright (c) Phigent Robotics. All rights reserved.
# align_after_view_transfromation=True
# mAP: 0.3605
# mATE: 0.6189
# mASE: 0.2739
# mAOE: 0.4818
# mAVE: 0.3959
# mAAE: 0.2037
# NDS: 0.4828
# Eval time: 142.2s
#
# Per-class results:
# Object Class AP ATE ASE AOE AVE AAE
# car 0.577 0.446 0.154 0.079 0.324 0.190
# truck 0.280 0.647 0.217 0.094 0.308 0.205
# bus 0.375 0.701 0.208 0.078 0.894 0.303
# trailer 0.172 0.952 0.244 0.392 0.332 0.169
# construction_vehicle 0.090 0.754 0.424 1.018 0.107 0.341
# pedestrian 0.413 0.676 0.305 0.812 0.490 0.234
# motorcycle 0.315 0.668 0.266 0.676 0.514 0.178
# bicycle 0.273 0.534 0.281 1.066 0.198 0.010
# traffic_cone 0.564 0.415 0.334 nan nan nan
# barrier 0.546 0.395 0.304 0.123 nan nan
# align_after_view_transfromation=False
# mAP: 0.3618
# mATE: 0.6168
# mASE: 0.2735
# mAOE: 0.4802
# mAVE: 0.3932
# mAAE: 0.2033
# NDS: 0.4842
# Eval time: 142.2s
#
# Per-class results:
# Object Class AP ATE ASE AOE AVE AAE
# car 0.577 0.445 0.154 0.079 0.320 0.190
# truck 0.280 0.648 0.217 0.094 0.305 0.205
# bus 0.373 0.700 0.208 0.082 0.896 0.304
# trailer 0.172 0.951 0.243 0.390 0.329 0.169
# construction_vehicle 0.091 0.750 0.422 1.008 0.109 0.339
# pedestrian 0.416 0.672 0.305 0.804 0.483 0.231
# motorcycle 0.318 0.665 0.267 0.674 0.506 0.179
# bicycle 0.274 0.533 0.281 1.070 0.199 0.010
# traffic_cone 0.567 0.411 0.334 nan nan nan
# barrier 0.550 0.391 0.304 0.122 nan nan
_base_ = ['../_base_/datasets/nus-3d.py', '../_base_/default_runtime.py']
# Global
# 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]
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
data_config = {
'cams': [
'CAM_FRONT_LEFT', 'CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_BACK_LEFT',
'CAM_BACK', 'CAM_BACK_RIGHT'
],
'Ncams':
6,
'input_size': (256, 704),
'src_size': (900, 1600),
# Augmentation
'resize': (-0.06, 0.11),
'rot': (-5.4, 5.4),
'flip': True,
'crop_h': (0.0, 0.0),
'resize_test': 0.00,
}
# Model
grid_config = {
'x': [-51.2, 51.2, 0.8],
'y': [-51.2, 51.2, 0.8],
'z': [-5, 3, 8],
'depth': [1.0, 60.0, 0.5],
}
voxel_size = [0.1, 0.1, 0.2]
numC_Trans = 80
multi_adj_frame_id_cfg = (1, 1+1, 1)
model = dict(
type='BEVDepth4D',
align_after_view_transfromation=False,
num_adj=len(range(*multi_adj_frame_id_cfg)),
img_backbone=dict(
pretrained='torchvision://resnet50',
type='ResNet',
depth=50,
num_stages=4,
out_indices=(2, 3),
frozen_stages=-1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=False,
with_cp=True,
style='pytorch'),
img_neck=dict(
type='CustomFPN',
in_channels=[1024, 2048],
out_channels=512,
num_outs=1,
start_level=0,
out_ids=[0]),
img_view_transformer=dict(
type='LSSViewTransformerBEVDepth',
grid_config=grid_config,
input_size=data_config['input_size'],
in_channels=512,
out_channels=numC_Trans,
depthnet_cfg=dict(use_dcn=False, aspp_mid_channels=96),
downsample=16),
img_bev_encoder_backbone=dict(
type='CustomResNet',
numC_input=numC_Trans * (len(range(*multi_adj_frame_id_cfg))+1),
num_channels=[numC_Trans * 2, numC_Trans * 4, numC_Trans * 8]),
img_bev_encoder_neck=dict(
type='FPN_LSS',
in_channels=numC_Trans * 8 + numC_Trans * 2,
out_channels=256),
pre_process=dict(
type='CustomResNet',
numC_input=numC_Trans,
num_layer=[2,],
num_channels=[numC_Trans,],
stride=[1,],
backbone_output_ids=[0,]),
pts_bbox_head=dict(
type='CenterHead',
in_channels=256,
tasks=[
dict(num_class=10, class_names=['car', 'truck',
'construction_vehicle',
'bus', 'trailer',
'barrier',
'motorcycle', 'bicycle',
'pedestrian', 'traffic_cone']),
],
common_heads=dict(
reg=(2, 2), height=(1, 2), dim=(3, 2), rot=(2, 2), vel=(2, 2)),
share_conv_channel=64,
bbox_coder=dict(
type='CenterPointBBoxCoder',
pc_range=point_cloud_range[:2],
post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
max_num=500,
score_threshold=0.1,
out_size_factor=8,
voxel_size=voxel_size[:2],
code_size=9),
separate_head=dict(
type='SeparateHead', init_bias=-2.19, final_kernel=3),
loss_cls=dict(type='GaussianFocalLoss', reduction='mean', loss_weight=6.),
loss_bbox=dict(type='L1Loss', reduction='mean', loss_weight=1.5),
norm_bbox=True),
# model training and testing settings
train_cfg=dict(
pts=dict(
point_cloud_range=point_cloud_range,
grid_size=[1024, 1024, 40],
voxel_size=voxel_size,
out_size_factor=8,
dense_reg=1,
gaussian_overlap=0.1,
max_objs=500,
min_radius=2,
code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])),
test_cfg=dict(
pts=dict(
pc_range=point_cloud_range[:2],
post_center_limit_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
max_per_img=500,
max_pool_nms=False,
min_radius=[4, 12, 10, 1, 0.85, 0.175],
score_threshold=0.1,
out_size_factor=8,
voxel_size=voxel_size[:2],
pre_max_size=1000,
post_max_size=500,
# Scale-NMS
nms_type=['rotate'],
nms_thr=[0.2],
nms_rescale_factor=[[1.0, 0.7, 0.7, 0.4, 0.55,
1.1, 1.0, 1.0, 1.5, 3.5]]
)
)
)
# Data
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
file_client_args = dict(backend='disk')
bda_aug_conf = dict(
rot_lim=(-22.5, 22.5),
scale_lim=(0.95, 1.05),
flip_dx_ratio=0.5,
flip_dy_ratio=0.5)
train_pipeline = [
dict(
type='PrepareImageInputs',
is_train=True,
data_config=data_config,
sequential=True),
dict(type='LoadAnnotations'),
dict(
type='BEVAug',
bda_aug_conf=bda_aug_conf,
classes=class_names),
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(type='PointToMultiViewDepth', downsample=1, grid_config=grid_config),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectNameFilter', classes=class_names),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D', keys=['img_inputs', 'gt_bboxes_3d', 'gt_labels_3d',
'gt_depth'])
]
test_pipeline = [
dict(type='PrepareImageInputs', data_config=data_config, sequential=True),
dict(type='LoadAnnotations'),
dict(type='BEVAug',
bda_aug_conf=bda_aug_conf,
classes=class_names,
is_train=False),
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=5,
use_dim=5,
file_client_args=file_client_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points', 'img_inputs'])
])
]
input_modality = dict(
use_lidar=False,
use_camera=True,
use_radar=False,
use_map=False,
use_external=False)
share_data_config = dict(
type=dataset_type,
classes=class_names,
modality=input_modality,
img_info_prototype='bevdet4d',
multi_adj_frame_id_cfg=multi_adj_frame_id_cfg,
)
test_data_config = dict(
pipeline=test_pipeline,
ann_file=data_root + 'bevdetv3-nuscenes_infos_val.pkl')
data = dict(
samples_per_gpu=8,
workers_per_gpu=4,
train=dict(
type='CBGSDataset',
dataset=dict(
data_root=data_root,
ann_file=data_root + 'bevdetv3-nuscenes_infos_train.pkl',
pipeline=train_pipeline,
classes=class_names,
test_mode=False,
use_valid_flag=True,
# 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')),
val=test_data_config,
test=test_data_config)
for key in ['val', 'test']:
data[key].update(share_data_config)
data['train']['dataset'].update(share_data_config)
# Optimizer
optimizer = dict(type='AdamW', lr=2e-4, weight_decay=1e-2)
optimizer_config = dict(grad_clip=dict(max_norm=5, norm_type=2))
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=200,
warmup_ratio=0.001,
step=[20,])
runner = dict(type='EpochBasedRunner', max_epochs=20)
custom_hooks = [
dict(
type='MEGVIIEMAHook',
init_updates=10560,
priority='NORMAL',
),
dict(
type='SequentialControlHook',
temporal_start_epoch=2,
),
]
# fp16 = dict(loss_scale='dynamic')