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LuPaoPao committed Oct 30, 2023
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296 changes: 296 additions & 0 deletions configs/PDBEV/pdbev-r50-cbgs-DA2X-dg.py
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# Copyright (c) Phigent Robotics. All rights reserved.

_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_train = [
'car', 'truck', 'van',
'motorcycle', 'cyclist', 'pedestrian'
]

class_names_test = [
'car', 'truck', 'van',
'motorcycle', 'cyclist', 'pedestrian'
]
class_names = class_names_train


data_config = {
'cams': ['Camera_FrontLeft', 'Camera_Front', 'Camera_FrontRight',
'Camera_BackLeft', 'Camera_Back', 'Camera_BackRight'],
'Ncams': 6,
'input_size': (384, 704), # 2.2727
'src_size': (900, 1600),
# Augmentation
'resize': (-0.06, 0.11),
'rot': (-5.4, 5.4),
'flip': True,
'crop_h': (0.0, 0.1),
'resize_test': 0.00,
're_ratio': False,
'pitch_aug': (-0.04, 0.04),
'yaw_aug': (-0.4, 0.4),
'roll_aug': (-0.04, 0.04),
'extri_x_aug': (-2.0, 2.0),
'extri_y_aug': (-2.0, 2.0),
'extri_z_aug': (-2.0, 2.0),
# # Intri Augmentation
# 'fc_w_ratio': 0.2, # 改变x光心
# 'fc_h_ratio': 0.2, # 改变y光心
# 'resize_intri': (0.5, 1.5), # 改变视角
}

# Model
grid_config = {
'x': [-51.2, 51.2, 0.8],
'y': [-51.2, 51.2, 0.8],
'z': [-5, 3, 8],
'depth': [1.0, 100.0, 1.0],
}

voxel_size = [0.1, 0.1, 0.2]

numC_Trans = 80

model = dict(
type='PCBEV_DG',
img_backbone=dict(
pretrained='/mnt/cfs/algorithm/yunpeng.zhang/pretrained/resnet50-0676ba61.pth',
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_aug=dict(
type='Img_Aux',
numC_input=512,
class_name=class_names_train,
upsample=[1, 2, 1],
num_channels=[256, 128, len(class_names_train) + len(class_names_train) * 6]),
bev_img_aux=dict(
type='Img_Aux_Dy',
numC_input=80,
class_name=class_names_train,
num_layer=[2, 1, 1],
upsample=[2, 1, 1],
num_channels=[256, 128, len(class_names_train) + len(class_names_train) * 6]),
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='LSSViewTransformer_pc',
BEV_Aux=dict(
type='BEV_Aux',
class_name=class_names_train,
numC_input=256,
num_layer=[2, 2, 2],
height_num=4,
num_channels=[numC_Trans, numC_Trans, numC_Trans + 4]),
downsample_from_ann=2,
grid_config=grid_config,
input_size=data_config['input_size'],
in_channels=512,
depthnet_cfg=dict(use_dcn=False),
out_channels=numC_Trans,
loss_depth_weight=3.0,
downsample=16,
),
img_bev_encoder_backbone=dict(
type='CustomResNet',
numC_input=numC_Trans,
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),
pts_bbox_head=dict(
type='CenterHead',
in_channels=256,
tasks=[
dict(num_class=1, class_names=['car']),
dict(num_class=2, class_names=['truck', 'van']),
dict(num_class=1, class_names=['bus']),
dict(num_class=2, class_names=['motorcycle', 'cyclist']),
dict(num_class=1, class_names=['pedestrian']),
],
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_bbox=dict(type='L1Loss', reduction='mean', loss_weight=0.25),
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, 0.2, 0.2])),
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=83,

# Scale-NMS
nms_type=[
'rotate', 'rotate', 'rotate', 'circle', 'rotate', 'rotate'
],
nms_thr=[0.2, 0.2, 0.2, 0.2, 0.2, 0.5],
nms_rescale_factor=[
1.0, [0.7, 0.7], [0.4, 0.55], 1.1, [1.0, 1.0], [4.5, 9.0]
])))


# Data
dataset_type = 'DeepAccidentDataset'
data_root = 'data/DeepAccident_data/'
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_UDA',
is_train=True,
data_config=data_config),
dict(
type='LoadAnnotationsBEVDepth',
bda_aug_conf=bda_aug_conf,
classes=class_names),
dict(
type='LoadPointsFromFile_UDA',
coord_type='LIDAR',
load_dim=5,
use_dim=3,
file_client_args=file_client_args),
dict(type='PointToMultiViewDepth_UDA', downsample=1, grid_config=grid_config),
dict(type='Load3DBoxesHeatmap', classes=class_names_train, downsample_feature=8), # CustomCenterNet_Single提高则downsample_feature降低
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', 'gt_depth_real',
'heatmaps_2d', 'ann_maps_2d', 'heatmap_masks_2d', 'heatmaps_2d_aug', 'ann_maps_2d_aug', 'heatmap_masks_2d_aug',
'bev_aug'])
]

test_pipeline = [
dict(type='PrepareImageInputs_UDA', data_config=data_config),
dict(
type='LoadAnnotationsBEVDepth',
bda_aug_conf=bda_aug_conf,
classes=class_names_test,
is_train=False),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1600, 900),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='DefaultFormatBundle3D',
class_names=class_names_test,
with_label=False),
dict(type='Collect3D', keys=['img_inputs', 'gt_bboxes_3d', 'gt_labels_3d'])
])
]

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_train,
modality=input_modality,
# img_info_prototype='bevdet',
)

test_data_config = dict(
pipeline=test_pipeline,
ann_file=data_root + 'bevdetv4-DeepAccident_infos_val.pkl')

data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
train=dict(
type='CBGSDataset',
dataset=dict(
data_root=data_root,
ann_file=data_root + 'bevdetv4-DeepAccident_infos_train.pkl',
pipeline=train_pipeline,
classes=class_names_train,
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=20,
warmup_ratio=0.001,
step=[24])
runner = dict(type='EpochBasedRunner', max_epochs=4)

custom_hooks = [
dict(
type='MEGVIIEMAHook',
init_updates=10560,
priority='NORMAL',
),
]


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