/
bevdet-occ-r50-4d-stereo-24e_384704.py
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bevdet-occ-r50-4d-stereo-24e_384704.py
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# Copyright (c) Phigent Robotics. All rights reserved.
# 2x/e24
# ===> per class IoU of 6019 samples:
# ===> others - IoU = 8.78
# ===> barrier - IoU = 45.22
# ===> bicycle - IoU = 19.1
# ===> bus - IoU = 43.52
# ===> car - IoU = 50.24
# ===> construction_vehicle - IoU = 23.7
# ===> motorcycle - IoU = 19.75
# ===> pedestrian - IoU = 22.9
# ===> traffic_cone - IoU = 20.7
# ===> trailer - IoU = 31.88
# ===> truck - IoU = 37.65
# ===> driveable_surface - IoU = 80.3
# ===> other_flat - IoU = 37.02
# ===> sidewalk - IoU = 50.51
# ===> terrain - IoU = 53.41
# ===> manmade - IoU = 47.11
# ===> vegetation - IoU = 41.93
# ===> mIoU of 6019 samples: 37.28
# 3x/36e
# ===> per class IoU of 6019 samples:
# ===> others - IoU = 9.49
# ===> barrier - IoU = 45.42
# ===> bicycle - IoU = 20.34
# ===> bus - IoU = 43.63
# ===> car - IoU = 50.52
# ===> construction_vehicle - IoU = 24.35
# ===> motorcycle - IoU = 20.97
# ===> pedestrian - IoU = 23.29
# ===> traffic_cone - IoU = 22.01
# ===> trailer - IoU = 33.4
# ===> truck - IoU = 37.67
# ===> driveable_surface - IoU = 80.45
# ===> other_flat - IoU = 38.03
# ===> sidewalk - IoU = 50.94
# ===> terrain - IoU = 53.77
# ===> manmade - IoU = 47.4
# ===> vegetation - IoU = 41.87
# ===> mIoU of 6019 samples: 37.86
# 4x/48e
# ===> per class IoU of 6019 samples:
# ===> others - IoU = 10.07
# ===> barrier - IoU = 45.76
# ===> bicycle - IoU = 20.35
# ===> bus - IoU = 43.76
# ===> car - IoU = 50.54
# ===> construction_vehicle - IoU = 25.02
# ===> motorcycle - IoU = 21.56
# ===> pedestrian - IoU = 23.36
# ===> traffic_cone - IoU = 22.76
# ===> trailer - IoU = 34.22
# ===> truck - IoU = 38.05
# ===> driveable_surface - IoU = 80.54
# ===> other_flat - IoU = 38.16
# ===> sidewalk - IoU = 51.06
# ===> terrain - IoU = 53.37
# ===> manmade - IoU = 47.45
# ===> vegetation - IoU = 41.77
# ===> mIoU of 6019 samples: 38.11
# 5x/e60
# ===> per class IoU of 6019 samples:
# ===> others - IoU = 10.12
# ===> barrier - IoU = 45.76
# ===> bicycle - IoU = 20.31
# ===> bus - IoU = 43.59
# ===> car - IoU = 50.45
# ===> construction_vehicle - IoU = 25.44
# ===> motorcycle - IoU = 22.02
# ===> pedestrian - IoU = 23.7
# ===> traffic_cone - IoU = 23.24
# ===> trailer - IoU = 33.77
# ===> truck - IoU = 37.78
# ===> driveable_surface - IoU = 80.54
# ===> other_flat - IoU = 38.4
# ===> sidewalk - IoU = 51.27
# ===> terrain - IoU = 53.85
# ===> manmade - IoU = 47.45
# ===> vegetation - IoU = 41.8
# ===> mIoU of 6019 samples: 38.2
# 6x/e72
# ===> per class IoU of 6019 samples:
# ===> others - IoU = 10.35
# ===> barrier - IoU = 45.79
# ===> bicycle - IoU = 19.87
# ===> bus - IoU = 43.96
# ===> car - IoU = 50.62
# ===> construction_vehicle - IoU = 25.5
# ===> motorcycle - IoU = 22.27
# ===> pedestrian - IoU = 23.95
# ===> traffic_cone - IoU = 23.62
# ===> trailer - IoU = 34.53
# ===> truck - IoU = 37.88
# ===> driveable_surface - IoU = 80.68
# ===> other_flat - IoU = 38.16
# ===> sidewalk - IoU = 51.32
# ===> terrain - IoU = 53.92
# ===> manmade - IoU = 47.3
# ===> vegetation - IoU = 41.82
# ===> mIoU of 6019 samples: 38.33
# e100
# ===> per class IoU of 6019 samples:
# ===> others - IoU = 10.82
# ===> barrier - IoU = 45.88
# ===> bicycle - IoU = 19.72
# ===> bus - IoU = 44.2
# ===> car - IoU = 50.61
# ===> construction_vehicle - IoU = 26.46
# ===> motorcycle - IoU = 22.44
# ===> pedestrian - IoU = 23.8
# ===> traffic_cone - IoU = 24.07
# ===> trailer - IoU = 34.93
# ===> truck - IoU = 37.48
# ===> driveable_surface - IoU = 80.75
# ===> other_flat - IoU = 38.51
# ===> sidewalk - IoU = 51.64
# ===> terrain - IoU = 54.38
# ===> manmade - IoU = 47.24
# ===> vegetation - IoU = 41.92
# ===> mIoU of 6019 samples: 38.52
_base_ = ['../_base_/datasets/nus-3d.py', '../_base_/default_runtime.py']
# 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': (384, 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': [-40, 40, 0.4],
'y': [-40, 40, 0.4],
'z': [-1, 5.4, 0.4],
'depth': [1.0, 45.0, 0.5],
}
voxel_size = [0.1, 0.1, 0.2]
numC_Trans = 32
multi_adj_frame_id_cfg = (1, 1+1, 1)
model = dict(
type='BEVStereo4DOCC',
align_after_view_transfromation=False,
num_adj=len(range(*multi_adj_frame_id_cfg)),
img_backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 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=256,
num_outs=1,
start_level=0,
out_ids=[0]),
img_view_transformer=dict(
type='LSSViewTransformerBEVStereo',
grid_config=grid_config,
input_size=data_config['input_size'],
in_channels=256,
out_channels=numC_Trans,
sid=False,
collapse_z=False,
loss_depth_weight=0.05,
depthnet_cfg=dict(use_dcn=False,
aspp_mid_channels=96,
stereo=True,
bias=5.),
downsample=16),
img_bev_encoder_backbone=dict(
type='CustomResNet3D',
numC_input=numC_Trans * (len(range(*multi_adj_frame_id_cfg))+1),
num_layer=[1, 2, 4],
with_cp=False,
num_channels=[numC_Trans,numC_Trans*2,numC_Trans*4],
stride=[1,2,2],
backbone_output_ids=[0,1,2]),
img_bev_encoder_neck=dict(type='LSSFPN3D',
in_channels=numC_Trans*7,
out_channels=numC_Trans),
pre_process=dict(
type='CustomResNet3D',
numC_input=numC_Trans,
with_cp=False,
num_layer=[1,],
num_channels=[numC_Trans,],
stride=[1,],
backbone_output_ids=[0,]),
loss_occ=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
use_mask=True,
)
# Data
dataset_type = 'NuScenesDatasetOccpancy'
data_root = 'data/nuscenes/'
file_client_args = dict(backend='disk')
bda_aug_conf = dict(
rot_lim=(-0., 0.),
scale_lim=(1., 1.),
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='LoadOccGTFromFile'),
dict(
type='LoadAnnotationsBEVDepth',
bda_aug_conf=bda_aug_conf,
classes=class_names,
is_train=True),
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='DefaultFormatBundle3D', class_names=class_names),
dict(
type='Collect3D', keys=['img_inputs', 'gt_depth', 'voxel_semantics',
'mask_lidar','mask_camera'])
]
test_pipeline = [
dict(type='PrepareImageInputs', data_config=data_config, sequential=True),
dict(
type='LoadAnnotationsBEVDepth',
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,
stereo=True,
filter_empty_gt=False,
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 + 'bevdetv2-nuscenes_infos_val.pkl')
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
train=dict(
data_root=data_root,
ann_file=data_root + 'bevdetv2-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', 'train', 'test']:
data[key].update(share_data_config)
# Optimizer
optimizer = dict(type='AdamW', lr=1e-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=[100,])
runner = dict(type='EpochBasedRunner', max_epochs=100)
custom_hooks = [
dict(
type='MEGVIIEMAHook',
init_updates=10560,
priority='NORMAL',
),
]
load_from="bevdet-r50-4d-stereo-cbgs.pth"
# fp16 = dict(loss_scale='dynamic')