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Commit 3b8d508a authored by lishj6's avatar lishj6 🏸
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init_0905

parent e968ab0f
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projects/configs/flashocc/flashocc-r50.py 0 → 100644
View file @ 3b8d508a
_base_ = ['../../../mmdetection3d/configs/_base_/datasets/nus-3d.py',
'../../../mmdetection3d/configs/_base_/default_runtime.py']
plugin = True
plugin_dir = 'projects/mmdet3d_plugin/'
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,
}
grid_config = {
'x': [-40, 40, 0.4],
'y': [-40, 40, 0.4],
'z': [-1, 5.4, 6.4],
'depth': [1.0, 45.0, 0.5],
}
voxel_size = [0.1, 0.1, 0.2]
numC_Trans = 64
model = dict(
type='BEVDetOCC',
img_backbone=dict(
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',
pretrained='torchvision://resnet50',
),
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='LSSViewTransformer',
grid_config=grid_config,
input_size=data_config['input_size'],
in_channels=256,
out_channels=numC_Trans,
sid=False,
collapse_z=True,
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),
occ_head=dict(
type='BEVOCCHead2D',
in_dim=256,
out_dim=256, # out_dim=128 for M0!!!
Dz=16,
use_mask=True,
num_classes=18,
use_predicter=True,
class_balance=False,
loss_occ=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
ignore_index=255,
loss_weight=1.0
),
)
)
# 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=False),
dict(
type='LoadAnnotationsBEVDepth',
bda_aug_conf=bda_aug_conf,
classes=class_names,
is_train=True),
dict(type='LoadOccGTFromFile'),
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=False),
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,
data_root=data_root,
classes=class_names,
modality=input_modality,
stereo=False,
filter_empty_gt=False,
img_info_prototype='bevdet',
)
test_data_config = dict(
pipeline=test_pipeline,
ann_file=data_root + 'bevdetv2-nuscenes_infos_val.pkl')
data = dict(
samples_per_gpu=34,
workers_per_gpu=34,
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=[24, ])
runner = dict(type='EpochBasedRunner', max_epochs=30)
custom_hooks = [
dict(
type='MEGVIIEMAHook',
init_updates=10560,
priority='NORMAL',
),
]
load_from = "ckpts/bevdet-r50-cbgs.pth"
# fp16 = dict(loss_scale='dynamic')
evaluation = dict(interval=1, start=20, pipeline=test_pipeline)
checkpoint_config = dict(interval=1, max_keep_ckpts=5)
# with det pretrain; use_mask=True;
# ===> per class IoU of 6019 samples:
# ===> others - IoU = 6.74
# ===> barrier - IoU = 37.65
# ===> bicycle - IoU = 10.26
# ===> bus - IoU = 39.55
# ===> car - IoU = 44.36
# ===> construction_vehicle - IoU = 14.88
# ===> motorcycle - IoU = 13.4
# ===> pedestrian - IoU = 15.79
# ===> traffic_cone - IoU = 15.38
# ===> trailer - IoU = 27.44
# ===> truck - IoU = 31.73
# ===> driveable_surface - IoU = 78.82
# ===> other_flat - IoU = 37.98
# ===> sidewalk - IoU = 48.7
# ===> terrain - IoU = 52.5
# ===> manmade - IoU = 37.89
# ===> vegetation - IoU = 32.24
# ===> mIoU of 6019 samples: 32.08
# with det pretrain; use_mask=False; class_balance=True
# ===> per class IoU of 6019 samples:
# ===> others - IoU = 4.49
# ===> barrier - IoU = 29.59
# ===> bicycle - IoU = 7.38
# ===> bus - IoU = 30.32
# ===> car - IoU = 32.22
# ===> construction_vehicle - IoU = 13.04
# ===> motorcycle - IoU = 11.91
# ===> pedestrian - IoU = 8.61
# ===> traffic_cone - IoU = 8.11
# ===> trailer - IoU = 7.66
# ===> truck - IoU = 20.84
# ===> driveable_surface - IoU = 48.59
# ===> other_flat - IoU = 26.62
# ===> sidewalk - IoU = 26.08
# ===> terrain - IoU = 20.86
# ===> manmade - IoU = 7.62
# ===> vegetation - IoU = 7.14
# ===> mIoU of 6019 samples: 18.3
projects/configs/flashocc/flashocc-stbase-4d-stereo-512x1408_4x4_1e-2.py 0 → 100644
View file @ 3b8d508a
_base_ = ['../../../mmdetection3d/configs/_base_/datasets/nus-3d.py',
'../../../mmdetection3d/configs/_base_/default_runtime.py']
plugin = True
plugin_dir = 'projects/mmdet3d_plugin/'
# 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': (512, 1408),
'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, 6.4],
'depth': [1.0, 45.0, 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='BEVStereo4DOCC',
align_after_view_transfromation=False,
num_adj=len(range(*multi_adj_frame_id_cfg)),
img_backbone=dict(
type='SwinTransformer',
pretrain_img_size=224,
patch_size=4,
window_size=12,
mlp_ratio=4,
embed_dims=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
strides=(4, 2, 2, 2),
out_indices=(2, 3),
qkv_bias=True,
qk_scale=None,
patch_norm=True,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.1,
use_abs_pos_embed=False,
return_stereo_feat=True,
act_cfg=dict(type='GELU'),
norm_cfg=dict(type='LN', requires_grad=True),
pretrain_style='official',
output_missing_index_as_none=False),
img_neck=dict(
type='FPN_LSS',
in_channels=512 + 1024,
out_channels=512,
# with_cp=False,
extra_upsample=None,
input_feature_index=(0, 1),
scale_factor=2),
img_view_transformer=dict(
type='LSSViewTransformerBEVStereo',
grid_config=grid_config,
input_size=data_config['input_size'],
in_channels=512,
out_channels=numC_Trans,
sid=False,
collapse_z=True,
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='CustomResNet',
with_cp=True,
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=[1, ],
num_channels=[numC_Trans, ],
stride=[1, ],
backbone_output_ids=[0, ]),
occ_head=dict(
type='BEVOCCHead2D',
in_dim=256,
out_dim=256,
Dz=16,
use_mask=True,
num_classes=18,
use_predicter=True,
class_balance=False,
loss_occ=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
ignore_index=255,
loss_weight=1.0
),
)
)
# 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='LoadAnnotationsBEVDepth',
bda_aug_conf=bda_aug_conf,
classes=class_names,
is_train=True),
dict(type='LoadOccGTFromFile'),
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(
data_root=data_root,
pipeline=test_pipeline,
ann_file=data_root + 'bevdetv2-nuscenes_infos_val.pkl')
data = dict(
samples_per_gpu=4, # with 32 GPU
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=[24, ])
runner = dict(type='EpochBasedRunner', max_epochs=24)
custom_hooks = [
dict(
type='MEGVIIEMAHook',
init_updates=10560,
priority='NORMAL',
),
dict(
type='SyncbnControlHook',
syncbn_start_epoch=0,
),
]
evaluation = dict(interval=6, start=0, pipeline=test_pipeline)
checkpoint_config = dict(interval=1, max_keep_ckpts=3)
# load_from="ckpts/bevdet-stbase-4d-stereo-512x1408-cbgs.pth"
resume_from="work_dirs/flashocc-stbase-4d-stereo-512x1408_4x4_1e-2/epoch_5.pth"
# fp16 = dict(loss_scale='dynamic')
# bash tools/dist_train.sh projects/configs/flashocc/flashocc-stbase-4d-stereo-512x1408_4x4_1e-2.py 4
\ No newline at end of file
projects/configs/flashocc/flashocc-stbase-4d-stereo-512x1408_4x4_2e-4.py 0 → 100644
View file @ 3b8d508a
_base_ = ['../../../mmdetection3d/configs/_base_/datasets/nus-3d.py',
'../../../mmdetection3d/configs/_base_/default_runtime.py']
plugin = True
plugin_dir = 'projects/mmdet3d_plugin/'
# 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': (512, 1408),
'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, 6.4],
'depth': [1.0, 45.0, 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='BEVStereo4DOCC',
align_after_view_transfromation=False,
num_adj=len(range(*multi_adj_frame_id_cfg)),
img_backbone=dict(
type='SwinTransformer',
pretrain_img_size=224,
patch_size=4,
window_size=12,
mlp_ratio=4,
embed_dims=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
strides=(4, 2, 2, 2),
out_indices=(2, 3),
qkv_bias=True,
qk_scale=None,
patch_norm=True,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.1,
use_abs_pos_embed=False,
return_stereo_feat=True,
act_cfg=dict(type='GELU'),
norm_cfg=dict(type='LN', requires_grad=True),
pretrain_style='official',
output_missing_index_as_none=False),
img_neck=dict(
type='FPN_LSS',
in_channels=512 + 1024,
out_channels=512,
# with_cp=False,
extra_upsample=None,
input_feature_index=(0, 1),
scale_factor=2),
img_view_transformer=dict(
type='LSSViewTransformerBEVStereo',
grid_config=grid_config,
input_size=data_config['input_size'],
in_channels=512,
out_channels=numC_Trans,
sid=False,
collapse_z=True,
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='CustomResNet',
with_cp=True,
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=[1, ],
num_channels=[numC_Trans, ],
stride=[1, ],
backbone_output_ids=[0, ]),
occ_head=dict(
type='BEVOCCHead2D',
in_dim=256,
out_dim=256,
Dz=16,
use_mask=True,
num_classes=18,
use_predicter=True,
class_wise=False,
loss_occ=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
ignore_index=255,
loss_weight=1.0
),
)
)
# 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='LoadAnnotationsBEVDepth',
bda_aug_conf=bda_aug_conf,
classes=class_names,
is_train=True),
dict(type='LoadOccGTFromFile'),
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(
data_root=data_root,
pipeline=test_pipeline,
ann_file=data_root + 'bevdetv2-nuscenes_infos_val.pkl')
data = dict(
samples_per_gpu=4, # with 32 GPU
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=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=[24, ])
runner = dict(type='EpochBasedRunner', max_epochs=24)
custom_hooks = [
dict(
type='MEGVIIEMAHook',
init_updates=10560,
priority='NORMAL',
),
dict(
type='SyncbnControlHook',
syncbn_start_epoch=0,
),
]
evaluation = dict(interval=6, start=0, pipeline=test_pipeline)
checkpoint_config = dict(interval=1, max_keep_ckpts=3)
load_from="ckpts/bevdet-stbase-4d-stereo-512x1408-cbgs.pth"
# fp16 = dict(loss_scale='dynamic')
# bash tools/dist_train.sh projects/configs/flashocc/flashocc-stbase-4d-stereo-512x1408.py 4
# ===> per class IoU of 6019 samples:
# ===> others - IoU = 13.42
# ===> barrier - IoU = 51.07
# ===> bicycle - IoU = 27.68
# ===> bus - IoU = 51.57
# ===> car - IoU = 56.22
# ===> construction_vehicle - IoU = 27.27
# ===> motorcycle - IoU = 29.98
# ===> pedestrian - IoU = 29.93
# ===> traffic_cone - IoU = 29.8
# ===> trailer - IoU = 37.77
# ===> truck - IoU = 43.52
# ===> driveable_surface - IoU = 83.81
# ===> other_flat - IoU = 46.55
# ===> sidewalk - IoU = 56.15
# ===> terrain - IoU = 59.56
# ===> manmade - IoU = 50.84
# ===> vegetation - IoU = 44.67
# ===> mIoU of 6019 samples: 43.52
# ===> per class IoU of 6019 samples:
# ===> others - IoU = 13.31
# ===> barrier - IoU = 51.62
# ===> bicycle - IoU = 28.07
# ===> bus - IoU = 50.91
# ===> car - IoU = 55.69
# ===> construction_vehicle - IoU = 27.46
# ===> motorcycle - IoU = 31.05
# ===> pedestrian - IoU = 29.98
# ===> traffic_cone - IoU = 29.2
# ===> trailer - IoU = 38.86
# ===> truck - IoU = 43.68
# ===> driveable_surface - IoU = 83.87
# ===> other_flat - IoU = 45.63
# ===> sidewalk - IoU = 56.33
# ===> terrain - IoU = 59.01
# ===> manmade - IoU = 50.63
# ===> vegetation - IoU = 44.56
# ===> mIoU of 6019 samples: 43.52
# {'mIoU': array([0.13311691, 0.51617081, 0.28070517, 0.50911942, 0.55694228,
# 0.27461342, 0.31050779, 0.29979125, 0.29204287, 0.38862984,
# 0.43680049, 0.83872518, 0.45630227, 0.56327839, 0.59008883,
# 0.50627122, 0.44564523, 0.90959399])}
\ No newline at end of file
projects/configs/panoptic-flashocc/panoptic-flashocc-r50-depth-tiny-pano-trt.py 0 → 100644
View file @ 3b8d508a
_base_ = ['./flashoccv2-r50-depth-tiny-pano.py',
]
model = dict(
wocc=True,
wdet3d=False,
)
projects/configs/panoptic-flashocc/panoptic-flashocc-r50-depth-tiny.py 0 → 100644
View file @ 3b8d508a
_base_ = ['../../../mmdetection3d/configs/_base_/datasets/nus-3d.py',
'../../../mmdetection3d/configs/_base_/default_runtime.py']
plugin = True
plugin_dir = 'projects/mmdet3d_plugin/'
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,
}
grid_config = {
'x': [-40, 40, 0.4],
'y': [-40, 40, 0.4],
'z': [-1, 5.4, 6.4],
'depth': [1.0, 45.0, 1.0],
}
voxel_size = [0.1, 0.1, 0.2]
numC_Trans = 64
model = dict(
type='BEVDepthOCC', # single-frame
img_backbone=dict(
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',
pretrained='torchvision://resnet50',
),
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='LSSViewTransformerBEVDepth',
grid_config=grid_config,
input_size=data_config['input_size'],
in_channels=256,
out_channels=numC_Trans,
loss_depth_weight=1,
depthnet_cfg=dict(use_dcn=False, aspp_mid_channels=96),
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=128),
occ_head=dict(
type='BEVOCCHead2D_V2',
in_dim=128,
out_dim=128,
Dz=16,
use_mask=False,
num_classes=18,
use_predicter=True,
class_balance=True,
loss_occ=dict(
type='CustomFocalLoss',
use_sigmoid=True,
loss_weight=1.0
),
)
)
# 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=False),
dict(
type='LoadAnnotationsBEVDepth',
bda_aug_conf=bda_aug_conf,
classes=class_names,
is_train=True),
dict(type='LoadOccGTFromFile'),
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=False),
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,
data_root=data_root,
classes=class_names,
modality=input_modality,
stereo=False,
filter_empty_gt=False,
img_info_prototype='bevdet',
)
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=[24, ])
runner = dict(type='EpochBasedRunner', max_epochs=24)
custom_hooks = [
dict(
type='MEGVIIEMAHook',
init_updates=10560,
priority='NORMAL',
),
]
load_from = "ckpts/bevdet-r50-4d-depth-cbgs.pth"
# fp16 = dict(loss_scale='dynamic')
evaluation = dict(interval=1, start=20, pipeline=test_pipeline)
checkpoint_config = dict(interval=1, max_keep_ckpts=5)
# use_mask = False
# ===> per class IoU of 6019 samples:
# ===> others - IoU = 10.69
# ===> barrier - IoU = 39.67
# ===> bicycle - IoU = 22.01
# ===> bus - IoU = 39.99
# ===> car - IoU = 40.46
# ===> construction_vehicle - IoU = 20.44
# ===> motorcycle - IoU = 24.52
# ===> pedestrian - IoU = 22.5
# ===> traffic_cone - IoU = 23.72
# ===> trailer - IoU = 25.93
# ===> truck - IoU = 29.75
# ===> driveable_surface - IoU = 58.29
# ===> other_flat - IoU = 31.46
# ===> sidewalk - IoU = 33.92
# ===> terrain - IoU = 31.25
# ===> manmade - IoU = 17.46
# ===> vegetation - IoU = 17.97
# ===> mIoU of 6019 samples: 28.83
# {'mIoU': array([0.1068576 , 0.3967071 , 0.220114 , 0.3998965 , 0.40462457,
# 0.20442682, 0.24516316, 0.22497209, 0.23719173, 0.25925541,
# 0.29754347, 0.58293305, 0.31458314, 0.33921965, 0.31254221,
# 0.17456574, 0.17970859, 0.8315865 ])}
# Starting Evaluation...
# 6019it [10:23, 9.65it/s]
# +----------------------+----------+----------+----------+
# | Class Names | RayIoU@1 | RayIoU@2 | RayIoU@4 |
# +----------------------+----------+----------+----------+
# | others | 0.094 | 0.107 | 0.111 |
# | barrier | 0.367 | 0.421 | 0.443 |
# | bicycle | 0.209 | 0.251 | 0.261 |
# | bus | 0.498 | 0.601 | 0.665 |
# | car | 0.472 | 0.550 | 0.581 |
# | construction_vehicle | 0.175 | 0.251 | 0.287 |
# | motorcycle | 0.205 | 0.292 | 0.315 |
# | pedestrian | 0.289 | 0.339 | 0.354 |
# | traffic_cone | 0.276 | 0.302 | 0.314 |
# | trailer | 0.203 | 0.289 | 0.380 |
# | truck | 0.396 | 0.493 | 0.546 |
# | driveable_surface | 0.528 | 0.611 | 0.702 |
# | other_flat | 0.280 | 0.315 | 0.346 |
# | sidewalk | 0.233 | 0.279 | 0.328 |
# | terrain | 0.218 | 0.286 | 0.353 |
# | manmade | 0.268 | 0.347 | 0.398 |
# | vegetation | 0.174 | 0.272 | 0.358 |
# +----------------------+----------+----------+----------+
# | MEAN | 0.287 | 0.353 | 0.397 |
# +----------------------+----------+----------+----------+
# {'RayIoU': 0.34574739050176573, 'RayIoU@1': 0.2873820616941079, 'RayIoU@2': 0.3533573712072785,
# 'RayIoU@4': 0.39650273860391083}
projects/configs/panoptic-flashocc/panoptic-flashocc-r50-depth-trt.py 0 → 100644
View file @ 3b8d508a
_base_ = ['./flashoccv2-r50-depth.py',
]
model = dict(
wocc=True,
wdet3d=False,
)
projects/mmdet3d_plugin/__init__.py 0 → 100644
View file @ 3b8d508a
from .datasets import *
from .core import *
from .models import *
projects/mmdet3d_plugin/core/__init__.py 0 → 100644
View file @ 3b8d508a
from .bbox import *
from .hook import *
projects/mmdet3d_plugin/core/bbox/__init__.py 0 → 100644
View file @ 3b8d508a
from .coders import *
\ No newline at end of file
projects/mmdet3d_plugin/core/bbox/coders/__init__.py 0 → 100644
View file @ 3b8d508a
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