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lanesegnet_r50_8x1_24e_olv2_subset_A.py
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_base_ = []
custom_imports = dict(imports=['projects.bevformer', 'projects.lanesegnet'])
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-51.2, -25.6, -2.3, 51.2, 25.6, 1.7]
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
class_names = ['lane_segment', 'ped_crossing']
class_nums = len(class_names)
input_modality = dict(
use_lidar=False,
use_camera=True,
use_radar=False,
use_map=False,
use_external=False)
num_cams = 7
pts_dim = 3
dataset_type = 'OpenLaneV2_subset_A_LaneSegNet_Dataset'
data_root = 'data/OpenLane-V2/'
para_method = 'fix_pts_interp'
method_para = dict(n_points=10)
code_size = 3 * method_para['n_points'] * pts_dim
_dim_ = 256
_pos_dim_ = _dim_//2
_ffn_dim_ = _dim_*2
_ffn_cfg_ = dict(
type='FFN',
embed_dims=_dim_,
feedforward_channels=_ffn_dim_,
num_fcs=2,
ffn_drop=0.1,
act_cfg=dict(type='ReLU', inplace=True),
),
_num_levels_ = 4
bev_h_ = 100
bev_w_ = 200
model = dict(
type='LaneSegNet',
img_backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
img_neck=dict(
type='FPN',
in_channels=[512, 1024, 2048],
out_channels=_dim_,
start_level=0,
add_extra_convs='on_output',
num_outs=_num_levels_,
relu_before_extra_convs=True),
bev_constructor=dict(
type='BEVFormerConstructer',
num_feature_levels=_num_levels_,
num_cams=num_cams,
embed_dims=_dim_,
rotate_prev_bev=True,
use_shift=True,
use_can_bus=True,
pc_range=point_cloud_range,
bev_h=bev_h_,
bev_w=bev_w_,
rotate_center=[bev_h_//2, bev_w_//2],
encoder=dict(
type='BEVFormerEncoder',
num_layers=3,
pc_range=point_cloud_range,
num_points_in_pillar=4,
return_intermediate=False,
transformerlayers=dict(
type='BEVFormerLayer',
attn_cfgs=[
dict(
type='TemporalSelfAttention',
embed_dims=_dim_,
num_levels=1),
dict(
type='SpatialCrossAttention',
embed_dims=_dim_,
num_cams=num_cams,
pc_range=point_cloud_range,
deformable_attention=dict(
type='MSDeformableAttention3D',
embed_dims=_dim_,
num_points=8,
num_levels=_num_levels_)
)
],
ffn_cfgs=_ffn_cfg_,
operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
'ffn', 'norm'))),
positional_encoding=dict(
type='LearnedPositionalEncoding',
num_feats=_pos_dim_,
row_num_embed=bev_h_,
col_num_embed=bev_w_),
),
lane_head=dict(
type='LaneSegHead',
num_classes=class_nums,
num_lane_type_classes=3,
in_channels=_dim_,
num_query=200,
bev_h=bev_h_,
bev_w=bev_w_,
pc_range=point_cloud_range,
pts_dim=pts_dim,
sync_cls_avg_factor=False,
with_box_refine=True,
code_size=code_size,
code_weights= [1.0 for i in range(code_size)],
transformer=dict(
type='LaneSegNetTransformer',
embed_dims=_dim_,
points_num=method_para['n_points'],
pts_dim=pts_dim,
decoder=dict(
type='LaneSegNetDecoder',
num_layers=6,
return_intermediate=True,
pc_range=point_cloud_range,
pts_dim=pts_dim,
transformerlayers=dict(
type='CustomDetrTransformerDecoderLayer',
attn_cfgs=[
dict(
type='MultiheadAttention',
embed_dims=_dim_,
num_heads=8,
dropout=0.1),
dict(
type='LaneAttention',
embed_dims=_dim_,
num_heads=8,
num_points=32,
num_levels=1),
],
ffn_cfgs=_ffn_cfg_,
operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
'ffn', 'norm')))),
bbox_coder=dict(type='LaneSegmentPseudoCoder'),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.5),
loss_bbox=dict(type='L1Loss', loss_weight=0.025),
loss_lane_type=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=0.1),
loss_mask=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
loss_weight=3.0),
loss_dice=dict(
type='DiceLoss',
use_sigmoid=True,
activate=True,
reduction='mean',
naive_dice=True,
eps=1.0,
loss_weight=3.0)),
lclc_head=dict(
type='RelationshipHead',
in_channels_o1=_dim_,
in_channels_o2=_dim_,
shared_param=False,
loss_rel=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=5)),
# model training and testing settings
train_cfg=dict(
lane=dict(
assigner=dict(
type='LaneSegmentHungarianAssigner3D',
cls_cost=dict(type='FocalLossCost', weight=1.5),
reg_cost=dict(type='LaneL1Cost', weight=0.025),
mask_cost=dict(type='CrossEntropyLossCost', weight=3.0, use_sigmoid=True),
dice_cost=dict(type='DiceCost', weight=3.0, pred_act=False, eps=1.0),
pc_range=point_cloud_range))))
train_pipeline = [
dict(type='CustomLoadMultiViewImageFromFiles', to_float32=True),
dict(type='LoadAnnotations3DLaneSegment',
with_lane_3d=True, with_lane_label_3d=True, with_lane_adj=True, with_lane_type=True,
with_bbox=False, with_label=False, with_lane_lste_adj=False),
dict(type='PhotoMetricDistortionMultiViewImage'),
dict(type='CropFrontViewImageForAv2'),
dict(type='RandomScaleImageMultiViewImage', scales=[0.5]),
dict(type='NormalizeMultiviewImage', **img_norm_cfg),
dict(type='PadMultiViewImageSame2Max', size_divisor=32),
dict(type='GridMaskMultiViewImage'),
dict(type='LaneSegmentParameterize3D', method=para_method, method_para=method_para),
dict(type='GenerateLaneSegmentMask', points_num=method_para['n_points'], bev_h=bev_h_, bev_w=bev_w_),
dict(type='CustomFormatBundle3DLane', class_names=class_names),
dict(type='CustomCollect3D', keys=[
'img', 'gt_lanes_3d', 'gt_lane_labels_3d', 'gt_lane_adj',
'gt_instance_masks', 'gt_lane_left_type', 'gt_lane_right_type'])
]
test_pipeline = [
dict(type='CustomLoadMultiViewImageFromFiles', to_float32=True),
dict(type='CropFrontViewImageForAv2'),
dict(type='RandomScaleImageMultiViewImage', scales=[0.5]),
dict(type='NormalizeMultiviewImage', **img_norm_cfg),
dict(type='PadMultiViewImageSame2Max', size_divisor=32),
dict(type='CustomFormatBundle3DLane', class_names=class_names),
dict(type='CustomCollect3D', keys=['img'])
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=8,
train=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'data_dict_subset_A_train_lanesegnet.pkl',
pipeline=train_pipeline,
classes=class_names,
modality=input_modality,
split='train',
filter_map_change=True,
points_num=method_para['n_points'],
test_mode=False),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'data_dict_subset_A_val_lanesegnet.pkl',
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
split='val',
points_num=method_para['n_points'],
test_mode=True),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'data_dict_subset_A_val_lanesegnet.pkl',
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
split='val',
points_num=method_para['n_points'],
test_mode=True)
)
optimizer = dict(
type='AdamW',
lr=2e-4,
paramwise_cfg=dict(
custom_keys={
'img_backbone': dict(lr_mult=0.1),
}),
weight_decay=0.01)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='CosineAnnealing',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
min_lr_ratio=1e-3)
total_epochs = 24
evaluation = dict(interval=24, pipeline=test_pipeline)
runner = dict(type='EpochBasedRunner', max_epochs=total_epochs)
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
checkpoint_config = dict(interval=1, max_keep_ckpts=1)
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = None
load_from = None
resume_from = None
workflow = [('train', 1)]
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# base_batch_size = (8 GPUs) x (1 samples per GPU)
auto_scale_lr = dict(base_batch_size=8)