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tpv_lidarseg.py
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tpv_lidarseg.py
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_base_ = [
'./_base_/dataset.py',
'./_base_/optimizer.py',
'./_base_/schedule.py',
]
occupancy = False
point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
_dim_ = 128
num_heads = 8
_pos_dim_ = [48, 48, 32]
_ffn_dim_ = _dim_*2
_num_levels_ = 4
_num_cams_ = 6
bev_h_ = 200
bev_w_ = 200
bev_z_ = 16
scale_h = 1
scale_w = 1
scale_z = 1
bev_encoder_layers = 5
num_points_in_pillar = [4, 32, 32]
num_points = [8, 64, 64]
hybrid_attn_anchors = 16
hybrid_attn_points = 32
hybrid_attn_init = 0
grid_size = [bev_h_*scale_h, bev_w_*scale_w, bev_z_*scale_z]
nbr_class = 17
self_cross_layer = dict(
type='TPVFormerLayer',
attn_cfgs=[
dict(
type='TPVTemporalSelfAttention',
bev_h=bev_h_,
bev_w=bev_w_,
bev_z=bev_z_,
num_anchors=hybrid_attn_anchors,
embed_dims=_dim_,
num_heads=num_heads,
num_points=hybrid_attn_points,
init_mode=hybrid_attn_init,
),
dict(
type='TPVSpatialCrossAttention',
pc_range=point_cloud_range,
num_cams=_num_cams_,
deformable_attention=dict(
type='TPVMSDeformableAttention3D',
embed_dims=_dim_,
num_heads=num_heads,
num_points=num_points,
num_z_anchors=num_points_in_pillar,
num_levels=_num_levels_,
floor_sampling_offset=False,
bev_h=bev_h_,
bev_w=bev_w_,
bev_z=bev_z_,
),
embed_dims=_dim_,
bev_h=bev_h_,
bev_w=bev_w_,
bev_z=bev_z_,
)
],
feedforward_channels=_ffn_dim_,
ffn_dropout=0.1,
operation_order=('self_attn', 'norm', 'cross_attn', 'norm', 'ffn', 'norm')
)
self_layer = dict(
type='TPVFormerLayer',
attn_cfgs=[
dict(
type='TPVTemporalSelfAttention',
bev_h=bev_h_,
bev_w=bev_w_,
bev_z=bev_z_,
num_anchors=hybrid_attn_anchors,
embed_dims=_dim_,
num_heads=num_heads,
num_points=hybrid_attn_points,
init_mode=hybrid_attn_init,
)
],
feedforward_channels=_ffn_dim_,
ffn_dropout=0.1,
operation_order=('self_attn', 'norm', 'ffn', 'norm')
)
model = dict(
type='TPVFormer',
use_grid_mask=True,
fusion_head = dict(
type='TPVFuser',
bev_h=bev_h_,
bev_w=bev_w_,
bev_z=bev_z_,
nbr_classes=nbr_class,
in_dims=_dim_,
hidden_dims=2*_dim_,
out_dims=_dim_,
scale_h=scale_h,
scale_w=scale_w,
scale_z=scale_z
),
img_backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN2d', requires_grad=False),
norm_eval=True,
style='caffe',
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), # original DCNv2 will print log when perform load_state_dict
stage_with_dcn=(False, False, True, True)),
img_neck=dict(
type='FPN',
in_channels=[512, 1024, 2048],
out_channels=_dim_,
start_level=0,
add_extra_convs='on_output',
num_outs=4,
relu_before_extra_convs=True),
pts_bbox_head=dict(
type='TPVFormerHead',
bev_h=bev_h_,
bev_w=bev_w_,
bev_z=bev_z_,
pc_range=point_cloud_range,
transformer=dict(
type='TPVPerceptionTransformer',
embed_dims=_dim_,
num_cams=_num_cams_,
encoder=dict(
type='TPVFormerEncoder',
bev_h=bev_h_,
bev_w=bev_w_,
bev_z=bev_z_,
num_layers=bev_encoder_layers,
pc_range=point_cloud_range,
num_points_in_pillar=num_points_in_pillar,
num_points_in_pillar_cross_view=[16, 16, 16],
return_intermediate=False,
transformerlayers=[
self_cross_layer,
self_cross_layer,
self_cross_layer,
self_layer,
self_layer,
])),
positional_encoding=dict(
type='CustomPositionalEncoding',
num_feats=_pos_dim_,
h=bev_h_,
w=bev_w_,
z=bev_z_
)))