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bevf_tf_4x8_10e_nusc_aug.py
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bevf_tf_4x8_10e_nusc_aug.py
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_base_ = [
'../_base_/datasets/nusc_tf_aug.py',
# '../_base_/schedules/schedule_1x.py',
]
point_cloud_range = [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
voxel_size = [0.075, 0.075, 0.2]
out_size_factor = 8
final_dim=(900, 1600) # HxW
downsample=8
imc = 256
input_modality = dict(
use_lidar=True,
use_camera=True,
use_radar=False,
use_map=False,
use_external=False)
num_views = 6
model = dict(
type='BEVF_TransFusion_Aug',
se=True,
camera_stream=True,
grid=0.6,
num_views=6,
final_dim=final_dim,
downsample=downsample,
freeze_img_backboneneck=True,
imc=imc,
lic=256 * 2,
lc_fusion=True,
pc_range = point_cloud_range,
img_backbone=dict(
type='CBSwinTransformer',
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4.,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.2,
ape=False,
patch_norm=True,
out_indices=(0, 1, 2, 3),
use_checkpoint=False),
img_neck=dict(
type='FPNC',
final_dim=final_dim,
downsample=downsample,
in_channels=[96, 192, 384, 768],
out_channels=256,
outC=imc,
use_adp=True,
num_outs=5),
pts_voxel_layer=dict(
max_num_points=10,
voxel_size=voxel_size,
max_voxels=(120000, 160000),
point_cloud_range=point_cloud_range),
pts_voxel_encoder=dict(
type='HardSimpleVFE',
num_features=5,
),
pts_middle_encoder=dict(
type='SparseEncoder',
in_channels=5,
sparse_shape=[41, 1440, 1440],
output_channels=128,
order=('conv', 'norm', 'act'),
encoder_channels=((16, 16, 32), (32, 32, 64), (64, 64, 128), (128, 128)),
encoder_paddings=((0, 0, 1), (0, 0, 1), (0, 0, [0, 1, 1]), (0, 0)),
block_type='basicblock'),
pts_backbone=dict(
type='SECOND',
in_channels=256,
out_channels=[128, 256],
layer_nums=[5, 5],
layer_strides=[1, 2],
norm_cfg=dict(type='BN', eps=0.001, momentum=0.01),
conv_cfg=dict(type='Conv2d', bias=False)),
pts_neck=dict(
type='SECONDFPN',
in_channels=[128, 256],
out_channels=[256, 256],
upsample_strides=[1, 2],
norm_cfg=dict(type='BN', eps=0.001, momentum=0.01),
upsample_cfg=dict(type='deconv', bias=False),
use_conv_for_no_stride=True),
pts_bbox_head=dict(
type='TransFusionHead',
fuse_img=False,
num_views=num_views,
in_channels_img=256,
out_size_factor_img=4,
num_proposals=200,
auxiliary=True,
in_channels=256 * 2,
hidden_channel=128,
num_classes=len(class_names),
num_decoder_layers=1,
num_heads=8,
learnable_query_pos=False,
initialize_by_heatmap=True,
nms_kernel_size=3,
ffn_channel=256,
dropout=0.1,
bn_momentum=0.1,
activation='relu',
common_heads=dict(center=(2, 2), height=(1, 2), dim=(3, 2), rot=(2, 2), vel=(2, 2)),
bbox_coder=dict(
type='TransFusionBBoxCoder',
pc_range=point_cloud_range[:2],
voxel_size=voxel_size[:2],
out_size_factor=out_size_factor,
post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
score_threshold=0.0,
code_size=10,
),
loss_cls=dict(type='FocalLoss', use_sigmoid=True, gamma=2, alpha=0.25, reduction='mean', loss_weight=1.0),
loss_bbox=dict(type='L1Loss', reduction='mean', loss_weight=0.25),
loss_heatmap=dict(type='GaussianFocalLoss', reduction='mean', loss_weight=1.0),
),
train_cfg=dict(
pts=dict(
dataset='nuScenes',
assigner=dict(
type='HungarianAssigner3D',
iou_calculator=dict(type='BboxOverlaps3D', coordinate='lidar'),
cls_cost=dict(type='FocalLossCost', gamma=2, alpha=0.25, weight=0.15),
reg_cost=dict(type='BBoxBEVL1Cost', weight=0.25),
iou_cost=dict(type='IoU3DCost', weight=0.25)
),
pos_weight=-1,
gaussian_overlap=0.1,
min_radius=2,
grid_size=[1440, 1440, 40], # [x_len, y_len, 1]
voxel_size=voxel_size,
out_size_factor=out_size_factor,
code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2],
point_cloud_range=point_cloud_range)),
test_cfg=dict(
pts=dict(
dataset='nuScenes',
grid_size=[1440, 1440, 40],
out_size_factor=out_size_factor,
pc_range=point_cloud_range[0:2],
voxel_size=voxel_size[:2],
nms_type=None,
)))
# optimizer = dict(type='AdamW', lr=0.001, betas=(0.9, 0.999), weight_decay=0.05,
# paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
# 'relative_position_bias_table': dict(decay_mult=0.),
# 'norm': dict(decay_mult=0.)}))
# optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))
# lr_config = dict(
# step=[4, 5])
# total_epochs = 6
optimizer = dict(
type='AdamW',
lr=0.0001,
betas=(0.9, 0.999),
weight_decay=0.05,
paramwise_cfg=dict(
custom_keys=dict(
absolute_pos_embed=dict(decay_mult=0.0),
relative_position_bias_table=dict(decay_mult=0.0),
norm=dict(decay_mult=0.0))))
optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
lr_config = dict(
policy='cyclic',
target_ratio=(10, 0.0001),
cyclic_times=1,
step_ratio_up=0.4)
momentum_config = dict(
policy='cyclic',
target_ratio=(0.8947368421052632, 1),
cyclic_times=1,
step_ratio_up=0.4)
total_epochs = 10
checkpoint_config = dict(interval=1)
log_config = dict(
interval=50,
hooks=[dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'models/lidar_tf.pth'
load_lift_from = 'models/cam_tf_e20.pth'
resume_from = None
workflow = [('train', 1)]
gpu_ids = range(0, 8)
freeze_lidar_components = True
find_unused_parameters = True
no_freeze_head = True
data = dict(
samples_per_gpu=4,
workers_per_gpu=6,)
checkpoint_config = dict(interval=1, out_dir='/model')
work_dir = '/model'
custom_hooks = [
dict(type='MindFreeHook')
]