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imvoxelnet_nuscenes.py
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imvoxelnet_nuscenes.py
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model = dict(
type='ImVoxelNet',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='pytorch',
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, False, True, True)),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=64,
num_outs=4),
neck_3d=dict(
type='NuScenesImVoxelNeck',
in_channels=64,
out_channels=256),
bbox_head=dict(
type='Anchor3DHead',
num_classes=1,
in_channels=256,
feat_channels=256,
use_direction_classifier=True,
anchor_generator=dict(
type='Anchor3DRangeGenerator',
sizes=[[1.98, 4.67, 1.74]],
ranges=[[-49.92, -49.92, -1.0, 49.92 - .32 * 2, 49.92 - .32 * 2, -1.0]]),
assigner_per_size=False,
diff_rad_by_sin=True,
dir_offset=0.7854, # pi/4
dir_limit_offset=0,
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
loss_dir=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
n_voxels=(312, 312, 12),
voxel_size=(.32, .32, .32))
train_cfg = dict(
assigner=dict(
type='MaxIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.6,
neg_iou_thr=0.3,
min_pos_iou=0.3,
ignore_iof_thr=-1),
allowed_border=0,
pos_weight=-1,
debug=False)
test_cfg = dict(
use_rotate_nms=True,
nms_across_levels=False,
nms_pre=1000,
nms_thr=0.2,
score_thr=0.05,
min_bbox_size=0,
max_num=500)
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
dataset_type = 'NuScenesMultiViewDataset'
data_root = 'data/nuscenes/'
point_cloud_range = [-49.92, -49.92, -2.92, 49.92, 49.92, 0.92]
class_names = [
'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
input_modality = dict(
use_lidar=False,
use_camera=True,
use_radar=False,
use_map=False,
use_external=False)
train_pipeline = [
dict(type='LoadAnnotations3D'),
dict(
type='MultiViewPipeline',
n_images=6,
transforms=[
dict(type='LoadImageFromFile'),
dict(type='Resize', img_scale=(1600, 900), keep_ratio=True),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32)]),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='KittiSetOrigin', point_cloud_range=point_cloud_range),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['img', 'gt_bboxes_3d', 'gt_labels_3d'])]
test_pipeline = [
dict(
type='MultiViewPipeline',
n_images=6,
transforms=[
dict(type='LoadImageFromFile'),
dict(type='Resize', img_scale=(1600, 900), keep_ratio=True),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32)]),
dict(type='KittiSetOrigin', point_cloud_range=point_cloud_range),
dict(type='DefaultFormatBundle3D', class_names=class_names, with_label=False),
dict(type='Collect3D', keys=['img'])]
data = dict(
samples_per_gpu=1,
workers_per_gpu=1,
train=dict(
type='RepeatDataset',
times=1,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'nuscenes_infos_train.pkl',
pipeline=train_pipeline,
classes=class_names,
modality=input_modality,
test_mode=False,
box_type_3d='LiDAR')),
val=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'nuscenes_infos_val.pkl',
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
test_mode=True,
box_type_3d='LiDAR'),
test=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'nuscenes_infos_val.pkl',
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
test_mode=True,
box_type_3d='LiDAR'))
optimizer = dict(
type='AdamW',
lr=0.0001,
weight_decay=0.0001,
paramwise_cfg=dict(
custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}))
optimizer_config = dict(grad_clip=dict(max_norm=35., norm_type=2))
lr_config = dict(policy='step', step=[8, 11])
total_epochs = 12
checkpoint_config = dict(interval=1)
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
evaluation = dict(interval=100)
dist_params = dict(backend='nccl')
find_unused_parameters = True # todo: fix number of FPN outputs
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]