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transfusion_waymo_voxel_LC.py
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transfusion_waymo_voxel_LC.py
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point_cloud_range = [-75.2, -75.2, -2, 75.2, 75.2, 4]
class_names = ['Car', 'Pedestrian', 'Cyclist']
voxel_size = [0.1, 0.1, 0.15]
out_size_factor = 8
evaluation = dict(interval=1)
dataset_type = 'WaymoDataset'
data_root = 'data/waymo/kitti_format'
input_modality = dict(
use_lidar=True,
use_camera=True,
use_radar=False,
use_map=False,
use_external=False)
img_scale = (640, 960)
num_views = 5
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=6, use_dim=5),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(type='LoadMultiViewImageFromFiles', img_scale=(1280, 1920)),
dict(type='MyResize', img_scale=img_scale, keep_ratio=True),
dict(type='MyNormalize', **img_norm_cfg),
dict(type='MyPad', size_divisor=32),
# dict(
# type='RandomFlip3D',
# sync_2d=True,
# flip_ratio_bev_horizontal=0.5,
# flip_ratio_bev_vertical=0.5),
# dict(
# type='GlobalRotScaleTrans',
# rot_range=[-0.78539816, 0.78539816],
# scale_ratio_range=[0.95, 1.05],
# ),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'img', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=6, use_dim=5),
dict(type='LoadMultiViewImageFromFiles', img_scale=(1280, 1920)),
dict(
type='MultiScaleFlipAug3D',
img_scale=img_scale,
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1.0, 1.0],
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(type='MyNormalize', **img_norm_cfg),
dict(type='MyResize', img_scale=img_scale, keep_ratio=True),
dict(type='MyPad', size_divisor=32),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points', 'img'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=6,
train=dict(
type='RepeatDataset',
times=1,
dataset=dict(
type=dataset_type,
data_root=data_root,
load_interval=1,
num_views=num_views,
ann_file=data_root + '/waymo_infos_train.pkl',
split='training',
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,
num_views=num_views,
ann_file=data_root + '/waymo_infos_val.pkl',
split='training',
load_interval=10,
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,
num_views=num_views,
ann_file=data_root + '/waymo_infos_val.pkl',
split='training',
load_interval=10,
pipeline=test_pipeline,
classes=class_names,
modality=input_modality,
test_mode=True,
box_type_3d='LiDAR'))
model = dict(
type='TransFusionDetector',
freeze_img=True,
img_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=True),
norm_eval=True,
style='pytorch'),
img_neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
pts_voxel_layer=dict(
max_num_points=5,
voxel_size=voxel_size,
max_voxels=150000,
point_cloud_range=point_cloud_range),
pts_voxel_encoder=dict(
type='HardVFE',
in_channels=5,
feat_channels=[64],
with_distance=False,
with_cluster_center=False,
with_voxel_center=False,
voxel_size=voxel_size,
norm_cfg=dict(type='BN1d', eps=0.001, momentum=0.01),
point_cloud_range=point_cloud_range,
),
pts_middle_encoder=dict(
type='SparseEncoder',
in_channels=64,
sparse_shape=[41, 1504, 1504],
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=True,
num_views=num_views,
in_channels_img=256,
out_size_factor_img=4,
num_proposals=300,
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)),
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=[-80, -80, -10.0, 80, 80, 10.0],
score_threshold=0.0,
code_size=8,
),
loss_cls=dict(type='FocalLoss', use_sigmoid=True, gamma=2, alpha=0.25, reduction='mean', loss_weight=1.0),
# loss_iou=dict(type='CrossEntropyLoss', use_sigmoid=True, reduction='mean', loss_weight=0.0),
loss_bbox=dict(type='L1Loss', reduction='mean', loss_weight=2.0),
loss_heatmap=dict(type='GaussianFocalLoss', reduction='mean', loss_weight=1.0),
),
train_cfg=dict(
pts=dict(
dataset='Waymo',
assigner=dict(
type='HungarianAssigner3D',
iou_calculator=dict(type='BboxOverlaps3D', coordinate='lidar'),
cls_cost=dict(type='FocalLossCost', gamma=2, alpha=0.25, weight=0.6),
reg_cost=dict(type='BBoxBEVL1Cost', weight=2.0),
iou_cost=dict(type='IoU3DCost', weight=2.0)
),
pos_weight=-1,
gaussian_overlap=0.1,
min_radius=2,
grid_size=[1504, 1504, 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],
point_cloud_range=point_cloud_range)),
test_cfg=dict(
pts=dict(
dataset='Waymo',
pc_range=point_cloud_range[:2],
grid_size=[1504, 1504, 40],
out_size_factor=out_size_factor,
voxel_size=voxel_size[:2],
nms_type=None,
)))
optimizer = dict(type='AdamW', lr=0.0001, weight_decay=0.01) # for 8gpu * 2sample_per_gpu
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 = 12
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'
work_dir = None
load_from = 'checkpoints/waymo_36e_R50.pth'
resume_from = None
workflow = [('train', 1)]
freeze_lidar_components = True
find_unused_parameters = True
gpu_ids = range(0, 8)