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Unverified Commit 40ec11aa authored by ChaimZhu's avatar ChaimZhu Committed by GitHub
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[Refactor] update benchmark config (#1868) 

* update benchmark config

* fix docstrings

* update docstrings

* update benchmark hook
parent 62e43671
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Showing with 542 additions and 324 deletions
configs/_base_/datasets/kitti-3d-3class.py
View file @ 40ec11aa
......@@ -67,7 +67,7 @@ test_pipeline = [
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
dict(type='Pack3DDetInputs', keys=['points']),
dict(type='Pack3DDetInputs', keys=['points'])
]
train_dataloader = dict(
batch_size=6,
......
configs/benchmark/hv_PartA2_secfpn_4x8_cyclic_80e_pcdet_kitti-3d-3class.py
View file @ 40ec11aa
......@@ -48,14 +48,16 @@ model = dict(
assign_per_class=True,
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
loss_cls=dict(
type='FocalLoss',
type='mmdet.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=2.0),
loss_bbox=dict(
type='mmdet.SmoothL1Loss', beta=1.0 / 9.0, loss_weight=2.0),
loss_dir=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
type='mmdet.CrossEntropyLoss', use_sigmoid=False,
loss_weight=0.2)),
roi_head=dict(
type='PartAggregationROIHead',
num_classes=3,
......@@ -66,14 +68,16 @@ model = dict(
seg_score_thr=0.3,
num_classes=3,
loss_seg=dict(
type='FocalLoss',
type='mmdet.FocalLoss',
use_sigmoid=True,
reduction='sum',
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_part=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
type='mmdet.CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0)),
seg_roi_extractor=dict(
type='Single3DRoIAwareExtractor',
roi_layer=dict(
......@@ -81,7 +85,7 @@ model = dict(
out_size=14,
max_pts_per_voxel=128,
mode='max')),
part_roi_extractor=dict(
bbox_roi_extractor=dict(
type='Single3DRoIAwareExtractor',
roi_layer=dict(
type='RoIAwarePool3d',
......@@ -105,12 +109,12 @@ model = dict(
roi_feat_size=14,
with_corner_loss=True,
loss_bbox=dict(
type='SmoothL1Loss',
type='mmdet.SmoothL1Loss',
beta=1.0 / 9.0,
reduction='sum',
loss_weight=1.0),
loss_cls=dict(
type='CrossEntropyLoss',
type='mmdet.CrossEntropyLoss',
use_sigmoid=True,
reduction='sum',
loss_weight=1.0))),
......@@ -119,21 +123,21 @@ model = dict(
rpn=dict(
assigner=[
dict( # for Pedestrian
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.5,
neg_iou_thr=0.35,
min_pos_iou=0.35,
ignore_iof_thr=-1),
dict( # for Cyclist
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.5,
neg_iou_thr=0.35,
min_pos_iou=0.35,
ignore_iof_thr=-1),
dict( # for Car
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.6,
neg_iou_thr=0.45,
......@@ -153,7 +157,7 @@ model = dict(
rcnn=dict(
assigner=[
dict( # for Pedestrian
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(
type='BboxOverlaps3D', coordinate='lidar'),
pos_iou_thr=0.55,
......@@ -161,7 +165,7 @@ model = dict(
min_pos_iou=0.55,
ignore_iof_thr=-1),
dict( # for Cyclist
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(
type='BboxOverlaps3D', coordinate='lidar'),
pos_iou_thr=0.55,
......@@ -169,7 +173,7 @@ model = dict(
min_pos_iou=0.55,
ignore_iof_thr=-1),
dict( # for Car
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(
type='BboxOverlaps3D', coordinate='lidar'),
pos_iou_thr=0.55,
......@@ -200,12 +204,13 @@ model = dict(
use_rotate_nms=True,
use_raw_score=True,
nms_thr=0.01,
score_thr=0.3)))
score_thr=0.1)))
# dataset settings
dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Pedestrian', 'Cyclist', 'Car']
metainfo = dict(CLASSES=class_names)
input_modality = dict(use_lidar=True, use_camera=False)
db_sampler = dict(
data_root=data_root,
......@@ -215,9 +220,8 @@ db_sampler = dict(
filter_by_difficulty=[-1],
filter_by_min_points=dict(Car=5, Pedestrian=5, Cyclist=5)),
classes=class_names,
sample_groups=dict(Car=20, Pedestrian=15, Cyclist=15),
points_loader=dict(
type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4))
sample_groups=dict(Car=20, Pedestrian=15, Cyclist=15))
train_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
......@@ -231,8 +235,9 @@ train_pipeline = [
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectNameFilter', classes=class_names),
dict(type='PointShuffle'),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_labels_3d', 'gt_bboxes_3d'])
]
test_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
......@@ -249,88 +254,133 @@ test_pipeline = [
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
type='PointsRangeFilter', point_cloud_range=point_cloud_range)
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
dict(type='Pack3DDetInputs', keys=['points'])
]
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
train=dict(
train_dataloader = dict(
batch_size=4,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_train.pkl',
split='training',
pts_prefix='velodyne_reduced',
ann_file='kitti_infos_train.pkl',
data_prefix=dict(pts='training/velodyne_reduced'),
pipeline=train_pipeline,
modality=input_modality,
classes=class_names,
test_mode=False),
val=dict(
test_mode=False,
metainfo=metainfo,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='LiDAR'))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
data_prefix=dict(pts='training/velodyne_reduced'),
ann_file='kitti_infos_val.pkl',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True),
test=dict(
type=dataset_type,
data_root=data_root,
test_mode=True,
metainfo=metainfo,
box_type_3d='LiDAR'))
test_dataloader = val_dataloader
val_evaluator = dict(
type='KittiMetric',
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True))
metric='bbox')
test_evaluator = val_evaluator
# optimizer
lr = 0.001 # max learning rate
optimizer = dict(type='AdamW', lr=lr, betas=(0.95, 0.99), weight_decay=0.01)
optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))
lr_config = dict(
policy='cyclic',
target_ratio=(10, 1e-4),
cyclic_times=1,
step_ratio_up=0.4)
momentum_config = dict(
policy='cyclic',
target_ratio=(0.85 / 0.95, 1),
cyclic_times=1,
step_ratio_up=0.4)
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, pipeline=eval_pipeline)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
# runtime settings
runner = dict(type='EpochBasedRunner', max_epochs=80)
dist_params = dict(backend='nccl', port=29506)
epoch_num = 80
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=lr, betas=(0.95, 0.99), weight_decay=0.01),
clip_grad=dict(max_norm=10, norm_type=2))
# learning policy
param_scheduler = [
dict(
type='CosineAnnealingLR',
T_max=epoch_num * 0.4,
eta_min=lr * 10,
begin=0,
end=epoch_num * 0.4,
by_epoch=True,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=epoch_num * 0.6,
eta_min=lr * 1e-4,
begin=epoch_num * 0.4,
end=epoch_num * 1,
by_epoch=True,
convert_to_iter_based=True),
dict(
type='CosineAnnealingMomentum',
T_max=epoch_num * 0.4,
eta_min=0.85 / 0.95,
begin=0,
end=epoch_num * 0.4,
by_epoch=True,
convert_to_iter_based=True),
dict(
type='CosineAnnealingMomentum',
T_max=epoch_num * 0.6,
eta_min=1,
begin=epoch_num * 0.4,
end=epoch_num * 1,
convert_to_iter_based=True)
]
train_cfg = dict(by_epoch=True, max_epochs=epoch_num, val_interval=50)
val_cfg = dict()
test_cfg = dict()
auto_scale_lr = dict(enable=False, base_batch_size=32)
default_scope = 'mmdet3d'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=1),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='Det3DVisualizationHook'))
custom_hooks = [
dict(type='BenchmarkHook'),
]
env_cfg = dict(
cudnn_benchmark=False,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
dist_cfg=dict(backend='nccl'),
)
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = None
resume = False
find_unused_parameters = True
work_dir = './work_dirs/parta2_secfpn_80e'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/benchmark/hv_pointpillars_secfpn_3x8_100e_det3d_kitti-3d-car.py
View file @ 40ec11aa
......@@ -46,18 +46,20 @@ model = dict(
diff_rad_by_sin=True,
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
loss_cls=dict(
type='FocalLoss',
type='mmdet.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=2.0),
loss_bbox=dict(
type='mmdet.SmoothL1Loss', beta=1.0 / 9.0, loss_weight=2.0),
loss_dir=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
type='mmdet.CrossEntropyLoss', use_sigmoid=False,
loss_weight=0.2)),
# model training and testing settings
train_cfg=dict(
assigner=dict(
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.6,
neg_iou_thr=0.45,
......@@ -79,6 +81,7 @@ model = dict(
dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Car']
metainfo = dict(CLASSES=class_names)
input_modality = dict(use_lidar=True, use_camera=False)
db_sampler = dict(
data_root=data_root,
......@@ -86,9 +89,7 @@ db_sampler = dict(
rate=1.0,
prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
sample_groups=dict(Car=15),
classes=class_names,
points_loader=dict(
type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4))
classes=class_names)
train_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
......@@ -108,99 +109,140 @@ train_pipeline = [
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', 'gt_bboxes_3d', 'gt_labels_3d'])
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_labels_3d', 'gt_bboxes_3d'])
]
test_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
dict(type='Pack3DDetInputs', keys=['points'])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
dict(type='Pack3DDetInputs', keys=['points'])
]
data = dict(
samples_per_gpu=3,
workers_per_gpu=3,
train=dict(
train_dataloader = dict(
batch_size=3,
num_workers=3,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type='RepeatDataset',
times=2,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_train.pkl',
split='training',
pts_prefix='velodyne_reduced',
ann_file='kitti_infos_train.pkl',
data_prefix=dict(pts='training/velodyne_reduced'),
pipeline=train_pipeline,
modality=input_modality,
classes=class_names,
test_mode=False)),
val=dict(
test_mode=False,
metainfo=metainfo,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='LiDAR')))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
data_prefix=dict(pts='training/velodyne_reduced'),
ann_file='kitti_infos_val.pkl',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True),
test=dict(
type=dataset_type,
data_root=data_root,
test_mode=True,
metainfo=metainfo,
box_type_3d='LiDAR'))
test_dataloader = val_dataloader
val_evaluator = dict(
type='KittiMetric',
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True))
metric='bbox')
test_evaluator = val_evaluator
# optimizer
lr = 0.001 # max learning rate
optimizer = dict(
type='AdamW',
lr=lr,
betas=(0.95, 0.99), # the momentum is change during training
weight_decay=0.01)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
epoch_num = 50
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=lr, betas=(0.95, 0.99), weight_decay=0.01),
clip_grad=dict(max_norm=10, norm_type=2))
# learning policy
lr_config = dict(
policy='cyclic',
target_ratio=(10, 1e-4),
cyclic_times=1,
step_ratio_up=0.4)
momentum_config = dict(
policy='cyclic',
target_ratio=(0.85 / 0.95, 1),
cyclic_times=1,
step_ratio_up=0.4)
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, pipeline=eval_pipeline)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
# runtime settings
runner = dict(type='EpochBasedRunner', max_epochs=50)
dist_params = dict(backend='nccl')
param_scheduler = [
dict(
type='CosineAnnealingLR',
T_max=epoch_num * 0.4,
eta_min=lr * 10,
begin=0,
end=epoch_num * 0.4,
by_epoch=True,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=epoch_num * 0.6,
eta_min=lr * 1e-4,
begin=epoch_num * 0.4,
end=epoch_num * 1,
by_epoch=True,
convert_to_iter_based=True),
dict(
type='CosineAnnealingMomentum',
T_max=epoch_num * 0.4,
eta_min=0.85 / 0.95,
begin=0,
end=epoch_num * 0.4,
by_epoch=True,
convert_to_iter_based=True),
dict(
type='CosineAnnealingMomentum',
T_max=epoch_num * 0.6,
eta_min=1,
begin=epoch_num * 0.4,
end=epoch_num * 1,
convert_to_iter_based=True)
]
train_cfg = dict(by_epoch=True, max_epochs=epoch_num, val_interval=50)
val_cfg = dict()
test_cfg = dict()
auto_scale_lr = dict(enable=False, base_batch_size=24)
default_scope = 'mmdet3d'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=1),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='Det3DVisualizationHook'))
custom_hooks = [
dict(type='BenchmarkHook'),
]
env_cfg = dict(
cudnn_benchmark=False,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
dist_cfg=dict(backend='nccl'),
)
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
work_dir = './work_dirs/pp_secfpn_100e'
load_from = None
resume_from = None
workflow = [('train', 50)]
resume = False
work_dir = './work_dirs/pp_secfpn_100e'
configs/benchmark/hv_pointpillars_secfpn_4x8_80e_pcdet_kitti-3d-3class.py
View file @ 40ec11aa
......@@ -56,34 +56,35 @@ model = dict(
diff_rad_by_sin=True,
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
loss_cls=dict(
type='FocalLoss',
type='mmdet.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=2.0),
loss_bbox=dict(
type='mmdet.SmoothL1Loss', beta=1.0 / 9.0, loss_weight=2.0),
loss_dir=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2),
type='mmdet.CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2),
),
# model training and testing settings
train_cfg=dict(
assigner=[
dict( # for Pedestrian
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.5,
neg_iou_thr=0.35,
min_pos_iou=0.35,
ignore_iof_thr=-1),
dict( # for Cyclist
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.5,
neg_iou_thr=0.35,
min_pos_iou=0.35,
ignore_iof_thr=-1),
dict( # for Car
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.6,
neg_iou_thr=0.45,
......@@ -106,6 +107,8 @@ model = dict(
dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Pedestrian', 'Cyclist', 'Car']
metainfo = dict(CLASSES=class_names)
input_modality = dict(use_lidar=True, use_camera=False)
db_sampler = dict(
data_root=data_root,
......@@ -123,9 +126,7 @@ db_sampler = dict(
Car=15,
Pedestrian=15,
Cyclist=15,
),
points_loader=dict(
type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4))
))
train_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
......@@ -139,8 +140,9 @@ train_pipeline = [
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', 'gt_bboxes_3d', 'gt_labels_3d']),
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_labels_3d', 'gt_bboxes_3d'])
]
test_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
......@@ -158,91 +160,132 @@ test_pipeline = [
dict(type='RandomFlip3D'),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
dict(type='Pack3DDetInputs', keys=['points'])
]
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
train=dict(
train_dataloader = dict(
batch_size=4,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_train.pkl',
split='training',
pts_prefix='velodyne_reduced',
ann_file='kitti_infos_train.pkl',
data_prefix=dict(pts='training/velodyne_reduced'),
pipeline=train_pipeline,
modality=input_modality,
classes=class_names,
test_mode=False),
val=dict(
test_mode=False,
metainfo=metainfo,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='LiDAR'))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
data_prefix=dict(pts='training/velodyne_reduced'),
ann_file='kitti_infos_val.pkl',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True),
test=dict(
type=dataset_type,
data_root=data_root,
test_mode=True,
metainfo=metainfo,
box_type_3d='LiDAR'))
test_dataloader = val_dataloader
val_evaluator = dict(
type='KittiMetric',
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True))
metric='bbox')
test_evaluator = val_evaluator
# optimizer
lr = 0.0003 # max learning rate
optimizer = dict(
type='AdamW',
lr=lr,
betas=(0.95, 0.99), # the momentum is change during training
weight_decay=0.01)
optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))
epoch_num = 80
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=lr, betas=(0.95, 0.99), weight_decay=0.01),
clip_grad=dict(max_norm=10, norm_type=2))
# learning policy
lr_config = dict(
policy='cyclic',
target_ratio=(10, 1e-4),
cyclic_times=1,
step_ratio_up=0.4)
momentum_config = dict(
policy='cyclic',
target_ratio=(0.85 / 0.95, 1),
cyclic_times=1,
step_ratio_up=0.4)
checkpoint_config = dict(interval=1)
evaluation = dict(interval=2, pipeline=eval_pipeline)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
# runtime settings
runner = dict(type='EpochBasedRunner', max_epochs=80)
dist_params = dict(backend='nccl')
param_scheduler = [
dict(
type='CosineAnnealingLR',
T_max=epoch_num * 0.4,
eta_min=lr * 10,
begin=0,
end=epoch_num * 0.4,
by_epoch=True,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=epoch_num * 0.6,
eta_min=lr * 1e-4,
begin=epoch_num * 0.4,
end=epoch_num * 1,
by_epoch=True,
convert_to_iter_based=True),
dict(
type='CosineAnnealingMomentum',
T_max=epoch_num * 0.4,
eta_min=0.85 / 0.95,
begin=0,
end=epoch_num * 0.4,
by_epoch=True,
convert_to_iter_based=True),
dict(
type='CosineAnnealingMomentum',
T_max=epoch_num * 0.6,
eta_min=1,
begin=epoch_num * 0.4,
end=epoch_num * 1,
convert_to_iter_based=True)
]
train_cfg = dict(by_epoch=True, max_epochs=epoch_num, val_interval=50)
val_cfg = dict()
test_cfg = dict()
auto_scale_lr = dict(enable=False, base_batch_size=32)
default_scope = 'mmdet3d'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=1),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='Det3DVisualizationHook'))
custom_hooks = [
dict(type='BenchmarkHook'),
]
env_cfg = dict(
cudnn_benchmark=False,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
dist_cfg=dict(backend='nccl'),
)
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
work_dir = './work_dirs/pp_secfpn_80e'
load_from = None
resume_from = None
workflow = [('train', 1)]
resume = False
work_dir = './work_dirs/pp_secfpn_80e'
configs/benchmark/hv_second_secfpn_4x8_80e_pcdet_kitti-3d-3class.py
View file @ 40ec11aa
......@@ -48,33 +48,35 @@ model = dict(
diff_rad_by_sin=True,
bbox_coder=dict(type='DeltaXYZWLHRBBoxCoder'),
loss_cls=dict(
type='FocalLoss',
type='mmdet.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=2.0),
loss_bbox=dict(
type='mmdet.SmoothL1Loss', beta=1.0 / 9.0, loss_weight=2.0),
loss_dir=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)),
type='mmdet.CrossEntropyLoss', use_sigmoid=False,
loss_weight=0.2)),
# model training and testing settings
train_cfg=dict(
assigner=[
dict( # for Pedestrian
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.5,
neg_iou_thr=0.35,
min_pos_iou=0.35,
ignore_iof_thr=-1),
dict( # for Cyclist
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.5,
neg_iou_thr=0.35,
min_pos_iou=0.35,
ignore_iof_thr=-1),
dict( # for Car
type='MaxIoUAssigner',
type='Max3DIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.6,
neg_iou_thr=0.45,
......@@ -97,7 +99,8 @@ model = dict(
dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Pedestrian', 'Cyclist', 'Car']
input_modality = dict(use_lidar=False, use_camera=False)
metainfo = dict(CLASSES=class_names)
input_modality = dict(use_lidar=True, use_camera=False)
db_sampler = dict(
data_root=data_root,
info_path=data_root + 'kitti_dbinfos_train.pkl',
......@@ -114,12 +117,7 @@ db_sampler = dict(
Car=20,
Pedestrian=15,
Cyclist=15,
),
points_loader=dict(
type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4))
file_client_args = dict(backend='disk')
# file_client_args = dict(
# backend='petrel', path_mapping=dict(data='s3://kitti_data/'))
))
train_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
......@@ -133,8 +131,9 @@ train_pipeline = [
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', 'gt_bboxes_3d', 'gt_labels_3d'])
dict(
type='Pack3DDetInputs',
keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
......@@ -151,87 +150,132 @@ test_pipeline = [
translation_std=[0, 0, 0]),
dict(type='RandomFlip3D'),
dict(
type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
])
type='PointsRangeFilter', point_cloud_range=point_cloud_range)
]),
dict(type='Pack3DDetInputs', keys=['points'])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
eval_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
dict(type='Pack3DDetInputs', keys=['points'])
]
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
train=dict(
train_dataloader = dict(
batch_size=4,
num_workers=4,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_train.pkl',
split='training',
pts_prefix='velodyne_reduced',
ann_file='kitti_infos_train.pkl',
data_prefix=dict(pts='training/velodyne_reduced'),
pipeline=train_pipeline,
modality=input_modality,
classes=class_names,
test_mode=False),
val=dict(
test_mode=False,
metainfo=metainfo,
# we use box_type_3d='LiDAR' in kitti and nuscenes dataset
# and box_type_3d='Depth' in sunrgbd and scannet dataset.
box_type_3d='LiDAR'))
val_dataloader = dict(
batch_size=1,
num_workers=1,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
data_prefix=dict(pts='training/velodyne_reduced'),
ann_file='kitti_infos_val.pkl',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True),
test=dict(
type=dataset_type,
data_root=data_root,
test_mode=True,
metainfo=metainfo,
box_type_3d='LiDAR'))
test_dataloader = val_dataloader
val_evaluator = dict(
type='KittiMetric',
ann_file=data_root + 'kitti_infos_val.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=test_pipeline,
modality=input_modality,
classes=class_names,
test_mode=True))
metric='bbox')
test_evaluator = val_evaluator
# optimizer
lr = 0.0003 # max learning rate
optimizer = dict(type='AdamW', lr=lr, betas=(0.95, 0.99), weight_decay=0.01)
optimizer_config = dict(grad_clip=dict(max_norm=10, norm_type=2))
lr_config = dict(
policy='cyclic',
target_ratio=(10, 1e-4),
cyclic_times=1,
step_ratio_up=0.4)
momentum_config = dict(
policy='cyclic',
target_ratio=(0.85 / 0.95, 1),
cyclic_times=1,
step_ratio_up=0.4)
checkpoint_config = dict(interval=1)
evaluation = dict(interval=2, pipeline=eval_pipeline)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
# runtime settings
runner = dict(type='EpochBasedRunner', max_epochs=80)
dist_params = dict(backend='nccl')
epoch_num = 80
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=lr, betas=(0.95, 0.99), weight_decay=0.01),
clip_grad=dict(max_norm=10, norm_type=2))
# learning policy
param_scheduler = [
dict(
type='CosineAnnealingLR',
T_max=epoch_num * 0.4,
eta_min=lr * 10,
begin=0,
end=epoch_num * 0.4,
by_epoch=True,
convert_to_iter_based=True),
dict(
type='CosineAnnealingLR',
T_max=epoch_num * 0.6,
eta_min=lr * 1e-4,
begin=epoch_num * 0.4,
end=epoch_num * 1,
by_epoch=True,
convert_to_iter_based=True),
dict(
type='CosineAnnealingMomentum',
T_max=epoch_num * 0.4,
eta_min=0.85 / 0.95,
begin=0,
end=epoch_num * 0.4,
by_epoch=True,
convert_to_iter_based=True),
dict(
type='CosineAnnealingMomentum',
T_max=epoch_num * 0.6,
eta_min=1,
begin=epoch_num * 0.4,
end=epoch_num * 1,
convert_to_iter_based=True)
]
train_cfg = dict(by_epoch=True, max_epochs=epoch_num, val_interval=50)
val_cfg = dict()
test_cfg = dict()
auto_scale_lr = dict(enable=False, base_batch_size=32)
default_scope = 'mmdet3d'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=1),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='Det3DVisualizationHook'))
custom_hooks = [
dict(type='BenchmarkHook'),
]
env_cfg = dict(
cudnn_benchmark=False,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
dist_cfg=dict(backend='nccl'),
)
vis_backends = [dict(type='LocalVisBackend')]
visualizer = dict(
type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
work_dir = './work_dirs/sec_secfpn_80e'
load_from = None
resume_from = None
workflow = [('train', 1)]
resume = False
work_dir = './work_dirs/pp_secfpn_100e'
mmdet3d/engine/__init__.py
View file @ 40ec11aa
# Copyright (c) OpenMMLab. All rights reserved.
from .hooks import Det3DVisualizationHook
from .hooks import BenchmarkHook, Det3DVisualizationHook
__all__ = ['Det3DVisualizationHook']
__all__ = ['Det3DVisualizationHook', 'BenchmarkHook']
mmdet3d/engine/hooks/__init__.py
View file @ 40ec11aa
# Copyright (c) OpenMMLab. All rights reserved.
from .benchmark_hook import BenchmarkHook
from .visualization_hook import Det3DVisualizationHook
__all__ = ['Det3DVisualizationHook']
__all__ = ['Det3DVisualizationHook', 'BenchmarkHook']
mmdet3d/engine/hooks/benchmark_hook.py 0 → 100644
View file @ 40ec11aa
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.hooks import Hook
from mmdet3d.registry import HOOKS
@HOOKS.register_module()
class BenchmarkHook(Hook):
"""A hook that logs the training speed of each epch."""
priority = 'NORMAL'
def after_train_epoch(self, runner) -> None:
"""We use the average throughput in iterations of the entire training
run and skip the first 50 iterations of each epoch to skip GPU warmup
time.
Args:
runner (Runner): The runner of the training process.
"""
message_hub = runner.message_hub
max_iter_num = len(runner.train_dataloader)
speed = message_hub.get_scalar('train/time').mean(max_iter_num - 50)
message_hub.update_scalar('train/speed', speed)
runner.logger.info(
f'Training speed of epoch {runner.epoch + 1} is {speed} s/iter')
def after_train(self, runner) -> None:
"""Log average training speed of entire training process.
Args:
runner (Runner): The runner of the training process.
"""
message_hub = runner.message_hub
avg_speed = message_hub.get_scalar('train/speed').mean()
runner.logger.info('Average training speed of entire training process'
f'is {avg_speed} s/iter')
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