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Commit 66600f95 authored by zhangwenwei's avatar zhangwenwei
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Merge branch 'update-cfg' into 'master' 

Update cfg

See merge request open-mmlab/mmdet.3d!69
parents 788f9b3e e8009e03
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configs/kitti/hv_pointpillars_secfpn_6x8_80e_kitti-3d-car.py deleted 100644 → 0
View file @ 788f9b3e
# model settings
voxel_size = [0.16, 0.16, 4]
point_cloud_range = [0, -39.68, -3, 69.12, 39.68, 1]
model = dict(
type='VoxelNet',
voxel_layer=dict(
max_num_points=64,
point_cloud_range=point_cloud_range,
voxel_size=voxel_size,
max_voxels=(12000, 20000) # (training, testing) max_coxels
),
voxel_encoder=dict(
type='PillarFeatureNet',
in_channels=4,
feat_channels=[64],
with_distance=False,
voxel_size=voxel_size,
point_cloud_range=point_cloud_range,
),
middle_encoder=dict(
type='PointPillarsScatter',
in_channels=64,
output_shape=[496, 432],
),
backbone=dict(
type='SECOND',
in_channels=64,
layer_nums=[3, 5, 5],
layer_strides=[2, 2, 2],
out_channels=[64, 128, 256],
),
neck=dict(
type='SECONDFPN',
in_channels=[64, 128, 256],
upsample_strides=[1, 2, 4],
out_channels=[128, 128, 128],
),
bbox_head=dict(
type='Anchor3DHead',
num_classes=1,
in_channels=384,
feat_channels=384,
use_direction_classifier=True,
anchor_generator=dict(
type='Anchor3DRangeGenerator',
ranges=[[0, -39.68, -1.78, 69.12, 39.68, -1.78]],
sizes=[[1.6, 3.9, 1.56]],
rotations=[0, 1.57],
reshape_out=True),
diff_rad_by_sin=True,
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=2.0),
loss_dir=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2),
),
)
# model training and testing settings
train_cfg = dict(
assigner=dict(
type='MaxIoUAssigner',
iou_calculator=dict(type='BboxOverlapsNearest3D'),
pos_iou_thr=0.6,
neg_iou_thr=0.45,
min_pos_iou=0.45,
ignore_iof_thr=-1),
allowed_border=0,
pos_weight=-1,
debug=False)
test_cfg = dict(
use_rotate_nms=True,
nms_across_levels=False,
nms_thr=0.01,
score_thr=0.1,
min_bbox_size=0,
nms_pre=100,
max_num=50)
# dataset settings
dataset_type = 'KittiDataset'
data_root = 'data/kitti/'
class_names = ['Car']
input_modality = dict(use_lidar=True, use_camera=False)
db_sampler = dict(
data_root=data_root,
info_path=data_root + 'kitti_dbinfos_train.pkl',
rate=1.0,
object_rot_range=[0.0, 0.0],
prepare=dict(
filter_by_difficulty=[-1],
filter_by_min_points=dict(Car=5),
),
sample_groups=dict(Car=15),
classes=class_names)
train_pipeline = [
dict(type='LoadPointsFromFile', load_dim=4, use_dim=4),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
dict(type='ObjectSample', db_sampler=db_sampler),
dict(
type='ObjectNoise',
num_try=100,
loc_noise_std=[0.25, 0.25, 0.25],
global_rot_range=[0.0, 0.0],
rot_uniform_noise=[-0.15707963267, 0.15707963267]),
dict(type='RandomFlip3D', flip_ratio=0.5),
dict(
type='GlobalRotScale',
rot_uniform_noise=[-0.78539816, 0.78539816],
scaling_uniform_noise=[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', 'gt_bboxes_3d', 'gt_labels_3d']),
]
test_pipeline = [
dict(type='LoadPointsFromFile', 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']),
]
data = dict(
samples_per_gpu=6,
workers_per_gpu=4,
train=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',
pipeline=train_pipeline,
modality=input_modality,
classes=class_names,
test_mode=False)),
val=dict(
type=dataset_type,
data_root=data_root,
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),
test=dict(
type=dataset_type,
data_root=data_root,
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))
# 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))
# 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)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
# runtime settings
total_epochs = 40
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = './work_dirs/pp_secfpn_80e'
load_from = None
resume_from = None
workflow = [('train', 1)]
configs/kitti/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py → configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-3class.py
View file @ 66600f95
......@@ -104,17 +104,9 @@ db_sampler = dict(
object_rot_range=[0.0, 0.0],
prepare=dict(
filter_by_difficulty=[-1],
filter_by_min_points=dict(
Car=5,
Pedestrian=10,
Cyclist=10,
)),
filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
classes=class_names,
sample_groups=dict(
Car=12,
Pedestrian=6,
Cyclist=6,
))
sample_groups=dict(Car=15, Pedestrian=10, Cyclist=10))
train_pipeline = [
dict(type='LoadPointsFromFile', load_dim=4, use_dim=4),
......
configs/kitti/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py → configs/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car.py
View file @ 66600f95
......@@ -7,34 +7,27 @@ model = dict(
max_num_points=64,
point_cloud_range=point_cloud_range,
voxel_size=voxel_size,
max_voxels=(12000, 20000) # (training, testing) max_coxels
),
max_voxels=(12000, 20000)),
voxel_encoder=dict(
type='PillarFeatureNet',
in_channels=4,
feat_channels=[64],
with_distance=False,
voxel_size=voxel_size,
point_cloud_range=point_cloud_range,
),
point_cloud_range=point_cloud_range),
middle_encoder=dict(
type='PointPillarsScatter',
in_channels=64,
output_shape=[496, 432],
),
type='PointPillarsScatter', in_channels=64, output_shape=[496, 432]),
backbone=dict(
type='SECOND',
in_channels=64,
layer_nums=[3, 5, 5],
layer_strides=[2, 2, 2],
out_channels=[64, 128, 256],
),
out_channels=[64, 128, 256]),
neck=dict(
type='SECONDFPN',
in_channels=[64, 128, 256],
upsample_strides=[1, 2, 4],
out_channels=[128, 128, 128],
),
out_channels=[128, 128, 128]),
bbox_head=dict(
type='Anchor3DHead',
num_classes=1,
......@@ -57,9 +50,7 @@ model = dict(
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=2.0),
loss_dir=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2),
),
)
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.2)))
# model training and testing settings
train_cfg = dict(
assigner=dict(
......@@ -91,10 +82,7 @@ db_sampler = dict(
info_path=data_root + 'kitti_dbinfos_train.pkl',
rate=1.0,
object_rot_range=[0.0, 0.0],
prepare=dict(
filter_by_difficulty=[-1],
filter_by_min_points=dict(Car=5),
),
prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
sample_groups=dict(Car=15),
classes=class_names)
......@@ -117,7 +105,7 @@ train_pipeline = [
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='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
dict(type='LoadPointsFromFile', load_dim=4, use_dim=4),
......@@ -126,7 +114,7 @@ test_pipeline = [
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points']),
dict(type='Collect3D', keys=['points'])
]
data = dict(
......@@ -178,14 +166,12 @@ lr_config = dict(
policy='cyclic',
target_ratio=(10, 1e-4),
cyclic_times=1,
step_ratio_up=0.4,
)
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,
)
step_ratio_up=0.4)
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1)
# yapf:disable
......
tests/test_config.py
View file @ 66600f95
......@@ -85,7 +85,7 @@ def test_config_build_pipeline():
# Other configs needs database sampler.
config_names = [
'nus/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py',
'pointpillars/hv_pointpillars_secfpn_sbn-all_4x8_2x_nus-3d.py',
]
print('Using {} config files'.format(len(config_names)))
......@@ -117,9 +117,9 @@ def test_config_data_pipeline():
# Only tests a representative subset of configurations
# TODO: test pipelines using Albu, current Albu throw None given empty GT
config_names = [
'nus/faster_rcnn_r50_fpn_caffe_2x8_1x_nus.py',
'nus/retinanet_r50_fpn_caffe_2x8_1x_nus.py',
'kitti/'
'mvxnet/faster_rcnn_r50_fpn_caffe_2x8_1x_nus.py',
'mvxnet/retinanet_r50_fpn_caffe_2x8_1x_nus.py',
'mvxnet/'
'faster_rcnn_r50_fpn_caffe_1x_kitti-2d-3class_coco-3x-pretrain.py',
]
......
tests/test_forward.py
View file @ 66600f95
......@@ -55,7 +55,7 @@ def _get_detector_cfg(fname):
def test_faster_rcnn_forward():
_test_two_stage_forward('nus/faster_rcnn_r50_fpn_caffe_2x8_1x_nus.py')
_test_two_stage_forward('mvxnet/faster_rcnn_r50_fpn_caffe_2x8_1x_nus.py')
def _test_two_stage_forward(cfg_file):
......
tests/test_heads.py
View file @ 66600f95
......@@ -71,7 +71,7 @@ def test_anchor3d_head_loss():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
bbox_head_cfg = _get_head_cfg(
'kitti/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class.py')
'second/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class.py')
from mmdet3d.models.builder import build_head
self = build_head(bbox_head_cfg)
......@@ -123,7 +123,7 @@ def test_anchor3d_head_getboxes():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
bbox_head_cfg = _get_head_cfg(
'kitti/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class.py')
'second/dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class.py')
from mmdet3d.models.builder import build_head
self = build_head(bbox_head_cfg)
......@@ -154,7 +154,7 @@ def test_parta2_rpnhead_getboxes():
if not torch.cuda.is_available():
pytest.skip('test requires GPU and torch+cuda')
rpn_head_cfg, proposal_cfg = _get_rpn_head_cfg(
'kitti/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py')
'parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class.py')
from mmdet3d.models.builder import build_head
self = build_head(rpn_head_cfg)
......
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