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kitti-3d-3class.py
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kitti-3d-3class.py
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# dataset settings
dataset_type = 'KittiDataset' # # 数据集类型 mmdet3d/datasets/kitti_dataset.py
data_root = 'data/kitti/' # # 数据路径
class_names = ['Pedestrian', 'Cyclist', 'Car'] # 类的名称
point_cloud_range = [0, -40, -3, 70.4, 40, 1]
input_modality = dict(use_lidar=True, use_camera=False)
db_sampler = dict( # mmdet3d/datasets/pipelines/dbsampler.py
data_root=data_root,
info_path=data_root + 'kitti_dbinfos_train.pkl', #
rate=1.0,
prepare=dict(
filter_by_difficulty=[-1],
filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
classes=class_names,
sample_groups=dict(Car=12, Pedestrian=6, Cyclist=6))
file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
# for more details.
# file_client_args = dict(
# backend='petrel', path_mapping=dict(data='s3://kitti_data/'))
# train的配置文件 # 训练流水线，更多细节请参考 mmdet3d.datasets.pipelines
train_pipeline = [
dict(
type='LoadPointsFromFile', # mmdet3d/datasets/pipelines/loading.py 第一个流程，用于读取点，更多细节请参考 mmdet3d.datasets.pipelines.indoor_loading
coord_type='LIDAR', # 雷达数据
load_dim=4, # 读取的点的维度 x,y,z,r
use_dim=4, # 使用所读取点的哪些维度
file_client_args=file_client_args),
dict(
type='LoadAnnotations3D', # # mmdet3d/datasets/pipelines/loading.py 第二个流程，用于读取标注GT，更多细节请参考 mmdet3d.datasets.pipelines.indoor_loading
with_bbox_3d=True, # 是否读取 3D 框
with_label_3d=True,# 是否读取 3D 框对应的类别标签
file_client_args=file_client_args),
dict(type='ObjectSample', db_sampler=db_sampler),
dict(
type='ObjectNoise',
num_try=100,
translation_std=[1.0, 1.0, 0.5],
global_rot_range=[0.0, 0.0],
rot_range=[-0.78539816, 0.78539816]),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5), # 数据增广流程，随机翻转点和 3D 框
dict(
type='GlobalRotScaleTrans', # 数据增广流程，旋转并放缩点和 3D 框，更多细节请参考 mmdet3d.datasets.pipelines.indoor_augment
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', 'gt_bboxes_3d', 'gt_labels_3d']) # 最后一个流程，mmdet3d/datasets/pipelines/formating.py 决定哪些键值对应的数据会被输入给检测器，更多细节请参考 mmdet3d.datasets.pipelines.formating
]
# # 测试流水线，更多细节请参考 mmdet3d.datasets.pipelines
test_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
file_client_args=file_client_args),
dict(
type='MultiScaleFlipAug3D',
img_scale=(1333, 800),
pts_scale_ratio=1,
flip=False,
transforms=[
dict(
type='GlobalRotScaleTrans',
rot_range=[0, 0],
scale_ratio_range=[1., 1.],
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'])
])
]
# construct a pipeline for data and gt loading in show function
# please keep its loading function consistent with test_pipeline (e.g. client)
# # 模型验证或可视化所使用的流水线，更多细节请参考 mmdet3d.datasets.pipelines
eval_pipeline = [
dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
file_client_args=file_client_args),
dict(
type='DefaultFormatBundle3D',
class_names=class_names,
with_label=False),
dict(type='Collect3D', keys=['points'])
]
data = dict(
samples_per_gpu=6, # 单张 GPU 上的样本数
workers_per_gpu=4, # 每张 GPU 上用于读取数据的进程数
train=dict( # 训练数据集配置
type='RepeatDataset', # 数据集嵌套，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/dataset_wrappers.py
times=2, # 重复次数
dataset=dict(
type=dataset_type, # KittiDataset # 数据集类型
data_root=data_root, # data_root = 'data/kitti/'
ann_file=data_root + 'kitti_infos_train.pkl',
split='training',
pts_prefix='velodyne_reduced',
pipeline=train_pipeline, # # 流水线，这里传入的就是上面创建的训练流水线变量 train_pipeline = [] 上面有配置文件
modality=input_modality,
classes=class_names, # 类别名称
test_mode=False,
# 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=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,
box_type_3d='LiDAR'),
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,
box_type_3d='LiDAR'))
# 流水线，这里传入的就是上面创建的验证流水线变量
evaluation = dict(interval=1, pipeline=eval_pipeline)