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# 后端支持
我们支持不同的文件客户端后端：磁盘、Ceph 和 LMDB 等。下面是修改配置使之从 Ceph 加载和保存数据的示例。
## 从 Ceph 读取数据和标注文件
我们支持从 Ceph 加载数据和生成的标注信息文件（pkl 和 json）：
```python
# set file client backends as Ceph
backend_args = dict(
backend='petrel',
path_mapping=dict({
'./data/nuscenes/':
's3://openmmlab/datasets/detection3d/nuscenes/', # replace the path with your data path on Ceph
'data/nuscenes/':
's3://openmmlab/datasets/detection3d/nuscenes/' # replace the path with your data path on Ceph
}))
db_sampler = dict(
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)),
sample_groups=dict(Car=15),
classes=class_names,
# set file client for points loader to load training data
points_loader=dict(
type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
backend_args=backend_args),
# set file client for data base sampler to load db info file
backend_args=backend_args)
train_pipeline = [
# set file client for loading training data
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, backend_args=backend_args),
# set file client for loading training data annotations
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, backend_args=backend_args),
dict(type='ObjectSample', db_sampler=db_sampler),
dict(
type='ObjectNoise',
num_try=100,
translation_std=[0.25, 0.25, 0.25],
global_rot_range=[0.0, 0.0],
rot_range=[-0.15707963267, 0.15707963267]),
dict(type='RandomFlip3D', flip_ratio_bev_horizontal=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', 'gt_bboxes_3d', 'gt_labels_3d'])
]
test_pipeline = [
# set file client for loading validation/testing data
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=4, use_dim=4, backend_args=backend_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'])
])
]
data = dict(
# set file client for loading training info files (.pkl)
train=dict(
type='RepeatDataset',
times=2,
dataset=dict(pipeline=train_pipeline, classes=class_names, backend_args=backend_args)),
# set file client for loading validation info files (.pkl)
val=dict(pipeline=test_pipeline, classes=class_names,backend_args=backend_args),
# set file client for loading testing info files (.pkl)
test=dict(pipeline=test_pipeline, classes=class_names, backend_args=backend_args))
```
## 从 Ceph 读取预训练模型
```python
model = dict(
pts_backbone=dict(
_delete_=True,
type='NoStemRegNet',
arch='regnetx_1.6gf',
init_cfg=dict(
type='Pretrained', checkpoint='s3://openmmlab/checkpoints/mmdetection3d/regnetx_1.6gf'), # replace the path with your pretrained model path on Ceph
...
```
## 从 Ceph 读取模型权重文件
```python
# replace the path with your checkpoint path on Ceph
load_from = 's3://openmmlab/checkpoints/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20200620_230614-77663cd6.pth'
resume_from = None
workflow = [('train', 1)]
```
## 保存模型权重文件至 Ceph
```python
# checkpoint saving
# replace the path with your checkpoint saving path on Ceph
checkpoint_config = dict(interval=1, max_keep_ckpts=2, out_dir='s3://openmmlab/mmdetection3d')
```
## EvalHook 保存最优模型权重文件至 Ceph
```python
# replace the path with your checkpoint saving path on Ceph
evaluation = dict(interval=1, save_best='bbox', out_dir='s3://openmmlab/mmdetection3d')
```
## 训练日志保存至 Ceph
训练后的训练日志会备份到指定的 Ceph 路径。
```python
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook', out_dir='s3://openmmlab/mmdetection3d'),
])
```
您还可以通过设置 `keep_local = False` 备份到指定的 Ceph 路径后删除本地训练日志。
```python
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook', out_dir='s3://openmmlab/mmdetection3d', keep_local=False),
])
```
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