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Getting Started

All dataset configs are located within tools/cfgs/dataset_configs.

UDA model configs are located within tools/cfgs/DA for different datasets.

ADA model configs are located within tools/cfgs/ADA for different datasets.

MDF model configs are located within tools/cfgs/MDF for different datasets.

SSDA model configs are located within tools/cfgs/SSDA for different datasets.

Other model configs are located within tools/cfgs/waymo_models, tools/cfgs/nuscenes_models, tools/cfgs/kitti_models for different datasets.

 

How to train our model using Ceph

  • Install petrel_client to enable the Ceph / PetrelBackend

  • Add '~/.petreloss.conf', which is a config file of Ceph, saving your KEY/ACCESS_KEY of S3 Ceph

  • You need to ensure that the file organization of all datasets in the Ceph (Petrel-OSS) is consistent with that described above.

  • For dataset config files, such as tools/cfgs/dataset_configs/waymo/OD/waymo_dataset.yaml, uncomment OSS_PATH and add the s3://path_of_your_dataset as follows:

OSS_PATH: 's3://${PATH_TO_DATASET}/waymo_0.5.0'

 

Dataset Preparation

We provide the dataloader for lyft, ONCE, waymo, KITTI and NuScenes datasets.

KITTI Dataset

  • Please download the official KITTI 3D object detection dataset and organize the downloaded files as follows (the road planes could be downloaded from [road plane], which are optional for data augmentation in the training):
  • NOTE: if you already have the data infos from pcdet v0.1, you can choose to use the old infos and set the DATABASE_WITH_FAKELIDAR option in tools/cfgs/dataset_configs/kitti/OD/kitti_dataset.yaml as True. The second choice is that you can create the infos and gt database again and leave the config unchanged.
3DTrans
├── data
│   ├── kitti
│   │   │── ImageSets
│   │   │── training
│   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes) & (optional: depth_2)
│   │   │── testing
│   │   │   ├──calib & velodyne & image_2
├── pcdet
├── tools
  • Generate the data infos by running the following command:
python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti/OD/kitti_dataset.yaml

 

NuScenes Dataset

3DTrans
├── data
│   ├── nuscenes
│   │   │── v1.0-trainval (or v1.0-mini if you use mini)
│   │   │   │── samples
│   │   │   │── sweeps
│   │   │   │── maps
│   │   │   │── v1.0-trainval  
├── pcdet
├── tools
  • Install the nuscenes-devkit with version 1.0.5 by running the following command:
pip install nuscenes-devkit==1.0.5
  • Generate the data infos by running the following command (it may take several hours):
python -m pcdet.datasets.nuscenes.nuscenes_dataset --func create_nuscenes_infos \
    --cfg_file tools/cfgs/dataset_configs/nuscenes/OD/nuscenes_dataset.yaml \
    --version v1.0-trainval

 

Waymo Open Dataset

  • Please download the official Waymo Open Dataset, including the training data training_0000.tar~training_0031.tar and the validation data validation_0000.tar~validation_0007.tar.
  • Unzip all the above xxxx.tar files to the directory of data/waymo/raw_data as follows (You could get 798 train tfrecord and 202 val tfrecord ):
3DTrans
├── data
│   ├── waymo
│   │   │── ImageSets
│   │   │── raw_data
│   │   │   │── segment-xxxxxxxx.tfrecord
|   |   |   |── ...
|   |   |── waymo_processed_data_v0_5_0
│   │   │   │── segment-xxxxxxxx/
|   |   |   |── ...
│   │   │── waymo_processed_data_v0_5_0_gt_database_train_sampled_1/
│   │   │── waymo_processed_data_v0_5_0_waymo_dbinfos_train_sampled_1.pkl
│   │   │── waymo_processed_data_v0_5_0_gt_database_train_sampled_1_global.npy (optional)
│   │   │── waymo_processed_data_v0_5_0_infos_train.pkl (optional)
│   │   │── waymo_processed_data_v0_5_0_infos_val.pkl (optional)
├── pcdet
├── tools
  • Install the official waymo-open-dataset by running the following command:
# tf 2.0.0
pip3 install waymo-open-dataset-tf-2-0-0
  • Extract point cloud data from tfrecord and generate data infos by running the following command (it takes several hours, and you could refer to data/waymo/waymo_processed_data_v0_5_0 to see how many records that have been processed):
python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos \
    --cfg_file tools/cfgs/dataset_configs/waymo/OD/waymo_dataset.yaml

Note that you do not need to install waymo-open-dataset if you have already processed the data before and do not need to evaluate with official Waymo Metrics.

 

ONCE Dataset

  • Please download the official ONCE dataset website. Please follow the instructions to unzip and organize the data.

  • Please organize the data as follows:

ONCE_Benchmark
├── data
│   ├── once
│   │   │── ImageSets
|   |   |   ├──train.txt
|   |   |   ├──val.txt
|   |   |   ├──test.txt
|   |   |   ├──raw_small.txt (100k unlabeled)
|   |   |   ├──raw_medium.txt (500k unlabeled)
|   |   |   ├──raw_large.txt (1M unlabeled)
│   │   │── data
│   │   │   ├──000000
|   |   |   |   |──000000.json (infos)
|   |   |   |   |──lidar_roof (point clouds)
|   |   |   |   |   |──frame_timestamp_1.bin
|   |   |   |   |  ...
|   |   |   |   |──cam0[1-9] (images)
|   |   |   |   |   |──frame_timestamp_1.jpg
|   |   |   |   |  ...
|   |   |   |  ...
├── pcdet
├── tools
  • Generate the data infos by running the following command:
python -m pcdet.datasets.once.once_dataset --func create_once_infos --cfg_file tools/cfgs/dataset_configs/once/OD/once_dataset.yaml

 

Lyft Dataset

3DTrans
├── data
│   ├── lyft
│   │   │── ImageSets
│   │   │── trainval
│   │   │   │── data & maps(train_maps) & images(train_images) & lidar(train_lidar) & train_lidar
│   │   │── test
│   │   │   │── data & maps(test_maps) & test_images & test_lidar
├── pcdet
├── tools
  • Install the lyft-dataset-sdk with version 0.0.8 by running the following command:
pip install -U lyft_dataset_sdk==0.0.8
  • Generate the training & validation data infos by running the following command (it may take several hours):
python -m pcdet.datasets.lyft.lyft_dataset --func create_lyft_infos \
    --cfg_file tools/cfgs/dataset_configs/lyft/OD/lyft_dataset.yaml
  • Generate the test data infos by running the following command:
python -m pcdet.datasets.lyft.lyft_dataset --func create_lyft_infos \
    --cfg_file tools/cfgs/dataset_configs/lyft/OD/lyft_dataset.yaml --version test
  • You need to check carefully since we don't provide a benchmark for it.