${ROOT}
└── checkpoints/    
    ├── rtm3d_resnet_18.pth
    ├── rtm3d_fpn_resnet_18.pth
└── dataset/    
    └── kitti/
        ├──ImageSets/
        │   ├── test.txt
        │   ├── train.txt
        │   └── val.txt
        ├── training/
        │   ├── image_2/ (left color camera)
        │   ├── image_3/ (right color camera)
        │   ├── calib/
        │   ├── label_2/
        └── testing/  
        │   ├── image_2/ (left color camera)
        │   ├── image_3/ (right color camera)
        │   ├── calib/
        └── classes_names.txt
└── src/
    ├── config/
    │   ├── train_config.py
    │   └── kitti_config.py
    ├── data_process/
    │   ├── kitti_dataloader.py
    │   ├── kitti_dataset.py
    │   └── kitti_data_utils.py
    ├── models/
    │   ├── fpn_resnet.py
    │   ├── resnet.py
    │   ├── model_utils.py
    └── utils/
    │   ├── evaluation_utils.py
    │   ├── logger.py
    │   ├── misc.py
    │   ├── torch_utils.py
    │   ├── train_utils.py
    ├── evaluate.py
    ├── test.py
    ├── train.py
    └── train.sh
├── README.md 
└── requirements.txt

usage: train.py [-h] [--seed SEED] [--saved_fn FN] [--root-dir PATH]
                [--arch ARCH] [--pretrained_path PATH] [--head_conv HEAD_CONV]
                [--hflip_prob HFLIP_PROB]
                [--use_left_cam_prob USE_LEFT_CAM_PROB] [--dynamic-sigma]
                [--no-val] [--num_samples NUM_SAMPLES]
                [--num_workers NUM_WORKERS] [--batch_size BATCH_SIZE]
                [--print_freq N] [--tensorboard_freq N] [--checkpoint_freq N]
                [--start_epoch N] [--num_epochs N] [--lr_type LR_TYPE]
                [--lr LR] [--minimum_lr MIN_LR] [--momentum M] [-wd WD]
                [--optimizer_type OPTIMIZER] [--steps [STEPS [STEPS ...]]]
                [--world-size N] [--rank N] [--dist-url DIST_URL]
                [--dist-backend DIST_BACKEND] [--gpu_idx GPU_IDX] [--no_cuda]
                [--multiprocessing-distributed] [--evaluate]
                [--resume_path PATH] [--K K]

The Implementation of RTM3D using PyTorch

optional arguments:
  -h, --help            show this help message and exit
  --seed SEED           re-produce the results with seed random
  --saved_fn FN         The name using for saving logs, models,...
  --root-dir PATH       The ROOT working directory
  --arch ARCH           The name of the model architecture
  --pretrained_path PATH
                        the path of the pretrained checkpoint
  --head_conv HEAD_CONV
                        conv layer channels for output head0 for no conv
                        layer-1 for default setting: 64 for resnets and 256
                        for dla.
  --hflip_prob HFLIP_PROB
                        The probability of horizontal flip
  --use_left_cam_prob USE_LEFT_CAM_PROB
                        The probability of using the left camera
  --dynamic-sigma       If true, compute sigma based on Amax, Amin then
                        generate heamapIf false, compute radius as CenterNet
                        did
  --no-val              If true, dont evaluate the model on the val set
  --num_samples NUM_SAMPLES
                        Take a subset of the dataset to run and debug
  --num_workers NUM_WORKERS
                        Number of threads for loading data
  --batch_size BATCH_SIZE
                        mini-batch size (default: 16), this is the totalbatch
                        size of all GPUs on the current node when usingData
                        Parallel or Distributed Data Parallel
  --print_freq N        print frequency (default: 50)
  --tensorboard_freq N  frequency of saving tensorboard (default: 50)
  --checkpoint_freq N   frequency of saving checkpoints (default: 5)
  --start_epoch N       the starting epoch
  --num_epochs N        number of total epochs to run
  --lr_type LR_TYPE     the type of learning rate scheduler (cosin or
                        multi_step)
  --lr LR               initial learning rate
  --minimum_lr MIN_LR   minimum learning rate during training
  --momentum M          momentum
  -wd WD, --weight_decay WD
                        weight decay (default: 1e-6)
  --optimizer_type OPTIMIZER
                        the type of optimizer, it can be sgd or adam
  --steps [STEPS [STEPS ...]]
                        number of burn in step
  --world-size N        number of nodes for distributed training
  --rank N              node rank for distributed training
  --dist-url DIST_URL   url used to set up distributed training
  --dist-backend DIST_BACKEND
                        distributed backend
  --gpu_idx GPU_IDX     GPU index to use.
  --no_cuda             If true, cuda is not used.
  --multiprocessing-distributed
                        Use multi-processing distributed training to launch N
                        processes per node, which has N GPUs. This is the
                        fastest way to use PyTorch for either single node or
                        multi node data parallel training
  --evaluate            only evaluate the model, not training
  --resume_path PATH    the path of the resumed checkpoint
  --K K                 the number of top K