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Getting Started

This page provides basic tutorials about the usage of ReDet. For installation instructions, please see INSTALL.md.

Prepare DOTA dataset.

It is recommended to symlink the dataset root to ReDet/data.

Here, we give an example for single scale data preparation of DOTA-v1.5.

First, make sure your initial data are in the following structure.

data/dota15
├── train
│   ├──images
│   └──labelTxt
├── val
│   ├──images
│   └──labelTxt
└── test
    └──images

Split the original images and create COCO format json.

python DOTA_devkit/prepare_dota1_5.py --srcpath path_to_dota --dstpath path_to_split_1024

Then you will get data in the following structure

dota15_1024
├── test1024
│   ├──DOTA_test1024.json
│   └──images
└── trainval1024
     ├──DOTA_trainval1024.json
     └──images

For data preparation with data augmentation, refer to "DOTA_devkit/prepare_dota1_5_v2.py"

Prepare HRSC2016 dataset.

First, make sure your initial data are in the following structure.

data/HRSC2016
├── Train
│   ├──AllImages
│   └──Annotations
└── Test
│   ├──AllImages
│   └──Annotations

Then you need to convert HRSC2016 to DOTA's format, i.e., rename AllImages to images, convert xml Annotations to DOTA's txt format. Here we provide a script from s2anet: HRSC2DOTA.py. Now, your data/HRSC2016 should contain the following folders.

data/HRSC2016
├── Train
│   ├──images
│   └── labelTxt
└── Test
    └── images

Then we need to generate json labels with COCO's format.

python DOTA_devkit/HRSC20162COCO.py

Inference with pretrained models

Test a dataset

  • single GPU testing
  • multiple GPU testing

You can use the following commands to test a dataset.

# single-gpu testing
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}]

# multi-gpu testing
./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}]

Optional arguments:

  • RESULT_FILE: Filename of the output results in pickle format. If not specified, the results will not be saved to a file.

Examples:

Assume that you have already downloaded the checkpoints to work_dirs/.

  1. Test ReDet with 1 GPU.
python tools/test.py configs/ReDet/ReDet_re50_refpn_1x_dota15.py \
    work_dirs/ReDet_re50_refpn_1x_dota15/ReDet_re50_refpn_1x_dota15-7f2d6dda.pth \ 
    --out work_dirs/ReDet_re50_refpn_1x_dota15/results.pkl
  1. Test ReDet with 4 GPUs.
./tools/dist_test.sh configs/ReDet/ReDet_re50_refpn_1x_dota15.py \
    work_dirs/ReDet_re50_refpn_1x_dota15/ReDet_re50_refpn_1x_dota15-7f2d6dda.pth \
    4 --out work_dirs/ReDet_re50_refpn_1x_dota15/results.pkl
  1. Parse results for DOTA evaluation
python tools/parse_results.py --config configs/ReDet/ReDet_re50_refpn_1x_dota15.py --type OBB
  1. Test and evaluate ReDet on HRSC2016.
# generate results
python tools/test.py configs/ReDet/ReDet_re50_refpn_3x_hrsc2016.py \
    work_dirs/ReDet_re50_refpn_3x_hrsc2016/ReDet_re50_refpn_3x_hrsc2016-d1b4bd29.pth \ 
    --out work_dirs/ReDet_re50_refpn_3x_hrsc2016/results.pkl

# evaluation
# remeber to modify the results path in hrsc2016_evaluation.py
python DOTA_devkit/hrsc2016_evaluation.py

Convert ReResNet+ReFPN to standard Pytorch layers

We provide a script to convert the pre-trained weights of ReResNet+ReFPN to standard Pytorch layers. Take ReDet on DOTA-v1.5 as an example.

  1. download pretrained weights at here, and convert it to standard pytorch layers.
python tools/convert_ReDet_to_torch.py configs/ReDet/ReDet_re50_refpn_1x_dota15.py \
        work_dirs/ReDet_re50_refpn_1x_dota15/ReDet_re50_refpn_1x_dota15-7f2d6dda.pth \
        work_dirs/ReDet_re50_refpn_1x_dota15/ReDet_r50_fpn_1x_dota15.pth
  1. use standard ResNet+FPN as the backbone of ReDet and test it on DOTA-v1.5.
mkdir work_dirs/ReDet_r50_fpn_1x_dota15

bash ./tools/dist_test.sh configs/ReDet/ReDet_r50_fpn_1x_dota15.py \
        work_dirs/ReDet_re50_refpn_1x_dota15/ReDet_r50_fpn_1x_dota15.pth 8 \
        --out work_dirs/ReDet_r50_fpn_1x_dota15/results.pkl

# submit parsed results to the evaluation server.
python tools/parse_results.py --config configs/ReDet/ReDet_r50_fpn_1x_dota15.py

Demo of inference in a large size image.

python demo_large_image.py

Train a model

mmdetection implements distributed training and non-distributed training, which uses MMDistributedDataParallel and MMDataParallel respectively.

All outputs (log files and checkpoints) will be saved to the working directory, which is specified by work_dir in the config file.

*Important*: The default learning rate in config files is for 8 GPUs. If you use less or more than 8 GPUs, you need to set the learning rate proportional to the GPU num, e.g., 0.01 for 4 GPUs and 0.04 for 16 GPUs.

Train with a single GPU

python tools/train.py ${CONFIG_FILE}

If you want to specify the working directory in the command, you can add an argument --work_dir ${YOUR_WORK_DIR}.

Train with multiple GPUs

./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]

Optional arguments are:

  • --validate (recommended): Perform evaluation at every k (default=1) epochs during the training.
  • --work_dir ${WORK_DIR}: Override the working directory specified in the config file.
  • --resume_from ${CHECKPOINT_FILE}: Resume from a previous checkpoint file.

Train with multiple machines

If you run mmdetection on a cluster managed with slurm, you can just use the script slurm_train.sh.

./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR} [${GPUS}]

Here is an example of using 16 GPUs to train Mask R-CNN on the dev partition.

./tools/slurm_train.sh dev mask_r50_1x configs/mask_rcnn_r50_fpn_1x.py /nfs/xxxx/mask_rcnn_r50_fpn_1x 16

You can check slurm_train.sh for full arguments and environment variables.

If you have just multiple machines connected with ethernet, you can refer to pytorch launch utility. Usually it is slow if you do not have high speed networking like infiniband.