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Getting Started

This page provides basic usage based MMdetection. For installation instructions, please see install.md

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.

  1. Prepare custom dataset files
python DOTA_devkit/ImgSplit_multi_process.py
python DOTA_devkit/DOTA2COCO.py
  1. Train with a single GPU
python tools/train.py --config 'configs/dota/r50_dota_demo.py'
  1. Train with multiple(4) GPUs
python -m torch.distributed.launch --nproc_per_node 4 tools/train.py --launcher pytorch \
    --config 'configs/dota/r50_dota_demo.py'
  1. Train with specified GPUs (for example with GPU=2,3)
CUDA_VISIBLE_DEVICES=2,3 python -m torch.distributed.launch --nproc_per_node 2 tools/train.py --launcher pytorch \
    --config 'configs/dota/r50_dota_demo.py'

or add code:

import os
    os.environ["CUDA_VISIBLE_DEVICES"] = "0"

Inferenece with pretrained models

We provide the testing scripts to evaluate the trained model.

Examples:

Assume that you have already downloaded the checkpoints to work_dirs/r50_dotav1/.

  1. Test OrientedRepPoints with single GPU
python tools/test.py \
    --config 'configs/dota/r50_dotav1.py' \
    --checkpoint 'work_dirs/r50_dotav1/epoch_40.pth' \
    --out 'work_dirs/r50_dotav1/results.pkl'
  1. Test OrientedRepPoints with 4 GPUs
python -m torch.distributed.launch --nproc_per_node 4 tools/test.py --launcher pytorch \
    --config 'configs/dota/r50_dotav1.py' \
    --checkpoint 'work_dirs/r50_dotav1/epoch_40.pth' \
    --out 'work_dirs/r50_dotav1/results.pkl'
  1. Parse the results
python tools/parse_results_pkl/parse_dota_evaluation.py \
    --detection_pkl_path 'work_dirs/r50_dotav1/results.pkl' \
    --val_json 'data/dataset_demo_split/test_datasetdemo.json' \
    --outpath 'work_dirs/r50_dotav1/Task1_results'
  1. Merge the results
python DOTA_devkit/ResultMerge_multi_process.py \
    --scrpath 'work_dirs/r50_dotav1/Task1_results' \
    --dstpath 'work_dirs/r50_dotav1/Task1_results_merged'
  1. Evaluate the results
python DOTA_devkit/dota_evaluation_task1.py \
    --detpath 'work_dirs/r50_dotav1/Task1_results_merged/Task1_{:s}.txt' \
    --annopath 'data/dataset_demo/labelTxt/{:s}.txt' \
    --imagesetfile 'data/dataset_demo/imgnamefile_demo.txt'
  1. Visualize the results
python tools/parse_results_pkl/show_learning_points_and_boxes.py

*If you want to evaluate the result on DOTA test-dev, please read the results.pkl, and run mergs the txt results. and zip the files and submit it to the evaluation server.