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Getting started with aistron

This document provides a brief intro of the usage of aistron

1. Inference Demo with Pre-trained Models

  1. Pick a model and its config file from model zoo, for example, configs/KINS2020/maskrcnn_R50_FPN_kins2020_6ep_bs1.yaml.
  2. We provide demo.py that is able to demo builtin configs. Run it with:
python demo/demo.py --config-file configs/KINS2020/maskrcnn_R50_FPN_kins2020_6ep_bs1.yaml \
  --input input1.jpg input2.jpg \
  --output demo_output.jpg \
  --segm_type amodal \ # visible or amodal
  [--other-options]
  --opts MODEL.WEIGHTS /path/to/checkpoint_file

The configs are made for training, therefore we need to specify MODEL.WEIGHTS to a model from model zoo for evaluation. This command will run the inference and output the result to output.jpg.

For details of the command line arguments, see demo.py -h or look at its source code to understand its behavior. Some common arguments are:

  • To run on your webcam, replace --input files with --webcam. We suggest using model weight trained on COCOA datasets for better experience.
  • To run on a video, replace --input files with --video-input video.mp4.
  • To run on cpu, add MODEL.DEVICE cpu after --opts.
  • To save outputs to a directory (for images) or a file (for webcam or video), use --output.

We provide some demo images from datasets to run the demo on, assets/demo_examples/. For example:

python demo/demo.py --config-file configs/KINS2020/maskrcnn_R50_FPN_kins2020_6ep_bs1.yaml \
        --input assets/demo_examples/kins_example.png \
        --output demo_output.png \
        --opts MODEL.WEIGHTS /path/to/the/weight.pth \

2. Train and Evaluation

We provide a script tools/train_net.py, that is made to train all the configs provided in aistron. It also can be used as an example for training your project that uses aistron as a library.

To train a model with tools/train_net.py, first setup the corresponding datasets following datasets/README.md, then run the following command to train with a specific config file:

export AISTRON_DATASETS=../data/datasets/
config_file=configs/KINS2020/maskrcnn_R50_FPN_kins_6ep_bs1.yaml
python tools/train_net.py --config-file ${config_file} --num-gpus 1 \

During training, the model configs, checkpoints and logs will be saved to the directory specified in the OUTPUT_DIR variable in the corresponding config file.

To evaluate a model's performance, use:

model_output='../data/train_outputs/aistron/maskrcnn/maskrcnn_R50_FPN_kins2020_6ep_bs1'
python3 tools/train_net.py --num-gpus 1 \
        --config-file ${model_output}/config.yaml \
        --eval-only MODEL.WEIGHTS ${model_output}/model_final.pth \
        2>&1 | tee ${model_output}/test_log.txt