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Getting Started with ODISE

This document provides a brief introduction on how to infer with and train ODISE.

For further reading, please refer to Getting Started with Detectron2.

Important Note: ODISE's demo/demo.py and tools/train_net.py scripts link to the original pre-trained models for Stable Diffusion v1.3 and CLIP. When you run them for the very first time, these scripts will automatically download the pre-trained models for Stable Diffuson and CLIP, from their original sources, to your local directories $HOME/.torch/ and $HOME/.cache/clip, respectively. Their use is subject to the original license terms defined at https://github.com/CompVis/stable-diffusion and https://github.com/openai/CLIP, respectively.

Inference Demo with Pre-trained ODISE Models

  1. Choose a model for ODISE and its corresponding configuration file from model zoo, for example, configs/Panoptic/odise_label_coco_50e.py. In demo/demo.py we also provide a default inbuilt configuration.
  2. Run the demo/demo.py with:
python demo/demo.py --config-file configs/Panoptic/odise_label_coco_50e.py \
  --input input1.jpg input2.jpg \
  --init-from /path/to/checkpoint_file
  [--other-options]

This command will run ODISE's inference and show visualizations in an OpenCV window.

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

  • To run with a customized vocabulary, use --vocab to specify additional vocabulary names.
  • To run with a caption, use --caption to specify a caption.
  • To run on your webcam, replace --input files with --webcam.
  • To run on a video, replace --input files with --video-input video.mp4.
  • To run on the cpu, add train.device=cpu at the end.
  • To save outputs to a directory (for images) or a file (for webcam or video), use the --output option.

The default behavior is to append the user-provided extra vocabulary to the labels from COCO, ADE20K and LVIS. To use only the user-provided vocabulary use --label "".

python demo/demo.py --input demo/examples/purse.jpeg --output demo/purse_pred.jpg --label "" --vocab "purse"

or

python demo/demo.py --input demo/examples/purse.jpeg --output demo/purse_pred.jpg --label "" --caption "there is a black purse"

Command line-based Training & Evaluation

We provide a script tools/train_net.py that trains all configurations of ODISE.

To train a model with tools/train_net.py, first prepare the datasets following the instructions in datasets/README.md and then run, for single-node (8-GPUs) NVIDIA AMP-based training:

(node0)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --num-gpus 8 --amp

For 4-node (32-GPUs) AMP-based training, run:

(node0)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --machine-rank 0 --num-machines 4 --dist-url tcp://${MASTER_ADDR}:29500 --num-gpus 8 --amp
(node1)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --machine-rank 1 --num-machines 4 --dist-url tcp://${MASTER_ADDR}:29500 --num-gpus 8 --amp
(node2)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --machine-rank 2 --num-machines 4 --dist-url tcp://${MASTER_ADDR}:29500 --num-gpus 8 --amp
(node3)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --machine-rank 3 --num-machines 4 --dist-url tcp://${MASTER_ADDR}:29500 --num-gpus 8 --amp

Note that our default training configurations are designed for 32 GPUs. Since we use the AdamW optimizer, it is not clear as to how to scale the learning rate with batch size. However, we provide the ability to automatically scale the learning rate and the batch size for any number of GPUs used for training by passing in the--ref $REFERENCE_WORLD_SIZE argument. For example, if you set $REFERENCE_WORLD_SIZE=32 while training on 8 GPUs, the batch size and learning rate will be set to 8/32 = 0.25 of the original ones.

(node0)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --num-gpus 8 --amp --ref 32

ODISE trains in 6 days on 32 NVIDIA V100 GPUs.

To evaluate a trained ODISE model's performance, run on single node

(node0)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --num-gpus 8 --eval-only --init-from /path/to/checkpoint

or for multi-node inference:

(node0)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --machine-rank 0 --num-machines 4 --dist-url tcp://${MASTER_ADDR}:29500 --num-gpus 8 --eval-only --init-from /path/to/checkpoint
(node1)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --machine-rank 1 --num-machines 4 --dist-url tcp://${MASTER_ADDR}:29500 --num-gpus 8 --eval-only --init-from /path/to/checkpoint
(node2)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --machine-rank 2 --num-machines 4 --dist-url tcp://${MASTER_ADDR}:29500 --num-gpus 8 --eval-only --init-from /path/to/checkpoint
(node3)$ ./tools/train_net.py --config-file configs/Panoptic/odise_label_coco_50e.py --machine-rank 3 --num-machines 4 --dist-url tcp://${MASTER_ADDR}:29500 --num-gpus 8 --eval-only --init-from /path/to/checkpoint

To use the our provided ODISE model zoo, you can pass in the arguments --config-file configs/Panoptic/odise_label_coco_50e.py --init-from odise://Panoptic/odise_label_coco_50e or --config-file configs/Panoptic/odise_label_coco_50e.py --init-from odise://Panoptic/odise_caption_coco_50e to ./tools/train_net.py, respectively.