Skip to content

Latest commit

 

History

History
135 lines (99 loc) · 6.64 KB

README.md

File metadata and controls

135 lines (99 loc) · 6.64 KB
Raw

HODOR: High-Level Object Descriptors for Object Re-segmentation in Video Learned from Static Images (CVPR 2022 Oral)

Ali Athar, Jonathon Luiten, Alexander Hermans, Deva Ramanan, Bastian Leibe

[arXiv] [BibTeX] [Related Workshop Paper] [YouTube]

This repository contains official code for the above-mentioned publication, as well as the related workshop paper titled 'Differentiable Soft-Maskted Attention' which was presented at the Transformers for Vision (T4V) Workshop at CVPR 2022.

Conceptual Overview

  • Idea: Can we learn to do VOS by training only on static images?
  • Unlike existing VOS methods which learn pixel-to-pixel correspondences, HODOR learns to encode object appearance information from an image frame into a concise object descriptors which can then be decoded into another video frame to "re-segment" that object.
  • We can also train using cyclic consistency on video clips where just one frame is annotated.

image

Differentiable Soft-Masked Attention

In case you followed the workshop paper and want to nose-dive into the implementation of our novel differentiable soft-masked attention, take a look at the PyTorch module here: hodor/modeling/encoder/soft_masked_attention.py. The API is similar to PyTorch's native nn.MultiHeadAttention with the main difference that the forward requires a soft mask for the attention to be given as input.

Installation

The following packages are required:

  • Python v3.7.10
  • PyTorch (v1.9.0)
  • Torchvision (v0.10.0)
  • Pillow
  • opencv-python
  • imgaug
  • einops
  • timm
  • tqdm
  • pyyaml
  • tensorboardX
  • pycocotools

Directory Setup

  1. Set the environment variable HODOR_WORKSPACE_DIR: This is the top-level directory under which all checkpoints will be loaded/saved, and also where the datasets are expected to be located. The directory structure should look like this:
$HODOR_WORKSPACE_DIR
    - dataset_images
        - coco_2017_train
        - davis_trainval
            - seq1
            - seq2
            - ...
        - davis_testdev
            - seq1
            - seq2
            - ...
        - youtube_vos_2019_train
            - seq1
            - seq2
            - ...
        - youtube_vos_2019_val
            - seq1
            - seq2
            - ...
    - dataset_json_annotations
        - coco_2017_train.json
        - davis_train.json
        - davis_val.json
        - youtube_vos_2019_train.json
        - youtube_vos_2019_val.json
    - pretrained_backbones
        - swin_tiny_pretrained.pth
    - checkpoints
        - my_training_session
        - another_training_session

Note that we convert all annotations for COCO, DAVIS and YouTube-VOS into a somewhat standardized JSON format so that data loading code can be easily re-used.

  1. Download annotations and pretrained models: Links to downloadable resources are given below. For the easiest setup, download the entire zipped workspace. This includes all model checkpoints (COCO training + finetuning on sparse and dense video) as well as train/val/test annotations in JSON format for all 3 datasets (COCO, DAVIS, YouTube-VOS). Note that you'll still have to copy the dataset images to the relevant dataset in $HODOR_WORKSPACE_DIR/dataset_images.
Content URLs
Zipped Workspace (Model Checkpoints + Dataset Annotations) LINK
Dataset Annotations LINK
Model Checkpoints LINK

Inference

DAVIS 2017 val: Run the following from the repository base directory:

python hodor/inference/main.py $HODOR_WORKSPACE_DIR/checkpoints/static_image/250000.pth --dataset davis_val --output_dir davis_inference_output --temporal_window 7 --min_image_dim 512

This will create a directory called davis_inference_output in $HODOR_WORKSPACE_DIR/checkpoints/static_image and write the output masks to it. For Likewise you can point the script to the checkpoints in video_dense or video_sparse to evaluate those.

YouTube-VOS or DAVIS testdev: To run inference on a different dataset, set the --dataset argument to davis_testdev or youtube_vos_val. For detailed inference options, run the script with --help. Note that you may need to adjust the --min_image_dim and/or --temporal_window options to get the exact results mentioned in the paper for different datasets.

Training

Static Images

For single GPU training on static images from COCO:

python hodor/training/main.py --model_dir my_training_on_static_images --cfg static_image.yaml

For multi-GPU training (e.g. 8 GPUs) on static images from COCO:

python -m torch.distributed.launch --nproc_per_node=8 hodor/training/main.py --model_dir my_training_on_static_images --cfg static_image.yaml --allow_multigpu

The checkpoints provided above were usually trained on 4 or 8 GPUs. Note that we use gradient accumulation so it is possible to train with the default batch size of 8 even on a single GPU, but the results will not be exactly reproducible.

Video

To fine-tuning the COCO trained model on sparse video (i.e. assuming that only one frame per video is annotated in the DAVIS and YouTube-VOS training sets):

python -m torch.distributed.launch --nproc_per_node=8 hodor/training/main.py --model_dir my_finetuning_on_video --cfg video_sparse.yaml --allow_multigpu --restore_path /path/to/coco/trained/checkpoint.pth

Likewise you can set --cfg video_dense.yaml to train with the full set of available training annotations.

Cite

@inproceedings{athar2022hodor,
  title={HODOR: High-level Object Descriptors for Object Re-segmentation in Video Learned from Static Images},
  author={Athar, Ali and Luiten, Jonathon and Hermans, Alexander and Ramanan, Deva and Leibe, Bastian},
  booktitle={CVPR},
  year={2022}
}