STEm-Seg

This repository contains the official inference and training implementation for the paper:

STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos

Ali Athar*, Sabarinath Mahadevan*, Aljoša Ošep, Laura Leal-Taixé, Bastian Leibe

ECCV 2020 | Paper | Video | Project Page

Pre-requisites

Python 3.7
PyTorch 1.4, 1.5 or 1.6
OpenCV, numpy, imgaug, pillow, tqdm, pyyaml, tensorboardX, scipy, pycocotools (see requirements.txt for exact versions in case you encounter issues)

Basic Setup

Clone the repository and append it to the PYTHONPATH variable:

git clone https://github.com/sabarim/STEm-Seg.git
cd STEm-Seg
export PYTHONPATH=$(pwd):$PYTHONPATH

Download the required datasets from their respective websites and the trained model checkpoints from the given links. For inference, you only need the validation sets of the target dataset. For training, the table below shows which dataset(s) you will need:

Target Dataset	Datasets Required for Training	Model Checkpoint
DAVIS	DAVIS'17, YouTubeVIS, COCO Instance Segmentation, PascalVOC	link
YouTube-VIS	YouTube-VIS, COCO Instance Segmentation, PascalVOC	link
KITTI-MOTS	Mapillary images, KITTI-MOTS, sequence `0002` from MOTSChallenge	link

Environment Variables

File paths to datasets and model checkpoints are configured using environment variables.

Required

STEMSEG_JSON_ANNOTATIONS_DIR: To streamline the code, we reorganized the annotations and file paths for every dataset into a standard JSON format. These JSON files can be downloaded from here. Set this variable to the directory holding these JSON files.
STEMSEG_MODELS_DIR: Base directory where models are saved to by default. Only required for training. You can initially point this to any empty directory.

Dataset Specific

For inference, you only need to set the relevant variable for the target dataset. For training, since multiple datasets are used, multiple variables will be required (as mentioned below).

Video Datasets

DAVIS_BASE_DIR: Set this to the full path of the JPEGImages/480p directory for the DAVIS dataset. The image frames for all 60 training and 30 validation videos should be present in the directory. This variable is required for training/inference on DAVIS'19 Unsupervised.
YOUTUBE_VIS_BASE_DIR: Set this to the parent directory of the train and val directories for the YouTube-VIS dataset. This variable is required for training/inference on YouTube-VIS and also for training for DAVIS.
KITTIMOTS_BASE_DIR: Set this to the images directory which contains the directories holding images for each video sequence.

Image Datasets (required only for training)

COCO_TRAIN_IMAGES_DIR: Set this to the train2017 directory of the COCO instance segmentation dataset. Remember to use the 2017 train/val split and not the 2014 one. This variable is required for training for DAVIS and YouTube-VIS.
PASCAL_VOC_IMAGES_DIR: Set this to the JPEGImages directory of the PascalVOC dataset.This variable is required for training for DAVIS and YouTube-VIS.
MAPILLARY_IMAGES_DIR: You will need to do two extra things here: (1) Put all the training and validation images into a single directory (18k + 2k = 20k images in total). (ii) Since Mapillary images are very large, we first down-sampled them. The expected size for each image is given in stemseg/data/metainfo/mapillary_image_dims.json as a dictionary from the image file name to a (width, height) tuple. Please use OpenCV's cv2.resize method with interpolation=cv2.INTER_LINEAR to ensure the best consistency between your down-sampled images and the annotations we provide in our JSON file. This variable is required for training for KITTI-MOTS.

Inference

Assuming the relevant dataset environment variables are correctly set, just run the following commands:

DAVIS:

python stemseg/inference/main.py /path/to/downloaded/checkpoints/davis.pth -o /path/to/output_dir --dataset davis

YouTube-VIS:

python stemseg/inference/main.py /path/to/downloaded/checkpoints/youtube_vis.pth -o /path/to/output_dir --dataset ytvis --resize_embeddings

KITTI-MOTS:

python stemseg/inference/main.py /path/to/downloaded/checkpoints/kitti_mots.pth -o /path/to/output_dir --dataset kittimots --max_dim 1948

For each dataset, the output written to /path/to/output_dir will be in the same format as that required for the official evaluation tool for each dataset. To obtain visualizations of the generated segmentation masks, you can add a --save_vis flag to the above commands.

Training

Make sure the required environment variables are set as mentioned in the above sections.
Run mkdir $STEMSEG_MODELS_DIR/pretrained and place the pre-trained backbone file in this directory.
Optional: To verify if the data loading pipeline is correctly configured, you can separately visualize the training clips by running python stemseg/data/visualize_data_loading.py (see --help for list of options).

DAVIS

The final inference reported in the paper is done using clips of length 16 frames. Training end-to-end with such lengthy clips requires too much GPU VRAM though, so we train in two steps:

First we train end-to-end with 8 frame long clips:

python stemseg/training/main.py --model_dir some_dir_name --cfg davis_1.yaml

Then we freeze the encoder network (backbone and FPN) and train only the decoders with 16 frame long clips:

python stemseg/training/main.py --model_dir another_dir_name --cfg davis_2.yaml --initial_ckpt /path/to/last/ckpt/from/previous/step.pth

The training code creates a directory at $STEMSEG_MODELS_DIR/checkpoints/DAVIS/some_dir_name and places all checkpoints and logs for that training session inside it. For the second step we want to restore the final weights from the first step, hence the additional --initial_ckpt argument.

YouTube-VIS

Here, the final inference was done on 8 frame clips, so the model can be trained in a single step:

python stemseg/training/main.py --model_dir some_dir_name --cfg youtube_vis.yaml

The training output for this will be placed in $STEMSEG_MODELS_DIR/checkpoints/youtube_vis/some_dir_name.

KITTI-MOTS

Here as well, the final inference was done on 8 frame clips, but we trained in two steps.

First on augmented images from the Mapillary dataset:

python stemseg/training/main.py --model_dir some_dir_name --cfg kitti_mots_1.yaml

Then on the KITTI-MOTS dataset itself:
```
python stemseg/training/main.py --model_dir another_dir_name --cfg kitti_mots_2.yaml --initial_ckpt /path/to/last/ckpt/from/previous/step.pth
```
For this step, we included video sequence 0002 from the MOTSChallenge training set into our training set. Simply copy the images directory for this video to $KITTIMOTS_BASE_DIR and rename the directory to 0050 (this is done because a video named 0002 already exists in KITTI-MOTS).

Further Notes on Training

In general, you will need at least 16GB VRAM for training any of the models. The VRAM requirement can be lowered by reducing the image dimensions in the config YAML file (INPUT.MIN_DIM and INPUT.MAX_DIM). Alternatively, you can also use mixed precision training by installing Nvidia apex and setting the TRAINING.MIXED_PRECISION option in the config YAML to true. In general, both these techniques will reduce performance.
Multi-GPU training is possible and has been implemented using torch.nn.parallel.DistributedDataParallel with one GPU per process. To utilize multiple GPUs, the above commands have to be modified as follows:
```
python -m torch.distributed.launch --nproc_per_node=<num_gpus> stemseg/training/main.py --model_dir some_dir_name --cfg <dataset_config.yaml> --allow_multigpu
```
You can visualize the training progress using tensorboard by pointing it to the logs sub-directory in the training directory.
By default, checkpoints are saved every 10k iterations, but this frequency can be modified using the --save_interval argument.
It is possible to terminate training and resume from a saved checkpoint by using the --restore_session argument and pointing it to the full path of the checkpoint.
We fix all random seeds prior to training, but the results reported in the paper may not be exactly reproducible when you train the model on your own.
Run python stemseg/training/main.py --help for the full list of options.

Implementing Other Datasets

Extending the training/inference to other datasets should be easy since most of the code is dataset agnostic.

Inference

See the if/else block in the main method in inference/main.py. You will just have to implement a class that converts the segmentation masks produced by the framework to whatever format you want (see any of the scripts in stemseg/inference/output_utils for examples).

Training

You will first have to convert the annotations for your dataset to the standard JSON format used by this code. Inspect any of the given JSON files to see what the format should be like. The segmentation masks are encoded in RLE format using pycocotools. To better understand the file format, you can also see stemseg/data/generic_video_dataset_parser.py and stemseg/data/generic_image_dataset_parser.py where these files are read and parsed.

Once this is done, you can utilize the VideoDataset API in stemseg/data/video_dataset.py to do most of the pre-processing and augmentations. You just have to inherit this class and implement the parse_sample_at method (see stemseg/data/davis_data_loader.py for an example of how to do this).

Cite

Use the following BibTeX to cite our work:

@inproceedings{Athar_Mahadevan20ECCV,
  title={STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos},
  author={Athar, Ali and Mahadevan, Sabarinath and O{\v{s}}ep, Aljo{\v{s}}a and Leal-Taix{\'e}, Laura and Leibe, Bastian},
  booktitle={ECCV},
  year={2020}
}

Schmiddo/STEm-Seg

STEm-Seg

Pre-requisites

Basic Setup

Environment Variables

Required

Dataset Specific

Video Datasets

Image Datasets (required only for training)

Inference

Training

DAVIS

YouTube-VIS

KITTI-MOTS

Further Notes on Training

Implementing Other Datasets

Inference

Training

Cite