aabbas90 / COPS

Official code for NeurIPS paper "Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach".

Combinatorial Optimization for Panoptic Segmentation (COPS)

Official code for NeurIPS'21 paper "Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach".

TLDR: Panoptic segmentation pipeline containing CNN and a combinatorial optimization problem (AMWC). The whole pipeline is trained with panoptic quality loss by backpropagating through AMWC.

Installation

Our codebase is built upon the detectron2 framework with some of our (minor) modifications such as printing per-image panoptic quality. For more information please consult the documentation of detectron2 as the codebase is designed according to their guidelines. The code is developed and tested on CUDA 10.1. We use conda package manager to manage the dependencies. The steps to set-up the environment are as follows:

conda create -n cops python=3.7
conda activate cops

Which creates and activates the environment. Afterwards check the contents of install.sh to install the dependencies (Make sure to fetch all submodules recursively).

Setting up the datasets

Please follow the guidelines of detectron2 from detectron2_datasets to set-up the datasets.

Organization

1. Please see affinityNet/panoptic_affinity/config.py to see all configurations parameters related to backbone, decoders, dataset etc. Instantiation of these parameters is done in config files present in affinityNet/configs folder.
2. Whole pipeline is defined in affinityNet/panoptic_affinity/panoptic_seg_affinity.py.
3. Panoptic quality surrogate loss, gradients of AMWC, instance segmentation confidence scores are computed from affinityNet/panoptic_affinity/losses.py.
4. In case of confusion w.r.t overall code structure, data generation etc. consulting detectron2 should help.

Training

Cityscapes

Pretraining:

python train_net.py --config-file configs/Cityscapes-PanopticSegmentation/panoptic_affinity_pretrain.yaml --num-gpus 1 --resume

Fully differentiable training:

Assuming that the output of pretraining phase is saved to PRETRAINED_DIR where WEIGHTS is the name of checkpoint file. Then run the following command by replacing the values of above-mentioned variables:

python train_net.py --config-file configs/Cityscapes-PanopticSegmentation/panoptic_affinity_end_to_end.yaml --base-config-file ${PRETRAINED_DIR}/config.yaml --num-gpus 1 --resume MODEL.WEIGHTS ${PRETRAINED_DIR}/${WEIGHTS}

COCO

Pretraining:

We use 4 GPUs. Please change appropriately according to your setup:

python train_net.py --config-file configs/COCO-PanopticSegmentation/panoptic_affinity_pretrain.yaml --num-gpus 4 --resume

Fully differentiable:

python train_net.py --config-file configs/COCO-PanopticSegmentation/panoptic_affinity_end_to_end.yaml --base-config-file ${PRETRAINED_DIR}/config.yaml --num-gpus 1 --resume MODEL.WEIGHTS ${PRETRAINED_DIR}/${WEIGHTS}

Evaluation

Assuming MODEL_DIR corresponds to the folder containing the checkpoint with name WEIGHTS. Folder by name OUT_FOLDER will be created which will contain the evaluation results. Setting MODEL.SAVE_RESULT_IMAGES to True will additionally save result images (can be slow).

python train_net.py --config-file ${MODEL_DIR}/config.yaml --num-gpus 1 --eval-only MODEL.WEIGHTS ${MODEL_DIR}/${WEIGHTS} OUTPUT_DIR ${MODEL_DIR}/${OUT_FOLDER} DATALOADER.EVAL_BATCH_SIZE 1 DATALOADER.NUM_WORKERS 0 MODEL.SAVE_RESULT_IMAGES False

Where DATALOADER.EVAL_BATCH_SIZE controls batch size during inference. Set to larger than 1 to evaluate faster (assuming enough computational resources are available).

Pretrained models

Pretrained models after full training and their results:

Dataset	Backbone	PQ	PQ_st	PQ_th	Per image inference time (s)	Checkpoint file
Cityscapes	ResNet50	62.1	67.2	55.1	1.8	one_drive_link
COCO	ResNet50	38.4	35.2	40.5	0.4	one_drive_link

Demo

See affinityNet/run_demo.sh