Awesome
All in Tokens: Unifying Output Space of Visual Tasks via Soft Token
By Jia Ning*, Chen Li*, Zheng Zhang*, Zigang Geng, Qi Dai, Kun He, Han Hu
Introduction
AiT is initially described in arxiv, which is a framework to unify the output space of visual tasks. We demonstrate a single unified model that simultaneously handles two typical visual tasks of instance segmentation and depth estimation, which have discrete/fixed-length and continuous/varied-length outputs, respectively. We propose several new techniques that take into account the particularity of visual tasks: 1) Soft tokens. We employ soft tokens to represent the task output. Unlike hard tokens in the common VQ-VAE which are assigned one-hot to discrete codebooks/vocabularies, the soft tokens are assigned softly to the codebook embeddings. Soft tokens can improve the accuracy of both the next token inference and decoding the task output; 2) Mask augmentation. Many visual tasks have corruption, undefined or invalid values in label annotations, i.e., occluded area of depth maps. We show that a mask augmentation technique can greatly benefit these tasks. With these new techniques and other designs, we show that the proposed general-purpose task solver can perform both instance segmentation and depth estimation well. Particularly, we achieve 0.275 RMSE on the specific task of NYUv2 depth estimation, setting a new record on this benchmark.
Results and Models
Results on COCO instance segmentation
| <div style="width: 100pt"> Model | Box AP | Mask AP | VQ-VAE Model | Task-Solver Model |
|---|---|---|---|---|
| AiT(SwinV2-B) | 43.3 | 34.2 | vqvae_insseg.pt | model |
| AiT(SwinV2-B) w/o soft token | 43.6 | 31.1(-3.1) | vqvae_insseg.pt | model |
Results on NYUv2 depth estimation
| <div style="width: 100pt"> Model</div> | D1 | D2 | D3 | Abs Rel | RMSE | Log10 | VQ-VAE <br> Model | Task-Solver <br> Model |
|---|---|---|---|---|---|---|---|---|
| AiT(SwinV2-B) | 0.934 | 0.991 | 0.998 | 0.087 | 0.305 | 0.037 | vqvae_depth.pt | model |
| AiT-P(SwinV2-B) | 0.940 | 0.992 | 0.998 | 0.085 | 0.301 | 0.036 | vqvae_depth.pt | model |
| AiT(SwinV2-B) w/o soft token | 0.932 | 0.991 | 0.998 | 0.089 | 0.318 | 0.038 | vqvae_depth.pt | model |
| AiT(SwinV2-L) | 0.949 | 0.993 | 0.999 | 0.079 | 0.284 | 0.034 | vqvae_depth.pt | model |
| AiT-P(SwinV2-L) | 0.954 | 0.994 | 0.999 | 0.076 | 0.275 | 0.033 | vqvae_depth.pt | model |
Joint training results on COCO and NYUv2
| <div style="width: 100pt"> Model</div> | Box AP | Mask AP | RMSE | VQ-VAE Model | Task-Solver <br> Model |
|---|---|---|---|---|---|
| AiT(SwinV2-B) | 42.2 | 34.1 | 0.310 | vqvae_depth.pt/vqvae_insseg.pt | model |
Usage
Installation
We recommend using pytorch>=1.10, other packages can be found in requirements.txt. To install boundary-iou-api, please using the following command:
git clone https://github.com/bowenc0221/boundary-iou-api && cd boundary-iou-api && pip install -e .
Data/Pre-training model Preparation
- Download the NYU Depth V2 dataset, COCO datasets, our preprocess box-cropped binary instance masks, named maskcoco, and organize the data according to the following directory structure:
AiT
├── ait
├── vae
├── data
│ ├── coco
│ │ ├── annotations
│ │ ├── train2017
│ │ ├── val2017
│ │ ├── test2017
│ ├── maskcoco
│ ├── nyu_depth_v2
- Create the data links using following commands:
ln -s data ait/data
ln -s data vae/data
- Download pre-trained backbone models swin_v2_base_densesimmim.pth and swin_v2_large_densesimmim.pth.
Training
Training VQ-VAE on depth estimation:
cd vae
python -m torch.distributed.launch --nproc_per_node=${N_GPUS} train_depth_vqvae_dist.py configs/depth/ait_depth_vqvae.py --cfg-options <custom-configs>
Training VQ-VAE on instance segmentation:
cd vae
python -m torch.distributed.launch --nproc_per_node=${N_GPUS} train_insseg_vqvae_dist.py configs/insseg/ait_insseg_vqvae.py --cfg-options <custom-configs>
Training task-solver on depth estimation:
cd ait
# Train auto-regressive model
python -m torch.distributed.launch --nproc_per_node=8 code/train.py configs/swinv2b_480reso_depthonly.py --cfg-options model.backbone.init_cfg.checkpoint=swin_v2_base_densesimmim.pth model.task_heads.depth.vae_cfg.pretrained=vqvae_depth.pt # for AR training
# Train parallel model
python -m torch.distributed.launch --nproc_per_node=8 code/train.py configs/swinv2b_480reso_parallel_depthonly.py --cfg-options model.backbone.init_cfg.checkpoint=swin_v2_base_densesimmim.pth model.task_heads.depth.vae_cfg.pretrained=vqvae_depth.pt # for parallel training
Training task-solver on object detection
cd ait
python -m torch.distributed.launch --nproc_per_node=16 --nnodes=2 --node_rank=${NODE_RANK} --master_addr=${MASTER_ADDR} --master_port=${MASTER_PORT} code/train.py configs/swinv2b_640reso_detonly.py --cfg-options model.backbone.init_cfg.checkpoint=swin_v2_base_densesimmim.pth
Note: We use the pre-trainined object detection model to initialize the instance segmentation models and joint-training models to save training cost, please download the pre-trained model (ait_det_swinv2b_wodec.pth) before training on instance segmentation and joint training setting.
Training task-solver on instance segmentation
python -m torch.distributed.launch --nproc_per_node=16 code/train.py configs/swinv2b_640reso_inssegonly.py --cfg-options model.backbone.init_cfg.checkpoint=swin_v2_base_densesimmim.pth model.task_heads.insseg.vae_cfg.pretrained=vqvae_insseg.pt load_from=ait_det_swinv2b_wodec.pth
Joint training on instance segmentation and depth estimation
python -m torch.distributed.launch --nproc_per_node=16 --nnodes=4 --node_rank=${NODE_RANK} --master_addr=${MASTER_ADDR} --master_port=${MASTER_PORT} code/train.py configs/swinv2b_640reso_joint.py --cfg-options model.backbone.init_cfg.checkpoint=swin_v2_base_densesimmim.pth model.task_heads.insseg.vae_cfg.pretrained=vqvae_insseg.pt model.task_heads.depth.vae_cfg.pretrained=vqvae_depth.pt load_from=ait_det_swinv2b_wodec.pth
Inference
Evaluate on depth estimation
cd ait
# Evaluating auto-regressive model
python -m torch.distributed.launch --nproc_per_node=8 code/train.py configs/swinv2b_480reso_depthonly.py --cfg-options model.task_heads.depth.vae_cfg.pretrained=vqvae_depth.pt --eval <model_checkpiont>
# Evaluating parallele model
python -m torch.distributed.launch --nproc_per_node=8 code/train.py configs/swinv2b_480reso_parallel_depthonly.py --cfg-options model.task_heads.depth.vae_cfg.pretrained=vqvae_depth.pt --eval <model_checkpiont>
Evaluate on instance segmentation
cd ait
python -m torch.distributed.launch --nproc_per_node=8 code/train.py configs/swinv2b_640reso_inssegonly.py --cfg-options model.task_heads.insseg.vae_cfg.pretrained=vqvae_insseg.pt --eval <model_checkpiont>
Evaluate on both depth estimation and instance segmentation
cd ait
python -m torch.distributed.launch --nproc_per_node=8 code/train.py configs/swinv2b_640reso_joint.py --cfg-options model.task_heads.insseg.vae_cfg.pretrained=vqvae_insseg.pt model.task_heads.depth.vae_cfg.pretrained=vqvae_depth.pt --eval <model_checkpiont>
Citation
@article{ning2023all,
title={All in Tokens: Unifying Output Space of Visual Tasks via Soft Token},
author={Ning, Jia and Li, Chen and Zhang, Zheng and Geng, Zigang and Dai, Qi and He, Kun and Hu, Han},
journal={arXiv preprint arXiv:2301.02229},
year={2023}
}