This repo is the official implementation of "InterFormer: Real-time Interactive Image Segmentation"
InterFormer follows a new pipeline to address the issues of existing pipeline's low computational efficiency. InterFormer extracts and preprocesses the computationally time-consuming part i.e. image processing from the existing process. Specifically, InterFormer employs a large vision transformer (ViT) on high-performance devices to preprocess images in parallel, and then uses a lightweight module called interactive multi-head self attention (I-MSA) for interactive segmentation. InterFormer achieves real-time high-quality interactive segmentation on CPU-only devices.
The following GIF animations were created on CPU-only devices:
Ensure the following requirements are met before proceeding with the installation process:
- Python 3.8+
- PyTorch 1.12.0
- mmcv-full 1.6.0
- mmsegmentation 0.26.0
To install PyTorch, please refer to the following resource: INSTALLING PREVIOUS VERSIONS OF PYTORCH
pip install -U openmim
mim install mmcv-full==1.6.0cd mmsegmentation
pip install -e .pip install -r requirements.txtTo download the COCO dataset, please refer to cocodataset. You will need to download the following: 2017 Train Images, 2017 Val images and 2017 Panoptic Train/Val annotations into data
Alternatively, you can use the following script:
cd data/coco2017
bash coco2017.shThe data is organized as follows:
data/coco2017/
├── annotations
│ ├── panoptic_train2017 [118287 entries exceeds filelimit, not opening dir]
│ ├── panoptic_train2017.json
│ ├── panoptic_val2017 [5000 entries exceeds filelimit, not opening dir]
│ └── panoptic_val2017.json
├── coco2017.sh
├── train2017 [118287 entries exceeds filelimit, not opening dir]
└── val2017 [5000 entries exceeds filelimit, not opening dir]
To download the LVIS dataset, please refer to lvisdataset to download the images and annotations.
The data is organized as follows:
data/lvis/
├── lvis_v1_train.json
├── lvis_v1_train.json.zip
├── lvis_v1_val.json
├── lvis_v1_val.json.zip
├── train2017 [118287 entries exceeds filelimit, not opening dir]
├── train2017.zip
├── val2017 [5000 entries exceeds filelimit, not opening dir]
└── val2017.zip
To download the SBD dataset, please refer to SBD.
The data is organized as follows:
data/sbd/
├── benchmark_RELEASE
│ ├── dataset
│ │ ├── cls [11355 entries exceeds filelimit, not opening dir]
│ │ ├── img [11355 entries exceeds filelimit, not opening dir]
│ │ ├── inst [11355 entries exceeds filelimit, not opening dir]
│ │ ├── train.txt
│ │ └── val.txt
└── benchmark.tgz
Please download DAVIS GrabCut Berkeley from Reviving Iterative Training with Mask Guidance for Interactive Segmentation
The data is organized as follows:
data/
├── berkeley
│ └── Berkeley
│ ├── gt [100 entries exceeds filelimit, not opening dir]
│ ├── img [100 entries exceeds filelimit, not opening dir]
│ └── list
│ └── val.txt
├── davis
│ └── DAVIS
│ ├── gt [345 entries exceeds filelimit, not opening dir]
│ ├── img [345 entries exceeds filelimit, not opening dir]
│ └── list
│ ├── val_ctg.txt
│ └── val.txt
└── grabcut
└── GrabCut
├── gt [50 entries exceeds filelimit, not opening dir]
├── img [50 entries exceeds filelimit, not opening dir]
└── list
└── val.txt
To download and transform the MAE-pretrained weights into mmseg-style, please refer to MAE.
E.g.
python tools/model_converters/beit2mmseg.py https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_base.pth pretrain/mae_pretrain_vit_base_mmcls.pthThe required weight files are located in the pretrain directory and are organized as follows:
pretrain
├── mae_pretrain_vit_base_mmcls.pth
└── mae_pretrain_vit_large_mmcls.pth
To start the training of InterFormer-Light, run the following script:
CUDA_VISIBLE_DEVICES=0,1,2,3 bash tools/dist_train.sh configs/interformer_light_coco_lvis_320k.py 4 --seed 42 --no-validateTo train InterFormer-Tiny, use the following script:
CUDA_VISIBLE_DEVICES=0,1,2,3 bash tools/dist_train.sh configs/interformer_tiny_coco_lvis_320k.py 4 --seed 42 --no-validateThe trained weights are stored in work_dirs/interformer_light_coco_lvis_320k or work_dirs/interformer_tiny_coco_lvis_320k.
The trained weights are available at InterFormer
To start the evaluation on the GrabCut, Berkeley, SBD, or DAVIS dataset, use the following script:
CUDA_VISIBLE_DEVICES=0,1,2,3 bash tools/dist_clicktest.sh ${CHECKPOINT_FILE} ${GPU_NUM} [--dataset ${DATASET_NAME}] [--size_divisor ${SIZE_DIVISOR}]where CHECKPOINT_FILE is the path to the trained weight file, GPU_NUM is the number of GPUs used for evaluation, DATASET_NAME is the name of the dataset to evaluate on, and SIZE_DIVISOR is the divisor used to pad the image. The script will look for the CONFIG_FILE in the same folder of CHECKPOINT_FILE (.py extension).
For example, assume the data is organized as follows:
work_dirs/
└── interformer_tiny_coco_lvis_320k
├── interformer_tiny_coco_lvis_320k.py
└── iter_320000.pth
To evaluate on SBD with InterFormer-Tiny, run:
CUDA_VISIBLE_DEVICES=0,1,2,3 bash tools/dist_clicktest.sh work_dirs/interformer_tiny_coco_lvis_320k/iter_320000.pth 4 --dataset sbd --size_divisor 32This command will start the evaluation by specifying the trained weight file work_dirs/interformer_tiny_coco_lvis_320k/iter_320000.pth and loading the configuration file interformer_tiny_coco_lvis_320k.py in the same folder.
The results are stored in work_dirs/interformer_tiny_coco_lvis_320k/clicktest_sbd_iter_320000_xxxx.json.
To run the demo directly with Python, use the following command in your terminal:
python demo/main.py path/to/checkpoint --device [cpu|cuda:0]where:
- path/to/checkpoint specifies the path to the checkpoint file that will be loaded before running the program.
- --device specifies the device to use, either cpu or gpu.
Here's an example script to run the demo:
python demo/main.py work_dirs/interformer_tiny_coco_lvis_320k/iter_320000.pth --device cpu
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