Awesome

Affordance Grounding from Demonstration Video to Target Image

This repository is the official implementation of Affordance Grounding from Demonstration Video to Target Image:

@inproceedings{afformer,
  author  = {Joya Chen and Difei Gao and Kevin Qinghong Lin and Mike Zheng Shou},
  title   = {Affordance Grounding from Demonstration Video to Target Image},
  booktitle = {CVPR},
  year    = {2023},
}

Install

1. PyTorch

We now support PyTorch 2.0. Other version should be okay.

conda install -y pytorch torchvision pytorch-cuda=11.8 -c pytorch -c nvidia

NOTE: If you want to use PyTorch 2.0, you should install CUDA >= 11.7. See https://pytorch.org/.

2. PyTorch Lightning

We use PyTorch Lightning 2.0 as the training and inference engines.

pip install lightning jsonargparse[signatures] --upgrade

3. xFormers

We use memory-efficient attention in xformers. Currently PyTorch 2.0 does not support memory-efficient attention relative positional encoding (see pytorch/issues/96099). We will update this repo when PyTorch supports this.

pip install triton --upgrade
pip install --pre xformers

4. Timm, Detectron2, Others

We borrow some implementations from timm and detectron2.

pip install timm opencv-python av imageio --upgrade
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'

Dataset

• Downloading OPRA dataset from https://github.com/kuanfang/opra. Due to the copyright issue, you may need to download the original video from YouTube.

• We have uploaded our organized annotation json files to datasets/opra/annotations. Now your datasets file tree should be:

datasets
└── opra
    ├── annotations
    │   ├── test.json
    │   ├── train.json
    ├── clips
    │   ├── aocom
    │   ├── appliances
    │   ├── bestkitchenreview
    │   ├── cooking
    │   ├── eguru
    │   └── seattle
    └── images
        ├── aocom
        ├── appliances
        ├── bestkitchenreview
        ├── cooking
        ├── eguru
        └── seattle

• We are working on organizing EPIC-Hotspot and AssistQ Buttons. They will be released as soon as possible.

Afformer Model

Hint: We recommend you to read LightningCLI if you firstly use it. That helps you better use these commands.

1. ResNet-50-FPN encoder

• You dont need to manually download pre-trained encoder weight. torchvision will automatically download it. See torchvision.models.detection.fasterrcnn_resnet50_fpn_v2 for details.

• Training Afformer with ResNet-50-FPN encoder with

python main.py fit --config configs/opra/r50fpn.yaml --trainer.devices 8 --data.batch_size_per_gpu 2

• The training log is saved in outputs/ by default. You can launch a tensorboard to monitor this folder:

tensorboard --logdir outputs/ --port 2333
# Then you can see real-time losses, metrics at http://localhost:2333/ 

• The evaluation would be done each 1k iterations during training. You can also evaluate with the validate command. For example,

python main.py validate --config configs/opra/r50fpn.yaml --trainer.devices 8 --data.batch_size_per_gpu 2 --ckpt outputs/opra/r50fpn/lightning_logs/version_0/checkpoints/xxxx.ckpt

2. ViTDet-B encoder

• Downloading ViTDet-B-COCO weights and then put it to weights/ folder: weights/mask_rcnn_vitdet_b_coco.pkl.

• Training Afformer with ViTDet-B encoder with

python main.py fit --config configs/opra/vitdet.yaml --trainer.devices 8 --data.batch_size_per_gpu 2

• The training log is saved in outputs/ by default. You can launch a tensorboard to monitor this folder:

tensorboard --logdir outputs/ --port 2333
# Then you can see real-time losses, metrics at http://localhost:2333/ 

• The evaluation would be done each 1k iterations during training. You can also evaluate with the validate command. For example,

python main.py validate --config configs/opra/vitdet_b.yaml --trainer.devices 8 --data.batch_size_per_gpu 2 --ckpt outputs/opra/vitdet_b/lightning_logs/version_0/checkpoints/xxxx.ckpt

3. Visualization

python demo.py --config configs/opra/vitdet_b.yaml --weight weights/afformer_vitdet_b_v1.ckpt --video demo/video.mp4 --image demo/image.jpg --output demo/output.gif

• Hint: we carefully fine-tuned a very strong ViTDet model, which is better than paper reported. Download it.

MaskAHand Pre-training

NOTE: A detailed tutorial will be done as soon as possible.

1. Hand Interaction Detection

• Downloading our trained hand interaction detector weights in this url. Then put it to weights/ folder: weights/hircnn_r50fpnv2_849.pth.

• The video demo by this hand interaction detector:

• Hint: we trained this simple and accurate hand interaction detector using 100DOH + some Ego datasets. It achieves 84.9 hand+interaction detection AP on 100DOH test set. For MaskAHand pre-training, this weight is enough. We will release its full source code at chenjoya/hircnn as soon as possible.

2. Hand Interaction Clip Mining

• Make sure your data preparation follows Dataset part.

• Running affominer/miner.py. The generated data will be saved at affominer/outputs.

3. Target Image Synthesis and Transformation

This would be done during training. You can set the hyper-parameters in configs/opra/maskahand/pretrain.yaml:

mask_ratio: 1.0
num_masks: 2
distortion_scale: 0.5
num_frames: 32
clip_interval: 16
contact_threshold: 0.99

4. MaskAHand Pre-training

python main.py fit --config configs/opra/maskahand/pretrain.yaml

5. Fine-tuning or Zero-shot Evaluation

• Fine-tuning the MaskAHand pre-trained weight by

python main.py fit --config configs/opra/maskahand/finetune.yaml 

• Zero-shot evaluate the MaskAHand pre-trained weight by

python main.py validate --config configs/opra/maskahand/pretrain.yaml

6. Visualization

You can refer to demo.py to visualize your model results.

Contact

This repository is developed by Joya Chen. Questions and discussions are welcome via joyachen@u.nus.edu.

Acknowledgement

Thanks to all co-authors of the paper, Difei Gao, Kevin Qinghong Lin, and Mike Shou (my supervisor). Also appreciate the assistance from Dongxing Mao and Jiawei Liu.