Skip to content

Latest commit

 

History

History
138 lines (113 loc) · 3.71 KB

setup.md

File metadata and controls

138 lines (113 loc) · 3.71 KB
Raw

Getting Started

Environment

DIGIT has been implemented and tested on Ubuntu 18.04 with python >= 3.7, PyTorch Lightning 0.9 and PyTorch 1.6.

Clone the repo:

git clone https://github.com/zc-alexfan/digit-interacting

Create folders needed:

make folders

Install conda environment:

conda create -n digit python=3.7
conda deactivate
conda activate digit
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch
pip install -r requirements.txt

Downloading InterHand2.6M

  • Download the 5fps.v1 of InterHand2.6M, following the instructions here
  • Place annotations, images, and rootnet_output from InterHand2.6M under ./data/InterHand/*:
./data/InterHand
├── annotations
├── images
│   ├── test
│   ├── train
│   └── val
├── rootnet_output
│   ├── rootnet_interhand2.6m_output_all_test.json
│   └── rootnet_interhand2.6m_output_machine_annot_val.json
|-- images
|   |-- test
|   |-- train
|   `-- val
`-- rootnet_output
    |-- rootnet_interhand2.6m_output_test.json
    `-- rootnet_interhand2.6m_output_val.json
  • The folder ./data/InterHand/annotations should look like this:
./data/InterHand/annotations
|-- skeleton.txt
|-- subject.txt
|-- test
|   |-- InterHand2.6M_test_MANO_NeuralAnnot.json
|   |-- InterHand2.6M_test_camera.json
|   |-- InterHand2.6M_test_data.json
|   `-- InterHand2.6M_test_joint_3d.json
|-- train
|   |-- InterHand2.6M_train_MANO_NeuralAnnot.json
|   |-- InterHand2.6M_train_camera.json
|   |-- InterHand2.6M_train_data.json
|   `-- InterHand2.6M_train_joint_3d.json
`-- val
    |-- InterHand2.6M_val_MANO_NeuralAnnot.json
    |-- InterHand2.6M_val_camera.json
    |-- InterHand2.6M_val_data.json
    `-- InterHand2.6M_val_joint_3d.json

Preparing data and backbone for training

Download the ImageNet-pretrained backbone from here and place it under:

./saved_models/pytorch/imagenet/hrnet_w32-36af842e.pt

Package images into lmdb:

cd scripts
python package_images_lmdb.py

Preprocess annotation:

python preprocess_annot.py

Render part segmentation masks:

  • Following the README.md of render_mano_ih to prepare an LMDB of part segmentation. For question in preparing the segmentation masks, please keep issues in there.

Place the LMDB from the images, the segmentation masks, and meta_dict_*.pkl to ./data/InterHand and it should look like the structure below. The cache files meta_dict_*.pkl are by-products of the step above.

|-- annotations
|   |-- skeleton.txt
|   |-- subject.txt
|   |-- test
|   |   |-- InterHand2.6M_test_MANO_NeuralAnnot.json
|   |   |-- InterHand2.6M_test_camera.json
|   |   |-- InterHand2.6M_test_data.json
|   |   |-- InterHand2.6M_test_data.pkl
|   |   `-- InterHand2.6M_test_joint_3d.json
|   |-- train
|   |   |-- InterHand2.6M_train_MANO_NeuralAnnot.json
|   |   |-- InterHand2.6M_train_camera.json
|   |   |-- InterHand2.6M_train_data.json
|   |   |-- InterHand2.6M_train_data.pkl
|   |   `-- InterHand2.6M_train_joint_3d.json
|   `-- val
|       |-- InterHand2.6M_val_MANO_NeuralAnnot.json
|       |-- InterHand2.6M_val_camera.json
|       |-- InterHand2.6M_val_data.json
|       |-- InterHand2.6M_val_data.pkl
|       `-- InterHand2.6M_val_joint_3d.json
|-- cache
|   |-- meta_dict_test.pkl
|   |-- meta_dict_train.pkl
|   `-- meta_dict_val.pkl
|-- images
|   |-- test
|   |-- train
|   `-- val
|-- rootnet_output
|   |-- rootnet_interhand2.6m_output_test.json
|   `-- rootnet_interhand2.6m_output_val.json
`-- segm_32.lmdb