# install pytorch 1.1 and torchvision
sudo pip3 install torch==1.1 torchvision
# install apex
cd $INSTALL_DIR
git clone https://github.com/NVIDIA/apex.git
cd apex
sudo python setup.py install --cuda_ext --cpp_ext
# clone Hier-R-CNN
git clone https://github.com/soeaver/Parsing-R-CNN.git
# install other requirements
pip3 install -r requirements.txt
# mask ops
cd Hier-R-CNN
sh make.sh
# make cocoapi
cd Parsing-R-CNN/cocoapi/PythonAPI
mask
cd ../../
ln -s cocoapi/PythonAPI/pycocotools/ ./
Make sure to put the files as the following structure:
├─data
│ ├─coco
│ │ ├─images
│ │ │ ├─train2017
│ │ │ ├─val2017
│ │ ├─annotations
│ │ │ ├─DensePoseData
│ │ │ │ ├─densepose_coco_train2017.json
│ │ │ │ ├─densepose_coco_val2017.json
│ │ │ │ ├─densepose_coco_test2017.json
| |
│ ├─CIHP
│ │ ├─train_img
│ │ │─train_parsing
│ │ │─train_seg
│ │ ├─val_img
│ │ │─val_parsing
│ │ │─val_seg
│ │ ├─annotations
│ │ │ ├─CIHP_train.json
│ │ │ ├─CIHP_val.json
| |
│ ├─MHP-v2
│ │ ├─train_img
│ │ │─train_parsing
│ │ │─train_seg
│ │ ├─val_img
│ │ │─val_parsing
│ │ │─val_seg
│ │ ├─annotations
│ │ │ ├─MHP-v2_train.json
│ │ │ ├─MHP-v2_val.json
|
├─weights
├─resnet50_caffe.pth
├─resnet101_caffe.pth
├─resnext101_32x8d-8ba56ff5.pth
- Densepose estimation using original coco images.
- For training and evaluating densepose estimation on Parsing R-CNN, you need fetch DensePose data following original repo