Contact: Maheen Rashid (mhnrashid@ucdavis.edu) ##Getting Started
Download the code from GitHub:
git clone https://github.com/menoRashid/animal_human_kp
cd animal_human_kp
Install Torch. Instructions are here
Install Torch requirements:
luarocks install torchx
- npy4th (You may need to checkout commit from 5-10-16)
git clone https://github.com/htwaijry/npy4th.git
cd npy4th
luarocks make
Install Python requirements if needed:
Install the Spatial Tranformer module provided:
cd stnbhwd-master
luarocks make
It is a modification of the code from Spatial Transformer Network (Jaderberg et al.) and includes a Thin Plate Spline grid generator layer.
##Dataset Download the Horse Dataset (580 MB)
Run the following commands
cd data
unzip <path to data zip file>
##Models To download all the pretrained and untrained models go here (8.6 GB)
Run the following commands
cd models
unzip <path to models zip file>
Otherwise add the individual models to models/
- Full model for horses with tps warping(4.4 GB)
- Full model for horses with affine warping(4.4 GB)
- TPS Warping model for horses(738 MB)
- Affine Warping model for horses(764 MB)
- Keypoint network trained on human faces(3.4 GB)
- Untrained TPS Warping model(111 MB)
- Untrained Affine Warping model(121 MB)
##Testing To test pretrained model run the following commands
cd torch
th test.th -out_dir_images <path to results directory>
python ../python/visualize_results.py --test_dir <path to results directory>
after replacing with the path to the folder where you would like the output images to be saved.
A webpage with the results would be in the results directory.
<path to results directory>/results.html
##Training The file for training the full model is
torch/train_full_model.th
For details on training run
cd torch
th train_full_model.th -help
To train the model with affine warping uncomment lines 373-375. Currently, all parameters are the parameters used in the paper.
The file for training the warping network is
torch/train_warping_net.th
For details on training run
cd torch
th train_warping_net.th -help
To train the model with affine warping uncomment lines 313-314.