Code for Which Tasks to Train Together in Multi-Task Learning
Trevor Standley, Amir R. Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, Silvio Savarese
ICML 2020
http://taskgrouping.stanford.edu/
- Install pytorch,torchvision
- Install apex
conda install -c conda-forge nvidia-apex
- (optional) install data loading speedups:
conda install -c thomasbrandon -c defaults -c conda-forge pillow-accel-avx2
conda install -c conda-forge libjpeg-turbo
- Get training data https://github.com/StanfordVL/taskonomy/tree/master/data The data must be aranged in
inputs:
root/rgb/building/point_x_view_x.png
labels:
root/$task$/$building$/point_x_view_x.png
order.
usage example
python3 train_taskonomy.py -d=/taskonomy_data/ -a=xception_taskonomy_new -j 4 -b 96 -lr=.1 --fp16 -sbn --tasks=sdnerac -r
Pretrained models from setting 2:
https://drive.google.com/drive/folders/1XQVpv6Yyz5CRGNxetO0LTXuTvMS_w5R5?usp=sharing
to test these models on the test set:
python3 train_taskonomy.py -d=/taskonomy_data/ -a=xception_taskonomy_new -j 4 -b 256 -lr=.1 --fp16 -sbn --tasks=[task letters] --resume=setting2_models/xception_taskonomy_new_[task letters].pth.tar -t -r
(contact for models from other settings)