Prerequisite: Tensorflow-2.4.1, CUDA-11, Keras 2.3.1
Follow instructions to install latest docker-ce, and nvidia-docker2
# Building docker image locally
cd Docker/TF-2.4_Keras-2.3.1
sudo docker build -t hangqiu/mcal:tf2.4 .
# or Pulling docker
sudo docker pull hangqiu/mcal:tf2.4
# Running in docker
sudo docker run -u $(id -u):$(id -g) -it --rm --gpus=all --ipc=host -v ~/research/ActiveLabeling:/ActiveLabeling -v ~/research/dataset:/dataset hangqiu/mcal:tf2.4
# An example parameter set:
TRIAL=1
HORIZON=0.99
GPU=0,1,2,3
CELL=3
LAYER=3
KERNEL=16
BATCHSIZE=1000
WARMSTARTSIZE=SAME
AUG=1
ACCThresh=0.95
MINIBATCH=256
DATASET=cifar10_keras
MODEL=resnet_grow
python3 run_experiment.py \
--dataset=${DATASET} \
--score_method=${MODEL} \
--standardize_data=False \
--train_horizon=${HORIZON} \
--trials=${TRIAL} \
--sampling_method=${SAMPLE} \
--data_dir=../dataset/tf-data \
--gpu=${GPU} \
--cell=${CELL} \
--layer=${LAYER} \
--kernel=${KERNEL} \
--warmstart_size=${WARMSTARTSIZE} \
--batch_size=${BATCHSIZE}\
--augmentation=${AUG}\
--accthresh=${ACCThresh}\
--minibatch=${MINIBATCH}
python3 optimized_labeling.py \
--dataset=${DATASET} \
--score_method=${MODEL} \
--standardize_data=False \
--train_horizon=${HORIZON} \
--trials=${TRIAL} \
--sampling_method=${SAMPLE} \
--data_dir=../dataset/tf-data \
--gpu=${GPU} \
--cell=${CELL} \
--layer=${LAYER} \
--kernel=${KERNEL} \
--warmstart_size=${WARMSTARTSIZE} \
--batch_size=${BATCHSIZE}\
--augmentation=${AUG}\
--accthresh=${ACCThresh}\
--minibatch=${MINIBATCH}
sh run_active_learning.sh
sh run_optimized_labeling.sh
@inproceedings{
qiu2023mcal,
title={{MCAL}: Minimum Cost Human-Machine Active Labeling},
author={Hang Qiu and Krishna Chintalapudi and Ramesh Govindan},
booktitle={International Conference on Learning Representations},
year={2023},
url={https://openreview.net/forum?id=1FxRPKrH8bw}
}
Note: This repo is developed based on google/active-learning.