data: dataset: name: office # choices are ['office','astro-nn', 'gz2', 'decals', 'adversaries'] n_share: 2 # number of classes to be shared n_source_private: 0 # number of classes in source private domain n_total: 10 # number of classes in total num_knowns: 3 dataloader: class_balance: true # data_workers: 3 # how many workers to use for train dataloaders batch_size: 36 # batch_size for source domain and target domain respectively model: base_model: resnet50 temp: 0.05 train: min_step: 10000 # minimum steps to run. run epochs until it exceeds the minStep lr: 0.01 # learning rate for new layers. learning rate for finetune is 1/10 of lr multi: 0.1 weight_decay: 0.0005 sgd_momentum: 0.9 momentum: 0.0 eta: 0.05 log_interval: 100 thr: 0.263 # trying to use the equation thr = log(K)/2 margin: 0.5 test: test_interval: 200 test_only: False # test a given model and exit resume_file: '' # model to test test_feat: False misc: gpus: 1 # how many GPUs to be used, 0 indicates CPU only log: root_dir: log # the log directory (log directory will be {root_dir}/{method}/time/) log_interval: 10 # steps to log scalars output: output_path: './deepastro_files/' model_path: './deepastro_files/models/' viz_path: './deepastro_files/plots/'