test-config-office-home.yaml

data:
  dataset:
    name: officehome # choices are ['office', 'officehome', 'VisDA+ImageCLEF-DA']
    root_path: /data/yinmingyue/Datasets/OfficeHome # /path/to/dataset/root
    source1: 1 # No.1 source domain index
    source2: 2 # No.2 source domain index
    source3: 3 # No.3 source domain index
    target: 0 # target domain index
    n_share1: 4 # number of the common classes between the No.1 source domain and the target domain
    n_source_private1: 2 # number of classes private to the No.1 source domain
    n_share2: 4 # number of the common classes between the No.2 source domain and the target domain
    n_source_private2: 2 # number of classes private to the No.2 source domain
    n_share3: 4 # number of the common classes between the No.3 source domain and the target domain
    n_source_private3: 2 # number of classes private to the No.3 source domain
    n_private_common: 1 # number of common classes between the private label sets of No.1 source domain and the No.2 source domain
    n_share_common: 10 # number of common classes between the whole source domain and the target domain
    n_total: 65 # number of classes in total

  dataloader:
    class_balance: true # if class_balance when loading datasets
    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 # backbone feature extractor
  pretrained_model: Pre-trained/resnet50-19c8e357.pth # /path/to/pretrained/model

train:
  min_step: 40000 # 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
  weight_decay: 0.0005 # weight_decay for SGD optimizer
  momentum: 0.9  # momentum for SGD optimizer
  continue_training: False  # continue to train on resume files: True / False
  continue_step: 0  # the step continue to train
  cut: .0  # cut threshold for normalizing weights

test:
  test_interval: 200 # interval of two continuous test phase
  test_only: True # test a given model and exit
  resume_file: log/ClPrRw-Ar/best.pkl # model to test
  w_0: 0.5 # the threshold for separating unknown-class samples from known-class samples

misc:
  gpus: 1  # how many GPUs to be used, 0 indicates CPU only, needed GPU memory < 6 GB
  gpu_id: "0"  # which GPU you want to use
  gpu_id_list: [0] # [0,...,gpus-1]

log:
  root_dir: log # the log directory (log directory will be {root_dir}/{method}/time/)
  log_interval: 10 # steps to log scalars