# retrieval settings

datasets:
  # number of images in a batch.
  batch_size: 16

  # function for stacking images in a batch.
  collate_fn:
    name: "CollateFn"  # name of the collate_fn.

  # function for loading images.
  folder:
    name: "Folder"  # name of the folder.

  # a list of data augmentation functions.
  transformers:
    names: ["ShorterResize", "CenterCrop", "ToCaffeTensor", "Normalize"]  # names of transformers.
    ShorterResize:
      size: 256  # target size of the shorter edge.
    CenterCrop:
      size: 224  # target size of the crop img.
    Normalize:
      mean: [104, 116, 124]
      std: [1.0, 1.0, 1.0]

model:
  name: "vgg16"  # name of the model.
  vgg16:
    load_checkpoint: "/data/places365_model/vgg16_hybrid1365.pt"  # path of the model checkpoint, If it is started with "torchvision://", the model will be loaded from torchvision.

extract:
  # way to assemble features if transformers produce multiple images (e.g. TwoFlip, TenCrop). 0 means concat these features and 1 means sum these features.
  assemble: 0

  # function for assigning output features.
  extractor:
    name: "VggSeries"  # name of the extractor.
    VggSeries:
      extract_features: ["pool5"]  # name of the output feature map. If it is ["all"], then all available features will be output.

  # function for splitting the output features (e.g. PCB).
  splitter:
    name: "Identity"  # name of the function for splitting features.

  # a list of pooling functions.
  aggregators:
    names: ["GAP"]  # names of aggregators.

index:
  # path of the query set features and gallery set features.
  query_fea_dir: "/data/features/best_features/oxford/query"
  gallery_fea_dir: "/data/features/best_features/oxford/gallery"

  # name of the features to be loaded. It should be "output feature map" + "_" + "aggregation".
  # If there are multiple elements in the list, they will be concatenated on the channel-wise.
  feature_names: ["pool5_GAP"]

  # a list of dimension process functions.
  dim_processors:
    names: ["L2Normalize", "SVD", "L2Normalize"]
    SVD:
      proj_dim: 511  # the dimension after reduction. If it is 0, then no reduction will be done.
      whiten: True  # whether do whiten when using SVD.
      train_fea_dir: "/data/features/best_features/paris"  # path of the features for training SVD.
      l2: True  # whether do l2-normalization on the training features.

  # function for enhancing the quality of features.
  feature_enhancer:
    name: "Identity"  # name of the feature enhancer.

  # function for calculating the distance between query features and gallery features.
  metric:
    name: "KNN"  # name of the metric.

  # function for re-ranking the results.
  re_ranker:
    name: "Identity"  # name of the re-ranker.

evaluate:
  # function for evaluating results.
  evaluator:
    name: "OxfordOverAll"  # name of the evaluator.