DCH.yaml

# global configs
Global:
  checkpoints: null
  pretrained_model: null
  output_dir: ./output
  device: gpu
  save_interval: 15
  eval_during_train: True
  eval_interval: 15
  epochs: 150
  print_batch_step: 10
  use_visualdl: False
  # used for static mode and model export
  image_shape: [3, 224, 224]
  save_inference_dir: ./inference
  eval_mode: retrieval
  use_dali: False
  to_static: False

  #feature postprocess
  feature_normalize: False
  feature_binarize: "sign"

# model architecture
Arch:
  name: RecModel
  infer_output_key: features
  infer_add_softmax: False

  Backbone:
    name: AlexNet
    pretrained: True
    class_num: 48

# loss function config for train/eval process
Loss:
  Train:
    - DCHLoss:
        weight: 1.0
        gamma:  20.0
        _lambda: 0.1
        n_class: 10
  Eval:
    - DCHLoss:
        weight: 1.0
        gamma:  20.0
        _lambda: 0.1
        n_class: 10

Optimizer:
  name: SGD
  lr:
    name: Piecewise
    learning_rate: 0.005
    decay_epochs: [200]
    values: [0.005, 0.0005]
  regularizer:
    name: 'L2'
    coeff: 0.00001

# data loader for train and eval
DataLoader:
  Train:
    dataset:
      name: ImageNetDataset
      image_root: ./dataset/
      cls_label_path: ./dataset/train.txt
      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - ResizeImage:
            size: 224
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''
    sampler:
      batch_size: 128
      drop_last: False
      shuffle: True
    loader:
      num_workers: 4
      use_shared_memory: True

  Eval:
    Query:
      dataset:
        name: ImageNetDataset
        image_root: ./dataset/
        cls_label_path: ./dataset/test.txt
        transform_ops:
          - DecodeImage:
              to_rgb: True
              channel_first: False
          - ResizeImage:
              size: 224
          - NormalizeImage:
              scale: 1.0/255.0
              mean: [0.485, 0.456, 0.406]
              std: [0.229, 0.224, 0.225]
              order: ''
      sampler:
        batch_size: 128
        drop_last: False
        shuffle: False
      loader:
        num_workers: 4
        use_shared_memory: True

    Gallery:
      dataset:
        name: ImageNetDataset
        image_root: ./dataset/
        cls_label_path: ./dataset/database.txt
        transform_ops:
          - DecodeImage:
              to_rgb: True
              channel_first: False
          - ResizeImage:
              size: 224
          - NormalizeImage:
              scale: 1.0/255.0
              mean: [0.485, 0.456, 0.406]
              std: [0.229, 0.224, 0.225]
              order: ''
      sampler:
        batch_size: 128
        drop_last: False
        shuffle: False
      loader:
        num_workers: 4
        use_shared_memory: True

Metric:
  Eval:
    - mAP: {}
    - Recallk:
        topk: [1, 5]