# global configs
Global:
  checkpoints: null
  pretrained_model: null
  output_dir: ./output/
  device: gpu
  class_num: 50030
  save_interval: 1
  eval_during_train: True
  eval_interval: 10
  epochs: 120
  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: classification

# model architecture
Arch:
  name: RecModel
  Backbone: 
    name: ResNet50_vd
    pretrained: True
  BackboneStopLayer:
    name: flatten_0
  Neck:
    name: FC
    embedding_size: 2048
    class_num: 512
  Head:
    name: FC  
    embedding_size: 512
    class_num: 50030
 
# loss function config for traing/eval process
Loss:
  Train:
    - CELoss:
        weight: 1.0
  Eval:
    - CELoss:
        weight: 1.0

Optimizer:
  name: Momentum
  momentum: 0.9
  lr:
    name: Cosine
    learning_rate: 0.05
  regularizer:
    name: 'L2'
    coeff: 0.00007

# data loader for train and eval
DataLoader:
  Train:
    dataset:
      name: ImageNetDataset
      image_root: ./dataset/Aliproduct/
      cls_label_path: ./dataset/Aliproduct/train_list.txt
      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - ResizeImage:
            size: 224
        - RandFlipImage:
            flip_code: 1
        - NormalizeImage:
            scale: 0.00392157
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''

    sampler:
      name: DistributedBatchSampler
      batch_size: 64
      drop_last: False
      shuffle: True
    loader:
      num_workers: 4
      use_shared_memory: True
  Eval:
    # TOTO: modify to the latest trainer
    dataset: 
      name: ImageNetDataset
      image_root: ./dataset/Aliproduct/
      cls_label_path: ./dataset/Aliproduct/val_list.txt
      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - ResizeImage:
            resize_short: 256
        - CropImage:
            size: 224
        - NormalizeImage:
            scale: 0.00392157
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''
    sampler:
      name: DistributedBatchSampler
      batch_size: 64
      drop_last: False
      shuffle: False
    loader:
      num_workers: 4
      use_shared_memory: True
Metric:
  Train:
    - TopkAcc:
        topk: [1, 5]
  Eval:
    - TopkAcc:
        topk: [1, 5]