res2net200_vd_distill_pphgnet_base.yaml

# global configs
Global:
  checkpoints: null
  pretrained_model: null
  output_dir: "./output/"
  device: "gpu"
  save_interval: 1
  eval_during_train: True
  eval_interval: 1
  epochs: 360
  print_batch_step: 10
  use_visualdl: False
  # used for static mode and model export
  image_shape: [3, 224, 224]
  save_inference_dir: "./inference"
  use_dali: false

# mixed precision training
AMP:
  scale_loss: 128.0
  use_dynamic_loss_scaling: True
  # O1: mixed fp16
  level: O1

# model architecture
Arch:
  name: "DistillationModel"
  class_num: &class_num 1000
  # if not null, its lengths should be same as models
  pretrained_list:
  # if not null, its lengths should be same as models
  freeze_params_list:
  - True
  - False
  models:
    - Teacher:
        name: Res2Net200_vd_26w_4s
        class_num: *class_num
        pretrained: True
        use_ssld: True
    - Student:
        name: PPHGNet_base
        class_num: *class_num
        pretrained: False

  infer_model_name: "Student"


# loss function config for traing/eval process
Loss:
  Train:
    - DistillationCELoss:
        weight: 1.0
        model_name_pairs:
        - ["Student", "Teacher"]
  Eval:
    - CELoss:
        weight: 1.0
        

Optimizer:
  name: Momentum
  momentum: 0.9
  lr:
    name: Cosine
    learning_rate: 0.5
    warmup_epoch: 5
  regularizer:
    name: 'L2'
    coeff: 0.00004


# data loader for train and eval
DataLoader:
  Train:
    dataset:
        name: ImageNetDataset
        image_root: "./dataset/ILSVRC2012/"
        cls_label_path: "./dataset/ILSVRC2012/train_list.txt"
        transform_ops:
          - DecodeImage:
              to_rgb: True
              channel_first: False
          - RandCropImage:
              size: 224
              interpolation: bicubic
              backend: pil
          - RandFlipImage:
              flip_code: 1
          - TimmAutoAugment:
              config_str: rand-m7-mstd0.5-inc1
              interpolation: bicubic
              img_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: 128
        drop_last: False
        shuffle: True
    loader:
        num_workers: 8
        use_shared_memory: True

  Eval:
    dataset: 
        name: ImageNetDataset
        image_root: "./dataset/ILSVRC2012/"
        cls_label_path: "./dataset/ILSVRC2012/val_list.txt"
        transform_ops:
          - DecodeImage:
              to_rgb: True
              channel_first: False
          - ResizeImage:
              resize_short: 236
              interpolation: bicubic
              backend: pil
          - 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: 128
        drop_last: False
        shuffle: False
    loader:
        num_workers: 8
        use_shared_memory: True

Infer:
  infer_imgs: "docs/images/inference_deployment/whl_demo.jpg"
  batch_size: 10
  transforms:
      - DecodeImage:
          to_rgb: True
          channel_first: False
      - ResizeImage:
          resize_short: 236
      - CropImage:
          size: 224
      - NormalizeImage:
          scale: 1.0/255.0
          mean: [0.485, 0.456, 0.406]
          std: [0.229, 0.224, 0.225]
          order: ''
      - ToCHWImage:
  PostProcess:
    name: DistillationPostProcess
    func: Topk
    topk: 5
    class_id_map_file: "ppcls/utils/imagenet1k_label_list.txt"

Metric:
    Train:
    - DistillationTopkAcc:
        model_key: "Student"
        topk: [1, 5]
    Eval:
    - DistillationTopkAcc:
        model_key: "Student"
        topk: [1, 5]