MobileViTv3_S.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: 300
  print_batch_step: 10
  use_visualdl: False
  # used for static mode and model export
  image_shape: [3, 256, 256]
  save_inference_dir: ./inference
  use_dali: False
  update_freq: 3  # for 4 gpus

# mixed precision training
AMP:
  scale_loss: 65536
  use_dynamic_loss_scaling: True
  # O1: mixed fp16
  level: O1

# model ema
EMA:
  decay: 0.9995

# model architecture
Arch:
  name: MobileViTv3_S
  class_num: 1000
  dropout: 0.1

# loss function config for traing/eval process
Loss:
  Train:
    - CELoss:
        weight: 1.0
        epsilon: 0.1
  Eval:
    - CELoss:
        weight: 1.0

Optimizer:
  name: AdamW
  beta1: 0.9
  beta2: 0.999
  epsilon: 1e-8
  weight_decay: 0.01
  lr:
    name: Cosine
    learning_rate: 0.002  # for total batch size 384
    eta_min: 0.0002
    warmup_epoch: 1  # 3000 iterations
    warmup_start_lr: 0.0002

# data loader for train and eval
DataLoader:
  Train:
    dataset:
      name: MultiScaleDataset
      image_root: ./dataset/ILSVRC2012/
      cls_label_path: ./dataset/ILSVRC2012/train_list.txt
      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - RandCropImage:
            size: 256
            interpolation: bilinear
            use_log_aspect: True
        - RandFlipImage:
            flip_code: 1
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.0, 0.0, 0.0]
            std: [1.0, 1.0, 1.0]
            order: ''
    # support to specify width and height respectively:
    # scales: [(256,256) (160,160), (192,192), (224,224) (288,288) (320,320)]
    sampler:
      name: MultiScaleSampler
      scales: [256, 160, 192, 224, 288, 320]
      # first_bs: batch size for the first image resolution in the scales list
      # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
      first_bs: 32
      divided_factor: 32
      is_training: True
    loader:
      num_workers: 4
      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: 288
            interpolation: bilinear
        - CropImage:
            size: 256
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.0, 0.0, 0.0]
            std: [1.0, 1.0, 1.0]
            order: ''
    sampler:
      name: DistributedBatchSampler
      batch_size: 48
      drop_last: False
      shuffle: False
    loader:
      num_workers: 4
      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: 288
        interpolation: bilinear
    - CropImage:
        size: 256
    - NormalizeImage:
        scale: 1.0/255.0
        mean: [0.0, 0.0, 0.0]
        std: [1.0, 1.0, 1.0]
        order: ''
    - ToCHWImage:
  PostProcess:
    name: Topk
    topk: 5
    class_id_map_file: ppcls/utils/imagenet1k_label_list.txt

Metric:
  Train:
    - TopkAcc:
        topk: [1, 5]
  Eval:
    - TopkAcc:
        topk: [1, 5]