PP-HGNetV2-B6_ml_decoder_448.yaml

# global configs
Global:
  checkpoints: null
  pretrained_model: null
  output_dir: ./output/
  device: gpu
  save_interval: 10
  eval_during_train: True
  eval_interval: 1
  epochs: 40
  print_batch_step: 10
  use_visualdl: False
  # used for static mode and model export
  image_shape: [3, 448, 448]
  save_inference_dir: ./inference
  # training model under @to_static
  to_static: False
  use_multilabel: True

# model ema
EMA:
  decay: 0.9997

# mixed precision
AMP:
  use_amp: True
  use_fp16_test: False
  scale_loss: 128.0
  use_dynamic_loss_scaling: True
  use_promote: False
  # O1: mixed fp16, O2: pure fp16
  level: O1

# model architecture
Arch:
  name: PPHGNetV2_B6
  class_num: 80
  pretrained: True # ssld pretrained
  use_ml_decoder: True

# ml-decoder head
MLDecoder:
  query_num: 80 # default: 80, query_num <= class_num
  in_channels: 2048

# loss function config for training/eval process
Loss:
  Train:
    - MultiLabelAsymmetricLoss:
        weight: 1.0
        gamma_pos: 0
        gamma_neg: 4
        clip: 0.05
        disable_focal_loss_grad: True

  Eval:
    - MultiLabelAsymmetricLoss:
        weight: 1.0
        gamma_pos: 0
        gamma_neg: 4
        clip: 0.05
        disable_focal_loss_grad: True

Optimizer:
  name: AdamW
  beta1: 0.9
  beta2: 0.999
  epsilon: 1e-8
  weight_decay: 1e-4 
  one_dim_param_no_weight_decay: True
  lr:
    name: Cosine
    learning_rate: 1e-4
    eta_min: 1e-10
    warmup_epoch: 5
    warmup_start_lr: 1e-6


# data loader for train and eval
DataLoader:
  Train:
    dataset:
      name: MultiLabelDataset
      image_root: dataset/coco_ml/images
      cls_label_path: dataset/coco_ml/train.txt
      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - ResizeImage:
            size: 448
            interpolation: bilinear
            backend: pil
        - Cutout:
            length: 224
            fill_value: none
        - RandAugmentV4:
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''

    sampler:
      name: DistributedBatchSampler
      batch_size: 32
      drop_last: False
      shuffle: True
    loader:
      num_workers: 8
      use_shared_memory: True

  Eval:
    dataset:
      name: MultiLabelDataset
      image_root: dataset/coco_ml/images
      cls_label_path: dataset/coco_ml/val.txt
      transform_ops:
        - DecodeImage:
            to_rgb: True
            channel_first: False
        - ResizeImage:
            size: 448
            interpolation: bilinear
            backend: pil
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''
    sampler:
      name: DistributedBatchSampler
      batch_size: 8
      drop_last: False
      shuffle: False
    loader:
      num_workers: 8
      use_shared_memory: True

Infer:
  infer_imgs: deploy/images/coco_000000570688.jpg
  batch_size: 10
  transforms:
    - DecodeImage:
        to_rgb: True
        channel_first: False
    - ResizeImage:
        size: 448
        interpolation: bilinear
        backend: pil
    - NormalizeImage:
        scale: 1.0/255.0
        mean: [0.485, 0.456, 0.406]
        std: [0.229, 0.224, 0.225]
        order: ''
    - ToCHWImage:
  PostProcess:
    name: MultiLabelThreshOutput
    threshold: 0.5
    class_id_map_file: ppcls/utils/COCO2017_label_list.txt

Metric:
  Train:
  Eval:
    - MultiLabelMAP:
        # support list: integral, 11point
        # default: integral
        map_type: integral