ResNet50.yaml

# global configs
Global:
  checkpoints: null
  pretrained_model: null
  output_dir: "./output/"
  device: "gpu"
  class_num: 431
  save_interval: 1
  eval_during_train: True
  eval_interval: 1
  epochs: 160
  print_batch_step: 10
  use_visualdl: False
  # used for static mode and model export
  image_shape: [3, 224, 224]
  save_inference_dir: "./inference"

# model architecture
Arch:
  name: "RecModel"
  Backbone: 
    name: "ResNet50_last_stage_stride1"
    pretrained: True
  BackboneStopLayer:
    name: "adaptive_avg_pool2d_0"
  Neck:
    name: "VehicleNeck"
    in_channels: 2048
    out_channels: 512
  Head:
    name: "ArcMargin"  
    embedding_size: 512
    class_num: 431
    margin: 0.15
    scale: 32
 
# loss function config for traing/eval process
Loss:
  Train:
    - CELoss:
        weight: 1.0
    - TripletLossV2:
        weight: 1.0
        margin: 0.5
  Eval:
    - CELoss:
        weight: 1.0

Optimizer:
  name: Momentum
  momentum: 0.9
  lr:
    name: MultiStepDecay
    learning_rate: 0.01
    milestones: [30, 60, 70, 80, 90, 100, 120, 140]
    gamma: 0.5
    verbose: False
    last_epoch: -1
  regularizer:
    name: 'L2'
    coeff: 0.0005


# data loader for train and eval
DataLoader:
  Train:
    dataset:
        name: "CompCars"
        image_root: "/work/dataset/CompCars/image/"
        label_root: "/work/dataset/CompCars/label/"
        bbox_crop: True
        cls_label_path: "/work/dataset/CompCars/train_test_split/classification/train_label.txt"
        transform_ops:
          - ResizeImage:
              size: 224
          - RandFlipImage:
              flip_code: 1
          - AugMix:
              prob: 0.5
          - NormalizeImage:
              scale: 0.00392157
              mean: [0.485, 0.456, 0.406]
              std: [0.229, 0.224, 0.225]
              order: ''
          - RandomErasing:
              EPSILON: 0.5
              sl: 0.02
              sh: 0.4
              r1: 0.3
              mean: [0., 0., 0.]

    sampler:
        name: DistributedRandomIdentitySampler
        batch_size: 128
        num_instances: 2
        drop_last: False
        shuffle: True
    loader:
        num_workers: 6
        use_shared_memory: False

  Eval:
    # TOTO: modify to the latest trainer
    dataset: 
        name: "CompCars"
        image_root: "/work/dataset/CompCars/image/"
        label_root: "/work/dataset/CompCars/label/"
        cls_label_path: "/work/dataset/CompCars/train_test_split/classification/test_label.txt"
        bbox_crop: True
        transform_ops:
          - ResizeImage:
              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: 6
        use_shared_memory: False

Infer:
  infer_imgs: "docs/images/whl/demo.jpg"
  batch_size: 10
  transforms:
      - DecodeImage:
          to_rgb: True
          channel_first: False
      - ResizeImage:
          resize_short: 256
      - 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: Topk
    topk: 5
    class_id_map_file: "ppcls/utils/imagenet1k_label_list.txt"

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