ResNet152.yaml 2.7 KB
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# global configs
Global:
  checkpoints: null
  pretrained_model: null
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  output_dir: ./output/
  device: gpu
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  class_num: 1000
  save_interval: 1
  eval_during_train: True
  eval_interval: 1
  epochs: 120
  print_batch_step: 10
  use_visualdl: False
  # used for static mode and model export
  image_shape: [3, 224, 224]
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  save_inference_dir: ./inference
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# model architecture
Arch:
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  name: ResNet152
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# loss function config for traing/eval process
Loss:
  Train:
    - CELoss:
        weight: 1.0
  Eval:
    - CELoss:
        weight: 1.0


Optimizer:
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  name: Momentum
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  momentum: 0.9
  lr:
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    name: Piecewise
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    learning_rate: 0.1
    decay_epochs: [30, 60, 90]
    values: [0.1, 0.01, 0.001, 0.0001]
  regularizer:
    name: 'L2'
    coeff: 0.0001


# data loader for train and eval
DataLoader:
  Train:
    dataset:
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      name: ImageNetDataset
      image_root: ./dataset/ILSVRC2012/
      cls_label_path: ./dataset/ILSVRC2012/train_list.txt
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      transform_ops:
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        - DecodeImage:
            to_rgb: True
            channel_first: False
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        - RandCropImage:
            size: 224
        - RandFlipImage:
            flip_code: 1
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''
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    sampler:
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      name: DistributedBatchSampler
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      batch_size: 64
      drop_last: False
      shuffle: True
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    loader:
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      num_workers: 4
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      use_shared_memory: True
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  Eval:
    # TOTO: modify to the latest trainer
    dataset: 
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      name: ImageNetDataset
      image_root: ./dataset/ILSVRC2012/
      cls_label_path: ./dataset/ILSVRC2012/val_list.txt
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      transform_ops:
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        - DecodeImage:
            to_rgb: True
            channel_first: False
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        - 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: ''
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    sampler:
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      name: DistributedBatchSampler
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      batch_size: 64
      drop_last: False
      shuffle: False
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    loader:
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      num_workers: 4
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      use_shared_memory: True
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Infer:
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  infer_imgs: docs/images/whl/demo.jpg
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  batch_size: 10
  transforms:
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    - 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:
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  PostProcess:
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    name: Topk
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    topk: 5
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    class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
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Metric:
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  Train:
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    - TopkAcc:
        topk: [1, 5]
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  Eval:
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    - TopkAcc:
        topk: [1, 5]