ENV:
  USE_PL: False
# SET GLOBAL SYSTEM
SOLVER:
  # NAME DESCRIPTION:  TYPE:  default: 'TrainValSolver'
  NAME: TrainValSolver
  # RESUME_FROM DESCRIPTION: Resume from some state of training! TYPE: str default: ''
  RESUME_FROM:
  # MAX_EPOCHS DESCRIPTION: Max epochs for training. TYPE: int default: 10
  MAX_EPOCHS: 200
  # NUM_FOLDS DESCRIPTION: Num folds for training. TYPE: int default: 0
  NUM_FOLDS: 1
  # WORK_DIR DESCRIPTION: Save dir of the training log or model. TYPE: str default: ''
  WORK_DIR: ./exp12/
  LOG_FILE: std_log.txt
  # EVAL_INTERVAL DESCRIPTION: Eval the model interval. TYPE: int default: 1
  EVAL_INTERVAL: 1
  ACCU_STEP: 1
  # DO_FINAL_EVAL DESCRIPTION: If do final evaluation or not. TYPE: bool default: False
  DO_FINAL_EVAL: True
  # SAVE_EVAL_DATA DESCRIPTION: If save the evaluation data or not. TYPE: bool default: False
  SAVE_EVAL_DATA: True
  # EXTRA_KEYS DESCRIPTION: The extra keys for metric. TYPE: list default: []
  EXTRA_KEYS: []
  # TRAIN_DATA DESCRIPTION: Train data config. TYPE:  default: ''
  TRAIN_DATA:
    # NAME DESCRIPTION:  TYPE:  default: 'ImageClassifyPublicDataset'
    NAME: ImageClassifyExampleDataset
    # DATASET DESCRIPTION: the public dataset name TYPE: str default: 'cifar10'
    DATASET: cifar10
    # DATA_ROOT DESCRIPTION: the download data save path TYPE: str default: ''
    DATA_ROOT: cifar10
    # MODE DESCRIPTION: test TYPE: str default: test
    MODE: train
    # PIN_MEMORY DESCRIPTION: pin_memory for data loader TYPE: bool default: False
    PIN_MEMORY: True
    # BATCH_SIZE DESCRIPTION: batch size for data TYPE: int default: 4
    BATCH_SIZE: 96
    # NUM_WORKERS DESCRIPTION: num workers for fetching data! TYPE: int default: 1
    NUM_WORKERS: 4
    # TRANSFORMS DESCRIPTION:  TYPE:  default:
    TRANSFORMS:
      # - DESCRIPTION:  TYPE:  default:
      - # NAME DESCRIPTION:  TYPE:  default: 'RandomResizedCrop'
        NAME: RandomResizedCrop
        SIZE: 32
        # RATIO DESCRIPTION: ratio TYPE: list default: [0.75, 1.3333333333333333]
        RATIO: [0.75, 1.33]
        # SCALE DESCRIPTION: scale TYPE: list default: [0.08, 1.0]
        SCALE: [0.8, 1.0]
        # INTERPOLATION DESCRIPTION: interpolation TYPE: str default: 'blilinear'
        INTERPOLATION: bilinear
        # INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
        INPUT_KEY: img
        # OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
        OUTPUT_KEY: img
        # BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
        BACKEND: pillow
      - # NAME DESCRIPTION:  TYPE:  default: 'RandomHorizontalFlip'
        NAME: RandomHorizontalFlip
        # P DESCRIPTION: P TYPE: float default: 0.5
        P: 0.5
        # INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
        INPUT_KEY: img
        # OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
        OUTPUT_KEY: img
        # BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
        BACKEND: pillow
      - # NAME DESCRIPTION:  TYPE:  default: 'ImageToTensor'
        NAME: ImageToTensor
        # INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
        INPUT_KEY: img
        # OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
        OUTPUT_KEY: img
        # BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
        BACKEND: pillow
      - # NAME DESCRIPTION:  TYPE:  default: 'Normalize'
        NAME: Normalize
        # MEAN DESCRIPTION: mean TYPE: list default: []
        MEAN: [0.4914, 0.4822, 0.4465]
        # STD DESCRIPTION: std TYPE: list default: []
        STD: [0.2023, 0.1994, 0.2010]
        # INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
        INPUT_KEY: img
        # OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
        OUTPUT_KEY: img
        # BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
        BACKEND: pillow
      - NAME: ToTensor
        # KEYS DESCRIPTION: keys TYPE: list default: []
        KEYS: ["img", "label"]
      - # NAME DESCRIPTION:  TYPE:  default: 'Select'
        NAME: Select
        # KEYS DESCRIPTION: keys TYPE: list default: []
        KEYS: ["img", "label"]
        # META_KEYS DESCRIPTION: meta keys TYPE: list default: []
        META_KEYS: []
  # EVAL_DATA DESCRIPTION: Eval data config. TYPE:  default: ''
  EVAL_DATA:
    # NAME DESCRIPTION:  TYPE:  default: 'ImageClassifyPublicDataset'
    NAME: ImageClassifyPublicDataset
    # DATASET DESCRIPTION: the public dataset name TYPE: str default: 'cifar10'
    DATASET: cifar10
    # DATA_ROOT DESCRIPTION: the download data save path TYPE: str default: ''
    DATA_ROOT: ./local_data/cifar10
    # MODE DESCRIPTION: test TYPE: str default: test
    MODE: test
    # PIN_MEMORY DESCRIPTION: pin_memory for data loader TYPE: bool default: False
    PIN_MEMORY: True
    # BATCH_SIZE DESCRIPTION: batch size for data TYPE: int default: 4
    BATCH_SIZE: 96
    # NUM_WORKERS DESCRIPTION: num workers for fetching data! TYPE: int default: 1
    NUM_WORKERS: 4
    # TRANSFORMS DESCRIPTION:  TYPE:  default:
    TRANSFORMS:
      # - DESCRIPTION:  TYPE:  default:
      - # NAME DESCRIPTION:  TYPE:  default: 'Resize'
        NAME: Resize
        SIZE: 32
        # INTERPOLATION DESCRIPTION: interpolation TYPE: str default: 'blilinear'
        INTERPOLATION: bilinear
        # INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
        INPUT_KEY: img
        # OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
        OUTPUT_KEY: img
        # BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
        BACKEND: pillow
      - # NAME DESCRIPTION:  TYPE:  default: 'ImageToTensor'
        NAME: ImageToTensor
        # INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
        INPUT_KEY: img
        # OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
        OUTPUT_KEY: img
        # BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
        BACKEND: pillow
      - # NAME DESCRIPTION:  TYPE:  default: 'Normalize'
        NAME: Normalize
        # MEAN DESCRIPTION: mean TYPE: list default: []
        MEAN: [0.4914, 0.4822, 0.4465]
        # STD DESCRIPTION: std TYPE: list default: []
        STD: [0.2023, 0.1994, 0.2010]
        # INPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
        INPUT_KEY: img
        # OUTPUT_KEY DESCRIPTION: input key TYPE: str default: 'img'
        OUTPUT_KEY: img
        # BACKEND DESCRIPTION: backend, choose from pillow, cv2, torchvision TYPE: str default: 'pillow'
        BACKEND: pillow
      - NAME: ToTensor
        # KEYS DESCRIPTION: keys TYPE: list default: []
        KEYS: ["img", "label"]
      - # NAME DESCRIPTION:  TYPE:  default: 'Select'
        NAME: Select
        # KEYS DESCRIPTION: keys TYPE: list default: []
        KEYS: ["img", "label"]
        # META_KEYS DESCRIPTION: meta keys TYPE: list default: []
        META_KEYS: []
  # TRAIN_HOOKS DESCRIPTION:  TYPE:  default: ''
  TRAIN_HOOKS:
    - # NAME DESCRIPTION:  TYPE:  default: 'LogHook'
      NAME: LogHook
      # LOG_INTERVAL DESCRIPTION: the interval for log print! TYPE: int default: 10
      LOG_INTERVAL: 10
  # EVAL_HOOKS DESCRIPTION:  TYPE:  default: ''
  EVAL_HOOKS:
    - # NAME DESCRIPTION:  TYPE:  default: 'LogHook'
      NAME: LogHook
      # LOG_INTERVAL DESCRIPTION: the interval for log print! TYPE: int default: 10
      LOG_INTERVAL: 10
  # TEST_HOOKS DESCRIPTION:  TYPE:  default: ''
  MODEL:
    # NAME DESCRIPTION:  TYPE:  default: 'Classifier'
    NAME: Classifier
    # ACT_NAME DESCRIPTION: the activation function for logits, select from [softmax, sigmoid]! TYPE: str default: 'softmax'
    ACT_NAME: softmax
    # FREEZE_BN DESCRIPTION: if freeze bn of not TYPE: bool default: False
    FREEZE_BN: False
    # BACKBONE DESCRIPTION:  TYPE:  default: ''
    BACKBONE:
      # NAME DESCRIPTION:  TYPE:  default: 'ResNet'
      NAME: ResNet
      # DEPTH DESCRIPTION: the depth of network for resnet! TYPE: int default: 18
      DEPTH: 18
      # PRETRAINED DESCRIPTION: if load the official pretrained model or not. TYPE: bool default: False
      PRETRAINED: false
      #
      KERNEL_SIZE: 3
      # USE_RELU DESCRIPTION: use relu or not! TYPE: bool default: True
      USE_RELU: True
      # USE_MAXPOOL DESCRIPTION: use maxpool or not! TYPE: bool default: True
      USE_MAXPOOL: false
      # FIRST_CONV_STRIDE DESCRIPTION: first conv stride 1 or 2! TYPE: int default: 1
      FIRST_CONV_STRIDE: 1
      # FIRST_MAX_POOL_STRIDE DESCRIPTION: first max pool stride 1 or 2! TYPE: int default: 1
      FIRST_MAX_POOL_STRIDE: 1
    # NECK DESCRIPTION:  TYPE:  default: ''
    NECK:
      # NAME DESCRIPTION:  TYPE:  default: 'GlobalAveragePooling'
      NAME: GlobalAveragePooling
      # DIM DESCRIPTION: GlobalAveragePooling dim! TYPE: int default: 2
      DIM: 2
    # HEAD DESCRIPTION:  TYPE:  default: ''
    HEAD:
      # NAME DESCRIPTION:  TYPE:  default: 'ClassifierHead'
      NAME: ClassifierHead
      # DIM DESCRIPTION: representation dim! TYPE: int default: 512
      DIM: 512
      # NUM_CLASSES DESCRIPTION: number of classes. TYPE: int default: 10
      NUM_CLASSES: 10
      # DROPOUT_RATE DESCRIPTION: dropout rate, default 0. TYPE: float default: 0.0
      DROPOUT_RATE: 0.0
    METRIC:
      # NAME DESCRIPTION:  TYPE:  default: 'AccuracyMetric'
      NAME: AccuracyMetric
      # TOPK DESCRIPTION: topk accuracy! TYPE: int default: 1
      TOPK: 1
    # LOSS DESCRIPTION:  TYPE:  default: ''
    LOSS:
      # NAME DESCRIPTION:  TYPE:  default: 'CrossEntropy'
      NAME: CrossEntropy
      # REDUCE DESCRIPTION: reduce is False, returns a loss per batch element instead and ignores :attr: size_average. Default: True TYPE: NoneType default: None
      # REDUCE: None
      # SIZE_AVERAGE DESCRIPTION: Deprecated (see :attr: reduction). By default,the losses are averaged over each loss element in the batch. Note that forsome losses, there are multiple elements per sample. If the field :attr: size_averageis set to False, the losses are instead summed for each minibatch. Ignoredwhen :attr: reduce is False. Default: True TYPE: NoneType default: None
      # SIZE_AVERAGE: None
      # IGNORE_INDEX DESCRIPTION: Specifies a target value that is ignoredand does not contribute to the input gradient. When :attr: size_average isTrue, the loss is averaged over non-ignored targets. Note that:attr: ignore_index is only applicable when the target contains class indices. TYPE: int default: -100
      # IGNORE_INDEX: -100
      # REDUCTION DESCRIPTION: Specifies the reduction to apply to the output:'none' | 'mean' | 'sum'. 'none': no reduction willbe applied, 'mean': the weighted mean of the output is taken,'sum': the output will be summed. Note: :attr: size_averageand :attr:`reduce` are in the process of being deprecated, and inthe meantime, specifying either of those two args will override:attr:`reduction`. Default: 'mean' TYPE: str default: 'mean'
      # REDUCTION: mean
      # LABEL_SMOOTHING DESCRIPTION: A float in [0.0, 1.0]. Specifies the amountof smoothing when computing the loss, where 0.0 means no smoothing.  TYPE: float default: 0.0
      # LABEL_SMOOTHING: 0.0
  # OPTIMIZER DESCRIPTION:  TYPE:  default: ''
  OPTIMIZER:
    # NAME DESCRIPTION:  TYPE:  default: 'SGD'
    NAME: SGD
    # LEARNING_RATE DESCRIPTION: the initial learning rate! TYPE: float default: 0.1
    LEARNING_RATE: 0.01
    # MOMENTUM DESCRIPTION: the momentum! TYPE: int default: 0
    MOMENTUM: 0.9
    # DAMPENING DESCRIPTION: the dampening! TYPE: int default: 0
    DAMPENING: 0
    # WEIGHT_DECAY DESCRIPTION: the weight decay! TYPE: int default: 0
    WEIGHT_DECAY: 5e-4
    # NESTEROV DESCRIPTION: the nesterov! TYPE: bool default: False
    NESTEROV: False
  # LR_SCHEDULER DESCRIPTION:  TYPE:  default: ''
  LR_SCHEDULER:
    # NAME DESCRIPTION:  TYPE:  default: 'CosineAnnealingLR'
    NAME: CosineAnnealingLR
    # T_MAX DESCRIPTION: the T max! TYPE: float default: 1.0
    T_MAX: 200.0
    # ETA_MIN DESCRIPTION: the eta min! TYPE: int default: 0
    ETA_MIN: 0
    # LAST_EPOCH DESCRIPTION: the last epoch! TYPE: int default: -1
    LAST_EPOCH: -1
  # METRICS DESCRIPTION:  TYPE:  default: ''
  METRICS:
    - # NAME DESCRIPTION:  TYPE:  default: 'AccuracyMetric'
       NAME: AccuracyMetric
       # TOPK DESCRIPTION: topk accuracy! TYPE: int default: 1
       TOPK: 1
       KEYS: ["logits", "label"]