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Configuration item

train_dataset

  • Training dataset

val_dataset

  • Validation dataset

batch_size

  • On a single card, the amount of data during each iteration of training. Generally speaking, the larger the video memory of the machine you are using, the larger the batch_size value.

iters

  • The process of using a batch of data to update the parameters of the semantic segmentation model is called one training, that is, one iteration. Iters is the number of iterations in the training process.

optimizer

  • Optimizer in training
  • Args
    • type : Optimizer type, currently only supports'sgd' and'adam'
    • momentum : Momentum optimization.
    • weight_decay : L2 regularized value.

lr_scheduler

  • Learning rate
  • Args
    • type : Learning rate type, supports 12 strategies: 'PolynomialDecay', 'PiecewiseDecay', 'StepDecay', 'CosineAnnealingDecay', 'ExponentialDecay', 'InverseTimeDecay', 'LinearWarmup', 'MultiStepDecay', 'NaturalExpDecay', 'NoamDecay', ReduceOnPlateau, LambdaDecay.
    • others : Please refer to Paddle official LRScheduler document

learning_rate（This configuration is not recommended and will be obsolete in the future. It is recommended to use lr_scheduler instead）

  • Learning rate
  • Args
    • value : Initial learning rate.
    • decay : Attenuation configuration.
      • type : Attenuation type, currently only supports poly.
      • power : Attenuation rate.
      • end_lr : Final learning rate.

loss

  • Loss function
  • Args
    • types : List of loss functions.
      • type : Loss function type, please refer to the loss function library for the supported values.
    • coef : List of coefficients corresponding to the loss function list.

model

  • Model to be trained

export

  • Model export configuration
  • Args
    • transforms : The preprocessing operation during prediction, the supported transforms are the same as train_dataset, val_dataset, etc. If you do not fill in this item, only the data will be normalized by default.

Example

batch_size: 4 # Set the number of pictures sent to the network at one iteration. Generally speaking, the larger the video memory of the machine you are using, the higher the batch_size value.
iters: 80000 # Number of iterations

train_dataset: # Training dataset
  type: Cityscapes # The name of the training dataset class
  dataset_root: data/cityscapes # The directory where the training dataset is stored
  transforms: # Data transformation and data augmentation
    - type: ResizeStepScaling # The image is scaled according to a certain ratio, and this ratio takes scale_step_size as the step size
      min_scale_factor: 0.5 # Parameters involved in the scaling process
      max_scale_factor: 2.0
      scale_step_size: 0.25
    - type: RandomPaddingCrop # Random cropping of images and annotations
      crop_size: [1024, 512]
    - type: RandomHorizontalFlip # Flip the image horizontally with a certain probability
    - type: Normalize # Normalize the image
  mode: train # Training mode

val_dataset: # Validation dataset
  type: Cityscapes # The name of the validating dataset class
  dataset_root: data/cityscapes # The directory where the validating dataset is stored
  transforms:
    - type: Normalize # Normalize the image
  mode: val # Validating mode

optimizer: # Which optimizer to use
  type: sgd # Stochastic gradient descent
  momentum: 0.9
  weight_decay: 4.0e-5

lr_scheduler: # Related settings for learning rate
  type: PolynomialDecay # A type of learning rate,a total of 12 strategies are supported
  learning_rate: 0.01
  power: 0.9
  end_lr: 0

loss: # What loss function to use
  types:
    - type: CrossEntropyLoss # Cross entropy loss function
  coef: [1] # When multiple loss functions are used, the ratio of each loss can be specified in coef

model: # Which semantic segmentation model to use
  type: FCN
  backbone: # What kind of backbone network to use
    type: HRNet_W18
    pretrained: pretrained_model/hrnet_w18_ssld # Specify the storage path of the pre-trained model
  num_classes: 19 # Number of pixel categories
  pretrained: Null
  backbone_indices: [-1]