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paddleseg.core

The interface for training, evaluation and prediction.

Training

paddleseg.core.train(model, train_dataset, val_dataset=None, optimizer=None, save_dir='output', iters=10000, batch_size=2, resume_model=None, save_interval=1000, log_iters=10, num_workers=0, use_vdl=False, losses=None)

Launch training.

Args

  • model（nn.Layer): A sementic segmentation model.
  • train_dataset (paddle.io.Dataset): Used to read and process training datasets.
  • val_dataset (paddle.io.Dataset, optional): Used to read and process validation datasets.
  • optimizer (paddle.optimizer.Optimizer): The optimizer.
  • save_dir (str, optional): The directory for saving the model snapshot. Default: 'output'.
  • iters (int, optional): How may iters to train the model. Defualt: 10000.
  • batch_size (int, optional): Mini batch size of one gpu or cpu. Default: 2.
  • resume_model (str, optional): The path of resume model.
  • save_interval (int, optional): How many iters to save a model snapshot once during training. Default: 1000.
  • log_iters (int, optional): Display logging information at every log_iters. Default: 10.
  • num_workers (int, optional): Num workers for data loader. Default: 0.
  • use_vdl (bool, optional): Whether to record the data to VisualDL during training. Default: False.
  • losses (dict): A dict including 'types' and 'coef'. The length of coef should equal to 1 or len(losses['types']). The 'types' item is a list of object of paddleseg.models.losses while the 'coef' item is a list of the relevant coefficient.

Evaluation

paddleseg.core.evaluate(model, eval_dataset, aug_eval=False, scales=1.0, flip_horizontal=True, flip_vertical=False, is_slide=False, stride=None, crop_size=None, num_workers=0)

Launch evaluation.

Args

  • model（nn.Layer): A sementic segmentation model.
  • eval_dataset (paddle.io.Dataset): Used to read and process validation datasets.
  • aug_eval (bool, optional): Whether to use mulit-scales and flip augment for evaluation. Default: False.
  • scales (list|float, optional): Scales for augment. It is valid when aug_eval is True. Default: 1.0.
  • flip_horizontal (bool, optional): Whether to use flip horizontally augment. It is valid when aug_eval is True. Default: True.
  • flip_vertical (bool, optional): Whether to use flip vertically augment. It is valid when aug_eval is True. Default: False.
  • is_slide (bool, optional): Whether to evaluate by sliding window. Default: False.
  • stride (tuple|list, optional): The stride of sliding window, the first is width and the second is height. It should be provided when is_slide is True.
  • crop_size (tuple|list, optional): The crop size of sliding window, the first is width and the second is height. It should be provided when is_slide is True.
  • num_workers (int, optional): Num workers for data loader. Default: 0.

Returns

  • float: The mIoU of validation datasets.
  • float: The accuracy of validation datasets.

Prediction

paddleseg.core.predict(model, model_path, transforms, image_list, image_dir=None, save_dir='output', aug_pred=False, scales=1.0, flip_horizontal=True, flip_vertical=False, is_slide=False, stride=None, crop_size=None)

Launch predict and visualize.

Args

  • model (nn.Layer): Used to predict for input image.
  • model_path (str): The path of pretrained model.
  • transforms (transform.Compose): Preprocess for input image.
  • image_list (list): A list of image path to be predicted.
  • image_dir (str, optional): The root directory of the images predicted. Default: None.
  • save_dir** (bool, optional): Whether to use mulit-scales and flip augment for predition. Default: False.
  • scales (list|float, optional): Scales for augment. It is valid when aug_pred is True. Default: 1.0.
  • flip_horizontal (bool, optional): Whether to use flip horizontally augment. It is valid when aug_pred is True. Default: True.
  • flip_vertical (bool, optional): Whether to use flip vertically augment. It is valid when aug_pred is True. Default: False.
  • is_slide (bool, optional): Whether to predict by sliding window. Default: False.
  • stride (tuple|list, optional): The stride of sliding window, the first is width and the second is height. It should be provided when is_slide is True.
  • crop_size (tuple|list, optional): The crop size of sliding window, the first is width and the second is height. It should be provided when is_slide is True.