Skip to content

Latest commit

 

History

History
115 lines (107 loc) · 5.09 KB

core.md

File metadata and controls

115 lines (107 loc) · 5.09 KB
Raw

English | 简体中文

paddleseg.core

The interface for training, evaluation and prediction.

train

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.

evaluate

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.
  • float: The kappa of validation datasets.

predict

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.