OperatorBase¶
- class towhee.operator.Operator[source]¶
Bases:
ABC
Operator base class, implements __init__ and __call__,
Examples
- class AddOperator(Operator):
- def __init__(self, factor: int):
self._factor = factor
- def __call__(self, num) -> NamedTuple(“Outputs”, [(“sum”, int)]):
Outputs = NamedTuple(“Outputs”, [(“sum”, int)]) return Outputs(self._factor + num)
- abstract __init__()[source]¶
Init operator, before a graph starts, the framework will call Operator __init__ function.
Args:
- Raises:
An exception during __init__ can terminate the graph run. –
- abstract __call__()[source]¶
The framework calls __call__ function repeatedly for every input data.
Args:
Returns:
- Raises:
An exception during __init__ can terminate the graph run. –
- __weakref__¶
list of weak references to the object (if defined)
- class towhee.operator.NNOperator(framework: str = 'pytorch')[source]¶
Bases:
Operator
Neural Network related operators that involve machine learning frameworks.
- Parameters:
framework (str) – The framework to apply.
- __init__(framework: str = 'pytorch')[source]¶
Init operator, before a graph starts, the framework will call Operator __init__ function.
Args:
- Raises:
An exception during __init__ can terminate the graph run. –
- train(training_config=None, train_dataset=None, eval_dataset=None, resume_checkpoint_path=None, **kwargs)[source]¶
Start to train an operator.
- Parameters:
training_config (TrainingConfig) – The config of this trainer.
train_dataset (Union[Dataset, TowheeDataSet]) – Training dataset.
eval_dataset (Union[Dataset, TowheeDataSet]) – Evaluate dataset.
resume_checkpoint_path (str) – If resuming training, pass into the path.
**kwargs (Any) – Keyword Args.
- setup_trainer(training_config=None, train_dataset=None, eval_dataset=None, train_dataloader=None, eval_dataloader=None, model_card=None)[source]¶
Set up the trainer instance in operator before training and set trainer parameters. :param training_config: The config of this trainer. :type training_config: TrainingConfig :param train_dataset: Training dataset. :type train_dataset: Union[Dataset, TowheeDataSet] :param eval_dataset: Evaluate dataset. :type eval_dataset: Union[Dataset, TowheeDataSet] :param train_dataloader: If specified, Trainer will use it to load training data.
Otherwise, Trainer will build dataloader from train_dataset.
- Parameters:
eval_dataloader (Union[DataLoader, Iterable]) – If specified, Trainer will use it to load evaluate data. Otherwise, Trainer will build dataloader from train_dataset.
model_card (ModelCard) – Model card contains the training informations.
Returns:
- class towhee.operator.PyOperator[source]¶
Bases:
Operator
Python function operator, no machine learning frameworks involved.
- towhee.register(name: Optional[str] = None, input_schema=None, output_schema=None, flag=None)¶
Register a class, function, or callable as a towhee operator.
- Parameters:
name (str, optional) – operator name, will use the class/function name if None.
input_schema –
output_schema –
operators. (flag for legacy) –
- Returns:
[description]
- Return type:
[type]