cascade.utils.torch#
- class cascade.utils.torch.TorchModel(model_class: Type | None = None, model: Module | None = None, **kwargs: Any)[source]#
The wrapper around
nn.Module
- __init__(model_class: Type | None = None, model: Module | None = None, **kwargs: Any) None [source]#
- Parameters:
model_class (type, optional) – The class created when new nn.Module was defined. Will be used to construct model. If any arguments needed, please pass them into
kwargs
.model (torch.nn.Module, optional) – The module that should be used as a model. Have higher priority if provided. model_class and model cannot both be None.
- evaluate(x: Any, y: Any, *args: Any, **kwargs: Any) None [source]#
Receives x and y validation sequences. Passes x to the model’s predict method along with any args or kwargs needed. Then updates self.metrics with what objects in
metrics
return.metrics
should contain Metric with compute() method or callables with the interface: f(true, predicted) -> metric_value, where metric_value is a scalar- Parameters:
x (Any) – Input of the model.
y (Any) – Desired output to compare with the values predicted.
metrics (List[Union[Metric, Callable[[Any, Any], MetricType]]]) – List of metrics or callables to compute metric values
- get_meta() List[Dict[Any, Any]] [source]#
- Returns:
meta – A list where first element is this object’s metadata. All other elements represent the other stages of pipeline if present.
Meta can be anything that is worth to document about the object and its properties.
Meta is a list (see Meta type alias) to allow the formation of pipelines.
- Return type:
Meta
- load_artifact(path: str, *args: Any, **kwargs: Any) None [source]#
Loads torch module. Additional args and kwargs are passed to torch.load
- Parameters:
path (str) – the folder from which to load pipeline.pkl
- Raises:
ValueError – if the path is not a valid directory