PointCloudSegmentation

class flash.pointcloud.segmentation.model.PointCloudSegmentation(num_classes, backbone='randlanet', backbone_kwargs=None, head=None, loss_fn=torch.nn.functional.cross_entropy, optimizer='Adam', lr_scheduler=None, metrics=None, learning_rate=None, multi_label=False)

The PointCloudClassifier is a ClassificationTask that classifies pointcloud data.

Parameters

• num_classes (int) – The number of classes (outputs) for this Task.

• backbone (Union[str, Tuple[Module, int]]) – The backbone name (or a tuple of nn.Module, output size) to use.

• backbone_kwargs (Optional[Dict]) – Any additional kwargs to pass to the backbone constructor.

• head (Optional[Module]) – a nn.Module to use on top of the backbone. The output dimension should match the num_classes argument. If not set will default to a single linear layer.

• loss_fn (TypeVar(LOSS_FN_TYPE, Callable, Mapping, Sequence, None)) – The loss function to use. If None, a default will be selected by the ClassificationTask depending on the multi_label argument.

• optimizer (TypeVar(OPTIMIZER_TYPE, str, Callable, Tuple[str, Dict[str, Any]], None)) – Optimizer to use for training.

• lr_scheduler (Optional[TypeVar(LR_SCHEDULER_TYPE, str, Callable, Tuple[str, Dict[str, Any]], Tuple[str, Dict[str, Any], Dict[str, Any]], None)]) – The LR scheduler to use during training.

• metrics (Optional[TypeVar(METRICS_TYPE, Metric, Mapping, Sequence, None)]) – Any metrics to use with this Task. If None, a default will be selected by the ClassificationTask depending on the multi_label argument.

• learning_rate (Optional[float]) – The learning rate for the optimizer.

• multi_label (bool) – If True, this will be treated as a multi-label classification problem.

classmethod available_finetuning_strategies(cls)

Returns a list containing the keys of the available Finetuning Strategies.

Return type

List[str]

classmethod available_lr_schedulers(cls)

Returns a list containing the keys of the available LR schedulers.

Return type

List[str]

classmethod available_optimizers(cls)

Returns a list containing the keys of the available Optimizers.

Return type

List[str]

classmethod available_outputs(cls)

Returns the list of available outputs (that can be used during prediction or serving) for this Task.

Examples

..testsetup:

>>> from flash import Task

>>> print(Task.available_outputs())
['preds', 'raw']

Return type

List[str]

forward(x)

First call the backbone, then the model head.

Return type

Tensor

modules_to_freeze()

Return the module attributes of the model to be frozen.

Return type

Union[Module, Iterable[Union[Module, Iterable]]]