Anglenet

class apps.expert.core.aggression.video_aggression.anglenet.AngleNet(pretrained: bool = True, device: torch.device | None = None)

Bases: Module

AngleNet implementation.

Model implementation for head rotation angles prediction using face mesh.

Example

>>> import torch
>>> anglenet = AngleNet(pretrained=True, device=torch.device('cuda')).eval()

property device: device

Check the device type.

Returns:

Device type on local machine.

Return type:

torch.device

forward(x: Tensor) → Tensor

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

apps.expert.core.aggression.video_aggression.anglenet.classify_rotation(angle_predictions: ndarray, threshold: int = 25) → int

Classification of turning away by the angles of head rotation.

Parameters:

angle_predictions (np.ndarray) – Head angle predictions represented as numpy ndarray.