anomalib.models.cflow.torch_model

PyTorch model for CFlow model implementation.

Module Contents

Classes

CflowModel

CFLOW: Conditional Normalizing Flows.
class anomalib.models.cflow.torch_model.CflowModel(input_size: Tuple[int, int], backbone: str, layers: List[str], fiber_batch_size: int = 64, decoder: str = 'freia-cflow', condition_vector: int = 128, coupling_blocks: int = 8, clamp_alpha: float = 1.9, permute_soft: bool = False)[source]

Bases: torch.nn.Module

CFLOW: Conditional Normalizing Flows.

forward(self, images)[source]

Forward-pass images into the network to extract encoder features and compute probability.

Parameters

images – Batch of images.

Returns

Predicted anomaly maps.