FastFlow#

FastFlow Lightning Model Implementation.

class anomalib.models.image.fastflow.lightning_model.Fastflow(backbone='resnet18', pre_trained=True, flow_steps=8, conv3x3_only=False, hidden_ratio=1.0)#

Bases: AnomalyModule

PL Lightning Module for the FastFlow algorithm.

Parameters:

backbone (str) – Backbone CNN network Defaults to resnet18.
pre_trained (bool, optional) – Boolean to check whether to use a pre_trained backbone. Defaults to True.
flow_steps (int, optional) – Flow steps. Defaults to 8.
conv3x3_only (bool, optinoal) – Use only conv3x3 in fast_flow model. Defaults to False.
hidden_ratio (float, optional) – Ratio to calculate hidden var channels. Defaults to ``1.0`.

configure_optimizers()#

Configure optimizers for each decoder.

Returns:: Adam optimizer for each decoder
Return type:: Optimizer

property learning_type: LearningType#

Return the learning type of the model.

Returns:: Learning type of the model.
Return type:: LearningType

property trainer_arguments: dict[str, Any]#: Return FastFlow trainer arguments.

training_step(batch, *args, **kwargs)#

Perform the training step input and return the loss.

Parameters:

(batch (batch) – dict[str, str | torch.Tensor]): Input batch
args – Additional arguments.
kwargs – Additional keyword arguments.

Returns:

Dictionary containing the loss value.

Return type:

STEP_OUTPUT

validation_step(batch, *args, **kwargs)#

Perform the validation step and return the anomaly map.

Parameters:

batch (dict[str, str | torch.Tensor]) – Input batch
args – Additional arguments.
kwargs – Additional keyword arguments.

Returns:

batch dictionary containing anomaly-maps.

Return type:

STEP_OUTPUT | None

FastFlow Torch Model Implementation.

class anomalib.models.image.fastflow.torch_model.FastflowModel(input_size, backbone, pre_trained=True, flow_steps=8, conv3x3_only=False, hidden_ratio=1.0)#

Bases: Module

FastFlow.

Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows.

Parameters:

input_size (tuple[int, int]) – Model input size.
backbone (str) – Backbone CNN network
pre_trained (bool, optional) – Boolean to check whether to use a pre_trained backbone. Defaults to True.
flow_steps (int, optional) – Flow steps. Defaults to 8.
conv3x3_only (bool, optinoal) – Use only conv3x3 in fast_flow model. Defaults to False.
hidden_ratio (float, optional) – Ratio to calculate hidden var channels. Defaults to 1.0.

Raises:

ValueError – When the backbone is not supported.

forward(input_tensor)#

Forward-Pass the input to the FastFlow Model.

Parameters:

input_tensor (torch.Tensor) – Input tensor.

Returns:

During training, return: (hidden_variables, log-of-the-jacobian-determinants). During the validation/test, return the anomaly map.

Return type:

Tensor | list[torch.Tensor] | tuple[list[torch.Tensor]]

Loss function for the FastFlow Model Implementation.

class anomalib.models.image.fastflow.loss.FastflowLoss(*args, **kwargs)#

Bases: Module

FastFlow Loss.

forward(hidden_variables, jacobians)#

Calculate the Fastflow loss.

Parameters:

hidden_variables (list[torch.Tensor]) – Hidden variables from the fastflow model. f: X -> Z
jacobians (list[torch.Tensor]) – Log of the jacobian determinants from the fastflow model.

Returns:

Fastflow loss computed based on the hidden variables and the log of the Jacobians.

Return type:

Tensor

FastFlow Anomaly Map Generator Implementation.

class anomalib.models.image.fastflow.anomaly_map.AnomalyMapGenerator(input_size)#

Bases: Module

Generate Anomaly Heatmap.

Parameters:: input_size (ListConfig | tuple) – Input size.

forward(hidden_variables)#

Generate Anomaly Heatmap.

This implementation generates the heatmap based on the flow maps computed from the normalizing flow (NF) FastFlow blocks. Each block yields a flow map, which overall is stacked and averaged to an anomaly map.

Parameters:: hidden_variables (list[torch.Tensor]) – List of hidden variables from each NF FastFlow block.
Returns:: Anomaly Map.
Return type:: Tensor

FastFlow

Contents

FastFlow#