Efficient AD#

EfficientAd: Accurate Visual Anomaly Detection at Millisecond-Level Latencies.

https://arxiv.org/pdf/2303.14535.pdf.

class anomalib.models.image.efficient_ad.lightning_model.EfficientAd(teacher_out_channels=384, model_size=EfficientAdModelSize.S, lr=0.0001, weight_decay=1e-05, padding=False, pad_maps=True, batch_size=1)#

Bases: AnomalyModule

PL Lightning Module for the EfficientAd algorithm.

Parameters:
  • teacher_out_channels (int) – number of convolution output channels Defaults to 384.

  • model_size (str) – size of student and teacher model Defaults to EfficientAdModelSize.S.

  • lr (float) – learning rate Defaults to 0.0001.

  • weight_decay (float) – optimizer weight decay Defaults to 0.00001.

  • padding (bool) – use padding in convoluional layers Defaults to False.

  • pad_maps (bool) – relevant if padding is set to False. In this case, pad_maps = True pads the output anomaly maps so that their size matches the size in the padding = True case. Defaults to True.

  • batch_size (int) – batch size for imagenet dataloader Defaults to 1.

configure_optimizers()#

Configure optimizers.

Return type:

Optimizer

configure_transforms(image_size=None)#

Default transform for Padim.

Return type:

Transform

property learning_type: LearningType#

Return the learning type of the model.

Returns:

Learning type of the model.

Return type:

LearningType

map_norm_quantiles(dataloader)#

Calculate 90% and 99.5% quantiles of the student(st) and autoencoder(ae).

Parameters:

dataloader (DataLoader) – Dataloader of the respective dataset.

Returns:

Dictionary of both the 90% and 99.5% quantiles of both the student and autoencoder feature maps.

Return type:

dict[str, torch.Tensor]

on_train_start()#

Called before the first training epoch.

First sets up the pretrained teacher model, then prepares the imagenette data, and finally calculates or loads the channel-wise mean and std of the training dataset and push to the model.

Return type:

None

on_validation_start()#

Calculate the feature map quantiles of the validation dataset and push to the model.

Return type:

None

prepare_imagenette_data(image_size)#

Prepare ImageNette dataset transformations.

Parameters:

image_size (tuple[int, int] | torch.Size) – Image size.

Return type:

None

prepare_pretrained_model()#

Prepare the pretrained teacher model.

Return type:

None

teacher_channel_mean_std(dataloader)#

Calculate the mean and std of the teacher models activations.

Adapted from https://math.stackexchange.com/a/2148949

Parameters:

dataloader (DataLoader) – Dataloader of the respective dataset.

Returns:

Dictionary of channel-wise mean and std

Return type:

dict[str, torch.Tensor]

property trainer_arguments: dict[str, Any]#

Return EfficientAD trainer arguments.

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

Perform the training step for EfficientAd returns the student, autoencoder and combined loss.

Parameters:
  • (batch (batch) – dict[str, str | torch.Tensor]): Batch containing image filename, image, label and mask

  • args – Additional arguments.

  • kwargs – Additional keyword arguments.

Return type:

dict[str, Tensor]

Returns:

Loss.

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

Perform the validation step of EfficientAd returns anomaly maps for the input image batch.

Parameters:
  • batch (dict[str, str | torch.Tensor]) – Input batch

  • args – Additional arguments.

  • kwargs – Additional keyword arguments.

Return type:

Union[Tensor, Mapping[str, Any], None]

Returns:

Dictionary containing anomaly maps.

Torch model for student, teacher and autoencoder model in EfficientAd.

class anomalib.models.image.efficient_ad.torch_model.EfficientAdModel(teacher_out_channels, model_size=EfficientAdModelSize.S, padding=False, pad_maps=True)#

Bases: Module

EfficientAd model.

Parameters:
  • teacher_out_channels (int) – number of convolution output channels of the pre-trained teacher model

  • model_size (str) – size of student and teacher model

  • padding (bool) – use padding in convoluional layers Defaults to False.

  • pad_maps (bool) – relevant if padding is set to False. In this case, pad_maps = True pads the output anomaly maps so that their size matches the size in the padding = True case. Defaults to True.

choose_random_aug_image(image)#

Choose a random augmentation function and apply it to the input image.

Parameters:

image (torch.Tensor) – Input image.

Returns:

Augmented image.

Return type:

Tensor

forward(batch, batch_imagenet=None, normalize=True)#

Perform the forward-pass of the EfficientAd models.

Parameters:
  • batch (torch.Tensor) – Input images.

  • batch_imagenet (torch.Tensor) – ImageNet batch. Defaults to None.

  • normalize (bool) – Normalize anomaly maps or not

Returns:

Predictions

Return type:

Tensor

is_set(p_dic)#

Check if any of the parameters in the parameter dictionary is set.

Parameters:

p_dic (nn.ParameterDict) – Parameter dictionary.

Returns:

Boolean indicating whether any of the parameters in the parameter dictionary is set.

Return type:

bool