anomalib.deploy.inferencers.torch

This module contains Torch inference implementations.

Module Contents

Classes

TorchInferencer

PyTorch implementation for the inference.
class anomalib.deploy.inferencers.torch.TorchInferencer(config: Union[omegaconf.DictConfig, omegaconf.ListConfig], model_source: Union[str, pathlib.Path, anomalib.models.components.AnomalyModule], meta_data_path: Union[str, pathlib.Path] = None)[source]

Bases: anomalib.deploy.inferencers.base.Inferencer

PyTorch implementation for the inference.

Parameters

  • config (DictConfig) – Configurable parameters that are used during the training stage.

  • model_source (Union[str, Path, AnomalyModule]) – Path to the model ckpt file or the Anomaly model.

  • meta_data_path (Union[str, Path], optional) – Path to metadata file. If none, it tries to load the params from the model state_dict. Defaults to None.

_load_meta_data(self, path: Optional[Union[str, pathlib.Path]] = None) → Union[Dict, omegaconf.DictConfig][source]

Load metadata from file or from model state dict.

Parameters

path (Optional[Union[str, Path]], optional) – Path to metadata file. If none, it tries to load the params from the model state_dict. Defaults to None.

Returns

Dictionary containing the meta_data.

Return type

Dict

load_model(self, path: Union[str, pathlib.Path]) → anomalib.models.components.AnomalyModule[source]

Load the PyTorch model.

Parameters

path (Union[str, Path]) – Path to model ckpt file.

Returns

PyTorch Lightning model.

Return type

(AnomalyModule)

pre_process(self, image: numpy.ndarray) → torch.Tensor[source]

Pre process the input image by applying transformations.

Parameters

image (np.ndarray) – Input image

Returns

pre-processed image.

Return type

Tensor

forward(self, image: torch.Tensor) → torch.Tensor[source]

Forward-Pass input tensor to the model.

Parameters

image (Tensor) – Input tensor.

Returns

Output predictions.

Return type

Tensor

post_process(self, predictions: torch.Tensor, meta_data: Optional[Union[Dict, omegaconf.DictConfig]] = None) → Tuple[numpy.ndarray, float][source]

Post process the output predictions.

Parameters

  • predictions (Tensor) – Raw output predicted by the model.

  • meta_data (Dict, optional) – Meta data. Post-processing step sometimes requires additional meta data such as image shape. This variable comprises such info. Defaults to None.

Returns

Post processed predictions that are ready to be visualized.

Return type

np.ndarray