Inference¶

Anomalib provides entrypoint scripts for using a trained model to generate predictions from a source of image data. This guide explains how to run inference with the standard PyTorch model and the exported OpenVINO model.

PyTorch (Lightning) Inference¶

The entrypoint script in tools/inference/lightning.py can be used to run inference with a trained PyTorch model. The script runs inference by loading a previously trained model into a PyTorch Lightning trainer and running the predict sequence. The entrypoint script has several command line arguments that can be used to configure inference:

Parameter	Required	Description
config	True	Path to the model config file.
weight_path	True	Path to the `.ckpt` model checkpoint file.
image_path	True	Path to the image source. This can be a single image or a folder of images.
save_path	False	Path to which the output images should be saved.
visualization_mode	False	Determines how the inference results are visualized. Options: “full”, “simple”.
disable_show_images	False	When this flag is passed, visualizations will not be shown on the screen.

To run inference, call the script from the command line with the with the following parameters, e.g.:

python tools/inference/lightning.py --config padim.yaml --weight_path results/weights/model.ckpt --image_path image.png

This will run inference on the specified image file or all images in the folder. A visualization of the inference results including the predicted heatmap and segmentation results (if applicable), will be displayed on the screen, like the example below.

OpenVINO Inference¶

To run OpenVINO inference, first make sure that your model has been exported to the OpenVINO IR format. Once the model has been exported, OpenVINO inference can be triggered by running the OpenVINO entrypoint script in tools/inference/openvino.py. The command line arguments are very similar to PyTorch inference entrypoint script:

Parameter	Required	Description
config	True	Path to the model config file.
weight_path	True	Path to the OpenVINO IR model file (either `.xml` or `.bin`)
image_path	True	Path to the image source. This can be a single image or a folder of images.
save_data	False	Path to which the output images should be saved. Leave empty for live visualization.
meta_data	True	Path to the JSON file containing the model’s meta data (e.g. normalization parameters and anomaly score threshold).

For correct inference results, the meta_data argument should be specified and point to the meta_data.json file that was generated when exporting the OpenVINO IR model. The file is stored in the same folder as the .xml and .bin files of the model.

As an example, OpenVINO inference can be triggered by the following command:

python tools/inference/openvino.py --config padim.yaml --weight_path results/openvino/model.xml --image_path image.png --meta_data results/openvino/meta_data.json

Similar to PyTorch inference, the visualization results will be displayed on the screen, and optionally saved to the file system location specified by the save_data parameter.