# Migration Guide ::::{grid} 1 2 2 2 :gutter: 2 :padding: 1 :::{grid-item-card} {octicon}`versions` v0.\* to v1.0 :link: migrating-from-0-to-1-0 :link-type: ref Learn how to migrate from v0.\* to v1.0, including changes to configuration, CLI, and API. ::: :::{grid-item-card} {octicon}`versions` v1.0 to v2.0 :link: migrating-from-1-0-to-2-0 :link-type: ref Learn how to migrate from v1.0 to v2.0. ::: :::: (migrating-from-0-to-1-0)= ## Migrating from 0.\* to 1.0 ### Overview The 1.0 release of the Anomaly Detection Library (AnomalyLib) introduces several changes to the library. This guide provides an overview of the changes and how to migrate from 0.\* to 1.0. ### Installation For installation instructions, refer to the [installation guide](anomalib.md). ### Changes to the CLI #### Upgrading the Configuration There are several changes to the configuration of Anomalib. The configuration file has been updated to include new parameters and remove deprecated parameters. In addition, some parameters have been moved to different sections of the configuration. Anomalib provides a python script to update the configuration file from 0.\* to 1.0. To update the configuration file, run the following command: ```bash python tools/upgrade/config.py \ --input_config \ --output_config ``` This script will ensure that the configuration file is updated to the 1.0 format. In the following sections, we will discuss the changes to the configuration file in more detail. #### Changes to the Configuration File ##### Data The `data` section of the configuration file has been updated such that the args can be directly used to instantiate the data object. Below are the differences between the old and new configuration files highlighted in a markdown diff format. ```diff -dataset: +data: - name: mvtec - format: mvtec + class_path: anomalib.data.MVTecAD + init_args: - path: ./datasets/MVTecAD + root: ./datasets/MVTecAD category: bottle image_size: 256 center_crop: null normalization: imagenet train_batch_size: 72 eval_batch_size: 32 num_workers: 8 task: segmentation test_split_mode: from_dir # options: [from_dir, synthetic] test_split_ratio: 0.2 # fraction of train images held out testing (usage depends on test_split_mode) val_split_mode: same_as_test # options: [same_as_test, from_test, synthetic] val_split_ratio: 0.5 # fraction of train/test images held out for validation (usage depends on val_split_mode) seed: null - transform_config: - train: null - eval: null + transform_config_train: null + transform_config_eval: null - tiling: - apply: false - tile_size: null - stride: null - remove_border_count: 0 - use_random_tiling: False - random_tile_count: 16+data: ``` Here is the summary of the changes to the configuration file: - The `name` and `format keys` from the old configuration are absent in the new configuration, possibly integrated into the design of the class at `class_path`. - Introduction of a `class_path` key in the new configuration specifies the Python class path for data handling. - The structure has been streamlined in the new configuration, moving everything under `data` and `init_args` keys, simplifying the hierarchy. - `transform_config` keys were split into `transform_config_train` and `transform_config_eval` to clearly separate training and evaluation configurations. - The `tiling` section present in the old configuration has been completely removed in the new configuration. v1.0.0 does not support tiling. This feature will be added back in a future release. ##### Model Similar to data configuration, the `model` section of the configuration file has been updated such that the args can be directly used to instantiate the model object. Below are the differences between the old and new configuration files highlighted in a markdown diff format. ```diff model: - name: patchcore - backbone: wide_resnet50_2 - pre_trained: true - layers: + class_path: anomalib.models.Patchcore + init_args: + backbone: wide_resnet50_2 + pre_trained: true + layers: - layer2 - layer3 - coreset_sampling_ratio: 0.1 - num_neighbors: 9 + coreset_sampling_ratio: 0.1 + num_neighbors: 9 - normalization_method: min_max # options: [null, min_max, cdf] +normalization: + normalization_method: min_max ``` Here is the summary of the changes to the configuration file: - Model Identification: Transition from `name` to `class_path` for specifying the model, indicating a more explicit reference to the model's implementation. - Initialization Structure: Introduction of `init_args` to encapsulate model initialization parameters, suggesting a move towards a more structured and possibly dynamically loaded configuration system. - Normalization Method: The `normalization_method` key is removed from the `model` section and moved to a separate `normalization` section in the new configuration. ##### Metrics The `metrics` section of the configuration file has been updated such that the args can be directly used to instantiate the metrics object. Below are the differences between the old and new configuration files highlighted in a markdown diff format. ```diff metrics: image: - F1Score - AUROC pixel: - F1Score - AUROC threshold: - method: adaptive #options: [adaptive, manual] - manual_image: null - manual_pixel: null + class_path: anomalib.metrics.F1AdaptiveThreshold + init_args: + default_value: 0.5 ``` Here is the summary of the changes to the configuration file: - Metric Identification: Transition from `method` to `class_path` for specifying the metric, indicating a more explicit reference to the metric's implementation. - Initialization Structure: Introduction of `init_args` to encapsulate metric initialization parameters, suggesting a move towards a more structured and possibly dynamically loaded configuration system. - Threshold Method: The `method` key is removed from the `threshold` section and moved to a separate `class_path` section in the new configuration. (migrating-from-1-0-to-2-0)= ## Migrating from 1.0 to 2.0 ### Overview The 2.0 release of Anomalib introduces several changes to the library. This guide will be updated with migration instructions when v2.0 is released.