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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.

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:

python tools/upgrade/config.py \
    --input_config <path_to_0.*_config> \
    --output_config <path_to_1.0_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.

-dataset:
+data:
-  name: mvtec
-  format: mvtec
+  class_path: anomalib.data.MVTec
+  init_args:
-  path: ./datasets/MVTec
+    root: ./datasets/MVTec
     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.

 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.

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.

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