Understanding the 'AttributeError' in Keras Segmentation Models Import

When utilizing advanced libraries like segmentation_models for image segmentation tasks, it is not uncommon to face compatibility issues. One such issue is encountering an AttributeError pointing out that module 'keras.utils.generic_utils' has no attribute 'get_custom_objects' . This article explores why this error occurs, its technical underpinnings, and how to resolve it.

Technical Explanation

Keras, a popular high-level neural networks API, occasionally undergoes updates that may deprecate or relocate certain functions. The segmentation_models library, which provides state-of-the-art image segmentation architectures like Unet, FPN, and Linknet, is designed to work with specific versions of Keras and its backend. Here are the technical reasons behind this AttributeError :

1. Version Compatibility: segmentation_models might be using Keras functions that exist in previous versions of Keras or TensorFlow. If get_custom_objects function is relocated or removed in the version you are using, it results in an AttributeError .
2. Function Relocation: In recent Keras versions, functions are being migrated between modules as the library evolves, possibly placing get_custom_objects in a different module or necessitating a different import statement.
3. Changes in TensorFlow: Since Keras is now embedded within TensorFlow (tf.keras ), changes within TensorFlow’s versioning and module integrations might play a role. For instance, if TensorFlow has moved it under a different namespace or module, it leads to such errors.

Example and Context

Consider you are trying to import segmentation models as shown:

• Ensure that the versions of keras , tensorflow , and segmentation_models are compatible with each other. You can check the library's documentation for compatibility information.
• If you are using the get_custom_objects function indirectly, check if it's available in tf.keras.utils . Adjust your import statements accordingly:
• Updating or downgrading your libraries can solve version-based discrepancies.
• Working within a virtual environment (using tools like venv or conda ) can help maintain a consistent environment specific to your project.
• Regularly checking the Keras release notes may offer insights into what changes have been made regarding function placements.
• Long-Term Stability: Aim for Keras and TensorFlow LTS (Long-Term Support) versions for more predictable behavior.
• Library Alternatives: If the problem persists and affects workflow, explore alternative libraries or implementations that could integrate seamlessly.
• Documentation and Community: Engage with community forums such as Stack Overflow or GitHub issues pages for the latest fixes and workarounds offered by the developer community.