The Validation Error Message

Oct 13, 2025 by wbrum.org 14 views

Introduction

The transforms.py file is a crucial component of many machine learning and computer vision projects. However, issues with this file can be frustrating and time-consuming to resolve. In this article, we will delve into a specific issue that has been reported by users, and provide a step-by-step guide on how to resolve it.

The Issue: A Warning and a Validation Error

The issue at hand is a warning and a validation error that occurs when using the ShiftScaleRotate transform in the transforms.py file. The warning message indicates that ShiftScaleRotate is a special case of Affine transform, and suggests using Affine instead. However, the validation error message is more cryptic, indicating that the size field is required, but is missing from the input.

The Warning Message

/EchoGraphs/.venv/lib/python3.10/site-packages/albumentations/core/validation.py:87: UserWarning: ShiftScaleRotate is a special case of Affine transform. Please use Affine transform instead.
  original_init(self, **validated_kwargs)

The Validation Error Message

Traceback (most recent call last):
  File "/EchoGraphs/.venv/lib/python3.10/site-packages/albumentations/core/validation.py", line 43, in _validate_parameters
    config = schema_cls(**{k: v for k, v in full_kwargs.items() if k in param_names})
  File "/EchoGraphs/.venv/lib/python3.10/site-packages/pydantic/main.py", line 214, in __init__
    validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
pydantic_core._pydantic_core.ValidationError: 1 validation error for InitSchema
size
  Field required [type=missing, input_value={'min_max_height': (112, ...'mask_interpolation': 0}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.10/v/missing

Resolving the Issue: A Step-by-Step Guide

To resolve this issue, we need to make two changes to the transforms.py file:

Replace ShiftScaleRotate with Affine: The warning message suggests that we should use Affine instead of ShiftScaleRotate. We can do this by replacing the ShiftScaleRotate transform with Affine in the transforms.py file.
Add the size field to the input: The validation error message indicates that the size field is required, but is missing from the input. We can add the size field to the input by specifying the size parameter when creating the Affine transform.

Step 1: Replace `ShiftScaleRotate` with `Affine`

To replace ShiftScaleRotate with Affine, we need to import the Affine transform from the albumentations library and use it instead of ShiftScaleRotate. We can do this by adding the following line to the transforms.py file:

from albumentations.augmentations.geometric import Affine

Then, we can replace the ShiftScaleRotate transform with Affine:

transform = Affine(
    scale=(0.5, 1.5),
    rotate=(-30, 30),
    shear=(-10, 10),
    translate_x=(-10, 10),
    translate_y=(-10, 10),
    p=0.5
)

Step 2: Add the `size` field to the input

To add the size field to the input, we need to specify the size parameter when creating the Affine transform. We can do this by adding the following line to the transforms.py file:

transform = Affine(
    scale=(0.5, 1.5),
    rotate=(-30, 30),
    shear=(-10, 10),
    translate_x=(-10, 10),
    translate_y=(-10, 10),
    size=(112, 112),
    p=0.5
)

Conclusion

In this article, we have resolved an issue with the transforms.py file that was causing a warning and a validation error. We replaced ShiftScaleRotate with Affine and added the size field to the input. By following these steps, we can resolve this issue and continue working on our machine learning and computer vision projects.

Additional Tips and Resources

Make sure to check the documentation for the albumentations library to learn more about the Affine transform and its parameters.
If you are still experiencing issues, try checking the transforms.py file for any other errors or warnings.
You can also try searching online for solutions to similar issues or seeking help from the albumentations community.

Resolving Issues with the albumentations Library
Using the Affine Transform in albumentations
Machine Learning and Computer Vision with albumentations
Q&A: Resolving Issues in transforms.py File =============================================

Q: What is the issue with the transforms.py file?

A: The issue with the transforms.py file is a warning and a validation error that occurs when using the ShiftScaleRotate transform. The warning message suggests using Affine transform instead, and the validation error message indicates that the size field is required, but is missing from the input.

Q: What is the difference between ShiftScaleRotate and Affine?

A: ShiftScaleRotate is a special case of Affine transform. Affine transform is a more general transform that includes ShiftScaleRotate as a specific case. Using Affine transform instead of ShiftScaleRotate can provide more flexibility and control over the transformation process.

Q: How do I replace ShiftScaleRotate with Affine?

A: To replace ShiftScaleRotate with Affine, you need to import the Affine transform from the albumentations library and use it instead of ShiftScaleRotate. You can do this by adding the following line to the transforms.py file:

from albumentations.augmentations.geometric import Affine

Then, you can replace the ShiftScaleRotate transform with Affine:

transform = Affine(
    scale=(0.5, 1.5),
    rotate=(-30, 30),
    shear=(-10, 10),
    translate_x=(-10, 10),
    translate_y=(-10, 10),
    p=0.5
)

Q: How do I add the size field to the input?

A: To add the size field to the input, you need to specify the size parameter when creating the Affine transform. You can do this by adding the following line to the transforms.py file:

transform = Affine(
    scale=(0.5, 1.5),
    rotate=(-30, 30),
    shear=(-10, 10),
    translate_x=(-10, 10),
    translate_y=(-10, 10),
    size=(112, 112),
    p=0.5
)

Q: What are some common issues that can occur with the transforms.py file?

A: Some common issues that can occur with the transforms.py file include:

Missing or incorrect import statements: Make sure to import the necessary libraries and modules.
Incorrect usage of transforms: Make sure to use the transforms correctly and according to their documentation.
Invalid or missing parameters: Make sure to specify all required parameters and provide valid values.
Conflicting or duplicate transforms: Make sure to avoid using conflicting or duplicate transforms.

Q: How can I troubleshoot issues with the transforms.py file?

A: To troubleshoot issues with the transforms.py file, you can try the following:

Check the documentation: Make sure to check the documentation for the albumentations library and the transforms you are using.
Check the import statements: Make sure to import the necessary libraries and modules.
Check the usage of transforms: Make sure to use the transforms correctly and according to their documentation.
Check the parameters: Make sure to specify all required parameters and provide valid values.
Check for conflicts or duplicates: Make sure to avoid using conflicting or duplicate transforms.

Q: Where can I find more information about the albumentations library and its transforms?

A: You can find more information about the albumentations library and its transforms on the official albumentations website, as well as on various online forums and communities. Some recommended resources include:

Albumentations documentation: The official albumentations documentation provides detailed information about the library and its transforms.
Albumentations GitHub repository: The albumentations GitHub repository provides access to the library's source code and issue tracker.
Albumentations community forums: The albumentations community forums provide a platform for users to ask questions and share knowledge about the library and its transforms.