Check if mask_dir is available for segmentation task in Folder data

[samet-akcay]

Description

• This PR checks if mask_dir is set for segmentation tasks .

• Fixes Unable read the mask image #1212

Changes

• Bug fix (non-breaking change which fixes an issue)
• Refactor (non-breaking change which refactors the code base)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

Checklist

• My code follows the pre-commit style and check guidelines of this project.
• I have performed a self-review of my code
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing tests pass locally with my changes
• I have added a summary of my changes to the CHANGELOG (not for minor changes, docs and tests).

[djdameln]

I feel this may be a bit too restricting, and could cause problems with the Lightning inferencer. The following is actually a valid dataset config, which will successfully train a model:

dataset:
  name: folder
  format: folder
  root: ./datasets/MVTec/bottle
  normal_dir: train/good
  abnormal_dir: null
  normal_test_dir: null
  mask_dir: null
  extensions: null
  task: segmentation
  train_batch_size: 32
  eval_batch_size: 32
  num_workers: 8
  image_size: 256 # dimensions to which images are resized (mandatory)
  center_crop: null # dimensions to which images are center-cropped after resizing (optional)
  normalization: imagenet # data distribution to which the images will be normalized: [none, imagenet]
  transform_config:
    train: null
    eval: null
  test_split_mode: none # 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: none # 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)

This is the config that one would use to train a model when no anomalous images are available, and then use that model for segmentation during inference.

As a solution, maybe we could check if the val_split_mode is NONE.

[djdameln]

Thanks!