Btech Dataset

BTech Dataset.

This script contains PyTorch Lightning DataModule for the BTech dataset.

If the dataset is not on the file system, the script downloads and extracts the dataset and create PyTorch data objects.

class anomalib.data.btech.BTech(root: Path | str, category: str, image_size: int | tuple[int, int] | None = None, center_crop: int | tuple[int, int] | None = None, normalization: str | InputNormalizationMethod = InputNormalizationMethod.IMAGENET, train_batch_size: int = 32, eval_batch_size: int = 32, num_workers: int = 8, task: TaskType = TaskType.SEGMENTATION, transform_config_train: str | A.Compose | None = None, transform_config_eval: str | A.Compose | None = None, test_split_mode: TestSplitMode = TestSplitMode.FROM_DIR, test_split_ratio: float = 0.2, val_split_mode: ValSplitMode = ValSplitMode.SAME_AS_TEST, val_split_ratio: float = 0.5, seed: int | None = None)[source]

Bases: AnomalibDataModule

BTech Lightning Data Module.

Parameters:

  • root (Path | str) – Path to the BTech dataset.

  • category (str) – Name of the BTech category.

  • image_size (int | tuple[int, int] | None, optional) – Variable to which image is resized. Defaults to None.

  • center_crop (int | tuple[int, int] | None, optional) – When provided, the images will be center-cropped to the provided dimensions. Defaults to None.

  • normalization (str | InputNormalizationMethod, optional) – When True, the images will be normalized to the ImageNet statistics. Defaults to InputNormalizationMethod.IMAGENET.

  • train_batch_size (int, optional) – Training batch size. Defaults to 32.

  • eval_batch_size (int, optional) – Eval batch size. Defaults to 32.

  • num_workers (int, optional) – Number of workers. Defaults to 8.

  • task (TaskType, optional) – Task type. Defaults to TaskType.SEGMENTATION.

  • transform_config_train (str | A.Compose | None, optional) – Config for pre-processing during training. Defaults to None.

  • transform_config_eval (str | A.Compose | None, optional) – Config for pre-processing during validation. Defaults to None.

  • test_split_mode (TestSplitMode, optional) – Setting that determines how the testing subset is obtained. Defaults to TestSplitMode.FROM_DIR.

  • test_split_ratio (float, optional) – Fraction of images from the train set that will be reserved for testing. Defaults to 0.2.

  • val_split_mode (ValSplitMode, optional) – Setting that determines how the validation subset is obtained. Defaults to ValSplitMode.SAME_AS_TEST.

  • val_split_ratio (float, optional) – Fraction of train or test images that will be reserved for validation. Defaults to 0.5.

  • seed (int | None, optional) – Seed which may be set to a fixed value for reproducibility. Defaults to None.

Examples

>>> from anomalib.data import BTech
>>> datamodule = BTech(
...     root="./datasets/BTech",
...     category="leather",
...     image_size=256,
...     train_batch_size=32,
...     test_batch_size=32,
...     num_workers=8,
...     transform_config_train=None,
...     transform_config_val=None,
... )
>>> datamodule.setup()

>>> i, data = next(enumerate(datamodule.train_dataloader()))
>>> data.keys()
dict_keys(['image'])
>>> data["image"].shape
torch.Size([32, 3, 256, 256])

>>> i, data = next(enumerate(datamodule.val_dataloader()))
>>> data.keys()
dict_keys(['image_path', 'label', 'mask_path', 'image', 'mask'])
>>> data["image"].shape, data["mask"].shape
(torch.Size([32, 3, 256, 256]), torch.Size([32, 256, 256]))
prepare_data_per_node

If True, each LOCAL_RANK=0 will call prepare data. Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare data.

allow_zero_length_dataloader_with_multiple_devices

If True, dataloader with zero length within local rank is allowed. Default value is False.

prepare_data() → None[source]

Download the dataset if not available.

class anomalib.data.btech.BTechDataset(root: str | Path, category: str, transform: A.Compose, split: str | Split | None = None, task: TaskType = TaskType.SEGMENTATION)[source]

Bases: AnomalibDataset

Btech Dataset class.

Parameters:

  • root – Path to the BTech dataset

  • category – Name of the BTech category.

  • transform (A.Compose) – Albumentations Compose object describing the transforms that are applied to the inputs.

  • split – ‘train’, ‘val’ or ‘test’

  • task – classification, detection or segmentation

  • create_validation_set – Create a validation subset in addition to the train and test subsets

Examples

>>> from anomalib.data.btech import BTechDataset
>>> from anomalib.pre_processing import get_transforms
>>> transform = get_transforms(image_size=256)
>>> dataset = BTechDataset(
...     root='./datasets/BTech',
...     category='leather',
...     transform=transform,
...     task="classification",
...     is_train=True,
... )
>>> dataset[0].keys()
dict_keys(['image'])

>>> dataset.split = "test"
>>> dataset[0].keys()
dict_keys(['image', 'image_path', 'label'])

>>> dataset.task = "segmentation"
>>> dataset.split = "train"
>>> dataset[0].keys()
dict_keys(['image'])

>>> dataset.split = "test"
>>> dataset[0].keys()
dict_keys(['image_path', 'label', 'mask_path', 'image', 'mask'])

>>> dataset[0]["image"].shape, dataset[0]["mask"].shape
(torch.Size([3, 256, 256]), torch.Size([256, 256]))
anomalib.data.btech.make_btech_dataset(path: Path, split: str | Split | None = None) → DataFrame[source]

Create BTech samples by parsing the BTech data file structure.

The files are expected to follow the structure:

path/to/dataset/split/category/image_filename.png path/to/dataset/ground_truth/category/mask_filename.png

Parameters:

  • path (Path) – Path to dataset

  • split (str | Split | None, optional) – Dataset split (ie., either train or test). Defaults to None.

  • split_ratio (float, optional) – Ratio to split normal training images and add to the test set in case test set doesn’t contain any normal images. Defaults to 0.1.

  • seed (int, optional) – Random seed to ensure reproducibility when splitting. Defaults to 0.

  • create_validation_set (bool, optional) – Boolean to create a validation set from the test set. BTech dataset does not contain a validation set. Those wanting to create a validation set could set this flag to True.

Example

The following example shows how to get training samples from BTech 01 category:

>>> root = Path('./BTech')
>>> category = '01'
>>> path = root / category
>>> path
PosixPath('BTech/01')

>>> samples = make_btech_dataset(path, split='train', split_ratio=0.1, seed=0)
>>> samples.head()
   path     split label image_path                  mask_path                     label_index
0  BTech/01 train 01    BTech/01/train/ok/105.bmp BTech/01/ground_truth/ok/105.png      0
1  BTech/01 train 01    BTech/01/train/ok/017.bmp BTech/01/ground_truth/ok/017.png      0
...
Returns:

an output dataframe containing samples for the requested split (ie., train or test)

Return type:

DataFrame