ShanghaiTech Datamodule

ShanghaiTech Datamodule#

ShanghaiTech Campus Data Module.

This module provides a PyTorch Lightning DataModule for the ShanghaiTech Campus dataset. If the dataset is not available locally, it will be downloaded and extracted automatically. The video files are also converted to a format readable by pyav.

Example

Create a ShanghaiTech datamodule:

>>> from anomalib.data import ShanghaiTech
>>> datamodule = ShanghaiTech(
...     root="./datasets/shanghaitech",
...     scene=1,
...     clip_length_in_frames=2,
...     frames_between_clips=1,
... )
>>> datamodule.setup()
>>> i, data = next(enumerate(datamodule.train_dataloader()))
>>> data.keys()
dict_keys(['image', 'video_path', 'frames', 'label'])

Notes

The directory structure after preparation will be:

root/
├── testing/
│   ├── frames/
│   ├── test_frame_mask/
│   └── test_pixel_mask/
└── training/
    ├── frames/
    ├── converted_videos/
    └── videos/
License:

ShanghaiTech Campus Dataset is released under the BSD 2-Clause License.

Reference:

Liu, W., Luo, W., Lian, D., & Gao, S. (2018). Future frame prediction for anomaly detection–a new baseline. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6536-6545).

class anomalib.data.datamodules.video.shanghaitech.ShanghaiTech(root='./datasets/shanghaitech', scene=1, clip_length_in_frames=2, frames_between_clips=1, target_frame=VideoTargetFrame.LAST, train_batch_size=32, eval_batch_size=32, num_workers=8, train_augmentations=None, val_augmentations=None, test_augmentations=None, augmentations=None, val_split_mode=ValSplitMode.SAME_AS_TEST, val_split_ratio=0.5, seed=None)#

Bases: AnomalibVideoDataModule

ShanghaiTech DataModule class.

Parameters:

  • root (Path | str) – Path to the root directory of the dataset. Defaults to "./datasets/shanghaitech".

  • scene (int) – Scene index in range [1, 13]. Defaults to 1.

  • clip_length_in_frames (int) – Number of frames in each video clip. Defaults to 2.

  • frames_between_clips (int) – Number of frames between consecutive clips. Defaults to 1.

  • target_frame (VideoTargetFrame) – Specifies which frame in the clip should be used for ground truth. Defaults to VideoTargetFrame.LAST.

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

  • eval_batch_size (int) – Test batch size. Defaults to 32.

  • num_workers (int) – Number of workers for data loading. Defaults to 8.

  • train_augmentations (Transform | None) – Augmentations to apply dto the training images Defaults to None.

  • val_augmentations (Transform | None) – Augmentations to apply to the validation images. Defaults to None.

  • test_augmentations (Transform | None) – Augmentations to apply to the test images. Defaults to None.

  • augmentations (Transform | None) – General augmentations to apply if stage-specific augmentations are not provided.

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

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

  • seed (int | None) – Random seed for reproducibility. Defaults to None.

prepare_data()#

Download the dataset and convert video files.

Return type:

None

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