dgs.models.dataset.dataset.VideoDataset¶
- class dgs.models.dataset.dataset.VideoDataset(*args: Any, **kwargs: Any)[source]¶
A dataset containing a single video.
Should support many file formats, but .mp4 works best.
Notes
The torchvision Video-API is in beta status and will most likely change. So make sure everything works before upgrading the version of torchvision.
Params¶
- data_path (FilePath):
The path to the file containing the data for this dataset. Either from within the
dataset_path
directory, or as absolute path. If you want to combine multiple files to a single (concatenated) dataset, check out the functionget_concatenated_dataset()
or thepaths
parameter.
Optional Params¶
- stream (str):
Default
DEF_VAL.video_dataset.stream
.- num_threads (int):
The number of threads used when loading the video. The default is 0 and lets ffmpeg decide the best configuration. Default
DEF_VAL.video_dataset.num_threads
.- video_backend (str):
The backend to use when loading the video. Default
DEF_VAL.video_dataset.video_backend
.- paths (list[FilePath], optional):
A list of file paths to concatenate into a single dataset. Will be ignored by the single dataset.
- __init__(config: dict[str, any], path: list[str]) None [source]¶
Methods
arbitrary_to_ds
(a, idx)Given an index, convert arbitrary data into a
State
or a list of States.configure_torch_module
(module[, train])Set compute mode and send model to the device or multiple parallel devices if applicable.
get_image_crops
(ds)Add the image crops and local key-points to a given state.
get_path_in_dataset
(path)Given an arbitrary file- or directory-path, return its absolute path.
Terminate this module and all of its submodules.
Given one single image, with its corresponding bounding boxes and key-points, obtain a cropped image for every bounding box with localized key-points.
Given an image, bboxes, and key-points, resize them with custom modes.
validate_params
(validations[, attrib_name])Given per key validations, validate this module's parameters.
Attributes
Get the device of this module.
Get whether this module is set to training-mode.
Get the name of the module.
Get the name of the module.
Get the escaped name of the module usable in filepaths by replacing spaces and underscores.
Get the (floating point) precision used in multiple parts of this module.
Arbitrary data, which will be converted using
self.arbitrary_to_ds()
The base path to the dataset.