dgs.models.dataset.torchreid_pose_dataset.TorchreidPoseDataManager.build_transforms

static TorchreidPoseDataManager.build_transforms(transforms: str | list[str] | callable | list[callable] | None = None, **kwargs)

Build transforms for pose data. Can’t use torchreid transforms.

Possible transforms:

random_flip

Randomly flip along the horizontal or vertical axis.

random_horizontal_flip

Randomly flip along the horizontal axis.

random_vertical_flip

Randomly flip along the vertical axis.

random_move

Adds normally distributed noise to the key points.

random_resize

Randomly resizes the key points by a factor in range (0.95, 1.05)

param transforms:

List of transform names or functions which will be applied to the data during training. Not used for testing! The transforms will be inserted into a tvt.Compose in the order they are defined in this list. Default is None.

keyword random_horizontal_flip_prob:

Probability of flipping the coordinates horizontally. Default 0.5

kwtype random_horizontal_flip_prob:

float

keyword random_vertical_flip_prob:

Probability of flipping the coordinates vertically. Default 0.5

kwtype random_vertical_flip_prob:

float

keyword random_move_prob:

Probability to use add normally distributed movement. Default 0.5

kwtype random_move_prob:

float

keyword random_resize_prob:

Probability to randomly resize. Default 0.5

kwtype random_resize_prob:

float

keyword random_flip_prob:

Probability of using random flipping. Default 0.5

kwtype random_flip_prob:

float

keyword random_flip_probs:

When a ‘random_flip’ is done, these are the probabilities of flipping horizontal and vertical. Default [0.8, 0.2]

kwtype random_flip_probs:

list[float]

returns:

One composed transform for training and testing.

rtype:

(tvt.Compose, tvt.Compose)

raises ValueError:

If transforms is an invalid object or contains invalid transform names.