Engine.loader

Engine.loader(
    model,
    split,
    *,
    augmented=False,
    shuffle=False,
    only_images=False,
    for_loss=True,
)

A torch DataLoader over one split, wrapping dataset.

Parameters

Name	Type	Description	Default
model	SegmentationModel	Which model’s settings to read - `input_shape`, `batch_size` and the augmentation all come from here.	required
split	str	`"train"`, `"validation"` or `"test"`.	required
augmented	bool	Apply the model’s augmentation. Training only: validation is read the same way every epoch, or two epochs measure different things.	`False`
shuffle	bool	Shuffle between epochs. Training only, for the same reason.	`False`
only_images	bool	Read images alone, with no targets.	`False`
for_loss	bool	Whether these batches are going to the loss. When they are and the loss asks for one, each batch carries a third item: the signed distance map for `boundary`, or the foreground mask for a regression loss scoring the foreground. Both are computed in the loader’s workers rather than in the loss - the distance transform costs more than an epoch of a small 3D model - and the paths that only score or predict should not pay for either.	`True`

Name	Type	Description
		A `torch.utils.data.DataLoader`. Batches hold two items, or three when the
		loss asked the loader for something.