BatchMeta

class lib.training.data.collate.BatchMeta(mask_face: list[Tensor] | None = None, mask_eye: list[Tensor] | None = None, mask_mouth: list[Tensor] | None = None)

Bases: object

Dataclass that holds meta information required for training a batch of images

All lists are of len(number model outputs per side) with tensors in shape (batch_size, num_inputs, 1, H, W)

Attributes Summary

mask_eye	The eye mask if eye loss multipliers > 1 for each output in NCHW order
mask_face	The selected face mask for penalized loss/learn mask for each output in NCHW order
mask_mouth	The mouth mask if mouth loss multipliers > 1 for each output in NCHW order

Methods Summary

to(device)

Place all contained tensors onto the given device

Attributes Documentation

Parameters:

• mask_face (list[Tensor] | None)

• mask_eye (list[Tensor] | None)

• mask_mouth (list[Tensor] | None)

mask_eye: list[Tensor] | None = None

The eye mask if eye loss multipliers > 1 for each output in NCHW order

mask_face: list[Tensor] | None = None

The selected face mask for penalized loss/learn mask for each output in NCHW order

mask_mouth: list[Tensor] | None = None

The mouth mask if mouth loss multipliers > 1 for each output in NCHW order

Methods Documentation

to(device: str | torch.Device) → T.Self

Place all contained tensors onto the given device

Parameters:

device (str | torch.Device) – The device to place the tensors on to

Return type:

This object with the tensors placed on the requested device

mask_eye: list[Tensor] | None = None

The eye mask if eye loss multipliers > 1 for each output in NCHW order

mask_face: list[Tensor] | None = None

The selected face mask for penalized loss/learn mask for each output in NCHW order

mask_mouth: list[Tensor] | None = None

The mouth mask if mouth loss multipliers > 1 for each output in NCHW order

to(device: str | torch.Device) → T.Self

Place all contained tensors onto the given device

Parameters:

device (str | torch.Device) – The device to place the tensors on to

Return type:

This object with the tensors placed on the requested device