mmagic.models.editors.tdan.tdan

Module Contents

Classes

TDAN	TDAN model for video super-resolution.

class mmagic.models.editors.tdan.tdan.TDAN(generator, pixel_loss, lq_pixel_loss, train_cfg=None, test_cfg=None, init_cfg=None, data_preprocessor=None)

Bases: mmagic.models.BaseEditModel

TDAN model for video super-resolution.

Paper:

TDAN: Temporally-Deformable Alignment Network for Video Super- Resolution, CVPR, 2020

Parameters

• generator (dict) – Config for the generator structure.

• pixel_loss (dict) – Config for pixel-wise loss.

• lq_pixel_loss (dict) – Config for pixel-wise loss for the LQ images.

• train_cfg (dict) – Config for training. Default: None.

• test_cfg (dict) – Config for testing. Default: None.

• init_cfg (dict, optional) – The weight initialized config for BaseModule.

• data_preprocessor (dict, optional) – The pre-process config of BaseDataPreprocessor.

forward_train(inputs, data_samples=None, **kwargs)

Forward training. Returns dict of losses of training.

Parameters

• inputs (torch.Tensor) – batch input tensor collated by data_preprocessor.

• data_samples (List[BaseDataElement], optional) – data samples collated by data_preprocessor.

Returns

Dict of losses.

Return type

dict

forward_tensor(inputs, data_samples=None, training=False, **kwargs)

Forward tensor. Returns result of simple forward.

Parameters

• inputs (torch.Tensor) – batch input tensor collated by data_preprocessor.

• data_samples (List[BaseDataElement], optional) – data samples collated by data_preprocessor.

• training (bool) – Whether is training. Default: False.

Returns

results of forward inference and

forward train.

Return type

(Tensor | List[Tensor])