mmagic.models.editors.basicvsr.basicvsr

Module Contents

Classes

BasicVSR

BasicVSR model for video super-resolution.

class mmagic.models.editors.basicvsr.basicvsr.BasicVSR(generator, pixel_loss, ensemble=None, train_cfg=None, test_cfg=None, init_cfg=None, data_preprocessor=None)

Bases: mmagic.models.BaseEditModel

BasicVSR model for video super-resolution.

Note that this model is used for IconVSR.

Paper:

BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond, CVPR, 2021

Parameters

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

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

• ensemble (dict) – Config for ensemble. Default: None.

• 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.

check_if_mirror_extended(lrs)

Check whether the input is a mirror-extended sequence.

If mirror-extended, the i-th (i=0, …, t-1) frame is equal to the (t-1-i)-th frame.

Parameters

lrs (tensor) – Input LR images with shape (n, t, c, h, w)

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

Forward inference. Returns predictions of validation, testing.

Parameters

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

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

Returns

predictions.

Return type

DataSample