mmagic.models.editors.cain.cain

Module Contents

Classes

CAIN	CAIN model for Video Interpolation.

class mmagic.models.editors.cain.cain.CAIN(generator: dict, pixel_loss: dict, train_cfg: Optional[dict] = None, test_cfg: Optional[dict] = None, required_frames: int = 2, step_frames: int = 1, init_cfg: Optional[dict] = None, data_preprocessor: Optional[dict] = None)

Bases: mmagic.models.base_models.BasicInterpolator

CAIN model for Video Interpolation.

Paper: Channel Attention Is All You Need for Video Frame Interpolation Ref repo: https://github.com/myungsub/CAIN

Parameters

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

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

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

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

• required_frames (int) – Required frames in each process. Default: 2

• step_frames (int) – Step size of video frame interpolation. Default: 1

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

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

init_cfg

Initialization config dict.

Type

dict, optional

data_preprocessor

Used for pre-processing data sampled by dataloader to the format accepted by forward().

Type

BaseDataPreprocessor

forward_inference(inputs, data_samples=None)

Forward inference. Returns predictions of validation, testing, and simple inference.

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

List[DataSample]