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Source code for mmedit.models.editors.cain.cain

# Copyright (c) OpenMMLab. All rights reserved.
from mmedit.models.base_models import BasicInterpolator
from mmedit.registry import MODELS


@MODELS.register_module()
[docs]class CAIN(BasicInterpolator): """CAIN model for Video Interpolation. Paper: Channel Attention Is All You Need for Video Frame Interpolation Ref repo: https://github.com/myungsub/CAIN Args: 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 :class:`BaseModule`. data_preprocessor (dict, optional): The pre-process config of :class:`BaseDataPreprocessor`. Attributes: init_cfg (dict, optional): Initialization config dict. data_preprocessor (:obj:`BaseDataPreprocessor`): Used for pre-processing data sampled by dataloader to the format accepted by :meth:`forward`. """
[docs] def forward_inference(self, inputs, data_samples=None): """Forward inference. Returns predictions of validation, testing, and simple inference. Args: inputs (torch.Tensor): batch input tensor collated by :attr:`data_preprocessor`. data_samples (List[BaseDataElement], optional): data samples collated by :attr:`data_preprocessor`. Returns: List[EditDataSample]: predictions. """ predictions = super().forward_inference( inputs, data_samples, padding_flag=True) return predictions
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