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mmagic.models.editors.flavr.flavr 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from mmagic.models.base_models import BasicInterpolator
from mmagic.registry import MODELS
from mmagic.utils import tensor2img

# TODO tensor2img will be move


@MODELS.register_module()
[文档]class FLAVR(BasicInterpolator): """FLAVR model for video interpolation. Paper: FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation Ref repo: https://github.com/tarun005/FLAVR 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`. """ @staticmethod
[文档] def merge_frames(input_tensors, output_tensors): """merge input frames and output frames. Interpolate a frame between the given two frames. Merged from [[in1, in2, in3, in4], [in2, in3, in4, in5], ...] [[out1], [out2], [out3], ...] to [in1, in2, out1, in3, out2, ..., in(-3), out(-1), in(-2), in(-1)] Args: input_tensors (Tensor): The input frames with shape [n, 4, c, h, w] output_tensors (Tensor): The output frames with shape [n, 1, c, h, w]. Returns: list[np.array]: The final frames. """ num_frames = input_tensors.shape[0] result = [tensor2img(input_tensors[0, 0])] for i in range(num_frames): result.append(tensor2img(input_tensors[i, 1])) result.append(tensor2img(output_tensors[i, 0])) result.append(tensor2img(input_tensors[-1, 2])) result.append(tensor2img(input_tensors[-1, 3])) return result
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