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mmedit.models.editors.tdan

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TDAN

TDAN model for video super-resolution.

TDANNet

TDAN network structure for video super-resolution.

class mmedit.models.editors.tdan.TDAN(generator, pixel_loss, lq_pixel_loss, train_cfg=None, test_cfg=None, init_cfg=None, data_preprocessor=None)[源代码]

Bases: mmedit.models.BaseEditModel

TDAN model for video super-resolution.

Paper:

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

参数
  • 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.

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

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

返回

Dict of losses.

返回类型

dict

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

Forward tensor. Returns result of simple forward.

参数
  • 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.

返回

results of forward inference and

forward train.

返回类型

(Tensor | List[Tensor])

class mmedit.models.editors.tdan.TDANNet(in_channels=3, mid_channels=64, out_channels=3, num_blocks_before_align=5, num_blocks_after_align=10)[源代码]

Bases: mmengine.model.BaseModule

TDAN network structure for video super-resolution.

Support only x4 upsampling.

Paper:

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

参数
  • in_channels (int) – Number of channels of the input image. Default: 3.

  • mid_channels (int) – Number of channels of the intermediate features. Default: 64.

  • out_channels (int) – Number of channels of the output image. Default: 3.

  • num_blocks_before_align (int) – Number of residual blocks before temporal alignment. Default: 5.

  • num_blocks_after_align (int) – Number of residual blocks after temporal alignment. Default: 10.

forward(lrs)

Forward function for TDANNet.

参数

lrs (Tensor) – Input LR sequence with shape (n, t, c, h, w).

返回

Output HR image with shape (n, c, 4h, 4w) and aligned LR images with shape (n, t, c, h, w).

返回类型

tuple[Tensor]

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