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mmedit.models.editors.real_basicvsr.real_basicvsr_net

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RealBasicVSRNet

RealBasicVSR network structure for real-world video super-resolution.

class mmedit.models.editors.real_basicvsr.real_basicvsr_net.RealBasicVSRNet(mid_channels=64, num_propagation_blocks=20, num_cleaning_blocks=20, dynamic_refine_thres=255, spynet_pretrained=None, is_fix_cleaning=False, is_sequential_cleaning=False)[source]

Bases: mmengine.model.BaseModule

RealBasicVSR network structure for real-world video super-resolution.

Support only x4 upsampling.

Paper:

Investigating Tradeoffs in Real-World Video Super-Resolution, arXiv

Parameters
  • mid_channels (int, optional) – Channel number of the intermediate features. Default: 64.

  • num_propagation_blocks (int, optional) – Number of residual blocks in each propagation branch. Default: 20.

  • num_cleaning_blocks (int, optional) – Number of residual blocks in the image cleaning module. Default: 20.

  • dynamic_refine_thres (int, optional) – Stop cleaning the images when the residue is smaller than this value. Default: 255.

  • spynet_pretrained (str, optional) – Pre-trained model path of SPyNet. Default: None.

  • is_fix_cleaning (bool, optional) – Whether to fix the weights of the image cleaning module during training. Default: False.

  • is_sequential_cleaning (bool, optional) – Whether to clean the images sequentially. This is used to save GPU memory, but the speed is slightly slower. Default: False.

forward(lqs, return_lqs=False)[source]

Forward function for BasicVSR++.

Parameters
  • lqs (tensor) – Input low quality (LQ) sequence with shape (n, t, c, h, w).

  • return_lqs (bool) – Whether to return LQ sequence. Default: False.

Returns

Output HR sequence.

Return type

Tensor

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