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mmedit.models.editors.basicvsr_plusplus_net

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Classes

BasicVSRPlusPlusNet

BasicVSR++ network structure.

class mmedit.models.editors.basicvsr_plusplus_net.BasicVSRPlusPlusNet(mid_channels=64, num_blocks=7, max_residue_magnitude=10, is_low_res_input=True, spynet_pretrained=None, cpu_cache_length=100)[源代码]

Bases: mmengine.model.BaseModule

BasicVSR++ network structure.

Support either x4 upsampling or same size output.

Paper:

BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

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

  • num_blocks (int, optional) – The number of residual blocks in each propagation branch. Default: 7.

  • max_residue_magnitude (int) – The maximum magnitude of the offset residue (Eq. 6 in paper). Default: 10.

  • is_low_res_input (bool, optional) – Whether the input is low-resolution or not. If False, the output resolution is equal to the input resolution. Default: True.

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

  • cpu_cache_length (int, optional) – When the length of sequence is larger than this value, the intermediate features are sent to CPU. This saves GPU memory, but slows down the inference speed. You can increase this number if you have a GPU with large memory. Default: 100.

check_if_mirror_extended(lqs)

Check whether the input is a mirror-extended sequence.

If mirror-extended, the i-th (i=0, …, t-1) frame is equal to the (t-1-i)-th frame.

参数

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

compute_flow(lqs)

Compute optical flow using SPyNet for feature alignment.

Note that if the input is an mirror-extended sequence, ‘flows_forward’ is not needed, since it is equal to ‘flows_backward.flip(1)’.

参数

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

返回

Optical flow. ‘flows_forward’ corresponds to the

flows used for forward-time propagation (current to previous). ‘flows_backward’ corresponds to the flows used for backward-time propagation (current to next).

返回类型

tuple(Tensor)

propagate(feats, flows, module_name)

Propagate the latent features throughout the sequence.

参数
  • dict (feats) – Features from previous branches. Each component is a list of tensors with shape (n, c, h, w).

  • flows (tensor) – Optical flows with shape (n, t - 1, 2, h, w).

  • module_name (str) – The name of the propagation branches. Can either be ‘backward_1’, ‘forward_1’, ‘backward_2’, ‘forward_2’.

返回

A dictionary containing all the propagated

features. Each key in the dictionary corresponds to a propagation branch, which is represented by a list of tensors.

返回类型

dict(list[tensor])

upsample(lqs, feats)

Compute the output image given the features.

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

  • feats (dict) – The features from the propagation branches.

返回

Output HR sequence with shape (n, t, c, 4h, 4w).

返回类型

Tensor

forward(lqs)

Forward function for BasicVSR++.

参数

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

返回

Output HR sequence with shape (n, t, c, 4h, 4w).

返回类型

Tensor

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