mmagic.models.editors.iconvsr

Package Contents

Classes

IconVSRNet

IconVSR network structure for video super-resolution.

class mmagic.models.editors.iconvsr.IconVSRNet(mid_channels=64, num_blocks=30, keyframe_stride=5, padding=2, spynet_pretrained=None, edvr_pretrained=None)

Bases: mmengine.model.BaseModule

IconVSR network structure for video super-resolution.

Support only x4 upsampling.

Paper:

BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond, CVPR, 2021

Parameters

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

• num_blocks (int) – Number of residual blocks in each propagation branch. Default: 30.

• keyframe_stride (int) – Number determining the keyframes. If stride=5, then the (0, 5, 10, 15, …)-th frame will be the keyframes. Default: 5.

• padding (int) – Number of frames to be padded at two ends of the sequence. 2 for REDS and 3 for Vimeo-90K. Default: 2.

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

• edvr_pretrained (str) – Pre-trained model path of EDVR (for refill). Default: None.

spatial_padding(lrs)

Apply padding spatially.

Since the PCD module in EDVR requires that the resolution is a multiple of 4, we apply padding to the input LR images if their resolution is not divisible by 4.

Parameters

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

Returns

Padded LR sequence with shape (n, t, c, h_pad, w_pad).

Return type

Tensor

check_if_mirror_extended(lrs)

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.

Parameters

lrs (tensor) – Input LR images with shape (n, t, c, h, w)

compute_refill_features(lrs, keyframe_idx)

Compute keyframe features for information-refill.

Since EDVR-M is used, padding is performed before feature computation. :param lrs: Input LR images with shape (n, t, c, h, w) :type lrs: Tensor :param keyframe_idx: The indices specifying the keyframes. :type keyframe_idx: list(int)

Returns

The keyframe features. Each key corresponds to the

indices in keyframe_idx.

Return type

dict(Tensor)

compute_flow(lrs)

Compute optical flow using SPyNet for feature warping.

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

Parameters

lrs (tensor) – Input LR images with shape (n, t, c, h, w)

Returns

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).

Return type

tuple(Tensor)

forward(lrs)

Forward function for IconVSR.

Parameters

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

Returns

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

Return type

Tensor