Args: aligned_feat (Tensor): Aligned features with shape (b, t, c, h, w). Returns: Tensor: Features aft

Forward function for BasicVSR++. Args: lqs (tensor): Input low quality (LQ) sequence with shape (n, t, c, h, w)

Args: x (Tensor): Input tensor with shape (b, num_feat, t, h, w). Returns: Tensor: Output with shape (b

Args: x (Tensor): Input tensor with shape (b, num_feat, t, h, w). Returns: Tensor: Output with shape (b

Forward function for DynamicUpsamplingFilter. Args: x (Tensor): Input image with 3 channels. The shape is (n, 3, h, w).

Args: x (Tensor): Input with shape (b, 7, c, h, w) Returns: Tensor: Output with shape (b, c, h * scale,

Get Inception feature maps. Args: x (Tensor): Input tensor of shape (b, 3, h, w). Values are expected to be i

Now only support testing with batch size = 0. Args: x (Tensor): Input faces with shape (b, c, 512, 512).

Args: tensor_input (Tensor): Input tensor with shape (b, 8, h, w). 8 channels contain: [refer

Args: ref (Tensor): Reference image with shape of (b, 3, h, w). supp: The supporting image to be warped: (b, 3, h,

Args: lrs: Input lr frames: (b, 7, 3, h, w). Returns: Tensor: SR frame: (b, 3, h, w).

Args: pred (Tensor): of shape (N, C, H, W). Predicted tensor. target (Tensor): of shape (N, C, H, W). Ground truth

Args: pred (Tensor): of shape (N, C, H, W). Predicted tensor. target (Tensor): of shape (N, C, H, W). Ground truth

Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). gt (Tensor): Ground-truth tensor with s

The input is a list of tensors, or a list of (a list of tensors)

Get values according to the filepath from one lmdb named client_key. Args: filepath (str | obj:`Path`): Here, filepath is the

Calculate gradient penalty for wgan-gp. Args: discriminator (nn.Module): Network for the discriminator. real_data (Tensor): R

Rotate image. Args: img (ndarray): Image to be rotated. angle (float): Rotation angle in degrees. Positive values mean

Load file form http url, will download models if necessary. Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py

Utility function for computing log_sum_exp while determining This will be used to determine unaveraged confidence loss across all examples i

Make lmdb from images. Contents of lmdb. The file structure is: example.lmdb ├── data.mdb ├── lock.mdb ├── meta_info.txt

Make noise for noise injection.

Make noise for noise injection.

Match each prior box with the ground truth box of the highest jaccard overlap, encode the bounding boxes, then return the matched indices co

return iof of a and b, numpy version for data augenmentation

return iou of a and b, numpy version for data augenmentation

A helper class for subspace visualization. Input and seg are different images. For the first n levels (including encoder) we use in

Apply non-maximum suppression at test time to avoid detecting too many overlapping bounding boxes for a given object. Args: boxes: