Args: image(tensor): batch x height x width Returns: Tensor: batch x height x width

Args: image(tensor): batch x height x width Returns: Tensor: batch x height x width

Args: image(tensor): batch x height x width Returns: Tensor: batch x height x width

Args: compressed(dict(tensor)): batch x h*w/64 x 8 x 8 imgh(int) imgw(int) factor(float)

Args: image(tensor): batch x height x width Returns: Tensor: batch x height x width

Args: image(tensor): batch x height x width Returns: Tensor: batch x height x width

Args: image(tensor): batch x height x width Returns: Tensor: batch x height x width

Args: patches(tensor) batch x height*width/64, height x width height(int) width(int) Returns:

Args: y(tensor): y channel image cb(tensor): cb channel cr(tensor): cr channel Returns:

Args: image(tensor): batch x height x width x 3 Returns: Tensor: batch x 3 x height x width

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

Args: input (Tensor): The input for the loss module, i.e., the network prediction. target_is_real (bo

Forward function. Args: x (Tensor): Tensor with shape (n, c, h, w) Returns: Tensor: The Gaussian-blurred ten

Forward function. Args: discriminator (nn.Module): Network for the discriminator. real_data (Tensor): Real input data

Forward function. Args: x (Tensor): Tensor with shape (n, c, h, w) Returns: Tensor: Loss.

Forward function. Args: img (Tensor): Input tensor with shape (n, c, h, w). Returns: Tensor: Forward results

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

Forward function.

Forward function for ResidualBlocksWithInputConv. Args: feat (Tensor): Input feature with shape (n, in_channels, h, w) R

Forward function of SPyNet. This function computes the optical flow from ref to supp. Args: ref (Tensor): Reference imag

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

Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). Returns: Tensor: Forward results.

Forward function for PixelShufflePack. Args: x (Tensor): Input tensor with shape (n, c, h, w). Returns: Tens

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

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

Forward function. Args: pred (torch.Tensor): Tensor with shape of (n, c, h, w). mask (torch.Tensor, optional): Tensor

Returns losses or predictions of training, validation, testing, and simple inference process. ``forward`` method of BaseModel is an a

Performs normalization、padding and channel order conversion. Args: data (dict): Input data to process. training (bool

Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). Returns: Tensor: Forward results.

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

Forward function. Args: maps (Tuple[Tensor]): Input tensors. soft_attention (Tensor): Soft-attention tensor.

Forward function. Args: x: input features with shape of (num_windows*B, N, C) mask: (0/-inf) mask with shape of (num_w

Forward function. Args: x: Input feature, tensor size (B, D, H, W, C). mask_matrix: Attention mask for cyclic shift.

Forward function. Args: x: Input feature, tensor size (B, D, H, W, C).

Forward function. Args: x: Input feature, tensor size (B, C, D, H, W).

Forward function.

Forward function.

Forward function. Args: x: input features with shape of (num_windows*B, N, C) mask: (0/-inf) mask with shape of (num_w

Forward function. Args: x: Input feature, tensor size (B, D, H, W, C). mask_matrix: Attention mask for cyclic shift.

Forward function. Args: x: Input feature, tensor size (B, D, H, W, C).

Forward function. Args: x: Input feature, tensor size (B, C, D, H, W).

Forward function.

Forward function.

Forward function. Args: x: input features with shape of (num_windows*B, N, C) mask: (0/-inf) mask with shape of (num_w

Forward function. Args: x: Input feature, tensor size (B, D, H, W, C). mask_matrix: Attention mask for cyclic shift.

Forward function. Args: x: Input feature, tensor size (B, D, H, W, C).