architecture/grl_common/upsample.py

import math

import torch.nn as nn


class Upsample(nn.Module):
    """Upsample module.
    Args:
        scale (int): Scale factor. Supported scales: 2^n and 3.
        num_feat (int): Channel number of intermediate features.
    """

    def __init__(self, scale, num_feat):
        super(Upsample, self).__init__()
        m = []
        if (scale & (scale - 1)) == 0:  # scale = 2^n
            for _ in range(int(math.log(scale, 2))):
                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
                m.append(nn.PixelShuffle(2))
        elif scale == 3:
            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
            m.append(nn.PixelShuffle(3))
        else:
            raise ValueError(
                f"scale {scale} is not supported. " "Supported scales: 2^n and 3."
            )
        self.up = nn.Sequential(*m)

    def forward(self, x):
        return self.up(x)


class UpsampleOneStep(nn.Module):
    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
       Used in lightweight SR to save parameters.
    Args:
        scale (int): Scale factor. Supported scales: 2^n and 3.
        num_feat (int): Channel number of intermediate features.
    """

    def __init__(self, scale, num_feat, num_out_ch):
        super(UpsampleOneStep, self).__init__()
        self.num_feat = num_feat
        m = []
        m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1))
        m.append(nn.PixelShuffle(scale))
        self.up = nn.Sequential(*m)

    def forward(self, x):
        return self.up(x)