CNN_Head.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import math

class Simple_Head(nn.Module):
    def __init__(self, in_c=3, out_c=3, nf=64, scale=1):
        super(Simple_Head, self).__init__()
        self.upscale = scale
        print('Head: Simple_Head')

        self.conv_first = nn.Conv2d(in_c, nf, 3, 1, 1, bias=True)


        # upsampling
        if self.upscale == 2:
            self.upconv1 = nn.Conv2d(nf, nf * 4, 3, 1, 1, bias=True)
            self.pixel_shuffle = nn.PixelShuffle(2)
        elif self.upscale == 3:
            self.upconv1 = nn.Conv2d(nf, nf * 9, 3, 1, 1, bias=True)
            self.pixel_shuffle = nn.PixelShuffle(3)
        elif self.upscale == 4:
            self.upconv1 = nn.Conv2d(nf, nf * 4, 3, 1, 1, bias=True)
            self.upconv2 = nn.Conv2d(nf, nf * 4, 3, 1, 1, bias=True)
            self.pixel_shuffle = nn.PixelShuffle(2)

        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        self.conv_last = nn.Conv2d(nf, out_c, 3, 1, 1, bias=True)

        # activation function
        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)


    def forward(self, x):
        out = self.lrelu(self.conv_first(x))

        if self.upscale == 4:
            out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))
            out = self.lrelu(self.pixel_shuffle(self.upconv2(out)))
        elif self.upscale == 3 or self.upscale == 2:
            out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))

        out = self.conv_last(self.lrelu(self.HRconv(out)))
        
        return out