models/networks/generator_module/DecoderCNN.py

import torch.nn as nn
class Decoder_MV(nn.Module):
    def __init__(self, d_model=768, seq_input=False):
        super(Decoder_MV, self).__init__()
        self.d_model = d_model
        self.seq_input = seq_input
        self.decoder = nn.Sequential(
            # Proccess Layer 1

            # Upsample Layer 2
            nn.ReflectionPad2d(1),
            nn.Conv2d(int(self.d_model), 256, 3, 1, 0),
            nn.ReLU(),
            nn.Upsample(scale_factor=2, mode='nearest'),
            nn.ReflectionPad2d(1),
            nn.Conv2d(256, 256, 3, 1, 0),
            nn.ReLU(),
            nn.ReflectionPad2d(1),
            nn.Conv2d(256, 256, 3, 1, 0),
            nn.ReLU(),
            nn.ReflectionPad2d(1),
            nn.Conv2d(256, 256, 3, 1, 0),
            nn.ReLU(),

            # Upsample Layer 3
            nn.ReflectionPad2d(1),
            nn.Conv2d(256, 128, 3, 1, 0),
            nn.ReLU(),
            nn.Upsample(scale_factor=2, mode='nearest'),
            nn.ReflectionPad2d(1),
            nn.Conv2d(128, 128, 3, 1, 0),
            nn.ReLU(),

            # Upsample Layer 4
            nn.ReflectionPad2d(1),
            nn.Conv2d(128, 64, 3, 1, 0),
            nn.ReLU(),
            nn.Upsample(scale_factor=2, mode='nearest'),
            nn.ReflectionPad2d(1),
            nn.Conv2d(64, 64, 3, 1, 0),
            nn.ReLU(),

            # Channel to 3
            nn.ReflectionPad2d(1),
            nn.Conv2d(64, 3, 3, 1, 0),
        )

    def forward(self, x, input_resolution):
        if self.seq_input == True:
            B, N, C = x.size()
            #       H, W = math.ceil(self.img_H//self.patch_size), math.ceil(self.img_W//self.patch_size)
            (H, W) = input_resolution
            x = x.permute(0, 2, 1).reshape(B, C, H, W)
        x = self.decoder(x)
        return x

vgg_structures = nn.Sequential(
    nn.Conv2d(3, 3, (1, 1)),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(3, 64, (3, 3)),
    nn.ReLU(),  # relu1-1
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(64, 64, (3, 3)),
    nn.ReLU(),  # relu1-2
    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(64, 128, (3, 3)),
    nn.ReLU(),  # relu2-1
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(128, 128, (3, 3)),
    nn.ReLU(),  # relu2-2
    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(128, 256, (3, 3)),
    nn.ReLU(),  # relu3-1
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(256, 256, (3, 3)),
    nn.ReLU(),  # relu3-2
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(256, 256, (3, 3)),
    nn.ReLU(),  # relu3-3
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(256, 256, (3, 3)),
    nn.ReLU(),  # relu3-4
    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(256, 512, (3, 3)),
    nn.ReLU(),  # relu4-1, this is the last layer used
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(512, 512, (3, 3)),
    nn.ReLU(),  # relu4-2
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(512, 512, (3, 3)),
    nn.ReLU(),  # relu4-3
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(512, 512, (3, 3)),
    nn.ReLU(),  # relu4-4
    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(512, 512, (3, 3)),
    nn.ReLU(),  # relu5-1
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(512, 512, (3, 3)),
    nn.ReLU(),  # relu5-2
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(512, 512, (3, 3)),
    nn.ReLU(),  # relu5-3
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(512, 512, (3, 3)),
    nn.ReLU()  # relu5-4
)

decoder_stem = nn.Sequential(
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(512, 256, (3, 3)),
    nn.ReLU(),
    nn.Upsample(scale_factor=2, mode='nearest'),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(256, 256, (3, 3)),
    nn.ReLU(),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(256, 256, (3, 3)),
    nn.ReLU(),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(256, 256, (3, 3)),
    nn.ReLU(),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(256, 128, (3, 3)),
    nn.ReLU(),
    nn.Upsample(scale_factor=2, mode='nearest'),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(128, 128, (3, 3)),
    nn.ReLU(),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(128, 64, (3, 3)),
    nn.ReLU(),
    nn.Upsample(scale_factor=2, mode='nearest'),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(64, 64, (3, 3)),
    nn.ReLU(),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(64, 3, (3, 3)),
)