image_gen_aux/preprocessors/lineart/model.py

# Copyright (c) 2022 Caroline Chan
#
# MIT License
import torch.nn as nn

from ...modeling_utils import ModelMixin


norm_layer = nn.InstanceNorm2d


class ResidualBlock(nn.Module):
    def __init__(self, in_features: int):
        super(ResidualBlock, self).__init__()

        conv_block = [
            nn.ReflectionPad2d(1),
            nn.Conv2d(in_features, in_features, 3),
            norm_layer(in_features),
            nn.ReLU(inplace=True),
            nn.ReflectionPad2d(1),
            nn.Conv2d(in_features, in_features, 3),
            norm_layer(in_features),
        ]

        self.conv_block = nn.Sequential(*conv_block)

    def forward(self, x):
        return x + self.conv_block(x)


class Generator(ModelMixin):
    def __init__(self, input_nc: int = 3, output_nc: int = 1, n_residual_blocks: int = 3, sigmoid: bool = True):
        super(Generator, self).__init__()

        # Initial convolution block
        model0 = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, 64, 7), norm_layer(64), nn.ReLU(inplace=True)]
        self.model0 = nn.Sequential(*model0)

        # Downsampling
        model1 = []
        in_features = 64
        out_features = in_features * 2
        for _ in range(2):
            model1 += [
                nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
                norm_layer(out_features),
                nn.ReLU(inplace=True),
            ]
            in_features = out_features
            out_features = in_features * 2
        self.model1 = nn.Sequential(*model1)

        model2 = []
        # Residual blocks
        for _ in range(n_residual_blocks):
            model2 += [ResidualBlock(in_features)]
        self.model2 = nn.Sequential(*model2)

        # Upsampling
        model3 = []
        out_features = in_features // 2
        for _ in range(2):
            model3 += [
                nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
                norm_layer(out_features),
                nn.ReLU(inplace=True),
            ]
            in_features = out_features
            out_features = in_features // 2
        self.model3 = nn.Sequential(*model3)

        # Output layer
        model4 = [nn.ReflectionPad2d(3), nn.Conv2d(64, output_nc, 7)]
        if sigmoid:
            model4 += [nn.Sigmoid()]

        self.model4 = nn.Sequential(*model4)

    def forward(self, x):
        out = self.model0(x)
        out = self.model1(out)
        out = self.model2(out)
        out = self.model3(out)
        out = self.model4(out)

        return out