models.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init

from utils import weights_init_normal


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

        conv_block = [  nn.ReflectionPad2d(1),
                        nn.Conv2d(in_features, in_features, 3),
                        nn.InstanceNorm2d(in_features),
                        nn.ReLU(inplace=True),
                        nn.ReflectionPad2d(1),
                        nn.Conv2d(in_features, in_features, 3),
                        nn.InstanceNorm2d(in_features)  ]

        self.conv_block = nn.Sequential(*conv_block)

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

class Generator(nn.Module):
    def __init__(self, input_nc, output_nc, n_residual_blocks=9):
        super(Generator, self).__init__()

        # Initial convolution block       
        model = [   nn.ReflectionPad2d(3),
                    nn.Conv2d(input_nc, 64, 7),
                    nn.InstanceNorm2d(64),
                    nn.ReLU(inplace=True) ]

        # Downsampling
        in_features = 64
        out_features = in_features*2
        for _ in range(2):
            model += [  nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
                        nn.InstanceNorm2d(out_features),
                        nn.ReLU(inplace=True) ]
            in_features = out_features
            out_features = in_features*2

        # Residual blocks
        for _ in range(n_residual_blocks):
            model += [ResidualBlock(in_features)]

        # Upsampling
        out_features = in_features//2
        for _ in range(2):
            model += [  nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
                        nn.InstanceNorm2d(out_features),
                        nn.ReLU(inplace=True) ]
            in_features = out_features
            out_features = in_features//2

        # Output layer
        model += [  nn.ReflectionPad2d(3),
                    nn.Conv2d(64, output_nc, 7),
                    nn.Tanh() ]

        self.model = nn.Sequential(*model)
        self.apply(weights_init_normal)

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

class Discriminator(nn.Module):
    def __init__(self, input_nc):
        super(Discriminator, self).__init__()

        # A bunch of convolutions one after another
        # 256 x 256
        model = [   nn.Conv2d(input_nc, 64, 4, stride=2, padding=1),
                    nn.LeakyReLU(0.2, inplace=True) ]

        # 128 x 128
        model += [  nn.Conv2d(64, 128, 4, stride=2, padding=1),
                    nn.InstanceNorm2d(128), 
                    nn.LeakyReLU(0.2, inplace=True) ]

        # 64 x 64
        model += [  nn.Conv2d(128, 256, 4, stride=2, padding=1),
                    nn.InstanceNorm2d(256), 
                    nn.LeakyReLU(0.2, inplace=True) ]

        # 32 x 32
        model += [  nn.Conv2d(256, 512, 4, stride=1, padding=1),
                    nn.InstanceNorm2d(512), 
                    nn.LeakyReLU(0.2, inplace=True) ]

        # FCN classification layer
        # 31 x 31
        model += [nn.Conv2d(512, 1, 4, stride=1, padding=1)]

        # 30 x 30
        self.model = nn.Sequential(*model)
        self.apply(weights_init_normal)

    def forward(self, x):
        x = self.model(x)
        # Average pooling and flatten
        return F.avg_pool2d(x, x.size()[2:]).view(x.size()[0], -1, 1, 1)
        # Do not flatten i.e. PatchGAN
        #return x

class Fire(nn.Module):
    def __init__(self, inplanes, squeeze_planes,
                 expand1x1_planes, expand3x3_planes):
        super(Fire, self).__init__()
        self.inplanes = inplanes
        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
        self.squeeze_activation = nn.ReLU(inplace=True)
        self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
                                   kernel_size=1)
        self.expand1x1_activation = nn.ReLU(inplace=True)
        self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
                                   kernel_size=3, padding=1)
        self.expand3x3_activation = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.squeeze_activation(self.squeeze(x))
        return torch.cat([
            self.expand1x1_activation(self.expand1x1(x)),
            self.expand3x3_activation(self.expand3x3(x))
        ], 1)

class SqueezeNet(nn.Module):
    def __init__(self, version='1_0', num_classes=9):
        super(SqueezeNet, self).__init__()
        self.num_classes = num_classes
        self.version = version
        if version == '1_0':
            self.features = nn.Sequential(
                nn.Conv2d(3, 96, kernel_size=7, stride=2),
                nn.ReLU(inplace=True),
                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
                Fire(96, 16, 64, 64),
                Fire(128, 16, 64, 64),
                Fire(128, 32, 128, 128),
                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
                Fire(256, 32, 128, 128),
                Fire(256, 48, 192, 192),
                Fire(384, 48, 192, 192),
                Fire(384, 64, 256, 256),
                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
                Fire(512, 64, 256, 256))
            # Final convolution is initialized differently from the rest
            final_conv = nn.Conv2d(512, self.num_classes, kernel_size=1)
            self.cams = nn.Sequential(
                nn.Dropout(p=0.5),
                final_conv,
                nn.ReLU(inplace=True))
            self.classifier = nn.Sequential(
                nn.AdaptiveAvgPool2d((1, 1)),
                nn.LogSoftmax(dim=1))
        elif version == '1_1':
            self.features = nn.Sequential(
                nn.Conv2d(3, 64, kernel_size=3, stride=2),
                nn.ReLU(inplace=True),
                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
                Fire(64, 16, 64, 64),
                Fire(128, 16, 64, 64),
                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
                Fire(128, 32, 128, 128),
                Fire(256, 32, 128, 128),
                nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
                Fire(256, 48, 192, 192),
                Fire(384, 48, 192, 192),
                Fire(384, 64, 256, 256),
                Fire(512, 64, 256, 256))
            # Final convolution is initialized differently from the rest
            final_conv = nn.Conv2d(512, self.num_classes, kernel_size=1)
            self.cams = nn.Sequential(
                nn.Dropout(p=0.5),
                final_conv,
                nn.ReLU(inplace=True))
            self.classifier = nn.Sequential(
                nn.AdaptiveAvgPool2d((1, 1)),
                nn.LogSoftmax(dim=1))
        else:
            raise ValueError("Unsupported SqueezeNet version {version}:"
                             "1_0/1_1 expected".format(version=version))

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                if m is final_conv:
                    init.normal_(m.weight, mean=0.0, std=0.01)
                else:
                    init.kaiming_uniform_(m.weight)
                if m.bias is not None:
                    init.constant_(m.bias, 0)

    def forward(self, x):
        x = self.features(x)
        masks = self.cams(x)
        x = self.classifier(masks)
        return x, masks