model.py

"""model.py"""

import torch
import torch.nn as nn
#import torch.nn.functional as F
import torch.nn.init as init
from torch.autograd import Variable


class View(nn.Module):
    def __init__(self, size):
        super(View, self).__init__()
        self.size = size

    def forward(self, tensor):
        return tensor.view(self.size)


class WAE(nn.Module):
    """Encoder-Decoder architecture for both WAE-MMD and WAE-GAN."""
    def __init__(self, z_dim=10, nc=3):
        super(WAE, self).__init__()
        self.z_dim = z_dim
        self.nc = nc
        self.encoder = nn.Sequential(
            nn.Conv2d(nc, 128, 4, 2, 1, bias=False),              # B,  128, 32, 32
            nn.BatchNorm2d(128),
            nn.ReLU(True),
            nn.Conv2d(128, 256, 4, 2, 1, bias=False),             # B,  256, 16, 16
            nn.BatchNorm2d(256),
            nn.ReLU(True),
            nn.Conv2d(256, 512, 4, 2, 1, bias=False),             # B,  512,  8,  8
            nn.BatchNorm2d(512),
            nn.ReLU(True),
            nn.Conv2d(512, 1024, 4, 2, 1, bias=False),            # B, 1024,  4,  4
            nn.BatchNorm2d(1024),
            nn.ReLU(True),
            View((-1, 1024*4*4)),                                 # B, 1024*4*4
            nn.Linear(1024*4*4, z_dim)                            # B, z_dim
        )
        self.decoder = nn.Sequential(
            nn.Linear(z_dim, 1024*8*8),                           # B, 1024*8*8
            View((-1, 1024, 8, 8)),                               # B, 1024,  8,  8
            nn.ConvTranspose2d(1024, 512, 4, 2, 1, bias=False),   # B,  512, 16, 16
            nn.BatchNorm2d(512),
            nn.ReLU(True),
            nn.ConvTranspose2d(512, 256, 4, 2, 1, bias=False),    # B,  256, 32, 32
            nn.BatchNorm2d(256),
            nn.ReLU(True),
            nn.ConvTranspose2d(256, 128, 4, 2, 1, bias=False),    # B,  128, 64, 64
            nn.BatchNorm2d(128),
            nn.ReLU(True),
            nn.ConvTranspose2d(128, nc, 1),                       # B,   nc, 64, 64
        )
        self.weight_init()

    def weight_init(self):
        for block in self._modules:
            for m in self._modules[block]:
                kaiming_init(m)

    def forward(self, x):
        z = self._encode(x)
        x_recon = self._decode(z)

        return x_recon, z

    def _encode(self, x):
        return self.encoder(x)

    def _decode(self, z):
        return self.decoder(z)


class Adversary(nn.Module):
    """Adversary architecture(Discriminator) for WAE-GAN."""
    def __init__(self, z_dim=10):
        super(Adversary, self).__init__()
        self.z_dim = z_dim
        self.net = nn.Sequential(
            nn.Linear(z_dim, 512),                                # B, 512
            nn.ReLU(True),
            nn.Linear(512, 512),                                  # B, 512
            nn.ReLU(True),
            nn.Linear(512, 512),                                  # B, 512
            nn.ReLU(True),
            nn.Linear(512, 512),                                  # B, 512
            nn.ReLU(True),
            nn.Linear(512, 1),                                    # B,   1
        )
        self.weight_init()

    def weight_init(self):
        for block in self._modules:
            for m in self._modules[block]:
                kaiming_init(m)

    def forward(self, z):
        return self.net(z)


def kaiming_init(m):
    if isinstance(m, (nn.Linear, nn.Conv2d)):
        init.kaiming_normal(m.weight)
        if m.bias is not None:
            m.bias.data.fill_(0)
    elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
        m.weight.data.fill_(1)
        if m.bias is not None:
            m.bias.data.fill_(0)


def normal_init(m, mean, std):
    if isinstance(m, (nn.Linear, nn.Conv2d)):
        m.weight.data.normal_(mean, std)
        if m.bias.data is not None:
            m.bias.data.zero_()
    elif isinstance(m, (nn.BatchNorm2d, nn.BatchNorm1d)):
        m.weight.data.fill_(1)
        if m.bias.data is not None:
            m.bias.data.zero_()


if __name__ == '__main__':
    pass