import torch
import torch.nn as nn

class Generator(nn.Module):
    def __init__(self, z_dim=100, img_channels=3):
        super(Generator, self).__init__()
        self.gen = nn.Sequential(
            # input is Z, going into a convolution
            nn.ConvTranspose2d(z_dim, 512, 4, 1, 0, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(True),
            # state size. 512 x 4 x 4
            nn.ConvTranspose2d(512, 256, 4, 2, 1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(True),
            # state size. 256 x 8 x 8
            nn.ConvTranspose2d(256, 128, 4, 2, 1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(True),
            # state size. 128 x 16 x 16
            nn.ConvTranspose2d(128, 64, 4, 2, 1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(True),
            # state size. 64 x 32 x 32
            nn.ConvTranspose2d(64, img_channels, 4, 2, 1, bias=False),
            nn.Tanh()
            # state size. img_channels x 64 x 64
        )

    def forward(self, input):
        return self.gen(input)

class Discriminator(nn.Module):
    def __init__(self, img_channels=3):
        super(Discriminator, self).__init__()
        self.disc = nn.Sequential(
            # input is img_channels x 64 x 64
            nn.Conv2d(img_channels, 64, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. 64 x 32 x 32
            nn.Conv2d(64, 128, 4, 2, 1, bias=False),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. 128 x 16 x 16
            nn.Conv2d(128, 256, 4, 2, 1, bias=False),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. 256 x 8 x 8
            nn.Conv2d(256, 512, 4, 2, 1, bias=False),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),
            # state size. 512 x 4 x 4
            nn.Conv2d(512, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

    def forward(self, input):
        return self.disc(input).view(-1, 1).squeeze(1)

batch_size = 32
latent_vector_size = 100

generator = Generator()
discriminator = Discriminator()

generator.load_state_dict(torch.load('netG.pth', map_location=torch.device('cpu') ))
discriminator.load_state_dict(torch.load('netD.pth', map_location=torch.device('cpu') ))