import argparse
import torch
import torchvision.utils as vutils
import torch.nn as nn

class NetG(nn.Module):
    def __init__(self, ngf, nz):
        super(NetG, self).__init__()
        self.layer1 = nn.Sequential(
            nn.ConvTranspose2d(nz, ngf * 8, kernel_size=4, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(ngf * 8),
            nn.ReLU(inplace=True)
        )
        self.layer2 = nn.Sequential(
            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 4),
            nn.ReLU(inplace=True)
        )
        self.layer3 = nn.Sequential(
            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf * 2),
            nn.ReLU(inplace=True)
        )
        self.layer4 = nn.Sequential(
            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf),
            nn.ReLU(inplace=True)
        )
        self.layer5 = nn.Sequential(
            nn.ConvTranspose2d(ngf, 3, 5, 3, 1, bias=False),
            nn.Tanh()
        )

    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.layer5(out)
        return out


#Hyperparameter
parser = argparse.ArgumentParser()
parser.add_argument('--batchSize', type=int, default=64)
parser.add_argument('--imageSize', type=int, default=96)
parser.add_argument('--nz', type=int, default=100, help='size of the latent z vector')
parser.add_argument('--ngf', type=int, default=64)
parser.add_argument('--outf', default='imgs/', help='folder to output images and model checkpoints')
opt = parser.parse_args()
device = torch.device("cpu")


netG = NetG(opt.ngf, opt.nz)
netG.load_state_dict(torch.load('imgs/netG_025.pth'))
NetG = netG.to(device)
noise = torch.randn(opt.batchSize, opt.nz, 1, 1)
noise=noise.to(device)
fake = netG(noise)
vutils.save_image(fake.data,'%s/test.png' % (opt.outf),normalize=True)



