num_gpu = 1 if torch.cuda.is_available() else 0
# load the models
from dcgan import Discriminator, Generator
D = Discriminator(ngpu=1).eval()
G = Generator(ngpu=1).eval()
D = D.double()
# load weights
D.load_state_dict(torch.load('weights/netD_epoch_199.pth', map_location='cpu'))
G.load_state_dict(torch.load('weights/netG_epoch_199.pth', map_location='cpu'))
if torch.cuda.is_available():
D = D.cuda()
G = G.cuda()
batch_size = 25
latent_size = 100
fixed_noise = torch.randn(batch_size, latent_size, 1, 1)
if torch.cuda.is_available():
fixed_noise = fixed_noise.cuda()
fake_images = G(fixed_noise)
# z = torch.randn(batch_size, latent_size).cuda()
# z = Variable(z)
# fake_images = G(z)
fake_images_np = fake_images.cpu().detach().numpy()
netG = Generator(ngpu, nz + t_in, ngf, nc).to(device)
netD = Discriminator(ngpu, nc, ndf, t_in).to(device)
net_g_path = '/content/drive/MyDrive/Deep_Learning/projects/dcgan/continue_train/checkpoints/netG_latest.pth'
net_d_path = '/content/drive/MyDrive/Deep_Learning/projects/dcgan/continue_train/checkpoints/netD_latest.pth'
netG.load_state_dict(torch.load(net_g_path))
netD.load_state_dict(torch.load(net_d_path))
print("Done loading model!")
# Handle multi-gpu if desired
# if (device.type == 'cuda') and (ngpu > 1):
# netG = nn.DataParallel(netG, list(range(ngpu)))
# netD = nn.DataParallel(netD, list(range(ngpu)))
# netG.apply(weights_init)
# netD.apply(weights_init)
netG = netG.cuda()
netD = netD.cuda()
# Initialize BCELoss function
criterion = nn.BCELoss()
fixed_noise = torch.randn(64, nz + t_in, 1, 1, device=device)
real_label = 1.
fake_label = 0.
optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))
# Training Loop
# Lists to keep track of progress
img_list = []
G_losses = []
dped_dir, vgg_dir, eval_step = utils.process_command_args(sys.argv)
# 使得随机数据可预测
np.random.seed(0)
# Loss function
adversarial_loss = torch.nn.BCELoss()
# Initialize generator and discriminator
generator = Generator()
discriminator = Discriminator()
cuda = True if torch.cuda.is_available() else False
if cuda:
generator.cuda()
discriminator.cuda()
adversarial_loss.cuda()
# Initialize weights
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)
# Configure data loader
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])
print('Using {} dataloader workers every process'.format(nw))
dataloader = torch.utils.data.DataLoader(
LoadData(
dped_dir=dped_dir,
dataset_size=train_size,
image_size=PATCH_SIZE,
test=False,
