Пример #1
0
print_every = config_d['print_every']
z_size = config_d['noise_dim']
n_critic = config_d['critic_iter']

lr = config_d['lr']
beta1 = config_d['beta1']
beta2 = config_d['beta2']

D = Discriminator()
G = Generator(z_size=z_size)
print(D)
print(G)

if cuda:
    G.cuda()
    D.cuda()
    print('GPU available for training. Models moved to GPU')
else:
    print('Training on CPU.')

d_optimizer = optim.Adam(D.parameters(), lr=lr, betas=[beta1, beta2])
g_optimizer = optim.Adam(G.parameters(), lr=lr, betas=[beta1, beta2])

losses_train = []
losses_val = []

reg_lambda = config_d['gp_lambda']

Nbatch = sdat_train.get_batch_size()
N_train_btot = sdat_train.get_Nbatches_tot()
N_val_btot = sdat_val.get_Nbatches_tot()
Пример #2
0
    # os.makedirs(save_path, exist_ok=True)

    root_path = "/Users/yuming/OneDrive/sync/semester/ML/hw/project/dataset/"
    data_path = root_path + "faces/"
    save_path = root_path + "fake-faces/"
    save_csv_loss_g = root_path + "csv/loss_g.csv"
    save_csv_loss_d = root_path + "csv/loss_d.csv"

    # Initialize generator and discriminator
    generator = Generator(opt.latent_dim, img_shape)
    discriminator = Discriminator(img_shape)

    cuda_enabled = torch.cuda.is_available()
    if cuda_enabled:
        generator.cuda()
        discriminator.cuda()
        Tensor = torch.cuda.FloatTensor
    else:
        Tensor = torch.FloatTensor
    # Tensor = torch.cuda.FloatTensor if cuda_enabled else torch.FloatTensor

    image_raw_data = fetch_dataset(data_path)
    data_loader = DataLoader(image_raw_data,
                             batch_size=opt.batch_size,
                             shuffle=True)

    # Optimizers
    optimizer_G = torch.optim.Adam(generator.parameters(),
                                   lr=opt.lr,
                                   betas=(opt.b1, opt.b2))
    optimizer_D = torch.optim.Adam(discriminator.parameters(),