def ex_cdcgan():
datasets = ReWrite.load_data_in_seq(source_files)
datasets = ReWrite.MyDataSet(datasets)
data_loader = DataLoader(
datasets,
batch_size=256,
shuffle=True,
)
latent_dim = 20
generator = G_D_Module.GeneratorCDCGAN(
latent_dim, opt.n_classes, img_shape) # latent_dim should be 20
discriminator = G_D_Module.DiscriminatorCDCGAN(opt.n_classes, img_shape,
latent_dim)
TrainFunction.train_cdcgan(generator,
discriminator,
data_loader,
opt.n_epochs,
opt.lr,
opt.b1,
opt.b2,
opt.latent_dim,
opt.n_classes,
cuda,
fist_train=False)
def show_cdcgan_data():
latent_dim = 20
data_list = os.listdir('coedatas')
data = []
for path in data_list:
data.append(data_read('coedatas/' + path))
FloatTensor = torch.FloatTensor
LongTensor = torch.LongTensor
generator = G_D_Module.GeneratorCDCGAN(latent_dim, 5, (1, 32, 32))
generator.load_state_dict(torch.load('GANParameters/CDCGAN/generator.pt'))
noise = FloatTensor(np.random.normal(0, 1, (len(data)**2, latent_dim)))
single_list = list(range(len(data)))
label = LongTensor(single_list * len(data))
gen_imags = generator(noise, label)
# real
# imgs = np.empty([len(data) ** 2, 1, 32, 32], dtype=float)
# for i in range(len(data)):
# for j in range(len(data)):
# index = random.randint(0, len(data[j]) - 1)
# imgs[i * len(data) + j][0] = data[j][index]
# for i in range(imgs.shape[0]):
# plt.subplot(len(data_list), len(data_list), i + 1)
# plt.axis('off')
# plt.contourf(imgs[i][0])
# plt.savefig('caches/real.jpg', bbox_inches='tight')
# plt.close()
for i in range(gen_imags.shape[0]):
plt.subplot(len(data), len(data), i + 1)
plt.axis('off')
plt.contourf(gen_imags[i][0].detach().numpy())
plt.savefig('caches/gen.jpg', bbox_inches='tight')
plt.close()