def show_1d_data():
real_data = read_csv_data()
n_class = len(real_data)
weight = 2
index = np.random.randint(450, size=(5, 2))
for i in range(n_class):
for j in range(weight):
plt.subplot(n_class, weight, i * weight + j + 1)
plt.xticks([])
# index = random.randint(0, real_data[i].shape[0] - 1)
plt.plot(real_data[i][index[i][j]])
plt.savefig('caches/real_1d_data.jpg', bbox_inches='tight')
plt.close()
cuda = torch.cuda.is_available()
FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if cuda else torch.LongTensor
latent_dim = 50
generator = G_D_Module.GeneratorConv1D(latent_dim, n_class)
generator.load_state_dict(
torch.load('GANParameters/CONV1DGAN/generator.pt'))
if cuda:
generator.cuda()
noise = FloatTensor(
np.random.normal(0, 1, (n_class * weight, 1, latent_dim)))
labels = LongTensor(list(range(0, n_class)) * weight)
gen_data = generator(noise, labels)
gen_data = gen_data.cpu().detach().numpy()
for i in range(gen_data.shape[0]):
plt.subplot(n_class, weight, i + 1)
plt.xticks([])
# plt.xlabel(str(i // weight))
plt.plot(gen_data[i % n_class][0])
plt.savefig('caches/gen_conv_1d_data.jpg', bbox_inches='tight')
plt.close()
generator = G_D_Module.GeneratorSelfNoise1D()
if cuda:
generator.cuda()
inputs = np.empty((n_class * weight, 1, 1024), dtype=float)
for i in range(inputs.shape[0]):
# index = random.randint(0, real_data[i // weight].shape[0] - 1)
inputs[i][0] = real_data[i // weight][index[i // 2][i % 2]]
inputs = FloatTensor(inputs)
gen_data = generator(inputs)
gen_data = gen_data.cpu().detach().numpy()
for i in range(gen_data.shape[0]):
plt.subplot(n_class, weight, i + 1)
plt.xticks([])
# plt.xlabel(str(i // weight))
plt.plot(gen_data[i][0])
plt.savefig('caches/gen_selfnoise_1d_data.jpg', bbox_inches='tight')
plt.close()
def gan_data_1d_perf(img_epoch_num, cuda=True):
"""This is WCGAN1D to performance
:param img_epoch_num:
:param cuda:
:return: gen_1d/data_wcgan.npz
"""
latent_dim = 50 # details in G_D_Module
n_class = 5 # details in G_D_Module
if cuda:
FloatTensor = torch.cuda.FloatTensor
LongTensor = torch.cuda.LongTensor
else:
FloatTensor = torch.FloatTensor
LongTensor = torch.LongTensor
generator = G_D_Module.GeneratorConv1D(latent_dim, n_class)
generator.load_state_dict(
torch.load('GANParameters/CONV1DGAN/generator.pt'))
noise = np.random.normal(0, 1, (img_epoch_num * n_class, latent_dim))
noise = noise[:, np.newaxis, :]
noise = FloatTensor(noise)
single_list = list(range(n_class))
label_cpu = single_list * img_epoch_num
label = LongTensor(label_cpu)
if cuda:
label.cuda()
generator.cuda()
gen_datas = generator(noise, label)
gen_datas = gen_datas.cpu()
datas = np.empty((n_class, img_epoch_num, 1024), dtype=float)
for i in range(len(label_cpu)):
datas[label_cpu[i]][i // 5] = gen_datas[i].detach().numpy()
np.savez(save_path + "gen_1d/data_wcgan.npz", datas)
def ex_conv1d_gan():
data_sets = ReWrite.load_data_in_seq_1d('data')
data_sets = ReWrite.MyDataSet1D(data_sets)
data_loader = DataLoader(
data_sets,
batch_size=512,
shuffle=True,
)
latent_dim = 50
generator = G_D_Module.GeneratorConv1D(latent_dim, opt.n_classes)
discriminator = G_D_Module.DiscriminatorConv1D(opt.n_classes)
TrainFunction.train_conv1d_gan(generator,
discriminator,
data_loader,
opt.n_epochs,
opt.lr,
opt.b1,
opt.b2,
latent_dim,
opt.n_classes,
cuda,
first_train=False)