def dcwcgan_data(img_epoch_num=50, cuda=True):
'''
:param img_epoch_num:
:param cuda:
:return: imgs(.jpg) note: change the save path
'''
latent_dim = 20 # details in G_D_Module
n_class = 5 # details in G_D_Module
img_shape = (1, 32, 32) # details in G_D_Module
data_list = os.listdir('coedatas')
data = []
for path in data_list:
data.append(data_read('coedatas/' + path))
if cuda:
FloatTensor = torch.cuda.FloatTensor
LongTensor = torch.cuda.LongTensor
generator = G_D_Module.GeneratorDCWCGAN(latent_dim, n_class, img_shape)
generator.load_state_dict(torch.load('GANParameters/DCWCGAN/generator.pt'))
noise = FloatTensor(
np.random.normal(0, 1, (img_epoch_num * n_class, latent_dim)))
single_list = list(range(n_class))
label_cpu = single_list * img_epoch_num
label = LongTensor(label_cpu)
if cuda:
label.cuda()
generator.cuda()
gen_imags = generator(noise, label)
gen_imags = gen_imags.cpu()
for i in range(gen_imags.shape[0]):
plt.axis('off')
plt.contourf(gen_imags[i][0].detach().numpy())
plt.subplots_adjust(top=1,
bottom=0,
right=1,
left=0,
hspace=0,
wspace=0)
plt.savefig(save_path + 'gen/label' + str(i % 5) + '_' + str(i // 5) +
'.jpg')
plt.close()
for i in range(img_epoch_num):
for j in range(len(data)):
index = random.randint(0, data[j].shape[0] - 1)
plt.axis('off')
plt.contourf(data[j][index])
plt.subplots_adjust(top=1,
bottom=0,
right=1,
left=0,
hspace=0,
wspace=0)
plt.savefig(save_path + 'real/label' + str(j) + '_' + str(i) +
'.jpg')
plt.close()
def dcwcgan_data_perf(img_epoch_num=50, cuda=True):
'''
:param img_epoch_num:
:param cuda:
:return:
note: generate the .npy in the Performance
'''
latent_dim = 20 # details in G_D_Module
n_class = 5 # details in G_D_Module
img_shape = (1, 32, 32) # details in G_D_Module
data_list = os.listdir('coedatas')
data = []
for path in data_list:
data.append(data_read('coedatas/' + path))
if cuda:
FloatTensor = torch.cuda.FloatTensor
LongTensor = torch.cuda.LongTensor
else:
FloatTensor = torch.FloatTensor
LongTensor = torch.LongTensor
generator = G_D_Module.GeneratorDCWCGAN(latent_dim, n_class, img_shape)
generator.load_state_dict(torch.load('GANParameters/DCWCGAN/generator.pt'))
noise = FloatTensor(
np.random.normal(0, 1, (img_epoch_num * n_class, latent_dim)))
single_list = list(range(n_class))
label_cpu = single_list * img_epoch_num
label = LongTensor(label_cpu)
if cuda:
label.cuda()
generator.cuda()
gen_imags = generator(noise, label)
gen_imags = gen_imags.cpu()
datas = []
for i in range(len(data_list)):
datas.append(np.empty((img_epoch_num, *img_shape), dtype=float))
for i in range(len(label_cpu)):
datas[label_cpu[i]][i // 5][0] = gen_imags[i][0].detach().numpy()
for i in range(len(datas)):
np.savez(save_path + "gen/%d.npz" % i, datas[i])
print(datas[i].shape)
def show_dcwcgan_data():
im_index = range(9)
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.GeneratorDCWCGAN(latent_dim, 5, (1, 32, 32))
generator.load_state_dict(torch.load('GANParameters/DCWCGAN/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)
gen_imags = gen_imags.cpu()
for i in range(gen_imags.size(0)):
plt.subplot(len(data), len(data), i + 1)
plt.axis('off')
plt.contourf(gen_imags[i][0].detach().numpy())
# plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
plt.savefig('caches/gen.jpg', bbox_inches='tight', pad_inches=0)
# plt.show()
plt.close()
for i in range(len(data)):
for j in range(len(data)):
index = random.randint(0, data[j].shape[0] - 1)
plt.subplot(len(data), len(data), i * len(data) + j + 1)
plt.axis('off')
plt.contourf(data[j][index])
# plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
plt.savefig('caches/real.jpg', bbox_inches='tight', pad_inches=0)
# plt.show()
plt.close()
def ex_dcwcgan():
data_sets = ReWrite.load_data_in_seq(source_files)
data_sets = ReWrite.MyDataSet(data_sets)
data_loader = DataLoader(
data_sets,
batch_size=256,
shuffle=True,
)
generator = G_D_Module.GeneratorDCWCGAN(
opt.latent_dim, opt.n_classes, img_shape) # latent_dim should be 20
discriminator = G_D_Module.DiscriminatorDCWCGAN(opt.n_classes, img_shape)
TrainFunction.train_dcwcgan(generator,
discriminator,
data_loader,
opt.n_epochs,
opt.lr,
opt.b1,
opt.b2,
opt.latent_dim,
opt.n_classes,
cuda,
fist_train=False)