def ex_info_gan():
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.GeneratorInfo(latent_dim=50,
n_classes=5,
code_dim=2,
img_shape=img_shape)
discriminator = G_D_Module.DiscriminatorInfo(n_classes=5,
code_dim=2,
img_shape=img_shape)
TrainFunction.train_info_gan(generator,
discriminator,
data_loader,
opt.n_epochs,
opt.lr,
opt.b1,
opt.b2,
latent_dim=50,
n_classes=5,
code_dim=2,
cuda=cuda,
first_train=False)
def ex_ponodcwcgan():
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,
)
latent_dim = 100
generator = G_D_Module.GeneratorPONODCWCGAN(
latent_dim, opt.n_classes, img_shape) # latent_dim should be 20
discriminator = G_D_Module.DiscriminatorPONODCWCGAN(
opt.n_classes, img_shape)
TrainFunction.train_ponodcwcgan(generator,
discriminator,
data_loader,
opt.n_epochs,
opt.lr,
opt.b1,
opt.b2,
latent_dim,
opt.n_classes,
cuda,
fist_train=False)
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_selfnoise_1d_perf(data_epoch_num, cuda=True):
n_class = 5
if cuda:
FloatTensor = torch.cuda.FloatTensor
LongTensor = torch.cuda.LongTensor
else:
FloatTensor = torch.FloatTensor
LongTensor = torch.LongTensor
generator = G_D_Module.GeneratorSelfNoise1D()
generator.load_state_dict(
torch.load('GANParameters/SELFNOISE1DGAN/generator.pt'))
if cuda:
generator.cuda()
datas = np.empty((n_class, data_epoch_num, 1024), dtype=float)
data_list = read_csv_data()
for i in range(data_epoch_num):
data = np.empty((n_class, 1, 1024), dtype=float)
for j in range(n_class):
index = random.randint(0, data_list[j].shape[0] - 1)
data[j][0] = data_list[j][index]
data = FloatTensor(data)
gen_data = generator(data).cpu().detach().numpy()
for j in range(n_class):
datas[j][i] = gen_data[j][0]
print('[%d/%d]' % (i, data_epoch_num))
np.savez(save_path + "gen_1d/data_selfnoise.npz", datas)
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 self_noise_data(img_epoch_num=50, cuda=True):
'''
:param img_epoch_num:
:param cuda:
:return: imgs(.jpg) note: change the save path
'''
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))
FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
generator = G_D_Module.GeneratorSelfNoise(img_shape)
generator.load_state_dict(
torch.load('GANParameters/SELFNOISEGAN/generator.pt'))
if cuda:
generator.cuda()
for epoch in range(img_epoch_num):
imgs = np.empty([len(data), 1, 32, 32], dtype=float)
for i in range(len(data)):
index = random.randint(0, len(data[i]) - 20 - 1)
imgs[i][0] = data[i][index]
if cuda:
imgs_torch = FloatTensor(imgs).cuda()
gen_imags = generator(imgs_torch)
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) + '_' + str(epoch) +
'.jpg')
plt.close()
for i in range(imgs.shape[0]):
plt.axis('off')
plt.contourf(imgs[i][0])
plt.subplots_adjust(top=1,
bottom=0,
right=1,
left=0,
hspace=0,
wspace=0)
plt.savefig(save_path + 'real/label' + str(i) + '_' + str(epoch) +
'.jpg')
plt.close()
def ex_self_noise_gan():
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.GeneratorSelfNoise(img_shape)
discriminator = G_D_Module.DiscriminatorSelfNoise(img_shape)
TrainFunction.train_self_noise_gan(generator,
discriminator,
data_loader,
opt.n_epochs,
opt.lr,
opt.b1,
opt.b2,
cuda,
first_train=False)
def self_noise_data_perf(img_epoch_num=50, cuda=True):
"""
:param img_epoch_num:
:param cuda:
:return: .npz in directory of gen
"""
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.GeneratorSelfNoise(img_shape)
generator.load_state_dict(
torch.load('GANParameters/SELFNOISEGAN/generator.pt'))
end_list = None
for i in range(img_epoch_num // 50):
datas_list = []
for j in range(n_class):
mid = np.empty([50, 1, 32, 32], dtype=float)
for k in range(50):
mid[k][0] = data[j][random.randint(0, data[j].shape[0] - 1)]
datas_list.append(mid)
img_list = []
for img in datas_list:
if cuda:
generator.cuda()
img = FloatTensor(img)
gen_imags = generator(img)
gen_imags = gen_imags.cpu()
img_list.append(gen_imags)
if end_list is None:
end_list = [
img_list[mid].detach().numpy() for mid in range(len(img_list))
]
else:
for count in range(len(end_list)):
end_list[count] = np.concatenate(
(end_list[count], img_list[count].detach().numpy()),
axis=0)
for i in range(len(end_list)):
np.savez(save_path + "gen/%d.npz" % i, end_list[i])
print(end_list[i].shape)
def ex_selfnoise_1d_gan():
data_sets = ReWrite.load_data_in_seq_1d('data')
data_sets = ReWrite.MyDataSet1D(data_sets)
data_loader = DataLoader(
data_sets,
batch_size=128,
shuffle=True,
)
generator = G_D_Module.GeneratorSelfNoise1D()
discriminator = G_D_Module.DiscriminatorSelfNoise1D()
TrainFunction.train_selfnoise_1d_gan(generator,
discriminator,
data_loader,
opt.n_epochs,
opt.lr,
opt.b1,
opt.b2,
-1,
opt.n_classes,
cuda,
first_train=False)
def ex_linear1d_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.GeneratorLinear1D(latent_dim, opt.n_classes)
discriminator = G_D_Module.DiscriminatorLinear1D(opt.n_classes)
TrainFunction.train_linear_1d_gan(generator,
discriminator,
data_loader,
opt.n_epochs,
opt.lr,
opt.b1,
opt.b2,
latent_dim,
opt.n_classes,
cuda,
first_train=False)
def show_self_noise_data():
data_list = os.listdir('coedatas')
data = []
for path in data_list:
data.append(data_read('coedatas/' + path))
FloatTensor = torch.cuda.FloatTensor
generator = G_D_Module.GeneratorSelfNoise((1, 32, 32))
generator.load_state_dict(
torch.load('GANParameters/SELFNOISEGAN/generator.pt'))
generator.cuda()
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]
imgs_torch = FloatTensor(imgs)
gen_imags = generator(imgs_torch)
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().cpu().numpy())
plt.savefig('caches/gen.jpg', bbox_inches='tight', pad_inches=0)
# plt.show()
plt.close()
# random
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]
# r
for i in range(len(imgs)):
plt.subplot(len(data), len(data), i + 1)
plt.axis('off')
plt.contourf(imgs[i][0])
plt.savefig('caches/real.jpg', bbox_inches='tight', pad_inches=0.0)
# plt.show()
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 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 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()
def img_data(img_epoch_num, cuda=True):
'''
:param img_epoch_num:
:param cuda:
:return: imgs(.jpg) note: change the save path
'''
os.makedirs('Performance/imgdatas/train/gen', exist_ok=True)
os.makedirs('Performance/imgdatas/train/real', exist_ok=True)
os.makedirs('Performance/imgdatas/test', exist_ok=True)
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))
FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
generator = G_D_Module.GeneratorSelfNoise(img_shape)
generator.load_state_dict(
torch.load('GANParameters/SELFNOISEGAN/generator.pt'))
if cuda:
generator.cuda()
for epoch in range(img_epoch_num):
imgs = np.empty([len(data), 1, 32, 32], dtype=float)
for i in range(len(data)):
index = random.randint(0, len(data[i]) - 20 - 1)
imgs[i][0] = data[i][index]
imgs_torch = FloatTensor(imgs)
gen_imags = generator(imgs_torch)
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('Performance/imgdatas/train/gen/' + str(i) + '_' +
str(epoch) + '.jpg',
pad_inches=0.0)
plt.close()
# real data 300 for train 170+ for test don't commend if need
for i in range(len(data)):
for j in range(data[i].shape[0]):
if j < 300:
plt.axis('off')
plt.contourf(data[i][j])
plt.subplots_adjust(top=1,
bottom=0,
right=1,
left=0,
hspace=0,
wspace=0)
plt.savefig('Performance/imgdatas/train/real/' + str(i) + '_' +
str(j) + '.jpg',
pad_inches=0.0)
plt.close()
else:
plt.axis('off')
plt.contourf(data[i][j])
plt.subplots_adjust(top=1,
bottom=0,
right=1,
left=0,
hspace=0,
wspace=0)
plt.savefig('Performance/imgdatas/test/' + str(i) + '_' +
str(j) + '.jpg',
pad_inches=0.0)
plt.close()