# Set the device to run on: GPU or CPU
device = torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu")
# Get the 'parameters' dictionary from the loaded file
parameters = model['parameters']
n_samp = parameters['n_smp']
# Initialise the network
netA = Augmenter_mnist(noise_dim=parameters['num_n'],
latent_dim=parameters['num_z']).to(device)
# netD = Discriminator().to(device)
# Load the trained augmenter weights
netA.load_state_dict(model['netA'])
# netD.load_state_dict(state_dict['netD'])
dataloader, _ = get_data(dataset='MNIST',
batch_size=parameters['batch_size'],
file='',
n_feature=[])
with torch.no_grad():
for i, (data, _) in enumerate(dataloader, 0):
# Get batch size
if i < 50:
b_size = parameters['batch_size']
# Transfer data tensor to GPU/CPU (device)
real_data = data.to(device)
augmented_img = []
err = []
for arm in range(parameters['n_smp']):
noise = torch.randn(b_size, parameters['num_n'], device=device)
noise += 0.1 * torch.sign(noise)
# Initialise the networks
netG = Generator_facs(latent_dim=parameters['num_z'],
input_dim=parameters['n_features']).to(device)
netG.load_state_dict(model_gen['netG'])
netAE = singleAE(input_dim=parameters['n_features'],
fc_dim=100,
latent_dim=2,
p_drop=0.).to(device)
netAE.load_state_dict(torch.load(load_file_ae))
dataloader, dataset = get_data(dataset=parameters['dataset'],
batch_size=parameters['batch_size'],
file=parameters['dataset_file'],
n_feature=parameters['n_features'],
training=False,
remove_nonneuron=parameters['remove_nonneuron'],
remove_CR_Meis2=parameters['remove_CR_Meis2'])
data_real_low = np.zeros((len(dataloader.dataset), 2))
data_gen_low = np.zeros((len(dataloader.dataset), 2))
data_bin_low = np.zeros((len(dataloader.dataset), 2))
data_generate = np.zeros((len(dataloader.dataset), parameters[
'n_features']))
data_origin = np.zeros((len(dataloader.dataset), parameters['n_features']))
samp_clr = ["" for x in range(len(dataloader.dataset))]
cluster = ["" for x in range(len(dataloader.dataset))]
with torch.no_grad():
for i, (data, data_bin, labels) in enumerate(dataloader):
def train_udagan(parameters, device):
dataloader, _ = get_data(dataset=parameters['dataset'],
batch_size=parameters['batch_size'],
file=parameters['dataset_file'],
n_feature=parameters['n_features'])
saving_path = os.path.abspath(os.path.join(os.getcwd(), '..'))
saving_path += '/results/augmenter'
if parameters['dataset'] == 'MNIST':
netA = Augmenter_mnist(noise_dim=parameters['num_n'],
latent_dim=parameters['num_z']).to(device)
netD = Discriminator_mnist().to(device)
saving_path += '/mnist/'
elif parameters['dataset'] == 'FACS':
netA = Augmenter_facs(noise_dim=parameters['num_n'],
latent_dim=parameters['num_z'],
input_dim=parameters['n_features']).to(device)
netA.apply(weights_init)
netD = Discriminator_facs(
input_dim=parameters['n_features']).to(device)
netD.apply(weights_init)
saving_path = '/scRNA/'
if parameters['initial_w']:
netA.apply(weights_init)
netD.apply(weights_init)
# Loss functions
criterionD = nn.BCELoss()
mseDist = nn.MSELoss()
# Set Adam optimiser for discriminator and augmenter
optimD = optim.Adam([{
'params': netD.parameters()
}],
lr=parameters['learning_rate'])
optimA = optim.Adam([{
'params': netA.parameters()
}],
lr=parameters['learning_rate'])
real_label = 1
fake_label = 0
A_losses = []
D_losses = []
print('-' * 50)
print('Starting the training ...')
for epoch in range(parameters['num_epochs']):
epoch_start_time = time.time()
A_loss_e = 0
D_loss_e = 0
recon_loss_e = 0
if parameters['dataset'] == 'MNIST':
for i, (data, _) in enumerate(dataloader, 0):
b_size = parameters['batch_size']
real_data = data.to(device)
# Updating the discriminator -----------------------------------
optimD.zero_grad()
# Original samples
label = torch.full((b_size, ), real_label, device=device)
_, probs_real = netD(real_data)
loss_real = criterionD(probs_real.view(-1), label)
loss_real.backward()
# Augmented samples
label.fill_(fake_label)
noise = torch.randn(b_size, parameters['num_n'], device=device)
noise += 0.1 * torch.sign(noise)
qz1, fake_data1 = netA(real_data, noise)
zeros = torch.zeros(b_size, parameters['num_n'], device=device)
qz2, fake_data2 = netA(real_data, zeros)
_, probs_fake1 = netD(fake_data1.detach())
_, probs_fake2 = netD(fake_data2.detach())
loss_fake = (criterionD(probs_fake1.view(-1), label) + \
criterionD(probs_fake2.view(-1), label)) / 2
loss_fake.backward()
# Loss value for the discriminator
D_loss = loss_real + loss_fake
optimD.step()
# Updating the augmenter ---------------------------------------
optimA.zero_grad()
# Augmented data treated as real data
noise = torch.randn(b_size, parameters['num_n'], device=device)
noise += 0.1 * torch.sign(noise)
qz1, fake_data1 = netA(real_data, noise)
z1, probs_fake1 = netD(fake_data1)
z2, probs_fake2 = netD(fake_data2)
# z0, _ = netD(real_data)
label.fill_(real_label)
gen_loss = (criterionD(probs_fake1.view(-1), label) + \
criterionD(probs_fake2.view(-1), label)) / 2
triplet_loss = TripletLoss(real_data.view(b_size, -1),
fake_data2.view(b_size, -1),
fake_data1.view(b_size, -1),
parameters['alpha'], 'BCE')
recon_loss = criterionD(fake_data2.view(b_size, -1),
real_data.view(b_size, -1))
# Loss value for the augmenter
A_loss = parameters['lambda'][0] * gen_loss + \
parameters['lambda'][1] * triplet_loss + \
parameters['lambda'][2] * KL_dist(qz1, qz2) + \
parameters['lambda'][3] * recon_loss
# parameters['lambda'][2] * mseDist(qz1, qz2)
A_loss.backward()
optimA.step()
A_losses.append(A_loss.data.item())
D_losses.append(D_loss.data.item())
A_loss_e += A_loss.data.item()
D_loss_e += D_loss.data.item()
recon_loss_e += recon_loss.data.item()
else:
for i, (data, data_bin, _) in enumerate(dataloader, 0):
b_size = parameters['batch_size']
real_data = data.to(device)
real_data_bin = data_bin.to(device)
# Updating the discriminator -----------------------------------
optimD.zero_grad()
# Original samples
label = torch.full((b_size, ), real_label, device=device)
_, probs_real = netD(real_data_bin)
loss_real = criterionD(probs_real.view(-1), label)
if F.relu(loss_real - 0.25) > 0:
loss_r = loss_real
else:
loss_r = F.relu(loss_real - 0.25)
loss_r.backward()
# Augmented samples
label.fill_(fake_label)
noise = torch.randn(b_size, parameters['num_n'], device=device)
noise += 0.1 * torch.sign(noise)
qz1, fake_data1 = netA(real_data_bin, noise)
zeros = torch.zeros(b_size, parameters['num_n'], device=device)
qz2, fake_data2 = netA(real_data_bin, zeros)
# binarizing the augmented sample
fake_data1_bin = fake_data1.clone()
fake_data2_bin = fake_data2.clone()
fake_data1_bin[fake_data1_bin > 0] = 1.
fake_data2_bin[fake_data2_bin > 0] = 1.
_, probs_fake1 = netD(fake_data1_bin.detach())
_, probs_fake2 = netD(fake_data2_bin.detach())
loss_fake = (criterionD(probs_fake1.view(-1), label) + \
criterionD(probs_fake2.view(-1), label)) / 2
if F.relu(loss_fake - 0.25) > 0:
loss_f = loss_fake
else:
loss_f = F.relu(loss_fake - 0.25)
loss_f.backward()
# Loss value for the discriminator
D_loss = loss_real + loss_fake
optimD.step()
# Updating the augmenter ---------------------------------------
optimA.zero_grad()
# Augmented data treated as real data
z1, probs_fake1 = netD(fake_data1_bin)
z2, probs_fake2 = netD(fake_data2_bin)
# z0, _ = netD(real_data)
label.fill_(real_label)
gen_loss = (criterionD(probs_fake1.view(-1), label) + \
criterionD(probs_fake2.view(-1), label)) / 2
triplet_loss = TripletLoss(real_data_bin.view(b_size, -1),
fake_data2_bin.view(b_size, -1),
fake_data1_bin.view(b_size, -1),
parameters['alpha'], 'BCE')
# recon_loss = mseDist(fake_data2.view(b_size, -1),
# real_data.view(b_size, -1))
recon_loss = F.mse_loss(fake_data2,
real_data,
reduction='mean')
# Loss value for the augmenter
A_loss = parameters['lambda'][0] * gen_loss + \
parameters['lambda'][1] * triplet_loss + \
parameters['lambda'][2] * mseDist(qz1, qz2) + \
parameters['lambda'][3] * recon_loss
A_loss.backward()
optimA.step()
A_losses.append(A_loss.data.item())
D_losses.append(D_loss.data.item())
A_loss_e += A_loss.data.item()
D_loss_e += D_loss.data.item()
recon_loss_e += recon_loss.data.item()
A_loss_epoch = A_loss_e / (i + 1)
D_loss_epoch = D_loss_e / (i + 1)
recon_loss_epoch = recon_loss_e / (i + 1)
print('=====> Epoch:{}, Augmenter Loss: {:.4f}, Discriminator Loss: {'
':.4f}, Recon Loss: {:.4f}, Elapsed Time:{:.2f}'.format(
epoch, A_loss_epoch, D_loss_epoch, recon_loss_epoch,
time.time() - epoch_start_time))
print("-" * 50)
# Save trained models
if parameters['save']:
torch.save(
{
'netA': netA.state_dict(),
'netD': netD.state_dict(),
'optimD': optimD.state_dict(),
'optimA': optimA.state_dict(),
'parameters': parameters
},
saving_path + 'model_bs_%d_dn_%d_dz_%d_l1_%f_l2_%f_l3_%f_l4_%f' %
(parameters['batch_size'], parameters['num_n'],
parameters['num_z'], parameters['lambda'][0], parameters['lambda']
[1], parameters['lambda'][2], parameters['lambda'][3]))
# Plot the training losses.
plt.figure()
plt.title("Augmenter and Discriminator Loss Values in Training")
plt.plot(A_losses, label="A")
plt.plot(D_losses, label="D")
plt.xlabel("Iterations")
plt.ylabel("Loss")
plt.legend()
plt.savefig(saving_path + 'loss_curve.png')
n_samp = parameters['n_arm']
# Initialise the networks
netA = Augmenter_facs(noise_dim=parameters['num_n'],
latent_dim=parameters['num_z'],
input_dim=parameters['n_features']).to(device)
netA.load_state_dict(model_aug['netA'])
netAE = singleAE(input_dim=parameters['n_features'],
fc_dim=100,
latent_dim=2,
p_drop=0.).to(device)
netAE.load_state_dict(torch.load(load_file_ae))
dataloader, dataset = get_data(dataset=parameters['dataset'],
batch_size=parameters['batch_size'],
file=parameters['dataset_path'],
n_feature=parameters['n_features'])
# training=False)
# remove_nonneuron=parameters['remove_nonneuron'],
# remove_CR_Meis2=parameters['remove_CR_Meis2'])
ngen = 200
for arm in range(2): #range(parameters['n_arm']):
print('-----> sample:{}'.format(arm))
data_real_low = np.zeros((len(dataloader.dataset), 2))
data_aug_low = np.zeros((len(dataloader.dataset), 2))
data_bin_low = np.zeros((len(dataloader.dataset), 2))
gene_samp_real = np.zeros((len(dataloader.dataset), ngen))
gene_samp_aug = np.zeros((len(dataloader.dataset), ngen))
samp_clr = ["" for x in range(len(dataloader.dataset))]
def train_vaegan(parameters, device):
dataloader, _ = get_data(dataset=parameters['dataset'],
batch_size=parameters['batch_size'],
file=parameters['dataset_file'],
n_feature=parameters['n_features'],
training=True,
remove_nonneuron=parameters['remove_nonneuron'],
remove_CR_Meis2=parameters['remove_CR_Meis2'])
saving_path = os.path.abspath(os.path.join(os.getcwd(), '..'))
saving_path += '/results/generator'
if parameters['dataset'] == 'MNIST':
netG = Generator_mnist(latent_dim=parameters['num_z']).to(device)
netD = Discriminator_mnist().to(device)
saving_path += '/mnist/'
elif parameters['dataset'] == 'FACS':
netG = Generator_facs(latent_dim=parameters['num_z'],
input_dim=parameters['n_features']).to(device)
netG.apply(weights_init)
netD = Discriminator_facs(
input_dim=parameters['n_features']).to(device)
netD.apply(weights_init)
saving_path += '/scRNA/'
if parameters['initial_w']:
netG.apply(weights_init)
netD.apply(weights_init)
# Loss functions
criterionD = nn.BCELoss()
criterionQ_con = NormalNLLLoss()
mseDist = nn.MSELoss()
# Set Adam optimiser for discriminator and augmenter
optimD = optim.Adam([{
'params': netD.parameters()
}],
lr=parameters['learning_rate'])
optimG = optim.Adam([{
'params': netG.parameters()
}],
lr=parameters['learning_rate'])
real_label = 1
fake_label = 0
G_losses = []
D_losses = []
print('-' * 50)
print('Starting the training ...')
for epoch in range(parameters['num_epochs']):
epoch_start_time = time.time()
G_loss_e = 0
D_loss_e = 0
recon_loss_e = 0
gen_loss_e = 0
if parameters['dataset'] == 'MNIST':
for i, (data, _) in enumerate(dataloader, 0):
b_size = parameters['batch_size']
real_data = data.to(device)
# Updating the discriminator -----------------------------------
optimD.zero_grad()
# Original samples
label = torch.full((b_size, ), real_label, device=device)
_, probs_real = netD(real_data)
loss_real = criterionD(probs_real.view(-1), label)
loss_real.backward()
# Augmented samples
label.fill_(fake_label)
_, fake_data = netG(real_data)
zeros = torch.zeros(b_size, parameters['num_n'], device=device)
_, probs_fake = netD(fake_data.detach())
loss_fake = criterionD(probs_fake.view(-1), label)
loss_fake.backward()
# Loss value for the discriminator
D_loss = loss_real + loss_fake
optimD.step()
# Updating the augmenter ---------------------------------------
optimG.zero_grad()
# Augmented data treated as real data
z, probs_fake = netD(fake_data)
label.fill_(real_label)
gen_loss = criterionD(probs_fake.view(-1), label)
recon_loss = criterionD(fake_data.view(b_size, -1),
real_data.view(b_size, -1))
# Loss value for the augmenter
G_loss = parameters['lambda'][0] * gen_loss + \
parameters['lambda'][3] * recon_loss
G_loss.backward()
optimG.step()
G_losses.append(G_loss.data.item())
D_losses.append(D_loss.data.item())
G_loss_e += G_loss.data.item()
D_loss_e += D_loss.data.item()
recon_loss_e += recon_loss.data.item()
gen_loss_e += gen_loss.data.item()
else:
for i, (data, data_bin, _) in enumerate(dataloader, 0):
b_size = parameters['batch_size']
real_data = data.to(device)
real_data_bin = data_bin.to(device)
# Updating the discriminator -----------------------------------
optimD.zero_grad()
# Original samples
label = torch.full((b_size, ), real_label, device=device)
_, probs_real = netD(real_data_bin)
loss_real = criterionD(probs_real.view(-1), label)
if F.relu(loss_real - np.log(2) / 2) > 0:
loss_real.backward()
optim_D = True
else:
optim_D = False
# Augmented samples
label.fill_(fake_label)
_, fake_data = netG(real_data_bin)
# binarizing the augmented sample
fake_data_bin = fake_data.clone()
fake_data_bin[fake_data_bin > 0] = 1.
_, probs_fake = netD(fake_data_bin.detach())
loss_fake = criterionD(probs_fake.view(-1), label)
if F.relu(loss_fake - np.log(2) / 2) > 0:
loss_fake.backward()
optim_D = True
# Loss value for the discriminator
D_loss = loss_real + loss_fake
if optim_D:
optimD.step()
# Updating the augmenter ---------------------------------------
optimG.zero_grad()
# Augmented data treated as real data
_, probs_fake = netD(fake_data_bin)
label.fill_(real_label)
gen_loss = criterionD(probs_fake.view(-1), label)
recon_loss = F.mse_loss(fake_data, real_data, reduction='mean')
# Loss value for the augmenter
G_loss = parameters['lambda'][0] * gen_loss + \
parameters['lambda'][3] * recon_loss
G_loss.backward()
optimG.step()
G_losses.append(G_loss.data.item())
D_losses.append(D_loss.data.item())
G_loss_e += G_loss.data.item()
D_loss_e += D_loss.data.item()
recon_loss_e += recon_loss.data.item()
gen_loss_e += gen_loss.data.item()
G_loss_epoch = G_loss_e / (i + 1)
D_loss_epoch = D_loss_e / (i + 1)
recon_loss_epoch = recon_loss_e / (i + 1)
gen_loss_epoch = gen_loss_e / (i + 1)
print('=====> Epoch:{}, Generator Loss: {:.4f}, Discriminator Loss: {'
':.4f}, Recon Loss: {:.4f}, Gen Loss: {:.4f}, Elapsed Time:{'
':.2f}'.format(epoch, G_loss_epoch, D_loss_epoch,
recon_loss_epoch, gen_loss_epoch,
time.time() - epoch_start_time))
print("-" * 50)
# Save trained models
if parameters['save']:
torch.save(
{
'netG': netG.state_dict(),
'netD': netD.state_dict(),
'optimD': optimD.state_dict(),
'optimG': optimG.state_dict(),
'parameters': parameters
}, saving_path + 'modelG_bs_%d_dn_%d_dz_%d' %
(parameters['batch_size'], parameters['num_z'],
parameters['num_epochs']))
# Plot the training losses.
plt.figure()
plt.title("Generator and Discriminator Loss Values in Training")
plt.plot(G_losses, label="G")
plt.plot(D_losses, label="D")
plt.xlabel("Iterations")
plt.ylabel("Loss")
plt.legend()
plt.savefig(saving_path + 'loss_curve_g.png')
