def main(args):
### config
global noise_multiplier
dataset = args.dataset
num_discriminators = args.num_discriminators
noise_multiplier = args.noise_multiplier
z_dim = args.z_dim
model_dim = args.model_dim
batchsize = args.batchsize
L_gp = args.L_gp
L_epsilon = args.L_epsilon
critic_iters = args.critic_iters
latent_type = args.latent_type
load_dir = args.load_dir
save_dir = args.save_dir
if_dp = (args.dp > 0.)
gen_arch = args.gen_arch
num_gpus = args.num_gpus
### CUDA
use_cuda = torch.cuda.is_available()
devices = [
torch.device("cuda:%d" % i if use_cuda else "cpu")
for i in range(num_gpus)
]
device0 = devices[0]
if use_cuda:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
### Random seed
random.seed(args.random_seed)
np.random.seed(args.random_seed)
torch.manual_seed(args.random_seed)
### Fix noise for visualization
if latent_type == 'normal':
fix_noise = torch.randn(10, z_dim)
elif latent_type == 'bernoulli':
p = 0.5
bernoulli = torch.distributions.Bernoulli(torch.tensor([p]))
fix_noise = bernoulli.sample((10, z_dim)).view(10, z_dim)
else:
raise NotImplementedError
### Set up models
print('gen_arch:' + gen_arch)
netG = GeneratorDCGAN(z_dim=z_dim, model_dim=model_dim, num_classes=10)
netGS = copy.deepcopy(netG)
netD_list = []
for i in range(num_discriminators):
netD = DiscriminatorDCGAN()
netD_list.append(netD)
### Load pre-trained discriminators
print("load pre-training...")
if load_dir is not None:
for netD_id in range(num_discriminators):
print('Load NetD ', str(netD_id))
network_path = os.path.join(load_dir, 'netD_%d' % netD_id,
'netD.pth')
netD = netD_list[netD_id]
netD.load_state_dict(torch.load(network_path))
netG = netG.to(device0)
for netD_id, netD in enumerate(netD_list):
device = devices[get_device_id(netD_id, num_discriminators, num_gpus)]
netD.to(device)
### Set up optimizers
optimizerD_list = []
for i in range(num_discriminators):
netD = netD_list[i]
optimizerD = optim.Adam(netD.parameters(), lr=1e-4, betas=(0.5, 0.99))
optimizerD_list.append(optimizerD)
optimizerG = optim.Adam(netG.parameters(), lr=1e-4, betas=(0.5, 0.99))
### Data loaders
if dataset == 'mnist' or dataset == 'fashionmnist':
transform_train = transforms.Compose([
transforms.CenterCrop((28, 28)),
transforms.ToTensor(),
#transforms.Grayscale(),
])
elif dataset == 'cifar_100' or dataset == 'cifar_10':
transform_train = transforms.Compose([
transforms.CenterCrop((28, 28)),
transforms.Grayscale(),
transforms.ToTensor(),
])
if dataset == 'mnist':
dataloader = datasets.MNIST
trainset = dataloader(root=os.path.join(DATA_ROOT, 'MNIST'),
train=True,
download=True,
transform=transform_train)
IMG_DIM = 784
NUM_CLASSES = 10
elif dataset == 'fashionmnist':
dataloader = datasets.FashionMNIST
trainset = dataloader(root=os.path.join(DATA_ROOT, 'FashionMNIST'),
train=True,
download=True,
transform=transform_train)
elif dataset == 'cifar_100':
dataloader = datasets.CIFAR100
trainset = dataloader(root=os.path.join(DATA_ROOT, 'CIFAR100'),
train=True,
download=True,
transform=transform_train)
IMG_DIM = 3072
NUM_CLASSES = 100
elif dataset == 'cifar_10':
IMG_DIM = 784
NUM_CLASSES = 10
dataloader = datasets.CIFAR10
trainset = dataloader(root=os.path.join(DATA_ROOT, 'CIFAR10'),
train=True,
download=True,
transform=transform_train)
else:
raise NotImplementedError
print('creat indices file')
indices_full = np.arange(len(trainset))
np.random.shuffle(indices_full)
#indices_full.dump(os.path.join(save_dir, 'indices.npy'))
trainset_size = int(len(trainset) / num_discriminators)
print('Size of the dataset: ', trainset_size)
input_pipelines = []
for i in range(num_discriminators):
start = i * trainset_size
end = (i + 1) * trainset_size
indices = indices_full[start:end]
trainloader = DataLoader(trainset,
batch_size=args.batchsize,
drop_last=False,
num_workers=args.num_workers,
sampler=SubsetRandomSampler(indices))
#input_data = inf_train_gen(trainloader)
input_pipelines.append(trainloader)
if if_dp:
### Register hook
global dynamic_hook_function
for netD in netD_list:
netD.conv1.register_backward_hook(master_hook_adder)
prg_bar = tqdm(range(args.iterations + 1))
for iters in prg_bar:
#########################
### Update D network
#########################
netD_id = np.random.randint(num_discriminators, size=1)[0]
device = devices[get_device_id(netD_id, num_discriminators, num_gpus)]
netD = netD_list[netD_id]
optimizerD = optimizerD_list[netD_id]
input_data = input_pipelines[netD_id]
for p in netD.parameters():
p.requires_grad = True
for iter_d in range(critic_iters):
real_data, real_y = next(iter(input_data))
real_data = real_data.view(-1, IMG_DIM)
real_data = real_data.to(device)
real_y = real_y.to(device)
real_data_v = autograd.Variable(real_data)
### train with real
dynamic_hook_function = dummy_hook
netD.zero_grad()
D_real_score = netD(real_data_v, real_y)
D_real = -D_real_score.mean()
### train with fake
batchsize = real_data.shape[0]
if latent_type == 'normal':
noise = torch.randn(batchsize, z_dim).to(device0)
elif latent_type == 'bernoulli':
noise = bernoulli.sample(
(batchsize, z_dim)).view(batchsize, z_dim).to(device0)
else:
raise NotImplementedError
noisev = autograd.Variable(noise)
fake = autograd.Variable(netG(noisev, real_y.to(device0)).data)
inputv = fake.to(device)
D_fake = netD(inputv, real_y.to(device))
D_fake = D_fake.mean()
'''
### train with gradient penalty
gradient_penalty = netD.calc_gradient_penalty(real_data_v.data, fake.data, real_y, L_gp, device)
D_cost = D_fake + D_real + gradient_penalty
### train with epsilon penalty
logit_cost = L_epsilon * torch.pow(D_real_score, 2).mean()
D_cost += logit_cost
'''
D_cost = D_fake + D_real
### update
D_cost.backward()
Wasserstein_D = -D_real - D_fake
optimizerD.step()
del real_data, real_y, fake, noise, inputv, D_real, D_fake #, logit_cost, gradient_penalty
torch.cuda.empty_cache()
############################
# Update G network
###########################
if if_dp:
### Sanitize the gradients passed to the Generator
dynamic_hook_function = dp_conv_hook
else:
### Only modify the gradient norm, without adding noise
dynamic_hook_function = modify_gradnorm_conv_hook
for p in netD.parameters():
p.requires_grad = False
netG.zero_grad()
### train with sanitized discriminator output
if latent_type == 'normal':
noise = torch.randn(batchsize, z_dim).to(device0)
elif latent_type == 'bernoulli':
noise = bernoulli.sample(
(batchsize, z_dim)).view(batchsize, z_dim).to(device0)
else:
raise NotImplementedError
label = torch.randint(0, NUM_CLASSES, [batchsize]).to(device0)
noisev = autograd.Variable(noise)
fake = netG(noisev, label)
#summary(netG, input_data=[noisev,label])
fake = fake.to(device)
label = label.to(device)
G = netD(fake, label)
G = -G.mean()
### update
G.backward()
G_cost = G
optimizerG.step()
### update the exponential moving average
exp_mov_avg(netGS, netG, alpha=0.999, global_step=iters)
############################
### Results visualization
############################
prg_bar.set_description(
'iter:{}, G_cost:{:.2f}, D_cost:{:.2f}, Wasserstein:{:.2f}'.format(
iters,
G_cost.cpu().data,
D_cost.cpu().data,
Wasserstein_D.cpu().data))
if iters % args.vis_step == 0:
if dataset == 'mnist':
generate_image_mnist(iters, netGS, fix_noise, save_dir,
device0)
elif dataset == 'cifar_100':
generate_image_cifar100(iters, netGS, fix_noise, save_dir,
device0)
elif dataset == 'cifar_10':
generate_image_mnist(iters, netGS, fix_noise, save_dir,
device0)
if iters % args.save_step == 0:
### save model
torch.save(netGS.state_dict(),
os.path.join(save_dir, 'netGS_%d.pth' % iters))
del label, fake, noisev, noise, G, G_cost, D_cost
torch.cuda.empty_cache()
if ((iters + 1) % 500 == 0):
classify_training(netGS, dataset, iters + 1)
### save model
torch.save(netG, os.path.join(save_dir, 'netG.pth'))
torch.save(netGS, os.path.join(save_dir, 'netGS.pth'))
def main(args):
### config
global noise_multiplier
dataset = args.dataset
num_discriminators = args.num_discriminators
noise_multiplier = args.noise_multiplier
z_dim = args.z_dim
if dataset == 'celeba':
z_dim = 100
model_dim = args.model_dim
batchsize = args.batchsize
L_gp = args.L_gp
L_epsilon = args.L_epsilon
critic_iters = args.critic_iters
latent_type = args.latent_type
load_dir = args.load_dir
save_dir = args.save_dir
if_dp = (args.noise_multiplier > 0.)
gen_arch = args.gen_arch
num_gpus = args.num_gpus
### CUDA
use_cuda = torch.cuda.is_available()
devices = [
torch.device("cuda:%d" % i if use_cuda else "cpu")
for i in range(num_gpus)
]
device0 = devices[0]
if use_cuda:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
### Random seed
if args.random_seed == 1:
args.random_seed = np.random.randint(10000, size=1)[0]
print('random_seed: {}'.format(args.random_seed))
os.system('rm ' + os.path.join(save_dir, 'seed*'))
os.system('touch ' +
os.path.join(save_dir, 'seed=%s' % str(args.random_seed)))
random.seed(args.random_seed)
np.random.seed(args.random_seed)
torch.manual_seed(args.random_seed)
### Set up models
print('gen_arch:' + gen_arch)
if dataset == 'celeba':
ngpu = 1
netG = Generator_celeba(ngpu).to(device0)
#netG.load_state_dict(torch.load('../results/celeba/main/d_1_2e-4_g_1_2e-4_SN_full/netG_15000.pth'))
# Handle multi-gpu if desired
if (device0.type == 'cuda') and (ngpu > 1):
netG = nn.DataParallel(netG, list(range(ngpu)))
# Apply the weights_init function to randomly initialize all weights
# to mean=0, stdev=0.02.
netG.apply(weights_init)
netGS = copy.deepcopy(netG).to(device0)
if dataset == 'celeba':
ngpu = 1
netD = Discriminator_celeba(ngpu).to(device0)
#netD.load_state_dict(torch.load('../results/celeba/main/d_1_2e-4_g_1_2e-4_SN_full/netD_15000.pth'))
# Handle multi-gpu if desired
if (device0.type == 'cuda') and (ngpu > 1):
netD = nn.DataParallel(netD, list(range(ngpu)))
# Apply the weights_init function to randomly initialize all weights
# to mean=0, stdev=0.2.
#netD.apply(weights_init)
### Set up optimizers
optimizerD = optim.Adam(netD.parameters(), lr=2e-4, betas=(0.5, 0.99))
optimizerG = optim.Adam(netG.parameters(), lr=2e-4, betas=(0.5, 0.99))
### Data loaders
if dataset == 'celeba':
transform_train = transforms.Compose([
transforms.Resize(64),
transforms.CenterCrop(64),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
if dataset == 'celeba':
IMG_DIM = 64 * 64 * 3
NUM_CLASSES = 2
trainset = CelebA(
root=os.path.join('/work/u5366584/exp/datasets/celeba'),
split='train',
transform=transform_train,
download=False) #, custom_subset=True)
#trainset = CelebA(root=os.path.join('../data'), split='train',
# transform=transform_train, download=False, custom_subset=True)
else:
raise NotImplementedError
###fix sub-training set (fix to 10000 training samples)
if args.update_train_dataset:
if dataset == 'mnist':
indices_full = np.arange(60000)
elif dataset == 'cifar_10':
indices_full = np.arange(50000)
elif dataset == 'celeba':
indices_full = np.arange(len(trainset))
np.random.shuffle(indices_full)
'''
#####ref
indices = np.loadtxt('index_20k.txt', dtype=np.int_)
remove_idx = [np.argwhere(indices_full==x) for x in indices]
indices_ref = np.delete(indices_full, remove_idx)
indices_slice = indices_ref[:20000]
np.savetxt('index_20k_ref.txt', indices_slice, fmt='%i') ##ref index is disjoint to original index
'''
### growing dataset
indices = np.loadtxt('index_20k.txt', dtype=np.int_)
remove_idx = [np.argwhere(indices_full == x) for x in indices]
indices_rest = np.delete(indices_full, remove_idx)
indices_rest = indices_rest[:20000]
indices_slice = np.concatenate((indices, indices_rest), axis=0)
np.savetxt('index_40k.txt', indices_slice, fmt='%i')
indices = np.loadtxt('index_100k.txt', dtype=np.int_)
trainset = torch.utils.data.Subset(trainset, indices)
print(len(trainset))
workers = 4
dataloader = torch.utils.data.DataLoader(trainset,
batch_size=batchsize,
shuffle=True,
num_workers=workers)
if if_dp:
### Register hook
global dynamic_hook_function
for netD in netD_list:
netD.conv1.register_backward_hook(master_hook_adder)
criterion = nn.BCELoss()
real_label = 1.
fake_label = 0.
nz = 100
fixed_noise = torch.randn(100, nz, 1, 1, device=device0)
iters = 0
num_epochs = 256 * 5 + 1
print("Starting Training Loop...")
# For each epoch
for epoch in range(num_epochs):
# For each batch in the dataloader
for i, (data, y) in enumerate(dataloader, 0):
############################
# (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
###########################
## Train with all-real batch
netD.zero_grad()
# Format batch
real_cpu = data.to(device0)
b_size = real_cpu.size(0)
label = torch.full((b_size, ),
real_label,
dtype=torch.float,
device=device0)
# Forward pass real batch through D
output = netD(real_cpu).view(-1)
# Calculate loss on all-real batch
errD_real = criterion(output, label)
# Calculate gradients for D in backward pass
errD_real.backward()
D_x = output.mean().item()
## Train with all-fake batch
# Generate batch of latent vectors
noise = torch.randn(b_size, nz, 1, 1, device=device0)
# Generate fake image batch with G
fake = netG(noise)
label.fill_(fake_label)
# Classify all fake batch with D
output = netD(fake.detach()).view(-1)
# Calculate D's loss on the all-fake batch
errD_fake = criterion(output, label)
# Calculate the gradients for this batch, accumulated (summed) with previous gradients
errD_fake.backward()
D_G_z1 = output.mean().item()
# Compute error of D as sum over the fake and the real batches
errD = errD_real + errD_fake
# Update D
optimizerD.step()
iters += 1
for iter_g in range(1):
############################
# Update G network
###########################
if if_dp:
### Sanitize the gradients passed to the Generator
dynamic_hook_function = dp_conv_hook
else:
### Only modify the gradient norm, without adding noise
dynamic_hook_function = modify_gradnorm_conv_hook
############################
# (2) Update G network: maximize log(D(G(z)))
###########################
noise = torch.randn(b_size, nz, 1, 1, device=device0)
fake = netG(noise)
label = torch.full((b_size, ),
real_label,
dtype=torch.float,
device=device0)
netG.zero_grad()
label.fill_(
real_label) # fake labels are real for generator cost
# Since we just updated D, perform another forward pass of all-fake batch through D
output = netD(fake).view(-1)
# Calculate G's loss based on this output
errG = criterion(output, label)
# Calculate gradients for G
errG.backward()
D_G_z2 = output.mean().item()
# Update G
optimizerG.step()
### update the exponential moving average
exp_mov_avg(netGS, netG, alpha=0.999, global_step=iters)
############################
### Results visualization
############################
if iters % 10 == 0:
print('iter:{}, G_cost:{:.2f}, D_cost:{:.2f}'.format(
iters,
errG.item(),
errD.item(),
))
if iters % args.vis_step == 0:
if dataset == 'celeba':
generate_image_celeba(str(iters + 0), netGS, fixed_noise,
save_dir, device0)
if iters % args.save_step == 0:
### save model
torch.save(
netGS.state_dict(),
os.path.join(save_dir, 'netGS_%s.pth' % str(iters + 0)))
torch.save(
netD.state_dict(),
os.path.join(save_dir, 'netD_%s.pth' % str(iters + 0)))
torch.cuda.empty_cache()
