def build_model(self):
if self.config.no_bn:
self.netG = dcgan.DCGAN_G_nobn(self.image_size, self.nz, self.nc,
self.ngf, self.ngpu,
self.config.n_extra_layers)
elif self.config.mlp_G:
self.netG = mlp.MLP_G(self.image_size, self.nz, self.nc, self.ngf,
self.ngpu)
else:
self.netG = dcgan.DCGAN_G(self.image_size, self.nz, self.nc,
self.ngf, self.ngpu,
self.config.n_extra_layers)
self.netG.apply(weights_init)
if self.config.netG != '': # load checkpoint if needed
self.netG.load_state_dict(torch.load(self.config.netG))
if self.config.mlp_D:
self.netD = mlp.MLP_D(self.image_size, self.nz, self.nc, self.ndf,
self.ngpu)
else:
self.netD = dcgan.DCGAN_D(self.image_size, self.nz, self.nc,
self.ndf, self.ngpu,
self.config.n_extra_layers)
self.netD.apply(weights_init)
if self.config.netD != '':
self.netD.load_state_dict(torch.load(self.config.netD))
def __init__(self, cfg_path, weights_path):
with open(cfg_path, 'r') as gencfg:
generator_config = json.loads(gencfg.read())
imageSize = generator_config["imageSize"]
nz = generator_config["nz"]
nc = generator_config["nc"]
ngf = generator_config["ngf"]
noBN = generator_config["noBN"]
ngpu = generator_config["ngpu"]
mlp_G = generator_config["mlp_G"]
n_extra_layers = generator_config["n_extra_layers"]
if noBN:
netG = dcgan.DCGAN_G_nobn(imageSize, nz, nc, ngf, ngpu,
n_extra_layers)
elif mlp_G:
netG = mlp.MLP_G(imageSize, nz, nc, ngf, ngpu)
else:
netG = dcgan.DCGAN_G(imageSize, nz, nc, ngf, ngpu, n_extra_layers)
# load weights
netG.load_state_dict(torch.load(weights_path))
if torch.cuda.is_available():
netG = netG.cuda()
self.model = netG
self.nz = nz
def __getGenerator(opt, ngpu, nz, ngf, ndf, nc, n_extra_layers):
if opt.noBN:
if isDebug: print("Using No Batch Norm (DCGAN_G_nobn) for Generator")
netG = dcgan.DCGAN_G_nobn(opt.imageSize, nz, nc, ngf, ngpu,
n_extra_layers)
elif opt.mlp_G:
if isDebug: print("Using MLP_G for Generator")
netG = mlp.MLP_G(opt.imageSize, nz, nc, ngf, ngpu)
else:
if isDebug: print("Using DCGAN_G for Generator")
netG = dcgan.DCGAN_G(opt.imageSize, nz, nc, ngf, ngpu, n_extra_layers)
netG.apply(weights_init)
if opt.netG != '': # load checkpoint if needed
netG.load_state_dict(torch.load(opt.netG))
print("netG:\n {0}".format(netG))
return netG
# custom weights initialization called on netG and netD
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
if opt.noBN:
netG = dcgan.DCGAN_G_nobn(opt.imageSize, nz, nc, ngf, ngpu, n_extra_layers)
elif opt.mlp_G:
netG = mlp.MLP_G(opt.imageSize, nz, nc, ngf, ngpu)
else:
netG = dcgan.DCGAN_G(opt.imageSize, nz, nc, ngf, ngpu, n_extra_layers)
netG.apply(weights_init)
if opt.netG != '': # load checkpoint if needed
netG.load_state_dict(torch.load(opt.netG))
print(netG)
if opt.mlp_D:
netD = mlp.MLP_D(opt.imageSize, nz, nc, ndf, ngpu)
else:
netD = dcgan.DCGAN_D(opt.imageSize, nz, nc, ndf, ngpu, n_extra_layers)
netD.apply(weights_init)
if opt.netD != '':
# custom weights initialization called on netG and netD
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
if args.BN_G:
netG = dcgan.DCGAN_G(args.imageSize, nz, nc, ngf, ngpu, n_extra_layers)
elif args.mlp_G:
netG = mlp.MLP_G(args.imageSize, nz, nc, ngf, ngpu)
else:
netG = dcgan.DCGAN_G_nobn(args.imageSize, nz, nc, ngf, ngpu, n_extra_layers)
netG.apply(weights_init)
if args.netG != '': # load checkpoint if needed
netG.load_state_dict(torch.load(args.netG))
print(netG)
if args.BN_D:
netD = dcgan.DCGAN_D(args.imageSize, nz, nc, ndf, ngpu, n_extra_layers)
elif args.mlp_D:
netD = mlp.MLP_D(args.imageSize, nz, nc, ndf, ngpu)
else:
netD = dcgan.DCGAN_D_nobn_bias(args.imageSize, nc, ndf, ngpu, n_extra_layers)
def main(opt, reporter=None):
writer = SummaryWriter()
with open(writer.file_writer.get_logdir() + '/args.json', 'w') as f:
json.dump(opt, f)
if opt['experiment'] is None:
opt['experiment'] = 'samples'
os.system('mkdir {0}'.format(opt['experiment']))
opt['manualSeed'] = random.randint(1, 10000) # fix seed
print("Random Seed: ", opt['manualSeed'])
random.seed(opt['manualSeed'])
torch.manual_seed(opt['manualSeed'])
cudnn.benchmark = True
if torch.cuda.is_available() and not opt['cuda']:
print("WARNING: You have a CUDA device,"
"so you should probably run with --cuda")
if opt['dataset'] in ['imagenet', 'folder', 'lfw']:
# folder dataset
dataset = dset.ImageFolder(root=opt['dataroot'],
transform=transforms.Compose([
transforms.Scale(opt['imageSize']),
transforms.CenterCrop(opt['imageSize']),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)),
]))
elif opt['dataset'] == 'lsun':
dataset = dset.LSUN(root=opt['dataroot'],
classes=['bedroom_train'],
transform=transforms.Compose([
transforms.Scale(opt['imageSize']),
transforms.CenterCrop(opt['imageSize']),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)),
]))
elif opt['dataset'] == 'cifar10':
dataset = dset.CIFAR10(root=opt['dataroot'],
download=True,
transform=transforms.Compose([
transforms.Scale(opt['imageSize']),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5)),
]))
assert dataset
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=opt['batchSize'],
shuffle=True,
num_workers=int(opt['workers']))
ngpu = int(opt['ngpu'])
nz = int(opt['nz'])
ngf = int(opt['ngf'])
ndf = int(opt['ndf'])
nc = int(opt['nc'])
n_extra_layers = int(opt['n_extra_layers'])
# custom weights initialization called on netG and netD
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
if opt['noBN']:
netG = dcgan.DCGAN_G_nobn(opt['imageSize'], nz, nc, ngf, ngpu,
n_extra_layers)
elif opt['type'] == 'mlp':
netG = mlp.MLP_G(opt['imageSize'], nz, nc, ngf, ngpu)
elif opt['type'] == 'resnet':
netG = resnet.Generator(nz)
else:
netG = dcgan.DCGAN_G(opt['imageSize'], nz, nc, ngf, ngpu,
n_extra_layers)
netG.apply(weights_init)
print(netG)
if opt['type'] == 'mlp':
netD = mlp.MLP_D(opt['imageSize'], nz, nc, ndf, ngpu)
elif opt['type'] == 'resnet':
netD = resnet.Discriminator(nz)
else:
netD = dcgan.DCGAN_D(opt['imageSize'], nz, nc, ndf, ngpu,
n_extra_layers)
netD.apply(weights_init)
print(netD)
inc_noise = torch.utils.data.TensorDataset(
torch.randn(50000, nz, 1, 1).cuda())
inc_noise_dloader = torch.utils.data.DataLoader(
inc_noise, batch_size=opt['batchSize'])
input = torch.FloatTensor(opt['batchSize'], 3, opt['imageSize'],
opt['imageSize'])
noise = torch.FloatTensor(opt['batchSize'], nz, 1, 1)
fixed_noise = torch.FloatTensor(opt['batchSize'], nz, 1, 1).normal_(0, 1)
one = torch.FloatTensor([1])
mone = one * -1
if opt['cuda']:
netD.cuda()
netG.cuda()
input = input.cuda()
one, mone = one.cuda(), mone.cuda()
noise, fixed_noise = noise.cuda(), fixed_noise.cuda()
# setup optimizer
if opt['adam']:
optimizerD = optim.Adam(netD.parameters(),
lr=opt['lrD'],
betas=(opt['beta1'], opt['beta2']))
optimizerG = optim.Adam(netG.parameters(),
lr=opt['lrG'],
betas=(opt['beta1'], opt['beta2']))
else:
optimizerD = optim.RMSprop(netD.parameters(), lr=opt['lrD'])
optimizerG = optim.RMSprop(netG.parameters(), lr=opt['lrG'])
var_weight = 0.5
w = torch.tensor(
[var_weight * (1 - var_weight)**i for i in range(9, -1, -1)]).cuda()
gen_iterations = 0
for epoch in range(opt['niter']):
data_iter = iter(dataloader)
i = 0
while i < len(dataloader):
# l_var = opt.l_var + (gen_iterations + 1)/3000
l_var = opt['l_var']
############################
# (1) Update D network
###########################
for p in netD.parameters(): # reset requires_grad
p.requires_grad = True
# train the discriminator Diters times
# if gen_iterations < 25 or gen_iterations % 500 == 0:
if gen_iterations % 500 == 0:
Diters = 100
else:
Diters = opt['Diters']
j = 0
while j < Diters and i < len(dataloader):
j += 1
# enforce constraint
if not opt['var_constraint']:
for p in netD.parameters():
p.data.clamp_(opt['clamp_lower'], opt['clamp_upper'])
data = data_iter.next()
i += 1
# train with real
real_cpu, _ = data
netD.zero_grad()
batch_size = real_cpu.size(0)
if opt['cuda']:
real_cpu = real_cpu.cuda()
input.resize_as_(real_cpu).copy_(real_cpu)
inputv = Variable(input)
out_D_real = netD(inputv)
errD_real = out_D_real.mean(0).view(1)
if opt['var_constraint']:
vm_real = out_D_real.var(0)
# train with fake
noise.resize_(opt['batchSize'], nz, 1, 1).normal_(0, 1)
with torch.no_grad():
noisev = Variable(noise) # totally freeze netG
fake = netG(noisev).data
inputv = fake
out_D_fake = netD(inputv)
errD_fake = out_D_fake.mean(0).view(1)
if opt['var_constraint']:
vm_fake = out_D_fake.var(0)
errD = errD_real - errD_fake
loss = -((errD_real - errD_fake) - l_var * torch.exp(
torch.sqrt(torch.log(vm_real)**2 + torch.log(vm_fake)**2)))
loss.backward()
optimizerD.step()
if opt['var_constraint']:
writer.add_scalars('train/variance', {
'real': vm_real.item(),
'fake': vm_fake.item()
},
epoch * len(dataloader) + i)
############################
# (2) Update G network
###########################
for p in netD.parameters():
p.requires_grad = False # to avoid computation
netG.zero_grad()
# in case our last batch was the tail batch of the dataloader,
# make sure we feed a full batch of noise
noise.resize_(opt['batchSize'], nz, 1, 1).normal_(0, 1)
noisev = Variable(noise)
fake = netG(noisev)
errG = -netD(fake).mean(0).view(1)
errG.backward()
optimizerG.step()
gen_iterations += 1
if torch.isnan(errG):
raise ValueError("Loss is nan")
############################
# Log Data
###########################
print('[%d/%d][%d/%d][%d] Loss_D: %f Loss_G: %f Loss_D_real: %f'
' Loss_D_fake %f' %
(epoch, opt['niter'], i, len(dataloader), gen_iterations,
errD.data[0], errG.data[0], errD_real.data[0],
errD_fake.data[0]))
writer.add_scalar('train/critic', -errD.item(), gen_iterations)
if gen_iterations % (500 * 64 / opt['batchSize']) == 0:
real_cpu = real_cpu.mul(0.5).add(0.5)
vutils.save_image(real_cpu,
f'{opt["experiment"]}/real_samples.png')
with torch.no_grad():
fake = netG(Variable(fixed_noise))
fake.data = fake.data.mul(0.5).add(0.5)
vutils.save_image(
fake.data, f'{opt["experiment"]}/'
f'fake_samples_{gen_iterations:010d}.png')
writer.add_image(
'train/sample',
fake.data.mul(255).clamp(0, 255).byte().cpu().numpy(),
gen_iterations)
############################
# (3) Compute Scores
############################
if gen_iterations % (500 * 64 / opt['batchSize']) == 0:
with torch.no_grad():
netG.eval()
samples = []
for (x, ) in inc_noise_dloader:
samples.append(netG(x))
netG.train()
samples = torch.cat(samples, dim=0).cpu()
samples = (samples - samples.mean()) / samples.std()
score, _ = inception_score(samples.numpy(),
cuda=True,
resize=True,
splits=10)
writer.add_scalar('test/inception_50k', score, gen_iterations)
# fids = fid_score(
# samples.permute(0, 2, 3,
# 1).mul(128).add(128).clamp(255).numpy(),
# 'cifar10'
# )
# writer.add_scalar('test/fid_50k', fids, gen_iterations)
if reporter:
reporter(inception=score, fid=0)
# do checkpointing
torch.save(netG.state_dict(),
f'{opt["experiment"]}/netG_epoch_{epoch}.pth')
torch.save(netD.state_dict(),
f'{opt["experiment"]}/netD_epoch_{epoch}.pth')
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
if opt.noBN:
print("A")
netG = dcgan.DCGAN_G_nobn(opt.imageSize, nz, z_dims, ngf, ngpu,
n_extra_layers)
elif opt.mlp_G:
print("B")
netG = mlp.MLP_G(opt.imageSize, nz, z_dims, ngf, ngpu)
else:
print("C")
netG = dcgan.DCGAN_G(opt.imageSize, nz, z_dims, ngf, ngpu, n_extra_layers)
netG.apply(weights_init)
if opt.netG != '': # load checkpoint if needed
netG.load_state_dict(torch.load(opt.netG))
print(netG)
if opt.mlp_D:
netD = mlp.MLP_D(opt.imageSize, nz, nc, ndf, ngpu)
else:
netD = dcgan.DCGAN_D(opt.imageSize, nz, z_dims, ndf, ngpu, n_extra_layers)
netD.apply(weights_init)