def get_discrim_model(opt):
nc = 1 if opt.data.startswith("mnist") else 3
if opt.model == "DCGAN":
return DCGAN_D()
elif opt.model == "WGAN":
return WGAN_D(64, 100, nc, 64, 1)
elif opt.model == "MDGAN":
return MDGAN_D()
assert (False)
workers = int(opt.workers)
lambda_ = int(opt.lambda_)
cuda = opt.cuda
#datapath='../../../../../'
#f = h5py.File(opt.datapath+'fields_z='+opt.redshift+'.hdf5', 'r')
#f = f['delta_HI']
if opt.MLP == True:
netG = MLP_G(s_sample, nz, nc, ngf, ngpu)
netD = MLP_D(s_sample, nz, nc, ngf, ngpu)
else:
netG = DCGAN_G(s_sample, nz, nc, ngf, ngpu)
netD = DCGAN_D(s_sample, nz, nc, ndf, ngpu)
#experiments/ch128_lr0005_tanh/netD_epoch_47.pth
epoch_load = opt.epoch_st - 1
wass_loss = []
if opt.load_weights == True:
netG.load_state_dict(
torch.load(opt.experiment + 'netG_epoch_' + str(epoch_load) +
'.pth'))
netD.load_state_dict(
torch.load(opt.experiment + 'netD_epoch_' + str(epoch_load) +
'.pth'))
wass_loss = pd.read_csv(opt.experiment + 'loss.csv', header=None)
wass_loss = wass_loss[wass_loss.columns[0]].tolist()
def DCGAN_main(opt):
g = Globals()
opt.workers = 2
opt.batchSize = 128
opt.imageSize = 64
opt.nz = 100
opt.ngf = 64
opt.ndf = 64
opt.niter = 50
opt.lr = 0.0002
opt.beta1 = 0.5
opt.cuda = True
opt.ngpu = 1
opt.netG = ''
opt.netD = ''
opt.outf = g.default_model_dir + "DCGAN/"
opt.manualSeed = None
opt = addDataInfo(opt)
opt.outf = opt.outf + opt.data + "/"
print_prop(opt)
try:
os.makedirs(opt.outf)
except OSError:
pass
if os.path.exists(opt.outf + "/mark"):
print("Already generated before. Now exit.")
return
if opt.manualSeed is None:
opt.manualSeed = random.randint(1, 10000)
print("Random Seed: ", opt.manualSeed)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
if opt.cuda:
torch.cuda.manual_seed_all(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")
dataset, dataloader = getDataSet(opt)
ngpu = int(opt.ngpu)
nz = int(opt.nz)
ngf = int(opt.ngf)
ndf = int(opt.ndf)
nc = 1 if opt.data.startswith("mnist") else 3
# 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)
netG = DCGAN_G(nz, nc, ngf)
netG.apply(weights_init)
if opt.netG != '':
netG.load_state_dict(torch.load(opt.netG))
print(netG)
netD = DCGAN_D(ngpu)
netD.apply(weights_init)
if opt.netD != '':
netD.load_state_dict(torch.load(opt.netD))
print(netD)
criterion = nn.BCELoss()
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)
label = torch.FloatTensor(opt.batchSize)
real_label = 1
fake_label = 0
if opt.cuda:
netD.to(device)
netG.to(device)
criterion.to(device)
input, label = input.to(device), label.to(device)
noise, fixed_noise = noise.to(device), fixed_noise.to(device)
input = Variable(input)
label = Variable(label)
noise = Variable(noise)
fixed_noise = Variable(fixed_noise)
# setup optimizer
optimizerD = optim.Adam(netD.parameters(), lr=opt.lr,
betas=(opt.beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=opt.lr,
betas=(opt.beta1, 0.999))
for epoch in range(opt.niter):
for i, data in enumerate(dataloader, 0):
############################
# (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
###########################
# train with real
netD.zero_grad()
real_cpu, _ = data
batch_size = real_cpu.size(0)
input.data.resize_(real_cpu.size()).copy_(real_cpu)
label.data.resize_(batch_size).fill_(real_label)
output = netD(input)
errD_real = criterion(output, label)
errD_real.backward()
D_x = output.data.mean()
# train with fake
noise.data.resize_(batch_size, nz, 1, 1)
noise.data.normal_(0, 1)
fake = netG(noise)
label.data.fill_(fake_label)
output = netD(fake.detach())
errD_fake = criterion(output, label)
errD_fake.backward()
D_G_z1 = output.data.mean()
errD = errD_real + errD_fake
optimizerD.step()
############################
# (2) Update G network: maximize log(D(G(z)))
###########################
netG.zero_grad()
# fake labels are real for generator cost
label.data.fill_(real_label)
output = netD(fake)
errG = criterion(output, label)
errG.backward()
D_G_z2 = output.data.mean()
optimizerG.step()
print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f'
% (epoch, opt.niter, i, len(dataloader),
errD.data[0], errG.data[0], D_x, D_G_z1, D_G_z2))
if i % 100 == 0:
saveImage(real_cpu, '%s/real_samples.png' % opt.outf)
fake = netG(fixed_noise)
saveImage(fake.data, '%s/fake_samples_epoch_%03d.png' %
(opt.outf, epoch))
# do checkpointing
torch.save(netG.state_dict(), '%s/netG_epoch_%d.pth' %
(opt.outf, epoch))
torch.save(netD.state_dict(), '%s/netD_epoch_%d.pth' %
(opt.outf, epoch))
with open(opt.outf + "/mark", "w") as f:
f.write("")
m.bias.data.fill_(0)
# In[33]:
from tqdm import tqdm
if model_name == 'DC':
G = DCGAN_G(isize=img_size,
nz=z_size,
nc=image_chanel,
ngf=hidden_size,
ngpu=0)
G.apply(weights_init)
D = DCGAN_D(isize=img_size,
nz=z_size,
nc=image_chanel,
ndf=hidden_size,
ngpu=0)
D.apply(weights_init)
if model_name == 'MLP':
G = MLP_G(isize=img_size,
nz=z_size,
nc=image_chanel,
ngf=hidden_size,
ngpu=0)
D = MLP_D(isize=img_size,
nz=z_size,
nc=image_chanel,
ndf=hidden_size,
ngpu=0)
print(G)
def DCGAN_main(opt):
g = Globals()
opt.workers = 2
opt.batchSize = 64
opt.imageSize = 64
opt.nz = 100
opt.ngf = 64
opt.ndf = 64
opt.niter = 50
opt.lr = 0.0002
opt.beta1 = 0.5
opt.cuda = True
opt.ngpu = 1
opt.netG = ''
opt.netD = ''
opt.outf = g.default_model_dir + "DCGAN/"
opt.manualSeed = None
opt = addDataInfo(opt)
opt.outf = opt.outf + opt.data + "/"
print_prop(opt)
try:
os.makedirs(opt.outf)
except OSError:
pass
if os.path.exists(opt.outf + "/mark"):
print("Already generated before. Now exit.")
return
if opt.manualSeed is None:
opt.manualSeed = random.randint(1, 10000)
print("Random Seed: ", opt.manualSeed)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
if opt.cuda:
torch.cuda.manual_seed_all(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")
dataset, dataloader = getDataSet(opt)
ngpu = int(opt.ngpu)
nz = int(opt.nz)
ngf = int(opt.ngf)
ndf = int(opt.ndf)
nc = 1 if opt.data.startswith("mnist") else 3
# 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)
netG = DCGAN_G(nz, nc, ngf)
netG.apply(weights_init)
if opt.netG != '':
netG.load_state_dict(torch.load(opt.netG))
print(netG)
netD = DCGAN_D(ngpu)
netD.apply(weights_init)
if opt.netD != '':
netD.load_state_dict(torch.load(opt.netD))
print(netD)
criterion = nn.BCELoss()
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)
label = torch.FloatTensor(opt.batchSize)
real_label = 1
fake_label = 0
if opt.cuda:
netD.cuda()
netG.cuda()
criterion.cuda()
input, label = input.cuda(), label.cuda()
noise, fixed_noise = noise.cuda(), fixed_noise.cuda()
input = Variable(input)
label = Variable(label)
noise = Variable(noise)
fixed_noise = Variable(fixed_noise)
# setup optimizer
optimizerD = optim.Adam(netD.parameters(), lr=opt.lr,
betas=(opt.beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=opt.lr,
betas=(opt.beta1, 0.999))
for epoch in range(opt.niter):
for i, data in enumerate(dataloader, 0):
############################
# (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
###########################
# train with real
netD.zero_grad()
real_cpu, _ = data
batch_size = real_cpu.size(0)
input.data.resize_(real_cpu.size()).copy_(real_cpu)
label.data.resize_(batch_size).fill_(real_label)
output = netD(input)
errD_real = criterion(output, label)
errD_real.backward()
D_x = output.data.mean()
# train with fake
noise.data.resize_(batch_size, nz, 1, 1)
noise.data.normal_(0, 1)
fake = netG(noise)
label.data.fill_(fake_label)
output = netD(fake.detach())
errD_fake = criterion(output, label)
errD_fake.backward()
D_G_z1 = output.data.mean()
errD = errD_real + errD_fake
optimizerD.step()
############################
# (2) Update G network: maximize log(D(G(z)))
###########################
netG.zero_grad()
# fake labels are real for generator cost
label.data.fill_(real_label)
output = netD(fake)
errG = criterion(output, label)
errG.backward()
D_G_z2 = output.data.mean()
optimizerG.step()
print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f'
% (epoch, opt.niter, i, len(dataloader),
errD.data[0], errG.data[0], D_x, D_G_z1, D_G_z2))
if i % 100 == 0:
saveImage(real_cpu, '%s/real_samples.png' % opt.outf)
fake = netG(fixed_noise)
saveImage(fake.data, '%s/fake_samples_epoch_%03d.png' %
(opt.outf, epoch))
# do checkpointing
torch.save(netG.state_dict(), '%s/netG_epoch_%d.pth' %
(opt.outf, epoch))
torch.save(netD.state_dict(), '%s/netD_epoch_%d.pth' %
(opt.outf, epoch))
with open(opt.outf + "/mark", "w") as f:
f.write("")
ngf = int(opt.ngf)
ndf = int(opt.ndf)
nc = 1 #By default our data has only one channel
niter = int(opt.niter)
Diters = int(opt.Diters)
workers = int(opt.workers)
lambda_ = int(opt.lambda_)
cuda = opt.cuda
#datapath='../../../../../'
#f = h5py.File(opt.datapath+'fields_z='+opt.redshift+'.hdf5', 'r')
#f = f['delta_HI']
netG = DCGAN_G(s_sample, nz, nc, ngf, ngpu, n_extra_layers)
netD = DCGAN_D(s_sample, nz, nc, ndf, ngpu, n_extra_layers)
#experiments/ch128_lr0005_tanh/netD_epoch_47.pth
epoch_load = opt.epoch_st - 1
wass_loss = []
if opt.load_weights == True:
netG.load_state_dict(
torch.load(opt.experiment + 'netG_epoch_' + str(epoch_load) +
'.pth'))
netD.load_state_dict(
torch.load(opt.experiment + 'netD_epoch_' + str(epoch_load) +
'.pth'))
#wass_loss=pd.read_csv(opt.experiment +'loss.csv', header=None)
#wass_loss=wass_loss[wass_loss.columns[0]].tolist()