def sigmoid_rampup(global_step, start_iter, end_iter):
if global_step < start_iter:
return 0.0
rampup_length = end_iter - start_iter
cur_ramp = global_step - start_iter
cur_ramp = np.clip(cur_ramp, 0, rampup_length)
phase = 1.0 - cur_ramp / rampup_length
return np.exp(-5.0 * phase * phase)
itr = inputs.get_data_iter(batch_size=FLAGS.bs_c, subset=FLAGS.n_labels)
# itr_u = inputs.get_data_iter(batch_size=FLAGS.bs_c)
netG, optim_G = inputs.get_generator_optimizer()
netD, optim_D = inputs.get_discriminator_optimizer()
netC, optim_c = inputs.get_classifier_optimizer()
netG, netD, netC = netG.to(device), netD.to(device), netC.to(device)
netG = nn.DataParallel(netG)
netD = nn.DataParallel(netD)
netC = nn.DataParallel(netC)
netC_T, _ = inputs.get_classifier_optimizer()
netC_T = netC_T.to(device)
netC_T = nn.DataParallel(netC_T)
netC.train()
netC_T.train()
Torture.update_average(netC_T, netC, 0)
for p in netC_T.parameters():
p.requires_grad_(False)
if FLAGS.c_step == "ramp_swa":
netC_swa, _ = inputs.get_classifier_optimizer()
netC_swa = netC_swa.to(device)
__file__, KEY_ARGUMENTS)
torch.manual_seed(1234)
torch.cuda.manual_seed(1235)
np.random.seed(1236)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
itr = inputs.get_data_iter(batch_size=FLAGS.bs_c, subset=1000)
itr_u = inputs.get_data_iter(batch_size=FLAGS.bs_c)
# itr_t = inputs.get_data_iter_twice(subset=1000)
# itr_ut = inputs.get_data_iter_twice()
netG, optim_G = inputs.get_generator_optimizer()
netD, optim_D = inputs.get_discriminator_optimizer()
netC, optim_c = inputs.get_classifier_optimizer()
netG, netD, netC = netG.to(device), netD.to(device), netC.to(device)
netG = nn.DataParallel(netG)
netD = nn.DataParallel(netD)
netC = nn.DataParallel(netC)
checkpoint_io = Torture.utils.checkpoint.CheckpointIO(
checkpoint_dir=MODELS_FOLDER)
checkpoint_io.register_modules(netG=netG,
netD=netD,
netC=netC,
optim_G=optim_G,
optim_D=optim_D,
optim_c=optim_c)
logger = Logger(log_dir=SUMMARIES_FOLDER)
