def main(args):
cfg = vars(args)
cfg["session_dir"] = create_session_dir("./sessions")
init_session_log(cfg)
m_gen = Generator(z_dim=cfg["z_dim"],
num_layers=cfg["num_layers"],
num_base_chans=cfg["num_base_chans"])
m_disc = Discriminator(num_base_chans=cfg["num_base_chans"],
num_layers=cfg["num_layers"] - 1)
logging.info("Generator:\n")
logging.info(str(m_gen))
logging.info("\n\n")
logging.info("Discriminator:\n")
logging.info(str(m_disc))
logging.info("\n\n")
train_loader = get_loader(cfg)
make_first_batch(train_loader, cfg)
cudev = cfg["cuda"]
if cudev >= 0 and not torch.cuda.is_available():
raise RuntimeError("CUDA device specified but CUDA not available")
if cudev >= 0:
m_gen.cuda(cudev)
m_disc.cuda(cudev)
betas = (cfg["beta1"], cfg["beta2"])
optD = torch.optim.Adam(m_disc.parameters(), lr=cfg["lr_d"], betas=betas)
optG = torch.optim.Adam(m_gen.parameters(), lr=cfg["lr_g"], betas=betas)
train(m_gen, m_disc, train_loader, (optD, optG), cfg)
def main(args):
cfg = vars(args)
cfg["session_dir"] = create_session_dir("./sessions")
m_gen = Generator(cfg["z_dim"])
m_disc = Discriminator()
if len( cfg["resume_path"] ) > 0:
cfg["session_dir"] = os.path.dirname( os.path.abspath(\
cfg["resume_path"] ) )
start_epoch = load_models(m_gen, m_disc, cfg)
filemode = "a"
else:
start_epoch = 0
filemode = "w"
init_session_log(cfg, filemode)
train_loader = get_loader(cfg)
cudev = cfg["cuda"]
if cudev >= 0 and not torch.cuda.is_available():
raise RuntimeError("CUDA device specified but CUDA not available")
if cudev >= 0:
m_gen.cuda(cudev)
m_disc.cuda(cudev)
optG = torch.optim.SGD([{"params" : m_gen.parameters()}], lr=cfg["lr_g"],
momentum=cfg["momentum"])
optD = torch.optim.SGD([{"params" : m_disc.parameters()}], lr=cfg["lr_d"],
momentum=cfg["momentum"])
train(m_gen, m_disc, train_loader, (optD,optG), cfg, start_epoch)
def main(args):
cfg = vars(args)
cfg["session_dir"] = create_session_dir("./sessions")
start_epoch = 0
filemode = "w"
init_session_log(cfg, filemode)
train_loader, test_loader = get_loaders(cfg)
cudev = cfg["cuda"]
if cudev >= 0 and not torch.cuda.is_available():
raise RuntimeError("CUDA device specified but CUDA not available")
cnn = SimpleCNN()
if cudev >= 0:
cnn = cnn.cuda(cudev)
optimizer = torch.optim.SGD([{
"params": cnn.parameters()
}],
lr=cfg["lr"],
momentum=cfg["momentum"])
train(cnn, (train_loader, test_loader), optimizer, cfg)
def main(args):
cfg = vars(args)
cfg["session_dir"] = create_session_dir("./sessions")
init_session_log(cfg)
train(cfg)