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jblitzar
/
github-python-corpus

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torch.backends.cudnn.benchmark = True # gotta go fast!
from utils import utils
if __name__ == "__main__":
# parse arguments
parser = argparse.ArgumentParser(description='Train an autoencoder')
parser.add_argument('experconfig', type=str, help='experiment config file')
parser.add_argument('--profile', type=str, default='Train', help='config profile')
parser.add_argument('--devices', type=int, nargs='+', default=[0], help='devices')
parser.add_argument('--resume', action='store_true', help='resume training')
parser.add_argument('--noprogress', action='store_true', help='don\'t output training progress images')
parser.add_argument('--nostab', action='store_true', help='don\'t check loss stability')
parser.add_argument('--scripting', action='store_true', help='use torch.jit.script')
parsed, unknown = parser.parse_known_args()
for arg in unknown:
if arg.startswith(("-", "--")):
parser.add_argument(arg, type=eval)
args = parser.parse_args()
outpath = os.path.dirname(args.experconfig)
if args.resume:
iternum = utils.findmaxiters("{}/log.txt".format(outpath))
else:
iternum = 0
log = utils.Logger("{}/log.txt".format(outpath), "a" if args.resume else "w")
print("Python", sys.version)
print("PyTorch", torch.__version__)
print(" ".join(sys.argv))
print("Output path:", outpath)
# load config
starttime = time.time()
experconfig = utils.import_module(args.experconfig, "config")
profile = getattr(experconfig, args.profile)(**{k: v for k, v in vars(args).items() if k not in parsed})
if not args.noprogress:
progressprof = experconfig.Progress()
print("Config loaded ({:.2f} s)".format(time.time() - starttime))
# build dataset & testing dataset
starttime = time.time()
if not args.noprogress:
testdataset = progressprof.get_dataset()
dataloader = torch.utils.data.DataLoader(testdataset,
batch_size=progressprof.batchsize, shuffle=False,
drop_last=True, num_workers=0)
for testbatch in dataloader:
break
dataset = profile.get_dataset()
print("len(dataset)=", len(dataset))
if hasattr(profile, "get_dataset_sampler"):
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=profile.batchsize,
sampler=profile.get_dataset_sampler(), drop_last=True,
num_workers=8, persistent_workers=True)
else:
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=profile.batchsize, shuffle=True, drop_last=True,
num_workers=8, persistent_workers=True)
print("Dataset instantiated ({:.2f} s)".format(time.time() - starttime))
# data writer
starttime = time.time()
if not args.noprogress:
writer = progressprof.get_writer()
print("Writer instantiated ({:.2f} s)".format(time.time() - starttime))
# build autoencoder
starttime = time.time()
ae = profile.get_autoencoder(dataset)
ae = ae.to("cuda").train()
if args.resume:
ae.load_state_dict(torch.load("{}/aeparams.pt".format(outpath)), strict=False)
print("Autoencoder instantiated ({:.2f} s)".format(time.time() - starttime))
# compile with jit
if args.scripting:
ae.encoder = torch.jit.script(ae.encoder)
ae.decoder = torch.jit.script(ae.decoder)
# build optimizer
starttime = time.time()
optim = profile.get_optimizer(ae)
if args.resume:
optim.load_state_dict(torch.load("{}/optimparams.pt".format(outpath)))
lossweights = profile.get_loss_weights()
print("Optimizer instantiated ({:.2f} s)".format(time.time() - starttime))
# train
starttime = time.time()
evalpoints = np.geomspace(1., profile.maxiter, 100).astype(np.int32)
prevloss = np.inf
for epoch in range(10000):
for data in dataloader:
# forward
cudadata = utils.tocuda(data)
output, losses = ae(
trainiter=iternum,