def main():
args = arg_parse()
# ---- setup device ----
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print('==> Using device ' + device)
# ---- setup config ----
cfg = get_cfg_defaults()
cfg.merge_from_file(args.cfg)
cfg.freeze()
seed.set_seed(cfg.SOLVER.SEED)
# ---- setup logger ----
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
logger = logging.construct_logger('context_cnns', cfg.OUTPUT_DIR)
logger.info(f'Using {device}')
logger.info('\n' + cfg.dump())
# ---- setup dataset ----
train_loader, val_loader = get_cifar(cfg)
# ---- setup model ----
print('==> Building model..')
net = get_model(cfg)
net = net.to(device)
model_stats = summary(net, (3, 32, 32), depth=10)
logger.info('\n'+str(model_stats))
if device=='cuda':
net = torch.nn.DataParallel(net)
# ---- setup trainers ----
optim = torch.optim.SGD(net.parameters(), lr=cfg.SOLVER.BASE_LR,
momentum=cfg.SOLVER.MOMENTUM,
weight_decay=cfg.SOLVER.WEIGHT_DECAY)
trainer = Trainer(
device,
train_loader,
val_loader,
net,
optim,
logger,
cfg
)
if args.resume:
# Load checkpoint
print('==> Resuming from checkpoint..')
cp = torch.load(args.resume)
trainer.model.load_state_dict(cp['net'])
trainer.optim.load_state_dict(cp['optim'])
trainer.epochs = cp['epoch']
trainer.train_acc = cp['train_accuracy']
trainer.val_acc = cp['test_accuracy']
trainer.train()
def main():
"""The main for this domain adapation example, showing the workflow"""
args = arg_parse()
# ---- setup device ----
device = "cuda" if torch.cuda.is_available() else "cpu"
print("==> Using device " + device)
# ---- setup configs ----
cfg = get_cfg_defaults()
cfg.merge_from_file(args.cfg)
cfg.freeze()
set_seed(cfg.SOLVER.SEED)
# ---- setup logger and output ----
output_dir = os.path.join(cfg.OUTPUT_DIR, cfg.DATASET.NAME, args.output)
os.makedirs(output_dir, exist_ok=True)
logger = construct_logger("isonet", output_dir)
logger.info("Using " + device)
logger.info("\n" + cfg.dump())
# ---- setup dataset ----
train_loader, val_loader = get_cifar(cfg)
print("==> Building model..")
net = get_model(cfg)
# print(net)
net = net.to(device)
# model_stats = summary(net, (3, 32, 32))
# logger.info('\n'+str(model_stats))
# Needed even for single GPU https://discuss.pytorch.org/t/attributeerror-net-object-has-no-attribute-module/45652
if device == "cuda":
net = torch.nn.DataParallel(net)
optim = torch.optim.SGD(
net.parameters(),
lr=cfg.SOLVER.BASE_LR,
momentum=cfg.SOLVER.MOMENTUM,
weight_decay=cfg.SOLVER.WEIGHT_DECAY,
dampening=cfg.SOLVER.DAMPENING,
nesterov=cfg.SOLVER.NESTEROV,
)
trainer = Trainer(device, train_loader, val_loader, net, optim, logger,
output_dir, cfg)
if args.resume:
# Load checkpoint
print("==> Resuming from checkpoint..")
cp = torch.load(args.resume)
trainer.model.load_state_dict(cp["net"])
trainer.optim.load_state_dict(cp["optim"])
trainer.epochs = cp["epoch"]
trainer.train_acc = cp["train_accuracy"]
trainer.val_acc = cp["test_accuracy"]
trainer.train()
def main():
args = arg_parse()
# ---- setup device ----
device = "cuda" if torch.cuda.is_available() else "cpu"
print("==> Using device " + device)
# ---- setup config ----
cfg = get_cfg_defaults()
cfg.merge_from_file(args.cfg)
cfg.freeze()
seed.set_seed(cfg.SOLVER.SEED)
# ---- setup logger ----
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
logger = logging.construct_logger("context_cnns", cfg.OUTPUT_DIR)
logger.info(f"Using {device}")
logger.info("\n" + cfg.dump())
# ---- setup dataset ----
train_loader, valid_loader = get_cifar(cfg)
# ---- setup model ----
print("==> Building model..")
net = get_model(cfg)
net = net.to(device)
model_stats = summary(net, (3, 32, 32))
logger.info("\n" + str(model_stats))
if device == "cuda":
net = torch.nn.DataParallel(net)
# ---- setup trainers ----
optim = torch.optim.SGD(
net.parameters(), lr=cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM, weight_decay=cfg.SOLVER.WEIGHT_DECAY
)
trainer = Trainer(device, train_loader, valid_loader, net, optim, logger, cfg)
if args.resume:
# Load checkpoint
print("==> Resuming from checkpoint..")
cp = torch.load(args.resume)
trainer.model.load_state_dict(cp["net"])
trainer.optim.load_state_dict(cp["optim"])
trainer.epochs = cp["epoch"]
trainer.train_acc = cp["train_accuracy"]
trainer.valid_acc = cp["test_accuracy"]
trainer.train()
def main():
args = arg_parse()
# ---- setup device ----
device = "cuda" if torch.cuda.is_available() else "cpu"
print("==> Using device " + device)
# ---- setup configs ----
cfg = get_cfg_defaults()
cfg.merge_from_file(args.cfg)
cfg.freeze()
seed.set_seed(cfg.SOLVER.SEED)
# ---- setup logger and output ----
output_dir = os.path.join(cfg.OUTPUT_DIR, cfg.DATASET.NAME, args.output)
os.makedirs(output_dir, exist_ok=True)
logger = lu.construct_logger("gripnet", output_dir)
logger.info("Using " + device)
logger.info(cfg.dump())
# ---- setup dataset ----
download_file_by_url(cfg.DATASET.URL, cfg.DATASET.ROOT, "pose.pt", "pt")
data = torch.load(os.path.join(cfg.DATASET.ROOT, "pose.pt"))
device = torch.device(device)
data = data.to(device)
# ---- setup model ----
print("==> Building model..")
model = GripNet(
cfg.GRIPN.GG_LAYERS, cfg.GRIPN.GD_LAYERS, cfg.GRIPN.DD_LAYERS, data.n_d_node, data.n_g_node, data.n_dd_edge_type
).to(device)
# TODO Visualize model
# ---- setup trainers ----
optimizer = torch.optim.Adam(model.parameters(), lr=cfg.SOLVER.BASE_LR)
# TODO
trainer = Trainer(cfg, device, data, model, optimizer, logger, output_dir)
if args.resume:
# Load checkpoint
print("==> Resuming from checkpoint..")
cp = torch.load(args.resume)
trainer.model.load_state_dict(cp["net"])
trainer.optim.load_state_dict(cp["optim"])
trainer.epochs = cp["epoch"]
trainer.train_auprc = cp["train_auprc"]
trainer.valid_auprc = cp["valid_auprc"]
trainer.train_auroc = cp["train_auroc"]
trainer.valid_auroc = cp["valid_auroc"]
trainer.train_ap = cp["train_ap"]
trainer.valid_ap = cp["valid_ap"]
trainer.train()
def main():
args = arg_parse()
# ---- setup device ----
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print('==> Using device ' + device)
# ---- setup configs ----
C.merge_from_file(args.cfg)
C.freeze()
seed.set_seed(C.SOLVER.SEED)
# ---- setup logger and output ----
output_dir = os.path.join(C.OUTPUT_DIR, C.DATASET.NAME, args.output)
os.makedirs(output_dir, exist_ok=True)
logger = lu.construct_logger('gripnet', output_dir)
logger.info('Using ' + device)
logger.info(C.dump())
# ---- setup dataset ----
data = construct_dataset(C)
device = torch.device(device)
data = data.to(device)
# ---- setup model ----
print('==> Building model..')
model = GripNet(C.GRIPN.GG_LAYERS, C.GRIPN.GD_LAYERS, C.GRIPN.DD_LAYERS,
data.n_d_node, data.n_g_node,
data.n_dd_edge_type).to(device)
# TODO Visualize model
# ---- setup trainers ----
optimizer = torch.optim.Adam(model.parameters(), lr=C.SOLVER.BASE_LR)
# TODO
trainer = Trainer(C, device, data, model, optimizer, logger, output_dir)
if args.resume:
# Load checkpoint
print('==> Resuming from checkpoint..')
cp = torch.load(args.resume)
trainer.model.load_state_dict(cp['net'])
trainer.optim.load_state_dict(cp['optim'])
trainer.epochs = cp['epoch']
trainer.train_auprc = cp['train_auprc']
trainer.val_auprc = cp['val_auprc']
trainer.train_auroc = cp['train_auroc']
trainer.val_auroc = cp['val_auroc']
trainer.train_ap = cp['train_ap']
trainer.val_ap = cp['val_ap']
trainer.train()
def testing_logger():
save_dir = os.getcwd()
testing_logger = logger.construct_logger("test", save_dir)
yield testing_logger
# Gather info
filehandler = testing_logger.handlers[0]
log_file_name = filehandler.baseFilename
# Teardown log file
filehandler.close()
os.remove(log_file_name)
# Teardown gitdiff.patch file
[folder, file] = os.path.split(log_file_name)
file_core = os.path.splitext(file)[0]
gitdiff_file_name = os.path.join(folder, file_core + ".gitdiff.patch")
os.remove(gitdiff_file_name)