def start_training(model_class, model_args, model_kwargs, chkpt_num, lr,
train_sets, val_sets, data_dir, **params):
#PyTorch Model
net = utils.create_network(model_class, model_args, model_kwargs)
monitor = utils.LearningMonitor()
#Loading model checkpoint (if applicable)
if chkpt_num != 0:
utils.load_chkpt(net, monitor, chkpt_num, params["model_dir"],
params["log_dir"])
#DataProvider Sampler
Sampler = params["sampler_class"]
train_sampler = utils.AsyncSampler(
Sampler(data_dir,
dsets=train_sets,
mode="train",
resize=params["resize"]))
val_sampler = utils.AsyncSampler(
Sampler(data_dir, dsets=val_sets, mode="val", resize=params["resize"]))
loss_fn = loss.BinomialCrossEntropyWithLogits()
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
train.train(net,
loss_fn,
optimizer,
train_sampler,
val_sampler,
last_iter=chkpt_num,
monitor=monitor,
**params)
def start_training(model_class, model_args, model_kwargs, sampler_class,
sampler_spec, augmentor_constr, chkpt_num, lr, train_sets,
val_sets, data_dir, model_dir, log_dir, tb_train, tb_val,
**params):
#PyTorch Model
net = utils.create_network(model_class, model_args, model_kwargs)
train_writer = tensorboardX.SummaryWriter(tb_train)
val_writer = tensorboardX.SummaryWriter(tb_val)
monitor = utils.LearningMonitor()
#Loading model checkpoint (if applicable)
if chkpt_num != 0:
utils.load_chkpt(net, monitor, chkpt_num, model_dir, log_dir)
#DataProvider Stuff
train_aug = augmentor_constr(True)
train_sampler = utils.AsyncSampler(
sampler_class(data_dir,
sampler_spec,
vols=train_sets,
mode="train",
aug=train_aug))
val_aug = augmentor_constr(False)
val_sampler = utils.AsyncSampler(
sampler_class(data_dir,
sampler_spec,
vols=val_sets,
mode="val",
aug=val_aug))
loss_fn = loss.BinomialCrossEntropyWithLogits()
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
train.train(net,
loss_fn,
optimizer,
train_sampler,
val_sampler,
train_writer=train_writer,
val_writer=val_writer,
last_iter=chkpt_num,
model_dir=model_dir,
log_dir=log_dir,
monitor=monitor,
**params)
