def main():
args = get_arguments()
utils.make_dirs(args.save)
name_model = args.model + "_" + args.dataset_name + "_" + utils.datestr()
# TODO visual3D_temp.Basewriter package
writer = SummaryWriter(log_dir='../runs/' + name_model, comment=name_model)
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(
args, path='.././datasets')
model, optimizer = medzoo.create_model(args)
criterion = DiceLoss(classes=11, skip_index_after=args.classes)
if args.cuda:
torch.cuda.manual_seed(seed)
model = model.cuda()
print("Model transferred in GPU.....")
print("START TRAINING...")
for epoch in range(1, args.nEpochs + 1):
train_stats = train.train_dice(args, epoch, model, training_generator,
optimizer, criterion)
val_stats = train.test_dice(args, epoch, model, val_generator,
criterion)
#old
utils.write_train_val_score(writer, epoch, train_stats, val_stats)
model.save_checkpoint(args.save,
epoch,
val_stats[0],
optimizer=optimizer)
def main():
args = get_arguments()
utils.make_dirs(args.save)
train_f, val_f = utils.create_stats_files(args.save)
name_model = args.model + "_" + args.dataset_name + "_" + utils.datestr()
writer = SummaryWriter(log_dir='../runs/' + name_model, comment=name_model)
best_prec1 = 100.
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(
args, path='.././datasets')
model, optimizer = medzoo.create_model(args)
criterion = DiceLoss(classes=args.classes)
if args.cuda:
torch.cuda.manual_seed(seed)
model = model.cuda()
print("Model transferred in GPU.....")
print("START TRAINING...")
for epoch in range(1, args.nEpochs + 1):
train_stats = train.train_dice(args, epoch, model, training_generator,
optimizer, criterion, train_f, writer)
val_stats = train.test_dice(args, epoch, model, val_generator,
criterion, val_f, writer)
utils.write_train_val_score(writer, epoch, train_stats, val_stats)
model.save_checkpoint(args.save,
epoch,
val_stats[0],
optimizer=optimizer)
# if epoch % 5 == 0:
# utils.visualize_no_overlap(args, full_volume, affine, model, epoch, DIM, writer)
#utils.save_model(model, args, val_stats[0], epoch, best_prec1)
train_f.close()
val_f.close()
def main():
args = get_arguments()
utils.make_dirs(args.save)
train_f, val_f = utils.create_stats_files(args.save)
name_model = args.model + "_" + args.dataset_name + "_" + utils.datestr()
writer = SummaryWriter(log_dir='../runs/' + name_model, comment=name_model)
best_pred = 1.01
samples_train = 200
samples_val = 200
training_generator, val_generator, full_volume, affine = medical_loaders.generate_datasets(
args,
path='.././datasets',
samples_train=samples_train,
samples_val=samples_val)
model, optimizer = medzoo.create_model(args)
criterion = medzoo.DiceLoss2D(args.classes)
if args.cuda:
torch.cuda.manual_seed(seed)
model = model.cuda()
for epoch in range(1, args.nEpochs + 1):
train_stats = train.train_dice(args, epoch, model, training_generator,
optimizer, criterion, train_f, writer)
val_stats = train.test_dice(args, epoch, model, val_generator,
criterion, val_f, writer)
utils.write_train_val_score(writer, epoch, train_stats, val_stats)
best_pred = utils.save_model(model=model,
args=args,
dice_loss=val_stats[0],
epoch=epoch,
best_pred_loss=best_pred)
train_f.close()
val_f.close()