def __init__(self, opt):
self.opt = opt
self.gpu_ids = opt.gpu_ids
self.is_train = opt.is_train
self.device = torch.device('cuda:{}'.format(
self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')
self.save_dir = join(opt.checkpoints_dir, opt.name)
self.optimizer = None
self.edge_features = None
self.labels = None
self.mesh = None
self.soft_label = None
self.loss = None
self.path = None
self.nclasses = opt.nclasses
# Adding input features additionally into the fully connected layer
self.feature_keys = opt.features
if self.feature_keys:
self.feature_dictionaries = {
feature: get_feature_dict(feature)
for feature in self.feature_keys
}
self.feature_values = None
# Logging results into a file for each testing epoch
self.save_dir = join(opt.checkpoints_dir, opt.name)
self.testacc_log = join(self.save_dir, 'testacc_full_log_')
self.final_testacc_log = join(self.save_dir, 'final_testacc_full_log_')
# Load/define networks
self.net = networks.define_classifier(opt.input_nc,
opt.ncf,
opt.ninput_edges,
opt.nclasses,
opt,
self.gpu_ids,
opt.arch,
opt.init_type,
opt.init_gain,
num_features=len(
self.feature_keys))
self.net.train(self.is_train)
self.criterion = networks.define_loss(opt).to(self.device)
if self.is_train:
self.optimizer = torch.optim.Adam(self.net.parameters(),
lr=opt.lr,
betas=(opt.beta1, 0.999))
self.scheduler = networks.get_scheduler(self.optimizer, opt)
print_network(self.net)
if not self.is_train or opt.continue_train:
self.load_network(opt.which_epoch)
def __init__(self, opt):
BaseDataset.__init__(self, opt)
self.opt = opt
self.device = torch.device('cuda:{}'.format(
opt.gpu_ids[0])) if opt.gpu_ids else torch.device('cpu')
self.root = opt.dataroot
self.dir = os.path.join(opt.dataroot)
if opt.verbose:
print("DEBUG dir path in dataset ", self.dir)
if opt.dataset_mode == 'regression':
self.class_to_idx = get_feature_dict(opt.batch_labels)
self.nclasses = 1
else:
self.classes, self.class_to_idx = self.find_classes(self.dir)
self.nclasses = len(self.classes)
self.paths = self.make_dataset_by_class(self.dir, self.class_to_idx,
opt.phase, opt.dataset_mode)
self.size = len(self.paths)
self.get_mean_std()
# modify for network later.
opt.nclasses = self.nclasses
opt.input_nc = self.ninput_channels
def test_scan_number(self):
self.assertEqual(
get_feature_dict("scan_number")[SUBJECT], 1, "Should be 1")
def test_scan_age(self):
self.assertEqual(round(get_feature_dict("scan_age")[SUBJECT], 3),
30.571, "Should be 1.03")
def test_birth_weight(self):
self.assertEqual(
get_feature_dict("birth_weight")[SUBJECT], 1.03, "Should be 1.03")
def test_birth_age(self):
self.assertEqual(round(get_feature_dict("birth_age")[SUBJECT], 3),
28.714, "Should be 28.714")
def test_gender(self):
self.assertEqual(
get_feature_dict("gender")[SUBJECT], "Male", "Should be Male")