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.nclasses = opt.nclasses
# 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)
self.net.train(self.is_train)
self.criterion = networks.define_loss(opt).to(self.device)
if self.is_train:
if opt.optim == 'RMSprop' :
self.optimizer = torch.optim.RMSprop(self.net.parameters(), lr=opt.lr)
else:
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):
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 = os.path.join(opt.checkpoints_dir, opt.name)
self.optimizer = None
self.loss = None
#
# load/define networks
self.net = network.define_prs(self.gpu_ids)
self.net.train(self.is_train)
self.criterion1 = network.define_loss_1(opt)
self.criterion2 = network.define_loss_2(opt)
if self.is_train:
self.optimizer = torch.optim.Adam(self.net.parameters(),
lr=opt.lr,
betas=(opt.beta1, 0.999))
self.scheduler = network.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):
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)