def __init__(self, args, ftype, data_loader, data_max, data_min, C, T, V, F, num_classes, n_z=1024, device='cuda:0'):
self.args = args
self.ftype = ftype
self.data_loader = data_loader
self.data_max = data_max
self.data_min = data_min
self.num_classes = num_classes
self.result = dict()
self.iter_info = dict()
self.epoch_info = dict()
self.meta_info = dict(epoch=0, iter=0)
self.device = device
self.io = torchlight.IO(
self.args.work_dir,
save_log=self.args.save_log,
print_log=self.args.print_log)
# model
self.C = C
self.T = T
self.V = V
self.F = F
self.n_z = n_z
if not os.path.isdir(self.args.work_dir):
os.mkdir(self.args.work_dir)
self.model = CVAE.CVAE(F, T, self.n_z, num_classes)
self.model.cuda('cuda:0')
self.model.apply(weights_init)
self.loss = vae_loss
self.best_loss = np.inf
self.loss_updated = False
self.step_epochs = [np.ceil(float(self.args.num_epoch * x)) for x in self.args.step]
self.best_epoch = None
self.mean = 0.
self.lsig = 1.
# optimizer
if self.args.optimizer == 'SGD':
self.optimizer = optim.SGD(
self.model.parameters(),
lr=self.args.base_lr,
momentum=0.9,
nesterov=self.args.nesterov,
weight_decay=self.args.weight_decay)
elif self.args.optimizer == 'Adam':
self.optimizer = optim.Adam(
self.model.parameters(),
lr=self.args.base_lr,
weight_decay=self.args.weight_decay)
else:
raise ValueError()
self.lr = self.args.base_lr
def __init__(self,
args,
data_loader,
C,
num_classes,
graph_dict,
device='cuda:0',
verbose=True):
self.args = args
self.data_loader = data_loader
self.num_classes = num_classes
self.result = dict()
self.iter_info = dict()
self.epoch_info = dict()
self.meta_info = dict(epoch=0, iter=0)
self.device = device
self.verbose = verbose
self.io = torchlight.IO(self.args.work_dir,
save_log=self.args.save_log,
print_log=self.args.print_log)
# model
if not os.path.isdir(self.args.work_dir):
os.mkdir(self.args.work_dir)
self.model = classifier.Classifier(C, num_classes, graph_dict)
self.model.cuda('cuda:0')
self.model.apply(weights_init)
self.loss = nn.CrossEntropyLoss()
self.best_loss = math.inf
self.step_epochs = [
math.ceil(float(self.args.num_epoch * x)) for x in self.args.step
]
self.best_epoch = None
self.best_accuracy = np.zeros((1, np.max(self.args.topk)))
self.accuracy_updated = False
# optimizer
if self.args.optimizer == 'SGD':
self.optimizer = optim.SGD(self.model.parameters(),
lr=self.args.base_lr,
momentum=0.9,
nesterov=self.args.nesterov,
weight_decay=self.args.weight_decay)
elif self.args.optimizer == 'Adam':
self.optimizer = optim.Adam(self.model.parameters(),
lr=self.args.base_lr,
weight_decay=self.args.weight_decay)
else:
raise ValueError()
self.lr = self.args.base_lr
def init_environment(self):
self.io = torchlight.IO(self.arg.work_dir,
save_log=self.arg.save_log,
print_log=self.arg.print_log)
self.io.save_arg(self.arg)
# gpu
if self.arg.use_gpu:
gpus = torchlight.visible_gpu(self.arg.device)
torchlight.occupy_gpu(gpus)
self.gpus = gpus
self.dev = "cuda:0"
else:
self.dev = "cpu"