def __init__(self):
self.args = self.parse_command_line()
self.checkpoint_dir, self.logfile, self.checkpoint_path_validation, self.checkpoint_path_final \
= get_log_files(self.args.checkpoint_dir)
print_and_log(self.logfile, "Options: %s\n" % self.args)
print_and_log(self.logfile, "Checkpoint Directory: %s\n" % self.checkpoint_dir)
gpu_device = 'cuda:0'
self.device = torch.device(gpu_device if torch.cuda.is_available() else 'cpu')
self.model = self.init_model()
self.train_set, self.validation_set, self.test_set = self.init_data()
self.metadataset = MetaDatasetReader(self.args.data_path, self.args.mode, self.train_set, self.validation_set,
self.test_set)
self.loss = loss
self.accuracy_fn = aggregate_accuracy
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.args.learning_rate)
self.optimizer.zero_grad()
self.validation_accuracies = ValidationAccuracies(self.validation_set)
def __init__(self):
self.args = self.parse_command_line()
self.checkpoint_dir, self.logfile, self.checkpoint_path_validation, self.checkpoint_path_final \
= get_log_files(self.args.checkpoint_dir, self.args.resume_from_checkpoint, self.args.mode == "test" or
self.args.mode == "attack")
print_and_log(self.logfile, "Options: %s\n" % self.args)
print_and_log(self.logfile,
"Checkpoint Directory: %s\n" % self.checkpoint_dir)
gpu_device = 'cuda:0'
self.device = torch.device(
gpu_device if torch.cuda.is_available() else 'cpu')
self.model = self.init_model()
self.train_set, self.validation_set, self.test_set = self.init_data()
if self.args.dataset == "meta-dataset":
self.dataset = MetaDatasetReader(
self.args.data_path, self.args.mode, self.train_set,
self.validation_set, self.test_set, self.args.max_way_train,
self.args.max_way_test, self.args.max_support_train,
self.args.max_support_test)
else:
self.dataset = SingleDatasetReader(self.args.data_path,
self.args.mode,
self.args.dataset,
self.args.way, self.args.shot,
self.args.query_train,
self.args.query_test)
self.loss = loss
self.accuracy_fn = aggregate_accuracy
self.optimizer = torch.optim.Adam(self.model.parameters(),
lr=self.args.learning_rate)
self.validation_accuracies = ValidationAccuracies(self.validation_set)
self.start_iteration = 0
if self.args.resume_from_checkpoint:
self.load_checkpoint()
self.optimizer.zero_grad()