def setup(self):
if self.scenario == 'ni':
self.train_set, self.val_set, self.test_set = construct_ns_multiple_wrapper(
self.train_data,
self.train_label,
self.test_data,
self.test_label,
self.task_nums,
32,
self.params.val_size,
self.params.ns_type,
self.params.ns_factor,
plot=self.params.plot_sample)
elif self.scenario == 'nc':
self.task_labels = create_task_composition(
class_nums=10,
num_tasks=self.task_nums,
fixed_order=self.params.fix_order)
self.test_set = []
for labels in self.task_labels:
x_test, y_test = load_task_with_labels(self.test_data,
self.test_label, labels)
self.test_set.append((x_test, y_test))
else:
raise Exception('wrong scenario')
def new_task(self, cur_task, **kwargs):
if self.scenario == 'ni':
x_train, y_train = self.train_set[cur_task]
labels = set(y_train)
elif self.scenario == 'nc':
labels = self.task_labels[cur_task]
x_train, y_train = load_task_with_labels(self.train_data,
self.train_label, labels)
return x_train, y_train, labels
def new_task(self, cur_task, **kwargs):
if self.scenario == 'ni':
x_train, y_train = self.train_set[cur_task]
labels = set(y_train)
elif self.scenario == 'nc':
labels = self.task_labels[cur_task]
x_train, y_train = load_task_with_labels(self.train_data,
self.train_label, labels)
else:
raise Exception('unrecognized scenario')
return x_train, y_train, labels
def setup(self, cur_run):
self.val_set = []
self.test_set = []
print('Loading test set...')
test_idx_list = self.LUP[self.scenario][cur_run][-1]
#test paths
test_paths = []
for idx in test_idx_list:
test_paths.append(os.path.join(self.root, self.paths[idx]))
# test imgs
self.test_data = self.get_batch_from_paths(test_paths)
self.test_label = np.asarray(self.labels[self.scenario][cur_run][-1])
if self.scenario == 'nc':
self.task_labels = self.labels[self.scenario][cur_run][:-1]
for labels in self.task_labels:
labels = list(set(labels))
x_test, y_test = load_task_with_labels(self.test_data,
self.test_label, labels)
self.test_set.append((x_test, y_test))
elif self.scenario == 'ni':
self.test_set = [(self.test_data, self.test_label)]