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)]