def test_initialisation(self):
module = SimpleMLP()
module = module.to(self.device)
old_classifier_weight = torch.clone(module.classifier.weight)
old_classifier_bias = torch.clone(module.classifier.bias)
module = as_multitask(module, "classifier")
module = module.to(self.device)
new_classifier_weight = torch.clone(
module.classifier.classifiers["0"].classifier.weight)
new_classifier_bias = torch.clone(
module.classifier.classifiers["0"].classifier.bias)
self.assertTrue(
torch.equal(old_classifier_weight, new_classifier_weight))
self.assertTrue(torch.equal(old_classifier_bias, new_classifier_bias))
final_train_data_x.append(temp_train_data_x)
final_train_data_y.append(temp_train_data_y)
final_test_data_x.append(temp_test_data_x)
final_test_data_y.append(temp_test_data_y)
final_train_data_y = [x.long() for x in final_train_data_y]
final_test_data_y = [x.long() for x in final_test_data_y]
return final_train_data_x, final_train_data_y, final_test_data_x, final_test_data_y
# MODEL CREATION
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = SimpleMLP(num_classes=2, input_size=38, hidden_size=100)
model.to(device)
perm = {
"1": [13, 22, 20, 14, 6],
"2": [9, 10, 0, 1, 2],
"3": [11, 15, 17, 21],
"4": [18, 19, 7, 8, 12],
"5": [3, 4, 5, 16],
}
task_order_list = [perm]
dataset = task_ordering(task_order_list[0])
generic_scenario = tensor_scenario(
train_data_x=dataset[0],
