def test_tensor_scenario_type(self):
n_experiences = 3
test_data_x = [[torch.zeros(2, 3)], torch.zeros(2, 3)]
test_data_y = [[torch.zeros(2)], torch.zeros(2)]
for complete_test in [True, False]:
for tdx, tdy in zip(test_data_x, test_data_y):
try:
tensor_scenario(
train_data_x=[torch.randn(2, 3)
for _ in range(n_experiences)],
train_data_y=[torch.zeros(2)
for _ in range(n_experiences)],
test_data_x=tdx,
test_data_y=tdy,
task_labels=[0]*n_experiences,
complete_test_set_only=complete_test)
except ValueError:
if complete_test and \
not isinstance(tdx, torch.Tensor) and \
not isinstance(tdy, torch.Tensor):
print("Value Error raised correctly")
"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],
train_data_y=dataset[1],
test_data_x=dataset[2],
test_data_y=dataset[3],
task_labels=[
0 for key in task_order_list[0].keys()
], # shouldn't provide task ID for inference
)
# log to Tensorboard
tb_logger = TensorboardLogger(f"./tb_data/{cur_time}-SimpleMLP/")
# log to text file
text_logger = TextLogger(open(f"./logs/{cur_time}-SimpleMLP.txt", "w+"))
# print to stdout
interactive_logger = InteractiveLogger()
eval_plugin = EvaluationPlugin(
perm = {
"1": [14, 9, 12, 15],
"2": [4, 3, 5, 16],
"3": [17, 11, 8],
"4": [7, 6, 10, 18],
"5": [2, 13, 1, 19],
}
dataset = task_ordering(perm)
generic_scenario = tensor_scenario(
train_data_x=dataset[0],
train_data_y=dataset[1],
test_data_x=dataset[2],
test_data_y=dataset[3],
task_labels=[0 for key in perm.keys()],
)
# Model Creation
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = SimpleMLP(num_classes=2, input_size=70, hidden_size=100)
# log to Tensorboard
tb_logger = TensorboardLogger(f"./tb_data/{cur_time}-simpleMLP_Domain/")
# log to text file
text_logger = TextLogger(open(f"./logs/{cur_time}-simpleMLP_Domain.txt", "w+"))
# print to stdout
