Error when train with sensor data

[irfanfadhullah]

Hello, can you help me to find where is the wrong part in my code.
In this code, i tried to simulate a simple continual learning with sensor data. There are two separated dataset, and i used the dataset_benchmark to train the data.
Here is my code.

class OptAvalanche:
    data_path1 = "/media/v2t/Data/iot/dataset_label_under_half1.csv"
    data_path2 = "/media/v2t/Data/iot/dataset_label_under_half2.csv"
    max_sensor = 20

opt = OptAvalanche()

data1 = pd.read_csv(opt.data_path1)
X_1 = data1.iloc[:,:-1]
Y_1 = data1.iloc[:,-1]

data2 = pd.read_csv(opt.data_path2)
X_2 = data2.iloc[:,:-1]
Y_2 = data2.iloc[:,-1]

X_1 = torch.tensor([padded_input_avalanche(x, opt.max_sensor) for x in X_1.values.tolist()])
Y_1 = torch.tensor(Y_1.values.tolist())
X_2 = torch.tensor([padded_input_avalanche(x, opt.max_sensor) for x in X_2.values.tolist()])
Y_2 = torch.tensor(Y_2.values.tolist())

#Convert to Avalanche Dataset
first_data = AvalancheTensorDataset(X_1,Y_1)
second_data = AvalancheTensorDataset(X_2,Y_2)

generic_scenario = dataset_benchmark(
    [first_data],[second_data]
)

#model
model = ResNetBaseline(in_channels=1, num_pred_classes=2)
criterion = CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

#Pluggin
replay_pluggin = ReplayPlugin(mem_size=10000)
early_stopping = EarlyStoppingPlugin(patience=10, val_stream_name='train')
ewc = EWCPlugin(ewc_lambda=0.001)
eval_plugin = EvaluationPlugin(
    WeightCheckpoint(),
    collect_all=True,
    benchmark=generic_scenario,
    strict_checks=False)

cl_strategy = Naive(
    model,
    optimizer,
    criterion,
    train_mb_size=32,
    train_epochs=20,
    eval_mb_size=32,
    device=device,
    plugins=[
        replay_pluggin,
        early_stopping,
        ewc
    ])

# TRAINING LOOP
print("Starting experiment...")
results = []
for experience in generic_scenario.train_stream:
    print("Start of experience ", experience.current_experience)
    cl_strategy.train(experience)
    print("Training completed")

    print("Computing accuracy on the whole test set")
    results.append(cl_strategy.eval(generic_scenario.test_stream))

Here is the detail of the error.

Starting experiment...
Start of experience  0
-- >> Start of training phase << --
Output exceeds the [size limit](command:workbench.action.openSettings?[). Open the full output data [in a text editor](command:workbench.action.openLargeOutput?04fdd0a6-1f90-430d-a74b-dca1d04aab5f)
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
/tmp/ipykernel_248055/198675998.py in <module>
     32 for experience in generic_scenario.train_stream:
     33     print("Start of experience ", experience.current_experience)
---> 34     cl_strategy.train(experience)
     35     print("Training completed")
     36 

~/anaconda3/envs/continual/lib/python3.7/site-packages/avalanche/training/templates/base_sgd.py in train(self, experiences, eval_streams, **kwargs)
    128                                                     ExpSequence]]] = None,
    129               **kwargs):
--> 130         super().train(experiences, eval_streams, **kwargs)
    131         return self.evaluator.get_last_metrics()
    132 

~/anaconda3/envs/continual/lib/python3.7/site-packages/avalanche/training/templates/base.py in train(self, experiences, eval_streams, **kwargs)
    105         for self.experience in experiences:
    106             self._before_training_exp(**kwargs)
--> 107             self._train_exp(self.experience, eval_streams, **kwargs)
    108             self._after_training_exp(**kwargs)
    109         self._after_training(**kwargs)

~/anaconda3/envs/continual/lib/python3.7/site-packages/avalanche/training/templates/base_sgd.py in _train_exp(self, experience, eval_streams, **kwargs)
    176                 break
...
---> 19     l_out = l_in = input.size(2)
     20     padding = (((l_out - 1) * stride) - l_in + (dilation * (kernel - 1)) + 1)
     21     if padding % 2 != 0:

IndexError: Dimension out of range (expected to be in range of [-2, 1], but got 2)

[irfanfadhullah]

I have found the way to overcome this error. I just did unsqueeze to the X data. I hope this answer can help the other who have same problem like this. Thank you.

[MagicHealer]

@irfanfadhullah Have you tried training using 2 different datasets? Like Imagenet for first task and CUB for second task?