Validation during training: time incoherence

Hi, everyone

I'm trying to run simultaneously the training and the validation of a CIL algorithm with eval_every = 1 to get the accuracy and the loss for each epoch in the test set. This is the code is use. Note that I set num_workers = 4 in the train call.

esp_plugin = EarlyStoppingPlugin(patience = 2, val_stream_name = 'test_stream', metric_name = "Top1_Acc_Exp")

cl_strategy = LwF(
    model, Adam(model.parameters(), lr = 0.001),
    CrossEntropyLoss(), train_mb_size = 256, train_epochs = 10, eval_mb_size = 256, plugins = [esp_plugin], 
    evaluator = eval_plugin, alpha = [0, 1], temperature = 1, eval_every = 1, device = device)
    
for experience in generic_scenario.train_stream:
    n_exp = experience.current_experience
    print("Start of experience: ", n_exp)
    print("Current Classes: ", experience.classes_in_this_experience)
    cl_strategy.train(experience, eval_streams = [generic_scenario.test_stream[0:n_exp+1]], num_workers = 4)
    print('Computed accuracy on the whole test set')

This is the problem I got. While the training iteration only lasts for 21'', the evaluation lasts for almost 3' when the size of the evaluation stream is 5x times shorter. I tried in both the beta version and the latest version but the same error was found for both.

[ggraffieti]

The bug is in the PeriodicEval class (avalanche/training/templates/base_sgd.py file).
During the periodic evaluation is called the eval method on the strategy without passing the number of workers (default to 1).

def _peval(self, strategy):
    for el in strategy._eval_streams:
        strategy.eval(el)  # <--- here 

It should be an easy fix, we'll notify you when is incorporated in the main branch.
Just to be sure, if you edit your code as:

for experience in generic_scenario.train_stream:
    n_exp = experience.current_experience
    print("Start of experience: ", n_exp)
    print("Current Classes: ", experience.classes_in_this_experience)
    cl_strategy.train(experience, eval_streams = [], num_workers = 4)
    cl_strategy.eval(generic_scenario.test_stream[0:n_exp+1], num_workers = 4)
    print('Computed accuracy on the whole test set')

should work with the same number of workers and the same speed between train and eval. Obviously is not a fix for your problem since the metrics are calculated only at the end of the experience and not after every epoch.

Thank you again, @ggraffieti

Yeah, I tried previously this code you proposed with and without passing the num_workers argument to the eval method. What I got is a very fast evaluation computation for num_workers = 4 and a more slower and comparable to the times presented in the screenshot without the workers.

About the bug, it is possible it also raises in _maybe_peval? I'm not a coding expert, but I passed the **kwargs to this methods and it started working for me. Surely/maybe it is not the most efficient way to code it, but it is already working and saving me lot of time.

def _peval(self, strategy, **kwargs):
    for el in strategy._eval_streams:
        strategy.eval(el, **kwargs)

def _maybe_peval(self, strategy, counter, **kwargs): # <-- ¿also here?
    if self.eval_every > 0 and counter % self.eval_every == 0:
        self._peval(strategy, **kwargs)

[ggraffieti]

You are right @PabloMese, good catch!
In fact '_maybe_peval' is used to eval the model after each epoch, not '_peval', which is used only at the end of the experience.
If you already fixed that in your code you can do a PR to avalanche and become an official contributor 😃

I will give it a try, @ggraffieti.

So we can now close this issue. Thanks for the help.

[ggraffieti]

Perfect @PabloMese!
I'll reopen just to keep track of the bug, we'll close the issue when the fix is included in the official code 👍

[ggraffieti]

@PabloMese solved by the linked PR.
When it will be accepted you can reinstall the "nightly" version of the library and the bug will be gone 😃