Possible Replay bug

Hi all,

I’m working on implementing Class-Balanced and Experience-balanced Replay (See #479). I want to provide the opportunity for the user to select a fixed capacity prior or adaptive to all seen experiences so far.

However, when looking at the Replay code I find it hard to follow:

single_task_mem_size = min(self.mem_size, len(curr_data))
h = single_task_mem_size // (strategy.training_exp_counter + 1)

remaining_example = single_task_mem_size % (
    strategy.training_exp_counter + 1)
# We recover it using the random_split method and getting rid of the
# second split.
rm_add, _ = random_split(
    curr_data, [h, len(curr_data) - h]
)

In the first line: https://github.com/ContinualAI/avalanche/blob/9cf3c53d83ffc1b3dfe4400d43356ebcd901cfc9/avalanche/training/plugins/replay.py#L64 What if len(curr_data) is smaller than self.mem_size? It would occur to me that ‘h’ is not correct anymore. Even though the current data length len(curr_data) may not equal the full memory size, it can still have capacity for a mem_size/n_observed_exps portion of the memory. So h should become self.mem_size // (strategy.training_exp_counter + 1)?

When taking the random_split however, then we should take the ‘min’ operation into account:

 h2 = min(capacity_per_exp, len(curr_data))
  rm_add, _ = random_split(
      curr_data, [h2, len(curr_data) - h2]
  )

Am I missing something here or is this a bug?

[AntonioCarta]

A single PR is ok.

[lrzpellegrini]

Thank you @Mattdl, I’ll try this immediately!