Is it efficient to pass model into a custom dataset to run model inference during training for sampling strategy

nlp
1

I’m trying to design a training flow for sampling samples during training.

My data look like this:

defaultdict(list,
        {'C1629836-28004480': [0, 5, 6, 12, 17, 19, 28],
         'C0021846-28004480': [1, 7, 15],
         'C0162832-28004480': [2, 9],
         'C0025929-28004480': [3, 10, 30],
         'C1515655-28004480': [4],
         ...
        }

where key is label and value is list of data index

I custom dataset class in which my __getitem__(self, idx) function need to calculate distance between an anchor (which is chosen randomly) and other data points. It looks like this:

def __getitem__(self, idx):
    item_label = self.labels[idx] # C1629836-28004480
    item_data = self.data[item_label] # [0, 5, 6, 12, 17, 19, 28]

    anchor_index = random.sample(item_data,1)
    mentions_indices = [idx for idx in item_data if idx != anchor_index]
    
    with torch.no_grad():
        self.model.eval()
        anchor_input = ...
        anchor_embedding = self.model.mention_encoder(anchor_input)

        for idx in mention_indices: 
        ...

Another way to prevent from passing the model into custom dataset is to run inference inside the training_step function during training.

But I read somewhere that, using dataset and dataloader to prepare data to feed into model might save the training time, as they have parallel mechanism or something like that.

But in fact, I need to compute these kind of distance base on the latest state of weight of my model during training, is this parallel mechanism ensure that? Though in python variable is reference variable instead of value variable.

So which way is more professional and correct?