Bug in dataloader ?

[Brandonnogithub]

Hi guys, I am trying to reproducing your work. In the dataloader, I found this code:

for sample_idx in range(self.num_sample):
    for query_idx in range(len(self.query_examples)):
        # If training, exclude the current example. Else keep all.
        if self.use_demo and args.demo_filter:
            # Demonstration filtering
            candidate = [support_idx for support_idx in support_indices
                           if support_idx != query_idx or mode != "train"]
            sim_score = []
            for support_idx in candidate:
                sim_score.append((support_idx, util.pytorch_cos_sim(self.support_emb[support_idx], self.query_emb[query_idx])))
            sim_score.sort(key=lambda x: x[1], reverse=True)
            if self.num_labels == 1:
                # Regression task
                limit_each_label = int(len(sim_score) // 2 * args.demo_filter_rate)
                count_each_label = {'0': 0, '1': 0}
                context_indices = []

                if args.debug_mode:
                    print("Query %s: %s" % (self.query_examples[query_idx].label, self.query_examples[query_idx].text_a)) # debug
                for support_idx, score in sim_score:
                    if count_each_label['0' if float(self.support_examples[support_idx].label) <= median_mapping[args.task_name] else '1'] < limit_each_label:
                        count_each_label['0' if float(self.support_examples[support_idx].label) <= median_mapping[args.task_name] else '1'] += 1
                        context_indices.append(support_idx)
                        if args.debug_mode:
                            print("    %.4f %s | %s" % (score, self.support_examples[support_idx].label, self.support_examples[support_idx].text_a)) # debug
            else:
                limit_each_label = int(len(sim_score) // self.num_labels * args.demo_filter_rate)
                count_each_label = {label: 0 for label in self.label_list}
                context_indices = []

                if args.debug_mode:
                    print("Query %s: %s" % (self.query_examples[query_idx].label, self.query_examples[query_idx].text_a)) # debug
                for support_idx, score in sim_score:
                    if count_each_label[self.support_examples[support_idx].label] < limit_each_label:
                        count_each_label[self.support_examples[support_idx].label] += 1
                        context_indices.append(support_idx)
                        if args.debug_mode:
                            print("    %.4f %s | %s" % (score, self.support_examples[support_idx].label, self.support_examples[support_idx].text_a)) # debug
        else:
            # Using demonstrations without filtering
            context_indices = [support_idx for support_idx in support_indices
                       if support_idx != query_idx or mode != "train"]

        # We'll subsample context_indices further later.
        self.example_idx.append((query_idx, context_indices, sample_idx))

Here it is calculating the similarity.
But I don't know why you use this loop: for sample_idx in range(self.num_sample) at outermost, the sample_idx is only used when you add the result into self.sample_idx

This codes is really slow, since you set the num_sample=16

I think you can remove for sample_idx in range(self.num_sample) and change the last line as

for query_idx in range(len(self.query_examples)):
    ....
    # We'll subsample context_indices further later.
    for sample_idx in range(self.num_sample):
        self.example_idx.append((query_idx, context_indices, sample_idx))

I don't know whether am I right.

[Brandonnogithub]

In my test, I found after changing, the result is different.

[gaotianyu1350]

Hi,

I think the two implementations are essentially equivalent. The difference is due to different orders having different random sampling results.

[Brandonnogithub]

Hi,

I think the two implementations are essentially equivalent. The difference is due to different orders having different random sampling results.

Thanks for your reply.