def __getitem__()

in anticipation/anticipation/datasets/epic_future_labels.py [0:0]

• 31 lines of code
• 4 McCabe index (conditional complexity)

    def __getitem__(self, idx):
        record = self.data[idx]
        data = dict(
            num_modalities=DC(to_tensor(1)),
            img_meta=DC(dict(), cpu_only=True),
            gt_label=DC(to_tensor(0)), # Dummy
            feature=DC(to_tensor(torch.zeros(2048))), # Dummy
            ratio_idx=DC(to_tensor(record.ratio_idx), stack=True, pad_dims=None)
        )

        if self.label=='int':
            ints = torch.zeros(self.num_actions)
            ints[record.ints] = 1
            data.update(dict(
                labels=DC(to_tensor(ints), stack=True, pad_dims=None),
                label_mask=DC(to_tensor(self.eval_ints), stack=True, pad_dims=None),
            ))
        elif self.label=='noun':
            nouns = torch.zeros(self.num_nouns)
            nouns[record.nouns] = 1
            data.update(dict(
                labels=DC(to_tensor(nouns), stack=True, pad_dims=None),
                label_mask=DC(to_tensor(self.eval_nouns), stack=True, pad_dims=None),
            ))
        elif self.label=='verb':
            verbs = torch.zeros(self.num_verbs)
            verbs[record.verbs] = 1
            data.update(dict(
                labels=DC(to_tensor(verbs), stack=True, pad_dims=None),
                label_mask=DC(to_tensor(self.eval_verbs), stack=True, pad_dims=None),
            ))
      
        return data