(self)
| 44 | return q_tuple |
| 45 | |
| 46 | def get_data(self): |
| 47 | q_tuple = [] |
| 48 | |
| 49 | while(1): |
| 50 | # if try data_count times still can not found a proper batch, resample |
| 51 | if self.data_count > (len(self.file_idxs)-self.config.TRAINING.BATCH.BATCH_SIZE): |
| 52 | self.shuffle_query() |
| 53 | continue |
| 54 | batch_keys = self.file_idxs[self.data_count] |
| 55 | # select the batch with qualified number of positives and negatives for training |
| 56 | if (len(self.queries[batch_keys]["positives"]) < self.config.TRAINING.BATCH.POSITIVES_PER_QUERY) or (len(self.queries[batch_keys]["negatives"]) < self.config.TRAINING.BATCH.NEGATIVES_PER_QUERY): |
| 57 | self.data_count+=1 |
| 58 | continue |
| 59 | q_tuple = self.get_tuple(self.queries[batch_keys], hard_neg=[], other_neg=True) |
| 60 | self.data_count+=1 |
| 61 | break |
| 62 | |
| 63 | return q_tuple |
| 64 | |
| 65 | def shuffle_query(self): |
| 66 | random.shuffle(self.file_idxs) |
no test coverage detected