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|
| import random |
| from typing import List |
|
|
| from fairseq.data import BaseWrapperDataset, data_utils |
|
|
|
|
| class RandomInputDataset(BaseWrapperDataset): |
| def __init__( |
| self, |
| dataset, |
| random_input_dataset, |
| input_key_path: List[str], |
| add_to_input, |
| pad_idx, |
| ): |
| super().__init__(dataset) |
| self.random_input_dataset = random_input_dataset |
| if isinstance(input_key_path, str): |
| input_key_path = [input_key_path] |
| assert len(input_key_path) > 0 |
| self.input_key_path = input_key_path |
| self.add_to_input = add_to_input |
| self.pad_idx = pad_idx |
|
|
| def get_target(self, item): |
| target_loc = item |
| for p in self.input_key_path[:-1]: |
| target_loc = target_loc[p] |
| return self.input_key_path[-1], target_loc |
|
|
| def get_target_value(self, item): |
| k, target_loc = self.get_target(item) |
| return target_loc[k] |
|
|
| def __getitem__(self, index): |
| item = self.dataset[index] |
| k, target_loc = self.get_target(item) |
| target_loc[k] = random.choice(self.random_input_dataset) |
| return item |
|
|
| def collater(self, samples): |
| collated = self.dataset.collater(samples) |
| if len(collated) == 0: |
| return collated |
| indices = set(collated["id"].tolist()) |
|
|
| random_inputs = data_utils.collate_tokens( |
| [self.get_target_value(s) for s in samples if s["id"] in indices], |
| pad_idx=self.pad_idx, |
| left_pad=False, |
| ) |
| k, target_loc = self.get_target( |
| collated if not self.add_to_input else collated["net_input"] |
| ) |
| target_loc[k] = random_inputs |
|
|
| return collated |
|
|
