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| import torch | |
| from dataclasses import dataclass | |
| from typing import Any, Dict, List, Optional, Tuple, Union | |
| from transformers import BatchEncoding, PreTrainedTokenizerBase | |
| from transformers.data.data_collator import ( | |
| DataCollatorMixin, | |
| _torch_collate_batch, | |
| ) | |
| from transformers.file_utils import PaddingStrategy | |
| from typing import NewType | |
| InputDataClass = NewType("InputDataClass", Any) | |
| def pre_calc_rel_mat(segment_ids): | |
| valid_span = torch.zeros((segment_ids.shape[0], segment_ids.shape[1], segment_ids.shape[1]), | |
| device=segment_ids.device, dtype=torch.bool) | |
| for i in range(segment_ids.shape[0]): | |
| for j in range(segment_ids.shape[1]): | |
| valid_span[i, j, :] = segment_ids[i, :] == segment_ids[i, j] | |
| return valid_span | |
| class DataCollatorForKeyValueExtraction(DataCollatorMixin): | |
| """ | |
| Data collator that will dynamically pad the inputs received, as well as the labels. | |
| Args: | |
| tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`): | |
| The tokenizer used for encoding the data. | |
| padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`True`): | |
| Select a strategy to pad the returned sequences (according to the model's padding side and padding index) | |
| among: | |
| * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single | |
| sequence if provided). | |
| * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the | |
| maximum acceptable input length for the model if that argument is not provided. | |
| * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of | |
| different lengths). | |
| max_length (:obj:`int`, `optional`): | |
| Maximum length of the returned list and optionally padding length (see above). | |
| pad_to_multiple_of (:obj:`int`, `optional`): | |
| If set will pad the sequence to a multiple of the provided value. | |
| This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= | |
| 7.5 (Volta). | |
| label_pad_token_id (:obj:`int`, `optional`, defaults to -100): | |
| The id to use when padding the labels (-100 will be automatically ignore by PyTorch loss functions). | |
| """ | |
| tokenizer: PreTrainedTokenizerBase | |
| padding: Union[bool, str, PaddingStrategy] = True | |
| max_length: Optional[int] = None | |
| pad_to_multiple_of: Optional[int] = None | |
| label_pad_token_id: int = -100 | |
| def __call__(self, features): | |
| label_name = "label" if "label" in features[0].keys() else "labels" | |
| labels = [feature[label_name] for feature in features] if label_name in features[0].keys() else None | |
| images = None | |
| if "images" in features[0]: | |
| images = torch.stack([torch.tensor(d.pop("images")) for d in features]) | |
| IMAGE_LEN = int(images.shape[-1] / 16) * int(images.shape[-1] / 16) + 1 | |
| batch = self.tokenizer.pad( | |
| features, | |
| padding=self.padding, | |
| max_length=self.max_length, | |
| pad_to_multiple_of=self.pad_to_multiple_of, | |
| # Conversion to tensors will fail if we have labels as they are not of the same length yet. | |
| return_tensors="pt" if labels is None else None, | |
| ) | |
| if images is not None: | |
| batch["images"] = images | |
| batch = {k: torch.tensor(v, dtype=torch.int64) if isinstance(v[0], list) and k == 'attention_mask' else v | |
| for k, v in batch.items()} | |
| visual_attention_mask = torch.ones((len(batch['input_ids']), IMAGE_LEN), dtype=torch.long) | |
| batch["attention_mask"] = torch.cat([batch['attention_mask'], visual_attention_mask], dim=1) | |
| if labels is None: | |
| return batch | |
| has_bbox_input = "bbox" in features[0] | |
| has_position_input = "position_ids" in features[0] | |
| padding_idx=self.tokenizer.pad_token_id | |
| sequence_length = torch.tensor(batch["input_ids"]).shape[1] | |
| padding_side = self.tokenizer.padding_side | |
| if padding_side == "right": | |
| batch["labels"] = [label + [self.label_pad_token_id] * (sequence_length - len(label)) for label in labels] | |
| if has_bbox_input: | |
| batch["bbox"] = [bbox + [[0, 0, 0, 0]] * (sequence_length - len(bbox)) for bbox in batch["bbox"]] | |
| if has_position_input: | |
| batch["position_ids"] = [position_id + [padding_idx] * (sequence_length - len(position_id)) | |
| for position_id in batch["position_ids"]] | |
| else: | |
| batch["labels"] = [[self.label_pad_token_id] * (sequence_length - len(label)) + label for label in labels] | |
| if has_bbox_input: | |
| batch["bbox"] = [[[0, 0, 0, 0]] * (sequence_length - len(bbox)) + bbox for bbox in batch["bbox"]] | |
| if has_position_input: | |
| batch["position_ids"] = [[padding_idx] * (sequence_length - len(position_id)) | |
| + position_id for position_id in batch["position_ids"]] | |
| if 'segment_ids' in batch: | |
| assert 'position_ids' in batch | |
| for i in range(len(batch['segment_ids'])): | |
| batch['segment_ids'][i] = batch['segment_ids'][i] + [batch['segment_ids'][i][-1] + 1] * (sequence_length - len(batch['segment_ids'][i])) + [ | |
| batch['segment_ids'][i][-1] + 2] * IMAGE_LEN | |
| batch = {k: torch.tensor(v, dtype=torch.int64) if isinstance(v[0], list) else v for k, v in batch.items()} | |
| if 'segment_ids' in batch: | |
| valid_span = pre_calc_rel_mat( | |
| segment_ids=batch['segment_ids'] | |
| ) | |
| batch['valid_span'] = valid_span | |
| del batch['segment_ids'] | |
| if images is not None: | |
| visual_labels = torch.ones((len(batch['input_ids']), IMAGE_LEN), dtype=torch.long) * -100 | |
| batch["labels"] = torch.cat([batch['labels'], visual_labels], dim=1) | |
| return batch | |