wehub-resource-sync
83 lines
3.9 KiB
Python
83 lines
3.9 KiB
Python
from dataclasses import dataclass
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from typing import Optional, Union
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import torch
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from detectron2.structures import ImageList
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from transformers import PreTrainedTokenizerBase
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from transformers.file_utils import PaddingStrategy
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@dataclass
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class DataCollatorForKeyValueExtraction:
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"""
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Data collator that will dynamically pad the inputs received, as well as the labels.
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Args:
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tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
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The tokenizer used for encoding the data.
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padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`True`):
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Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
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among:
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* :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
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sequence if provided).
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* :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
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maximum acceptable input length for the model if that argument is not provided.
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* :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
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different lengths).
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max_length (:obj:`int`, `optional`):
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Maximum length of the returned list and optionally padding length (see above).
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pad_to_multiple_of (:obj:`int`, `optional`):
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If set will pad the sequence to a multiple of the provided value.
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This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
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7.5 (Volta).
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label_pad_token_id (:obj:`int`, `optional`, defaults to -100):
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The id to use when padding the labels (-100 will be automatically ignore by PyTorch loss functions).
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"""
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tokenizer: PreTrainedTokenizerBase
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padding: Union[bool, str, PaddingStrategy] = True
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max_length: Optional[int] = None
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pad_to_multiple_of: Optional[int] = None
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label_pad_token_id: int = -100
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def __call__(self, features):
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label_name = "label" if "label" in features[0].keys() else "labels"
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labels = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
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has_image_input = "image" in features[0]
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has_bbox_input = "bbox" in features[0]
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if has_image_input:
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image = ImageList.from_tensors([torch.tensor(feature["image"]) for feature in features], 32)
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for feature in features:
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del feature["image"]
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batch = self.tokenizer.pad(
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features,
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padding=self.padding,
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max_length=self.max_length,
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pad_to_multiple_of=self.pad_to_multiple_of,
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# Conversion to tensors will fail if we have labels as they are not of the same length yet.
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return_tensors="pt" if labels is None else None,
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)
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if labels is None:
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return batch
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sequence_length = torch.tensor(batch["input_ids"]).shape[1]
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padding_side = self.tokenizer.padding_side
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if padding_side == "right":
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batch["labels"] = [label + [self.label_pad_token_id] * (sequence_length - len(label)) for label in labels]
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if has_bbox_input:
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batch["bbox"] = [bbox + [[0, 0, 0, 0]] * (sequence_length - len(bbox)) for bbox in batch["bbox"]]
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else:
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batch["labels"] = [[self.label_pad_token_id] * (sequence_length - len(label)) + label for label in labels]
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if has_bbox_input:
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batch["bbox"] = [[[0, 0, 0, 0]] * (sequence_length - len(bbox)) + bbox for bbox in batch["bbox"]]
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batch = {k: torch.tensor(v, dtype=torch.int64) if isinstance(v[0], list) else v for k, v in batch.items()}
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if has_image_input:
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batch["image"] = image
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return batch
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