156 lines
6.5 KiB
Python
156 lines
6.5 KiB
Python
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from datasets import load_dataset
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from torch.utils.data import Dataset
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import json
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from typing import Any, List, Tuple
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import random
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import torch
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from transformers import DonutProcessor, VisionEncoderDecoderModel
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class DonutDataset(Dataset):
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"""
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DonutDataset which is saved in huggingface datasets format. (see details in https://huggingface.co/docs/datasets)
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Each row, consists of image path(png/jpg/jpeg) and gt data (json/jsonl/txt),
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and it will be converted into input_tensor(vectorized image) and input_ids(tokenized string).
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Args:
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dataset_name_or_path: name of dataset (available at huggingface.co/datasets) or the path containing image files and metadata.jsonl
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max_length: the max number of tokens for the target sequences
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split: whether to load "train", "validation" or "test" split
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ignore_id: ignore_index for torch.nn.CrossEntropyLoss
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task_start_token: the special token to be fed to the decoder to conduct the target task
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prompt_end_token: the special token at the end of the sequences
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sort_json_key: whether or not to sort the JSON keys
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"""
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def __init__(
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self,
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dataset_name_or_path: str,
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max_length: int,
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processor: DonutProcessor,
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model: VisionEncoderDecoderModel,
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split: str = "train",
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ignore_id: int = -100,
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task_start_token: str = "<s>",
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prompt_end_token: str = None,
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sort_json_key: bool = True,
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added_tokens: list = []
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):
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super().__init__()
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self.max_length = max_length
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self.split = split
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self.processor = processor
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self.model = model
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self.ignore_id = ignore_id
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self.task_start_token = task_start_token
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self.prompt_end_token = prompt_end_token if prompt_end_token else task_start_token
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self.sort_json_key = sort_json_key
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self.added_tokens = added_tokens
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self.dataset = load_dataset(dataset_name_or_path, split=self.split, stream=True).with_format("torch")
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self.dataset_length = len(self.dataset)
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self.gt_token_sequences = []
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for sample in self.dataset:
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ground_truth = json.loads(sample["ground_truth"])
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if "gt_parses" in ground_truth: # when multiple ground truths are available, e.g., docvqa
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assert isinstance(ground_truth["gt_parses"], list)
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gt_jsons = ground_truth["gt_parses"]
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else:
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assert "gt_parse" in ground_truth and isinstance(ground_truth["gt_parse"], dict)
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gt_jsons = [ground_truth["gt_parse"]]
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self.gt_token_sequences.append(
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[
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self.json2token(
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gt_json,
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update_special_tokens_for_json_key=self.split == "train",
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sort_json_key=self.sort_json_key,
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)
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+ self.processor.tokenizer.eos_token
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for gt_json in gt_jsons # load json from list of json
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]
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)
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self.add_tokens([self.task_start_token, self.prompt_end_token])
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self.prompt_end_token_id = self.processor.tokenizer.convert_tokens_to_ids(self.prompt_end_token)
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def json2token(self, obj: Any, update_special_tokens_for_json_key: bool = True, sort_json_key: bool = True):
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"""
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Convert an ordered JSON object into a token sequence
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"""
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if type(obj) == dict:
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if len(obj) == 1 and "text_sequence" in obj:
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return obj["text_sequence"]
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else:
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output = ""
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if sort_json_key:
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keys = sorted(obj.keys(), reverse=True)
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else:
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keys = obj.keys()
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for k in keys:
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if update_special_tokens_for_json_key:
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self.add_tokens([fr"<s_{k}>", fr"</s_{k}>"])
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output += (
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fr"<s_{k}>"
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+ self.json2token(obj[k], update_special_tokens_for_json_key, sort_json_key)
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+ fr"</s_{k}>"
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)
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return output
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elif type(obj) == list:
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return r"<sep/>".join(
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[self.json2token(item, update_special_tokens_for_json_key, sort_json_key) for item in obj]
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)
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else:
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obj = str(obj)
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if f"<{obj}/>" in self.added_tokens:
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obj = f"<{obj}/>" # for categorical special tokens
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return obj
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def add_tokens(self, list_of_tokens: List[str]):
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"""
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Add special tokens to tokenizer and resize the token embeddings of the decoder
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"""
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newly_added_num = self.processor.tokenizer.add_tokens(list_of_tokens)
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if newly_added_num > 0:
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self.model.decoder.resize_token_embeddings(len(self.processor.tokenizer))
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self.added_tokens.extend(list_of_tokens)
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# def __len__(self) -> int:
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# return self.dataset_length
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def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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Load image from image_path of given dataset_path and convert into input_tensor and labels
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Convert gt data into input_ids (tokenized string)
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Returns:
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input_tensor : preprocessed image
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input_ids : tokenized gt_data
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labels : masked labels (model doesn't need to predict prompt and pad token)
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"""
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sample = self.dataset[idx]
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# change if not 3 channels
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if sample['image'].mode != "RGB":
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sample['image'] = sample['image'].convert("RGB")
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# inputs
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pixel_values = self.processor(sample["image"], random_padding=self.split == "train", return_tensors="pt").pixel_values
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pixel_values = pixel_values.squeeze()
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# targets
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target_sequence = random.choice(self.gt_token_sequences[idx]) # can be more than one, e.g., DocVQA Task 1
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input_ids = self.processor.tokenizer(
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target_sequence,
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add_special_tokens=False,
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max_length=self.max_length,
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padding="max_length",
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truncation=True,
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return_tensors="pt",
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)["input_ids"].squeeze(0)
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labels = input_ids.clone()
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labels[labels == self.processor.tokenizer.pad_token_id] = self.ignore_id # model doesn't need to predict pad token
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# labels[: torch.nonzero(labels == self.prompt_end_token_id).sum() + 1] = self.ignore_id # model doesn't need to predict prompt (for VQA)
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return pixel_values, labels, target_sequence
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