wehub-resource-sync
318 lines
12 KiB
Python
318 lines
12 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import os
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import paddle
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from paddle.io import Dataset
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logger = logging.getLogger(__name__)
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class FunsdDataset(Dataset):
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def __init__(self, args, tokenizer, labels, pad_token_label_id, mode):
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logger.info("Creating features from dataset file at %s", args.data_dir)
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examples = read_examples_from_file(args.data_dir, mode)
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features = convert_examples_to_features(
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examples,
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labels,
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args.max_seq_length,
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tokenizer,
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cls_token_at_end=False,
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cls_token=tokenizer.cls_token,
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cls_token_segment_id=0,
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sep_token=tokenizer.sep_token,
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sep_token_extra=False,
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pad_on_left=False,
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pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
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pad_token_segment_id=0,
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pad_token_label_id=pad_token_label_id,
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)
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self.features = features
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# Convert to Tensors and build dataset
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self.all_input_ids = paddle.to_tensor([f.input_ids for f in features], dtype="int64")
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self.all_input_mask = paddle.to_tensor([f.input_mask for f in features], dtype="int64")
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self.all_segment_ids = paddle.to_tensor([f.segment_ids for f in features], dtype="int64")
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self.all_label_ids = paddle.to_tensor([f.label_ids for f in features], dtype="int64")
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self.all_bboxes = paddle.to_tensor([f.boxes for f in features], dtype="int64")
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def __len__(self):
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return len(self.features)
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def __getitem__(self, index):
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return (
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self.all_input_ids[index],
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self.all_input_mask[index],
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self.all_segment_ids[index],
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self.all_label_ids[index],
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self.all_bboxes[index],
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)
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class InputExample(object):
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"""A single training/test example for token classification."""
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def __init__(self, guid, words, labels, boxes, actual_bboxes, file_name, page_size):
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"""Constructs a InputExample.
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Args:
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guid: Unique id for the example.
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words: list. The words of the sequence.
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labels: (Optional) list. The labels for each word of the sequence. This should be
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specified for train and dev examples, but not for test examples.
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"""
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self.guid = guid
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self.words = words
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self.labels = labels
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self.boxes = boxes
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self.actual_bboxes = actual_bboxes
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self.file_name = file_name
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self.page_size = page_size
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class InputFeatures(object):
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"""A single set of features of data."""
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def __init__(
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self,
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input_ids,
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input_mask,
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segment_ids,
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label_ids,
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boxes,
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actual_bboxes,
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file_name,
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page_size,
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):
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assert (
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0 <= all(boxes) <= 1000
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), "Error with input bbox ({}): the coordinate value is not between 0 and 1000".format(boxes)
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self.input_ids = input_ids
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self.input_mask = input_mask
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self.segment_ids = segment_ids
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self.label_ids = label_ids
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self.boxes = boxes
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self.actual_bboxes = actual_bboxes
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self.file_name = file_name
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self.page_size = page_size
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def read_examples_from_file(data_dir, mode):
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file_path = os.path.join(data_dir, "{}.txt".format(mode))
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box_file_path = os.path.join(data_dir, "{}_box.txt".format(mode))
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image_file_path = os.path.join(data_dir, "{}_image.txt".format(mode))
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guid_index = 1
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examples = []
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with open(file_path, encoding="utf-8") as f, open(box_file_path, encoding="utf-8") as fb, open(
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image_file_path, encoding="utf-8"
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) as fi:
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words = []
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boxes = []
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actual_bboxes = []
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file_name = None
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page_size = None
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labels = []
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for line, bline, iline in zip(f, fb, fi):
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if line.startswith("-DOCSTART-") or line == "" or line == "\n":
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if words:
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examples.append(
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InputExample(
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guid="{}-{}".format(mode, guid_index),
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words=words,
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labels=labels,
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boxes=boxes,
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actual_bboxes=actual_bboxes,
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file_name=file_name,
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page_size=page_size,
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)
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)
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guid_index += 1
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words = []
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boxes = []
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actual_bboxes = []
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file_name = None
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page_size = None
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labels = []
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else:
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splits = line.split("\t")
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bsplits = bline.split("\t")
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isplits = iline.split("\t")
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assert len(splits) == 2
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assert len(bsplits) == 2
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assert len(isplits) == 4
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assert splits[0] == bsplits[0]
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words.append(splits[0])
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if len(splits) > 1:
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labels.append(splits[-1].replace("\n", ""))
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box = bsplits[-1].replace("\n", "")
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box = [int(b) for b in box.split()]
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boxes.append(box)
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actual_bbox = [int(b) for b in isplits[1].split()]
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actual_bboxes.append(actual_bbox)
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page_size = [int(i) for i in isplits[2].split()]
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file_name = isplits[3].strip()
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else:
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# Examples could have no label for mode = "test"
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labels.append("O")
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if words:
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examples.append(
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InputExample(
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guid=f"{mode}-{guid_index}",
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words=words,
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labels=labels,
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boxes=boxes,
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actual_bboxes=actual_bboxes,
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file_name=file_name,
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page_size=page_size,
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)
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)
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return examples
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def convert_examples_to_features(
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examples,
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label_list,
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max_seq_length,
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tokenizer,
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cls_token_at_end=False,
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cls_token="[CLS]",
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cls_token_segment_id=1,
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sep_token="[SEP]",
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sep_token_extra=False,
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pad_on_left=False,
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pad_token=0,
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cls_token_box=[0, 0, 0, 0],
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sep_token_box=[1000, 1000, 1000, 1000],
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pad_token_box=[0, 0, 0, 0],
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pad_token_segment_id=0,
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pad_token_label_id=-1,
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sequence_a_segment_id=0,
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mask_padding_with_zero=True,
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):
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label_map = {label: i for i, label in enumerate(label_list)}
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features = []
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for (ex_index, example) in enumerate(examples):
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file_name = example.file_name
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page_size = example.page_size
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width, height = page_size
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if ex_index % 10000 == 0:
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logger.info("Writing example %d of %d", ex_index, len(examples))
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tokens = []
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token_boxes = []
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actual_bboxes = []
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label_ids = []
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for word, label, box, actual_bbox in zip(example.words, example.labels, example.boxes, example.actual_bboxes):
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word_tokens = tokenizer.tokenize(word)
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tokens.extend(word_tokens)
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token_boxes.extend([box] * len(word_tokens))
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actual_bboxes.extend([actual_bbox] * len(word_tokens))
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# Use the real label id for the first token of the word, and padding ids for the remaining tokens
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label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1))
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# Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
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special_tokens_count = 3 if sep_token_extra else 2
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if len(tokens) > max_seq_length - special_tokens_count:
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tokens = tokens[: (max_seq_length - special_tokens_count)]
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token_boxes = token_boxes[: (max_seq_length - special_tokens_count)]
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actual_bboxes = actual_bboxes[: (max_seq_length - special_tokens_count)]
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label_ids = label_ids[: (max_seq_length - special_tokens_count)]
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# The convention in BERT is:
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# (a) For sequence pairs:
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# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
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# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
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# (b) For single sequences:
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# tokens: [CLS] the dog is hairy . [SEP]
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# type_ids: 0 0 0 0 0 0 0
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#
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# Where "type_ids" are used to indicate whether this is the first
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# sequence or the second sequence. The embedding vectors for `type=0` and
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# `type=1` were learned during pre-training and are added to the wordpiece
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# embedding vector (and position vector). This is not *strictly* necessary
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# since the [SEP] token unambiguously separates the sequences, but it makes
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# it easier for the model to learn the concept of sequences.
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#
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# For classification tasks, the first vector (corresponding to [CLS]) is
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# used as the "sentence vector". Note that this only makes sense because
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# the entire model is fine-tuned.
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tokens += [sep_token]
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token_boxes += [sep_token_box]
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actual_bboxes += [[0, 0, width, height]]
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label_ids += [pad_token_label_id]
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if sep_token_extra:
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# roberta uses an extra separator b/w pairs of sentences
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tokens += [sep_token]
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token_boxes += [sep_token_box]
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actual_bboxes += [[0, 0, width, height]]
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label_ids += [pad_token_label_id]
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segment_ids = [sequence_a_segment_id] * len(tokens)
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if cls_token_at_end:
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tokens += [cls_token]
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token_boxes += [cls_token_box]
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actual_bboxes += [[0, 0, width, height]]
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label_ids += [pad_token_label_id]
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segment_ids += [cls_token_segment_id]
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else:
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tokens = [cls_token] + tokens
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token_boxes = [cls_token_box] + token_boxes
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actual_bboxes = [[0, 0, width, height]] + actual_bboxes
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label_ids = [pad_token_label_id] + label_ids
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segment_ids = [cls_token_segment_id] + segment_ids
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input_ids = tokenizer.convert_tokens_to_ids(tokens)
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# The mask has 1 for real tokens and 0 for padding tokens. Only real
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# tokens are attended to.
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input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
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# Zero-pad up to the sequence length.
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padding_length = max_seq_length - len(input_ids)
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if pad_on_left:
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input_ids = ([pad_token] * padding_length) + input_ids
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input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
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segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
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label_ids = ([pad_token_label_id] * padding_length) + label_ids
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token_boxes = ([pad_token_box] * padding_length) + token_boxes
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else:
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input_ids += [pad_token] * padding_length
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input_mask += [0 if mask_padding_with_zero else 1] * padding_length
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segment_ids += [pad_token_segment_id] * padding_length
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label_ids += [pad_token_label_id] * padding_length
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token_boxes += [pad_token_box] * padding_length
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assert len(input_ids) == max_seq_length
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assert len(input_mask) == max_seq_length
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assert len(segment_ids) == max_seq_length
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assert len(label_ids) == max_seq_length
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assert len(token_boxes) == max_seq_length
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features.append(
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InputFeatures(
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input_ids=input_ids,
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input_mask=input_mask,
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segment_ids=segment_ids,
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label_ids=label_ids,
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boxes=token_boxes,
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actual_bboxes=actual_bboxes,
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file_name=file_name,
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page_size=page_size,
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)
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)
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return features
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