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# Lint as: python3
import json
import logging
import os
import datasets
from PIL import Image
import numpy as np
from transformers import AutoTokenizer
def load_image(image_path, size):
image = Image.open(image_path).convert("RGB")
w, h = image.size
if size is not None:
# resize image
image = image.resize((size, size))
image = np.asarray(image)
image = image[:, :, ::-1] # flip color channels from RGB to BGR
image = image.transpose(2, 0, 1) # move channels to first dimension
return image, (w, h)
def normalize_bbox(bbox, size):
return [
int(1000 * bbox[0] / size[0]),
int(1000 * bbox[1] / size[1]),
int(1000 * bbox[2] / size[0]),
int(1000 * bbox[3] / size[1]),
]
def simplify_bbox(bbox):
return [
min(bbox[0::2]),
min(bbox[1::2]),
max(bbox[2::2]),
max(bbox[3::2]),
]
def merge_bbox(bbox_list):
x0, y0, x1, y1 = list(zip(*bbox_list))
return [min(x0), min(y0), max(x1), max(y1)]
_URL = "https://github.com/doc-analysis/XFUN/releases/download/v1.0/"
_LANG = ["zh", "de", "es", "fr", "en", "it", "ja", "pt"]
logger = logging.getLogger(__name__)
class XFUNConfig(datasets.BuilderConfig):
"""BuilderConfig for XFUN."""
def __init__(self, lang, additional_langs=None, **kwargs):
"""
Args:
lang: string, language for the input text
**kwargs: keyword arguments forwarded to super.
"""
super(XFUNConfig, self).__init__(**kwargs)
self.lang = lang
self.additional_langs = additional_langs
class XFUN(datasets.GeneratorBasedBuilder):
"""XFUN dataset."""
BUILDER_CONFIGS = [XFUNConfig(name=f"xfun.{lang}", lang=lang) for lang in _LANG]
tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features(
{
"id": datasets.Value("string"),
"input_ids": datasets.Sequence(datasets.Value("int64")),
"bbox": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))),
"labels": datasets.Sequence(
datasets.ClassLabel(
names=["O", "B-QUESTION", "B-ANSWER", "B-HEADER", "I-ANSWER", "I-QUESTION", "I-HEADER"]
)
),
"image": datasets.Array3D(shape=(3, 224, 224), dtype="uint8"),
"entities": datasets.Sequence(
{
"start": datasets.Value("int64"),
"end": datasets.Value("int64"),
"label": datasets.ClassLabel(names=["HEADER", "QUESTION", "ANSWER"]),
}
),
"relations": datasets.Sequence(
{
"head": datasets.Value("int64"),
"tail": datasets.Value("int64"),
"start_index": datasets.Value("int64"),
"end_index": datasets.Value("int64"),
}
),
}
),
supervised_keys=None,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": [f"{_URL}{self.config.lang}.train.json", f"{_URL}{self.config.lang}.train.zip"],
"val": [f"{_URL}{self.config.lang}.val.json", f"{_URL}{self.config.lang}.val.zip"],
# "test": [f"{_URL}{self.config.lang}.test.json", f"{_URL}{self.config.lang}.test.zip"],
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
train_files_for_many_langs = [downloaded_files["train"]]
val_files_for_many_langs = [downloaded_files["val"]]
# test_files_for_many_langs = [downloaded_files["test"]]
if self.config.additional_langs:
additional_langs = self.config.additional_langs.split("+")
if "all" in additional_langs:
additional_langs = [lang for lang in _LANG if lang != self.config.lang]
for lang in additional_langs:
urls_to_download = {"train": [f"{_URL}{lang}.train.json", f"{_URL}{lang}.train.zip"]}
additional_downloaded_files = dl_manager.download_and_extract(urls_to_download)
train_files_for_many_langs.append(additional_downloaded_files["train"])
logger.info(f"Training on {self.config.lang} with additional langs({self.config.additional_langs})")
logger.info(f"Evaluating on {self.config.lang}")
logger.info(f"Testing on {self.config.lang}")
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_files_for_many_langs}),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": val_files_for_many_langs}
),
# datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepaths": test_files_for_many_langs}),
]
def _generate_examples(self, filepaths):
for filepath in filepaths:
logger.info("Generating examples from = %s", filepath)
with open(filepath[0], "r", encoding="utf-8") as f:
data = json.load(f)
for doc in data["documents"]:
doc["img"]["fpath"] = os.path.join(filepath[1], doc["img"]["fname"])
image, size = load_image(doc["img"]["fpath"], size=224)
original_image, _ = load_image(doc["img"]["fpath"])
document = doc["document"]
tokenized_doc = {"input_ids": [], "bbox": [], "labels": []}
entities = []
relations = []
id2label = {}
entity_id_to_index_map = {}
empty_entity = set()
for line in document:
if len(line["text"]) == 0:
empty_entity.add(line["id"])
continue
id2label[line["id"]] = line["label"]
relations.extend([tuple(sorted(l)) for l in line["linking"]])
tokenized_inputs = self.tokenizer(
line["text"],
add_special_tokens=False,
return_offsets_mapping=True,
return_attention_mask=False,
)
text_length = 0
ocr_length = 0
bbox = []
for token_id, offset in zip(tokenized_inputs["input_ids"], tokenized_inputs["offset_mapping"]):
if token_id == 6:
bbox.append(None)
continue
text_length += offset[1] - offset[0]
tmp_box = []
while ocr_length < text_length:
ocr_word = line["words"].pop(0)
ocr_length += len(
self.tokenizer._tokenizer.normalizer.normalize_str(ocr_word["text"].strip())
)
tmp_box.append(simplify_bbox(ocr_word["box"]))
if len(tmp_box) == 0:
tmp_box = last_box
bbox.append(normalize_bbox(merge_bbox(tmp_box), size))
last_box = tmp_box # noqa
bbox = [
[bbox[i + 1][0], bbox[i + 1][1], bbox[i + 1][0], bbox[i + 1][1]] if b is None else b
for i, b in enumerate(bbox)
]
if line["label"] == "other":
label = ["O"] * len(bbox)
else:
label = [f"I-{line['label'].upper()}"] * len(bbox)
label[0] = f"B-{line['label'].upper()}"
tokenized_inputs.update({"bbox": bbox, "labels": label})
if label[0] != "O":
entity_id_to_index_map[line["id"]] = len(entities)
entities.append(
{
"start": len(tokenized_doc["input_ids"]),
"end": len(tokenized_doc["input_ids"]) + len(tokenized_inputs["input_ids"]),
"label": line["label"].upper(),
}
)
for i in tokenized_doc:
tokenized_doc[i] = tokenized_doc[i] + tokenized_inputs[i]
relations = list(set(relations))
relations = [rel for rel in relations if rel[0] not in empty_entity and rel[1] not in empty_entity]
kvrelations = []
for rel in relations:
pair = [id2label[rel[0]], id2label[rel[1]]]
if pair == ["question", "answer"]:
kvrelations.append(
{"head": entity_id_to_index_map[rel[0]], "tail": entity_id_to_index_map[rel[1]]}
)
elif pair == ["answer", "question"]:
kvrelations.append(
{"head": entity_id_to_index_map[rel[1]], "tail": entity_id_to_index_map[rel[0]]}
)
else:
continue
def get_relation_span(rel):
bound = []
for entity_index in [rel["head"], rel["tail"]]:
bound.append(entities[entity_index]["start"])
bound.append(entities[entity_index]["end"])
return min(bound), max(bound)
relations = sorted(
[
{
"head": rel["head"],
"tail": rel["tail"],
"start_index": get_relation_span(rel)[0],
"end_index": get_relation_span(rel)[1],
}
for rel in kvrelations
],
key=lambda x: x["head"],
)
chunk_size = 512
for chunk_id, index in enumerate(range(0, len(tokenized_doc["input_ids"]), chunk_size)):
item = {}
for k in tokenized_doc:
item[k] = tokenized_doc[k][index : index + chunk_size]
entities_in_this_span = []
global_to_local_map = {}
for entity_id, entity in enumerate(entities):
if (
index <= entity["start"] < index + chunk_size
and index <= entity["end"] < index + chunk_size
):
entity["start"] = entity["start"] - index
entity["end"] = entity["end"] - index
global_to_local_map[entity_id] = len(entities_in_this_span)
entities_in_this_span.append(entity)
relations_in_this_span = []
for relation in relations:
if (
index <= relation["start_index"] < index + chunk_size
and index <= relation["end_index"] < index + chunk_size
):
relations_in_this_span.append(
{
"head": global_to_local_map[relation["head"]],
"tail": global_to_local_map[relation["tail"]],
"start_index": relation["start_index"] - index,
"end_index": relation["end_index"] - index,
}
)
item.update(
{
"id": f"{doc['id']}_{chunk_id}",
"image": image,
"original_image": original_image,
"entities": entities_in_this_span,
"relations": relations_in_this_span,
}
)
yield f"{doc['id']}_{chunk_id}", item |