# coding:utf-8
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import base64
import json
import os
import re
from typing import List
import numpy as np
import paddle
from huggingface_hub import hf_hub_download
from ..datasets import load_dataset
from ..layers import GlobalPointerForEntityExtraction, GPLinkerForRelationExtraction
from ..transformers import UIE, UIEM, UIEX, AutoModel, AutoTokenizer
from ..utils.doc_parser import DocParser
from ..utils.env import CONFIG_NAME, LEGACY_CONFIG_NAME
from ..utils.ie_utils import map_offset, pad_image_data
from ..utils.log import logger
from ..utils.tools import get_bool_ids_greater_than, get_span
from .task import Task
from .utils import DataCollatorGP, SchemaTree, dbc2sbc, get_id_and_prob, gp_decode
usage = r"""
from paddlenlp import Taskflow
# Entity Extraction
schema = ['时间', '选手', '赛事名称'] # Define the schema for entity extraction
ie = Taskflow('information_extraction', schema=schema)
ie("2月8日上午北京冬奥会自由式滑雪女子大跳台决赛中中国选手谷爱凌以188.25分获得金牌！")
'''
[{'时间': [{'text': '2月8日上午', 'start': 0, 'end': 6, 'probability': 0.9857378532924486}], '选手': [{'text': '谷爱凌', 'start': 28, 'end': 31, 'probability': 0.8981548639781138}], '赛事名称': [{'text': '北京冬奥会自由式滑雪女子大跳台决赛', 'start': 6, 'end': 23, 'probability': 0.8503089953268272}]}]
'''
# Relation Extraction
schema = [{"歌曲名称":["歌手", "所属专辑"]}] # Define the schema for relation extraction
ie.set_schema(schema) # Reset schema
ie("《告别了》是孙耀威在专辑爱的故事里面的歌曲")
'''
[{'歌曲名称': [{'text': '告别了', 'start': 1, 'end': 4, 'probability': 0.6296155977145546, 'relations': {'歌手': [{'text': '孙耀威', 'start': 6, 'end': 9, 'probability': 0.9988381005599081}], '所属专辑': [{'text': '爱的故事', 'start': 12, 'end': 16, 'probability': 0.9968462078543183}]}}, {'text': '爱的故事', 'start': 12, 'end': 16, 'probability': 0.2816869478191606, 'relations': {'歌手': [{'text': '孙耀威', 'start': 6, 'end': 9, 'probability': 0.9951415104192272}]}}]}]
'''
# Event Extraction
schema = [{'地震触发词': ['地震强度', '时间', '震中位置', '震源深度']}] # Define the schema for event extraction
ie.set_schema(schema) # Reset schema
ie('中国地震台网正式测定：5月16日06时08分在云南临沧市凤庆县(北纬24.34度，东经99.98度)发生3.5级地震，震源深度10千米。')
'''
[{'地震触发词': [{'text': '地震', 'start': 56, 'end': 58, 'probability': 0.9977425555988333, 'relations': {'地震强度': [{'text': '3.5级', 'start': 52, 'end': 56, 'probability': 0.998080217831891}], '时间': [{'text': '5月16日06时08分', 'start': 11, 'end': 22, 'probability': 0.9853299772936026}], '震中位置': [{'text': '云南临沧市凤庆县(北纬24.34度，东经99.98度)', 'start': 23, 'end': 50, 'probability': 0.7874012889740385}], '震源深度': [{'text': '10千米', 'start': 63, 'end': 67, 'probability': 0.9937974422968665}]}}]}]
'''
# Opinion Extraction
schema = [{'评价维度': ['观点词', '情感倾向[正向，负向]']}] # Define the schema for opinion extraction
ie.set_schema(schema) # Reset schema
ie("地址不错，服务一般，设施陈旧")
'''
[{'评价维度': [{'text': '地址', 'start': 0, 'end': 2, 'probability': 0.9888139270606509, 'relations': {'观点词': [{'text': '不错', 'start': 2, 'end': 4, 'probability': 0.9927847072459528}], '情感倾向[正向，负向]': [{'text': '正向', 'probability': 0.998228967796706}]}}, {'text': '设施', 'start': 10, 'end': 12, 'probability': 0.9588297379365116, 'relations': {'观点词': [{'text': '陈旧', 'start': 12, 'end': 14, 'probability': 0.9286753967902683}], '情感倾向[正向，负向]': [{'text': '负向', 'probability': 0.9949389795770394}]}}, {'text': '服务', 'start': 5, 'end': 7, 'probability': 0.9592857070501211, 'relations': {'观点词': [{'text': '一般', 'start': 7, 'end': 9, 'probability': 0.9949359182521675}], '情感倾向[正向，负向]': [{'text': '负向', 'probability': 0.9952498258302498}]}}]}]
'''
# Sentence-level Sentiment Classification
schema = ['情感倾向[正向，负向]'] # Define the schema for sentence-level sentiment classification
ie.set_schema(schema) # Reset schema
ie('这个产品用起来真的很流畅，我非常喜欢')
'''
[{'情感倾向[正向，负向]': [{'text': '正向', 'probability': 0.9990024058203417}]}]
'''
# English Model
schema = [{'Person': ['Company', 'Position']}]
ie_en = Taskflow('information_extraction', schema=schema, model='uie-base-en')
ie_en('In 1997, Steve was excited to become the CEO of Apple.')
'''
[{'Person': [{'text': 'Steve', 'start': 9, 'end': 14, 'probability': 0.999631971804547, 'relations': {'Company': [{'text': 'Apple', 'start': 48, 'end': 53, 'probability': 0.9960158209451642}], 'Position': [{'text': 'CEO', 'start': 41, 'end': 44, 'probability': 0.8871063806420736}]}}]}]
'''
schema = ['Sentiment classification [negative, positive]']
ie_en.set_schema(schema)
ie_en('I am sorry but this is the worst film I have ever seen in my life.')
'''
[{'Sentiment classification [negative, positive]': [{'text': 'negative', 'probability': 0.9998415771287057}]}]
'''
# Multilingual Model
schema = [{'Person': ['Company', 'Position']}]
ie_m = Taskflow('information_extraction', schema=schema, model='uie-m-base', schema_lang="en")
ie_m('In 1997, Steve was excited to become the CEO of Apple.')
'''
[{'Person': [{'text': 'Steve', 'start': 9, 'end': 14, 'probability': 0.9998436034905893, 'relations': {'Company': [{'text': 'Apple', 'start': 48, 'end': 53, 'probability': 0.9842775467359672}], 'Position': [{'text': 'CEO', 'start': 41, 'end': 44, 'probability': 0.9628799853543271}]}}]}]
'''
"""
MODEL_MAP = {"UIE": UIE, "UIEM": UIEM, "UIEX": UIEX}
[文档]def get_dynamic_max_length(examples, default_max_length: int, dynamic_max_length: List[int]) -> int:
"""get max_length by examples which you can change it by examples in batch"""
cur_length = len(examples[0]["input_ids"])
max_length = default_max_length
for max_length_option in sorted(dynamic_max_length):
if cur_length <= max_length_option:
max_length = max_length_option
break
return max_length
[文档]class UIETask(Task):
"""
Universal Information Extraction Task.
Args:
task(string): The name of task.
model(string): The model name in the task.
kwargs (dict, optional): Additional keyword arguments passed along to the specific task.
"""
resource_files_names = {
"model_state": "model_state.pdparams",
"config": "config.json",
"vocab_file": "vocab.txt",
"special_tokens_map": "special_tokens_map.json",
"tokenizer_config": "tokenizer_config.json",
}
# vocab.txt/special_tokens_map.json/tokenizer_config.json are common to the default model.
resource_files_urls = {
"uie-base": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base_v1.1/model_state.pdparams",
"47b93cf6a85688791699548210048085",
],
"config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/config.json",
"ad8b5442c758fb2dc18ea53b61e867f7",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/vocab.txt",
"1c1c1f4fd93c5bed3b4eebec4de976a8",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/tokenizer_config.json",
"59acb0ce78e79180a2491dfd8382b28c",
],
},
"uie-medium": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_medium_v1.1/model_state.pdparams",
"c34475665eb05e25f3c9cd9b020b331a",
],
"config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_medium/config.json",
"7fb22b3e07c5af76371c25ab814f06b8",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/vocab.txt",
"1c1c1f4fd93c5bed3b4eebec4de976a8",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/tokenizer_config.json",
"59acb0ce78e79180a2491dfd8382b28c",
],
},
"uie-mini": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_mini_v1.1/model_state.pdparams",
"9a0805762c41b104d590c15fbe9b19fd",
],
"config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_mini/config.json",
"8ddebbf64c3f32a49e6f9e1c220e7322",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/vocab.txt",
"1c1c1f4fd93c5bed3b4eebec4de976a8",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/tokenizer_config.json",
"59acb0ce78e79180a2491dfd8382b28c",
],
},
"uie-micro": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_micro_v1.1/model_state.pdparams",
"da67287bca2906864929e16493f748e4",
],
"config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_micro/config.json",
"544ddc65c758536cd3ba122f55b8709c",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/vocab.txt",
"1c1c1f4fd93c5bed3b4eebec4de976a8",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/tokenizer_config.json",
"59acb0ce78e79180a2491dfd8382b28c",
],
},
"uie-nano": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_nano_v1.1/model_state.pdparams",
"48db5206232e89ef16b66467562d90e5",
],
"config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_nano/config.json",
"e0e0a2c0d9651ed1a8492be5507590a9",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/vocab.txt",
"1c1c1f4fd93c5bed3b4eebec4de976a8",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/tokenizer_config.json",
"59acb0ce78e79180a2491dfd8382b28c",
],
},
# Rename to `uie-medium` and the name of `uie-tiny` will be deprecated in future.
"uie-tiny": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_medium_v1.1/model_state.pdparams",
"c34475665eb05e25f3c9cd9b020b331a",
],
"config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_medium/config.json",
"7fb22b3e07c5af76371c25ab814f06b8",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/vocab.txt",
"1c1c1f4fd93c5bed3b4eebec4de976a8",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/tokenizer_config.json",
"59acb0ce78e79180a2491dfd8382b28c",
],
},
"uie-medical-base": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_medical_base_v0.2/model_state.pdparams",
"7582d3b01f6faf00b7000111ea853796",
],
"config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/config.json",
"ad8b5442c758fb2dc18ea53b61e867f7",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/vocab.txt",
"1c1c1f4fd93c5bed3b4eebec4de976a8",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base/tokenizer_config.json",
"59acb0ce78e79180a2491dfd8382b28c",
],
},
"uie-base-en": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base_en_v1.2/model_state.pdparams",
"8c5d5c8faa76681a0aad58f982cd6141",
],
"config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base_en/config.json",
"257b80ea8b7889fd8b83a9ace7a8a220",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base_en/vocab.txt",
"64800d5d8528ce344256daf115d4965e",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base_en/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_base_en/tokenizer_config.json",
"59acb0ce78e79180a2491dfd8382b28c",
],
},
"uie-m-base": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_base_v1.1/model_state.pdparams",
"eb00c06bd7144e76343d750f5bf36ff6",
],
"config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_base/config.json",
"f03de3ce1b83c13e7bee18e6f323d33f",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_base/vocab.txt",
"e6e1091c984592e72c4460e8eb25045e",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_base/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_base/tokenizer_config.json",
"f144bd065ea90cc26eaa91197124bdcc",
],
"sentencepiece_model_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_base/sentencepiece.bpe.model",
"bf25eb5120ad92ef5c7d8596b5dc4046",
],
},
"uie-m-large": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_large_v1.1/model_state.pdparams",
"9db83a67f34a9c2483dbe57d2510b4c2",
],
"config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_large/config.json",
"8f540de05de57ecc66336b41f3a7ffdb",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_large/vocab.txt",
"e6e1091c984592e72c4460e8eb25045e",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_large/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_large/tokenizer_config.json",
"f144bd065ea90cc26eaa91197124bdcc",
],
"sentencepiece_model_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_large/sentencepiece.bpe.model",
"bf25eb5120ad92ef5c7d8596b5dc4046",
],
},
"uie-x-base": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_x_base_v1.0/model_state.pdparams",
"a953b55f7639ae73d1df6c2c5f7667dd",
],
"config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_x_base/config.json",
"6bcd7d4b119717121fa0276c20bd9224",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_x_base/vocab.txt",
"e6e1091c984592e72c4460e8eb25045e",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_x_base/special_tokens_map.json",
"ba000b17745bb5b5b40236789318847f",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_x_base/tokenizer_config.json",
"09456ba644dac6f9d0b367353a36abe7",
],
"sentencepiece_model_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_x_base/sentencepiece.bpe.model",
"bf25eb5120ad92ef5c7d8596b5dc4046",
],
},
"__internal_testing__/tiny-random-uie": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-uie/model_state.pdparams",
"9e89a3bf94081b2d9ed89118419a3061",
],
"config": [
"https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-uie/config.json",
"113667d59b84133a99b4f1f1ec5784d7",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-uie/vocab.txt",
"1c1c1f4fd93c5bed3b4eebec4de976a8",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-uie/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-uie/tokenizer_config.json",
"dcb0f3257830c0eb1f2de47f2d86f89a",
],
},
"__internal_testing__/tiny-random-uie-m": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-uie-m/model_state.pdparams",
"9fd51b19ba96ab634185744e0a214378",
],
"config": [
"https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-uie-m/config.json",
"7fc6b1503db1e68bec4e6035cc7705c5",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-uie-m/vocab.txt",
"e6e1091c984592e72c4460e8eb25045e",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-uie-m/special_tokens_map.json",
"8b3fb1023167bb4ab9d70708eb05f6ec",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-uie-m/tokenizer_config.json",
"66651e1427b0936da3f964f640303d16",
],
"sentencepiece_model_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_m_base/sentencepiece.bpe.model",
"bf25eb5120ad92ef5c7d8596b5dc4046",
],
},
"__internal_testing__/tiny-random-uie-x": {
"model_state": [
"https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-uie-x_v1.0/model_state.pdparams",
"d9b573b31a82b860b6e5a3005d7b879e",
],
"config": [
"https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-uie-x_v1.0/config.json",
"27d715e680596a69d882056a400d97db",
],
"vocab_file": [
"https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-uie-x/vocab.txt",
"e6e1091c984592e72c4460e8eb25045e",
],
"special_tokens_map": [
"https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-uie-x/special_tokens_map.json",
"ba000b17745bb5b5b40236789318847f",
],
"tokenizer_config": [
"https://bj.bcebos.com/paddlenlp/models/community/__internal_testing__/tiny-random-uie-x/tokenizer_config.json",
"c19bdbcec62476176d268e4dc7f1e506",
],
"sentencepiece_model_file": [
"https://bj.bcebos.com/paddlenlp/taskflow/information_extraction/uie_x_base/sentencepiece.bpe.model",
"bf25eb5120ad92ef5c7d8596b5dc4046",
],
},
}
def __init__(self, task, model, schema=None, **kwargs):
super().__init__(task=task, model=model, **kwargs)
self._convert_from_torch = kwargs.get("convert_from_torch", None)
self._max_seq_len = kwargs.get("max_seq_len", 512)
self._dynamic_max_length = kwargs.get("dynamic_max_length", None)
self._batch_size = kwargs.get("batch_size", 16)
self._split_sentence = kwargs.get("split_sentence", False)
self._position_prob = kwargs.get("position_prob", 0.5)
self._lazy_load = kwargs.get("lazy_load", False)
self._num_workers = kwargs.get("num_workers", 0)
self._use_fast = kwargs.get("use_fast", False)
self._layout_analysis = kwargs.get("layout_analysis", False)
self._ocr_lang = kwargs.get("ocr_lang", "ch")
self._schema_lang = kwargs.get("schema_lang", "ch")
self._expand_to_a4_size = False if self._custom_model else True
if self.model in [
"uie-m-base",
"uie-m-large",
"uie-x-base",
"__internal_testing__/tiny-random-uie-m",
"__internal_testing__/tiny-random-uie-x",
]:
self.resource_files_names["sentencepiece_model_file"] = "sentencepiece.bpe.model"
elif "sentencepiece_model_file" in self.resource_files_names.keys():
del self.resource_files_names["sentencepiece_model_file"]
# TODO: temporary solution to support HF Hub due to lack of AutoModel
# change this logic to use AutoConfig when available
if self.from_hf_hub:
config_file_path = hf_hub_download(repo_id=self._task_path, filename=CONFIG_NAME)
with open(config_file_path) as f:
self._init_class = json.load(f)["architectures"].pop()
else:
# Compatible with the model fine-tuned without PretrainedConfig
if os.path.exists(os.path.join(self._task_path, LEGACY_CONFIG_NAME)):
if "config" in self.resource_files_names.keys():
del self.resource_files_names["config"]
with open(os.path.join(self._task_path, LEGACY_CONFIG_NAME)) as f:
self._init_class = json.load(f)["init_class"]
self._check_task_files()
else:
self._check_task_files()
with open(os.path.join(self._task_path, CONFIG_NAME)) as f:
self._init_class = json.load(f)["architectures"].pop()
self._is_en = True if model in ["uie-base-en"] or self._schema_lang == "en" else False
if self._init_class in ["UIEX"]:
self._summary_token_num = 4 # [CLS] prompt [SEP] [SEP] text [SEP] for UIE-X
else:
self._summary_token_num = 3 # [CLS] prompt [SEP] text [SEP]
self._parser_map = {
"ch": None, # OCR-CH
"en": None, # OCR-EN
"ch-layout": None, # Layout-CH
"en-layout": None, # Layout-EN
}
if not schema:
logger.warning(
"The schema has not been set yet, please set a schema via set_schema(). "
"More details about the setting of schema please refer to https://github.com/PaddlePaddle/PaddleNLP/blob/develop/applications/information_extraction/taskflow_text.md"
)
self._schema_tree = None
else:
self.set_schema(schema)
self._check_predictor_type()
self._get_inference_model()
self._usage = usage
self._construct_tokenizer()
def set_argument(self, argument: dict):
for k, v in argument.items():
if k == "input":
continue
setattr(self, f"_{k}", v)
def set_schema(self, schema):
if isinstance(schema, dict) or isinstance(schema, str):
schema = [schema]
self._schema_tree = self._build_tree(schema)
def _construct_input_spec(self):
"""
Construct the input spec for the convert dygraph model to static model.
"""
if paddle.get_device().split(":", 1)[0] == "npu":
input_spec_dtype = "int32"
else:
input_spec_dtype = "int64"
if self._init_class in ["UIEX"]:
self._input_spec = [
paddle.static.InputSpec(shape=[None, None], dtype="int64", name="input_ids"),
paddle.static.InputSpec(shape=[None, None], dtype="int64", name="token_type_ids"),
paddle.static.InputSpec(shape=[None, None], dtype="int64", name="position_ids"),
paddle.static.InputSpec(shape=[None, None], dtype="int64", name="attention_mask"),
paddle.static.InputSpec(shape=[None, None, 4], dtype="int64", name="bbox"),
paddle.static.InputSpec(shape=[None, 3, 224, 224], dtype="float32", name="image"),
]
elif self._init_class in ["UIEM"]:
self._input_spec = [
paddle.static.InputSpec(shape=[None, None], dtype="int64", name="input_ids"),
paddle.static.InputSpec(shape=[None, None], dtype="int64", name="position_ids"),
]
else:
self._input_spec = [
paddle.static.InputSpec(shape=[None, None], dtype=input_spec_dtype, name="input_ids"),
paddle.static.InputSpec(shape=[None, None], dtype=input_spec_dtype, name="token_type_ids"),
paddle.static.InputSpec(shape=[None, None], dtype=input_spec_dtype, name="position_ids"),
paddle.static.InputSpec(shape=[None, None], dtype=input_spec_dtype, name="attention_mask"),
]
def _construct_model(self, model):
"""
Construct the inference model for the predictor.
"""
model_instance = MODEL_MAP[self._init_class].from_pretrained(
self._task_path, from_hf_hub=self.from_hf_hub, convert_from_torch=self._convert_from_torch
)
self._model = model_instance
self._model.eval()
def _construct_tokenizer(self):
"""
Construct the tokenizer for the predictor.
"""
self._tokenizer = AutoTokenizer.from_pretrained(
self._task_path, use_fast=self._use_fast, from_hf_hub=self.from_hf_hub
)
def _preprocess(self, inputs):
"""
Transform the raw text to the model inputs, two steps involved:
1) Transform the raw text to token ids.
2) Generate the other model inputs from the raw text and token ids.
"""
inputs = self._check_input_text(inputs)
outputs = {}
outputs["text"] = inputs
return outputs
def _check_input_text(self, inputs):
"""
Check whether the input meet the requirement.
"""
self._ocr_lang_choice = (self._ocr_lang + "-layout") if self._layout_analysis else self._ocr_lang
inputs = inputs[0]
if isinstance(inputs, dict) or isinstance(inputs, str):
inputs = [inputs]
if isinstance(inputs, list):
input_list = []
for example in inputs:
data = {}
if isinstance(example, dict):
if "doc" in example.keys():
if not self._parser_map[self._ocr_lang_choice]:
self._parser_map[self._ocr_lang_choice] = DocParser(
ocr_lang=self._ocr_lang, layout_analysis=self._layout_analysis
)
if "layout" in example.keys():
data = self._parser_map[self._ocr_lang_choice].parse(
{"doc": example["doc"]}, do_ocr=False, expand_to_a4_size=self._expand_to_a4_size
)
data["layout"] = example["layout"]
else:
data = self._parser_map[self._ocr_lang_choice].parse(
{"doc": example["doc"]}, expand_to_a4_size=self._expand_to_a4_size
)
elif "text" in example.keys():
if not isinstance(example["text"], str):
raise TypeError(
"Invalid inputs, the input text should be string. but type of {} found!".format(
type(example["text"])
)
)
data["text"] = example["text"]
else:
raise ValueError("Invalid inputs, the input should contain a doc or a text.")
input_list.append(data)
elif isinstance(example, str):
input_list.append(example)
else:
raise TypeError(
"Invalid inputs, the input should be dict or list of dict, but type of {} found!".format(
type(example)
)
)
else:
raise TypeError("Invalid input format!")
return input_list
def _single_stage_predict(self, inputs):
input_texts = [d["text"] for d in inputs]
prompts = [d["prompt"] for d in inputs]
# max predict length should exclude the length of prompt and summary tokens
max_predict_len = self._max_seq_len - len(max(prompts)) - self._summary_token_num
if self._init_class in ["UIEX"]:
bbox_list = [d["bbox"] for d in inputs]
short_input_texts, short_bbox_list, input_mapping = self._auto_splitter(
input_texts, max_predict_len, bbox_list=bbox_list, split_sentence=self._split_sentence
)
else:
short_input_texts, input_mapping = self._auto_splitter(
input_texts, max_predict_len, split_sentence=self._split_sentence
)
short_texts_prompts = []
for k, v in input_mapping.items():
short_texts_prompts.extend([prompts[k] for _ in range(len(v))])
if self._init_class in ["UIEX"]:
image_list = []
for k, v in input_mapping.items():
image_list.extend([inputs[k]["image"] for _ in range(len(v))])
short_inputs = [
{
"text": short_input_texts[i],
"prompt": short_texts_prompts[i],
"bbox": short_bbox_list[i],
"image": image_list[i],
}
for i in range(len(short_input_texts))
]
else:
short_inputs = [
{"text": short_input_texts[i], "prompt": short_texts_prompts[i]} for i in range(len(short_input_texts))
]
def text_reader(inputs):
for example in inputs:
if self._dynamic_max_length is not None:
temp_encoded_inputs = self._tokenizer(
text=[example["prompt"]],
text_pair=[example["text"]],
truncation=True,
max_seq_len=self._max_seq_len,
return_attention_mask=True,
return_position_ids=True,
return_dict=False,
return_offsets_mapping=True,
)
max_length = get_dynamic_max_length(
examples=temp_encoded_inputs,
default_max_length=self._max_seq_len,
dynamic_max_length=self._dynamic_max_length,
)
encoded_inputs = self._tokenizer(
text=[example["prompt"]],
text_pair=[example["text"]],
truncation=True,
max_seq_len=max_length,
pad_to_max_seq_len=True,
return_attention_mask=True,
return_position_ids=True,
return_offsets_mapping=True,
)
logger.info("Inference with dynamic max length in {}".format(max_length))
else:
encoded_inputs = self._tokenizer(
text=[example["prompt"]],
text_pair=[example["text"]],
truncation=True,
max_seq_len=self._max_seq_len,
pad_to_max_seq_len=True,
return_attention_mask=True,
return_position_ids=True,
return_offsets_mapping=True,
)
if self._init_class in ["UIEM"]:
tokenized_output = [
encoded_inputs["input_ids"][0],
encoded_inputs["position_ids"][0],
encoded_inputs["offset_mapping"][0],
]
else:
tokenized_output = [
encoded_inputs["input_ids"][0],
encoded_inputs["token_type_ids"][0],
encoded_inputs["position_ids"][0],
encoded_inputs["attention_mask"][0],
encoded_inputs["offset_mapping"][0],
]
tokenized_output = [np.array(x, dtype="int64") for x in tokenized_output]
yield tuple(tokenized_output)
def doc_reader(inputs, pad_id=1, c_sep_id=2):
def _process_bbox(tokens, bbox_lines, offset_mapping, offset_bias):
bbox_list = [[0, 0, 0, 0] for x in range(len(tokens))]
for index, bbox in enumerate(bbox_lines):
index_token = map_offset(index + offset_bias, offset_mapping)
if 0 <= index_token < len(bbox_list):
bbox_list[index_token] = bbox
return bbox_list
def _encode_doc(
tokenizer, offset_mapping, last_offset, prompt, this_text_line, inputs_ids, q_sep_index, max_seq_len
):
if len(offset_mapping) == 0:
content_encoded_inputs = tokenizer(
text=[prompt],
text_pair=[this_text_line],
max_seq_len=max_seq_len,
return_dict=False,
return_offsets_mapping=True,
)
content_encoded_inputs = content_encoded_inputs[0]
inputs_ids = content_encoded_inputs["input_ids"][:-1]
sub_offset_mapping = [list(x) for x in content_encoded_inputs["offset_mapping"]]
q_sep_index = content_encoded_inputs["input_ids"].index(2, 1)
bias = 0
for i in range(len(sub_offset_mapping)):
if i == 0:
continue
mapping = sub_offset_mapping[i]
if mapping[0] == 0 and mapping[1] == 0 and bias == 0:
bias = sub_offset_mapping[i - 1][-1] + 1
if mapping[0] == 0 and mapping[1] == 0:
continue
if mapping == sub_offset_mapping[i - 1]:
continue
sub_offset_mapping[i][0] += bias
sub_offset_mapping[i][1] += bias
offset_mapping = sub_offset_mapping[:-1]
last_offset = offset_mapping[-1][-1]
else:
content_encoded_inputs = tokenizer(
text=this_text_line, max_seq_len=max_seq_len, return_dict=False, return_offsets_mapping=True
)
inputs_ids += content_encoded_inputs["input_ids"][1:-1]
sub_offset_mapping = [list(x) for x in content_encoded_inputs["offset_mapping"]]
for i, sub_list in enumerate(sub_offset_mapping[1:-1]):
if i == 0:
org_offset = sub_list[1]
else:
if sub_list[0] != org_offset and sub_offset_mapping[1:-1][i - 1] != sub_list:
last_offset += 1
org_offset = sub_list[1]
offset_mapping += [[last_offset, sub_list[1] - sub_list[0] + last_offset]]
last_offset = offset_mapping[-1][-1]
return offset_mapping, last_offset, q_sep_index, inputs_ids
for example in inputs:
content = example["text"]
prompt = example["prompt"]
bbox_lines = example.get("bbox", None)
image_buff_string = example.get("image", None)
# Text
if bbox_lines is None:
encoded_inputs = self._tokenizer(
text=[example["prompt"]],
text_pair=[example["text"]],
truncation=True,
max_seq_len=self._max_seq_len,
pad_to_max_seq_len=True,
return_attention_mask=True,
return_position_ids=True,
return_offsets_mapping=True,
return_dict=False,
)
encoded_inputs = encoded_inputs[0]
inputs_ids = encoded_inputs["input_ids"]
position_ids = encoded_inputs["position_ids"]
attention_mask = encoded_inputs["attention_mask"]
q_sep_index = inputs_ids.index(2, 1)
c_sep_index = attention_mask.index(0)
offset_mapping = [list(x) for x in encoded_inputs["offset_mapping"]]
bbox_list = [[0, 0, 0, 0] for x in range(len(inputs_ids))]
token_type_ids = [
1 if token_index <= q_sep_index or token_index > c_sep_index else 0
for token_index in range(self._max_seq_len)
]
padded_image = np.zeros([3, 224, 224])
# Doc
else:
inputs_ids = []
prev_bbox = [-1, -1, -1, -1]
this_text_line = ""
q_sep_index = -1
offset_mapping = []
last_offset = 0
for char_index, (char, bbox) in enumerate(zip(content, bbox_lines)):
if char_index == 0:
prev_bbox = bbox
this_text_line = char
continue
if all([bbox[x] == prev_bbox[x] for x in range(4)]):
this_text_line += char
else:
offset_mapping, last_offset, q_sep_index, inputs_ids = _encode_doc(
self._tokenizer,
offset_mapping,
last_offset,
prompt,
this_text_line,
inputs_ids,
q_sep_index,
self._max_seq_len,
)
this_text_line = char
prev_bbox = bbox
if len(this_text_line) > 0:
offset_mapping, last_offset, q_sep_index, inputs_ids = _encode_doc(
self._tokenizer,
offset_mapping,
last_offset,
prompt,
this_text_line,
inputs_ids,
q_sep_index,
self._max_seq_len,
)
if len(inputs_ids) > self._max_seq_len:
inputs_ids = inputs_ids[: (self._max_seq_len - 1)] + [c_sep_id]
offset_mapping = offset_mapping[: (self._max_seq_len - 1)] + [[0, 0]]
else:
inputs_ids += [c_sep_id]
offset_mapping += [[0, 0]]
if len(offset_mapping) > 1:
offset_bias = offset_mapping[q_sep_index - 1][-1] + 1
else:
offset_bias = 0
seq_len = len(inputs_ids)
inputs_ids += [pad_id] * (self._max_seq_len - seq_len)
token_type_ids = [1] * (q_sep_index + 1) + [0] * (seq_len - q_sep_index - 1)
token_type_ids += [pad_id] * (self._max_seq_len - seq_len)
bbox_list = _process_bbox(inputs_ids, bbox_lines, offset_mapping, offset_bias)
offset_mapping += [[0, 0]] * (self._max_seq_len - seq_len)
# Reindex the text
text_start_idx = offset_mapping[1:].index([0, 0]) + self._summary_token_num - 1
for idx in range(text_start_idx, self._max_seq_len):
offset_mapping[idx][0] -= offset_bias
offset_mapping[idx][1] -= offset_bias
position_ids = list(range(seq_len))
position_ids = position_ids + [0] * (self._max_seq_len - seq_len)
attention_mask = [1] * seq_len + [0] * (self._max_seq_len - seq_len)
image_data = base64.b64decode(image_buff_string.encode("utf8"))
padded_image = pad_image_data(image_data)
input_list = [
inputs_ids,
token_type_ids,
position_ids,
attention_mask,
bbox_list,
padded_image,
offset_mapping,
]
input_list = [inputs_ids, token_type_ids, position_ids, attention_mask, bbox_list]
return_list = [np.array(x, dtype="int64") for x in input_list]
return_list.append(np.array(padded_image, dtype="float32"))
return_list.append(np.array(offset_mapping, dtype="int64"))
assert len(inputs_ids) == self._max_seq_len
assert len(token_type_ids) == self._max_seq_len
assert len(position_ids) == self._max_seq_len
assert len(attention_mask) == self._max_seq_len
assert len(bbox_list) == self._max_seq_len
yield tuple(return_list)
reader = doc_reader if self._init_class in ["UIEX"] else text_reader
infer_ds = load_dataset(reader, inputs=short_inputs, lazy=self._lazy_load)
batch_sampler = paddle.io.BatchSampler(dataset=infer_ds, batch_size=self._batch_size, shuffle=False)
infer_data_loader = paddle.io.DataLoader(
dataset=infer_ds, batch_sampler=batch_sampler, num_workers=self._num_workers, return_list=True
)
sentence_ids = []
probs = []
for batch in infer_data_loader:
if self._init_class in ["UIEX"]:
input_ids, token_type_ids, pos_ids, att_mask, bbox, image, offset_maps = batch
elif self._init_class in ["UIEM"]:
input_ids, pos_ids, offset_maps = batch
else:
input_ids, token_type_ids, pos_ids, att_mask, offset_maps = batch
if self._predictor_type == "paddle-inference":
if self._init_class in ["UIEX"]:
self.input_handles[0].copy_from_cpu(input_ids.numpy())
self.input_handles[1].copy_from_cpu(token_type_ids.numpy())
self.input_handles[2].copy_from_cpu(pos_ids.numpy())
self.input_handles[3].copy_from_cpu(att_mask.numpy())
self.input_handles[4].copy_from_cpu(bbox.numpy())
self.input_handles[5].copy_from_cpu(image.numpy())
elif self._init_class in ["UIEM"]:
self.input_handles[0].copy_from_cpu(input_ids.numpy())
self.input_handles[1].copy_from_cpu(pos_ids.numpy())
else:
self.input_handles[0].copy_from_cpu(input_ids.numpy())
self.input_handles[1].copy_from_cpu(token_type_ids.numpy())
self.input_handles[2].copy_from_cpu(pos_ids.numpy())
self.input_handles[3].copy_from_cpu(att_mask.numpy())
self.predictor.run()
start_prob = self.output_handle[0].copy_to_cpu().tolist()
end_prob = self.output_handle[1].copy_to_cpu().tolist()
else:
if self._init_class in ["UIEX"]:
input_dict = {
"input_ids": input_ids.numpy(),
"token_type_ids": token_type_ids.numpy(),
"position_ids": pos_ids.numpy(),
"attention_mask": att_mask.numpy(),
"bbox": bbox.numpy(),
"image": image.numpy(),
}
elif self._init_class in ["UIEM"]:
input_dict = {
"input_ids": input_ids.numpy(),
"position_ids": pos_ids.numpy(),
}
else:
input_dict = {
"input_ids": input_ids.numpy(),
"token_type_ids": token_type_ids.numpy(),
"position_ids": pos_ids.numpy(),
"attention_mask": att_mask.numpy(),
}
start_prob, end_prob = self.predictor.run(None, input_dict)
start_prob = start_prob.tolist()
end_prob = end_prob.tolist()
start_ids_list = get_bool_ids_greater_than(start_prob, limit=self._position_prob, return_prob=True)
end_ids_list = get_bool_ids_greater_than(end_prob, limit=self._position_prob, return_prob=True)
for start_ids, end_ids, offset_map in zip(start_ids_list, end_ids_list, offset_maps.tolist()):
span_set = get_span(start_ids, end_ids, with_prob=True)
sentence_id, prob = get_id_and_prob(span_set, offset_map)
sentence_ids.append(sentence_id)
probs.append(prob)
results = self._convert_ids_to_results(short_inputs, sentence_ids, probs)
results = self._auto_joiner(results, short_input_texts, input_mapping)
return results
def _auto_joiner(self, short_results, short_inputs, input_mapping):
concat_results = []
is_cls_task = False
for short_result in short_results:
if short_result == []:
continue
elif "start" not in short_result[0].keys() and "end" not in short_result[0].keys():
is_cls_task = True
break
else:
break
for k, vs in input_mapping.items():
if is_cls_task:
cls_options = {}
single_results = []
for v in vs:
if len(short_results[v]) == 0:
continue
if short_results[v][0]["text"] not in cls_options.keys():
cls_options[short_results[v][0]["text"]] = [1, short_results[v][0]["probability"]]
else:
cls_options[short_results[v][0]["text"]][0] += 1
cls_options[short_results[v][0]["text"]][1] += short_results[v][0]["probability"]
if len(cls_options) != 0:
cls_res, cls_info = max(cls_options.items(), key=lambda x: x[1])
concat_results.append([{"text": cls_res, "probability": cls_info[1] / cls_info[0]}])
else:
concat_results.append([])
else:
offset = 0
single_results = []
for v in vs:
if v == 0:
single_results = short_results[v]
offset += len(short_inputs[v])
else:
for i in range(len(short_results[v])):
if "start" not in short_results[v][i] or "end" not in short_results[v][i]:
continue
short_results[v][i]["start"] += offset
short_results[v][i]["end"] += offset
offset += len(short_inputs[v])
single_results.extend(short_results[v])
concat_results.append(single_results)
return concat_results
def _run_model(self, inputs):
raw_inputs = inputs["text"]
_inputs = self._parse_inputs(raw_inputs)
results = self._multi_stage_predict(_inputs)
inputs["result"] = results
return inputs
def _parse_inputs(self, inputs):
_inputs = []
for d in inputs:
if isinstance(d, dict):
if "doc" in d.keys():
text = ""
bbox = []
img_w, img_h = d["img_w"], d["img_h"]
offset_x, offset_y = d["offset_x"], d["offset_x"]
for segment in d["layout"]:
org_box = segment[0] # bbox before expand to A4 size
box = [
org_box[0] + offset_x,
org_box[1] + offset_y,
org_box[2] + offset_x,
org_box[3] + offset_y,
]
box = self._parser_map[self._ocr_lang_choice]._normalize_box(box, [img_w, img_h], [1000, 1000])
text += segment[1]
bbox.extend([box] * len(segment[1]))
_inputs.append({"text": text, "bbox": bbox, "image": d["image"], "layout": d["layout"]})
else:
_inputs.append({"text": d["text"], "bbox": None, "image": None})
else:
_inputs.append({"text": d, "bbox": None, "image": None})
return _inputs
def _multi_stage_predict(self, data):
"""
Traversal the schema tree and do multi-stage prediction.
Args:
data (list): a list of strings
Returns:
list: a list of predictions, where the list's length
equals to the length of `data`
"""
results = [{} for _ in range(len(data))]
# Input check to early return
if len(data) < 1 or self._schema_tree is None:
return results
# Copy to stay `self._schema_tree` unchanged
schema_list = self._schema_tree.children[:]
while len(schema_list) > 0:
node = schema_list.pop(0)
examples = []
input_map = {}
cnt = 0
idx = 0
if not node.prefix:
for one_data in data:
examples.append(
{
"text": one_data["text"],
"bbox": one_data["bbox"],
"image": one_data["image"],
"prompt": dbc2sbc(node.name),
}
)
input_map[cnt] = [idx]
idx += 1
cnt += 1
else:
for pre, one_data in zip(node.prefix, data):
if len(pre) == 0:
input_map[cnt] = []
else:
for p in pre:
if self._is_en:
if re.search(r"\[.*?\]$", node.name):
prompt_prefix = node.name[: node.name.find("[", 1)].strip()
cls_options = re.search(r"\[.*?\]$", node.name).group()
# Sentiment classification of xxx [positive, negative]
prompt = prompt_prefix + p + " " + cls_options
else:
prompt = node.name + p
else:
prompt = p + node.name
examples.append(
{
"text": one_data["text"],
"bbox": one_data["bbox"],
"image": one_data["image"],
"prompt": dbc2sbc(prompt),
}
)
input_map[cnt] = [i + idx for i in range(len(pre))]
idx += len(pre)
cnt += 1
if len(examples) == 0:
result_list = []
else:
result_list = self._single_stage_predict(examples)
if not node.parent_relations:
relations = [[] for i in range(len(data))]
for k, v in input_map.items():
for idx in v:
if len(result_list[idx]) == 0:
continue
if node.name not in results[k].keys():
results[k][node.name] = result_list[idx]
else:
results[k][node.name].extend(result_list[idx])
if node.name in results[k].keys():
relations[k].extend(results[k][node.name])
else:
relations = node.parent_relations
for k, v in input_map.items():
for i in range(len(v)):
if len(result_list[v[i]]) == 0:
continue
if "relations" not in relations[k][i].keys():
relations[k][i]["relations"] = {node.name: result_list[v[i]]}
elif node.name not in relations[k][i]["relations"].keys():
relations[k][i]["relations"][node.name] = result_list[v[i]]
else:
relations[k][i]["relations"][node.name].extend(result_list[v[i]])
new_relations = [[] for i in range(len(data))]
for i in range(len(relations)):
for j in range(len(relations[i])):
if "relations" in relations[i][j].keys() and node.name in relations[i][j]["relations"].keys():
for k in range(len(relations[i][j]["relations"][node.name])):
new_relations[i].append(relations[i][j]["relations"][node.name][k])
relations = new_relations
prefix = [[] for _ in range(len(data))]
for k, v in input_map.items():
for idx in v:
for i in range(len(result_list[idx])):
if self._is_en:
prefix[k].append(" of " + result_list[idx][i]["text"])
else:
prefix[k].append(result_list[idx][i]["text"] + "的")
for child in node.children:
child.prefix = prefix
child.parent_relations = relations
schema_list.append(child)
results = self._add_bbox_info(results, data)
return results
def _add_bbox_info(self, results, data):
def _add_bbox(result, char_boxes):
for vs in result.values():
for v in vs:
if "start" in v.keys() and "end" in v.keys():
boxes = []
for i in range(v["start"], v["end"]):
cur_box = char_boxes[i][1]
if i == v["start"]:
box = cur_box
continue
_, cur_y1, cur_x2, cur_y2 = cur_box
if cur_y1 == box[1] and cur_y2 == box[3]:
box[2] = cur_x2
else:
boxes.append(box)
box = cur_box
if box:
boxes.append(box)
boxes = [[int(b) for b in box] for box in boxes]
v["bbox"] = boxes
if v.get("relations"):
_add_bbox(v["relations"], char_boxes)
return result
new_results = []
for result, one_data in zip(results, data):
if "layout" in one_data.keys():
layout = one_data["layout"]
char_boxes = []
for segment in layout:
sbox = segment[0]
text_len = len(segment[1])
if text_len == 0:
continue
if len(segment) == 2 or (len(segment) == 3 and segment[2] != "table"):
char_w = (sbox[2] - sbox[0]) * 1.0 / text_len
for i in range(text_len):
cbox = [sbox[0] + i * char_w, sbox[1], sbox[0] + (i + 1) * char_w, sbox[3]]
char_boxes.append((segment[1][i], cbox))
else:
cell_bbox = [(segment[1][i], sbox) for i in range(text_len)]
char_boxes.extend(cell_bbox)
result = _add_bbox(result, char_boxes)
new_results.append(result)
return new_results
def _convert_ids_to_results(self, examples, sentence_ids, probs):
"""
Convert ids to raw text in a single stage.
"""
results = []
for example, sentence_id, prob in zip(examples, sentence_ids, probs):
if len(sentence_id) == 0:
results.append([])
continue
result_list = []
text = example["text"]
prompt = example["prompt"]
for i in range(len(sentence_id)):
start, end = sentence_id[i]
if start < 0 and end >= 0:
continue
if end < 0:
start += len(prompt) + 1
end += len(prompt) + 1
result = {"text": prompt[start:end], "probability": prob[i]}
result_list.append(result)
else:
result = {"text": text[start:end], "start": start, "end": end, "probability": prob[i]}
result_list.append(result)
results.append(result_list)
return results
@classmethod
def _build_tree(cls, schema, name="root"):
"""
Build the schema tree.
"""
schema_tree = SchemaTree(name)
for s in schema:
if isinstance(s, str):
schema_tree.add_child(SchemaTree(s))
elif isinstance(s, dict):
for k, v in s.items():
if isinstance(v, str):
child = [v]
elif isinstance(v, list):
child = v
else:
raise TypeError(
"Invalid schema, value for each key:value pairs should be list or string"
"but {} received".format(type(v))
)
schema_tree.add_child(cls._build_tree(child, name=k))
else:
raise TypeError("Invalid schema, element should be string or dict, " "but {} received".format(type(s)))
return schema_tree
def _postprocess(self, inputs):
"""
This function will convert the model output to raw text.
"""
return inputs["result"]
[文档]class GPTask(Task):
"""
Global Pointer for closed-domain information extraction Task.
Args:
task(string): The name of task.
model(string): The model name in the task.
kwargs (dict, optional): Additional keyword arguments passed along to the specific task.
"""
resource_files_names = {
"model_state": "model_state.pdparams",
"model_config": "model_config.json",
"vocab_file": "vocab.txt",
"special_tokens_map": "special_tokens_map.json",
"tokenizer_config": "tokenizer_config.json",
}
def __init__(self, task, model, **kwargs):
super().__init__(task=task, model=model, **kwargs)
self._schema_tree = None
self._load_config()
self._construct_tokenizer()
self._get_inference_model()
self._max_seq_len = kwargs.get("max_seq_len", 256)
self._batch_size = kwargs.get("batch_size", 64)
self._lazy_load = kwargs.get("lazy_load", False)
self._num_workers = kwargs.get("num_workers", 0)
def _load_config(self):
model_config_file = os.path.join(self._task_path, self.resource_files_names["model_config"])
with open(model_config_file, encoding="utf-8") as f:
model_config = json.load(f)
self._label_maps = model_config["label_maps"]
self._task_type = model_config["task_type"]
self._encoder = model_config["encoder"]
schema = model_config["label_maps"]["schema"]
self._set_schema(schema)
def _set_schema(self, schema):
if isinstance(schema, dict) or isinstance(schema, str):
schema = [schema]
self._schema_tree = self._build_tree(schema)
def _construct_input_spec(self):
"""
Construct the input spec for the convert dygraph model to static model.
"""
self._input_spec = [
paddle.static.InputSpec(shape=[None, None], dtype="int64", name="input_ids"),
paddle.static.InputSpec(shape=[None, None], dtype="int64", name="att_mask"),
]
def _construct_model(self, model):
"""
Construct the inference model for the predictor.
"""
encoder = AutoModel.from_pretrained(self._encoder)
if self._task_type == "entity_extraction":
model_instance = GlobalPointerForEntityExtraction(encoder, self._label_maps)
else:
model_instance = GPLinkerForRelationExtraction(encoder, self._label_maps)
model_path = os.path.join(self._task_path, "model_state.pdparams")
state_dict = paddle.load(model_path)
model_instance.set_dict(state_dict)
self._model = model_instance
self._model.eval()
def _construct_tokenizer(self):
"""
Construct the tokenizer for the predictor.
"""
# TODO(zhoushunjie): Will set use_fast=True in future.
self._tokenizer = AutoTokenizer.from_pretrained(self._task_path)
def _preprocess(self, inputs):
"""
Transform the raw text to the model inputs, two steps involved:
1) Transform the raw text to token ids.
2) Generate the other model inputs from the raw text and token ids.
"""
inputs = self._check_input_text(inputs)
def read(inputs):
for x in inputs:
tokenized_inputs = self._tokenizer(
x,
max_length=self._max_seq_len,
padding=False,
truncation=True,
return_attention_mask=True,
return_offsets_mapping=True,
return_token_type_ids=False,
)
tokenized_inputs["text"] = x
yield tokenized_inputs
infer_ds = load_dataset(read, inputs=inputs, lazy=self._lazy_load)
data_collator = DataCollatorGP(self._tokenizer, label_maps=self._label_maps, task_type=self._task_type)
batch_sampler = paddle.io.BatchSampler(dataset=infer_ds, batch_size=self._batch_size, shuffle=False)
infer_data_loader = paddle.io.DataLoader(
dataset=infer_ds,
batch_sampler=batch_sampler,
collate_fn=data_collator,
num_workers=self._num_workers,
return_list=True,
)
outputs = {}
outputs["data_loader"] = infer_data_loader
outputs["input_texts"] = inputs
return outputs
def _run_model(self, inputs):
all_preds = ([], []) if self._task_type in ["opinion_extraction", "relation_extraction"] else []
for batch in inputs["data_loader"]:
input_ids, attention_masks, offset_mappings, texts = batch
self.input_handles[0].copy_from_cpu(input_ids.numpy().astype("int64"))
self.input_handles[1].copy_from_cpu(attention_masks.numpy().astype("int64"))
self.predictor.run()
logits = [paddle.to_tensor(self.output_handle[i].copy_to_cpu()) for i in range(len(self.output_handle))]
batch_outputs = gp_decode(logits, offset_mappings, texts, self._label_maps, self._task_type)
if isinstance(batch_outputs, tuple):
all_preds[0].extend(batch_outputs[0]) # Entity output
all_preds[1].extend(batch_outputs[1]) # Relation output
else:
all_preds.extend(batch_outputs)
inputs["result"] = all_preds
return inputs
@classmethod
def _build_tree(cls, schema, name="root"):
"""
Build the schema tree.
"""
schema_tree = SchemaTree(name)
for s in schema:
if isinstance(s, str):
schema_tree.add_child(SchemaTree(s))
elif isinstance(s, dict):
for k, v in s.items():
if isinstance(v, str):
child = [v]
elif isinstance(v, list):
child = v
else:
raise TypeError(
"Invalid schema, value for each key:value pairs should be list or string"
"but {} received".format(type(v))
)
schema_tree.add_child(cls._build_tree(child, name=k))
else:
raise TypeError("Invalid schema, element should be string or dict, " "but {} received".format(type(s)))
return schema_tree
def _postprocess(self, inputs):
if self._task_type == "entity_extraction":
results = self._postprocess_entity_extraction(inputs)
elif self._task_type == "opinion_extraction":
results = self._postprocess_opinion_extraction(inputs)
else:
results = self._postprocess_relation_extraction(inputs)
return results
def _postprocess_opinion_extraction(self, inputs):
all_ent_preds, all_rel_preds = inputs["result"]
results = []
for i in range(len(inputs["input_texts"])):
result = {}
aspect_maps = {}
for ent in all_ent_preds[i]:
ent_res = {
"text": ent["text"],
"start": ent["start_index"],
"end": ent["start_index"] + len(ent["text"]),
"probability": ent["probability"],
}
result.setdefault(ent["type"], []).append(ent_res)
if ent["type"] == "评价维度":
for r in result["评价维度"]:
if ent["text"] == r["text"] and ent["start_index"] == r["start"]:
aspect_maps[(ent["text"], ent["start_index"])] = r
break
for rel in all_rel_preds[i]:
r = aspect_maps[(rel["aspect"], rel["aspect_start_index"])]
r["relations"] = {}
sentiment = {"probability": rel["probability"], "text": rel["sentiment"]}
opinion = {
"text": rel["opinion"],
"start": rel["opinion_start_index"],
"end": rel["opinion_start_index"] + len(rel["opinion"]),
"probability": rel["probability"],
}
r["relations"].setdefault("情感倾向[正向，负向]", []).append(sentiment)
r["relations"].setdefault("观点词", []).append(opinion)
results.append(result)
return results
def _postprocess_relation_extraction(self, inputs):
all_ent_preds, all_rel_preds = inputs["result"]
results = []
for input_text_idx in range(len(inputs["input_texts"])):
result = {}
schema_list = self._schema_tree.children[:]
while len(schema_list) > 0:
node = schema_list.pop(0)
if node.parent_relations is None:
prefix = []
relations = [[]]
cnt = -1
for ent in all_ent_preds[input_text_idx]:
if node.name == ent["type"]:
ent_res = {
"text": ent["text"],
"start": ent["start_index"],
"end": ent["start_index"] + len(ent["text"]),
"probability": ent["probability"].astype("float"),
}
result.setdefault(node.name, []).append(ent_res)
cnt += 1
result[node.name][cnt]["relations"] = {}
relations[0].append(result[node.name][cnt])
else:
relations = [[] for _ in range(len(node.parent_relations))]
for i, rs in enumerate(node.parent_relations):
for r in rs:
cnt = -1
for rel in all_rel_preds[input_text_idx]:
if (
r["text"] == rel["subject"]
and r["start"] == rel["subject_start_index"]
and node.name == rel["predicate"]
):
rel_res = {
"text": rel["object"],
"start": rel["object_start_index"],
"end": rel["object_start_index"] + len(rel["object"]),
"probability": rel["probability"].astype("float"),
}
r["relations"].setdefault(node.name, []).append(rel_res)
cnt += 1
r["relations"][node.name][cnt]["relations"] = {}
relations[i].append(r["relations"][node.name][cnt])
for child in node.children:
child.prefix = prefix
child.parent_relations = relations
schema_list.append(child)
results.append(result)
return results
def _postprocess_entity_extraction(self, inputs):
all_preds = inputs["result"]
results = []
for input_text_idx in range(len(inputs["input_texts"])):
result = {}
schema_list = self._schema_tree.children[:]
while len(schema_list) > 0:
node = schema_list.pop(0)
for ent in all_preds[input_text_idx]:
if node.name == ent["type"]:
ent_res = {
"text": ent["text"],
"start": ent["start_index"],
"end": ent["start_index"] + len(ent["text"]),
"probability": ent["probability"].astype("float"),
}
result.setdefault(node.name, []).append(ent_res)
results.append(result)
return results
