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本文为 https://space.bilibili.com/21060026/channel/collectiondetail?sid=1357748的视频学习笔记

项目地址为：https://github.com/zyds/transformers-code

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一、什么是Pipeline

• 将数据预处理、模型调用、结果后处理三部分组装成的流水线，如下流程图
• 使我们能够直接输入文本便获得最终的答案，不需要我们关注细节

二、查看PipeLine支持的任务类型

from transformers.pipelines import SUPPORTED_TASKS
from pprint import pprint
for k, v in SUPPORTED_TASKS.items():print(k, v)

输出但其概念PipeLine支持的任务类型以及可以调用的
举例输出：

audio-classification {'impl': <class 'transformers.pipelines.audio_classification.AudioClassificationPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForAudioClassification'>,), 'default': {'model': {'pt': ('superb/wav2vec2-base-superb-ks', '372e048')}}, 'type': 'audio'}

• key: 任务的名称，如音频分类
• v：关于任务的实现，如具体哪个Pipeline，有没有TF模型，有没有pytorch模型， 模型具体是哪一个
  

三、Pipeline的创建和使用

3.1 根据任务类型，直接创建Pipeline，默认是英文模型

from transformers import pipeline
pipe = pipeline("text-classification") # 根据pipeline直接创建一个任务类
pipe("very good") # 测试一个句子，输出结果

3.2 指定任务类型，再指定模型，创建基于指定模型的Pipeline

注，这里我已经将模型离线下载到本地了

# https://huggingface.co/models
pipe = pipeline("text-classification", model="./models/roberta-base-finetuned-dianping-chinese")

3.3 预先加载模型，再创建Pipeline

rom transformers import AutoModelForSequenceClassification, AutoTokenizer# 这种方式，必须同时指定model和tokenizer
model = AutoModelForSequenceClassification.from_pretrained("./models_roberta-base-finetuned-dianping-chinese")
tokenizer = AutoTokenizer.from_pretrained("./models_roberta-base-finetuned-dianping-chinese")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)

3.4 使用Gpu进行推理

pipe = pipeline("text-classification", model="./models_roberta-base-finetuned-dianping-chinese", device=0)

3.5 查看Device

pipe.model.device

3.6 测试一下耗时

import torch
import time
times = []
for i in range(100):torch.cuda.synchronize()start = time.time()pipe("我觉得不太行！")torch.cuda.synchronize()end = time.time()times.append(end - start)
print(sum(times) / 100)

3.7 确定的Pipeline的参数

# 先创建一个pipeline
qa_pipe = pipeline("question-answering", model="../../models/models")
qa_pipe

输出
QuestionAnsweringPipeline

查看定义，会告诉我们这个pipeline该如何使用

class QuestionAnsweringPipeline(ChunkPipeline):"""Question Answering pipeline using any `ModelForQuestionAnswering`. See the [question answeringexamples](../task_summary#question-answering) for more information.Example:```python>>> from transformers import pipeline>>> oracle = pipeline(model="deepset/roberta-base-squad2")>>> oracle(question="Where do I live?", context="My name is Wolfgang and I live in Berlin"){'score': 0.9191, 'start': 34, 'end': 40, 'answer': 'Berlin'}```Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)This question answering pipeline can currently be loaded from [`pipeline`] using the following task identifier:`"question-answering"`.The models that this pipeline can use are models that have been fine-tuned on a question answering task. See theup-to-date list of available models on[huggingface.co/models](https://huggingface.co/models?filter=question-answering)."""

进入pipeline，看__call__，查看可以支持的更多的参数
列出了更多的参数

    def __call__(self, *args, **kwargs):"""Answer the question(s) given as inputs by using the context(s).Args:args ([`SquadExample`] or a list of [`SquadExample`]):One or several [`SquadExample`] containing the question and context.X ([`SquadExample`] or a list of [`SquadExample`], *optional*):One or several [`SquadExample`] containing the question and context (will be treated the same way as ifpassed as the first positional argument).data ([`SquadExample`] or a list of [`SquadExample`], *optional*):One or several [`SquadExample`] containing the question and context (will be treated the same way as ifpassed as the first positional argument).question (`str` or `List[str]`):One or several question(s) (must be used in conjunction with the `context` argument).context (`str` or `List[str]`):One or several context(s) associated with the question(s) (must be used in conjunction with the`question` argument).topk (`int`, *optional*, defaults to 1):The number of answers to return (will be chosen by order of likelihood). Note that we return less thantopk answers if there are not enough options available within the context.doc_stride (`int`, *optional*, defaults to 128):If the context is too long to fit with the question for the model, it will be split in several chunkswith some overlap. This argument controls the size of that overlap.max_answer_len (`int`, *optional*, defaults to 15):The maximum length of predicted answers (e.g., only answers with a shorter length are considered).max_seq_len (`int`, *optional*, defaults to 384):The maximum length of the total sentence (context + question) in tokens of each chunk passed to themodel. The context will be split in several chunks (using `doc_stride` as overlap) if needed.max_question_len (`int`, *optional*, defaults to 64):The maximum length of the question after tokenization. It will be truncated if needed.handle_impossible_answer (`bool`, *optional*, defaults to `False`):Whether or not we accept impossible as an answer.align_to_words (`bool`, *optional*, defaults to `True`):Attempts to align the answer to real words. Improves quality on space separated langages. Might hurt onnon-space-separated languages (like Japanese or Chinese)Return:A `dict` or a list of `dict`: Each result comes as a dictionary with the following keys:- **score** (`float`) -- The probability associated to the answer.- **start** (`int`) -- The character start index of the answer (in the tokenized version of the input).- **end** (`int`) -- The character end index of the answer (in the tokenized version of the input).- **answer** (`str`) -- The answer to the question."""

如下面的例子

我们输出问题：中国的首都是哪里？ 给的上下文是：中国的首都是北京

qa_pipe(question="中国的首都是哪里？", context="中国的首都是北京")

如果通过 max_answer_len参数来限定输出的最大长度，会进行强行截断

qa_pipe(question="中国的首都是哪里？", context="中国的首都是北京", max_answer_len=1)

四、Pipeline的背后实现

• step1 初始化组件，Tokenizer，model

# step1 初始化tokenizer， model
tokenizer = AutoTokenizer.from_pretrained("../../models/models_roberta-base-finetuned-dianping-chinese")
model = AutoModelForSequenceClassification.from_pretrained("../../models/models_roberta-base-finetuned-dianping-chinese")

• step2 预处理

# 预处理，返回pytorch的tensor，是一个dict
input_text = "我觉得不太行！"
inputs = tokenizer(input_text, return_tensors="pt")
inputs

• step3 模型预测

res = model(**inputs)
res

预测的结果，包括的内容有点多，如loss,logits等

• step4 结果后处理

logits = res.logits
logits = torch.softmax(logits, dim=-1)
pred = torch.argmax(logits).item()
result = model.config.id2label.get(pred)
result