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# Pipelines

pipelines是使用模型进行推理的一种简单方法。这些pipelines是抽象了库中大部分复杂代码的对象，提供了一个专用于多个任务的简单API，包括专名识别、掩码语言建模、情感分析、特征提取和问答等。请参阅[任务摘要](../task_summary)以获取使用示例。

有两种pipelines抽象类需要注意：

- [`pipeline`]，它是封装所有其他pipelines的最强大的对象。
- 针对特定任务pipelines，适用于[音频](#audio)、[计算机视觉](#computer-vision)、[自然语言处理](#natural-language-processing)和[多模态](#multimodal)任务。

## pipeline抽象类

*pipeline*抽象类是对所有其他可用pipeline的封装。它可以像任何其他pipeline一样实例化，但进一步提供额外的便利性。

简单调用一个项目：


```python
>>> pipe = pipeline("text-classification")
>>> pipe("This restaurant is awesome")
[{'label': 'POSITIVE', 'score': 0.9998743534088135}]
```

如果您想使用 [hub](https://huggingface.co) 上的特定模型，可以忽略任务，如果hub上的模型已经定义了该任务：

```python
>>> pipe = pipeline(model="FacebookAI/roberta-large-mnli")
>>> pipe("This restaurant is awesome")
[{'label': 'NEUTRAL', 'score': 0.7313136458396912}]
```

要在多个项目上调用pipeline，可以使用*列表*调用它。

```python
>>> pipe = pipeline("text-classification")
>>> pipe(["This restaurant is awesome", "This restaurant is awful"])
[{'label': 'POSITIVE', 'score': 0.9998743534088135},
 {'label': 'NEGATIVE', 'score': 0.9996669292449951}]
```

为了遍历整个数据集，建议直接使用 `dataset`。这意味着您不需要一次性分配整个数据集，也不需要自己进行批处理。这应该与GPU上的自定义循环一样快。如果不是，请随时提出issue。

```python
import datasets
from transformers import pipeline
from transformers.pipelines.pt_utils import KeyDataset
from tqdm.auto import tqdm

pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0)
dataset = datasets.load_dataset("superb", name="asr", split="test")

# KeyDataset (only *pt*) will simply return the item in the dict returned by the dataset item
# as we're not interested in the *target* part of the dataset. For sentence pair use KeyPairDataset
for out in tqdm(pipe(KeyDataset(dataset, "file"))):
    print(out)
    # {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"}
    # {"text": ....}
    # ....
```

为了方便使用，也可以使用生成器：


```python
from transformers import pipeline

pipe = pipeline("text-classification")


def data():
    while True:
        # This could come from a dataset, a database, a queue or HTTP request
        # in a server
        # Caveat: because this is iterative, you cannot use `num_workers > 1` variable
        # to use multiple threads to preprocess data. You can still have 1 thread that
        # does the preprocessing while the main runs the big inference
        yield "This is a test"


for out in pipe(data()):
    print(out)
    # {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"}
    # {"text": ....}
    # ....
```

[[autodoc]] pipeline

## Pipeline batching

所有pipeline都可以使用批处理。这将在pipeline使用其流处理功能时起作用（即传递列表或 `Dataset` 或 `generator` 时）。

```python
from transformers import pipeline
from transformers.pipelines.pt_utils import KeyDataset
import datasets

dataset = datasets.load_dataset("imdb", name="plain_text", split="unsupervised")
pipe = pipeline("text-classification", device=0)
for out in pipe(KeyDataset(dataset, "text"), batch_size=8, truncation="only_first"):
    print(out)
    # [{'label': 'POSITIVE', 'score': 0.9998743534088135}]
    # Exactly the same output as before, but the content are passed
    # as batches to the model
```

<Tip warning={true}>

然而，这并不自动意味着性能提升。它可能是一个10倍的加速或5倍的减速，具体取决于硬件、数据和实际使用的模型。

主要是加速的示例：

</Tip>

```python
from transformers import pipeline
from torch.utils.data import Dataset
from tqdm.auto import tqdm

pipe = pipeline("text-classification", device=0)


class MyDataset(Dataset):
    def __len__(self):
        return 5000

    def __getitem__(self, i):
        return "This is a test"


dataset = MyDataset()

for batch_size in [1, 8, 64, 256]:
    print("-" * 30)
    print(f"Streaming batch_size={batch_size}")
    for out in tqdm(pipe(dataset, batch_size=batch_size), total=len(dataset)):
        pass
```

```
# On GTX 970
------------------------------
Streaming no batching
100%|██████████████████████████████████████████████████████████████████████| 5000/5000 [00:26<00:00, 187.52it/s]
------------------------------
Streaming batch_size=8
100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:04<00:00, 1205.95it/s]
------------------------------
Streaming batch_size=64
100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:02<00:00, 2478.24it/s]
------------------------------
Streaming batch_size=256
100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:01<00:00, 2554.43it/s]
(diminishing returns, saturated the GPU)
```

主要是减速的示例：

```python
class MyDataset(Dataset):
    def __len__(self):
        return 5000

    def __getitem__(self, i):
        if i % 64 == 0:
            n = 100
        else:
            n = 1
        return "This is a test" * n
```

与其他句子相比，这是一个非常长的句子。在这种情况下，**整个**批次将需要400个tokens的长度，因此整个批次将是 [64, 400] 而不是 [64, 4]，从而导致较大的减速。更糟糕的是，在更大的批次上，程序会崩溃。

```
------------------------------
Streaming no batching
100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [00:05<00:00, 183.69it/s]
------------------------------
Streaming batch_size=8
100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [00:03<00:00, 265.74it/s]
------------------------------
Streaming batch_size=64
100%|██████████████████████████████████████████████████████████████████████| 1000/1000 [00:26<00:00, 37.80it/s]
------------------------------
Streaming batch_size=256
  0%|                                                                                 | 0/1000 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "/home/nicolas/src/transformers/test.py", line 42, in <module>
    for out in tqdm(pipe(dataset, batch_size=256), total=len(dataset)):
....
    q = q / math.sqrt(dim_per_head)  # (bs, n_heads, q_length, dim_per_head)
RuntimeError: CUDA out of memory. Tried to allocate 376.00 MiB (GPU 0; 3.95 GiB total capacity; 1.72 GiB already allocated; 354.88 MiB free; 2.46 GiB reserved in total by PyTorch)
```

对于这个问题，没有好的（通用）解决方案，效果可能因您的用例而异。经验法则如下：

对于用户，一个经验法则是：

- **使用硬件测量负载性能。测量、测量、再测量。真实的数字是唯一的方法。**
- 如果受到延迟的限制（进行推理的实时产品），不要进行批处理。
- 如果使用CPU，不要进行批处理。
- 如果您在GPU上处理的是吞吐量（您希望在大量静态数据上运行模型），则：
  - 如果对序列长度的大小没有概念（"自然"数据），默认情况下不要进行批处理，进行测试并尝试逐渐添加，添加OOM检查以在失败时恢复（如果您不能控制序列长度，它将在某些时候失败）。
  - 如果您的序列长度非常规律，那么批处理更有可能非常有趣，进行测试并推动它，直到出现OOM。
  - GPU越大，批处理越有可能变得更有趣
- 一旦启用批处理，确保能够很好地处理OOM。

## Pipeline chunk batching

`zero-shot-classification` 和 `question-answering` 在某种意义上稍微特殊，因为单个输入可能会导致模型的多次前向传递。在正常情况下，这将导致 `batch_size` 参数的问题。

为了规避这个问题，这两个pipeline都有点特殊，它们是 `ChunkPipeline` 而不是常规的 `Pipeline`。简而言之：


```python
preprocessed = pipe.preprocess(inputs)
model_outputs = pipe.forward(preprocessed)
outputs = pipe.postprocess(model_outputs)
```

现在变成：


```python
all_model_outputs = []
for preprocessed in pipe.preprocess(inputs):
    model_outputs = pipe.forward(preprocessed)
    all_model_outputs.append(model_outputs)
outputs = pipe.postprocess(all_model_outputs)
```

这对您的代码应该是非常直观的，因为pipeline的使用方式是相同的。

这是一个简化的视图，因为Pipeline可以自动处理批次！这意味着您不必担心您的输入实际上会触发多少次前向传递，您可以独立于输入优化 `batch_size`。前面部分的注意事项仍然适用。

## Pipeline自定义

如果您想要重载特定的pipeline。

请随时为您手头的任务创建一个issue，Pipeline的目标是易于使用并支持大多数情况，因此 `transformers` 可能支持您的用例。

如果您想简单地尝试一下，可以：

- 继承您选择的pipeline

```python
class MyPipeline(TextClassificationPipeline):
    def postprocess():
        # Your code goes here
        scores = scores * 100
        # And here


my_pipeline = MyPipeline(model=model, tokenizer=tokenizer, ...)
# or if you use *pipeline* function, then:
my_pipeline = pipeline(model="xxxx", pipeline_class=MyPipeline)
```

这样就可以让您编写所有想要的自定义代码。


## 实现一个pipeline

[实现一个新的pipeline](../add_new_pipeline)

## 音频

可用于音频任务的pipeline包括以下几种。

### AudioClassificationPipeline

[[autodoc]] AudioClassificationPipeline
    - __call__
    - all

### AutomaticSpeechRecognitionPipeline

[[autodoc]] AutomaticSpeechRecognitionPipeline
    - __call__
    - all

### TextToAudioPipeline

[[autodoc]] TextToAudioPipeline
    - __call__
    - all


### ZeroShotAudioClassificationPipeline

[[autodoc]] ZeroShotAudioClassificationPipeline
    - __call__
    - all

## 计算机视觉

可用于计算机视觉任务的pipeline包括以下几种。

### DepthEstimationPipeline
[[autodoc]] DepthEstimationPipeline
    - __call__
    - all

### ImageClassificationPipeline

[[autodoc]] ImageClassificationPipeline
    - __call__
    - all

### ImageSegmentationPipeline

[[autodoc]] ImageSegmentationPipeline
    - __call__
    - all

### ImageToImagePipeline

[[autodoc]] ImageToImagePipeline
    - __call__
    - all

### ObjectDetectionPipeline

[[autodoc]] ObjectDetectionPipeline
    - __call__
    - all

### VideoClassificationPipeline

[[autodoc]] VideoClassificationPipeline
    - __call__
    - all

### ZeroShotImageClassificationPipeline

[[autodoc]] ZeroShotImageClassificationPipeline
    - __call__
    - all

### ZeroShotObjectDetectionPipeline

[[autodoc]] ZeroShotObjectDetectionPipeline
    - __call__
    - all

## 自然语言处理

可用于自然语言处理任务的pipeline包括以下几种。

### ConversationalPipeline

[[autodoc]] Conversation

[[autodoc]] ConversationalPipeline
    - __call__
    - all

### FillMaskPipeline

[[autodoc]] FillMaskPipeline
    - __call__
    - all

### NerPipeline

[[autodoc]] NerPipeline

See [`TokenClassificationPipeline`] for all details.

### QuestionAnsweringPipeline

[[autodoc]] QuestionAnsweringPipeline
    - __call__
    - all

### SummarizationPipeline

[[autodoc]] SummarizationPipeline
    - __call__
    - all

### TableQuestionAnsweringPipeline

[[autodoc]] TableQuestionAnsweringPipeline
    - __call__

### TextClassificationPipeline

[[autodoc]] TextClassificationPipeline
    - __call__
    - all

### TextGenerationPipeline

[[autodoc]] TextGenerationPipeline
    - __call__
    - all

### Text2TextGenerationPipeline

[[autodoc]] Text2TextGenerationPipeline
    - __call__
    - all

### TokenClassificationPipeline

[[autodoc]] TokenClassificationPipeline
    - __call__
    - all

### TranslationPipeline

[[autodoc]] TranslationPipeline
    - __call__
    - all

### ZeroShotClassificationPipeline

[[autodoc]] ZeroShotClassificationPipeline
    - __call__
    - all

## 多模态

可用于多模态任务的pipeline包括以下几种。

### DocumentQuestionAnsweringPipeline

[[autodoc]] DocumentQuestionAnsweringPipeline
    - __call__
    - all

### FeatureExtractionPipeline

[[autodoc]] FeatureExtractionPipeline
    - __call__
    - all

### ImageFeatureExtractionPipeline

[[autodoc]] ImageFeatureExtractionPipeline
    - __call__
    - all

### ImageToTextPipeline

[[autodoc]] ImageToTextPipeline
    - __call__
    - all

### MaskGenerationPipeline

[[autodoc]] MaskGenerationPipeline
    - __call__
    - all

### VisualQuestionAnsweringPipeline

[[autodoc]] VisualQuestionAnsweringPipeline
    - __call__
    - all

## Parent class: `Pipeline`

[[autodoc]] Pipeline
