PaddleX/docs/pipeline_usage/instructions/pipeline_python_API.en.md

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PaddleX Model Pipeline Python Usage Instructions

Before using Python scripts for rapid inference on model pipelines, please ensure you have installed PaddleX following the PaddleX Local Installation Guide.

I. Usage Example

Taking the image classification pipeline as an example, the usage is as follows:

from paddlex import create_pipeline
pipeline = create_pipeline("image_classification")
output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_image_classification_001.jpg", batch_size=1)
for res in output:
    res.print(json_format=False)
    res.save_to_img("./output/")
    res.save_to_json("./output/res.json")

In short, there are only three steps:

• Call the create_pipeline() method to instantiate the prediction model pipeline object;
• Call the predict() method of the prediction model pipeline object for inference;
• Call print(), save_to_xxx() and other related methods to visualize or save the prediction results.

II. API Description

1. Instantiate the Prediction Model Pipeline Object by Calling create_pipeline()

• create_pipeline: Instantiates the prediction model pipeline object;
  • Parameters:
  • pipeline_name: str type, the pipeline name or the local pipeline configuration file path, such as "image_classification", "/path/to/image_classification.yaml";
  • device: str type, used to set the model inference device, such as "cpu" or "gpu:2" for GPU settings;
  • pp_option: PaddlePredictorOption type, used to set the model inference backend;
  • Return Value: BasePredictor type.

2. Perform Inference by Calling the predict() Method of the Prediction Model Pipeline Object

• predict: Uses the defined prediction model pipeline to predict input data;
  • Parameters:
  • input: Any type, supporting str representing the path of the file to be predicted, or a directory containing files to be predicted, or a network URL; for CV tasks, supports numpy.ndarray representing image data; for TS tasks, supports pandas.DataFrame type data; also supports lists of the above types;
  • Return Value: generator, returns the prediction result of one sample per call;

3. Visualize the Prediction Results

The prediction results of the model pipeline support access, visualization, and saving, which can be achieved through corresponding attributes or methods, specifically as follows:

Attributes:

• str: str type representation of the prediction result;
  • Return Value: str type, string representation of the prediction result;
• json: Prediction result in JSON format;
  • Return Value: dict type;
• img: Visualization image of the prediction result;
  • Return Value: PIL.Image type;
• html: HTML representation of the prediction result;
  • Return Value: str type;

3. Visualize the Prediction Results

The prediction results support to be accessed, visualized, and saved, which can be achieved through corresponding attributes or methods, specifically as follows:

Attributes:

• str: Representation of the prediction result in str type;
  • Returns: A str type, the string representation of the prediction result.
• json: The prediction result in JSON format;
  • Returns: A dict type.
• img: The visualization image of the prediction result;
  • Returns: A PIL.Image type.
• html: The HTML representation of the prediction result;
  • Returns: A str type.

Methods:

• print(): Outputs the prediction result. Note that when the prediction result is not convenient for direct output, relevant content will be omitted;
  • Parameters:
  • json_format: bool type, default is False, indicating that json formatting is not used;
  • indent: int type, default is 4, valid when json_format is True, indicating the indentation level for json formatting;
  • ensure_ascii: bool type, default is False, valid when json_format is True;
  • Return Value: None;
• save_to_json(): Saves the prediction result as a JSON file. Note that when the prediction result contains data that cannot be serialized in JSON, automatic format conversion will be performed to achieve serialization and saving;
  • Parameters:
  • save_path: str type, the path to save the result;
  • indent: int type, default is 4, valid when json_format is True, indicating the indentation level for json formatting;
  • ensure_ascii: bool type, default is False, valid when json_format is True;
  • Return Value: None;
• save_to_img(): Visualizes the prediction result and saves it as an image;
  • Parameters:
  • save_path: str type, the path to save the result.
  • Returns: None.
• save_to_csv(): Saves the prediction result as a CSV file;
  • Parameters:
  • save_path: str type, the path to save the result.
  • Returns: None.
• save_to_html(): Saves the prediction result as an HTML file;
  • Parameters:
  • save_path: str type, the path to save the result.
  • Returns: None.
• save_to_xlsx(): Saves the prediction result as an XLSX file;
  • Parameters:
  • save_path: str type, the path to save the result.
  • Returns: None.

4. Inference Backend Configuration

PaddleX supports configuring the inference backend through PaddlePredictorOption. Relevant APIs are as follows:

Attributes:

• device: Inference device;
  • Supports setting the device type and card number represented by str. Device types include 'gpu', 'cpu', 'npu', 'xpu', 'mlu'. When using an accelerator card, you can specify the card number, e.g., 'gpu:0' for GPU 0. The default is 'gpu:0';
  • Return value: str type, the currently set inference device.
• run_mode: Inference backend;
  • Supports setting the inference backend as a str type, options include 'paddle', 'trt_fp32', 'trt_fp16', 'trt_int8', 'mkldnn', 'mkldnn_bf16'. 'mkldnn' is only selectable when the inference device is 'cpu'. The default is 'paddle';
  • Return value: str type, the currently set inference backend.
• cpu_threads: Number of CPU threads for the acceleration library, only valid when the inference device is 'cpu';
  • Supports setting an int type for the number of CPU threads for the acceleration library during CPU inference;
  • Return value: int type, the currently set number of threads for the acceleration library.

Methods:

• get_support_run_mode: Get supported inference backend configurations;
  • Parameters: None;
  • Return value: List type, the available inference backend configurations.
• get_support_device: Get supported device types for running;
  • Parameters: None;
  • Return value: List type, the available device types.
• get_device: Get the currently set device;
  • Parameters: None;
  • Return value: str type. ```