PaddleX/docs/module_usage/instructions/model_python_API.en.md

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PaddleX Single Model Python Usage Instructions

Before using Python scripts for single model quick inference, please ensure you have completed the installation of PaddleX following the PaddleX Local Installation Tutorial.

I. Usage Example

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

from paddlex import create_model
model = create_model(model_name="PP-LCNet_x1_0")
output = model.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, just three steps:

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

II. API Description

1. Instantiate the Prediction Model Object by Calling the create_model() Method

• create_model: Instantiate the prediction model object;
  • Parameters:
  • model_name: str type, model name, such as "PP-LCNet_x1_0", "/path/to/PP-LCNet_x1_0_infer/";
  • model_dir: str type, local path to directory of inference model files ，such as "/path/to/PP-LCNet_x1_0_infer/", default to None, means that use the official model specified by model_name;
  • batch_size: int type, default to 1;
  • device: str type, used to set the inference device, such as "cpu", "gpu:2" for GPU settings. By default, using 0 id GPU if available, otherwise CPU;
  • pp_option: PaddlePredictorOption type, used to change inference settings (e.g. the operating mode). Please refer to 4-Inference Configuration for more details;
  • use_hpip：bool type, whether to enable the high-performance inference plugin;
  • hpi_config：dict | None type, high-performance inference configuration;
  • inference hyperparameters: used to set common inference hyperparameters. Please refer to specific model description document for details.

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

• predict: Use the defined prediction model to predict the input data;
  • Parameters:
  • input: Any type, supports str type representing the path of the file to be predicted, or a directory containing files to be predicted, or a network URL; for CV models, supports numpy.ndarray representing image data; for TS models, supports pandas.DataFrame type data; also supports list types composed of the above types;
  • Return Value: generator, using for-in or next() to iterate, and the prediction result of one sample would be returned per call.

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. Available only when the results support visual representation;
  • Returns: A PIL.Image type.
• html: The HTML representation of the prediction result. Available only when the results support representation in HTML format;
  • Returns: A str type.
• more attrs: The prediction result of different models support different representation methods. Please refer to the specific model tutorial documentation for details.

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. Available only when the results support representation in the form of images;
  • Parameters:
  • save_path: str type, the path to save the result.
  • Returns: None.
• save_to_csv(): Saves the prediction result as a CSV file. Available only when the results support representation in CSV format;
  • Parameters:
  • save_path: str type, the path to save the result.
  • Returns: None.
• save_to_html(): Saves the prediction result as an HTML file. Available only when the results support representation in HTML format;
  • Parameters:
  • save_path: str type, the path to save the result.
  • Returns: None.
• save_to_xlsx(): Saves the prediction result as an XLSX file. Available only when the results support representation in XLSX format;
  • Parameters:
  • save_path: str type, the path to save the result.
  • Returns: None.

4. Inference Configuration

PaddleX supports modifying the inference configuration 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', 'dcu'. When using an accelerator card, you can specify the card number, e.g., 'gpu:0' for GPU 0. By default, if a GPU is available, GPU 0 will be used; otherwise, the CPU will be used.
  • Return value: str type, the currently set inference device.
• run_mode: Operating mode.
  • Supports setting the operating mode as a str type, options include 'paddle', 'trt_fp32', 'trt_fp16', 'trt_int8', 'mkldnn', 'mkldnn_bf16'. Note that 'trt_fp32' and 'trt_fp16' correspond to using the TensorRT subgraph engine for inference with FP32 and FP16 precision respectively; these options are only available when the inference device is a GPU. Additionally, 'mkldnn' is only available when the inference device is a CPU. The default value is 'paddle'.
  • Return value: str type, the currently set operating mode.
• 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.
• trt_dynamic_shapes: TensorRT dynamic shape configuration, only effective when run_mode is set to 'trt_fp32' or 'trt_fp16'.
  • Supports setting a value of type dict or None. If it is a dict, the keys are the input tensor names and the values are two-level nested lists formatted as [{minimum shape}, {optimal shape}, {maximum shape}], for example [[1, 2], [1, 2], [2, 2]].
  • Return value: dict type or None, the current TensorRT dynamic shape configuration.
• trt_dynamic_shape_input_data: For TensorRT usage, this parameter provides the fill data for the input tensors used to build the engine, and it is only valid when run_mode is set to 'trt_fp32' or 'trt_fp16'.
  • Supports setting a value of type dict or None. If it is a dict, the keys are the input tensor names and the values are two-level nested lists formatted as [{fill data corresponding to the minimum shape}, {fill data corresponding to the optimal shape}, {fill data corresponding to the maximum shape}], for example [[1.0, 1.0], [1.0, 1.0], [1.0, 1.0, 1.0, 1.0]]. The data are floating point numbers filled in row-major order.
  • Return value: dict type or None, the currently set input tensor fill data.

Methods:

• get_support_run_mode: Get supported operating modes;
  • Parameters: None;
  • Return value: List type, the available operating modes.
• 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.