AI开发平台MODELARTS-TensorFlow 2.1:推理代码

时间：2024-09-05 08:29:59

AI开发平台MODELARTS

推理代码

在模型代码推理文件customize_service.py中，需要添加一个子类，该子类继承对应模型类型的父类，各模型类型的父类名称和导入语句如请参考表1。

import logging
import threading

import numpy as np
import tensorflow as tf
from PIL import Image

from model_service.tfserving_model_service import TfServingBaseService

logger = logging.getLogger()
logger.setLevel(logging.INFO)


class MnistService(TfServingBaseService):

    def __init__(self, model_name, model_path):
        self.model_name = model_name
        self.model_path = model_path
        self.model = None
        self.predict = None

        # label文件可以在这里加载,在后处理函数里使用
        # label.txt放在obs和模型包的目录

        # with open(os.path.join(self.model_path, 'label.txt')) as f:
        #     self.label = json.load(f)
        # 非阻塞方式加载saved_model模型，防止阻塞超时
        thread = threading.Thread(target=self.load_model)
        thread.start()

    def load_model(self):
        # load saved_model 格式的模型
        self.model = tf.saved_model.load(self.model_path)

        signature_defs = self.model.signatures.keys()

        signature = []
        # only one signature allowed
        for signature_def in signature_defs:
            signature.append(signature_def)

        if len(signature) == 1:
            model_signature = signature[0]
        else:
            logging.warning("signatures more than one, use serving_default signature from %s", signature)
            model_signature = tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY

        self.predict = self.model.signatures[model_signature]

    def _preprocess(self, data):
        images = []
        for k, v in data.items():
            for file_name, file_content in v.items():
                image1 = Image.open(file_content)
                image1 = np.array(image1, dtype=np.float32)
                image1.resize((28, 28, 1))
                images.append(image1)

        images = tf.convert_to_tensor(images, dtype=tf.dtypes.float32)
        preprocessed_data = images

        return preprocessed_data

    def _inference(self, data):

        return self.predict(data)

    def _postprocess(self, data):

        return {
            "result": int(data["output"].numpy()[0].argmax())
        }

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