如何解决TensorFlow Serving报错“Failed to get input map for signature: serving_default”

该错误通常出现在向TensorFlow Serving API提供的输入数据格式与模型签名定义的预期输入格式不匹配时，可按以下步骤解决：

• 检查模型签名：确保输入数据的格式（如数据类型、形状）与模型签名要求的输入格式一致。
• 数据预处理：若模型对输入数据有特定预处理要求（如缩放、归一化），需在发送至模型前完成相应预处理。
• JSON请求格式：确保发送给TensorFlow Serving的JSON请求格式正确，instances字段应包含符合模型输入签名预期格式的输入列表。
• 检查模型版本：若使用特定版本的模型，需在API请求的url变量中指定正确版本。

以下是使用TensorFlow Serving发送图像预测请求的格式正确的代码示例：

import requests
import json
import numpy as np
import base64
import cv2

# Replace this with the actual image path you want to test
image_path = 'H_L_.jpg'

# Read and preprocess the image
image = cv2.imread(image_path)
image = cv2.resize(image, (256, 256))
image = image.astype(np.float32) / 255.0
image = np.expand_dims(image, axis=0)

# Convert the NumPy array to bytes before encoding
encoded_image = base64.b64encode(image.tobytes()).decode('utf-8')

# Prepare the JSON request with the signature name
data = {
    "signature_name": "serving_default",
    "instances": [{"input_1": encoded_image}]  # Adjust the input key based on your model's signature
}
# Replace these labels with your actual labels
labels = ['Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy']

# Send the inference request to TensorFlow Serving
url = 'http://localhost:8501/v1/models/model:predict'  # Replace 'model' with the actual model name and version
headers = {"content-type": "application/json"}
response = requests.post(url, data=json.dumps(data), headers=headers)

# Process the response
if response.status_code == 200:
    predictions = response.json()['predictions'][0]
    predicted_class_idx = np.argmax(predictions)
    predicted_label = labels[predicted_class_idx]
    print("Predicted Label:", predicted_label)
    print("Class Probabilities:", predictions)
else:
    print("Error: Unable to get predictions. Status code:", response.status_code)
    print("Response content:", response.content)

内容的提问来源于stack exchange，提问作者Mayur Katre