如何在 raspberry-pi 上运行 tf.lite 建模而不是保存的 keras 模型

Question

我正在尝试使用 raspery-pi 对流量进行分类，为此我训练并保存了一个 .h5 文件的 keras 模型，但它消耗太多 cpu 所以我将其转换为 .tflite模型并尝试运行。但是它给出了错误 OSError: SavedModel file does not exist at: yourmodel.tflite/{saved_model.pbtxt|saved_model.pb} 我检查了路径，这是我的代码。另外我刚刚更改了那行： model = tensorflow.keras.models.load_model("my_model.h5") 到 model = tensorflow.keras.models.load_model("yourmodel.tflite")

import numpy as np
import cv2
import tensorflow
from tensorflow import keras
from tensorflow.keras.preprocessing import image
 
#############################################
frameWidth= 600         # CAMERA RESOLUTION
frameHeight = 480
brightness = 180
threshold = 0.75         # PROBABLITY THRESHOLD
font = cv2.FONT_HERSHEY_SIMPLEX
##############################################
 
# SETUP THE VIDEO CAMERA
cap = cv2.VideoCapture(0)
cap.set(3, frameWidth)
cap.set(4, frameHeight)
cap.set(10, brightness)
cap.set(cv2.CAP_PROP_FPS, 3)

# IMPORT THE TRANNIED MODEL
model = tensorflow.keras.models.load_model("yourmodel.tflite")
#model = load_model('best_model.h5')

def equalize(img):
    img = cv2.equalizeHist(img)
    return img
def grayscale(img):
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    return img
def preprocessing(img):
    img = grayscale(img)
    img = equalize(img)
    img = img/255
    return img
 
def getCalssName(classNo):
    if   classNo == 0: return 'Speed Limit 20 km/h'
    
    elif classNo == 9: return 'No passing'

    elif classNo == 12: return 'Priority road'
    elif classNo == 13: return 'Yield'
    elif classNo == 14: return 'Stop'

    elif classNo == 38: return 'Keep right'
    elif classNo == 39: return 'Keep left'
    
while True:
    success, imgOrignal = cap.read()
    img = np.asarray(imgOrignal)
    #img = cv2.resize(img, (32, 32))
    img = preprocessing(img)
    cv2.imshow("Processed Image", img)
    img = img.reshape(1, 32, 32, 1)
    cv2.putText(imgOrignal, "CLASS: " , (20, 35), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
    cv2.putText(imgOrignal, "PROBABILITY: ", (20, 75), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
    
    # PREDICT IMAGE
    predictions = model.predict(img)
    classIndex = model.predict_classes(img)
    probabilityValue =np.amax(predictions)
    if probabilityValue > threshold:
        print(getCalssName(classIndex))
        #cv2.rectangle(image, coordinate[0],coordinate[1], (0, 255, 0), 1)
        cv2.putText(imgOrignal,str(classIndex)+" "+str(getCalssName(classIndex)), (120, 35), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
        cv2.putText(imgOrignal, str(round(probabilityValue*100,2) )+"%", (180, 75), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
        cv2.imshow("Result", imgOrignal)
        
        if cv2.waitKey(1) and 0xFF == ord('q'):
            break

cap.release()

cv2.destroyAllWindows()

Answer 1

尝试使用此代码保存您的 keras 模型

# model is your keras model
tflite_model = tf.lite.TFLiteConverter.from_keras_model(model).convert()

with open('model.tflite', 'wb') as f:
    f.write(tflite_model)

要加载和使用它，您需要 tf.lite.Interpreter

# instead of `model = tensorflow.keras.models.load_model("yourmodel.tflite")`
# use this code to load tflite model
interpreter = tf.lite.Interpreter(model_path="model.tflite")
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# replace `predictions = model.predict(img)` with this code
interpreter.set_tensor(input_details[0]['index'], img)
interpreter.invoke()
predictions = interpreter.get_tensor(output_details[0]['index'])

如何在 raspberry-pi 上运行 tf.lite 建模而不是保存的 keras 模型

How to run tf.lite model on raspery-pi instead of saved keras model

python

raspberry-pi

keras

tensorflow

如何在 raspberry-pi 上 运行 tf.lite 建模而不是保存的 keras 模型

How to run tf.lite model on raspery-pi instead of saved keras model

python

raspberry-pi

keras

tensorflow

如何在 raspberry-pi 上运行 tf.lite 建模而不是保存的 keras 模型