keras和tensorflow模型同时读取要慎重

快来打我* 2021-11-17 09:22 333阅读 0赞

项目中，先读取了一个keras模型获取模型输入size，再加载keras转tensorflow后的pb模型进行预测。

报错：

Attempting to use uninitialized value batch\_normalization\_14/moving\_mean

逛论坛，有建议加上初始化：

sess.run(tf.global_variables_initializer())

但是这样的话，会导致模型参数全部变成初始化数据。无法使用预测模型参数。

最后发现，将keras模型的加载去掉即可。

猜测原因：keras模型和tensorflow模型同时读取有坑。

import cv2
    import numpy as np
    from keras.models import load_model
    from utils.datasets import get_labels
    from utils.preprocessor import preprocess_input
    import time
    import os
    import tensorflow as tf
    from tensorflow.python.platform import gfile
    
    os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
    
    emotion_labels = get_labels('fer2013')
    emotion_target_size = (64,64)
    #emotion_model_path = './models/emotion_model.hdf5'
    #emotion_classifier = load_model(emotion_model_path)
    #emotion_target_size = emotion_classifier.input_shape[1:3]
    
    path = '/mnt/nas/cv_data/emotion/test'
    filelist = os.listdir(path)
    total_num = len(filelist)
    timeall = 0
    n = 0
    
    sess = tf.Session()
    #sess.run(tf.global_variables_initializer())
    with gfile.FastGFile("./trans_model/emotion_mode.pb", 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        sess.graph.as_default()
        tf.import_graph_def(graph_def, name='')
    
        pred = sess.graph.get_tensor_by_name("predictions/Softmax:0")
    
        ######################img##########################
        for item in filelist:
            if (item == '.DS_Store') | (item == 'Thumbs.db'):
                continue
            src = os.path.join(os.path.abspath(path), item)
            bgr_image = cv2.imread(src)
            gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
            gray_face = gray_image
            try:
                gray_face = cv2.resize(gray_face, (emotion_target_size))
            except:
                continue
    
            gray_face = preprocess_input(gray_face, True)
            gray_face = np.expand_dims(gray_face, 0)
            gray_face = np.expand_dims(gray_face, -1)
    
            input = sess.graph.get_tensor_by_name('input_1:0')
            res = sess.run(pred, {input: gray_face})
            print("src:", src)
    
            emotion_probability = np.max(res[0])
            emotion_label_arg = np.argmax(res[0])
            emotion_text = emotion_labels[emotion_label_arg]
            print("predict:", res[0], ",prob:", emotion_probability, ",label:", emotion_label_arg, ",text:",emotion_text)