keras和tensorflow模型同時讀取要慎重

項目中，先讀取了一個keras模型獲取模型輸入size，再加載keras轉tensorflow後的pb模型進行預測。

報錯：

Attempting to use uninitialized value batch_normalization_14/moving_mean

逛論壇，有建議加上初始化：

sess.run(tf.global_variables_initializer())

但是這樣的話，會導致模型參數全部變成初始化數據。無法使用預測模型參數。

最後發現，將keras模型的加載去掉即可。

猜測原因：keras模型和tensorflow模型同時讀取有坑。

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)