AttributeError:模块“ tensorflow”在tensorflow中没有属性“ get_default_graph”

时间:2020-08-17 17:52:50

标签: python tensorflow deep-learning

我的代码中出现以下错误。

import mtcnn
# print version
print(mtcnn.__version__)

# demonstrate face detection on 5 Celebrity Faces Dataset
from os import listdir
from PIL import Image
from numpy import asarray
from matplotlib import pyplot
from mtcnn.mtcnn import MTCNN
print("MTCNN: {}".format(mtcnn.__version__))
import tensorflow as tf
from tensorflow import keras

# extract a single face from a given photograph
def extract_face(filename, required_size=(160, 160)):
    # load image from file
    image = Image.open(filename)
    # convert to RGB, if needed
    image = image.convert('RGB')
    # convert to array
    pixels = asarray(image)
    # create the detector, using default weights
    detector = MTCNN()
    # detect faces in the image
    results = detector.detect_faces(pixels)
    # extract the bounding box from the first face
    x1, y1, width, height = results[0]['box']
    # bug fix
    x1, y1 = abs(x1), abs(y1)
    x2, y2 = x1 + width, y1 + height
    # extract the face
    face = pixels[y1:y2, x1:x2]
    # resize pixels to the model size
    image = Image.fromarray(face)
    image = image.resize(required_size)
    face_array = asarray(image)
    return face_array

# specify folder to plot
#folder = '5-celebrity-faces-dataset/train/ben_afflek/'
folder = '5-celebrity-faces-dataset/train/ben_afflek'
i = 1
# enumerate files
for filename in listdir(folder):
    # path
    path = folder + '/' + filename
    # get face
    face = extract_face(path)
    print(i, face.shape)
    # plot
    pyplot.subplot(2, 7, i)
    pyplot.axis('off')
    pyplot.imshow(face)
    i += 1
pyplot.show()

错误:

anaconda3 \ envs \ py3 \ lib \ site-packages \ keras \ backend \ tensorflow_backend.py”, 第68行,在get_uid中 图= tf.get_default_graph()

AttributeError:模块“ tensorflow”没有属性 'get_default_graph'

我尝试了几种不同的进口方式,但是没有任何效果。看来这个错误很常见,但是我找不到能解决我问题的任何东西。

1 个答案:

答案 0 :(得分:1)

在答案here中,像您一样从tensorflow导入keras即可解决问题。

但是您遇到的问题是MTCNN在纯Keras而不是TensorFlow上运行,因此从tensorflow加载“ main.py” keras中这一事实没有任何效果。您要么需要降级tensorflow版本,要么修改MTCNN中的每个导入,但不能保证无法正常工作。