模块“tensorflow”没有属性“GraphDef”

Question

所以我已经在 Tensorflow 工作了一段时间并更改了一些配置等。

我一直在安装和卸载 tensorflow vesions，我一直在这些错误中循环：如果我安装 Tensorflow 2.0，我会收到这个错误

模块“tensorflow”没有属性“GraphDef”

然后，如果我降级到 Tensorflow 1.14，我会收到此错误：

python -c "import tensorflow as tf; print(tf.__version__)"

回溯（最近一次调用）：文件“C:\Users\PPE DETECTION\AppData\Roaming\Python\Python35\site-packages\tensorflow\python\pywrap_tensorflow.py”，第 58 行，来自 tensorflow.python.pywrap_tensorflow_internal import * 文件“C:\Users\PPE DETECTION\AppData\Roaming\Python\Python35\site-packages\tensorflow\python\pywrap_tensorflow_internal.py”，第 28 行，在 _pywrap_tensorflow_internal = swig_import_helper() 文件“C:\Users\PPE DETECTION \AppData\Roaming\Python\Python35\site-packages\tensorflow\python\pywrap_tensorflow_internal.py”，第 24 行，在 swig_import_helper _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description) 文件“C:\Users\ PPE DETECTION\Anaconda3\envs\ppe\lib\imp.py”，第 243 行，在 load_module 中 return load_dynamic(name,filename, file) File "C:\Users\PPE DETECTION\Anaconda3\envs\ppe\lib\imp.py", line 343, in load_dynamic return _load(spec) ImportError: DLL load failed: The specified module could not be found .

在处理上述异常的过程中，又发生了一个异常：

回溯（最近一次调用）：文件“”，第 1 行，在文件“C:\Users\PPE DETECTION\AppData\Roaming\Python\Python35\site-packages\tensorflow__init__.py”中，第 28 行，来自 tensorflow。 python import pywrap_tensorflow # pylint: disable=unused-import File "C:\Users\PPE DETECTION\AppData\Roaming\Python\Python35\site-packages\tensorflow\python__init__.py", line 49, in from tensorflow.python import pywrap_tensorflow文件“C:\Users\PPE DETECTION\AppData\Roaming\Python\Python35\site-packages\tensorflow\python\pywrap_tensorflow.py”，第 74 行，在 raise ImportError(msg) ImportError: Traceback（最后一次调用）中：文件“C:\Users\PPE DETECTION\AppData\Roaming\Python\Python35\site-packages\tensorflow\python\pywrap_tensorflow.py”，第 58 行，从 tensorflow.python.pywrap_tensorflow_internal 导入 * 文件“C:\Users\PPE DETECTION\AppData\Roaming\Python\Python35\site-packages\tensorflow\python\pywrap_tensorflow_internal.py”，第 28 行，在 _pywrap_tensorflow_internal = swig_import_helper “C:\Users\PPE DETECTION\AppData\Roaming\Python\Python35\site-packages\tensorflow\python\pywrap_tensorflow_internal.py”，第 24 行，在 swig_import_helper _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description ) 文件 "C:\Users\PPE DETECTION\Anaconda3\envs\ppe\lib\imp.py", line 243, in load_module return load_dynamic(name, filename, file) File "C:\Users\PPE DETECTION\Anaconda3\ envs\ppe\lib\imp.py", line 343, in load_dynamic return _load(spec) ImportError: DLL load failed:指定的模块无法找到。

无法加载本机 TensorFlow 运行时。

请参阅https://www.tensorflow.org/install/errors

出于一些常见的原因和解决方案。寻求帮助时，请在此错误消息上方包含整个堆栈跟踪。

正如过去一直在测试解决方案一样，我被困在一个漏洞中，但如果有人能总结我这部分文档，我会很高兴，因为我相信它可以解决我的问题https://www.tensorflow.org/guide /versions#compatibility_of_savedmodels_graphs_and_checkpoints

这是我一直在使用的代码：它是https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10 Object_detection_webcam.py

> ######## Webcam Object Detection Using Tensorflow-trained Classifier #########
> #
> # Author: Evan Juras
> # Date: 1/20/18
> # Description:
> # This program uses a TensorFlow-trained classifier to perform object detection.
> # It loads the classifier uses it to perform object detection on a webcam feed.
> # It draws boxes and scores around the objects of interest in each frame from
> # the webcam.
> 
> ## Some of the code is copied from Google's example at
> ## https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
> 
> ## and some is copied from Dat Tran's example at
> ## https://github.com/datitran/object_detector_app/blob/master/object_detection_app.py
> 
> ## but I changed it to make it more understandable to me.
> 
> 
> # Import packages import os import cv2 import numpy as np import tensorflow as tf import sys
> 
> # This is needed since the notebook is stored in the object_detection folder. sys.path.append("..")
> 
> # Import utilites from utils import label_map_util from utils import visualization_utils as vis_util
> 
> # Name of the directory containing the object detection module we're using MODEL_NAME = 'inference_graph'
> 
> # Grab path to current working directory CWD_PATH = os.getcwd()
> 
> # Path to frozen detection graph .pb file, which contains the model that is used
> # for object detection. PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb')
> 
> # Path to label map file PATH_TO_LABELS = os.path.join(CWD_PATH,'training','labelmap.pbtxt')
> 
> # Number of classes the object detector can identify NUM_CLASSES = 6
> 
> ## Load the label map.
> # Label maps map indices to category names, so that when our convolution
> # network predicts `5`, we know that this corresponds to `king`.
> # Here we use internal utility functions, but anything that returns a
> # dictionary mapping integers to appropriate string labels would be fine label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
> categories = label_map_util.convert_label_map_to_categories(label_map,
> max_num_classes=NUM_CLASSES, use_display_name=True) category_index =
> label_map_util.create_category_index(categories)
> 
> # Load the Tensorflow model into memory. detection_graph = tf.Graph() with detection_graph.as_default():
>     od_graph_def = tf.GraphDef()
>     with tf.io.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
>         serialized_graph = fid.read()
>         od_graph_def.ParseFromString(serialized_graph)
>         tf.import_graph_def(od_graph_def, name='')
> 
>     sess = tf.Session(graph=detection_graph)
> 
> 
> # Define input and output tensors (i.e. data) for the object detection classifier
> 
> # Input tensor is the image image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
> 
> # Output tensors are the detection boxes, scores, and classes
> # Each box represents a part of the image where a particular object was detected detection_boxes =
> detection_graph.get_tensor_by_name('detection_boxes:0')
> 
> # Each score represents level of confidence for each of the objects.
> # The score is shown on the result image, together with the class label. detection_scores =
> detection_graph.get_tensor_by_name('detection_scores:0')
> detection_classes =
> detection_graph.get_tensor_by_name('detection_classes:0')
> 
> # Number of objects detected num_detections = detection_graph.get_tensor_by_name('num_detections:0')
> 
> # Initialize webcam feed video = cv2.VideoCapture(0) video.open("rtsp://admin:Password1@192.168.100.60:554/Streaming/channels/2/")
> video.set(cv2.cv2.CAP_PROP_FPS, 5) ret = video.set(3,640) ret =
> video.set(4,480)
> 
> while(True):
> 
>     # Acquire frame and expand frame dimensions to have shape: [1, None, None, 3]
>     # i.e. a single-column array, where each item in the column has the pixel RGB value
>     ret, frame = video.read()
>     frame_expanded = np.expand_dims(frame, axis=0)
> 
>     # Perform the actual detection by running the model with the image as input
>     (boxes, scores, classes, num) = sess.run(
>         [detection_boxes, detection_scores, detection_classes, num_detections],
>         feed_dict={image_tensor: frame_expanded})
> 
>     # Draw the results of the detection (aka 'visulaize the results')
>     vis_util.visualize_boxes_and_labels_on_image_array(
>         frame,
>         np.squeeze(boxes),
>         np.squeeze(classes).astype(np.int32),
>         np.squeeze(scores),
>         category_index,
>         use_normalized_coordinates=True,
>         line_thickness=8,
>         min_score_thresh=0.60)
> 
>     # All the results have been drawn on the frame, so it's time to display it.
>     cv2.imshow('Object detector', frame)
> 
>     # Press 'q' to quit
>     if cv2.waitKey(1) == ord('q'):
>         break
> 
> # Clean up video.release() cv2.destroyAllWindows()

TensorFlow 2.0

版本检查器：

python -c "import tensorflow as tf; print(tf.__version__)"

输出：

2.0.0

但在提供的测试仪中：

AttributeError Traceback（最近一次调用最后一次）在 1 detection_graph = tf.Graph() 2 with detection_graph.as_default(): ----> 3 od_graph_def = tf.GraphDef() 4 with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb ') as fid: 5 serialized_graph = fid.read()

AttributeError: 模块“tensorflow”没有属性“GraphDef”

应该可以正常运行

在 tensorflow 1.14 中

版本检查器：

python -c "import tensorflow as tf; print(tf.__version__)"

输出：

回溯（最近一次调用）：文件“C:\Users\PPE DETECTION\AppData\Roaming\Python\Python35\site-packages\tensorflow\python\pywrap_tensorflow.py”，第 58 行，来自 tensorflow.python.pywrap_tensorflow_internal import * 文件“C:\Users\PPE DETECTION\AppData\Roaming\Python\Python35\site-packages\tensorflow\python\pywrap_tensorflow_internal.py”，第 28 行，在 _pywrap_tensorflow_internal = swig_import_helper() 文件“C:\Users\PPE DETECTION \AppData\Roaming\Python\Python35\site-packages\tensorflow\python\pywrap_tensorflow_internal.py”，第 24 行，在 swig_import_helper _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description) 文件“C:\Users\ PPE DETECTION\Anaconda3\envs\ppe\lib\imp.py”，第 243 行，在 load_module 中 return load_dynamic(name,filename, file) File "C:\Users\PPE DETECTION\Anaconda3\envs\ppe\lib\imp.py", line 343, in load_dynamic return _load(spec) ImportError: DLL load failed: The specified module could not be found .

在处理上述异常的过程中，又发生了一个异常：

回溯（最近一次调用）：文件“”，第 1 行，在文件“C:\Users\PPE DETECTION\AppData\Roaming\Python\Python35\site-packages\tensorflow__init__.py”中，第 28 行，来自 tensorflow。 python import pywrap_tensorflow # pylint: disable=unused-import File "C:\Users\PPE DETECTION\AppData\Roaming\Python\Python35\site-packages\tensorflow\python__init__.py", line 49, in from tensorflow.python import pywrap_tensorflow文件“C:\Users\PPE DETECTION\AppData\Roaming\Python\Python35\site-packages\tensorflow\python\pywrap_tensorflow.py”，第 74 行，在 raise ImportError(msg) ImportError: Traceback（最后一次调用）中：文件“C:\Users\PPE DETECTION\AppData\Roaming\Python\Python35\site-packages\tensorflow\python\pywrap_tensorflow.py”，第 58 行，从 tensorflow.python.pywrap_tensorflow_internal 导入 * 文件“C:\Users\PPE DETECTION\AppData\Roaming\Python\Python35\site-packages\tensorflow\python\pywrap_tensorflow_internal.py”，第 28 行，在 _pywrap_tensorflow_internal = swig_import_helper “C:\Users\PPE DETECTION\AppData\Roaming\Python\Python35\site-packages\tensorflow\python\pywrap_tensorflow_internal.py”，第 24 行，在 swig_import_helper _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description ) 文件 "C:\Users\PPE DETECTION\Anaconda3\envs\ppe\lib\imp.py", line 243, in load_module return load_dynamic(name, filename, file) File "C:\Users\PPE DETECTION\Anaconda3\ envs\ppe\lib\imp.py", line 343, in load_dynamic return _load(spec) ImportError: DLL load failed:指定的模块无法找到。

无法加载本机 TensorFlow 运行时。

请参阅https://www.tensorflow.org/install/errors

出于一些常见的原因和解决方案。寻求帮助时，请在此错误消息上方包含整个堆栈跟踪。

提前致谢

Answer 1

你需要这样做

  graph = tf.Graph()
  graph_def = tf.compat.v1.GraphDef()