我一直在以下github存储库中关注该教程：https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10
在开始运行Jupyter笔记本中的最后一段代码之前，我已经能够顺利完成所有步骤：
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
# Definite input and output Tensors for detection_graph
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# 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 represent how level of confidence for each of the objects.
# 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')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
for image_path in TEST_IMAGE_PATHS:
image = Image.open(image_path)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
image_np = load_image_into_numpy_array(image)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
# Actual detection.
(boxes, scores, classes, num) = sess.run(
[detection_boxes, detection_scores, detection_classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8)
plt.figure(figsize=IMAGE_SIZE)
plt.imshow(image_np)
我收到此错误：
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-1-7493eea60222> in <module>
----> 1 with detection_graph.as_default():
2 with tf.Session(graph=detection_graph) as sess:
3 # Definite input and output Tensors for detection_graph
4 image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
5 # Each box represents a part of the image where a particular object was detected.
NameError: name 'detection_graph' is not defined
不是正确输出带有两个检测到的狗，并且在它们周围有边框的图像。
我尝试在开头添加以下代码行：
detection_graph = tf.Graph()
但是那没有用，我最终遇到了另一个错误：
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-12-88ea994f3b00> in <module>
3 with tf.Session(graph=detection_graph) as sess:
4 # Definite input and output Tensors for detection_graph
----> 5 image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
6 # Each box represents a part of the image where a particular object was detected.
7 detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
c:\users\ja230262\appdata\local\continuum\anaconda3\envs\tensorflow1\lib\site-packages\tensorflow\python\framework\ops.py in get_tensor_by_name(self, name)
3497 raise TypeError("Tensor names are strings (or similar), not %s." %
3498 type(name).__name__)
-> 3499 return self.as_graph_element(name, allow_tensor=True, allow_operation=False)
3500
3501 def _get_tensor_by_tf_output(self, tf_output):
c:\users\ja230262\appdata\local\continuum\anaconda3\envs\tensorflow1\lib\site-packages\tensorflow\python\framework\ops.py in as_graph_element(self, obj, allow_tensor, allow_operation)
3321
3322 with self._lock:
-> 3323 return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
3324
3325 def _as_graph_element_locked(self, obj, allow_tensor, allow_operation):
c:\users\ja230262\appdata\local\continuum\anaconda3\envs\tensorflow1\lib\site-packages\tensorflow\python\framework\ops.py in _as_graph_element_locked(self, obj, allow_tensor, allow_operation)
3363 raise KeyError("The name %s refers to a Tensor which does not "
3364 "exist. The operation, %s, does not exist in the "
-> 3365 "graph." % (repr(name), repr(op_name)))
3366 try:
3367 return op.outputs[out_n]
KeyError: "The name 'image_tensor:0' refers to a Tensor which does not exist. The operation, 'image_tensor', does not exist in the graph."
关于如何解决此问题的任何想法？