FailedPreconditionError运行时，TF目标检测API和自己的模型

Question

我真的试图运行模式，我在TensorFlow目标检测API做了我自己的数据集，但运行脚本的时候，我得到这样的错误：

python object_detection/detect_test.py 

Traceback (most recent call last): 
    File "object_detection/detect_test.py", line 81, in <module> 
    feed_dict={image_tensor: image_np_expanded}) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 789, in run 
    run_metadata_ptr) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 997, in _run 
    feed_dict_string, options, run_metadata) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1132, in _do_run 
    target_list, options, run_metadata) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1152, in _do_call 
    raise type(e)(node_def, op, message) 
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value SecondStageBoxPredictor/ClassPredictor/biases 
     [[Node: SecondStageBoxPredictor/ClassPredictor/biases/read = Identity[T=DT_FLOAT, _class=["loc:@SecondStageBoxPredictor/ClassPredictor/biases"], _device="/job:localhost/replica:0/task:0/cpu:0"](SecondStageBoxPredictor/ClassPredictor/biases)]] 

Caused by op u'SecondStageBoxPredictor/ClassPredictor/biases/read', defined at: 
    File "object_detection/detect_test.py", line 40, in <module> 
    tf.import_graph_def(od_graph_def, name='') 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/importer.py", line 311, in import_graph_def 
    op_def=op_def) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2506, in create_op 
    original_op=self._default_original_op, op_def=op_def) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1269, in __init__ 
    self._traceback = _extract_stack() 

FailedPreconditionError (see above for traceback): Attempting to use uninitialized value SecondStageBoxPredictor/ClassPredictor/biases 
     [[Node: SecondStageBoxPredictor/ClassPredictor/biases/read = Identity[T=DT_FLOAT, _class=["loc:@SecondStageBoxPredictor/ClassPredictor/biases"], _device="/job:localhost/replica:0/task:0/cpu:0"](SecondStageBoxPredictor/ClassPredictor/biases)]] 

这有点奇怪，因为我遵循their tutorial的模型用法，错误可能是说某些变量没有初始化。

这里是我的代码：

detect_test.py

import numpy as np 
import os 
import six.moves.urllib as urllib 
import sys 
import tarfile 
import tensorflow as tf 
import zipfile 

from collections import defaultdict 
from io import StringIO 
from matplotlib import pyplot as plt 
from PIL import Image 

from utils import label_map_util 
from utils import visualization_utils as vis_util 

# Path to frozen detection graph. This is the actual model that is used for the object detection. 
PATH_TO_CKPT = '/home/jun/PycharmProjects/tf_workspace/models/output_inference_graph_151.pb' 

# List of the strings that is used to add correct label for each box. 
PATH_TO_LABELS = '/home/jun/PycharmProjects/tf_workspace/models/object_detection/data/pascal_label_map_new.pbtxt' 

NUM_CLASSES = 3 

detection_graph = tf.Graph() 
with detection_graph.as_default(): 
    od_graph_def = tf.GraphDef() 
    with tf.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='') 

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) 

def load_image_into_numpy_array(image): 
    (im_width, im_height) = image.size 
    return np.array(image.getdata()).reshape(
     (im_height, im_width, 3)).astype(np.uint8) 

# For the sake of simplicity we will use only 2 images: 
# image1.jpg 
# image2.jpg 
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS. 
PATH_TO_TEST_IMAGES_DIR = 'object_detection/test_images' 
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ] 

# Size, in inches, of the output images. 
IMAGE_SIZE = (12, 8) 

with detection_graph.as_default(): 
    with tf.Session(graph=detection_graph) as sess: 
    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) 
     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. 
     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. 
     scores = detection_graph.get_tensor_by_name('detection_scores:0') 
     classes = detection_graph.get_tensor_by_name('detection_classes:0') 
     num_detections = detection_graph.get_tensor_by_name('num_detections:0') 
     # Actual detection. 
     (boxes, scores, classes, num_detections) = sess.run(
      [boxes, scores, 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) 

我会在这种情况下，任何帮助，所以感激！提前致谢！

Answer 1

最后，我已经改变了行，其中matplotlib显示图像后评估，以简单地保存结果图像。他们在他们的例子中一直使用jupyter笔记本，因此可能会有一些功能。

最终代码：

import numpy as np 
import os 
import six.moves.urllib as urllib 
import sys 
import tarfile 
import tensorflow as tf 
import zipfile 

from collections import defaultdict 
from io import StringIO 
from matplotlib import pyplot as plt 
from PIL import Image 

from object_detection.utils import label_map_util 
from object_detection.utils import visualization_utils as vis_util 

NUM_CLASSES = 3 

detection_graph = tf.Graph() 
with detection_graph.as_default(): 
    od_graph_def = tf.GraphDef() 
    with tf.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='') 

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) 

def load_image_into_numpy_array(image): 
    (im_width, im_height) = image.size 
    return np.array(image.getdata()).reshape(
     (im_height, im_width, 3)).astype(np.uint8) 

# For the sake of simplicity we will use only 2 images: 
# image1.jpg 
# image2.jpg 
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS. 
PATH_TO_TEST_IMAGES_DIR = 'object_detection/test_images/' 
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 6) ] 

# Size, in inches, of the output images. 
IMAGE_SIZE = (12, 8) 

with detection_graph.as_default(): 
    with tf.Session(graph=detection_graph) as sess: 
    sess.run(tf.global_variables_initializer()) 
    img = 1 
    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) 
     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. 
     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. 
     scores = detection_graph.get_tensor_by_name('detection_scores:0') 
     classes = detection_graph.get_tensor_by_name('detection_classes:0') 
     num_detections = detection_graph.get_tensor_by_name('num_detections:0') 
     # Actual detection. 
     (boxes, scores, classes, num_detections) = sess.run(
      [boxes, scores, 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.imsave(str(img) + '.jpg', image_np) 
     img += 1 

Answer 2

在with tf.Session(graph=detection_graph) as sess:之后插入sess.run(tf.global_variable_initializers())。