Ошибка неверного аргумента при работе с обнаружением объекта tenorflow

Question

Я работал над API обнаружения объектов tenorflow после стольких ошибок, я застрял на этой ошибке, и искал по всему миру, но я не получил свой ответ, я получаю некоторые длинные и большие ошибки, и я не могу понять это.

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

import cv2
cap = cv2.VideoCapture(0)

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")


# ## Object detection imports
# Here are the imports from the object detection module.

# In[3]:

from utils import label_map_util

from utils import visualization_utils as vis_util


# # Model preparation 

# ## Variables
# 
# Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_CKPT` to point to a new .pb file.  
# 
# By default we use an "SSD with Mobilenet" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.

# In[4]:

# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 90


# ## Download Model

# In[5]:

opener = urllib.request.URLopener()
opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
tar_file = tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
  file_name = os.path.basename(file.name)
  if 'frozen_inference_graph.pb' in file_name:
    tar_file.extract(file, os.getcwd())


# ## Load a (frozen) Tensorflow model into memory.

# In[6]:

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='')


# ## Loading label map
# Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`.  Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine

# In[7]:

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)


# ## Helper code

# In[8]:

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)


# # Detection

# In[9]:

# 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 = '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)


# In[10]:

with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    while True:
      ret, image_np = cap.read()
      # 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)

      cv2.imshow('object detection', cv2.resize(image_np, (800,600)))
      if cv2.waitKey(25) & 0xFF == ord('q'):
        cv2.destroyAllWindows()
        break

и это ошибка, которую я получаю

InvalidArgumentError                      Traceback (most recent call last)
~\Miniconda3\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
   1021     fetch_handler = _FetchHandler(self._graph, fetches, {})
-> 1022 
   1023     # Set up a graph with feeds and fetches for partial run.

~\Miniconda3\lib\site-packages\tensorflow\python\client\session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1003     for feed in feeds:
-> 1004       for subfeed in _feed_fn(feed):
   1005         try:

~\Miniconda3\lib\contextlib.py in __exit__(self, type, value, traceback)
     65             try:
---> 66                 next(self.gen)
     67             except StopIteration:

~\Miniconda3\lib\site-packages\tensorflow\python\framework\errors_impl.py in raise_exception_on_not_ok_status()
    468     ABORTED: AbortedError,
--> 469     OUT_OF_RANGE: OutOfRangeError,
    470     UNIMPLEMENTED: UnimplementedError,

InvalidArgumentError: NodeDef mentions attr 'data_format' not in Op<name=DepthwiseConv2dNative; signature=input:T, filter:T -> output:T; attr=T:type,allowed=[DT_FLOAT, DT_DOUBLE]; attr=strides:list(int); attr=padding:string,allowed=["SAME", "VALID"]>; NodeDef: FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise = DepthwiseConv2dNative[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], _device="/job:localhost/replica:0/task:0/cpu:0"](FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Relu6, FeatureExtractor/MobilenetV1/Conv2d_1_depthwise/depthwise_weights/read)
     [[Node: FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise = DepthwiseConv2dNative[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], _device="/job:localhost/replica:0/task:0/cpu:0"](FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Relu6, FeatureExtractor/MobilenetV1/Conv2d_1_depthwise/depthwise_weights/read)]]

During handling of the above exception, another exception occurred:

InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-104-4c1ce36f9a76> in <module>()
     14       num_detections = detection_graph.get_tensor_by_name('num_detections:0')
     15       # Actual detection.
---> 16       (boxes, scores, classes, num_detections) = sess.run([boxes, scores, classes, num_detections],feed_dict={image_tensor: image_np_expanded})
     17       # Visualization of the results of a detection.
     18       vis_util.visualize_boxes_and_labels_on_image_array(

~\Miniconda3\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
    765       print(c.eval())
    766 
--> 767     sess.close()
    768     ```
    769 

~\Miniconda3\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    963     Note that contrary to `run`, `feeds` only specifies the graph elements.
    964     The tensors will be supplied by the subsequent `partial_run` calls.
--> 965 
    966     Args:
    967       fetches: A single graph element, or a list of graph elements.

~\Miniconda3\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1013             feed_list.append(compat.as_bytes(subfeed_t.name))
   1014         except Exception as e:
-> 1015           e.message = ('Cannot interpret feed_list key as Tensor: ' + e.message)
   1016           e.args = (e.message,)
   1017           raise e

~\Miniconda3\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
   1033 
   1034     if self._created_with_new_api:
-> 1035       # pylint: disable=protected-access
   1036       final_fetches = [t._as_tf_output() for t in fetch_handler.fetches()]
   1037       final_targets = [op._c_op for op in fetch_handler.targets()]

InvalidArgumentError: NodeDef mentions attr 'data_format' not in Op<name=DepthwiseConv2dNative; signature=input:T, filter:T -> output:T; attr=T:type,allowed=[DT_FLOAT, DT_DOUBLE]; attr=strides:list(int); attr=padding:string,allowed=["SAME", "VALID"]>; NodeDef: FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise = DepthwiseConv2dNative[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], _device="/job:localhost/replica:0/task:0/cpu:0"](FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Relu6, FeatureExtractor/MobilenetV1/Conv2d_1_depthwise/depthwise_weights/read)
     [[Node: FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise = DepthwiseConv2dNative[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], _device="/job:localhost/replica:0/task:0/cpu:0"](FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Relu6, FeatureExtractor/MobilenetV1/Conv2d_1_depthwise/depthwise_weights/read)]]

Caused by op 'FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise', defined at:
  File "C:\Users\Johny Sir\Miniconda3\lib\runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "C:\Users\Johny Sir\Miniconda3\lib\runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\ipykernel_launcher.py", line 16, in <module>
    app.launch_new_instance()
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\traitlets\config\application.py", line 658, in launch_instance
    app.start()
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\ipykernel\kernelapp.py", line 486, in start
    self.io_loop.start()
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\tornado\platform\asyncio.py", line 127, in start
    self.asyncio_loop.run_forever()
  File "C:\Users\Johny Sir\Miniconda3\lib\asyncio\base_events.py", line 421, in run_forever
    self._run_once()
  File "C:\Users\Johny Sir\Miniconda3\lib\asyncio\base_events.py", line 1425, in _run_once
    handle._run()
  File "C:\Users\Johny Sir\Miniconda3\lib\asyncio\events.py", line 127, in _run
    self._callback(*self._args)
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\tornado\ioloop.py", line 759, in _run_callback
    ret = callback()
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\tornado\stack_context.py", line 276, in null_wrapper
    return fn(*args, **kwargs)
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 536, in <lambda>
    self.io_loop.add_callback(lambda : self._handle_events(self.socket, 0))
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 450, in _handle_events
    self._handle_recv()
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 480, in _handle_recv
    self._run_callback(callback, msg)
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 432, in _run_callback
    callback(*args, **kwargs)
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\tornado\stack_context.py", line 276, in null_wrapper
    return fn(*args, **kwargs)
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\ipykernel\kernelbase.py", line 283, in dispatcher
    return self.dispatch_shell(stream, msg)
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\ipykernel\kernelbase.py", line 233, in dispatch_shell
    handler(stream, idents, msg)
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\ipykernel\kernelbase.py", line 399, in execute_request
    user_expressions, allow_stdin)
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\ipykernel\ipkernel.py", line 208, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\ipykernel\zmqshell.py", line 537, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2662, in run_cell
    raw_cell, store_history, silent, shell_futures)
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2785, in _run_cell
    interactivity=interactivity, compiler=compiler, result=result)
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2903, in run_ast_nodes
    if self.run_code(code, result):
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2963, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-100-0d8b8f2357e8>", line 7, in <module>
    tf.import_graph_def(od_graph_def, name='')
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\tensorflow\python\framework\importer.py", line 287, in import_graph_def
    if colocation_node_name != op.name:
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 2395, in create_op
    for a description of the valid feed values.
  File "C:\Users\Johny Sir\Miniconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1264, in __init__
    (dtypes.as_dtype(dtype).name, value.dtype.name, str(value)))

InvalidArgumentError (see above for traceback): NodeDef mentions attr 'data_format' not in Op<name=DepthwiseConv2dNative; signature=input:T, filter:T -> output:T; attr=T:type,allowed=[DT_FLOAT, DT_DOUBLE]; attr=strides:list(int); attr=padding:string,allowed=["SAME", "VALID"]>; NodeDef: FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise = DepthwiseConv2dNative[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], _device="/job:localhost/replica:0/task:0/cpu:0"](FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Relu6, FeatureExtractor/MobilenetV1/Conv2d_1_depthwise/depthwise_weights/read)
     [[Node: FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise = DepthwiseConv2dNative[T=DT_FLOAT, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], _device="/job:localhost/replica:0/task:0/cpu:0"](FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Relu6, FeatureExtractor/MobilenetV1/Conv2d_1_depthwise/depthwise_weights/read)]]

Я проверил, что камера работает, но главная проблема в последних 40 строках, я думаю.