Models: Error using mask networks

[gustavz]

System information

• What is the top-level directory of the model you are using: object_detection
• Have I written custom code (as opposed to using a stock example script provided in TensorFlow): yes
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): 16.04
• TensorFlow installed from (source or binary): source
• TensorFlow version (use command below): 1.4.0
• Bazel version (if compiling from source): idk
• CUDA/cuDNN version: 9.0 / 7.0
• GPU model and memory: geforce gtx 1050 4GB
• Exact command to reproduce:

I am unable to run any mask networks, my detection looks like the following and works for any model, but not for the new mask models.

My Code:

def detection(detection_graph, category_index):
    outputs = ['num_detections', 'detection_boxes', 'detection_scores','detection_classes', 'detection_masks']
    config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=log_device)
    config.gpu_options.allow_growth=True
    with detection_graph.as_default():
        with tf.Session(graph=detection_graph, config=config) as sess:
            tensor_dict = get_tensordict(detection_graph, outputs)            
            image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
            if 'detection_masks' in tensor_dict:
                # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
                detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
                detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
                real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
                detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
                detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
                detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
                        detection_masks, detection_boxes, width, height)
                detection_masks_reframed = tf.cast(tf.greater(detection_masks_reframed, 0.5), tf.uint8)
                # Follow the convention by adding back the batch dimension
                tensor_dict['detection_masks'] = tf.expand_dims(detection_masks_reframed, 0)
            # Start Video Stream
            video_stream = WebcamVideoStream(video_input,width,height).start()
            while video_stream.isActive():
                image = video_stream.read()
                output_dict = sess.run(tensor_dict, feed_dict={image_tensor: np.expand_dims(image, 0)})
                num = int(output_dict['num_detections'][0])
                classes = output_dict['detection_classes'][0].astype(np.uint8)
                boxes = output_dict['detection_boxes'][0]
                scores = output_dict['detection_scores'][0]
                if 'detection_masks' in output_dict:
                    masks = output_dict['detection_masks'][0]

The Error Output is:

2018-02-23 10:45:50.166904: E tensorflow/core/framework/op_segment.cc:53] Create kernel failed: Invalid argument: NodeDef mentions attr 'identical_element_shapes' not in Op<name=TensorArrayV3; signature=size:int32 -> handle:resource, flow:float; attr=dtype:type; attr=element_shape:shape,default=<unknown>; attr=dynamic_size:bool,default=false; attr=clear_after_read:bool,default=true; attr=tensor_array_name:string,default=""; is_stateful=true>; NodeDef: Preprocessor/map/TensorArray_1 = TensorArrayV3[clear_after_read=true, dtype=DT_FLOAT, dynamic_size=false, element_shape=<unknown>, identical_element_shapes=true, tensor_array_name="", _device="/job:localhost/replica:0/task:0/device:GPU:0"](Preprocessor/map/TensorArrayUnstack/strided_slice). (Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating binary.).
2018-02-23 10:45:50.166959: E tensorflow/core/common_runtime/executor.cc:643] Executor failed to create kernel. Invalid argument: NodeDef mentions attr 'identical_element_shapes' not in Op<name=TensorArrayV3; signature=size:int32 -> handle:resource, flow:float; attr=dtype:type; attr=element_shape:shape,default=<unknown>; attr=dynamic_size:bool,default=false; attr=clear_after_read:bool,default=true; attr=tensor_array_name:string,default=""; is_stateful=true>; NodeDef: Preprocessor/map/TensorArray_1 = TensorArrayV3[clear_after_read=true, dtype=DT_FLOAT, dynamic_size=false, element_shape=<unknown>, identical_element_shapes=true, tensor_array_name="", _device="/job:localhost/replica:0/task:0/device:GPU:0"](Preprocessor/map/TensorArrayUnstack/strided_slice). (Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating binary.).
     [[Node: Preprocessor/map/TensorArray_1 = TensorArrayV3[clear_after_read=true, dtype=DT_FLOAT, dynamic_size=false, element_shape=<unknown>, identical_element_shapes=true, tensor_array_name="", _device="/job:localhost/replica:0/task:0/device:GPU:0"](Preprocessor/map/TensorArrayUnstack/strided_slice)]]
Traceback (most recent call last):
  File "test.py", line 291, in <module>
    main()
  File "test.py", line 287, in main
    detection(graph, category, score, expand)
  File "test.py", line 233, in detection
    output_dict = sess.run(tensor_dict, feed_dict={image_tensor: np.expand_dims(image, 0)})
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 889, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1120, in _run
    feed_dict_tensor, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1317, in _do_run
    options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1336, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: NodeDef mentions attr 'identical_element_shapes' not in Op<name=TensorArrayV3; signature=size:int32 -> handle:resource, flow:float; attr=dtype:type; attr=element_shape:shape,default=<unknown>; attr=dynamic_size:bool,default=false; attr=clear_after_read:bool,default=true; attr=tensor_array_name:string,default=""; is_stateful=true>; NodeDef: Preprocessor/map/TensorArray_1 = TensorArrayV3[clear_after_read=true, dtype=DT_FLOAT, dynamic_size=false, element_shape=<unknown>, identical_element_shapes=true, tensor_array_name="", _device="/job:localhost/replica:0/task:0/device:GPU:0"](Preprocessor/map/TensorArrayUnstack/strided_slice). (Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating binary.).
     [[Node: Preprocessor/map/TensorArray_1 = TensorArrayV3[clear_after_read=true, dtype=DT_FLOAT, dynamic_size=false, element_shape=<unknown>, identical_element_shapes=true, tensor_array_name="", _device="/job:localhost/replica:0/task:0/device:GPU:0"](Preprocessor/map/TensorArrayUnstack/strided_slice)]]

Caused by op u'Preprocessor/map/TensorArray_1', defined at:
  File "test.py", line 291, in <module>
    main()
  File "test.py", line 285, in main
    graph, score, expand = load_frozenmodel()
  File "test.py", line 89, in load_frozenmodel
    tf.import_graph_def(od_graph_def, name='')
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/importer.py", line 313, in import_graph_def
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2956, in create_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1470, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

InvalidArgumentError (see above for traceback): NodeDef mentions attr 'identical_element_shapes' not in Op<name=TensorArrayV3; signature=size:int32 -> handle:resource, flow:float; attr=dtype:type; attr=element_shape:shape,default=<unknown>; attr=dynamic_size:bool,default=false; attr=clear_after_read:bool,default=true; attr=tensor_array_name:string,default=""; is_stateful=true>; NodeDef: Preprocessor/map/TensorArray_1 = TensorArrayV3[clear_after_read=true, dtype=DT_FLOAT, dynamic_size=false, element_shape=<unknown>, identical_element_shapes=true, tensor_array_name="", _device="/job:localhost/replica:0/task:0/device:GPU:0"](Preprocessor/map/TensorArrayUnstack/strided_slice). (Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating binary.).
     [[Node: Preprocessor/map/TensorArray_1 = TensorArrayV3[clear_after_read=true, dtype=DT_FLOAT, dynamic_size=false, element_shape=<unknown>, identical_element_shapes=true, tensor_array_name="", _device="/job:localhost/replica:0/task:0/device:GPU:0"](Preprocessor/map/TensorArrayUnstack/strided_slice)]]

[nguyeho7]

Maybe try updating to TF 1.5?

[gustavz]

@nguyeho7 yeah that solved the problem.
In the tutorial is still written that 1.4 is sufficient.
@tensorflowbutler should maybe update this

[AcodeWang]

Hi, I got the same problem in Object detection API and i'm using Tf 1.6.0.
What should I update for the object detection module?

Much Thanks

[mazzz56]

E tensorflow/core/framework/op_segment.cc:53] Create kernel failed: Invalid argument: NodeDef mentions attr 'identical_element_shapes' not in Op handle:resource, flow:float; attr=dtype:type; attr=element_shape:shape,default=; attr=dynamic_size:bool,default=false; attr=clear_after_read:bool,default=true; attr=tensor_array_name:string,default=""; is_stateful=true>; NodeDef: bidirectional_rnn/bw/bw/TensorArray_1 = TensorArrayV3clear_after_read=true, dtype=DT_FLOAT, dynamic_size=false, element_shape=[?,4096], identical_element_shapes=true, tensor_array_name="bidirectional_rnn/bw/bw/dynamic_rnn/input_0", _device="/job:localhost/replica:0/task:0/device:CPU:0". (Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating binary.).
i want to run urdu language on deepspeech but its giving me this error

[jamesguoxin]

This problem is caused by the fact that frozen_inference_graph.pb is generated by tf v1.5.0(any other version is not guaranteed to work, no matter below 1.5 or higher), simply regenerate .pb by your own tf, should fix this problem. I had same issue and fix it by regenerating .pb file.
see the marks about frozen inference graph: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md