Utils folder has a few scripts that are not useful and need to be removed.

Hi, I have walk through inception_v3 example, and have got a good result from the converted coreml model.

But I meet a question: 'Shapes not found for 506 tensors' when I convert inception_v3 model to coreml model.

Is this a bug?

Hello

I am having this error message:

dyld: warning, LC_RPATH $ORIGIN/../../_solib_darwin_x86_64/_U_S_Stensorflow_Spython_C_Upywrap_Utensorflow_Uinternal.so___Utensorflow in /Library/Python/2.7/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so being ignored in restricted program because it is a relative path 2018-01-04 19:47:30.977648: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.2 AVX Traceback (most recent call last): File "co.py", line 15, in <module> image_scale = 2.0/255.0) File "/Library/Python/2.7/site-packages/tfcoreml/_tf_coreml_converter.py", line 478, in convert predicted_probabilities_output=predicted_probabilities_output) File "/Library/Python/2.7/site-packages/tfcoreml/_tf_coreml_converter.py", line 143, in _convert_pb_to_mlmodel _check_unsupported_ops(OPS, output_feature_names) File "/Library/Python/2.7/site-packages/tfcoreml/_tf_coreml_converter.py", line 111, in _check_unsupported_ops ','.join(unsupported_op_types))) NotImplementedError: Unsupported Ops of type: PlaceholderWithDefault

When i am trying to convert a tensorflow model i trained with tensorflow for poets to a CoreML model. I am using this as python code:

`import tfcoreml as tf_converter

tf_model_path = "retrained_graph.pb"
mlmodel_path = "mobilenet_v1_1.0_224.mlmodel"

mlmodel = tf_converter.convert(
tf_model_path = tf_model_path,
mlmodel_path = mlmodel_path,
output_feature_names = ["MobilenetV1/Predictions/Softmax:0"],
input_name_shape_dict = {"input:0":[1,224,224,3]},
image_input_names = ["input:0"],
red_bias = -1,
green_bias = -1,
blue_bias = -1,
image_scale = 2.0/255.0)
`

Machine: mac OS Sierra

Hope you can help me.

Greetings Sieuwe

After converting from tensorflow to coreml file I found that tensorflow labels are not take in account. While running model on xcode for iOS app, code returns because VNClassificationObservation not found.

Do we have some special method to handle it unlike provide models on developers.apple.com?

best regards,
Kishwar

Can anyone explain what the mappings are of the two tensors which are produced from the output of the ssd_mobilenet_v1_android_export model which the example converts to a .mlmodel file?

When I look at this example:
https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb

Which uses:
ssd_mobilenet_v1_coco_2017_11_17

You can see the code does this:

(boxes, scores, classes, num) = sess.run(
    [detection_boxes, detection_scores, detection_classes, num_detections],
    feed_dict={image_tensor: image_np_expanded}
)

print(boxes.shape)
print(boxes)

(1, 100, 4)
[  3.90840471e-02   1.92150325e-02   8.72103453e-01   3.15773487e-01]
[  1.09515011e-01   4.02835608e-01   9.24646080e-01   9.73047853e-01]
[  5.07123828e-01   3.85651529e-01   8.76479626e-01   7.03940928e-01]

Looks good!

So I assume what we have here is the first 100 boxes with 4 dimensions each.

I traced the values and code and did this:

box_coords = ymin, xmin, ymax, xmax
(left, right, top, bottom) = (xmin, xmax, ymin, ymax)
e.g.
(0.10951501131057739, 0.4028356075286865, 0.9246460795402527, 0.9730478525161743)
left: 0.4028356075286865
right: 0.9730478525161743
top: 0.10951501131057739
bottom: 0.9246460795402527

These are % of the entire image so multiplied by the input size of 300 and you should get the original pixel locations.

This all makes sense.

However, I need the model in CoreML so I followed this guide:
https://github.com/tf-coreml/tf-coreml/blob/master/examples/ssd_example.ipynb

Which uses: ssd_mobilenet_v1_android_export
I assume from the README that its the same model:
https://github.com/tensorflow/models/tree/master/research/object_detection

August 11, 2017
We have released an update to the Android Detect demo which will now run models trained using the Tensorflow Object Detection API on an Android device. By default, it currently runs a frozen SSD w/Mobilenet detector trained on COCO, but we encourage you to try out other detection models!

Obviously its slightly different but to what degree I don't know. Can someone clarify?

Now, after the export process I load it into Xcode and when I run the model, I get this kind of output from the tensor.

boxes in concat__0 e.g. concat:0

concat:0 are the bounding-box encodings of the 1917 anchor boxes

Why are there negative values in the bounding box coords?
[ 0.35306236, -0.48976013, -2.5883727 , -4.0799093 ]
[ 0.8760979 , 1.1190459 , -2.6803727 , -1.5514386 ]
[ 1.3935553 , 0.85614955, -0.92042184, -2.7950268 ]

Also can anyone explain what the 1917 are? There are 91 categories in COCO but why 1917 anchor boxes?

I even looked at the android example and its not much easier to understand:

Even better would be an explanation of the CoreML files output tensors?
It would be great to have the end of the file map the output in python to show what they are.

Perhaps draw the box and show the category label just like the object_detection_tutorial.ipynb that would be great! :)

I am completely lost with this so any help would be greatly appreciated!

I have tried to convert the stylize model given in the README.md. Because the input_shape is unknown, I do not specify the keyword input_name_shape_dict. The following code raises an exception.

import tfcoreml
tfcoreml.convert(tf_model_path="stylize_quantized.pb", mlmodel_path="stylize.mlmodel", output_feature_names=["output"])

AssertionError                            Traceback (most recent call last)
<ipython-input-23-147bf52650a0> in <module>()
----> 1 tfcoreml.convert(tf_model_path="/home/long/stylize_quantized.pb", mlmodel_path="stylize.mlmodel", output_feature_names=["output"])

/home/long/anaconda3/envs/fractal_frosties/lib/python2.7/site-packages/tfcoreml/_tf_coreml_converter.pyc in convert(tf_model_path, mlmodel_path, output_feature_names, input_name_shape_dict, image_input_names, is_bgr, red_bias, green_bias, blue_bias, gray_bias, image_scale, class_labels, predicted_feature_name, predicted_probabilities_output)
    476       class_labels=class_labels,
    477       predicted_feature_name=predicted_feature_name,
--> 478       predicted_probabilities_output=predicted_probabilities_output)

/home/long/anaconda3/envs/fractal_frosties/lib/python2.7/site-packages/tfcoreml/_tf_coreml_converter.pyc in _convert_pb_to_mlmodel(tf_model_path, mlmodel_path, output_feature_names, input_name_shape_dict, image_input_names, is_bgr, red_bias, green_bias, blue_bias, gray_bias, image_scale, class_labels, predicted_feature_name, predicted_probabilities_output)
    172       if not (shape.is_fully_defined() or input_name in input_name_shape_dict):
    173         assert False, (
--> 174             "%s is a placehoder with incomplete shape %s" %(input_name, str(shape)))
    175       if shape.is_fully_defined():
    176         shape = shape.as_list()

AssertionError: input:0 is a placehoder with incomplete shape <unknown>

is this related to #45 ? although, I don't even get to the prediction phase.

I think its because some of the slim ops have been deprecated. @slin07 I think you can modify/remove those tests with deprecated ops.

Check the list of ops in the graph, error out even before converting, if an op is not supported. Along with the message that the user can try using the Graph transform tool to simplify the graph and try the converter again.

Hi All,

I am beginner to neural networks and I just started to learn tensorflow from tutorials, and while converting the .pb model to mlmodel , I m getting the following error:

/anaconda2/lib/python2.7/site-packages/coremltools/models/_feature_management.py", line 295, in process_or_validate_features
num_dimensions = 1 + max(chain(*[il for k, il in features.items()]))
ValueError: max() arg is an empty sequence.

This is the code I am following to convert:

import tfcoreml as tf_converter
tf_converter.convert(tf_model_path = 'image.pb',
mlmodel_path = 'myFirstImageModel.mlmodel',
output_feature_names = ['Softmax_2:0'])

Can you guys please help me out here.

Thanks

On lastest master:
Running:
python setup.py bdist_wheel
I get the following error:
Traceback (most recent call last):
File "setup.py", line 14, in
with open(path.join(path.abspath(path.dirname(file)), 'README.md'), encoding='utf-8') as f:
TypeError: 'encoding' is an invalid keyword argument for this function

@slin07 @miaout17 are you also getting this error?

Hi there,

I'm running the following script and receive an "output feature cannot be a placeholder error" (tfcoreml/_tf_coreml_converter.py line 175)

error:

Traceback (most recent call last):
File "converttest2.py", line 6, in
image_input_names = ['input:0'])
File "/Users/tgwagner/fresh/tf-coreml/tfcoreml/_tf_coreml_converter.py", line 483, in convert
predicted_probabilities_output=predicted_probabilities_output)
File "/Users/tgwagner/fresh/tf-coreml/tfcoreml/_tf_coreml_converter.py", line 175, in _convert_pb_to_mlmodel
('Output feature cannot be a placeholder')
AssertionError: Output feature cannot be a placeholder

Script is:

import tfcoreml as tf_converter
tf_converter.convert(tf_model_path = '/users/fresh/darkflow/built_graph/tiny-yolo-NFPA.pb',
                     mlmodel_path = 'my_model.mlmodel',
                     output_feature_names = ['output:0'],
                     input_name_shape_dict = {'input:0':[1, 416, 416, 3]},
                     image_input_names = ['input:0'])

I summarized my model using summarize_graph (from tensorflow tools) with the following output:

=/users/tgwagner/fresh/darkflow/built_graph/tiny-yolo-NFPA.pb
Found 1 possible inputs: (name=input, type=float(1), shape=[?,416,416,3])
No variables spotted.
Found 1 possible outputs: (name=output, op=Identity)
Found 15770590 (15.77M) const parameters, 0 (0) variable parameters, and 0 control_edges
Op types used: 59 Const, 16 Mul, 9 Conv2D, 9 BiasAdd, 9 Pad, 8 Sub, 8 Maximum, 8 RealDiv, 6 MaxPool, 1 Placeholder, 1 Identity, 1 NoOp

what am I missing? This is a model derived from tiny_yolo_voc.cfg

Thanks

Hi,

I follow tensorflow for poets sample. I want to create core ml with 2 outputs, classLabel and classLabelProbs. First step is to add in param my class label

import tfcoreml as tf_converter
tf_converter.convert(tf_model_path = 'tf_files/optimized_graph.pb',
mlmodel_path = 'tf_files/my_model.mlmodel',
output_feature_names = ['final_result:0'],
input_name_shape_dict = {'input:0':[1,224,224,3]},
image_input_names = ['input:0'],
red_bias = -1,
green_bias = -1,
blue_bias = -1,
image_scale = 2.0/255.0,
class_labels = 'tf_files/retrained_labels.txt')

But add got this issue:
420/420: Converting op name: final_result ( type: Softmax )
Traceback (most recent call last):
File "tfcoremlconverter.py", line 12, in
class_labels = 'tf_files/retrained_labels.txt')
File "/host/Desktop/workspace/tf-coreml/tfcoreml/_tf_coreml_converter.py", line 478, in convert
predicted_probabilities_output=predicted_probabilities_output)
File "/host/Desktop/workspace/tf-coreml/tfcoreml/_tf_coreml_converter.py", line 354, in _convert_pb_to_mlmodel
optimize_nn_spec(builder=builder)
File "/host/Desktop/workspace/tf-coreml/tfcoreml/optimizations/_optimize_nn_spec.py", line 47, in optimize_nn_spec
_optimize_disconnected_components(builder)
File "/host/Desktop/workspace/tf-coreml/tfcoreml/optimizations/_optimize_nn_spec.py", line 37, in _optimize_disconnected_components
_optimize._remove_disconnected_components(builder)
File "/host/Desktop/workspace/tf-coreml/tfcoreml/optimizations/_optimize.py", line 355, in _remove_disconnected_components
start_ids.append(blob_src[out.name])
KeyError: u'classLabel'

I ran a MobilenetV1 model through tf-coreml, and got the following error:

AssertionError: Weight input has to be an identity op

Here's the op printed out:

140/209: Converting op name: MobilenetV1/MobilenetV1/Conv2d_5_pointwise/BatchNorm/batchnorm/mul_1 ( type:  Conv2D )
name: "MobilenetV1/Conv2d_5_pointwise/weights/read/_52__cf__52"
op: "Dequantize"
input: "MobilenetV1/Conv2d_5_pointwise/weights/read/_52__cf__52_quantized_const"
input: "MobilenetV1/Conv2d_5_pointwise/weights/read/_52__cf__52_quantized_min"
input: "MobilenetV1/Conv2d_5_pointwise/weights/read/_52__cf__52_quantized_max"
attr {
  key: "T"
  value {
    type: DT_QUINT8
  }
}
attr {
  key: "mode"
  value {
    s: "MIN_FIRST"
  }
}

Traceback (most recent call last):
  File "tf2coreml.py", line 7, in <module>
    class_labels = '/tmp/output_labels.txt')
  File "/Users/andypang/code/tf-coreml/tfcoreml/_tf_coreml_converter.py", line 477, in convert
    predicted_probabilities_output=predicted_probabilities_output)
  File "/Users/andypang/code/tf-coreml/tfcoreml/_tf_coreml_converter.py", line 275, in _convert_pb_to_mlmodel
    convert_ops_to_layers(context)
  File "/Users/andypang/code/tf-coreml/tfcoreml/_ops_to_layers.py", line 142, in convert_ops_to_layers
    translator(op, context)
  File "/Users/andypang/code/tf-coreml/tfcoreml/_layers.py", line 130, in conv2d
    'Weight input has to be an identity op')
AssertionError: Weight input has to be an identity op

The last layer of the model was retrained with my own images, following the image retraining tutorial from tensorflow: https://www.tensorflow.org/tutorials/image_retraining using the architecture option "mobilenet_1.0_224_quantized".

First thought is I should avoid Dequantized by dropping the quantized option out of the model.

I am trying to use your converter to make my .pb model into a .mlmodel

In my convertor.py I have the following
import tfcoreml as tf_converter tf_converter.convert(tf_model_path = 'retrained_graph.pb', mlmodel_path = 'mymodel.mlmodel', output_feature_names = ['final_result:0'])

When trying to build this, I get the following error:

File "convertor.py", line 4, in
output_feature_names = ['final_result:0'])
File "/Users/xxxx/Documents/NewTry/retrainedversion/tf-coreml/tfcoreml/_tf_coreml_converter.py", line 477, in convert
predicted_probabilities_output=predicted_probabilities_output)
File "/Users/xxxx/Documents/NewTry/retrainedversion/tf-coreml/tfcoreml/_tf_coreml_converter.py", line 143, in _convert_pb_to_mlmodel
_check_unsupported_ops(OPS, output_feature_names)
File "/Users/xxxx/Documents/NewTry/retrainedversion/tf-coreml/tfcoreml/_tf_coreml_converter.py", line 111, in _check_unsupported_ops
','.join(unsupported_op_types)))
NotImplementedError: Unsupported Ops of type: DecodeJpeg

I feel like it's an issue in your code. I've followed numerous tutorials on this (including: https://hackernoon.com/integrating-tensorflow-model-in-an-ios-app-cecf30b9068d) and it still fails.

tf_to_coreml raises an error when trying to convert Inception Model
released by Google in 2015.

Notice that the model file is different from the one in the other bug.

The Python code:

tf_converter.convert(
    tf_model_path='models/inception-2015-12-05/classify_image_graph_def.pb',
    mlmodel_path='Inception2015.mlmodel',
    output_feature_names=['softmax:0'],
    image_input_names=['Mul:0'],
    red_bias=-0.5,
    green_bias=-0.5,
    blue_bias=-0.5,
    image_scale=1.0 / 255.0,
    )

The error message is:

Traceback (most recent call last):
  File "tf_coreml.py", line 18, in <module>
    image_scale=1.0 / 255.0,
  File "/tf_to_coreml/local/lib/python2.7/site-packages/tfcoreml/_tf_coreml_converter.py", line 363, in convert
    predicted_probabilities_output = predicted_probabilities_output)              
  File "/tf_to_coreml/local/lib/python2.7/site-packages/tfcoreml/_tf_coreml_converter.py", line 231, in _convert_pb_to_mlmodel
    builder = NeuralNetworkBuilder(input_features, output_features, mode=mode) 
  File "/tf_to_coreml/local/lib/python2.7/site-packages/coremltools/models/neural_network.py", line 140, in __init__
    out_features_with_shape)
  File "/tf_to_coreml/local/lib/python2.7/site-packages/coremltools/models/_interface_management.py", line 137, in set_transform_interface_params
    input_features = _fm.process_or_validate_features(input_features)
  File "/tf_to_coreml/local/lib/python2.7/site-packages/coremltools/models/_feature_management.py", line 295, in process_or_validate_features
    num_dimensions = 1 + max(chain(*[il for k, il in features.items()]))
ValueError: max() arg is an empty sequence

Add an optimization that removes disconnected sub-graphs which are not involved in the computation of the output node. An example: "test_flatten" Keras nosetest. the graph in that nosetest has a disconnected component which should be removed after this optimization.

Currently the handling of 1D conv and pooling layers is not very robust. As of now, either "test_conv1d_avepool" and "test_conv1d_avepool" tests in test_tf_converter.py pass or "test_max_pooling_1d" in test_tf_keras_layers.py, not all.

'test_slim_conv_bn'
'test_slim_conv_bn_no_beta'
Both of these tests are failing on my machine with the same error:
File "/Users/aseem/Documents/code/tf-coreml/tfcoreml/_layers.py", line 88, in batchnorm
op.inputs[idx].op.inputs[0].name)] for idx in range(1,5)]
File "/Users/aseem/Aseem_env/env/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1865, in getitem
return self._op._inputs[i]
IndexError: list index out of range

@slin07 This seems related to PR #41. Did you also see these errors in your test run?

my environment:

OS: Ubuntu 16
Python: 2.7.12
Package:
coremltools (0.7)
numpy (1.13.3)
protobuf (3.5.0.post1)
six (1.10.0)
tensorflow-gpu (1.2.0)
tfcoreml (0.1.0)

here is part of my net structure:

def My_CustomLayer(net, train=True, layer_id = ''):
    batch, rows, cols, channels = [i.value for i in net.get_shape()]
    var_shape = [channels]
    mu, sigma_sq = tf.nn.moments(net, [1,2], keep_dims=True)
    shift = tf.Variable(tf.zeros(var_shape), name = 'norm_shift_' + layer_id)
    scale = tf.Variable(tf.ones(var_shape), name = 'norm_scale_' + layer_id)
    epsilon = 1e-3
    normalized = (net-mu)* tf.rsqrt(sigma_sq + epsilon)
    return scale * normalized + shift

Shift and scale are trainable variable in tensorflow and .pb work perfectly when I predict.
The converting from .pb to .mlmodel do not show any error.

But when I use .mlmodel to predict, it will return totally wrong result.

once I remove shift and scale it will work fine when I use .mlmodel.
(but I need shift and scale to improve my net accuracy and training speed.....)

It seem the weight of shift and scale are not converted to .mlmodel.

any suggestion? Thanks~~~

This mobile SSD model can't be converted:
[https://storage.googleapis.com/download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_android_export.zip]
In this mobile SSD there's a map operation and a while loop in preprocessing step, making it a loop. CoreML does not have support to realize map/while loop related ops.
See test_mobile_ssd in tests/test_pretrained_models.py in branch dev/ssd-test.

Instead use pytest and add a description in the readme on how to install and use it.

I am running the following commands and getting this result.

I'm unsure if it's a XCode bug or a converter problem.

I used to test tf-coreml with Swift and it worked well.
I tried to test it with Objective C, but it doesn't work when input/output name contains '/'.

For example, the output name is MobilenetV1/Predictions/Reshape in the mobilenet model we're using.
The generated Objective C become:

- (instancetype)initWithMobilenetV1/Predictions/Reshape_1__0:(MLMultiArray *)MobilenetV1_Predictions_Reshape_1__0;

Tried this with XCode 9.0 and 9.2 beta, and got the same result.

Hi

I have fine tuned some graphs using supported architectures (Mobilenet v1, 1.0 224) with Tensorflow 1.5 (from source), and have split my resulting graph into a main inference engine and a few various fine tuned classifiers via the graph transform tool, and am able to successfully run my networks in TF Lite / and the C++ API.

However, i'd love to run them in Core ML.

Upon attempting to run the a basic conversion routine tf_converter.convert - and am getting

NotImplementedError: Unsupported Ops of type: PlaceholderWithDefault

I imagine this could be resolved with an implementation, but is there a workaround with the transform graph tool to convert a PlaceholderWithDefault node to a regular Placeholder node which is reported as supported.

Thank you.

Hi,

Updated Mac from 10.12.6 to 10.13.2.
examples/inception_v3.ipynb stoped working and crashing on fatal (information below).
On previous version (10.12.6) it did not crash.
I tried to reinstall the tf-coreml package (from source) with no luck.
Ran it from both jupyter notebook and as a standalone python script, both are crashing.

The crash is in the convert process, the conversion finishes and creates the file (MLcore model) and then crashes.

Any ideas?

The crash info:

`Process: Python [1908]
Path: /Library/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python
Identifier: Python
Version: 2.7.14 (2.7.14)
Code Type: X86-64 (Native)
Parent Process: bash [921]
Responsible: Python [1908]
User ID: 501

Date/Time: 2018-01-01 21:56:01.782 +0200
OS Version: Mac OS X 10.13.2 (17C88)
Report Version: 12
Bridge OS Version: 3.0 (14Y618b)
Anonymous UUID: 319AA880-5FD9-6B6C-9A7C-A4E9F601D912

Time Awake Since Boot: 3400 seconds

System Integrity Protection: enabled

Crashed Thread: 0 Dispatch queue: com.apple.main-thread

Exception Type: EXC_CRASH (SIGABRT)
Exception Codes: 0x0000000000000000, 0x0000000000000000
Exception Note: EXC_CORPSE_NOTIFY

Application Specific Information:
abort() called

Thread 0 Crashed:: Dispatch queue: com.apple.main-thread
0 libsystem_kernel.dylib 0x00007fff74920e3e _pthread_kill + 10
1 libsystem_pthread.dylib 0x00007fff74a5f150 pthread_kill + 333
2 libsystem_c.dylib 0x00007fff7487d312 abort + 127
3 org.python.python 0x00007fff57534dae Py_FatalError + 49
4 org.python.python 0x00007fff57534287 PyThreadState_Get + 28
5 org.python.python 0x00007fff57531372 Py_InitModule4_64 + 58
6 libcoremlpython.so 0x000000016f12b4c2 pybind11::module::module(char const*, char const*) + 130
7 libcoremlpython.so 0x000000016f12ae95 pybind11::module::module(char const*, char const*) + 37
8 libcoremlpython.so 0x000000016f12ad4f pybind11_init() + 31
9 libcoremlpython.so 0x000000016f1293db pybind11_init_wrapper() + 171
10 libcoremlpython.so 0x000000016f12931d initlibcoremlpython + 13
11 org.python.python 0x00000001000e42f1 PyImport_LoadDynamicModule + 177
12 org.python.python 0x00000001000e274b import_submodule + 315
13 org.python.python 0x00000001000e2c0a load_next + 234
14 org.python.python 0x00000001000e2f03 PyImport_ImportModuleLevel + 339
15 org.python.python 0x00000001000bcdd3 builtin___import + 131
16 org.python.python 0x000000010000c2e2 PyObject_Call + 98
17 org.python.python 0x00000001000be027 PyEval_CallObjectWithKeywords + 87
18 org.python.python 0x00000001000c5681 PyEval_EvalFrameEx + 25457
19 org.python.python 0x00000001000c8fe2 PyEval_EvalFrameEx + 40146
20 org.python.python 0x00000001000cac23 PyEval_EvalCodeEx + 2115
21 org.python.python 0x000000010003e7a0 function_call + 176
22 org.python.python 0x000000010000c2e2 PyObject_Call + 98
23 org.python.python 0x000000010001ec3d instancemethod_call + 365
24 org.python.python 0x000000010000c2e2 PyObject_Call + 98
25 org.python.python 0x000000010007a228 slot_tp_init + 88
26 org.python.python 0x0000000100076ba5 type_call + 245
27 org.python.python 0x000000010000c2e2 PyObject_Call + 98
28 org.python.python 0x00000001000c30b1 PyEval_EvalFrameEx + 15777
29 org.python.python 0x00000001000cac23 PyEval_EvalCodeEx + 2115
30 org.python.python 0x00000001000c8f1f PyEval_EvalFrameEx + 39951
31 org.python.python 0x00000001000cac23 PyEval_EvalCodeEx + 2115
32 org.python.python 0x00000001000c8f1f PyEval_EvalFrameEx + 39951
33 org.python.python 0x00000001000cac23 PyEval_EvalCodeEx + 2115
34 org.python.python 0x00000001000cad46 PyEval_EvalCode + 54
35 org.python.python 0x00000001000efcbe PyRun_FileExFlags + 174
36 org.python.python 0x00000001000eff5a PyRun_SimpleFileExFlags + 458
37 org.python.python 0x00000001001071dd Py_Main + 3101
38 org.python.python 0x0000000100000f14 0x100000000 + 3860

Thread 1:
0 libsystem_kernel.dylib 0x00007fff74920cee __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff74a5d662 _pthread_cond_wait + 732
2 libc++.1.dylib 0x00007fff72806cb0 std::__1::condition_variable::wait(std::__1::unique_lockstd::__1::mutex&) + 18
3 libtensorflow_framework.so 0x000000010ab07976 Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 278
4 libtensorflow_framework.so 0x000000010ab0768c Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WaitForWork(Eigen::EventCount::Waiter*, tensorflow::thread::EigenEnvironment::Task*) + 876
5 libtensorflow_framework.so 0x000000010ab07239 Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WorkerLoop(int) + 1849
6 libtensorflow_framework.so 0x000000010ab069f4 std::__1::__function::__func<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'(), std::__1::allocator<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()>, void ()>::operator()() + 52
7 libtensorflow_framework.so 0x000000010ab29000 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::function<void ()> > >(void*) + 96
8 libsystem_pthread.dylib 0x00007fff74a5c6c1 _pthread_body + 340
9 libsystem_pthread.dylib 0x00007fff74a5c56d _pthread_start + 377
10 libsystem_pthread.dylib 0x00007fff74a5bc5d thread_start + 13

Thread 2:
0 libsystem_kernel.dylib 0x00007fff74920cee __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff74a5d662 _pthread_cond_wait + 732
2 libc++.1.dylib 0x00007fff72806cb0 std::__1::condition_variable::wait(std::__1::unique_lockstd::__1::mutex&) + 18
3 libtensorflow_framework.so 0x000000010ab07976 Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 278
4 libtensorflow_framework.so 0x000000010ab0768c Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WaitForWork(Eigen::EventCount::Waiter*, tensorflow::thread::EigenEnvironment::Task*) + 876
5 libtensorflow_framework.so 0x000000010ab07239 Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WorkerLoop(int) + 1849
6 libtensorflow_framework.so 0x000000010ab069f4 std::__1::__function::__func<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'(), std::__1::allocator<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()>, void ()>::operator()() + 52
7 libtensorflow_framework.so 0x000000010ab29000 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::function<void ()> > >(void*) + 96
8 libsystem_pthread.dylib 0x00007fff74a5c6c1 _pthread_body + 340
9 libsystem_pthread.dylib 0x00007fff74a5c56d _pthread_start + 377
10 libsystem_pthread.dylib 0x00007fff74a5bc5d thread_start + 13

Thread 3:
0 libsystem_kernel.dylib 0x00007fff74920cee __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff74a5d662 _pthread_cond_wait + 732
2 libc++.1.dylib 0x00007fff72806cb0 std::__1::condition_variable::wait(std::__1::unique_lockstd::__1::mutex&) + 18
3 libtensorflow_framework.so 0x000000010ab07976 Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 278
4 libtensorflow_framework.so 0x000000010ab0768c Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WaitForWork(Eigen::EventCount::Waiter*, tensorflow::thread::EigenEnvironment::Task*) + 876
5 libtensorflow_framework.so 0x000000010ab07239 Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WorkerLoop(int) + 1849
6 libtensorflow_framework.so 0x000000010ab069f4 std::__1::__function::__func<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'(), std::__1::allocator<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()>, void ()>::operator()() + 52
7 libtensorflow_framework.so 0x000000010ab29000 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::function<void ()> > >(void*) + 96
8 libsystem_pthread.dylib 0x00007fff74a5c6c1 _pthread_body + 340
9 libsystem_pthread.dylib 0x00007fff74a5c56d _pthread_start + 377
10 libsystem_pthread.dylib 0x00007fff74a5bc5d thread_start + 13

Thread 4:
0 libsystem_kernel.dylib 0x00007fff74920cee __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff74a5d662 _pthread_cond_wait + 732
2 libc++.1.dylib 0x00007fff72806cb0 std::__1::condition_variable::wait(std::__1::unique_lockstd::__1::mutex&) + 18
3 libtensorflow_framework.so 0x000000010ab07976 Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 278
4 libtensorflow_framework.so 0x000000010ab0768c Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WaitForWork(Eigen::EventCount::Waiter*, tensorflow::thread::EigenEnvironment::Task*) + 876
5 libtensorflow_framework.so 0x000000010ab07239 Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WorkerLoop(int) + 1849
6 libtensorflow_framework.so 0x000000010ab069f4 std::__1::__function::__func<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'(), std::__1::allocator<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()>, void ()>::operator()() + 52
7 libtensorflow_framework.so 0x000000010ab29000 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::function<void ()> > >(void*) + 96
8 libsystem_pthread.dylib 0x00007fff74a5c6c1 _pthread_body + 340
9 libsystem_pthread.dylib 0x00007fff74a5c56d _pthread_start + 377
10 libsystem_pthread.dylib 0x00007fff74a5bc5d thread_start + 13

Thread 5:
0 libsystem_kernel.dylib 0x00007fff74920cee __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff74a5d662 _pthread_cond_wait + 732
2 libc++.1.dylib 0x00007fff72806cb0 std::__1::condition_variable::wait(std::__1::unique_lockstd::__1::mutex&) + 18
3 libtensorflow_framework.so 0x000000010ab07976 Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 278
4 libtensorflow_framework.so 0x000000010ab0768c Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WaitForWork(Eigen::EventCount::Waiter*, tensorflow::thread::EigenEnvironment::Task*) + 876
5 libtensorflow_framework.so 0x000000010ab07239 Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WorkerLoop(int) + 1849
6 libtensorflow_framework.so 0x000000010ab069f4 std::__1::__function::__func<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'(), std::__1::allocator<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()>, void ()>::operator()() + 52
7 libtensorflow_framework.so 0x000000010ab29000 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::function<void ()> > >(void*) + 96
8 libsystem_pthread.dylib 0x00007fff74a5c6c1 _pthread_body + 340
9 libsystem_pthread.dylib 0x00007fff74a5c56d _pthread_start + 377
10 libsystem_pthread.dylib 0x00007fff74a5bc5d thread_start + 13

Thread 6:
0 libsystem_kernel.dylib 0x00007fff74920cee __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff74a5d662 _pthread_cond_wait + 732
2 libc++.1.dylib 0x00007fff72806cb0 std::__1::condition_variable::wait(std::__1::unique_lockstd::__1::mutex&) + 18
3 libtensorflow_framework.so 0x000000010ab07976 Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 278
4 libtensorflow_framework.so 0x000000010ab0768c Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WaitForWork(Eigen::EventCount::Waiter*, tensorflow::thread::EigenEnvironment::Task*) + 876
5 libtensorflow_framework.so 0x000000010ab07239 Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WorkerLoop(int) + 1849
6 libtensorflow_framework.so 0x000000010ab069f4 std::__1::__function::__func<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'(), std::__1::allocator<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()>, void ()>::operator()() + 52
7 libtensorflow_framework.so 0x000000010ab29000 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::function<void ()> > >(void*) + 96
8 libsystem_pthread.dylib 0x00007fff74a5c6c1 _pthread_body + 340
9 libsystem_pthread.dylib 0x00007fff74a5c56d _pthread_start + 377
10 libsystem_pthread.dylib 0x00007fff74a5bc5d thread_start + 13

Thread 7:
0 libsystem_kernel.dylib 0x00007fff74920cee __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff74a5d662 _pthread_cond_wait + 732
2 libc++.1.dylib 0x00007fff72806cb0 std::__1::condition_variable::wait(std::__1::unique_lockstd::__1::mutex&) + 18
3 libtensorflow_framework.so 0x000000010ab07976 Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 278
4 libtensorflow_framework.so 0x000000010ab0768c Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WaitForWork(Eigen::EventCount::Waiter*, tensorflow::thread::EigenEnvironment::Task*) + 876
5 libtensorflow_framework.so 0x000000010ab07239 Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WorkerLoop(int) + 1849
6 libtensorflow_framework.so 0x000000010ab069f4 std::__1::__function::__func<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'(), std::__1::allocator<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()>, void ()>::operator()() + 52
7 libtensorflow_framework.so 0x000000010ab29000 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::function<void ()> > >(void*) + 96
8 libsystem_pthread.dylib 0x00007fff74a5c6c1 _pthread_body + 340
9 libsystem_pthread.dylib 0x00007fff74a5c56d _pthread_start + 377
10 libsystem_pthread.dylib 0x00007fff74a5bc5d thread_start + 13

Thread 8:
0 libsystem_kernel.dylib 0x00007fff74920cee __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff74a5d662 _pthread_cond_wait + 732
2 libc++.1.dylib 0x00007fff72806cb0 std::__1::condition_variable::wait(std::__1::unique_lockstd::__1::mutex&) + 18
3 libtensorflow_framework.so 0x000000010ab07976 Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 278
4 libtensorflow_framework.so 0x000000010ab0768c Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WaitForWork(Eigen::EventCount::Waiter*, tensorflow::thread::EigenEnvironment::Task*) + 876
5 libtensorflow_framework.so 0x000000010ab07239 Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WorkerLoop(int) + 1849
6 libtensorflow_framework.so 0x000000010ab069f4 std::__1::__function::__func<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'(), std::__1::allocator<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()>, void ()>::operator()() + 52
7 libtensorflow_framework.so 0x000000010ab29000 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::function<void ()> > >(void*) + 96
8 libsystem_pthread.dylib 0x00007fff74a5c6c1 _pthread_body + 340
9 libsystem_pthread.dylib 0x00007fff74a5c56d _pthread_start + 377
10 libsystem_pthread.dylib 0x00007fff74a5bc5d thread_start + 13

Thread 9:
0 libsystem_kernel.dylib 0x00007fff74920cee __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff74a5d662 _pthread_cond_wait + 732
2 libc++.1.dylib 0x00007fff72806cb0 std::__1::condition_variable::wait(std::__1::unique_lockstd::__1::mutex&) + 18
3 libtensorflow_framework.so 0x000000010ab07976 Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 278
4 libtensorflow_framework.so 0x000000010ab0768c Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WaitForWork(Eigen::EventCount::Waiter*, tensorflow::thread::EigenEnvironment::Task*) + 876
5 libtensorflow_framework.so 0x000000010ab07239 Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WorkerLoop(int) + 1849
6 libtensorflow_framework.so 0x000000010ab069f4 std::__1::__function::__func<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'(), std::__1::allocator<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()>, void ()>::operator()() + 52
7 libtensorflow_framework.so 0x000000010ab29000 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::function<void ()> > >(void*) + 96
8 libsystem_pthread.dylib 0x00007fff74a5c6c1 _pthread_body + 340
9 libsystem_pthread.dylib 0x00007fff74a5c56d _pthread_start + 377
10 libsystem_pthread.dylib 0x00007fff74a5bc5d thread_start + 13

Thread 10:
0 libsystem_kernel.dylib 0x00007fff74920cee __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff74a5d662 _pthread_cond_wait + 732
2 libc++.1.dylib 0x00007fff72806cb0 std::__1::condition_variable::wait(std::__1::unique_lockstd::__1::mutex&) + 18
3 libtensorflow_framework.so 0x000000010ab07976 Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 278
4 libtensorflow_framework.so 0x000000010ab0768c Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WaitForWork(Eigen::EventCount::Waiter*, tensorflow::thread::EigenEnvironment::Task*) + 876
5 libtensorflow_framework.so 0x000000010ab07239 Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WorkerLoop(int) + 1849
6 libtensorflow_framework.so 0x000000010ab069f4 std::__1::__function::__func<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'(), std::__1::allocator<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()>, void ()>::operator()() + 52
7 libtensorflow_framework.so 0x000000010ab29000 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::function<void ()> > >(void*) + 96
8 libsystem_pthread.dylib 0x00007fff74a5c6c1 _pthread_body + 340
9 libsystem_pthread.dylib 0x00007fff74a5c56d _pthread_start + 377
10 libsystem_pthread.dylib 0x00007fff74a5bc5d thread_start + 13

Thread 11:
0 libsystem_kernel.dylib 0x00007fff74920cee __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff74a5d662 _pthread_cond_wait + 732
2 libc++.1.dylib 0x00007fff72806cb0 std::__1::condition_variable::wait(std::__1::unique_lockstd::__1::mutex&) + 18
3 libtensorflow_framework.so 0x000000010ab07976 Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 278
4 libtensorflow_framework.so 0x000000010ab0768c Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WaitForWork(Eigen::EventCount::Waiter*, tensorflow::thread::EigenEnvironment::Task*) + 876
5 libtensorflow_framework.so 0x000000010ab07239 Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WorkerLoop(int) + 1849
6 libtensorflow_framework.so 0x000000010ab069f4 std::__1::__function::__func<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'(), std::__1::allocator<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()>, void ()>::operator()() + 52
7 libtensorflow_framework.so 0x000000010ab29000 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::function<void ()> > >(void*) + 96
8 libsystem_pthread.dylib 0x00007fff74a5c6c1 _pthread_body + 340
9 libsystem_pthread.dylib 0x00007fff74a5c56d _pthread_start + 377
10 libsystem_pthread.dylib 0x00007fff74a5bc5d thread_start + 13

Thread 12:
0 libsystem_kernel.dylib 0x00007fff74920cee __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff74a5d662 _pthread_cond_wait + 732
2 libc++.1.dylib 0x00007fff72806cb0 std::__1::condition_variable::wait(std::__1::unique_lockstd::__1::mutex&) + 18
3 libtensorflow_framework.so 0x000000010ab07976 Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 278
4 libtensorflow_framework.so 0x000000010ab0768c Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WaitForWork(Eigen::EventCount::Waiter*, tensorflow::thread::EigenEnvironment::Task*) + 876
5 libtensorflow_framework.so 0x000000010ab07239 Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WorkerLoop(int) + 1849
6 libtensorflow_framework.so 0x000000010ab069f4 std::__1::__function::__func<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'(), std::__1::allocator<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()>, void ()>::operator()() + 52
7 libtensorflow_framework.so 0x000000010ab29000 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::function<void ()> > >(void*) + 96
8 libsystem_pthread.dylib 0x00007fff74a5c6c1 _pthread_body + 340
9 libsystem_pthread.dylib 0x00007fff74a5c56d _pthread_start + 377
10 libsystem_pthread.dylib 0x00007fff74a5bc5d thread_start + 13

Thread 13:
0 libsystem_kernel.dylib 0x00007fff74920cee __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff74a5d662 _pthread_cond_wait + 732
2 libc++.1.dylib 0x00007fff72806cb0 std::__1::condition_variable::wait(std::__1::unique_lockstd::__1::mutex&) + 18
3 libtensorflow_framework.so 0x000000010ab07976 Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 278
4 libtensorflow_framework.so 0x000000010ab0768c Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WaitForWork(Eigen::EventCount::Waiter*, tensorflow::thread::EigenEnvironment::Task*) + 876
5 libtensorflow_framework.so 0x000000010ab07239 Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WorkerLoop(int) + 1849
6 libtensorflow_framework.so 0x000000010ab069f4 std::__1::__function::__func<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'(), std::__1::allocator<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()>, void ()>::operator()() + 52
7 libtensorflow_framework.so 0x000000010ab29000 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::function<void ()> > >(void*) + 96
8 libsystem_pthread.dylib 0x00007fff74a5c6c1 _pthread_body + 340
9 libsystem_pthread.dylib 0x00007fff74a5c56d _pthread_start + 377
10 libsystem_pthread.dylib 0x00007fff74a5bc5d thread_start + 13

Thread 14:
0 libsystem_kernel.dylib 0x00007fff74920cee __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff74a5d662 _pthread_cond_wait + 732
2 libc++.1.dylib 0x00007fff72806cb0 std::__1::condition_variable::wait(std::__1::unique_lockstd::__1::mutex&) + 18
3 libtensorflow_framework.so 0x000000010ab07976 Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 278
4 libtensorflow_framework.so 0x000000010ab0768c Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WaitForWork(Eigen::EventCount::Waiter*, tensorflow::thread::EigenEnvironment::Task*) + 876
5 libtensorflow_framework.so 0x000000010ab07239 Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WorkerLoop(int) + 1849
6 libtensorflow_framework.so 0x000000010ab069f4 std::__1::__function::__func<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'(), std::__1::allocator<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()>, void ()>::operator()() + 52
7 libtensorflow_framework.so 0x000000010ab29000 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::function<void ()> > >(void*) + 96
8 libsystem_pthread.dylib 0x00007fff74a5c6c1 _pthread_body + 340
9 libsystem_pthread.dylib 0x00007fff74a5c56d _pthread_start + 377
10 libsystem_pthread.dylib 0x00007fff74a5bc5d thread_start + 13

Thread 15:
0 libsystem_kernel.dylib 0x00007fff74920cee __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff74a5d662 _pthread_cond_wait + 732
2 libc++.1.dylib 0x00007fff72806cb0 std::__1::condition_variable::wait(std::__1::unique_lockstd::__1::mutex&) + 18
3 libtensorflow_framework.so 0x000000010ab07976 Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 278
4 libtensorflow_framework.so 0x000000010ab0768c Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WaitForWork(Eigen::EventCount::Waiter*, tensorflow::thread::EigenEnvironment::Task*) + 876
5 libtensorflow_framework.so 0x000000010ab07239 Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WorkerLoop(int) + 1849
6 libtensorflow_framework.so 0x000000010ab069f4 std::__1::__function::__func<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'(), std::__1::allocator<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()>, void ()>::operator()() + 52
7 libtensorflow_framework.so 0x000000010ab29000 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::function<void ()> > >(void*) + 96
8 libsystem_pthread.dylib 0x00007fff74a5c6c1 _pthread_body + 340
9 libsystem_pthread.dylib 0x00007fff74a5c56d _pthread_start + 377
10 libsystem_pthread.dylib 0x00007fff74a5bc5d thread_start + 13

Thread 16:
0 libsystem_kernel.dylib 0x00007fff74920cee __psynch_cvwait + 10
1 libsystem_pthread.dylib 0x00007fff74a5d662 _pthread_cond_wait + 732
2 libc++.1.dylib 0x00007fff72806cb0 std::__1::condition_variable::wait(std::__1::unique_lockstd::__1::mutex&) + 18
3 libtensorflow_framework.so 0x000000010ab07976 Eigen::EventCount::CommitWait(Eigen::EventCount::Waiter*) + 278
4 libtensorflow_framework.so 0x000000010ab0768c Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WaitForWork(Eigen::EventCount::Waiter*, tensorflow::thread::EigenEnvironment::Task*) + 876
5 libtensorflow_framework.so 0x000000010ab07239 Eigen::NonBlockingThreadPoolTempltensorflow::thread::EigenEnvironment::WorkerLoop(int) + 1849
6 libtensorflow_framework.so 0x000000010ab069f4 std::__1::__function::__func<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'(), std::__1::allocator<tensorflow::thread::EigenEnvironment::CreateThread(std::__1::function<void ()>)::'lambda'()>, void ()>::operator()() + 52
7 libtensorflow_framework.so 0x000000010ab29000 void* std::__1::__thread_proxy<std::__1::tuple<std::__1::function<void ()> > >(void*) + 96
8 libsystem_pthread.dylib 0x00007fff74a5c6c1 _pthread_body + 340
9 libsystem_pthread.dylib 0x00007fff74a5c56d _pthread_start + 377
10 libsystem_pthread.dylib 0x00007fff74a5bc5d thread_start + 13

Thread 0 crashed with X86 Thread State (64-bit):
rax: 0x0000000000000000 rbx: 0x00007fffad8ea340 rcx: 0x00007ffeefbfdd88 rdx: 0x0000000000000000
rdi: 0x0000000000000307 rsi: 0x0000000000000006 rbp: 0x00007ffeefbfddc0 rsp: 0x00007ffeefbfdd88
r8: 0x00007fffad8ca048 r9: 0x0000000000000040 r10: 0x0000000000000000 r11: 0x0000000000000206
r12: 0x0000000000000307 r13: 0x000000016f1990d4 r14: 0x0000000000000006 r15: 0x000000000000002d
rip: 0x00007fff74920e3e rfl: 0x0000000000000206 cr2: 0x00007fffad8c8148

Logical CPU: 0
Error Code: 0x02000148
Trap Number: 133

Binary Images:
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Calls made by this process:
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Calls made by all processes on this machine:
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`

flake8 testing of https://github.com/tf-coreml/tf-coreml on Python 2.7.14

$ flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics

./tfcoreml/optimizations/_optimize.py:116:23: F821 undefined name 'beta'
    beta = np.reshape(beta,(1, shape[0],1,1))
                      ^

https://github.com/tf-coreml/tf-coreml/blob/master/tfcoreml/optimizations/_optimize.py#L116

hi, I use the inception_v1_preprocessing_steps.ipynb to run, but convert model error , the error info is :

1014/1015: Converting op name: InceptionV1/Logits/Predictions/Softmax ( type:  Softmax )
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-7-0c798ef17d31> in <module>()
     18         output_feature_names=output_tensor_names,
     19         image_input_names = image_input_name,
---> 20         class_labels = class_labels)

/Users/a123/anaconda/envs/tensorflow27/lib/python2.7/site-packages/tfcoreml/_tf_coreml_converter.pyc in convert(tf_model_path, mlmodel_path, output_feature_names, input_name_shape_dict, image_input_names, is_bgr, red_bias, green_bias, blue_bias, gray_bias, image_scale, class_labels, predicted_feature_name, predicted_probabilities_output)
    476       class_labels=class_labels,
    477       predicted_feature_name=predicted_feature_name,
--> 478       predicted_probabilities_output=predicted_probabilities_output)

/Users/a123/anaconda/envs/tensorflow27/lib/python2.7/site-packages/tfcoreml/_tf_coreml_converter.pyc in _convert_pb_to_mlmodel(tf_model_path, mlmodel_path, output_feature_names, input_name_shape_dict, image_input_names, is_bgr, red_bias, green_bias, blue_bias, gray_bias, image_scale, class_labels, predicted_feature_name, predicted_probabilities_output)
    352 
    353   #optimizations on the nn spec
--> 354   optimize_nn_spec(builder=builder)
    355 
    356   utils.save_spec(builder.spec, mlmodel_path)

/Users/a123/anaconda/envs/tensorflow27/lib/python2.7/site-packages/tfcoreml/optimizations/_optimize_nn_spec.pyc in optimize_nn_spec(builder)
     45   _optimize_spatial_reduce_operation(builder.nn_spec.layers)
     46   _optimize_conv_mul_add(builder.nn_spec.layers)
---> 47   _optimize_disconnected_components(builder)
     48 
     49 

/Users/a123/anaconda/envs/tensorflow27/lib/python2.7/site-packages/tfcoreml/optimizations/_optimize_nn_spec.pyc in _optimize_disconnected_components(builder)
     35   """
     36 
---> 37   _optimize._remove_disconnected_components(builder)
     38 
     39 def optimize_nn_spec(builder):

/Users/a123/anaconda/envs/tensorflow27/lib/python2.7/site-packages/tfcoreml/optimizations/_optimize.pyc in _remove_disconnected_components(builder)
    353   start_ids = []
    354   for out in builder.spec.description.output:
--> 355     start_ids.append(blob_src[out.name])
    356 
    357   #Lets do BFS Graph traversal

KeyError: u'classLabel'

what happen to this and how to fix? please !

and i convert tensorflow model to coreML model , it success, but the result value is wrong? do you have any idea?

Could not find a version that satisfies the requirement coremltools>=0.6.3 (from tfcoreml==0.1.0) (from versions: )
No matching distribution found for coremltools>=0.6.3 (from tfcoreml==0.1.0)
What do this happen?

The current setting for test_pretrained_models.py does not remove the downloaded models. This improves the test time by avoiding multiple downloads for the same model. However, I'm also seeing a large tmp folder being created with all kinds of models for the tests:

shuoxinlin$ du -sh /tmp/tfcoreml/
816M	/tmp/tfcoreml/

So... to remove or not to remove, that's the question.
Any thoughts?

Hello,

Thank you for the conversion script. i get this error when running on a valid frozen TF model + careful usage with all required params:

File "/private/tmp/lib/python2.7/site-packages/tfcoreml/_tf_coreml_converter.py", line 478, in convert
predicted_probabilities_output=predicted_probabilities_output)
File "/private/tmp/lib/python2.7/site-packages/tfcoreml/_tf_coreml_converter.py", line 255, in _convert_pb_to_mlmodel
consumer_op = input_tensor.consumers()[0]

Can you please advise?
Thanks!
Reut

CoreML automatic code generation fails if model input and/or output names has '/' literal in it. We had a similar issue with ':'. Modify the converter so that '/' characters are replaced with '__'.

We tried tr_coreml on both MobileNet and InceptionV3. It works well with
MobileNet end to end (including conversion and inference). However, the
inference results are all NaNs with InceptionV3.

Steps to reproduce

1. Download and setup tf_coreml Library.
2. Download the frozen InceptionV3 model published by Google in 2016.
3. Run the tf_coreml tool to convert TensorFlow GraphDef to MLModel file.
4. Import the MLModel file into a iOS/Cocoa project, and feed it with an image.
5. All the inference resluts are NaNs.

Bash commands to download and extract the InceptionV3 model:

wget https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz
tar zxvf inception_v3_2016_08_28_frozen.pb.tar.gz

The Python code for conversion:

import tf_coreml as tf_converter
tf_converter.convert(
    tf_model_path='inception_v3_2016_08_28_frozen.pb',
    mlmodel_path = 'Inception.mlmodel',
    output_feature_names = ['InceptionV3/Predictions/Reshape_1:0'],
    image_input_names = ['input:0'],
    red_bias = -1.0,
    green_bias = -1.0,
    blue_bias = -1.0,
    image_scale = 2.0 / 255.0,
    )

The bias and scale paramters normalizes the RGB values from 0~255 to -1.0~1.0.

Environment

Python 2
OSX

From inside tf-coreml root folder:

pip install -e .
pytest tests

branch: master afec144

Encountered Issue

AssertionError: Output feature cannot be a placeholder

Is being thrown on most tests

Hello,

I am trying to convert model with 2 input parameter - int and image. For now I am getting something like this:
Core ML input(s): [name: "style_num__0" shortDescription: "This input is a sequence of length 26" type { imageType { width: 1 height: 1 colorSpace: GRAYSCALE } } , name: "input__0" type { imageType { width: 1000 height: 1000 colorSpace: RGB } } ] Core ML output(s): [name: "Squeeze__0" type { multiArrayType { dataType: DOUBLE } } ]

after doing that:
`import tfcoreml

tfcoreml.convert('styl.pb',
mlmodel_path="stylize3.mlmodel",
input_name_shape_dict={"input:0":[1,1000,1000,3]},
output_feature_names=["Squeeze:0"],
image_input_names= ["input:0", "style_num:0"])`

Add support for tf.max op in the converter.

This style transfer network can't be converted:
[https://storage.googleapis.com/download.tensorflow.org/models/stylize_v1.zip]
In this style transfer network there're a bunch of quantization related ops that are not supported in CoreML.
See test_style_transfer in tests/test_pretrained_models.py.

After converting TensorFlow model to Core ML I created an iOS app to try to use it and check out the predictions. I used my already built app and replaced it with the new model. Unfortunately, the predictions it give were completely wrong. The predictions works correctly if I get the Core ML model from Apple's Machine Learning website.

But when I use the TensorFlow to Core ML converted model then it gives wrong predictions. I am attaching an image to show the problem and also a link to the Github repository that contains all the code I used to create the app.

Github URL: https://github.com/azamsharp/ImageRecognitionTensorFlow2CoreML

• The app works correctly and give correct predictions if I use the Core ML model from Apple's website and not the TensorFlow generated model.

While trying the "Simple Speech Recognition" frozen graph given in this link:
https://storage.googleapis.com/download.tensorflow.org/models/speech_commands_v0.01.zip

The TF model fails to load in tensorflow. Here's the output:
ValueError: No op named DecodeWav in defined operations.

I'm using TensorFlow 1.3.

Create a Readme.rst file that will be displayed in the pypi package description.

Currently before converting the TF graph we check for unsupported ops and error out early if there is an unsupported op. However, this function should also take into account the output tensors that the user in interested in. We should not error out if an unsupported op comes after the desired output.

TF converter cannot handle LRN op in GoogleNet V1 because of a missing TF op LRN.

`File "convert_temp.py", line 7, in
input_name_shape_dict = {'input:0' : [1, 299, 299, 3]})
tfcoreml/_tf_coreml_converter.py", line 478, in convert
predicted_probabilities_output=predicted_probabilities_output)

/tfcoreml/_tf_coreml_converter.py", line 143, in _convert_pb_to_mlmodel
_check_unsupported_ops(OPS, output_feature_names)
tfcoreml/_tf_coreml_converter.py", line 111, in _check_unsupported_ops

','.join(unsupported_op_types)))
NotImplementedError: Unsupported Ops of type: DecodeJpeg,ResizeBilinear,PlaceholderWithDefault`
Anyone meet this issue?

These are currently skipped in the converter but this is not the correct behavior. This is will lead to converted .mlmodel files but its highly likely that they will be incorrect. Since BatchToSpaceND is not supported by CoreML, converter should error out.

after i converted the tfmodel to mlmodel, when i run the predict program, the following errors appeared:
coremlc: Error: compiler error: Invalid data blob: 'cnn_net_1/conv-maxpool-5/pool_5:0' shape (C,H,W = 150,146,-144) for output of layer: 'cnn_net_1/conv-maxpool-5/pool_5:0'.

in fact, it's a max_pooling operation, and the previous layers's shape is （C,H,W）= (150, 146, 1), the operation is ：
pooled = tf.nn.max_pool(
conv_h,
ksize=[1, self.sequence_length - filter_size + 1, 1, 1],
strides=[1, 1, 1, 1],
padding='VALID',
name="pool_%s" % filter_size)

how could i solve the problem? thank you

Expose the scripts as functions that can be called after installing the pip package.

Hi there,

this is a really exciting project! I'm getting this error when trying to convert MobileNet from the code poets example:

NotImplementedError: Unsupported Ops of type: PlaceholderWithDefault

I noticed that Placeholder is implemented. Do you think PlaceholderWithDefault will be in scope?

Many thanks!

after installing tf-coreml with the following

pip install -e .

I attempt to run python test_tf_converter.py

and get the following error:

return f(*args, **kwds)
Traceback (most recent call last):
File "test_tf_converter.py", line 12, in
import tfcoreml as tf_converter
File "/Users/tgwagner/fresh/tf-coreml/tfcoreml/init.py", line 1, in
from ._tf_coreml_converter import convert
File "/Users/tgwagner/fresh/tf-coreml/tfcoreml/_tf_coreml_converter.py", line 9, in
from _ops_to_layers import convert_ops_to_layers
ModuleNotFoundError: No module named '_ops_to_layers'

Do I need to add something to my PATH or ?

Note that _ops_to_layers.py does exist in my tf-coreml/tfcoreml directory

Thanks
Todd