Tensorflow for other custom program

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Hello,

I am using with raspberry pi and I want to run my own program with custom created graph. Is there any way I can do with movidius???

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@smit.idevices You should be able to do this, although you will need to use a graph file compiled by the NCSDK. You can generate a compatible graph file by using mvnCCompile with your custom network. See https://movidius.github.io/ncsdk/tools/compile.html for more information regarding compiling a graph file.

In regards to writing your own program, you can visit https://movidius.github.io/ncsdk/c_api/ for more information on the C API and for the Python API please visit https://movidius.github.io/ncsdk/py_api/. You may also feel free to view and re-use example code from the NCAPP ZOO at: https://github.com/Movidius/ncappzoo. Good luck with your project!

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Thank you for the answer. I understood the flow but when I am converting my meta file to graph I am encountering following error:

Traceback (most recent call last):

File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1323, in _do_call return fn(*args) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1293, in _run_fn self._extend_graph() File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1354, in _extend_graph self._session, graph_def.SerializeToString(), status) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/errors_impl.py", line 473, in exit c_api.TF_GetCode(self.status.status))tensorflow.python.framework.errors_impl.InvalidArgumentError: No OpKernel was registered to support Op 'Unpack' with these attrs. Registered devices: [CPU], Registered kernels: device='CPU'; T in [DT_BOOL] device='CPU'; T in [DT_FLOAT] device='CPU'; T in [DT_INT32] [[Node: unstack_20 = Unpack[T=DT_STRING, axis=0, num=24, _device="/device:GPU:0"](prefetch_queue_Dequeue:20)]]

Edit:

This is a checkpoint file from tensorflow object detection API and it is trained on GPU with tf version 1.4

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@Tome_at_Intel can you please let me know how can I convert my trained check point file into movidius graph??

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@smit.idevices Converting your network is done using mvNCCompile. You have to specify the input node by using the -in and output node by using the -on flags. In order to help debug your issue, can you provide me with the command that you tried to use to run your network?

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@Tome_at_Intel I am using tensorflow object detection model ssd_mobilenet_v1_coco and I have a frozen .pb graph and checkpoint files like model.ckpt.meta/.index etc. I tried using model.ckpt.meta and I got following error (input and output nodes are default):

smit@raspberrypi:~/DevelopmentGit/models/research $ mvNCCompile object_detection/model/model.ckpt.meta -in input_tensor -o network.graph

mvNCCompile v02.00, Copyright @ Movidius Ltd 2016

/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py:814: DeprecationWarning: builtin type EagerTensor has no module attribute

EagerTensor = c_api.TFE_Py_InitEagerTensor(_EagerTensorBase)/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead if d.decorator_argspec is not None), _inspect.getargspec(target))/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead if d.decorator_argspec is not None), _inspect.getargspec(target))Traceback (most recent call last): File "/usr/local/bin/mvNCCompile", line 118, in create_graph(args.network, args.inputnode, args.outputnode, args.outfile, args.nshaves, args.inputsize, args.weights) File "/usr/local/bin/mvNCCompile", line 104, in create_graph net = parse_tensor(args, myriad_config) File "/usr/local/bin/ncsdk/Controllers/TensorFlowParser.py", line 194, in parse_tensor inputTensor = graph.get_tensor_by_name(inputnode + ':0') File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 3328, in get_tensor_by_name return self.as_graph_element(name, allow_tensor=True, allow_operation=False) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 3156, in as_graph_element return self._as_graph_element_locked(obj, allow_tensor, allow_operation) File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 3198, in _as_graph_element_locked "graph." % (repr(name), repr(op_name)))KeyError: "The name 'input_tensor:0' refers to a Tensor which does not exist. The operation, 'input_tensor', does not exist in the graph."

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@smit.idevices Have you solved the issue? I am trying to compile a custom model, and I know the input and output nodes, but when I try to compile it shows me the same error as you: The name 'tensorname:0' refers to a Tensor which does not exist. The operation, 'tensorname', does not exist in the graph.

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@albertcliment no as movidius is not supporting SSD mobilenet from tensorflow object detection model

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looking forward to a SSD Mobilenet support for Tensorflow in the future!

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@smit.idevices @albertcliment hello guys I have the same problems as you, did you solve it? Please give me some help…And @Tome_at_Intel do you know how to fix this problem???

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@WuXinyang no as SSD Mobilenet for Tesnorflow is still not supported :(

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@WuXinyang There currently isn't support for SSD MobileNet for TensorFlow. We do have TinyYoloV2 Tensorflow support via Darkflow transformation if you are interested @ https://github.com/movidius/ncsdk/releases/tag/v1.12.00.01