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It's the names of\nnodes you want to output, seperated by comma for multiple values.\n\nIn graph theory terminology, they are ``leaf`` nodes of your graph.\n\nexample\n~~~~~~~\n\n.. code-block:: console\n\n $ utensor-cli convert simple_model.pb --output-nodes=pred,logits\n\nRun ``utensor-cli convert --help`` for detailed information.\n\n:mod:`utensor_cgen` as Library\n==============================\n\n.. subgraph-match-begine\n\nSubgraph Isomorphic Matcher\n---------------------------\n\nWith :class:`.uTensorGraphMatcher`, performing isomorphic subgraph matching\nalong with replacing or manipulating the matched subgraph(s) takes just a\nfew line of code:\n\n.. code-block:: python\n\n from utensor_cgen.matcher import uTensorGraphMatcher\n\n # `pattrn_ugraph` is the pattern to match with\n pattrn_ugraph = ...\n matcher = uTensorGraphMatcher(pattrn_ugraph)\n\n # a larget graph to perform subgraph match\n subject_ugraph = ...\n\n # matches is a list of `uTensorGraphMatch` objects\n matches = matcher.match_all(subject_ugraph)\n if matches:\n # do stuff with the matches\n\nUse Case: Node Fusion\n~~~~~~~~~~~~~~~~~~~~~\n\nNote: we'll use **operation**/**node**/**layer** interchangeably in the\ndocumentation\n\n- It's commonly seen pattern in convolution neural network (``CNN``),\n ``conv -> relu -> pooling``. That is, a 2D convolution followed by a\n relu layer and then a pooling down sampling layer.\n- With our :class:`.uTensorGraphMatcher`, you can locate such pattern in your\n ``CNN`` model and fuse/replace matched nodes into one optimized\n :class:`.QuantizedFusedConv2DMaxpool` node.\n\n - Left: original graph\n - Middle: matched convolution layer\n - Right: replace the matched layer with specialized\n ``QuantizedFusedConv2DMaxpool`` node\n\n\\ |conv-pool-fuse|\n\n\nUse Case: Dropout Layer Removal\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n- Though ``dropout`` is an effective technique to improve training\n performance of your model, it's not necessary during inference\n phrase.\n- In the mainstream frameworks such as `Tensorflow`_ or `PyTorch`_,\n an ``dropout`` layer is typically implemented with other elementary\n operations/nodes. As a result, finding and removing those nodes for\n inference optimization (both in model size and prediciton time) is\n not trivial and error prone.\n- With our :class:`.uTensorGraphMatcher`, you can find and remove the dropout\n nodes as illustrated in the following picture.\n\n - Left: original graph with dropout Layers\n - Middle: matched dropout layers\n - Right: graph with dropout layers removed\n\n\\ |cnn-dropout|\n\n.. subgraph-match-end\n\nWe use mainly `Tensorflow`_ for declaring the pattern graph for matcher now.\n\nHigh-level graph builder is on its way, see `Future Works <#future-works>`_ for detail.\n\nTutorials\n=========\n\n- `Deep Multilayer\n Perceptron `_\n- `End-to-End Convolution\n NN `_\n\nHow to Serve Your Model on uTenosr\n==================================\n\nTensorFlow_\n-----------\n\n1. Freeze your `tensorflow.Graph`\n\n - please refer to the `official doc `_\n and read the `Freezing `_ section\n\n2. Follow instructions in :ref:`install` section to install :mod:`utensor_cgen`\n\n - then `utensor-cli` should be available in your console\n\n3. Inspect your pb file to find the output node\n\n .. code-block:: console\n\n # verbose mode\n $ utensor-cli show graph.pb\n\n # or oneline mode\n $ utensor-cli show graph.pb --oneline\n\n4. convert the protobuf file to C/C++ source code with `utensor-cli`\n\n - supose the output node is ``pred`` in **graph.pb**\n\n .. code-block:: console\n\n $ utensor-cli convert --output-nodes=pred graph.pb\n\n5. Compile your application code with generated C/C++ and weights files\n\n - You should find your model C/C++ and weights files in directories\n **models** and **constants** respectively\n\n\\ |convert-example|\n\nTesting\n=======\n\n1. follow the steps in :ref:`install_dev` section\n2. run tests as following\n\n .. code-block:: console\n\n # run with `make`\n $ make tests\n\n # run with `pipenv`\n $ pipenv run pytest tests\n\n.. design philosophy\n.. `12 Factor CLI App `_\n\n\nFuture Works\n============\n\n1. High-level graph builder api for building :class:`.uTensorGraph`.\n\n - Currently ``utensor_cgen`` uses ``TensorFlow`` api for building IR graph, ``uTensorGraph``.\n - With high-level graph builder, users can build their ``uTensorGraph`` easily and do not need\n to take care of the integrity of the graph.\n The builder will take care of it automatically.\n\n.. _pip: https://pip.pypa.io/en/stable/\n.. _pipenv: https://github.com/pypa/pipenv\n.. _Tensorflow: https://www.tensorflow.org\n.. _PyTorch: https://pytorch.org/\n.. _uTensor: https://github.com/uTensor/uTensor\n\n.. readme_end\n\n.. |cnn-dropout| image:: doc/source/_images/cnn_dropout.png\n :alt: cnn-dropout\n.. |conv-pool-fuse| image:: doc/source/_images/conv_pool_fuse.png\n :alt: conv-pool-fuse\n.. |convert-example| image:: doc/source/_images/convert_example.png\n :alt: convert-example\n\n\n.. TODOs\n.. =====\n\n.. 1. (done?) core code generator implementation\n\n.. - We need some refactoring, PRs are welcomed!\n\n.. 2. type alias in C/C++\n\n.. - ex: use ``uint8_t`` or ``unsigned char``?\n.. - a lot more about this....\n\n.. 3. Relation among snippets/containers\n\n.. - shared template variables? (headers, shared placeholders...etc)\n\n.. 4. 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dboyliao/utensor-cli

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