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Commit 97940fb

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Fix issue for code-blocks not rendering correctly on readthedocs + Minor updates
1 parent 5c2c986 commit 97940fb

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‎docs/source/index.rst‎

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@@ -32,7 +32,7 @@ The software tools which we shall use throughout this tutorial are listed in the
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.. toctree::
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:maxdepth: 4
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:caption: Contents:
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:caption: Contents
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install
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training

‎docs/source/install.rst‎

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- Run the following command in a **NEW** `Terminal` window:
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.. code-block:: posh
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.. code-block:: bash
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python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
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- Once the above is run, you should see a print-out similar to the one bellow:
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.. code-block:: posh
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.. code-block:: bash
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:emphasize-lines: 1,2,6,7,8,9,10,11,12,20,21,22,23,24,25,26,31
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2020年06月22日 20:24:31.355541: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
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- Add ``<PATH_TO_PB>`` to your ``Path`` environment variable (see :ref:`set_env`)
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- In a new `Terminal` [#]_, ``cd`` into ``TensorFlow/models/research/`` directory and run the following command:
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.. code-block:: python
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.. code-block:: bash
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# From within TensorFlow/models/research/
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protoc object_detection/protos/*.proto --python_out=.
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.. tab:: Windows Powershell
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.. code-block::bash
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# From within TensorFlow/models/research/
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Get-ChildItem object_detection/protos/*.proto | foreach {protoc "object_detection/protos/$($_.Name)" --python_out=.}
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.. tab:: Command Prompt
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.. code-block::
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.. code-block::bash
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# From within TensorFlow/models/research/
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for /f %i in ('dir /b object_detection\protos\*.proto') do protoc object_detection\protos\%i --python_out=.
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********************************
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Installation of the Object Detection API is achieved by installing the ``object_detection`` package. This is done by running the following commands from within ``Tensorflow\models\research``:
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.. code-block:: bash
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# From within TensorFlow/models/research/
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cp object_detection/packages/tf2/setup.py .
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During the above installation, you may observe the following error:
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.. code-block::
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.. code-block:: bash
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ERROR: Command errored out with exit status 1:
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command: 'C:\Users\sglvladi\Anaconda3\envs\tf2\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\sglvladi\\AppData\\Local\\Temp\\pip-install-yn46ecei\\pycocotools\\setup.py'"'"'; __file__='"'"'C:\\Users\\sglvladi\\AppData\\Local\\Temp\\pip-install-yn46ecei\\pycocotools\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\sglvladi\AppData\Local\Temp\pip-record-wpn7b6qo\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\Users\sglvladi\Anaconda3\envs\tf2\Include\pycocotools'
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To test the installation, run the following command from within ``Tensorflow\models\research``:
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.. code-block:: bash
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# From within TensorFlow/models/research/
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python object_detection/builders/model_builder_tf2_test.py
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Once the above is run, allow some time for the test to complete and once done you should observe a
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printout similar to the one below:
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.. code-block::
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.. code-block:: bash
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...
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[ OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config
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time to have a look at the :ref:`examples` section.
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.. _labelImg_install:
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LabelImg Installation
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---------------------
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There exist several ways to install ``labelImg``. Below are 3 of the most common.
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Get from PyPI (Recommended)
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***************************
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1. Open a new `Terminal` window and activate the `tensorflow_gpu` environment (if you have not done so already)
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2. Run the following command to install ``labelImg``:
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.. code-block:: bash
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pip install labelImg
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3. ``labelImg`` can then be run as follows:
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.. code-block:: bash
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labelImg
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# or
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labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
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Use precompiled binaries (Easy)
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*******************************
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Precompiled binaries for both Windows and Linux can be found `here <http://tzutalin.github.io/labelImg/>`_ .
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Installation is the done in three simple steps:
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1. Inside you ``TensorFlow`` folder, create a new directory, name it ``addons`` and then ``cd`` into it.
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2. Download the latest binary for your OS from `here <http://tzutalin.github.io/labelImg/>`_. and extract its contents under ``Tensorflow/addons/labelImg``.
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3. You should now have a single folder named ``addons/labelImg`` under your ``TensorFlow`` folder, which contains another 4 folders as such:
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.. code-block:: bash
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TensorFlow/
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├─ addons/
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│ └─ labelImg/
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└─ models/
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├─ community/
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├─ official/
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├─ orbit/
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├─ research/
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└─ ...
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4. ``labelImg`` can then be run as follows:
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# From within Tensorflow/addons/labelImg
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labelImg
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# or
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labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
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Build from source (Hard)
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************************
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The steps for installing from source follow below.
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**1. Download labelImg**
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- Inside you ``TensorFlow`` folder, create a new directory, name it ``addons`` and then ``cd`` into it.
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- To download the package you can either use `Git <https://git-scm.com/downloads>`_ to clone the `labelImg repo <https://github.com/tzutalin/labelImg>`_ inside the ``TensorFlow\addons`` folder, or you can simply download it as a `ZIP <https://github.com/tzutalin/labelImg/archive/master.zip>`_ and extract it's contents inside the ``TensorFlow\addons`` folder. To keep things consistent, in the latter case you will have to rename the extracted folder ``labelImg-master`` to ``labelImg``. [#]_
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- You should now have a single folder named ``addons\labelImg`` under your ``TensorFlow`` folder, which contains another 4 folders as such:
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.. code-block:: bash
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TensorFlow/
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├─ addons
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│ └─ labelImg/
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└─ models/
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├─ community/
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├─ official/
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├─ orbit/
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├─ research/
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└─ ...
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.. [#] The latest repo commit when writing this tutorial is `8d1bd68 <https://github.com/tzutalin/labelImg/commit/8d1bd68ab66e8c311f2f45154729bba301a81f0b>`_.
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**2. Install dependencies and compiling package**
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- ``cd`` into ``TensorFlow/addons/labelImg`` and run the following commands:
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.. tabs::
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.. tab:: Windows
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.. code-block:: bash
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conda install pyqt=5
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pyrcc5 -o libs/resources.py resources.qrc
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sudo pip install -r requirements/requirements-linux-python3.txt
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make qt5py3
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**3. Test your installation**
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- ``cd`` into ``TensorFlow/addons/labelImg`` and run the following command:
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python labelImg.py
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python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
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‎docs/source/issues.rst‎

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get the above error. As per the discussion is in `this Stack Overflow thread <https://stackoverflow.com/a/61961016/3474873>`_,
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upgrading the Python protobuf version seems to solve this issue:
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When trying to export oyu trained model using the ``exporter_main_v2.py`` script, you may come
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across an error that looks like this:
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.. code-block:: bash
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:linenos:
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:emphasize-lines: 9
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