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Commit fee5f84

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Fixed sphinx warnings
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‎docs/source/install.rst‎

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@@ -21,22 +21,22 @@ Install Anaconda Python 3.7
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- Go to `<https://www.anaconda.com/products/individual>`_ and click the "Download" button
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- Download the `Python 3.7 64-Bit Graphical Installer <https://repo.anaconda.com/archive/Anaconda3-2020.02-Windows-x86_64.exe>`_ or the `32-Bit Graphical Installer <https://repo.anaconda.com/archive/Anaconda3-2020.02-Windows-x86.exe>`_ installer, per your system requirements
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- Run the downloaded executable (``.exe``) file to begin the installation. See `here <https://docs.anaconda.com/anaconda/install/windows/>`_ for more details
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- Run the downloaded executable (``.exe``) file to begin the installation. See `here <https://docs.anaconda.com/anaconda/install/windows/>`__ for more details
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- (Optional) In the next step, check the box "Add Anaconda3 to my PATH environment variable". This will make Anaconda your default Python distribution, which should ensure that you have the same default Python distribution across all editors.
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.. tab:: Linux
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- Go to `<https://www.anaconda.com/products/individual>`_ and click the "Download" button
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- Download the `Python 3.7 64-Bit (x86) Installer <https://repo.anaconda.com/archive/Anaconda3-2020.02-Linux-x86_64.sh>`_
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- Run the downloaded bash script (``.sh``) file to begin the installation. See `here <https://docs.anaconda.com/anaconda/install/linux/>`_ for more details.
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- Run the downloaded bash script (``.sh``) file to begin the installation. See `here <https://docs.anaconda.com/anaconda/install/linux/>`__ for more details.
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- When prompted with the question "Do you wish the installer to prepend the Anaconda<2 or 3> install location to PATH in your /home/<user>/.bashrc ?", answer "Yes". If you enter "No", you must manually add the path to Anaconda or conda will not work.
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Create a new Anaconda virtual environment
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*****************************************
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- Open a new `Terminal` window
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- Type the following command:
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conda create -n tensorflow pip python=3.8
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@@ -48,7 +48,7 @@ Activate the Anaconda virtual environment
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*****************************************
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- Activating the newly created virtual environment is achieved by running the following in the `Terminal` window:
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conda activate tensorflow
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@@ -73,21 +73,21 @@ Install the TensorFlow PIP package
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**********************************
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- Run the following command in a `Terminal` window:
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pip install --ignore-installed --upgrade tensorflow==2.2.0
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Verify your Installation
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************************
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- Run the following command in a `Terminal` window:
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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:: default
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2020年06月22日 19:20:32.614181: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'cudart64_101.dll'; dlerror: cudart64_101.dll not found
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2020年06月22日 19:20:32.620571: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
@@ -147,13 +147,13 @@ Install CUDA Toolkit
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.. tab:: Windows
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- Follow this `link <https://developer.nvidia.com/cuda-10.1-download-archive-update2?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exenetwork>`_ to download and install CUDA Toolkit 10.1
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- Installation instructions can be found `here <https://docs.nvidia.com/cuda/archive/10.1/cuda-installation-guide-microsoft-windows/index.html>`_
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- Follow this `link <https://developer.nvidia.com/cuda-10.1-download-archive-update2?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exenetwork>`__ to download and install CUDA Toolkit 10.1
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- Installation instructions can be found `here <https://docs.nvidia.com/cuda/archive/10.1/cuda-installation-guide-microsoft-windows/index.html>`__
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.. tab:: Linux
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- Follow this `link <https://developer.nvidia.com/cuda-10.1-download-archive-update2?target_os=Linux&target_arch=x86_64>`_ to download and install CUDA Toolkit 10.1 for your Linux distribution.
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- Installation instructions can be found `here <https://docs.nvidia.com/cuda/archive/10.1/cuda-installation-guide-linux/index.html>`_
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- Follow this `link <https://developer.nvidia.com/cuda-10.1-download-archive-update2?target_os=Linux&target_arch=x86_64>`__ to download and install CUDA Toolkit 10.1 for your Linux distribution.
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- Installation instructions can be found `here <https://docs.nvidia.com/cuda/archive/10.1/cuda-installation-guide-linux/index.html>`__
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.. _cudnn_install:
@@ -201,7 +201,7 @@ Environment Setup
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As per Section 7.1.1 of the `CUDA Installation Guide for Linux <https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#install-linux>`_, append the following lines to ``~/.bashrc``:
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# CUDA related exports
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export PATH=/usr/local/cuda-10.1/bin${PATH:+:${PATH}}
@@ -220,7 +220,7 @@ Verify the installation
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- Run the following command in a **NEW** `Terminal` window:
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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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: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
@@ -286,7 +286,7 @@ Downloading the TensorFlow Model Garden
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- To download the models you can either use `Git <https://git-scm.com/downloads>`_ to clone the `TensorFlow Models repository <https://github.com/tensorflow/models>`_ inside the ``TensorFlow`` folder, or you can simply download it as a `ZIP <https://github.com/tensorflow/models/archive/master.zip>`_ and extract its contents inside the ``TensorFlow`` folder. To keep things consistent, in the latter case you will have to rename the extracted folder ``models-master`` to ``models``.
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- You should now have a single folder named ``models`` under your ``TensorFlow`` folder, which contains another 4 folders as such:
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TensorFlow/
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└─ models/
@@ -311,7 +311,7 @@ This should be done as follows:
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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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# 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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# 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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# 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=.
@@ -354,19 +354,19 @@ As of TensorFlow 2.x, the ``pycocotools`` package is listed as `a dependency of
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Run the following command to install ``pycocotools`` with Windows support:
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pip install cython
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pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI
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Note that, according to the `package's instructions <https://github.com/philferriere/cocoapi#this-clones-readme>`_, Visual C++ 2015 build tools must be installed and on your path. If they are not, make sure to install them from `here <https://go.microsoft.com/fwlink/?LinkId=691126>`_.
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Note that, according to the `package's instructions <https://github.com/philferriere/cocoapi#this-clones-readme>`_, Visual C++ 2015 build tools must be installed and on your path. If they are not, make sure to install them from `here <https://go.microsoft.com/fwlink/?LinkId=691126>`__.
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.. tab:: Linux
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Download `cocoapi <https://github.com/cocodataset/cocoapi>`_ to a directory of your choice, then ``make`` and copy the pycocotools subfolder to the ``Tensorflow/models/research`` directory, as such:
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git clone https://github.com/cocodataset/cocoapi.git
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@@ -386,7 +386,7 @@ Install the Object Detection API
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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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During the above installation, you may observe the following error:
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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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# 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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...
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[ OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config

‎docs/source/issues.rst‎

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@@ -10,7 +10,7 @@ Python crashes - TensorFlow GPU
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If you are using :ref:`tensorflow_gpu` and when you try to run some Python object detection script (e.g. :ref:`test_tf_models`), after a few seconds, Windows reports that Python has crashed then have a look at the `Anaconda/Command Prompt` window you used to run the script and check for a line similar (maybe identical) to the one below:
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2018年03月22日 03:07:54.623130: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY36円\tensorflow\stream_executor\cuda\cuda_dnn.cc:378] Loaded runtime CuDNN library: 7101 (compatibility version 7100) but source was compiled with 7003 (compatibility version 7000). If using a binary install, upgrade your CuDNN library to match. If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration.
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- Failure to restart training of a model. Look for the following errors in the debugging:
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2018年03月23日 03:03:10.326902: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY36円\tensorflow\stream_executor\cuda\cuda_dnn.cc:385] could not create cudnn handle: CUDNN_STATUS_ALLOC_FAILED
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2018年03月23日 03:03:10.330475: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY36円\tensorflow\stream_executor\cuda\cuda_dnn.cc:352] could not destroy cudnn handle: CUDNN_STATUS_BAD_PARAM
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In some versions of Tensorflow, you may see errors that look similar to the ones below:
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WARNING:tensorflow:Entity <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x000001E92103EDD8>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Conv.call of <tensorflow.python.layers.convolutional.Conv2D object at 0x000001E92103EDD8>>: AssertionError: Bad argument number for Name: 3, expecting 4
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These warnings appear to be harmless form my experience, however they can saturate the console with unnecessary messages, which makes it hard to scroll through the output of the training/evaluation process.
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As reported `here <https://github.com/tensorflow/tensorflow/issues/34551>`_, this issue seems to
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As reported `here <https://github.com/tensorflow/tensorflow/issues/34551>`__, this issue seems to
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be caused by a mismatched version of `gast <https://github.com/serge-sans-paille/gast/>`_. Simply
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:linenos:
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- Change:
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