Implementation for Cognitive Mapping and Planning paper.

cognitive_mapping_and_planning/.gitignore

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+deps
+*.pyc
+lib*.so
+lib*.so*

cognitive_mapping_and_planning/README.md

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+# Cognitive Mapping and Planning for Visual Navigation
+**Saurabh Gupta, James Davidson, Sergey Levine, Rahul Sukthankar, Jitendra Malik**
+
+**Computer Vision and Pattern Recognition (CVPR) 2017.**
+
+**[ArXiv](https://arxiv.org/abs/1702.03920), 
+[Project Website](https://sites.google.com/corp/view/cognitive-mapping-and-planning/)**
+
+### Citing
+If you find this code base and models useful in your research, please consider
+citing the following paper:
+  ```
+  @inproceedings{gupta2017cognitive,
+    title={Cognitive Mapping and Planning for Visual Navigation},
+    author={Gupta, Saurabh and Davidson, James and Levine, Sergey and
+      Sukthankar, Rahul and Malik, Jitendra},
+    booktitle={CVPR},
+    year={2017}
+  }
+  ```
+
+### Contents
+1.  [Requirements: software](#requirements-software)
+2.  [Requirements: data](#requirements-data)
+3.  [Test Pre-trained Models](#test-pre_trained-models)
+4.  [Train your Own Models](#train-your-own-models)
+
+### Requirements: software
+1.  Python Virtual Env Setup: All code is implemented in Python but depends on a
+    small number of python packages and a couple of C libraries. We recommend
+    using virtual environment for installing these python packages and python
+    bindings for these C libraries.
+      ```Shell
+      VENV_DIR=venv
+      pip install virtualenv
+      virtualenv $VENV_DIR
+      source $VENV_DIR/bin/activate
+
+      # You may need to upgrade pip for installing openv-python.
+      pip install --upgrade pip
+      # Install simple dependencies.
+      pip install -r requirements.txt
+
+      # Patch bugs in dependencies.
+      sh patches/apply_patches.sh
+      ```
+
+2.  Install [Tensorflow](https://www.tensorflow.org/) inside this virtual
+    environment. Typically done with `pip install --upgrade tensorflow-gpu`.
+
+3.  Swiftshader: We use
+    [Swiftshader](https://github.com/google/swiftshader.git), a CPU based
+    renderer to render the meshes.  It is possible to use other renderers,
+    replace `SwiftshaderRenderer` in `render/swiftshader_renderer.py` with
+    bindings to your renderer. 
+    ```Shell
+    mkdir -p deps
+    git clone --recursive https://github.com/google/swiftshader.git deps/swiftshader-src
+    cd deps/swiftshader-src && git checkout 91da6b00584afd7dcaed66da88e2b617429b3950
+    mkdir build && cd build && cmake .. && make -j 16 libEGL libGLESv2
+    cd ../../../
+    cp deps/swiftshader-src/build/libEGL* libEGL.so.1
+    cp deps/swiftshader-src/build/libGLESv2* libGLESv2.so.2
+    ```
+
+4.  PyAssimp: We use [PyAssimp](https://github.com/assimp/assimp.git) to load
+    meshes.  It is possible to use other libraries to load meshes, replace
+    `Shape` `render/swiftshader_renderer.py` with bindings to your library for
+    loading meshes. 
+    ```Shell
+    mkdir -p deps
+    git clone https://github.com/assimp/assimp.git deps/assimp-src
+    cd deps/assimp-src
+    git checkout 2afeddd5cb63d14bc77b53740b38a54a97d94ee8
+    cmake CMakeLists.txt -G 'Unix Makefiles' && make -j 16
+    cd port/PyAssimp && python setup.py install
+    cd ../../../..
+    cp deps/assimp-src/lib/libassimp* .
+    ```
+
+5.  graph-tool: We use [graph-tool](https://git.skewed.de/count0/graph-tool)
+    library for graph processing.
+    ```Shell
+    mkdir -p deps
+    # If the following git clone command fails, you can also download the source
+    # from https://downloads.skewed.de/graph-tool/graph-tool-2.2.44.tar.bz2
+    git clone https://git.skewed.de/count0/graph-tool deps/graph-tool-src
+    cd deps/graph-tool-src && git checkout 178add3a571feb6666f4f119027705d95d2951ab
+    bash autogen.sh
+    ./configure --disable-cairo --disable-sparsehash --prefix=$HOME/.local
+    make -j 16
+    make install
+    cd ../../
+    ```
+
+### Requirements: data
+1.  Download the Stanford 3D Inddor Spaces Dataset (S3DIS Dataset) and ImageNet
+    Pre-trained models for initializing different models. Follow instructions in
+    `data/README.md`
+
+### Test Pre-trained Models
+1.  Download pre-trained models using
+    `scripts/scripts_download_pretrained_models.sh`
+
+2.  Test models using `scripts/script_test_pretrained_models.sh`.
+
+### Train Your Own Models
+All models were trained asynchronously with 16 workers each worker using data
+from a single floor. The default hyper-parameters coorespond to this setting.
+See [distributed training with
+Tensorflow](https://www.tensorflow.org/deploy/distributed) for setting up
+distributed training. Training with a single worker is possible with the current
+code base but will require some minor changes to allow each worker to load all
+training environments.
+
+### Contact
+For questions or issues open an issue on the tensorflow/models [issues
+tracker](https://github.com/tensorflow/models/issues). Please assign issues to
+@s-gupta.
+
+### Credits
+This code was written by Saurabh Gupta (@s-gupta).