Add sphinx-argparse, fix doc errors

doc/source/conf.py

@@ -43,6 +43,7 @@
     'sphinx.ext.viewcode',
     'sphinx.ext.autosummary',
     'sphinx.ext.napoleon',
+    'sphinxarg.ext',
     'sphinx.ext.githubpages',
 ]
 
@@ -79,7 +80,7 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'alabaster'
+html_theme = 'pyramid'
 
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the

doc/source/index.rst

@@ -11,6 +11,7 @@ Welcome to DeepVO's documentation!
    :caption: Contents:
 
    modules
+   command_line_usage
 
 
 

doc/source/modules.rst

@@ -1,4 +1,4 @@
-final_project
+DeepVO
 =============
 
 .. toctree::

main.py

@@ -2,9 +2,8 @@
 '''
 .. module:: main
 
-Module for starting the training, testing and plotting functions
-
 .. moduleauthor:: Rasmus Diederichsen
+
 '''
 
 import sys
@@ -21,8 +20,8 @@
 from utils import DataManager, OptimizerSpec
 from performance_visualizer import PerformanceVisualizer
 
-def main():
-
+def make_parser():
+    '''Function returning parser is necessary for sphinx-argparse'''
     tf_optimizers = {class_name[:-len('Optimizer')] for class_name in dir(tf.train) if 'Optimizer'
                      in class_name and class_name != 'Optimizer'}
     parser = ArgumentParser('Train the DeepVO model')
@@ -49,7 +48,11 @@ def main():
                         help='Resize images to long edge')
     parser.add_argument('-c', '--load-checkpoint', action='store_true', default=False,
                         help='Load checkpoint from checkpoints/deepvo.ckpt')
-    args = parser.parse_args()
+    return parser
+
+def main():
+
+    args = make_parser().parse_args()
 
     data_manager_args = {
         'dataset_path': args.dataset,

model.py

@@ -1,7 +1,8 @@
 '''
 .. module:: model
-    Contains :py:class:`VOModel`, an implementation of DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent
-    Convolutional Neural Networks by Wang et al.
+
+Contains :py:class:`VOModel`, an implementation of DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent
+Convolutional Neural Networks by Wang et al.
 
 .. moduleauthor:: Rasmus Diederichsen
 
@@ -67,7 +68,7 @@ def __init__(self,
         use_dropout :   bool
                         Do not use dropout for LSTM cells
         use_flownet :   bool
-                        Name CNN vars according to flownet naming scheme. You _must_ call
+                        Name CNN vars according to flownet naming scheme. You *must* call
                         :py:meth:`load_flownet`  before pushing stuff through the graph.
         '''
         if not optimizer_spec:

performance_visualizer.py

@@ -8,7 +8,7 @@
 
 class PerformanceVisualizer(object):
 
-    """This class visualizes the performance of the network by plotting the percentage of the offset"""
+    '''This class visualizes the performance of the network by plotting the percentage of the offset'''
 
     def __init__(self):
         super(PerformanceVisualizer, self).__init__()
@@ -98,5 +98,5 @@ def main():
     p.plot()
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     main()

preprocess_data.py

@@ -1,7 +1,8 @@
 #!/usr/bin/env python
 '''
 .. module:: preprocess_data
-    Contains functions for normalizing and converting the raw data.
+
+Contains functions for normalizing and converting the raw data.
 
 .. moduleauthor:: Rasmus Diederichsen
 '''

sequence_visualizer.py

@@ -1,6 +1,7 @@
 '''
 .. module:: sequence_visualizer
-    Contains the :py:class:`SequenceVisualizer` to visualise training success.
+
+Contains the :py:class:`SequenceVisualizer` to visualise trajectory
 
 .. moduleauthor:: Christian Heiden
 '''

utils.py

@@ -1,8 +1,9 @@
 '''
 .. module:: utils
-    Miscellaneous functions for data processing and batching. This module defines - among other things -
-    :py:class:`OptimizerSpec` for specifying optimizers, and :py:class:`DataManager` to partition
-    the data into batches.
+
+Miscellaneous functions for data processing and batching. This module defines - among other things -
+:py:class:`OptimizerSpec` for specifying optimizers, and :py:class:`DataManager` to partition
+the data into batches.
 
 .. moduleauthor Rasmus Diederichsen, Alexander Mock
 '''
@@ -16,7 +17,7 @@ def tensor_from_lstm_tuple(tuples, validate_shape=False):
         We do not check all possible error cases. For instance, the different LSTMStateTuples could
         not only have differing shapes (which we check for to some extend see ``validate_shape``
         parameter), but also the state members ``c`` and ``h`` could differ in their data type (Tensor,
-        array), which we _do not_ check.
+        array), which we *do not* check.
 
 
     Parameters