Merge branch 'fixing-ci' into 'master'

Fixing ci

See merge request !3

LICENSE

+Copyright (c) 2016 Idiap Research Institute, http://www.idiap.ch/
+Written by Andre Anjos <andre.anjos@idiap.ch>
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+this list of conditions and the following disclaimer in the documentation
+and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors
+may be used to endorse or promote products derived from this software without
+specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

MANIFEST.in

 include README.rst bootstrap-buildout.py buildout.cfg COPYING version.txt requirements.txt
 recursive-include doc *.py *.rst
-recursive-include bob *.wav *.hdf5
\ No newline at end of file
+recursive-include bob *.wav *.hdf5 *.pickle *.meta *.ckp
\ No newline at end of file

bob/learn/__init__.py

 # see https://docs.python.org/3/library/pkgutil.html
 from pkgutil import extend_path
-__path__ = extend_path(__path__, __name__)
-
+__path__ = extend_path(__path__, __name__)
\ No newline at end of file

bob/learn/tensorflow/__init__.py

-# gets sphinx autodoc done right - don't remove it
-__all__ = [_ for _ in dir() if not _.startswith('_')]
-
-
-from . import analyzers
-from . import datashuffler
-from . import initialization
-from . import layers
-from . import loss
-from . import network
-from . import trainers
-from . import utils
\ No newline at end of file
+def get_config():
+    """
+    Returns a string containing the configuration information.
+    """
+    import bob.extension
+    return bob.extension.get_config(__name__)
\ No newline at end of file

bob/learn/tensorflow/layers/FullyConnected.py

@@ -67,7 +67,7 @@ class FullyConnected(Layer):
     def create_variables(self, input_layer):
         self.input_layer = input_layer
         if self.W is None:
-            input_dim = reduce(mul, self.input_layer.get_shape().as_list()[1:])
+            input_dim = numpy.prod(self.input_layer.get_shape().as_list()[1:])
             if self.init_value is None:
                 self.init_value = input_dim
 

bob/learn/tensorflow/loss/__init__.py

-
 from .BaseLoss import BaseLoss
 from .ContrastiveLoss import ContrastiveLoss
 from .TripletLoss import TripletLoss
-from NegLogLoss import NegLogLoss
+from .NegLogLoss import NegLogLoss
 
 
 # gets sphinx autodoc done right - don't remove it

bob/learn/tensorflow/network/SequenceNetwork.py

@@ -317,14 +317,14 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
 
         session = Session.instance().session
 
-        open(path+"_sequence_net.pickle", 'w').write(self.pickle_architecture)
+        open(path+"_sequence_net.pickle", 'wb').write(self.pickle_architecture)
         return saver.save(session, path)
 
     def load(self, path, clear_devices=False, session_from_scratch=False):
 
         session = Session.instance(new=session_from_scratch).session
 
-        self.sequence_net = pickle.loads(open(path+"_sequence_net.pickle").read())
+        self.sequence_net = pickle.loads(open(path+"_sequence_net.pickle", 'rb').read())
         if clear_devices:
             saver = tf.train.import_meta_graph(path + ".meta", clear_devices=clear_devices)
         else:

bob/learn/tensorflow/network/__init__.py

-
 from .SequenceNetwork import SequenceNetwork
 from .Lenet import Lenet
 from .Chopra import Chopra
@@ -9,7 +8,7 @@ from .FaceNet import FaceNet
 from .FaceNetSimple import FaceNetSimple
 from .VGG16 import VGG16
 from .VGG16_mod import VGG16_mod
-from SimpleAudio import SimpleAudio
+from .SimpleAudio import SimpleAudio
 
 # gets sphinx autodoc done right - don't remove it
 def __appropriate__(*args):

bob/learn/tensorflow/test/__init__.py

-# see https://docs.python.org/3/library/pkgutil.html
-from pkgutil import extend_path
-__path__ = extend_path(__path__, __name__)
\ No newline at end of file

doc/user_guide.rst

@@ -16,7 +16,7 @@ The example consists in training a very simple **CNN** with `MNIST` dataset in 4
 
 1. Preparing your input data
 
-.. doctest::
+.. code-block:: python
 
     >>> import tensorflow as tf
     >>> import bob.learn.tensorflow
@@ -28,7 +28,7 @@ The example consists in training a very simple **CNN** with `MNIST` dataset in 4
 
 2. Create an architecture
 
-.. doctest::
+.. code-block:: python
 
     >>> architecture = bob.learn.tensorflow.network.SequenceNetwork()
     >>> architecture.add(bob.learn.tensorflow.layers.Conv2D(name="conv1", kernel_size=3, filters=10, activation=tf.nn.tanh))
@@ -36,7 +36,7 @@ The example consists in training a very simple **CNN** with `MNIST` dataset in 4
 
 3. Defining a loss and training
 
-.. doctest::
+.. code-block:: python
 
     >>> loss = bob.learn.tensorflow.loss.BaseLoss(tf.nn.sparse_softmax_cross_entropy_with_logits, tf.reduce_mean)
     >>> trainer = bob.learn.tensorflow.trainers.Trainer(architecture=architecture, loss=loss, iterations=100, temp_dir="./cnn")
@@ -45,7 +45,7 @@ The example consists in training a very simple **CNN** with `MNIST` dataset in 4
 
 4. Predicting and computing the accuracy
 
-.. doctest::
+.. code-block:: python
 
     >>> # Loading the model
     >>> architecture = bob.learn.tensorflow.network.SequenceNetwork()
@@ -124,7 +124,7 @@ The data can be fetched either from the memory (:py:class:`bob.learn.tensorflow.
 disk (:py:class:`bob.learn.tensorflow.datashuffler.Disk`).
 To train networks fetched from the disk, your training data must be a list of paths like in the example below:
 
-.. doctest::
+.. code-block:: python
 
     >>> train_data = ['./file/id1_0.jpg', './file/id1_1.jpg', './file/id2_1.jpg']
     >>> train_labels = [0, 0, 1]
@@ -171,7 +171,7 @@ The library has already some crafted networks implemented in `Architectures <py_
 It is also possible to craft simple MLPs with this library using the class :py:class:`bob.learn.tensorflow.network.MLP`.
 The example bellow shows how to create a simple MLP with 10 putputs and 2 hidden layers.
 
-.. doctest::
+.. code-block:: python
 
     >>> architecture = bob.learn.tensorflow.network.MLP(10, hidden_layers=[20, 40])
 
@@ -221,7 +221,7 @@ Loss
 Loss functions must be wrapped as a :py:class:`bob.learn.tensorflow.loss.BaseLoss` objects.
 For instance, if you want to use the sparse softmax cross entropy loss between logits and labels you should do like this.
 
-.. doctest::
+.. code-block:: python
 
     >>> loss = BaseLoss(tf.nn.sparse_softmax_cross_entropy_with_logits, tf.reduce_mean)
 

requirements.txt

@@ -10,9 +10,10 @@ bob.io.image
 bob.learn.activation
 bob.learn.em
 bob.learn.linear
+bob.ip.base
 bob.math
 bob.measure
 bob.sp
+bob.db.mnist
 gridtk
 scipy
-setuptools
\ No newline at end of file

setup.py

@@ -2,93 +2,66 @@
 # vim: set fileencoding=utf-8 :
 # Andre Anjos <andre.anjos@idiap.ch>
 # Mon 16 Apr 08:18:08 2012 CEST
-#
-# Copyright (C) Idiap Research Institute, Martigny, Switzerland
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, version 3 of the License.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program. If not, see <http://www.gnu.org/licenses/>.
-
-# This file contains the python (distutils/setuptools) instructions so your
-# package can be installed on **any** host system. It defines some basic
-# information like the package name for instance, or its homepage.
-#
-# It also defines which other packages this python package depends on and that
-# are required for this package's operation. The python subsystem will make
-# sure all dependent packages are installed or will install them for you upon
-# the installation of this package.
-#
-# The 'buildout' system we use here will go further and wrap this package in
-# such a way to create an isolated python working environment. Buildout will
-# make sure that dependencies which are not yet installed do get installed, but
-# **without** requiring administrative privileges on the host system. This
-# allows you to test your package with new python dependencies w/o requiring
-# administrative interventions.
 
 from setuptools import setup
 
+from setuptools import setup, dist
+dist.Distribution(dict(setup_requires=['bob.extension']))
+
+from bob.extension.utils import load_requirements, find_packages
+install_requires = load_requirements()
+
 # The only thing we do in this file is to call the setup() function with all
 # parameters that define our package.
 setup(
 
     # This is the basic information about your project. Modify all this
     # information before releasing code publicly.
-    name = 'bob.learn.tensorflow',
-    version = open("version.txt").read().rstrip(),
-    description = 'Bob bindings for tensorflow',
+    name='bob.learn.tensorflow',
+    version=open("version.txt").read().rstrip(),
+    description='Bob bindings for tensorflow',
 
-    url = '',
-    license = 'BSD',
-    author = 'Tiago de Freitas Pereira',
-    author_email = 'tiago.pereira@idiap.ch',
-    keywords = 'tensorflow',
+    url='',
+    license='BSD',
+    author='Tiago de Freitas Pereira',
+    author_email='tiago.pereira@idiap.ch',
+    keywords='tensorflow',
 
     # If you have a better, long description of your package, place it on the
     # 'doc' directory and then hook it here
-    long_description = open('README.rst').read(),
+    long_description=open('README.rst').read(),
 
     # This line is required for any distutils based packaging.
-    include_package_data = True,
+    include_package_data=True,
 
     # This line defines which packages should be installed when you "install"
     # this package. All packages that are mentioned here, but are not installed
     # on the current system will be installed locally and only visible to the
     # scripts of this package. Don't worry - You won't need administrative
     # privileges when using buildout.
-    install_requires = [
-        'setuptools',
-        'docopt',
-        'ipython',
-        'bob.db.mnist',
-    ],
+    install_requires=install_requires,
+    packages=find_packages(),
+    zip_safe=False,
 
-    entry_points = {
+    entry_points={
 
-      # scripts should be declared using this entry:
-      'console_scripts': [
-          'compute_statistics.py = bob.learn.tensorflow.script.compute_statistics:main'
-      ],
+        # scripts should be declared using this entry:
+        'console_scripts': [
+            'compute_statistics.py = bob.learn.tensorflow.script.compute_statistics:main'
+        ],
 
     },
 
     # Classifiers are important if you plan to distribute this package through
     # PyPI. You can find the complete list of classifiers that are valid and
     # useful here (http://pypi.python.org/pypi?%3Aaction=list_classifiers).
-    classifiers = [
-      'Framework :: Tensorflow',
-      'Development Status :: 3 - Alpha',
-      'Intended Audience :: Developers',
-      'License :: OSI Approved ::  BSD License',
-      'Natural Language :: English',
-      'Programming Language :: Python',
-      'Topic :: Scientific/Engineering :: Artificial Intelligence',
+    classifiers=[
+        'Framework :: Tensorflow',
+        'Development Status :: 3 - Alpha',
+        'Intended Audience :: Developers',
+        'License :: OSI Approved ::  BSD License',
+        'Natural Language :: English',
+        'Programming Language :: Python',
+        'Topic :: Scientific/Engineering :: Artificial Intelligence',
     ],
 )