Merge pull request #3885 from abergeron/faster_travis

.travis.yml

@@ -23,19 +23,25 @@ before_install:
   - export PATH=/home/travis/miniconda2/bin:$PATH
   - conda update --yes conda
 
+addons:
+  apt_packages:
+   - texlive-latex-recommended
+   - texlive-latex-extra
+   - texlive-fonts-recommended
+   - dvipng
+
 install:
-  - if [[ $TRAVIS_PYTHON_VERSION == '2.6' ]]; then conda create --yes -q -n pyenv mkl python=2.6 numpy=1.7.1 scipy=0.11 nose=1.3.0 pyparsing=1.5 pip flake8=2.3 six=1.9.0 pep8=1.6.2 pyflakes=0.8.1; fi
-  - if [[ $TRAVIS_PYTHON_VERSION == '3.3' ]]; then conda create --yes -q -n pyenv mkl python=3.3 numpy=1.9.1 scipy=0.14.0 nose=1.3.4 pyparsing=1.5 pip flake8=2.3 six=1.9.0 pep8=1.6.2 pyflakes=0.8.1; fi
+  - if [[ $TRAVIS_PYTHON_VERSION == '2.6' ]]; then conda create --yes -q -n pyenv mkl python=2.6 numpy=1.7.1 scipy=0.11 nose=1.3.0 pyparsing=1.5 pip flake8=2.3 six=1.9.0 pep8=1.6.2 pyflakes=0.8.1 sphinx; fi
+  - if [[ $TRAVIS_PYTHON_VERSION == '3.3' ]]; then conda create --yes -q -n pyenv mkl python=3.3 numpy=1.9.1 scipy=0.14.0 nose=1.3.4 pyparsing=1.5 pip flake8=2.3 six=1.9.0 pep8=1.6.2 pyflakes=0.8.1 sphinx; fi
   - source activate pyenv
   - if [[ $TRAVIS_PYTHON_VERSION == '2.6' ]]; then pip install pydot; fi
   - pip install . --no-deps
-  - pip install -U Sphinx
 
 # command to run tests
 env:
-  - PART="theano/sandbox theano/sparse theano/scalar theano/tensor/nnet theano/scan_module"
-  - PART="theano/tensor/tests/test_basic.py theano/tensor/signal theano/compile theano/gof theano/misc theano/tests theano/compat"
-  - PART="-e test_basic.py theano/tensor/tests theano/d3viz"
+  - PART="theano/tests/test_flake8.py" DOC=1
+  - PART="-e theano/tests/test_flake8.py theano/compat theano/compile theano/d3viz theano/gof theano/misc theano/sandbox theano/scalar theano/scan_module theano/sparse theano/tests theano/typed_list"
+  - PART="theano/tensor"
 
 matrix:
   fast_finish: true
@@ -44,8 +50,6 @@ matrix:
       env: PART="." THEANO_FLAGS="mode=FAST_COMPILE"
     - python: "2.6"
       env: PART="." THEANO_FLAGS="mode=FAST_COMPILE,floatX=float32"
-    - python: 2.6
-      env: PART="." DOC=1
 
 script:
   - export THEANO_FLAGS=$THEANO_FLAGS,warn.ignore_bug_before=all,on_opt_error=raise,on_shape_error=raise,gcc.cxxflags=-pipe
@@ -60,13 +64,9 @@ script:
   - cd $(python -c 'import theano; import os; print(os.path.split(theano.__file__)[0])')
   - echo "$PART"
   - cd -; cd Theano
-  - if [[ $DOC == "1" ]]; then python doc/scripts/docgen.py --nopdf; else theano-nose -v $PART; fi
-  - if [[ $DOC == "1" ]]; then python doc/scripts/docgen.py --test; fi
-#  - theano-cache list
+  - theano-nose -v $PART
+  - if [[ $DOC == "1" ]]; then python doc/scripts/docgen.py --nopdf --check; fi
+#  - if [[ $DOC == "1" ]]; then python doc/scripts/docgen.py --test --check; fi
 
-#after_script:
 after_failure:
   - cat /home/travis/.pip/pip.log
-#after_success:
-
-cache: apt

doc/sandbox/ccodegen.txt

@@ -132,7 +132,7 @@ The following ResultBase represents a double (we only care about the C part).
 .. code-block:: python
 
     class Double(ResultBase):
-      <snip>
+      # <snip>
       def c_declare(self):
         return "double %(name)s;"
       def c_init(self):
@@ -153,7 +153,7 @@ The following ResultBase represents addition of two nonnegative doubles (we only
 .. code-block:: python
 
     class Add(Op):
-      <snip>
+      # <snip>
       def c_var_names(self):
         return "[['x', 'y'], ['z']]"
       def c_validate_update(self):
@@ -170,7 +170,7 @@ Generating a C function
 
 For the example Op, the generated C function will typically look like this:
 
-.. code-block:: python
+.. code-block:: cpp
 
     void add(PyObject* storage_x, PyObject* storage_y, PyObject* storage_z) {
       PyObject* py_x = PyList_GET_ITEM(storage_x, 0); Py_XINCREF(py_x); // automatic
@@ -232,16 +232,16 @@ Here is a sketch of the struct equivalent of the previous function:
       double z; // z.c_declare
 
       void init(PyObject* storage_x, PyObject* storage_y, PyObject* storage_z) {
-        <set the struct members of the same names>
-        <init the struct members corresponding to z>
+        // <set the struct members of the same names>
+        // <init the struct members corresponding to z>
       }
 
       void cleanup(void) {
-        <cleanup z>
+        // <cleanup z>
       }
 
       void run(void) {
-        <same code as before minus z's cleanup>
+        // <same code as before minus z's cleanup>
       }
 
       add() { this->init(); }

doc/scripts/docgen.py

@@ -16,7 +16,7 @@ if __name__ == '__main__':
     opts, args = getopt.getopt(
         sys.argv[1:],
         'o:f:',
-        ['rst', 'help', 'nopdf', 'cache', 'test'])
+        ['rst', 'help', 'nopdf', 'cache', 'check', 'test'])
     options.update(dict([x, y or True] for x, y in opts))
     if options['--help']:
         print('Usage: %s [OPTIONS] [files...]' % sys.argv[0])
@@ -25,6 +25,7 @@ if __name__ == '__main__':
         print('  --rst: only compile the doc (requires sphinx)')
         print('  --nopdf: do not produce a PDF file from the doc, only HTML')
         print('  --test: run all the code samples in the documentaton')
+        print('  --check: treat warnings as errors')
         print('  --help: this help')
         print('If one or more files are specified after the options then only '
               'those files will be built. Otherwise the whole tree is '
@@ -54,17 +55,21 @@ if __name__ == '__main__':
     pythonpath = os.pathsep.join([throot, pythonpath])
     sys.path[0:0] = [throot]  # We must not use os.environ.
 
-    def call_sphinx(builder, workdir, extraopts=None):
+    def call_sphinx(builder, workdir):
         import sphinx
-        if extraopts is None:
+        if options['--check']:
             extraopts = ['-W']
+        else:
+            extraopts = []
         if not options['--cache'] and files is None:
             extraopts.append('-E')
         docpath = os.path.join(throot, 'doc')
         inopt = [docpath, workdir]
         if files is not None:
             inopt.extend(files)
-        sphinx.build_main(['', '-b', builder] + extraopts + inopt)
+        ret = sphinx.build_main(['', '-b', builder] + extraopts + inopt)
+        if ret != 0:
+            sys.exit(ret)
 
     if options['--all'] or options['--rst']:
         mkdir("doc")
@@ -92,5 +97,6 @@ if __name__ == '__main__':
         mkdir("doc")
         sys.path[0:0] = [os.path.join(throot, 'doc')]
         call_sphinx('doctest', '.')
+
     # To go back to the original current directory.
     os.chdir(currentdir)

doc/tutorial/extending_theano_c.txt

@@ -997,7 +997,7 @@ down computation on the GPU, but it is enabled by default on the CPU).
 Then you must start Python inside GDB and in it start your Python
 process (e.g. theano-nose):
 
-.. code-block::
+.. code-block:: sh
 
     $gdb python
     (gdb)r bin/theano-nose theano/

theano/misc/pkl_utils.py

@@ -124,12 +124,15 @@ if PY3:
         Examples
         --------
 
-        with open(fname, 'rb') as fp:
-            if PY3:
-                u = CompatUnpickler(fp, encoding="latin1")
-            else:
-                u = CompatUnpickler(fp)
-            mat = u.load()
+        ::
+
+            with open(fname, 'rb') as fp:
+                if PY3:
+                    u = CompatUnpickler(fp, encoding="latin1")
+                else:
+                    u = CompatUnpickler(fp)
+                mat = u.load()
+
         """
         pass
 
@@ -145,13 +148,15 @@ else:
         Examples
         --------
 
-        with open(fname, 'rb') as fp:
-            if PY3:
-                u = CompatUnpickler(fp, encoding="latin1")
-            else:
-                u = CompatUnpickler(fp)
+        ::
+
+            with open(fname, 'rb') as fp:
+                if PY3:
+                    u = CompatUnpickler(fp, encoding="latin1")
+                else:
+                    u = CompatUnpickler(fp)
+                mat = u.load()
 
-            mat = u.load()
         """
         pass
 

theano/sparse/basic.py

@@ -790,7 +790,7 @@ shape
 
 Returns
 -------
-matrix
+sparse matrix
     A sparse matrix having the properties specified by the inputs.
 
 Notes
@@ -820,7 +820,7 @@ shape
 
 Returns
 -------
-matrix
+sparse matrix
     A sparse matrix having the properties specified by the inputs.
 
 Notes
@@ -1053,7 +1053,7 @@ x
 
 Returns
 -------
-matrix
+theano.tensor.matrix
     A dense matrix, the same as `x`.
 
 Notes
@@ -1121,7 +1121,7 @@ x
 
 Returns
 -------
-matrix
+sparse matrix
     The same as `x` in a sparse csr matrix format.
 
 """
@@ -1137,7 +1137,7 @@ x
 
 Returns
 -------
-matrix
+sparse matrix
     The same as `x` in a sparse csc matrix format.
 
 """
@@ -1187,7 +1187,7 @@ index
 
 Returns
 -------
-matrix
+sparse matrix
     The corresponding rows in `x`.
 
 """
@@ -1284,7 +1284,7 @@ index
 
 Returns
 -------
-vector
+theano.tensor.vector
     The corresponding elements in `x`.
 
 """
@@ -1436,7 +1436,8 @@ index
 
 Returns
 -------
-The corresponding slice in `x`.
+sparse matrix
+    The corresponding slice in `x`.
 
 
 Notes
@@ -1511,7 +1512,7 @@ index
 
 Returns
 -------
-scalar
+theano.tensor.scalar
     The corresponding item in `x`.
 
 Notes
@@ -1566,7 +1567,7 @@ x
 
 Returns
 -------
-matrix
+sparse matrix
     `x` transposed.
 
 Notes
@@ -1617,7 +1618,7 @@ x
 
 Returns
 -------
-matrix
+sparse matrix
     -`x`.
 
 Notes
@@ -1930,7 +1931,7 @@ x
 
 Returns
 -------
-vector
+theano.tensor.vector
     A dense vector representing the diagonal elements.
 
 Notes
@@ -1985,7 +1986,7 @@ x
 
 Returns
 -------
-matrix
+sparse matrix
     A sparse matrix having `x` as diagonal.
 
 Notes
@@ -2044,7 +2045,7 @@ x
 
 Returns
 -------
-matrix
+sparse matrix
     The same as `x` with indices sorted.
 
 Notes
@@ -2854,7 +2855,7 @@ y
 
 Returns
 -------
-matrix
+matrix variable
     `x` == `y`
 
 Notes
@@ -2875,7 +2876,7 @@ y
 
 Returns
 -------
-matrix
+matrix variable
     `x` != `y`
 
 Notes
@@ -2896,7 +2897,7 @@ y
 
 Returns
 -------
-matrix
+matrix variable
     `x` < `y`
 
 Notes
@@ -2917,7 +2918,7 @@ y
 
 Returns
 -------
-matrix
+matrix variable
     `x` > `y`
 
 Notes
@@ -2937,6 +2938,7 @@ y
 
 Returns
 -------
+matrix variable
     `x` <= `y`
 
 Notes
@@ -2957,7 +2959,7 @@ y
 
 Returns
 -------
-matrix
+matrix variable
     `x` >= `y`
 
 Notes
@@ -3199,7 +3201,7 @@ x
 
 Returns
 -------
-matrix
+sparse matrix
     Exactly `x` but with a data attribute exempt of zeros.
 
 Notes
@@ -4112,7 +4114,7 @@ p
 
 Returns
 -------
-matrix
+sparse matrix
     A dense matrix containing the dot product of `x` by `y`.T only
     where `p` is 1.
 

theano/tensor/signal/pool.py

@@ -30,6 +30,7 @@ def max_pool_2d_same_size(input, patch_size):
     of non-overlapping patches of size (patch_size[0],patch_size[1]) to zero,
     keeping only the maximum values. The output has the same dimensions as
     the input.
+
     Parameters
     ----------
     input : 4-D theano tensor of input images
@@ -37,6 +38,7 @@ def max_pool_2d_same_size(input, patch_size):
     patch_size : tuple of length 2
         Size of the patch (patch height, patch width).
         (2,2) will retain only one non-zero value per patch of 4 values.
+
     """
     output = Pool(patch_size, True)(input)
     outs = MaxPoolGrad(patch_size, True)(input, output, output)
@@ -49,6 +51,7 @@ def pool_2d(input, ds, ignore_border=None, st=None, padding=(0, 0),
     Takes as input a N-D tensor, where N >= 2. It downscales the input image by
     the specified factor, by keeping only the maximum value of non-overlapping
     patches of size (ds[0],ds[1])
+
     Parameters
     ----------
     input : N-D theano tensor of input images
@@ -64,13 +67,14 @@ def pool_2d(input, ds, ignore_border=None, st=None, padding=(0, 0),
         next pool region. If st is None, it is considered equal to ds
         (no overlap on pooling regions).
     padding : tuple of two ints
-        (pad_h, pad_w), pad zeros to extend beyond four borders
-            of the images, pad_h is the size of the top and bottom margins,
-            and pad_w is the size of the left and right margins.
+        (pad_h, pad_w), pad zeros to extend beyond four borders of the
+        images, pad_h is the size of the top and bottom margins, and
+        pad_w is the size of the left and right margins.
     mode : {'max', 'sum', 'average_inc_pad', 'average_exc_pad'}
         Operation executed on each window. `max` and `sum` always exclude
         the padding in the computation. `average` gives you the choice to
         include or exclude it.
+
     """
     if input.ndim < 2:
         raise NotImplementedError('pool_2d requires a dimension >= 2')
@@ -121,15 +125,17 @@ class Pool(Op):
     For N-dimensional tensors, consider that the last two dimensions span
     images. This Op downsamples these images by taking the max, sum or average
     over different patch.
+
     The constructor takes the max, sum or average or different input patches.
+
     Parameters
     ----------
     ds : list or tuple of two ints
         Downsample factor over rows and column.
         ds indicates the pool region size.
     ignore_border : bool
-        If ds doesn't divide imgshape, do we include an extra row/col of partial
-        downsampling (False) or ignore it (True).
+        If ds doesn't divide imgshape, do we include an extra row/col
+        of partial downsampling (False) or ignore it (True).
     st : list or tuple of two ints or None
         Stride size, which is the number of shifts over rows/cols to get the
         next pool region. If st is None, it is considered equal to ds
@@ -141,6 +147,7 @@ class Pool(Op):
     mode : {'max', 'sum', 'average_inc_pad', 'average_exc_pad'}
         ('average_inc_pad' excludes the padding from the count,
         'average_exc_pad' include it)
+
     """
 
     __props__ = ('ds', 'ignore_border', 'st', 'padding', 'mode')
@@ -150,6 +157,7 @@ class Pool(Op):
         """
         Return the shape of the output from this op, for input of given
         shape and flags.
+
         Parameters
         ----------
         imgshape : tuple, list, or similar of integer or scalar Theano variable
@@ -168,12 +176,14 @@ class Pool(Op):
             (pad_h, pad_w), pad zeros to extend beyond four borders
             of the images, pad_h is the size of the top and bottom margins,
             and pad_w is the size of the left and right margins.
+
         Returns
         -------
         list
             The shape of the output from this op, for input of given shape.
             This will have the same length as imgshape, but with last two
             elements reduced as per the downsampling & ignore_border flags.
+
         """
         if len(imgshape) < 2:
             raise TypeError('imgshape must have at least two elements '
@@ -528,27 +538,36 @@ class PoolGrad(Op):
     def out_shape(imgshape, ds, ignore_border=False, st=None, padding=(0, 0)):
         """Return the shape of the output from this op, for input of given
         shape and flags.
-        :param imgshape: the shape of a tensor of images. The last two elements
-            are interpreted as the number of rows, and the number of cols.
-        :type imgshape: tuple, list, or similar of integer or
-            scalar Theano variable.
-        :param ds: downsample factor over rows and columns
-                   this parameter indicates the size of the pooling region
-        :type ds: list or tuple of two ints
-        :param st: the stride size. This is the distance between the pooling
-                   regions. If it's set to None, in which case it equlas ds.
-        :type st: list or tuple of two ints
-        :param ignore_border: if ds doesn't divide imgshape, do we include an
-            extra row/col of partial downsampling (False) or ignore it (True).
-        :type ignore_border: bool
-        :param padding: (pad_h, pad_w), pad zeros to extend beyond four borders
-            of the images, pad_h is the size of the top and bottom margins,
-            and pad_w is the size of the left and right margins.
-        :type padding: tuple of two ints
-        :rtype: list
-        :returns: the shape of the output from this op, for input of given
-            shape.  This will have the same length as imgshape, but with last
-            two elements reduced as per the downsampling & ignore_border flags.
+
+        Parameters
+        ----------
+        imgshape : tuple of integers or scalar Theano variables
+            the shape of a tensor of images. The last two elements are
+            interpreted as the number of rows, and the number of cols.
+        ds : tuple of two ints
+            downsample factor over rows and columns this parameter
+            indicates the size of the pooling region
+        st : tuple of two ints
+            the stride size. This is the distance between the pooling
+            regions. If it's set to None, in which case it equlas ds.
+        ignore_border : bool
+            if ds doesn't divide imgshape, do we include an extra
+            row/col of partial downsampling (False) or ignore it
+            (True).
+        padding : tuple of two ints
+            (pad_h, pad_w), pad zeros to extend beyond four borders of
+            the images, pad_h is the size of the top and bottom
+            margins, and pad_w is the size of the left and right
+            margins.
+
+        Returns
+        -------
+        list :
+            the shape of the output from this op, for input of given
+            shape.  This will have the same length as imgshape, but
+            with last two elements reduced as per the downsampling &
+            ignore_border flags.
+
         """
         if len(imgshape) < 2:
             raise TypeError('imgshape must have at least two elements '