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提交 5c31ea6b authored 作者: Pascal Lamblin's avatar Pascal Lamblin
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Merge pull request #3885 from abergeron/faster_travis

Faster travis
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.travis.yml
浏览文件 @ 5c31ea6b
......@@ -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
浏览文件 @ 5c31ea6b
......@@ -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
浏览文件 @ 5c31ea6b
......@@ -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
浏览文件 @ 5c31ea6b
......@@ -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
浏览文件 @ 5c31ea6b
......@@ -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
浏览文件 @ 5c31ea6b
......@@ -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
浏览文件 @ 5c31ea6b
......@@ -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 '
......
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