Allow indexing using Ellipsis.

.travis.yml

@@ -30,7 +30,7 @@ addons:
    - 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 pip flake8=2.3 six=1.9.0 pep8=1.6.2 pyflakes=0.8.1 sphinx; fi
+  - if [[ $TRAVIS_PYTHON_VERSION == '2.6' ]]; then conda create --yes -q -n pyenv mkl python=2.6 numpy=1.9.1 scipy=0.14.0 nose=1.3.0 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 pip flake8=2.3 six=1.9.0 pep8=1.6.2 pyflakes=0.8.1 sphinx; fi
   - source activate pyenv
 # pydot 1.2 broke support of python 2.6. They won't try to maintain it.

theano/tensor/tests/test_subtensor.py

-from __future__ import absolute_import, print_function, division
+from __future__ import absolute_import, division, print_function
+
 import logging
 import sys
 import unittest
 
-from nose.plugins.skip import SkipTest
 import numpy
+from nose.plugins.skip import SkipTest
+from nose.tools import assert_equal
+from numpy.testing import assert_array_equal
 from six import StringIO
 from six.moves import xrange
 
 import theano
-from theano.compat import exc_message, izip, PY3
-from theano.compile import DeepCopyOp
-from theano import config
-from theano import gof
 import theano.scalar as scal
 import theano.tensor as tensor
-from theano.tests import unittest_tools as utt
-from theano.tensor.subtensor import (inc_subtensor, set_subtensor,
+from theano import config, gof
+from theano.compat import PY3, exc_message, izip
+from theano.compile import DeepCopyOp
+from theano.tensor import (MakeSlice, NotScalarConstantError, _shared,
+                           as_tensor_variable, cscalar, ctensor3, dmatrix,
+                           dscalar, dtensor4, dvector, fmatrix, fscalar,
+                           fvector, iscalar, lmatrix, lrow, lvector, matrix,
+                           vector)
+from theano.tensor.basic import DimShuffle
+from theano.tensor.subtensor import (AdvancedIncSubtensor,
+                                     AdvancedIncSubtensor1, AdvancedSubtensor,
+                                     AdvancedSubtensor1, IncSubtensor,
+                                     Subtensor, advanced_inc_subtensor,
                                      advanced_inc_subtensor1,
-                                     advanced_set_subtensor1,
-                                     advanced_inc_subtensor,
                                      advanced_set_subtensor,
-                                     Subtensor, IncSubtensor,
-                                     AdvancedSubtensor1, AdvancedSubtensor,
-                                     advanced_subtensor1, inplace_increment,
-                                     AdvancedIncSubtensor1,
-                                     AdvancedIncSubtensor,
-                                     get_canonical_form_slice)
-from theano.tensor import (as_tensor_variable, _shared,
-                           NotScalarConstantError,
-                           fscalar, iscalar, dscalar, cscalar,
-                           vector, dvector, fvector, lvector, lrow,
-                           fmatrix, dmatrix, lmatrix, matrix,
-                           ctensor3, dtensor4)
-from theano.tensor.tests.test_basic import rand, randint_ranged, inplace_func
+                                     advanced_set_subtensor1,
+                                     advanced_subtensor1,
+                                     get_canonical_form_slice, inc_subtensor,
+                                     inplace_increment, set_subtensor)
+from theano.tensor.tests.test_basic import inplace_func, rand, randint_ranged
+from theano.tests import unittest_tools as utt
 from theano.tests.unittest_tools import attr
 
 if PY3:
@@ -65,6 +66,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
         self.adv_incsub1 = adv_incsub1
         if mode is None:
             mode = theano.compile.mode.get_default_mode()
+            mode = mode.including("local_useless_subtensor")
         self.mode = mode
         self.dtype = dtype
         self.type = type
@@ -97,18 +99,18 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
         Subtensor.debug = False
         utt.seed_rng()
 
-    def eval_output_and_check(self, t, list=False, mode=None):
+    def eval_output_and_check(self, t, op_type=None, mode=None, length=1):
+        if op_type is None:
+            op_type = self.sub
         if mode is None:
             mode = self.mode
         f = inplace_func([], t, mode=mode)
         topo = f.maker.fgraph.toposort()
         topo_ = [node for node in topo if not isinstance(node.op,
                                                          self.ignore_topo)]
-        assert len(topo_) == 1
-        if not list:
-            assert isinstance(topo_[0].op, self.sub)
-        else:
-            assert isinstance(topo_[0].op, self.adv_sub1)
+        assert_equal(len(topo_), length)
+        if length == 1:
+            assert isinstance(topo_[0].op, op_type)
         tval = f()
         return tval
 
@@ -337,6 +339,30 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
         ret = f()
         assert ret.shape == (1, 1, 4)
 
+    def test_ellipsis(self):
+        numpy_n = numpy.arange(24, dtype=self.dtype).reshape((2, 3, 4))
+        n = self.shared(numpy_n)
+        for length, op_type, slice_ in [
+                (0, self.sub, numpy.index_exp[...]),
+                (1, self.sub, numpy.index_exp[..., 1]),
+                (1, self.sub, numpy.index_exp[1, ...]),
+                (1, self.sub, numpy.index_exp[..., 1, 2, 3]),
+                (1, self.sub, numpy.index_exp[1, ..., 2, 3]),
+                (1, self.sub, numpy.index_exp[1, 2, 3, ...]),
+                (3, DimShuffle, numpy.index_exp[..., [0, 2, 3]]),
+                (1, DimShuffle,
+                 numpy.index_exp[numpy.newaxis, ...]),
+                (1, AdvancedSubtensor,
+                 numpy.index_exp[..., numpy.newaxis, [1, 2]])]:
+            numpy_tval = numpy_n[slice_]
+            t = n[slice_]
+            self.assertTrue(isinstance(t.owner.op, op_type))
+            tval = self.eval_output_and_check(t,
+                                              op_type=op_type,
+                                              length=length)
+            assert_equal(tval.shape, numpy_tval.shape)
+            assert_array_equal(tval, numpy_tval)
+
     def test_newaxis(self):
         """
         newaxis support comes from logic in the __getitem__ of TensorType
@@ -433,7 +459,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
         topo_ = [node for node in topo if not isinstance(node.op,
              self.ignore_topo)]
         if not self.fast_compile:
-            assert len(topo_) == 6
+            assert_equal(len(topo_), 6)
         assert numpy.sum([isinstance(node.op, self.inc_sub)
              for node in topo_]) == 1
         assert numpy.sum([isinstance(node.op, self.sub)
@@ -467,7 +493,7 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
             # We test again AdvancedSubtensor1 as we transfer data to the cpu.
             self.assertTrue(isinstance(t.owner.op, tensor.AdvancedSubtensor1))
 
-            val = self.eval_output_and_check(t, list=True)
+            val = self.eval_output_and_check(t, op_type=self.adv_sub1)
             if isinstance(idx, list):
                 good = data[idx]
             else:

theano/tensor/var.py

@@ -471,6 +471,24 @@ class _tensor_py_operators(object):
             pass
         elif not isinstance(args, tuple):
             args = args,
+
+        # Convert an Ellipsis if provided into an appropriate number of
+        # slice(None).
+        ellipses = [i
+                    for i, index in enumerate(args)
+                    if index is Ellipsis]
+        if len(ellipses) > 1:
+            raise IndexError(
+                "an index can only have a single Ellipsis (`...`)")
+        elif len(ellipses) == 1:
+            new_axes = sum(1
+                           for index in args
+                           if index is numpy.newaxis)  # numpy.newaxis is None
+            ellipsis_at = ellipses[0]
+            args = list(args)
+            args[ellipsis_at: ellipsis_at + 1] = (
+                [slice(None)] * (self.ndim - (len(args) - 1 - new_axes)))
+
         # Convert python literals to theano constants
         args = theano.tensor.subtensor.make_constant(args)
         # Determine if advanced indexing is needed or not