Merge pull request #1644 from nouiz/gh-1122

theano/tensor/basic.py

@@ -566,6 +566,9 @@ def get_scalar_constant_value(v):
         if isinstance(v.owner.op, scal.Second):
             x, y = v.owner.inputs
             return get_scalar_constant_value(y)
+        if (isinstance(v.owner.op, theano.compile.ops.Shape_i) and
+            isinstance(v.owner.inputs[0], Constant)):
+            return v.owner.inputs[0].data.shape[v.owner.op.i]
         # Don't act as the constant_folding optimization here as this
         # fct is used too early in the optimization phase.  This would
         # mess with the stabilization optimization.

theano/tensor/opt.py

@@ -731,7 +731,12 @@ class ShapeFeature(object):
             return self.lscalar_one
         else:
             # Do not call make_node for test_value
-            return Shape_i(i)(r)
+            s = Shape_i(i)(r)
+            try:
+                s = get_scalar_constant_value(s)
+            except NotScalarConstantError:
+                pass
+            return s
 
     def shape_tuple(self, r):
         """Return a tuple of symbolic shape vars for tensor variable r"""

theano/tensor/tests/test_basic.py

@@ -5921,6 +5921,13 @@ class T_get_scalar_constant_value(unittest.TestCase):
             get_scalar_constant_value,
             mv[t()])
 
+    def test_shape_i(self):
+        c = theano.tensor.constant(numpy.random.rand(3, 4))
+        s = opt.Shape_i(0)(c)
+        assert get_scalar_constant_value(s) == 3
+        s = opt.Shape_i(1)(c)
+        assert get_scalar_constant_value(s) == 4
+
 
 class T_as_tensor_variable(unittest.TestCase):
     """

theano/tensor/tests/test_opt.py

@@ -236,12 +236,12 @@ class test_canonize(unittest.TestCase):
         fyv = theano._asarray(numpy.random.rand(*shp), dtype='float32')
         fzv = theano._asarray(numpy.random.rand(*shp), dtype='float32')
         fvv = theano._asarray(numpy.random.rand(shp[0]), dtype=
-                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        'float32').reshape(1, shp[0])
+                              'float32').reshape(1, shp[0])
         dxv = theano._asarray(numpy.random.rand(*shp), dtype='float64')
         dyv = theano._asarray(numpy.random.rand(*shp), dtype='float64')
         dzv = theano._asarray(numpy.random.rand(*shp), dtype='float64')
         dvv = theano._asarray(numpy.random.rand(shp[0]), dtype=
-                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        'float64').reshape(1, shp[0])
+                              'float64').reshape(1, shp[0])
         cases = [
             (fx + fy, (fx, fy), (fxv, fyv), 1, 'float32'),
             (fx * fy, (fx, fy), (fxv, fyv), 1, 'float32'),
@@ -2571,6 +2571,15 @@ class test_shapeoptimizer(unittest.TestCase):
         f = theano.function([], out, mode=mode)
         f()
 
+    def test_constant_merge(self):
+        """This test the error in gh-1122 that is a caused by the
+        combination of merge optimizer and ShapeFeature.
+        """
+        x = tensor.constant([0, 0])
+        y = x[1:]
+        x1 = x - tensor.join(0, y, y)
+        x1.eval()
+
     def test_local_track_shape_i(self):
         class IdentityNoShape(gof.Op):
             '''Op that does not infer the output shape from the input one'''