Added GetItem2Lists function, its grad and all its tests.

theano/sparse/basic.py

@@ -1099,16 +1099,14 @@ class GetItem2Lists(gof.op.Op):
         ind1 = tensor.as_tensor_variable(ind1)
         ind2 = tensor.as_tensor_variable(ind2)
 
-        return gof.Apply(self, [x, ind1, ind2], [theano.tensor.vector()])
+        return gof.Apply(self, [x, ind1, ind2],
+                         [theano.tensor.vector()])
 
     def perform(self, node, inp, (out, )):
         x = inp[0]
         ind1 = inp[1]
         ind2 = inp[2]
-        p = []
-        for i in ind1:
-            p.append(x[(ind1[i],ind2[i])])
-        out[0] = numpy.asarray(p)
+        out[0] = numpy.asarray(x[ind1, ind2]).flatten()
 
     def grad(self, inputs, g_outputs):
         x, ind1, ind2 = inputs
@@ -1136,13 +1134,11 @@ class GetItem2ListsGrad(gof.op.Op):
 
     def make_node(self, x, ind1, ind2, gz):
         x = as_sparse_variable(x)
-        gz = as_sparse_variable(gz)
 
         assert x.format in ["csr", "csc"]
-        assert gz.format in ["csr", "csc"]
 
-        ind1 = tensor.as_tensor_variable(index)
-        ind2 = tensor.as_tensor_variable(index)
+        ind1 = tensor.as_tensor_variable(ind1)
+        ind2 = tensor.as_tensor_variable(ind2)
         assert ind1.ndim == 1
         assert ind2.ndim == 1
 

theano/sparse/tests/test_basic.py

@@ -32,7 +32,7 @@ from theano.sparse import (
     AddSS, AddSD, MulSS, MulSD, Transpose, Neg, Remove0,
     add, mul, structured_dot, transpose,
     csc_from_dense, csr_from_dense, dense_from_sparse,
-    Dot, Usmm, sp_ones_like, GetItemScalar, GetItemList,
+    Dot, Usmm, sp_ones_like, GetItemScalar, GetItemList, GetItem2Lists,
     SparseFromDense,
     Cast, cast, HStack, VStack, AddSSData, add_s_s_data,
     structured_minimum, structured_maximum, structured_add,
@@ -2056,6 +2056,41 @@ class Test_getitem(unittest.TestCase):
 
         verify_grad_sparse(op_with_fixed_index, x_val)
 
+    def test_GetItem2Lists(self):
+
+        a, A = sparse_random_inputs('csr', (4, 5))
+        b, B = sparse_random_inputs('csc', (4, 5))
+        y = a[0][[0, 0, 1, 3], [0, 1, 2, 4]]
+        z = b[0][[0, 0, 1, 3], [0, 1, 2, 4]]
+
+        fa = theano.function([a[0]], y)
+        fb = theano.function([b[0]], z)
+
+        t_geta = fa(A[0])
+        t_getb = fb(B[0])
+
+        s_geta = numpy.asarray(scipy.sparse.csr_matrix(A[0])[[0, 0, 1, 3], [0, 1, 2, 4]])
+        s_getb = numpy.asarray(scipy.sparse.csc_matrix(B[0])[[0, 0, 1, 3], [0, 1, 2, 4]])
+
+        utt.assert_allclose(t_geta, s_geta)
+        utt.assert_allclose(t_getb, s_getb)
+
+    def test_GetItem2Lists_wrong_index(self):
+        a, A = sparse_random_inputs('csr', (4, 5))
+        y = a[0][[0, 4], [0, 4]]
+        f = theano.function([a[0]], y)
+
+        self.assertRaises(IndexError, f, A[0])
+
+    def test_get_item_2lists_grad(self):
+        op = theano.sparse.basic.GetItem2Lists()
+        def op_with_fixed_index(x):
+            return op(x, ind1=numpy.asarray([0, 1]), ind2=numpy.asarray([1, 1]))
+
+        x, x_val = sparse_random_inputs("csr", (4,5), out_dtype="float64")
+
+        verify_grad_sparse(op_with_fixed_index, x_val)
+
     def test_GetItem2D(self):
         sparse_formats = ('csc', 'csr')
         for format in sparse_formats: