Merge pull request #220 from yaoli/EnsureSortedIndicesReview

theano/sparse/sandbox/sp.py

@@ -254,25 +254,35 @@ class EnsureSortedIndices(Op):
     """
     Remove explicit zeros from a sparse matrix, and resort indices
     """
-    inplace=False
 
     def __init__(self, inplace):
-        self.inplace=inplace
+        self.inplace = inplace
         if self.inplace:
             self.view_map = {0:[0]}
 
     def make_node(self, x):
         return gof.Apply(self, [x], [x.type()])
 
-    def perform(self,node, (x,), (z,)):
+    def perform(self, node, inputs, output_storage):
+        x = inputs[0]
+        z = output_storage[0]
         if self.inplace:
             x.sort_indices()
             z[0] = x
         else:
             z[0] = x.sorted_indices()
 
-    def grad(self, (x,), (gz,)):
-        return [gz]
+    def grad(self, inputs, output_grad):
+        return [output_grad[0]]
+
+    def infer_shape(self, node, i0_shapes):
+        return i0_shapes
+            
+    def __str__(self):
+        if self.inplace:
+            return self.__class__.__name__ + "{inplace}"
+        else:
+            return self.__class__.__name__ + "{no_inplace}"
 
 ensure_sorted_indices = EnsureSortedIndices(inplace=False)
 

theano/sparse/sandbox/test_sp.py

@@ -16,7 +16,7 @@ from theano import function, tensor
 import theano
 from theano.sparse.sandbox import sp
 from theano.tests import unittest_tools as utt
-
+from theano.sparse.tests.test_basic import random_lil
 
 class TestSP(unittest.TestCase):
     def test_convolution(self):
@@ -413,6 +413,27 @@ def test_diagonal():
 
         assert numpy.all(n == f(range(K)).toarray())
 
+def test_ensure_sorted_indices():
+    x = 2000
+    y = 2000
+    sparsity = 1000
+    for i in range(2):
+        # testing both csc and csr
+        if i is 0:
+            # csc
+            input_tensor = theano.sparse.csc_dmatrix()
+            sample = scipy.sparse.csc_matrix(random_lil((x,y),'float64',sparsity))
+        else:
+            # csr
+            input_tensor = theano.sparse.csr_dmatrix()
+            sample = scipy.sparse.csr_matrix(random_lil((x,y),'float64',sparsity))
+            
+        sort_op = sp.ensure_sorted_indices(input_tensor)
+        f = theano.function([input_tensor], sort_op)
+        sorted_scipy = sample.sorted_indices()
+        sorted_theano = f(sample)
+        assert numpy.all(sorted_theano.todense() == sorted_scipy.todense())
+    
 def test_diagonal_grad():
     def d(x):
         return sp.sp_sum(sp.square_diagonal(x), sparse_grad=True)