Merge pull request #605 from ynd/csm_fix

theano/sparse/basic.py

@@ -558,14 +558,10 @@ class CSMProperties(gof.Op):
         out[2][0] = theano._asarray(csm.indptr, dtype='int32')
         out[3][0] = theano._asarray(csm.shape, dtype='int32')
 
-    # TODO FIX THIS
     def grad(self, (csm,), g):
         assert [gg is None for gg in g[1:]]
         data, indices, indptr, shape = csm_properties(csm)
-        if csm.format == 'csc':
-            return [CSM('csc')(g_data, indices, indptr, shape)]
-        else:
-            return [CSR('csm')(g_data, indices, indptr, shape)]
+        return [CSM(csm.format)(g[0], indices, indptr, shape)]
 # don't make this a function or it breaks some optimizations below
 csm_properties = CSMProperties()
 

theano/sparse/tests/test_basic.py

@@ -20,7 +20,8 @@ if enable_sparse == False:
 from theano.sparse.basic import _is_dense, _is_sparse, _mtypes
 from theano.sparse.basic import _is_dense_variable, _is_sparse_variable
 from theano.sparse.basic import verify_grad_sparse
-from theano.sparse import as_sparse_variable, CSC, CSR, CSM, CSMProperties
+from theano.sparse import (as_sparse_variable, CSC, CSR, CSM, CSMProperties,
+    csm_properties)
 from theano.sparse import SparseType, CSMGrad
 from theano.sparse import StructuredDot, StructuredDotCSC
 from theano.sparse import StructuredDotGradCSC, StructuredDotGradCSR
@@ -560,6 +561,49 @@ class T_conversion(unittest.TestCase):
             self.assertRaises(TypeError, self.check_format_ndim, format, 4)
 
 
+class test_csm_properties(unittest.TestCase):
+    def setUp(self):
+        utt.seed_rng()
+
+    def test_csm_properties_grad(self):
+        sp_types = {'csc': sp.csc_matrix,
+            'csr': sp.csr_matrix}
+        
+        for format in ['csc', 'csr']:
+            for dtype in ['float32', 'float64']:
+                spmat = sp_types[format](random_lil((4, 3), dtype, 3))
+                
+                verify_grad_sparse(lambda *x: CSMProperties()(*x)[0], [spmat],
+                    structured=True)
+                
+                verify_grad_sparse(lambda *x: CSMProperties()(*x)[1], [spmat],
+                    structured=True)
+                
+                verify_grad_sparse(lambda *x: CSMProperties()(*x)[2], [spmat],
+                    structured=True)
+                
+                verify_grad_sparse(lambda *x: CSMProperties()(*x)[2], [spmat],
+                    structured=True)
+    
+    def test_csm_properties(self):
+        sp_types = {'csc': sp.csc_matrix,
+            'csr': sp.csr_matrix}
+        
+        for format in ['csc', 'csr']:
+            for dtype in ['float32', 'float64']:
+                x = SparseType(format, dtype=dtype)()
+                f = theano.function([x], csm_properties(x))
+                
+                spmat = sp_types[format](random_lil((4, 3), dtype, 3))
+                
+                data, indices, indptr, shape = f(spmat)
+                
+                assert numpy.all(data == spmat.data)
+                assert numpy.all(indices == spmat.indices)
+                assert numpy.all(indptr == spmat.indptr)
+                assert numpy.all(shape == spmat.shape)
+
+
 class test_structureddot(unittest.TestCase):
     def setUp(self):
         utt.seed_rng()