re-enabled grad_sources_inputs tests

theano/gradient.py

@@ -607,6 +607,11 @@ def grad_sources_inputs(sources, graph_inputs, warn_type = 'ignored'):
 
 
     outputs, output_grads = zip(*sources)
+
+
+    if graph_inputs is None:
+        graph_inputs = gof.graph.inputs(outputs)
+
     wrt = graph_inputs
 
 

theano/tests/test_gradient.py

@@ -6,22 +6,17 @@ import unittest
 import theano
 from theano import gof
 
-#from theano.gradient import grad_sources_inputs
+from theano.gradient import grad_sources_inputs
 from theano import gradient
 from theano.tensor.nnet.Conv3D import conv3D
 from theano import config
 
 
-#def _grad_sources_inputs(*args):
+def _grad_sources_inputs(*args):
     # warn_type was introduced after this code, it complains throughout for nothing.
-#    return grad_sources_inputs(warn_type=False, *args)
-
-if 0:
-    #most of these tests are no longer relevant now that grad_sources_inputs is gone
-    #also, some of our policies about what is allowed or not have changed
-    #nonetheless, it may be a good idea to resurrect some of these tests and write
-    #them in terms of tensor.grad directly
-    class test_grad_sources_inputs(unittest.TestCase):
+    return grad_sources_inputs(warn_type=False, *args)
+
+class test_grad_sources_inputs(unittest.TestCase):
     def test_retNone1(self):
         """Test that it is not ok to return None from op.grad()"""
         class retNone(gof.op.Op):
@@ -119,6 +114,7 @@ if 0:
         a1 = O().make_node()
         g = _grad_sources_inputs([(a1.outputs[0], 1)], None)
         self.assertTrue(g[a1.inputs[0]] is gval)
+
     def test_Nin_1out(self):
         """Test grad is called correctly for a many-to-1 op"""
         gval0 = gof.generic()
@@ -136,6 +132,7 @@ if 0:
         g = _grad_sources_inputs([(a1.outputs[0], 1)], None)
         self.assertTrue(g[a1.inputs[0]] is gval0)
         self.assertTrue(g[a1.inputs[1]] is gval1)
+
     def test_Nin_Nout(self):
         """Test grad is called correctly for a many-to-many op"""
         gval0 = gof.generic()
@@ -151,6 +148,7 @@ if 0:
         g = _grad_sources_inputs([(a1.outputs[0], 1)], None)
         self.assertTrue(g[a1.inputs[0]] is gval0)
         self.assertTrue(g[a1.inputs[1]] is gval1)
+
     def test_some_None_ograds(self):
         """Test grad is called when some output gradients are None"""
         class O(gof.op.Op):