Merge pull request #4092 from abergeron/fix_buildbot2

theano/sandbox/cuda/basic_ops.py

@@ -3684,7 +3684,7 @@ class GpuAllocEmpty(GpuOp):
         return Apply(self, shape, [output])
 
     def debug_perform(self, node, inputs, out_):
-        self.perform(self, node, inputs, out_)
+        self.perform(node, inputs, out_)
         # __setitem__ is limited on CudaNdarray
         tmp = numpy.empty(out_[0][0].shape, dtype='float32')
         tmp.fill(-123456789)
@@ -3769,6 +3769,10 @@ class GpuAlloc(GpuAllocEmpty):
         shape, output = self.validate_shape(shape)
         return Apply(self, [v] + shape, [output])
 
+    # This is required because the superclass (GpuAllocEmpty) also has it.
+    def debug_perform(self, node, inputs, out_):
+        self.perform(node, inputs, out_)
+
     def perform(self, node, inputs, out_):
         # the super class (GpuAllocEmpty) allocates memory, we fill it
         value = inputs[0]

theano/sandbox/cuda/tests/test_blas.py

@@ -17,7 +17,7 @@ if cuda_ndarray.cuda_available == False:
 import theano.sandbox.cuda as tcn
 
 from theano.tensor.signal.pool import (Pool,
-        DownsampleFactorMaxGrad, DownsampleFactorMaxGradGrad)
+        PoolGrad, DownsampleFactorMaxGradGrad)
 
 import theano.compile.mode
 from theano.tensor.tests.test_blas import BaseGemv, TestBlasStrides, TestGer
@@ -394,7 +394,7 @@ def test_downsample():
                 assert any([isinstance(node.op,
                                        tcn.blas.GpuDownsampleFactorMaxGrad)
                             for node in g.maker.fgraph.toposort()])
-                assert any([isinstance(node.op, DownsampleFactorMaxGrad)
+                assert any([isinstance(node.op, PoolGrad)
                             for node in g2.maker.fgraph.toposort()])
                 assert numpy.allclose(g(), g2()), shp
 

theano/scan_module/scan_opt.py

@@ -926,7 +926,7 @@ class PushOutScanOutput(gof.Optimizer):
         # For the inner_vars that don't already exist in the outer graph, add
         # them as new nitsot outputs to the scan node.
         idx_add_as_nitsots = [i for i in range(len(outer_vars))
-                              if outer_vars[i] == None]
+                              if outer_vars[i] is None]
         add_as_nitsots = [inner_vars[idx] for idx in idx_add_as_nitsots]
 
         if len(add_as_nitsots) > 0:

theano/sparse/tests/test_basic.py

@@ -67,6 +67,15 @@ def eval_outputs(outputs):
     return compile.function([], outputs)()[0]
 
 
+# scipy 0.17 will return sparse values in all cases while previous
+# version sometimes wouldn't.  This will make everything dense so that
+# we can use assert_allclose.
+def as_ndarray(val):
+    if hasattr(val, 'toarray'):
+        return val.toarray()
+    return val
+
+
 def random_lil(shape, dtype, nnz):
     rval = sp.lil_matrix(shape, dtype=dtype)
     huge = 2 ** 30
@@ -2956,7 +2965,7 @@ class StructuredAddSVTester(unittest.TestCase):
 
                 out = f(spmat, mat)
 
-                utt.assert_allclose(spones.multiply(spmat + mat),
+                utt.assert_allclose(as_ndarray(spones.multiply(spmat + mat)),
                                     out.toarray())
 
 
@@ -3072,7 +3081,7 @@ class SamplingDotTester(utt.InferShapeTester):
         x, y, p = self.a
         expected = p.multiply(numpy.dot(x, y.T))
 
-        utt.assert_allclose(expected, tested.toarray())
+        utt.assert_allclose(as_ndarray(expected), tested.toarray())
         assert tested.format == 'csr'
         assert tested.dtype == expected.dtype