Merge pull request #792 from nouiz/fix_py2.4

theano/misc/pycuda_init.py

@@ -41,17 +41,23 @@ if (not hasattr(theano.sandbox, 'cuda') or
             import pycuda.autoinit
             pycuda_available = True
 else:
-    import pycuda.driver
-    if hasattr(pycuda.driver.Context, "attach"):
-        pycuda.driver.Context.attach()
-    else:
-        # Now we always import this file when we call theano.sandbox.cuda.use
-        # So this should not happen normally.
-        # TODO: make this an error.
-        warnings.warn("For some unknow reason, theano.misc.pycuda_init was not"
-                      " imported before Theano initialized the GPU and"
-                      " your PyCUDA version is 2011.2.2 or earlier."
-                      " To fix the problem, import theano.misc.pycuda_init"
-                      " manually before using/initializing the GPU, use the"
-                      " Theano flag pycuda.init=True or use a"
-                      " more recent version of PyCUDA.")
+    try:
+        import pycuda.driver
+        pycuda_available = True
+    except ImportError:
+        pass
+    if pycuda_available:
+        if hasattr(pycuda.driver.Context, "attach"):
+            pycuda.driver.Context.attach()
+        else:
+            # Now we always import this file when we call
+            # theano.sandbox.cuda.use. So this should not happen
+            # normally.
+            # TODO: make this an error.
+            warnings.warn("For some unknow reason, theano.misc.pycuda_init was"
+                          " not imported before Theano initialized the GPU and"
+                          " your PyCUDA version is 2011.2.2 or earlier."
+                          " To fix the problem, import theano.misc.pycuda_init"
+                          " manually before using/initializing the GPU, use the"
+                          " Theano flag pycuda.init=True or use a"
+                          " more recent version of PyCUDA.")

theano/tensor/fourier.py

@@ -89,7 +89,7 @@ class Fourier(gof.Op):
         if len(shape_a) == 1:
             return [(n,)]
         elif isinstance(axis, tensor.TensorConstant):
-            out_shape = list(shape_a[0: axis.data]) + [n] +\
+            out_shape = list(shape_a[0: axis.data.item()]) + [n] +\
             list(shape_a[axis.data + 1:])
         else:
             l = len(shape_a)
@@ -105,7 +105,7 @@ class Fourier(gof.Op):
         a = inputs[0]
         n = inputs[1]
         axis = inputs[2]
-        output_storage[0][0] = numpy.fft.fft(a, n=int(n), axis=axis)
+        output_storage[0][0] = numpy.fft.fft(a, n=int(n), axis=axis.item())
 
     def grad(self, inputs, cost_grad):
         """

theano/tensor/nnet/Conv3D.py

@@ -121,7 +121,7 @@ class Conv3D(theano.Op):
 
     def perform(self, node, inputs, output_storage):
         V, W, b, d = inputs
-        print "Conv3D python code"
+#        print "Conv3D python code"
         output_storage[0][0] = computeH(V,W,b,d)
 
     def infer_shape(self, node, input_shapes):

theano/tensor/nnet/ConvGrad3D.py

@@ -44,7 +44,7 @@ class ConvGrad3D(theano.Op):
 
     def perform(self, node, inputs, output_storage):
         V, d, WShape, dCdH = inputs
-        print "ConvGradW3D python code"
+#        print "ConvGradW3D python code"
 
         #partial C / partial W[j,z,k,l,m] = sum_i sum_p sum_q sum_r (partial C /partial H[i,j,p,q,r] ) *  V[i,z,dr*p+k,dc*q+l,dt*r+m]
 

theano/tensor/nnet/ConvTransp3D.py

@@ -83,7 +83,7 @@ class ConvTransp3D(theano.Op):
 
     def perform(self, node, inputs, output_storage):
         W, b, d, H, RShape = inputs
-        print "\t\t\t\tConvTransp3D python code"
+#        print "\t\t\t\tConvTransp3D python code"
         output_storage[0][0] = computeR(W,b,d,H,RShape)
 
     def c_code(self, node, nodename, inputs, outputs, sub):

theano/tensor/nnet/tests/test_conv3d.py

@@ -97,8 +97,8 @@ class DummyConvTransp3D:
         self.dW = rng.uniform(-1, 1, self.W.get_value(borrow=True).shape)
         self.db = rng.uniform(-1, 1, self.b.get_value(borrow=True).shape)
         self.dH = rng.uniform(-1, 1, self.H.get_value(borrow=True).shape)
-        self.dW, self.db, self.dH = shared(self.dW), shared(self.db),
-        shared(self.dH)
+        self.dW, self.db = shared(self.dW), shared(self.db),
+        self.dH = shared(self.dH)
 
         self.d = d
         self.RShape = RShape

theano/tensor/nnet/tests/test_nnet.py

@@ -276,7 +276,7 @@ class T_prepend(utt.InferShapeTester):
     def test0(self):
         x = tensor.matrix('x')
         y = Prepend_scalar_constant_to_each_row(4.)(x)
-        f = theano.function([x], [y])
+        f = theano.function([x], y)
         m = numpy.random.rand(3, 5)
         my = f(m)
         self.assertTrue(my.shape == (3, 6), my.shape)

theano/tensor/tests/test_basic.py

@@ -1245,6 +1245,8 @@ if imported_scipy_special:
 else:
     expected_erf = []
     expected_erfc = []
+    expected_gammaln = []
+    expected_psi = []
     skip_scipy = "scipy is not present"
 
 ErfTester = makeBroadcastTester(

theano/tensor/tests/test_elemwise.py

@@ -8,6 +8,7 @@ import numpy
 from numpy.testing import dec
 
 import theano
+from theano.gof.python25 import all
 from theano.gof import Variable, Op
 from theano import gof, scalar, config
 

theano/tests/test_ifelse.py

@@ -16,6 +16,7 @@ from theano import tensor
 import theano.ifelse
 from theano.ifelse import IfElse, ifelse
 from theano.tests  import unittest_tools as utt
+from theano.gof.python25 import all
 
 
 class test_ifelse(unittest.TestCase, utt.TestOptimizationMixin):