s/stderr/stdout (in most cases) in cuda tests

theano/sandbox/cuda/tests/test_basic_ops.py

@@ -140,20 +140,20 @@ def test_elemwise1():
     b = tensor.fmatrix()
 
     #let debugmode catch any mistakes
-    print >> sys.stderr, "STARTING FUNCTION 1"
+    print >> sys.stdout, "STARTING FUNCTION 1"
     f = pfunc([b], [], updates=[(a, b**a)], mode=mode_with_gpu)
     for i, node in enumerate(f.maker.env.toposort()):
         print i, node
     f(numpy.random.rand(*shape)+0.3)
 
-    print >> sys.stderr, "STARTING FUNCTION 2"
+    print >> sys.stdout, "STARTING FUNCTION 2"
     #let debugmode catch any mistakes
     f = pfunc([b], [], updates=[(a, tensor.exp(b**a))], mode=mode_with_gpu)
     for i, node in enumerate(f.maker.env.toposort()):
         print i, node
     f(numpy.random.rand(*shape)+0.3)
 
-    print >> sys.stderr, "STARTING FUNCTION 3"
+    print >> sys.stdout, "STARTING FUNCTION 3"
     #let debugmode catch any mistakes
     f = pfunc([b], [], updates=[(a, a+b * tensor.exp(b**a))], mode=mode_with_gpu)
     f(numpy.random.rand(*shape)+0.3)
@@ -169,11 +169,11 @@ def test_elemwise2():
         f = pfunc([b], [], updates=[(a, (a+b).dimshuffle(pattern))], mode=mode_with_gpu)
         has_elemwise = False
         for i, node in enumerate(f.maker.env.toposort()):
-            print >> sys.stderr, i, node
+            print >> sys.stdout, i, node
             has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise)
         assert not has_elemwise
         #let debugmode catch errors
-        print >> sys.stderr, 'pattern', pattern
+        print >> sys.stdout, 'pattern', pattern
         f(rng.rand(*shape)*.3)
     
     shape = (3,4,5,6)
@@ -204,7 +204,7 @@ def test_elemwise3():
         b**a).dimshuffle([2,0,3,1]))], mode=mode_with_gpu)
     has_elemwise = False
     for i, node in enumerate(f.maker.env.toposort()):
-        print >> sys.stderr, i, node
+        print >> sys.stdout, i, node
         has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise)
     assert not has_elemwise
     #let debugmode catch errors
@@ -220,7 +220,7 @@ def test_elemwise4():
     f = pfunc([b,c], [], updates=[(a, (a+b.dimshuffle('x', 0)*c.dimshuffle(0, 'x')))], mode=mode_with_gpu)
     has_elemwise = False
     for i, node in enumerate(f.maker.env.toposort()):
-        print >> sys.stderr, i, node
+        print >> sys.stdout, i, node
         has_elemwise = has_elemwise or isinstance(node.op, tensor.Elemwise)
     assert not has_elemwise
     #let debugmode catch errors

theano/sandbox/cuda/tests/test_conv_cuda_ndarray.py

@@ -360,7 +360,7 @@ def test_subsample():
 
 def test_logical_shapes():
     # implement when
-    print >> sys.stderr, "INFO: test_logical_shapes not implemented (i.e. imshp_logical, kshp_logical, kshp_logical_top_aligned)"
+    print >> sys.stderr, "WARNING TODO: test_logical_shapes not implemented (i.e. imshp_logical, kshp_logical, kshp_logical_top_aligned)"
 
 
 def _test_dummy():

theano/sandbox/cuda/tests/test_cuda_ndarray.py

@@ -8,7 +8,7 @@ if cuda_ndarray.enable_cuda == False:
 import numpy
 
 def test_host_to_device():
-    print >>sys.stderr, 'starting test_host_to_dev'
+    print >>sys.stdout, 'starting test_host_to_dev'
     for shape in ((), (3,), (2,3), (3,4,5,6)):
         a = theano._asarray(numpy.random.rand(*shape), dtype='float32')
         b = cuda_ndarray.CudaNdarray(a)
@@ -53,7 +53,7 @@ def test_add():
 
 
 def test_exp():
-    print >>sys.stderr, 'starting test_exp'
+    print >>sys.stdout, 'starting test_exp'
     for shape in ((), (3,), (2,3), (1,10000000),(10,1000000), (100,100000),(1000,10000),(10000,1000)):
         a0 = theano._asarray(numpy.random.rand(*shape), dtype='float32')
         a1 = a0.copy()
@@ -74,25 +74,25 @@ def test_exp():
 
 
 def test_copy():
-    print >>sys.stderr, 'starting test_copy'
+    print >>sys.stdout, 'starting test_copy'
     shape = (5,)
     a = theano._asarray(numpy.random.rand(*shape), dtype='float32')
 
-    print >>sys.stderr, '.. creating device object'
+    print >>sys.stdout, '.. creating device object'
     b = cuda_ndarray.CudaNdarray(a)
 
-    print >>sys.stderr, '.. copy'
+    print >>sys.stdout, '.. copy'
     c = copy.copy(b)
-    print >>sys.stderr, '.. deepcopy'
+    print >>sys.stdout, '.. deepcopy'
     d = copy.deepcopy(b)
 
-    print >>sys.stderr, '.. comparisons'
+    print >>sys.stdout, '.. comparisons'
     assert numpy.allclose(a, numpy.asarray(b))
     assert numpy.allclose(a, numpy.asarray(c))
     assert numpy.allclose(a, numpy.asarray(d))
 
 def test_dot():
-    print >>sys.stderr, 'starting test_dot'
+    print >>sys.stdout, 'starting test_dot'
     a0 = theano._asarray(numpy.random.rand(4, 7), dtype='float32')
     a1 = theano._asarray(numpy.random.rand(7, 6), dtype='float32')
 
@@ -101,7 +101,7 @@ def test_dot():
 
     assert numpy.allclose(numpy.dot(a0, a1), cuda_ndarray.dot(b0, b1))
 
-    print >> sys.stderr, 'WARNING test_dot: not testing all 8 transpose cases of dot'
+    print >> sys.stderr, 'WARNING TODO test_dot: not testing all 8 transpose cases of dot'
 
 def test_sum():
     shape = (2,3)
@@ -147,7 +147,7 @@ def test_reshape():
              ]
 
     def subtest(shape_1, shape_2):
-        #print >> sys.stderr, "INFO: shapes", shape_1, shape_2
+        #print >> sys.stdout, "INFO: shapes", shape_1, shape_2
         a = theano._asarray(numpy.random.rand(*shape_1), dtype='float32')
         b = cuda_ndarray.CudaNdarray(a)