Small test refactorization to remove duplicate code about the mode.

theano/scan_module/tests/test_scan.py

@@ -24,7 +24,11 @@ from numpy.testing.noseclasses import KnownFailureTest
    * There is some of scan functionality that is not well documented
 '''
 
-
+if theano.config.mode == 'FAST_COMPILE':
+    mode_with_opt = theano.compile.mode.get_mode('FAST_RUN')
+else:
+    mode_with_opt = theano.compile.mode.get_default_mode()
+mode_with_gpu = mode_with_opt.including('gpu','scan')
 class multiple_outputs_numeric_grad:
     """WRITEME"""
     type_eps = {'float64': 1e-7,
@@ -255,14 +259,9 @@ class T_Scan(unittest.TestCase):
         W_in = theano.tensor.fscalar('win')
         W    = theano.tensor.fscalar('w')
 
-        if theano.config.mode == 'FAST_COMPILE':
-            mode = theano.compile.mode.get_mode('FAST_RUN')
-        else:
-            mode = theano.compile.mode.get_default_mode()
-        mode = mode.including('gpu','scan')
         # The following line is needed to have the first case being used
         # Otherwise, it is the second that is tested.
-        mode = mode.excluding('InputToGpuOptimizer')
+        mode = mode_with_gpu.excluding('InputToGpuOptimizer')
         output, updates = theano.scan(f_rnn, u,x0,[W_in,W]
                                       , n_steps           = None
                                       , truncate_gradient = -1
@@ -328,20 +327,15 @@ class T_Scan(unittest.TestCase):
         x0   = theano.tensor.fscalar('x0')
         W_in = theano.tensor.fscalar('win')
         W    = theano.tensor.fscalar('w')
-        if theano.config.mode == 'FAST_COMPILE':
-            mode = theano.compile.mode.get_mode('FAST_RUN')
-        else:
-            mode = theano.compile.mode.get_default_mode()
-        mode = mode.including('gpu','scan')
         output, updates = theano.scan(f_rnn, u,x0,[W_in,W]
                                       , n_steps           = None
                                       , truncate_gradient = -1
                                       , go_backwards      = False
-                                      , mode = mode)
+                                      , mode = mode_with_gpu)
 
         f2   = theano.function([u,x0,W_in,W], output, updates = updates,
                                allow_input_downcast = True,
-                               mode = mode)
+                               mode = mode_with_gpu)
 
         # get random initial values
         rng  = numpy.random.RandomState(utt.fetch_seed())
@@ -914,13 +908,6 @@ class T_Scan(unittest.TestCase):
         if cuda.cuda_available == False:
             raise SkipTest('Optional package cuda disabled')
 
-        if theano.config.mode == 'FAST_COMPILE':
-            mode = theano.compile.mode.get_mode('FAST_RUN')
-        else:
-            mode = theano.compile.mode.get_default_mode()
-        mode = mode.including('gpu','scan')
-
-
         rng = numpy.random.RandomState(utt.fetch_seed())
         v_vsample = numpy.array(rng.binomial(1,0.5, size=(3,20), )
                                 , dtype = 'float32')
@@ -936,12 +923,12 @@ class T_Scan(unittest.TestCase):
                                                , n_steps = 10
                                                , truncate_gradient=-1
                                                , go_backwards = False
-                                               , mode = mode
+                                               , mode = mode_with_gpu
                                               )
         my_f = theano.function([], theano_vsamples[-1]
                                , updates = updates
                                , allow_input_downcast = True
-                               , mode = mode
+                               , mode = mode_with_gpu
                               )
 
         # I leave this to tested by debugmode, this test was anyway more of
@@ -1963,10 +1950,6 @@ class T_Scan(unittest.TestCase):
                                       dtype = theano.config.floatX),
                     m + trng.uniform(size=[3])]
 
-        if theano.config.mode == 'FAST_COMPILE':
-            mode = theano.compile.mode.get_mode('FAST_RUN')
-        else:
-            mode = theano.compile.mode.get_default_mode()
         [o1,o2], updates = theano.scan( lm,
                                        sequences = x,
                                        n_steps = None,
@@ -1975,7 +1958,7 @@ class T_Scan(unittest.TestCase):
                                        go_backwards = False)
         go1 = theano.tensor.grad(o1.mean(), wrt = x)
         f = theano.function([x],go1, updates = updates,
-                            allow_input_downcast = True, mode= mode)
+                            allow_input_downcast = True, mode= mode_with_opt)
         self.assertTrue(numpy.allclose(f([1,2,3]), 2./3))
 
         #theano.printing.debugprint(f, print_type=True)
@@ -2001,14 +1984,10 @@ class T_Scan(unittest.TestCase):
         def sum(s):
             return s+1
 
-        if theano.config.mode == 'FAST_COMPILE':
-            mode = theano.compile.mode.get_mode('FAST_RUN')
-        else:
-            mode = theano.compile.mode.get_default_mode()
         sx, upx = theano.scan(sum, sequences = [x])
         sy, upy = theano.scan(sum, sequences = [y])
 
-        f = theano.function([x, y], [sx, sy], mode = mode)
+        f = theano.function([x, y], [sx, sy], mode = mode_with_opt)
         topo = f.maker.env.toposort()
         scans = filter(lambda n: isinstance(n.op, theano.scan_module.scan_op.Scan), topo)
         self.assertTrue(len(scans) == 2)
@@ -2016,7 +1995,7 @@ class T_Scan(unittest.TestCase):
         sx, upx = theano.scan(sum, sequences = [x], n_steps=2)
         sy, upy = theano.scan(sum, sequences = [y], n_steps=3)
 
-        f = theano.function([x, y], [sx, sy], mode= mode)
+        f = theano.function([x, y], [sx, sy], mode= mode_with_opt)
         topo = f.maker.env.toposort()
         scans = filter(lambda n: isinstance(n.op, theano.scan_module.scan_op.Scan), topo)
         self.assertTrue(len(scans) == 2)
@@ -2024,7 +2003,7 @@ class T_Scan(unittest.TestCase):
         sx, upx = theano.scan(sum, sequences = [x], n_steps=4)
         sy, upy = theano.scan(sum, sequences = [y], n_steps=4)
 
-        f = theano.function([x, y], [sx, sy], mode = mode)
+        f = theano.function([x, y], [sx, sy], mode = mode_with_opt)
         topo = f.maker.env.toposort()
         scans = filter(lambda n: isinstance(n.op, theano.scan_module.scan_op.Scan), topo)
         self.assertTrue(len(scans) == 1)
@@ -2032,7 +2011,7 @@ class T_Scan(unittest.TestCase):
         sx, upx = theano.scan(sum, sequences = [x])
         sy, upy = theano.scan(sum, sequences = [x])
 
-        f = theano.function([x, y], [sx, sy], mode= mode)
+        f = theano.function([x, y], [sx, sy], mode= mode_with_opt)
         topo = f.maker.env.toposort()
         scans = filter(lambda n: isinstance(n.op, theano.scan_module.scan_op.Scan), topo)
         self.assertTrue(len(scans) == 1)
@@ -2040,7 +2019,7 @@ class T_Scan(unittest.TestCase):
         sx, upx = theano.scan(sum, sequences = [x])
         sy, upy = theano.scan(sum, sequences = [x], mode='FAST_COMPILE')
 
-        f = theano.function([x, y], [sx, sy], mode= mode)
+        f = theano.function([x, y], [sx, sy], mode= mode_with_opt)
         topo = f.maker.env.toposort()
         scans = filter(lambda n: isinstance(n.op, theano.scan_module.scan_op.Scan), topo)
         self.assertTrue(len(scans) == 2)
@@ -2048,7 +2027,7 @@ class T_Scan(unittest.TestCase):
         sx, upx = theano.scan(sum, sequences = [x])
         sy, upy = theano.scan(sum, sequences = [x], truncate_gradient=1)
 
-        f = theano.function([x, y], [sx, sy], mode = mode)
+        f = theano.function([x, y], [sx, sy], mode = mode_with_opt)
         topo = f.maker.env.toposort()
         scans = filter(lambda n: isinstance(n.op, theano.scan_module.scan_op.Scan), topo)
         self.assertTrue(len(scans) == 2)
@@ -2241,10 +2220,6 @@ class T_Scan(unittest.TestCase):
         W2 = tensor.matrix('W2')
         h0 = tensor.vector('h0')
 
-        if theano.config.mode == 'FAST_COMPILE':
-            mode = theano.compile.mode.get_mode('FAST_RUN')
-        else:
-            mode = theano.compile.mode.get_default_mode()
         def lambda_fn(h, W1, W2):
             return tensor.dot(h, W1 + W2)
 
@@ -2252,7 +2227,7 @@ class T_Scan(unittest.TestCase):
                            non_sequences =[W1,W2],
                            n_steps = 5)
 
-        f = theano.function([h0,W1,W2], o, mode= mode)
+        f = theano.function([h0,W1,W2], o, mode= mode_with_opt)
 
         scan_node = [x for x in f.maker.env.toposort()
                      if isinstance(x.op,
@@ -2273,11 +2248,7 @@ class T_Scan(unittest.TestCase):
                            non_sequences =[W1,tensor.zeros_like(W2)],
                            n_steps = 5)
 
-        if theano.config.mode == 'FAST_COMPILE':
-            mode = theano.compile.mode.get_mode('FAST_RUN')
-        else:
-            mode = theano.compile.mode.get_default_mode()
-        f = theano.function([h0,W1,W2], o, mode = mode)
+        f = theano.function([h0,W1,W2], o, mode = mode_with_opt)
         scan_node = [x for x in f.maker.env.toposort()
                      if isinstance(x.op,
                                    theano.scan_module.scan_op.Scan)][0]
@@ -2302,11 +2273,7 @@ class T_Scan(unittest.TestCase):
                            non_sequences =[tensor.zeros_like(W2)],
                            n_steps = 5)
 
-        if theano.config.mode == 'FAST_COMPILE':
-            mode = theano.compile.mode.get_mode('FAST_RUN')
-        else:
-            mode = theano.compile.mode.get_default_mode()
-        f = theano.function([h0,W1,W2], o, mode = mode)
+        f = theano.function([h0,W1,W2], o, mode = mode_with_opt)
         scan_node = [x for x in f.maker.env.toposort()
                      if isinstance(x.op,
                                    theano.scan_module.scan_op.Scan)][0]
@@ -2334,11 +2301,7 @@ class T_Scan(unittest.TestCase):
                            non_sequences =[tensor.zeros_like(W2)],
                            n_steps = 5)
 
-        if theano.config.mode == 'FAST_COMPILE':
-            mode = theano.compile.mode.get_mode('FAST_RUN')
-        else:
-            mode = theano.compile.mode.get_default_mode()
-        f = theano.function([_h0,_W1,_W2], o, mode = mode)
+        f = theano.function([_h0,_W1,_W2], o, mode = mode_with_opt)
         scan_node = [x for x in f.maker.env.toposort()
                      if isinstance(x.op,
                                    theano.scan_module.scan_op.Scan)][0]
@@ -2384,11 +2347,7 @@ class T_Scan(unittest.TestCase):
         o2, _ = theano.scan(lambda x_t:( x_t + 2, theano.scan_module.until(x_t>3)),
                             x)
 
-        if theano.config.mode == 'FAST_COMPILE':
-            mode = theano.compile.mode.get_mode('FAST_RUN')
-        else:
-            mode = theano.compile.mode.get_default_mode()
-        f = theano.function([x], [o,o2], mode=mode)
+        f = theano.function([x], [o,o2], mode=mode_with_opt)
         vx = numpy.zeros((50,), dtype = theano.config.floatX)
         vx[23] = 4
         out, out2 = f(vx)