Merge pull request #4310 from nouiz/small

theano/gof/op.py

@@ -965,8 +965,14 @@ class Op(utils.object2, PureOp, CLinkerOp):
                                      compute_map=compute_map)
         if new_node is not None:
             node = new_node
-
-        if self._op_use_c_code:
+        if not hasattr(self, '_op_use_c_code'):
+            warnings.warn(
+                "The  __getstate__ method of '%s' is not implemented correctly."
+                " It should keep the attributes added by the base class."
+                " To implement it correctly, it should keep all attributes"
+                " and only remove those it does not want." % (self),
+                stacklevel=2)
+        if getattr(self, '_op_use_c_code', theano.config.cxx):
             try:
                 return self.make_c_thunk(node, storage_map, compute_map,
                                          no_recycling)

theano/misc/do_nightly_build_send

@@ -19,9 +19,9 @@ msgs=['Theano buildbot',
       'Deep Learning Tutorial buildbot',
       'Theano Python3.3.0 buildbot']
 
-print 'files', files
-print "msgs", msgs
-print "args", sys.argv
+print('files', files)
+print("msgs", msgs)
+print("args", sys.argv)
 if len(sys.argv) == 3:
     #We send just a file with a message
     files = [sys.argv[1]]
@@ -31,7 +31,7 @@ elif len(sys.argv) == 2:
     files=[os.path.join(sys.argv[1], x) for x in files]
 else:
     files=[os.path.join('/tmp', x) for x in files]
-print 'path', files
+print('path', files)
 
 # Here are the email package modules we'll need
 from email.mime.text import MIMEText
@@ -42,7 +42,7 @@ COMMASPACE = ', '
 def mysend(subject, file):
     # Create the container (outer) email message.
     if not os.path.isfile(file):
-        print "Error: no file",file
+        print("Error: no file", file)
         return
 
     msg = MIMEMultipart()
@@ -89,8 +89,8 @@ def mysend(subject, file):
             ran=False
             try: 
                 nb_ran+=int(token)
-            except Exception, e:
-                print e
+            except Exception as e:
+                print(e)
                 
     start = ""
     for line in s.splitlines():
@@ -124,12 +124,12 @@ def mysend(subject, file):
     else:
         msg['Subject'] = subject+" Fail="+str(failures)+" Err="+str(errors)+" Ran="+str(nb_ran)+" Skip="+str(skip)+" KnownFail="+str(knownfail)
 
-    print msg['Subject']
+    print(msg['Subject'])
     s = smtplib.SMTP()
     s.connect()
     s.sendmail(me, family, msg.as_string())
     s.close()
-    print "Finished sending email for",subject
+    print("Finished sending email for", subject)
 
 for msg, file in zip(msgs, files):
     mysend(msg, file)

theano/sandbox/cuda/tests/test_dnn.py

@@ -568,11 +568,11 @@ class test_DnnSoftMax(test_nnet.test_SoftMax):
         x_val = numpy.random.normal(0, 1, (3, 4, 2, 5)).astype('float32')
         x_val2 = numpy.random.normal(0, 1, (3, 4, 1, 1)).astype('float32')
 
-        utt.verify_grad(softmax_op, [x_val])
+        utt.verify_grad(softmax_op, [x_val], mode=mode_with_gpu)
 
         # Gradient is broken for (n, c, 1, 1) in v3 rc1
         if cuda.dnn.version() != (3000, 3000):
-            utt.verify_grad(softmax_op, [x_val2])
+            utt.verify_grad(softmax_op, [x_val2], mode=mode_with_gpu)
 
     def test_cudnn_softmax_grad_opt(self):
         # Verify that the SoftmaxGrad -> GpuDnnSoftmaxGrad optimization is
@@ -1353,9 +1353,9 @@ def test_dnn_conv_grad():
         return dnn.GpuDnnConvGradW()(img, out, kern, desc, alpha=0.75,
                                      beta=-1.0)
 
-    utt.verify_grad(dconv, [img_val, kern_val, out_val])
-    utt.verify_grad(dconvi, [img_val, kern_val, out_val])
-    utt.verify_grad(dconvw, [img_val, kern_val, out_val])
+    utt.verify_grad(dconv, [img_val, kern_val, out_val], mode=mode_with_gpu)
+    utt.verify_grad(dconvi, [img_val, kern_val, out_val], mode=mode_with_gpu)
+    utt.verify_grad(dconvw, [img_val, kern_val, out_val], mode=mode_with_gpu)
 
 
 def get_conv3d_test_cases():

theano/tensor/elemwise.py

@@ -140,7 +140,10 @@ class DimShuffle(Op):
                 # numpy integers.  See
                 # <http://projects.scipy.org/numpy/ticket/2235>.
                 if not isinstance(j, (integer_types, numpy.integer)):
-                    raise TypeError("DimShuffle indices must be python ints.")
+                    raise TypeError(
+                        "DimShuffle indices must be python ints. "
+                        "Got: '%s' of type '%s'.",
+                        str(j), str(type(j)))
                 if j >= len(input_broadcastable):
                     raise ValueError(("new_order[%d] is %d, but the input "
                                       "only has %d axes.") %

theano/tensor/raw_random.py

@@ -133,15 +133,27 @@ class RandomFunction(gof.Op):
         self.__setstate__([fn, outtype, inplace, ndim_added])
 
     def __getstate__(self):
-        return self.state
-
-    def __setstate__(self, state):
-        self.state = state
+        d = dict(self.__dict__)
+        del d['exec_fn']
+        if 'destroy_map' in d:
+            del d['destroy_map']
+        return d
+
+    def __setstate__(self, dct):
+        if isinstance(dct, dict):
+            state = [dct['fn'],
+                     dct['outtype'],
+                     dct['inplace'],
+                     dct['ndim_added']]
+            self.__dict__.update(dct)
+        else:
+            state = dct
         fn, outtype, inplace, ndim_added = state
+        self.fn = fn
         if isinstance(fn, string_types):
-            self.fn = getattr(numpy.random.RandomState, fn)
+            self.exec_fn = getattr(numpy.random.RandomState, fn)
         else:
-            self.fn = fn
+            self.exec_fn = fn
         self.outtype = outtype
         self.inplace = inplace
         if self.inplace:
@@ -149,7 +161,7 @@ class RandomFunction(gof.Op):
         self.ndim_added = ndim_added
 
     def __str__(self):
-        return 'RandomFunction{%s}' % self.fn.__name__
+        return 'RandomFunction{%s}' % self.exec_fn.__name__
 
     def make_node(self, r, shape, *args):
         """
@@ -247,7 +259,7 @@ class RandomFunction(gof.Op):
         if not self.inplace:
             r = copy(r)
         rout[0] = r
-        rval = self.fn(r, *(args + [shape]))
+        rval = self.exec_fn(r, *(args + [shape]))
         if (not isinstance(rval, numpy.ndarray) or
                 str(rval.dtype) != node.outputs[1].type.dtype):
             rval = theano._asarray(rval, dtype=node.outputs[1].type.dtype)
@@ -904,7 +916,7 @@ def random_make_inplace(node):
     if isinstance(op, RandomFunction) and not op.inplace:
         # Read op_fn from op.state, not from op.fn, since op.fn
         # may not be picklable.
-        op_fn, op_outtype, op_inplace, op_ndim_added = op.__getstate__()
+        op_fn, op_outtype, op_inplace, op_ndim_added = op._props()
         new_op = RandomFunction(op_fn, op_outtype, inplace=True,
                                 ndim_added=op_ndim_added)
         return new_op.make_node(*node.inputs).outputs

theano/tensor/tests/test_raw_random.py

@@ -310,8 +310,6 @@ class T_random_function(utt.InferShapeTester):
         self.assertRaises(ValueError, fm11)
         self.assertRaises(ValueError, fm12)
         u01, u02 = f0()
-        print(u01)
-        print(u02)
         self.assertTrue(numpy.allclose(u01, u02[0]))
 
     def test_uniform(self):
@@ -332,10 +330,6 @@ class T_random_function(utt.InferShapeTester):
         val1 = f()
         numpy_val0 = numpy_rng.uniform(-2.0, 2.0, size=(4,))
         numpy_val1 = numpy_rng.uniform(-2.0, 2.0, size=(4,))
-        print(val0)
-        print(numpy_val0)
-        print(val1)
-        print(numpy_val1)
         self.assertTrue(numpy.allclose(val0, numpy_val0))
         self.assertTrue(numpy.allclose(val1, numpy_val1))
 
@@ -359,10 +353,6 @@ class T_random_function(utt.InferShapeTester):
         val1 = f()
         numpy_val0 = numpy_rng.binomial(5, 0.8, size=(7, 12))
         numpy_val1 = numpy_rng.binomial(5, 0.8, size=(7, 12))
-        print(val0)
-        print(numpy_val0)
-        print(val1)
-        print(numpy_val1)
         self.assertTrue(numpy.all(val0 == numpy_val0))
         self.assertTrue(numpy.all(val1 == numpy_val1))
 
@@ -384,10 +374,6 @@ class T_random_function(utt.InferShapeTester):
         val1 = f()
         numpy_val0 = numpy_rng.normal(4.0, 2.0, size=(2, 3))
         numpy_val1 = numpy_rng.normal(4.0, 2.0, size=(2, 3))
-        print(val0)
-        print(numpy_val0)
-        print(val1)
-        print(numpy_val1)
         self.assertTrue(numpy.allclose(val0, numpy_val0))
         self.assertTrue(numpy.allclose(val1, numpy_val1))
 
@@ -411,10 +397,6 @@ class T_random_function(utt.InferShapeTester):
         val1 = f()
         numpy_val0 = numpy_rng.random_integers(-3, 16, size=(11, 8))
         numpy_val1 = numpy_rng.random_integers(-3, 16, size=(11, 8))
-        print(val0)
-        print(numpy_val0)
-        print(val1)
-        print(numpy_val1)
         self.assertTrue(numpy.allclose(val0, numpy_val0))
         self.assertTrue(numpy.allclose(val1, numpy_val1))
 
@@ -447,10 +429,6 @@ class T_random_function(utt.InferShapeTester):
                                     for i in range(7)])
         numpy_val1 = numpy.asarray([numpy_rng.permutation(8)
                                     for i in range(7)])
-        print(val0)
-        print(numpy_val0)
-        print(val1)
-        print(numpy_val1)
         self.assertTrue(numpy.all(val0 == numpy_val0))
         self.assertTrue(numpy.all(val1 == numpy_val1))
 
@@ -502,10 +480,6 @@ class T_random_function(utt.InferShapeTester):
         val1 = f()
         numpy_val0 = numpy_rng.choice(10, (11, 8), True, None)
         numpy_val1 = numpy_rng.choice(10, (11, 8), True, None)
-        print(val0)
-        print(numpy_val0)
-        print(val1)
-        print(numpy_val1)
         self.assertTrue(numpy.allclose(val0, numpy_val0))
         self.assertTrue(numpy.allclose(val1, numpy_val1))
 
@@ -529,10 +503,6 @@ class T_random_function(utt.InferShapeTester):
         val1 = f()
         numpy_val0 = numpy_rng.poisson(5, size=(11, 8))
         numpy_val1 = numpy_rng.poisson(5, size=(11, 8))
-        print(val0)
-        print(numpy_val0)
-        print(val1)
-        print(numpy_val1)
         self.assertTrue(numpy.allclose(val0, numpy_val0))
         self.assertTrue(numpy.allclose(val1, numpy_val1))
 
@@ -541,7 +511,6 @@ class T_random_function(utt.InferShapeTester):
         results as numpy."""
         rng_R = random_state_type()
         post_r, out = permutation(rng_R, size=(9,), n=6)
-        print('OUT NDIM', out.ndim)
         f = compile.function(
                 [compile.In(rng_R,
                     value=numpy.random.RandomState(utt.fetch_seed()),
@@ -558,10 +527,6 @@ class T_random_function(utt.InferShapeTester):
                                     for i in range(9)])
         numpy_val1 = numpy.asarray([numpy_rng.permutation(6)
                                     for i in range(9)])
-        print(val0)
-        print(numpy_val0)
-        print(val1)
-        print(numpy_val1)
         self.assertTrue(numpy.all(val0 == numpy_val0))
         self.assertTrue(numpy.all(val1 == numpy_val1))
 
@@ -596,10 +561,6 @@ class T_random_function(utt.InferShapeTester):
         val1, = f()
         numpy_val0 = numpy_rng.multinomial(6, [0.2] * 5, (7, 3))
         numpy_val1 = numpy_rng.multinomial(6, [0.2] * 5, (7, 3))
-        print(val0)
-        print(numpy_val0)
-        print(val1)
-        print(numpy_val1)
         self.assertTrue(numpy.all(val0 == numpy_val0))
         self.assertTrue(numpy.all(val1 == numpy_val1))
 
@@ -1199,6 +1160,7 @@ class T_random_function(utt.InferShapeTester):
         post_int_r, int_sample = random_integers(rng_r, (3, 5), -1, 8)
         g = theano.function([rng_r], [post_int_r, int_sample], mode=mode)
         pkl_g = pickle.dumps(g)
+        pickle.loads(pkl_g)
 
 
 if __name__ == '__main__':