Fix the overfix of 2to3 for print function

Author: velconia

CMakeLists.txt

@@ -72,6 +72,7 @@ option(PY_VERSION       "Compile PaddlePaddle with python3 support"     ${PY_VER
 if(NOT PY_VERSION)
   set(PY_VERSION 2.7)
 endif()
+set(PYBIND11_PYTHON_VERSION ${PY_VERSION})
 
 # CMAKE_BUILD_TYPE
 if(NOT CMAKE_BUILD_TYPE)

python/paddle/fluid/optimizer.py

@@ -106,7 +106,7 @@ def _create_param_lr(self, param_and_grad):
         param_lr = param.optimize_attr['learning_rate']
         if type(param_lr) == Variable:
             # param learning rate has been updated (LARS)
-            print(("returns updated param lr ", param_lr))
+            print("returns updated param lr ", param_lr)
             return param_lr
         else:
             if param_lr == 1.0:

python/paddle/fluid/tests/book/high-level-api/fit_a_line/test_fit_a_line.py

@@ -94,7 +94,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
     tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
 
     results = inferencer.infer({'x': tensor_x})
-    print(("infer results: ", results[0]))
+    print("infer results: ", results[0])
 
 
 def main(use_cuda):

python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_resnet.py

@@ -105,7 +105,7 @@ def event_handler(event):
             avg_cost, accuracy = trainer.test(
                 reader=test_reader, feed_order=['pixel', 'label'])
 
-            print(('Loss {0:2.2}, Acc {1:2.2}'.format(avg_cost, accuracy)))
+            print('Loss {0:2.2}, Acc {1:2.2}'.format(avg_cost, accuracy))
 
             if accuracy > 0.01:  # Low threshold for speeding up CI
                 if params_dirname is not None:
@@ -134,7 +134,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
     tensor_img = numpy.random.rand(1, 3, 32, 32).astype("float32")
     results = inferencer.infer({'pixel': tensor_img})
 
-    print(("infer results: ", results))
+    print("infer results: ", results)
 
 
 def main(use_cuda):

python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_vgg.py

@@ -82,7 +82,7 @@ def event_handler(event):
             avg_cost, accuracy = trainer.test(
                 reader=test_reader, feed_order=['pixel', 'label'])
 
-            print(('Loss {0:2.2}, Acc {1:2.2}'.format(avg_cost, accuracy)))
+            print('Loss {0:2.2}, Acc {1:2.2}'.format(avg_cost, accuracy))
 
             if accuracy > 0.01:  # Low threshold for speeding up CI
                 if params_dirname is not None:
@@ -111,7 +111,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
     tensor_img = numpy.random.rand(1, 3, 32, 32).astype("float32")
     results = inferencer.infer({'pixel': tensor_img})
 
-    print(("infer results: ", results))
+    print("infer results: ", results)
 
 
 def main(use_cuda):

python/paddle/fluid/tests/book/high-level-api/label_semantic_roles/test_label_semantic_roles_newapi.py

@@ -171,7 +171,7 @@ def event_handler(event):
             # get avg cost
             avg_cost = np.array(avg_cost_set).mean()
 
-            print(("avg_cost: %s" % avg_cost))
+            print("avg_cost: %s" % avg_cost)
 
             if float(avg_cost) < 100.0:  # Large value to increase CI speed
                 trainer.save_params(params_dirname)
@@ -183,8 +183,8 @@ def event_handler(event):
                     sys.exit("got NaN loss, training failed.")
 
         elif isinstance(event, fluid.EndStepEvent):
-            print(("Step {0}, Epoch {1} Metrics {2}".format(
-                event.step, event.epoch, list(map(np.array, event.metrics)))))
+            print("Step {0}, Epoch {1} Metrics {2}".format(
+                event.step, event.epoch, list(map(np.array, event.metrics))))
             if event.step == 1:  # Run 2 iterations to speed CI
                 trainer.save_params(params_dirname)
                 trainer.stop()
@@ -206,14 +206,14 @@ def infer(use_cuda, inference_program, params_dirname):
         inference_program, param_path=params_dirname, place=place)
 
     # Setup input by creating LoDTensor to represent sequence of words.
-    # Here each word is the basic element of the LoDTensor and the shape of 
-    # each word (base_shape) should be [1] since it is simply an index to 
+    # Here each word is the basic element of the LoDTensor and the shape of
+    # each word (base_shape) should be [1] since it is simply an index to
     # look up for the corresponding word vector.
     # Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
-    # which has only one level of detail. Then the created LoDTensor will have only 
-    # one higher level structure (sequence of words, or sentence) than the basic 
-    # element (word). Hence the LoDTensor will hold data for three sentences of 
-    # length 3, 4 and 2, respectively. 
+    # which has only one level of detail. Then the created LoDTensor will have only
+    # one higher level structure (sequence of words, or sentence) than the basic
+    # element (word). Hence the LoDTensor will hold data for three sentences of
+    # length 3, 4 and 2, respectively.
     # Note that recursive_sequence_lengths should be a list of lists.
     recursive_seq_lens = [[3, 4, 2]]
     base_shape = [1]
@@ -248,7 +248,7 @@ def infer(use_cuda, inference_program, params_dirname):
         },
         return_numpy=False)
 
-    print(("infer results: ", np.array(results[0]).shape))
+    print("infer results: ", np.array(results[0]).shape)
 
 
 def main(use_cuda):

python/paddle/fluid/tests/book/high-level-api/machine_translation/test_machine_translation.py

@@ -197,7 +197,7 @@ def train(use_cuda, is_sparse, is_local=True):
 
     def event_handler(event):
         if isinstance(event, fluid.EndStepEvent):
-            print(('pass_id=' + str(event.epoch) + ' batch=' + str(event.step)))
+            print('pass_id=' + str(event.epoch) + ' batch=' + str(event.step))
             if event.step == 10:
                 trainer.stop()
 
@@ -259,7 +259,7 @@ def decode_main(use_cuda, is_sparse):
             feed=feed_dict,
             fetch_list=[translation_ids, translation_scores],
             return_numpy=False)
-        print((result_ids.recursive_sequence_lengths()))
+        print(result_ids.recursive_sequence_lengths())
         break
 
 

python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_conv.py

@@ -78,14 +78,14 @@ def event_handler(event):
             avg_cost, acc = trainer.test(
                 reader=test_reader, feed_order=['img', 'label'])
 
-            print(("avg_cost: %s" % avg_cost))
-            print(("acc     : %s" % acc))
+            print("avg_cost: %s" % avg_cost)
+            print("acc     : %s" % acc)
 
             if acc > 0.2:  # Smaller value to increase CI speed
                 trainer.save_params(params_dirname)
             else:
-                print(('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
-                    event.epoch + 1, avg_cost, acc)))
+                print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
+                    event.epoch + 1, avg_cost, acc))
                 if math.isnan(avg_cost):
                     sys.exit("got NaN loss, training failed.")
         elif isinstance(event, fluid.EndStepEvent):
@@ -118,7 +118,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
 
     results = inferencer.infer({'img': tensor_img})
 
-    print(("infer results: ", results[0]))
+    print("infer results: ", results[0])
 
 
 def main(use_cuda):

python/paddle/fluid/tests/book/high-level-api/recognize_digits/test_recognize_digits_mlp.py

@@ -61,14 +61,14 @@ def event_handler(event):
             avg_cost, acc = trainer.test(
                 reader=test_reader, feed_order=['img', 'label'])
 
-            print(("avg_cost: %s" % avg_cost))
-            print(("acc     : %s" % acc))
+            print("avg_cost: %s" % avg_cost)
+            print("acc     : %s" % acc)
 
             if acc > 0.2:  # Smaller value to increase CI speed
                 trainer.save_params(params_dirname)
             else:
-                print(('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
-                    event.epoch + 1, avg_cost, acc)))
+                print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
+                    event.epoch + 1, avg_cost, acc))
                 if math.isnan(avg_cost):
                     sys.exit("got NaN loss, training failed.")
 
@@ -96,7 +96,7 @@ def infer(use_cuda, inference_program, params_dirname=None):
 
     results = inferencer.infer({'img': tensor_img})
 
-    print(("infer results: ", results[0]))
+    print("infer results: ", results[0])
 
 
 def main(use_cuda):

python/paddle/fluid/tests/book/high-level-api/recommender_system/test_recommender_system_newapi.py

@@ -180,7 +180,7 @@ def event_handler(event):
             # get avg cost
             avg_cost = np.array(avg_cost_set).mean()
 
-            print(("avg_cost: %s" % avg_cost))
+            print("avg_cost: %s" % avg_cost)
 
             if float(avg_cost) < 4:  # Smaller value to increase CI speed
                 trainer.save_params(params_dirname)
@@ -240,7 +240,7 @@ def infer(use_cuda, inference_program, params_dirname):
         },
         return_numpy=False)
 
-    print(("infer results: ", np.array(results[0])))
+    print("infer results: ", np.array(results[0]))
 
 
 def main(use_cuda):