Port object_detection to Python3

fluid/object_detection/reader.py

@@ -22,6 +22,7 @@ import xml.etree.ElementTree
 import os
 import time
 import copy
+import six
 
 
 class Settings(object):
@@ -151,7 +152,7 @@ def preprocess(img, bbox_labels, mode, settings):
         mirror = int(random.uniform(0, 2))
         if mirror == 1:
             img = img[:, ::-1, :]
-            for i in xrange(len(sampled_labels)):
+            for i in six.moves.xrange(len(sampled_labels)):
                 tmp = sampled_labels[i][1]
                 sampled_labels[i][1] = 1 - sampled_labels[i][3]
                 sampled_labels[i][3] = 1 - tmp

fluid/object_detection/train.py

@@ -88,16 +88,16 @@ def train(args,
     if 'coco' in data_args.dataset:
         # learning rate decay in 12, 19 pass, respectively
         if '2014' in train_file_list:
-            epocs = 82783 / batch_size
+            epocs = 82783 // batch_size
             boundaries = [epocs * 12, epocs * 19]
         elif '2017' in train_file_list:
-            epocs = 118287 / batch_size
+            epocs = 118287 // batch_size
             boundaries = [epocs * 12, epocs * 19]
         values = [
             learning_rate, learning_rate * 0.5, learning_rate * 0.25
         ]
     elif 'pascalvoc' in data_args.dataset:
-        epocs = 19200 / batch_size
+        epocs = 19200 // batch_size
         boundaries = [epocs * 40, epocs * 60, epocs * 80, epocs * 100]
         values = [
             learning_rate, learning_rate * 0.5, learning_rate * 0.25,
@@ -137,7 +137,7 @@ def train(args,
         model_path = os.path.join(model_save_dir, postfix)
         if os.path.isdir(model_path):
             shutil.rmtree(model_path)
-        print 'save models to %s' % (model_path)
+        print('save models to %s' % (model_path))
         fluid.io.save_persistables(exe, model_path)
 
     best_map = 0.
@@ -193,15 +193,15 @@ def train(args,
             total_time += end_time - start_time
             train_avg_loss = np.mean(every_pass_loss)
             if devices_num == 1:
-                print ("kpis	train_cost	%s" % train_avg_loss)
-                print ("kpis	test_acc	%s" % mean_map)
-                print ("kpis	train_speed	%s" % (total_time / epoch_idx))
+                print("kpis	train_cost	%s" % train_avg_loss)
+                print("kpis	test_acc	%s" % mean_map)
+                print("kpis	train_speed	%s" % (total_time / epoch_idx))
             else:
-                print ("kpis	train_cost_card%s	%s" %
+                print("kpis	train_cost_card%s	%s" %
                        (devices_num, train_avg_loss))
-                print ("kpis	test_acc_card%s	%s" %
+                print("kpis	test_acc_card%s	%s" %
                        (devices_num, mean_map))
-                print ("kpis	train_speed_card%s	%f" %
+                print("kpis	train_speed_card%s	%f" %
                        (devices_num, total_time / epoch_idx))
 
 

fluid/object_detection/utility.py

@@ -16,8 +16,10 @@
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
+
 import distutils.util
 import numpy as np
+import six
 from paddle.fluid import core
 
 
@@ -37,7 +39,7 @@ def print_arguments(args):
     :type args: argparse.Namespace
     """
     print("-----------  Configuration Arguments -----------")
-    for arg, value in sorted(vars(args).iteritems()):
+    for arg, value in sorted(six.iteritems(vars(args))):
         print("%s: %s" % (arg, value))
     print("------------------------------------------------")