port python3

fluid/face_detection/profile.py

@@ -59,7 +59,7 @@ def train(args, config, train_file_list, optimizer_method):
         loss = network.vgg_ssd_loss()
         fetches = [loss]
 
-    epocs = 12880 / batch_size
+    epocs = 12880 // batch_size
     boundaries = [epocs * 40, epocs * 60, epocs * 80, epocs * 100]
     values = [
         learning_rate, learning_rate * 0.5, learning_rate * 0.25,

fluid/face_detection/pyramidbox.py

 import numpy as np
-
+import six
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
 from paddle.fluid.initializer import Xavier
@@ -27,13 +27,13 @@ def conv_block(input, groups, filters, ksizes, strides=None, with_pool=True):
     w_attr = ParamAttr(learning_rate=1., initializer=Xavier())
     b_attr = ParamAttr(learning_rate=2., regularizer=L2Decay(0.))
     conv = input
-    for i in xrange(groups):
+    for i in six.moves.xrange(groups):
         conv = fluid.layers.conv2d(
             input=conv,
             num_filters=filters[i],
             filter_size=ksizes[i],
             stride=strides[i],
-            padding=(ksizes[i] - 1) / 2,
+            padding=(ksizes[i] - 1) // 2,
             param_attr=w_attr,
             bias_attr=b_attr,
             act='relu')
@@ -220,7 +220,7 @@ class PyramidBox(object):
         def permute_and_reshape(input, last_dim):
             trans = fluid.layers.transpose(input, perm=[0, 2, 3, 1])
             compile_shape = [
-                trans.shape[0], np.prod(trans.shape[1:]) / last_dim, last_dim
+                trans.shape[0], np.prod(trans.shape[1:]) // last_dim, last_dim
             ]
             run_shape = fluid.layers.assign(
                 np.array([0, -1, last_dim]).astype("int32"))
@@ -291,7 +291,7 @@ class PyramidBox(object):
         def permute_and_reshape(input, last_dim):
             trans = fluid.layers.transpose(input, perm=[0, 2, 3, 1])
             compile_shape = [
-                trans.shape[0], np.prod(trans.shape[1:]) / last_dim, last_dim
+                trans.shape[0], np.prod(trans.shape[1:]) // last_dim, last_dim
             ]
             run_shape = fluid.layers.assign(
                 np.array([0, -1, last_dim]).astype("int32"))

fluid/face_detection/reader.py

@@ -24,6 +24,7 @@ import time
 import copy
 import random
 import cv2
+import six
 from data_util import GeneratorEnqueuer
 
 
@@ -151,7 +152,7 @@ def preprocess(img, bbox_labels, mode, settings, image_path):
         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/face_detection/train.py

@@ -57,7 +57,7 @@ def train(args, config, train_file_list, optimizer_method):
         loss = network.vgg_ssd_loss()
         fetches = [loss]
 
-    steps_per_pass = 12880 / batch_size
+    steps_per_pass = 12880 // batch_size
     boundaries = [steps_per_pass * 50, steps_per_pass * 80,
                   steps_per_pass * 120, steps_per_pass * 140]
     values = [
@@ -110,7 +110,7 @@ def train(args, config, train_file_list, optimizer_method):
         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)
 
     def tensor(data, place, lod=None):

fluid/face_detection/utility.py

@@ -16,6 +16,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 import distutils.util
+import six
 
 
 def print_arguments(args):
@@ -34,7 +35,7 @@ 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("------------------------------------------------")