Support Python3.0 .

fluid/image_classification/_ce.py

 ####this file is only used for continuous evaluation test!
-
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import os
 import sys
 sys.path.append(os.environ['ceroot'])
@@ -7,14 +9,14 @@ from kpi import CostKpi, DurationKpi, AccKpi
 
 #### NOTE kpi.py should shared in models in some way!!!!
 
-train_acc_top1_kpi = AccKpi('train_acc_top1', 0.005, 0, desc='TOP1 ACC')
+train_acc_top1_kpi = AccKpi('train_acc_top1', 0.05, 0, desc='TOP1 ACC')
 train_acc_top5_kpi = AccKpi(
-    'train_acc_top5', 0.005, 0, actived=True, desc='TOP5 ACC')
+    'train_acc_top5', 0.05, 0, actived=True, desc='TOP5 ACC')
 train_cost_kpi = CostKpi('train_cost', 0.5, 0, actived=True, desc='train cost')
-test_acc_top1_kpi = AccKpi('test_acc_top1', 0.005, 0, desc='TOP1 ACC')
+test_acc_top1_kpi = AccKpi('test_acc_top1', 0.05, 0, desc='TOP1 ACC')
 test_acc_top5_kpi = AccKpi(
-    'test_acc_top5', 0.005, 0, actived=True, desc='TOP5 ACC')
-test_cost_kpi = CostKpi('test_cost', 0.005, 0, actived=True, desc='train cost')
+    'test_acc_top5', 0.05, 0, actived=True, desc='TOP5 ACC')
+test_cost_kpi = CostKpi('test_cost', 0.05, 0, actived=True, desc='train cost')
 train_speed_kpi = AccKpi(
     'train_speed',
     0.5,
@@ -23,17 +25,17 @@ train_speed_kpi = AccKpi(
     unit_repr='seconds/image',
     desc='train speed in one GPU card')
 train_acc_top1_card4_kpi = AccKpi(
-    'train_acc_top1_card4', 0.005, 0, desc='TOP1 ACC')
+    'train_acc_top1_card4', 0.05, 0, desc='TOP1 ACC')
 train_acc_top5_card4_kpi = AccKpi(
-    'train_acc_top5_card4', 0.005, 0, actived=True, desc='TOP5 ACC')
+    'train_acc_top5_card4', 0.05, 0, actived=True, desc='TOP5 ACC')
 train_cost_card4_kpi = CostKpi(
-    'train_cost_kpi', 0.005, 0, actived=True, desc='train cost')
+    'train_cost_kpi', 0.05, 0, actived=True, desc='train cost')
 test_acc_top1_card4_kpi = AccKpi(
-    'test_acc_top1_card4', 0.005, 0, desc='TOP1 ACC')
+    'test_acc_top1_card4', 0.05, 0, desc='TOP1 ACC')
 test_acc_top5_card4_kpi = AccKpi(
-    'test_acc_top5_card4', 0.005, 0, actived=True, desc='TOP5 ACC')
+    'test_acc_top5_card4', 0.05, 0, actived=True, desc='TOP5 ACC')
 test_cost_card4_kpi = CostKpi(
-    'test_cost_card4', 0.005, 0, actived=True, desc='train cost')
+    'test_cost_card4', 0.05, 0, actived=True, desc='train cost')
 train_speed_card4_kpi = AccKpi(
     'train_speed_card4',
     0.5,

fluid/image_classification/eval.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import os
 import numpy as np
 import time

fluid/image_classification/infer.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import os
 import numpy as np
 import time
@@ -15,7 +18,6 @@ import math
 parser = argparse.ArgumentParser(description=__doc__)
 # yapf: disable
 add_arg = functools.partial(add_arguments, argparser=parser)
-add_arg('batch_size',       int,  256,                  "Minibatch size.")
 add_arg('use_gpu',          bool, True,                 "Whether to use GPU or not.")
 add_arg('class_dim',        int,  1000,                 "Class number.")
 add_arg('image_shape',      str,  "3,224,224",          "Input image size")

fluid/image_classification/models/alexnet.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 import math

fluid/image_classification/models/dpn.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import os
 import numpy as np
 import time
 import sys
-import paddle
-import paddle.fluid as fluid
-import paddle.fluid.layers.control_flow as control_flow
-import paddle.fluid.layers.nn as nn
-import paddle.fluid.layers.tensor as tensor
 import math
+import paddle.fluid as fluid
 
 __all__ = ["DPN", "DPN68", "DPN92", "DPN98", "DPN107", "DPN131"]
 
@@ -67,7 +66,7 @@ class DPN(object):
         for gc in range(4):
             bw = bws[gc]
             inc = inc_sec[gc]
-            R = (k_r * bw) / rs[gc]
+            R = (k_r * bw) // rs[gc]
             if gc == 0:
                 _type1 = 'proj'
                 _type2 = 'normal'
@@ -178,8 +177,8 @@ class DPN(object):
                           _type='normal'):
         kw = 3
         kh = 3
-        pw = (kw - 1) / 2
-        ph = (kh - 1) / 2
+        pw = (kw - 1) // 2
+        ph = (kh - 1) // 2
 
         # type
         if _type is 'proj':

fluid/image_classification/models/googlenet.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 
@@ -36,7 +39,7 @@ class GoogleNet():
             num_filters=num_filters,
             filter_size=filter_size,
             stride=stride,
-            padding=(filter_size - 1) / 2,
+            padding=(filter_size - 1) // 2,
             groups=groups,
             act=act,
             param_attr=param_attr,

fluid/image_classification/models/inception_v4.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 import math

fluid/image_classification/models/learning_rate.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.layers.ops as ops

fluid/image_classification/models/mobilenet.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle.fluid as fluid
 from paddle.fluid.initializer import MSRA
 from paddle.fluid.param_attr import ParamAttr

fluid/image_classification/models/resnet.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 import math
@@ -75,7 +78,7 @@ class ResNet():
             num_filters=num_filters,
             filter_size=filter_size,
             stride=stride,
-            padding=(filter_size - 1) / 2,
+            padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
             bias_attr=False)

fluid/image_classification/models/se_resnext.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 import math
@@ -156,7 +159,7 @@ class SE_ResNeXt():
             num_filters=num_filters,
             filter_size=filter_size,
             stride=stride,
-            padding=(filter_size - 1) / 2,
+            padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
             bias_attr=False)
@@ -167,7 +170,7 @@ class SE_ResNeXt():
             input=input, pool_size=0, pool_type='avg', global_pooling=True)
         stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
         squeeze = fluid.layers.fc(input=pool,
-                                  size=num_channels / reduction_ratio,
+                                  size=num_channels // reduction_ratio,
                                   act='relu',
                                   param_attr=fluid.param_attr.ParamAttr(
                                       initializer=fluid.initializer.Uniform(

fluid/image_classification/models/vgg.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 

fluid/image_classification/train.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import os
 import numpy as np
 import time
 import sys
+import functools
+import math
 import paddle
 import paddle.fluid as fluid
 import paddle.dataset.flowers as flowers
 import models
 import reader
 import argparse
-import functools
 from models.learning_rate import cosine_decay
 from utility import add_arguments, print_arguments
-import math
 
 parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
@@ -106,8 +109,9 @@ def train(args):
         assert model_name == "SE_ResNeXt50_32x4d"
         fluid.default_startup_program().random_seed = 1000
         model.params["dropout_seed"] = 100
+        class_dim = 102
 
-    if model_name is "GoogleNet":
+    if model_name == "GoogleNet":
         out0, out1, out2 = model.net(input=image, class_dim=class_dim)
         cost0 = fluid.layers.cross_entropy(input=out0, label=label)
         cost1 = fluid.layers.cross_entropy(input=out1, label=label)

fluid/image_classification/utility.py

@@ -18,6 +18,7 @@ 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 +38,7 @@ 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("------------------------------------------------")