Merge pull request #2007 from Yancey1989/fix_py3

fix fast imagenet with py3

PaddleCV/image_classification/fast_imagenet/train.py

@@ -335,7 +335,7 @@ def print_paddle_envs():
     print("DEVICE_NUM: %d" % DEVICE_NUM)
     for k in os.environ:
         if "PADDLE_" in k:
-            print "ENV %s:%s" % (k, os.environ[k])
+            print("ENV %s:%s" % (k, os.environ[k]))
     print('------------------------------------------------')
 
 

PaddleCV/image_classification/models/fast_imagenet.py

@@ -22,8 +22,6 @@ import time
 import os
 import math
 
-import cProfile, pstats, StringIO
-
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
@@ -135,7 +133,7 @@ def lr_decay(lrs, epochs, bs, total_image):
             step += 1
         ratio = (lrs[idx][1] - lrs[idx][0])*1.0 / (epoch[1] - epoch[0])
         lr_base = lrs[idx][0]
-        for s in xrange(epoch[0], epoch[1]):
+        for s in range(epoch[0], epoch[1]):
             if boundaries:
                 boundaries.append(boundaries[-1] + step + 1)
             else:

PaddleCV/image_classification/utils/learning_rate.py

@@ -38,7 +38,7 @@ def cosine_decay_with_warmup(learning_rate, step_each_epoch, epochs=120):
         shape=[1], dtype='float32', value=float(5), force_cpu=True)
 
     with init_on_cpu():
-	epoch = ops.floor(global_step / step_each_epoch)
+        epoch = ops.floor(global_step / step_each_epoch)
         with control_flow.Switch() as switch:
             with switch.case(epoch < warmup_epoch):
                 decayed_lr = learning_rate * (global_step / (step_each_epoch * warmup_epoch))