Make gan support for python3 (#1311)

fluid/gan/c_gan/c_gan.py

@@ -12,8 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import sys
 import os
+import six
 import argparse
 import functools
 import matplotlib
@@ -102,7 +106,7 @@ def train(args):
             noise_data = np.random.uniform(
                 low=-1.0, high=1.0,
                 size=[args.batch_size, NOISE_SIZE]).astype('float32')
-            real_image = np.array(map(lambda x: x[0], data)).reshape(
+            real_image = np.array(list(map(lambda x: x[0], data))).reshape(
                 -1, 784).astype('float32')
             conditions_data = np.array([x[1] for x in data]).reshape(
                 [-1, 1]).astype("float32")
@@ -138,7 +142,7 @@ def train(args):
 
             d_loss_np = [d_loss_1[0][0], d_loss_2[0][0]]
 
-            for _ in xrange(NUM_TRAIN_TIMES_OF_DG):
+            for _ in six.moves.xrange(NUM_TRAIN_TIMES_OF_DG):
                 noise_data = np.random.uniform(
                     low=-1.0, high=1.0,
                     size=[args.batch_size, NOISE_SIZE]).astype('float32')
@@ -159,7 +163,7 @@ def train(args):
                 total_images = np.concatenate([real_image, generated_images])
                 fig = plot(total_images)
                 msg = "Epoch ID={0}\n Batch ID={1}\n D-Loss={2}\n DG-Loss={3}\n gen={4}".format(
-                    pass_id, batch_id, d_loss_np, dg_loss_np,
+                    pass_id, batch_id, np.mean(d_loss_np), dg_loss_np,
                     check(generated_images))
                 print(msg)
                 plt.title(msg)

fluid/gan/c_gan/dc_gan.py

@@ -12,11 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import sys
 import os
 import argparse
 import functools
 import matplotlib
+import six
 import numpy as np
 import paddle
 import paddle.fluid as fluid
@@ -98,7 +102,7 @@ def train(args):
             noise_data = np.random.uniform(
                 low=-1.0, high=1.0,
                 size=[args.batch_size, NOISE_SIZE]).astype('float32')
-            real_image = np.array(map(lambda x: x[0], data)).reshape(
+            real_image = np.array(list(map(lambda x: x[0], data))).reshape(
                 -1, 784).astype('float32')
             real_labels = np.ones(
                 shape=[real_image.shape[0], 1], dtype='float32')
@@ -128,7 +132,7 @@ def train(args):
 
             d_loss_np = [d_loss_1[0][0], d_loss_2[0][0]]
 
-            for _ in xrange(NUM_TRAIN_TIMES_OF_DG):
+            for _ in six.moves.xrange(NUM_TRAIN_TIMES_OF_DG):
                 noise_data = np.random.uniform(
                     low=-1.0, high=1.0,
                     size=[args.batch_size, NOISE_SIZE]).astype('float32')
@@ -146,7 +150,7 @@ def train(args):
                 fig = plot(total_images)
                 msg = "Epoch ID={0} Batch ID={1} D-Loss={2} DG-Loss={3}\n gen={4}".format(
                     pass_id, batch_id,
-                    np.sum(d_loss_np), dg_loss_np, check(generated_images))
+                    np.mean(d_loss_np), dg_loss_np, check(generated_images))
                 print(msg)
                 plt.title(msg)
                 plt.savefig(

fluid/gan/c_gan/network.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 from utility import get_parent_function_name
@@ -104,13 +107,13 @@ def D_cond(image, y):
 
 def G_cond(z, y):
     s_h, s_w = output_height, output_width
-    s_h2, s_h4 = int(s_h / 2), int(s_h / 4)
-    s_w2, s_w4 = int(s_w / 2), int(s_w / 4)
+    s_h2, s_h4 = int(s_h // 2), int(s_h // 4)
+    s_w2, s_w4 = int(s_w // 2), int(s_w // 4)
 
     yb = fluid.layers.reshape(y, [-1, y_dim, 1, 1])  #NCHW
 
     z = fluid.layers.concat([z, y], 1)
-    h0 = bn(fc(z, gfc_dim / 2), act='relu')
+    h0 = bn(fc(z, gfc_dim // 2), act='relu')
     h0 = fluid.layers.concat([h0, y], 1)
 
     h1 = bn(fc(h0, gf_dim * 2 * s_h4 * s_w4), act='relu')
@@ -134,8 +137,8 @@ def D(x):
 
 def G(x):
     x = bn(fc(x, gfc_dim))
-    x = bn(fc(x, gf_dim * 2 * img_dim / 4 * img_dim / 4))
-    x = fluid.layers.reshape(x, [-1, gf_dim * 2, img_dim / 4, img_dim / 4])
+    x = bn(fc(x, gf_dim * 2 * img_dim // 4 * img_dim // 4))
+    x = fluid.layers.reshape(x, [-1, gf_dim * 2, img_dim // 4, img_dim // 4])
     x = deconv(x, gf_dim * 2, act='relu', output_size=[14, 14])
     x = deconv(x, 1, filter_size=5, padding=2, act='tanh', output_size=[28, 28])
     x = fluid.layers.reshape(x, shape=[-1, 28 * 28])

fluid/gan/c_gan/utility.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import math
 import distutils.util
 import numpy as np
 import inspect
 import matplotlib
+import six
 matplotlib.use('agg')
 import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
@@ -54,7 +58,7 @@ 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("------------------------------------------------")
 

fluid/gan/cycle_gan/data_reader.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import os
 from PIL import Image
 import numpy as np
-from itertools import izip
 
 A_LIST_FILE = "./data/horse2zebra/trainA.txt"
 B_LIST_FILE = "./data/horse2zebra/trainB.txt"
@@ -70,11 +72,3 @@ def b_test_reader():
     Reader of images with B style for test.
     """
     return reader_creater(B_TEST_LIST_FILE, cycle=False, return_name=True)
-
-
-if __name__ == "__main__":
-    for A, B in izip(a_test_reader()(), a_test_reader()()):
-        print A[0].shape
-        print A[1]
-        print B[0].shape
-        print B[1]

fluid/gan/cycle_gan/train.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 import data_reader
 import os
 import random
@@ -9,7 +12,6 @@ import paddle.fluid as fluid
 import numpy as np
 from paddle.fluid import core
 from trainer import *
-from itertools import izip
 from scipy.misc import imsave
 import paddle.fluid.profiler as profiler
 from utility import add_arguments, print_arguments, ImagePool
@@ -66,7 +68,7 @@ def train(args):
         if not os.path.exists(out_path):
             os.makedirs(out_path)
         i = 0
-        for data_A, data_B in izip(A_test_reader(), B_test_reader()):
+        for data_A, data_B in zip(A_test_reader(), B_test_reader()):
             A_name = data_A[1]
             B_name = data_B[1]
             tensor_A = core.LoDTensor()
@@ -114,7 +116,7 @@ def train(args):
             exe, out_path + "/d_a", main_program=d_A_trainer.program)
         fluid.io.save_persistables(
             exe, out_path + "/d_b", main_program=d_B_trainer.program)
-        print "saved checkpoint to [%s]" % out_path
+        print("saved checkpoint to {}".format(out_path))
         sys.stdout.flush()
 
     def init_model():
@@ -128,7 +130,7 @@ def train(args):
             exe, args.init_model + "/d_a", main_program=d_A_trainer.program)
         fluid.io.load_persistables(
             exe, args.init_model + "/d_b", main_program=d_B_trainer.program)
-        print "Load model from [%s]" % args.init_model
+        print("Load model from {}".format(args.init_model))
 
     if args.init_model:
         init_model()
@@ -136,8 +138,8 @@ def train(args):
     for epoch in range(args.epoch):
         batch_id = 0
         for i in range(max_images_num):
-            data_A = A_reader.next()
-            data_B = B_reader.next()
+            data_A = next(A_reader)
+            data_B = next(B_reader)
             tensor_A = core.LoDTensor()
             tensor_B = core.LoDTensor()
             tensor_A.set(data_A, place)
@@ -174,9 +176,9 @@ def train(args):
                 feed={"input_A": tensor_A,
                       "fake_pool_A": fake_pool_A})
 
-            print "epoch[%d]; batch[%d]; g_A_loss: %s; d_B_loss: %s; g_B_loss: %s; d_A_loss: %s;" % (
+            print("epoch{}; batch{}; g_A_loss: {}; d_B_loss: {}; g_B_loss: {}; d_A_loss: {};".format(
                 epoch, batch_id, g_A_loss[0], d_B_loss[0], g_B_loss[0],
-                d_A_loss[0])
+                d_A_loss[0]))
             sys.stdout.flush()
             batch_id += 1
 

fluid/gan/cycle_gan/trainer.py

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

fluid/gan/cycle_gan/utility.py

@@ -17,6 +17,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 import distutils.util
+import six
 import random
 import glob
 import numpy as np
@@ -39,7 +40,7 @@ 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("------------------------------------------------")