change set (#3042)

change set to list in c_gan

PaddleCV/PaddleGAN/c_gan/c_gan.py

@@ -31,7 +31,6 @@ matplotlib.use('agg')
 import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
 
-
 NOISE_SIZE = 100
 LEARNING_RATE = 2e-4
 
@@ -98,8 +97,7 @@ def train(args):
     exe.run(fluid.default_startup_program())
     if args.run_ce:
         train_reader = paddle.batch(
-                paddle.dataset.mnist.train(),
-                batch_size=args.batch_size)
+            paddle.dataset.mnist.train(), batch_size=args.batch_size)
     else:
         train_reader = paddle.batch(
             paddle.reader.shuffle(
@@ -111,7 +109,7 @@ def train(args):
         low=-1.0, high=1.0,
         size=[args.batch_size, NOISE_SIZE]).astype('float32')
     t_time = 0
-    losses = [[],[]]
+    losses = [[], []]
     for pass_id in range(args.epoch):
         for batch_id, data in enumerate(train_reader()):
             if len(data) != args.batch_size:
@@ -133,7 +131,7 @@ def train(args):
                 g_program,
                 feed={'noise': noise_data,
                       'conditions': conditions_data},
-                fetch_list={g_img})[0]
+                fetch_list=[g_img])[0]
 
             total_images = np.concatenate([real_image, generated_image])
 
@@ -143,7 +141,7 @@ def train(args):
                                    'label': fake_labels,
                                    'conditions': conditions_data
                                },
-                               fetch_list={d_loss})[0][0]
+                               fetch_list=[d_loss])[0][0]
 
             d_loss_2 = exe.run(d_program,
                                feed={
@@ -151,7 +149,7 @@ def train(args):
                                    'label': real_labels,
                                    'conditions': conditions_data
                                },
-                               fetch_list={d_loss})[0][0]
+                               fetch_list=[d_loss])[0][0]
 
             d_loss_n = d_loss_1 + d_loss_2
             losses[0].append(d_loss_n)
@@ -163,13 +161,11 @@ def train(args):
                     dg_program,
                     feed={'noise': noise_data,
                           'conditions': conditions_data},
-                    fetch_list={dg_loss})[0][0]
+                    fetch_list=[dg_loss])[0][0]
                 losses[1].append(dg_loss_n)
             batch_time = time.time() - s_time
             t_time += batch_time
 
-            
-
             if batch_id % 10 == 0 and not args.run_ce:
                 if not os.path.exists(args.output):
                     os.makedirs(args.output)
@@ -178,7 +174,7 @@ def train(args):
                     g_program_test,
                     feed={'noise': const_n,
                           'conditions': conditions_data},
-                    fetch_list={g_img})[0]
+                    fetch_list=[g_img])[0]
                 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}\n " \
@@ -196,7 +192,7 @@ def train(args):
         print("kpis,cgan_d_train_cost,{}".format(np.mean(losses[0])))
         print("kpis,cgan_g_train_cost,{}".format(np.mean(losses[1])))
         print("kpis,cgan_duration,{}".format(t_time / args.epoch))
-            
+
 
 if __name__ == "__main__":
     args = parser.parse_args()

PaddleCV/PaddleGAN/c_gan/dc_gan.py

@@ -93,8 +93,7 @@ def train(args):
 
     if args.run_ce:
         train_reader = paddle.batch(
-                paddle.dataset.mnist.train(),
-                batch_size=args.batch_size)
+            paddle.dataset.mnist.train(), batch_size=args.batch_size)
     else:
         train_reader = paddle.batch(
             paddle.reader.shuffle(
@@ -125,7 +124,7 @@ def train(args):
             s_time = time.time()
             generated_image = exe.run(g_program,
                                       feed={'noise': noise_data},
-                                      fetch_list={g_img})[0]
+                                      fetch_list=[g_img])[0]
 
             total_images = np.concatenate([real_image, generated_image])
 
@@ -134,14 +133,14 @@ def train(args):
                                    'img': generated_image,
                                    'label': fake_labels,
                                },
-                               fetch_list={d_loss})[0][0]
+                               fetch_list=[d_loss])[0][0]
 
             d_loss_2 = exe.run(d_program,
                                feed={
                                    'img': real_image,
                                    'label': real_labels,
                                },
-                               fetch_list={d_loss})[0][0]
+                               fetch_list=[d_loss])[0][0]
 
             d_loss_n = d_loss_1 + d_loss_2
             losses[0].append(d_loss_n)
@@ -150,8 +149,8 @@ def train(args):
                     low=-1.0, high=1.0,
                     size=[args.batch_size, NOISE_SIZE]).astype('float32')
                 dg_loss_n = exe.run(dg_program,
-                                     feed={'noise': noise_data},
-                                     fetch_list={dg_loss})[0][0]
+                                    feed={'noise': noise_data},
+                                    fetch_list=[dg_loss])[0][0]
                 losses[1].append(dg_loss_n)
             t_time += (time.time() - s_time)
             if batch_id % 10 == 0 and not args.run_ce:
@@ -160,12 +159,12 @@ def train(args):
                 # generate image each batch
                 generated_images = exe.run(g_program_test,
                                            feed={'noise': const_n},
-                                           fetch_list={g_img})[0]
+                                           fetch_list=[g_img])[0]
                 total_images = np.concatenate([real_image, generated_images])
                 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,
-                    d_loss_n, dg_loss_n, check(generated_images))
+                    pass_id, batch_id, d_loss_n, dg_loss_n,
+                    check(generated_images))
                 print(msg)
                 plt.title(msg)
                 plt.savefig(
@@ -177,7 +176,7 @@ def train(args):
         print("kpis,dcgan_d_train_cost,{}".format(np.mean(losses[0])))
         print("kpis,dcgan_g_train_cost,{}".format(np.mean(losses[1])))
         print("kpis,dcgan_duration,{}".format(t_time / args.epoch))
- 
+
 
 if __name__ == "__main__":
     args = parser.parse_args()