Correction in README

README.md

@@ -45,7 +45,7 @@ Using pretrained models not only get high performance, but fastly attach converg
 
 # References 
 - \[1\] 'Improved Training of Wasserstein GANs' by Ishaan Gulrajani et. al, https://arxiv.org/abs/1704.00028, (https://github.com/igul222/improved_wgan_training)[code] 
-- \[2\] 'GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium' by Martin Heusel  et. al, https://arxiv.org/abs/1704.00028
+- \[2\] 'GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium' by Martin Heusel  et. al, https://arxiv.org/abs/1706.08500, (https://github.com/bioinf-jku/TTUR)[code]
 # Contact
 
 If you run into any problems with this code, please submit a bug report on the Github site of the project. For another inquries pleace contact with me: yaxing@cvc.uab.es

tflib/lsun_label.py

@@ -16,64 +16,67 @@
        'restaurant':9}
 
 def make_generator(path, n_files, batch_size,image_size, IW = False, pharse='train'):
-    epoch_count = [1]
-    image_list_main = listdir(path)
-    image_list = []
-    for sub_class  in image_list_main:
-        # pdb.set_trace()
-        sub_class_path =path + '/'+ sub_class + '/'+ pharse
-        sub_class_image = listdir(sub_class_path)        
-        image_list.extend([sub_class_path + '/' + i for i in sub_class_image])
+  epoch_count = [1]
+  image_list_main = listdir(path)
+  image_list = []
+  for sub_class  in image_list_main:
+    # pdb.set_trace()
+    sub_class_path =path + '/'+ sub_class + '/'+ pharse
+    sub_class_image = listdir(sub_class_path)
+    image_list.extend([sub_class_path + '/' + i for i in sub_class_image])
 
-    def get_epoch():
-        images = np.zeros((batch_size, 3, 64, 64), dtype='int32')
-        labels = np.zeros((batch_size,), dtype='int32')
-        files = range(len(image_list))
-        random_state = np.random.RandomState(epoch_count[0])
-        random_state.shuffle(files)
-        epoch_count[0] += 1
-        for n, i in enumerate(files):
-            #image = scipy.misc.imread("{}/{}.png".format(path, str(i+1).zfill(len(str(n_files)))))
-            image = scipy.misc.imread("{}".format(image_list[i]))
-            label = Label[image_list[i].split('/')[2]]
-	    image = scipy.misc.imresize(image,(image_size,image_size))
-            images[n % batch_size] = image.transpose(2,0,1)
-            labels[n % batch_size] = label
-            if n > 0 and n % batch_size == 0:
-                yield (images,labels)
-    def get_epoch_from_end():
-        images = np.zeros((batch_size, 3, 64, 64), dtype='int32')
-        files = range(n_files)
-        random_state = np.random.RandomState(epoch_count[0])
-        random_state.shuffle(files)
-        epoch_count[0] += 1
-        for n, i in enumerate(files):
-            #image = scipy.misc.imread("{}/{}.png".format(path, str(i+1).zfill(len(str(n_files)))))
+  def get_epoch():
+    images = np.zeros((batch_size, 3, 64, 64), dtype='int32')
+    labels = np.zeros((batch_size,), dtype='int32')
+    files = range(len(image_list))
+    random_state = np.random.RandomState(epoch_count[0])
+    random_state.shuffle(files)
+    epoch_count[0] += 1
+    for n, i in enumerate(files):
+      #image = scipy.misc.imread("{}/{}.png".format(path, str(i+1).zfill(len(str(n_files)))))
+      image = scipy.misc.imread("{}".format(image_list[i]))
+      label = Label[image_list[i].split('/')[2]]
+      image = scipy.misc.imresize(image,(image_size,image_size))
+      images[n % batch_size] = image.transpose(2,0,1)
+      labels[n % batch_size] = label
+      if n > 0 and n % batch_size == 0:
+        yield (images,labels)
+  '''
+  def get_epoch_from_end():
+    images = np.zeros((batch_size, 3, 64, 64), dtype='int32')
+    files = range(n_files)
+    random_state = np.random.RandomState(epoch_count[0])
+    random_state.shuffle(files)
+    epoch_count[0] += 1
+    for n, i in enumerate(files):
+      #image = scipy.misc.imread("{}/{}.png".format(path, str(i+1).zfill(len(str(n_files)))))
 
-            image = scipy.misc.imread("{}".format(path + image_list[-i-1]))
+      image = scipy.misc.imread("{}".format(path + image_list[-i-1]))
 
-	    image = scipy.misc.imresize(image,(image_size,image_size))
-            images[n % batch_size] = image.transpose(2,0,1)
-            if n > 0 and n % batch_size == 0:
-                yield (images,labels)
-    return get_epoch_from_end if IW else get_epoch
+      image = scipy.misc.imresize(image,(image_size,image_size))
+      images[n % batch_size] = image.transpose(2,0,1)
+      #if n > 0 and n % batch_size == 0:
+      #    yield (images,labels)
+  '''
+  return get_epoch
 
 def load_from_end(batch_size, data_dir='/home/ishaan/data/imagenet64',image_size = 64, NUM_TRAIN = 7000):
-    return (
-        make_generator(data_dir+'/train/', NUM_TRAIN, batch_size,image_size, IW =True),
-        make_generator(data_dir+'/val/', 10000, batch_size,image_size, IW =True)
-    )
+  return (
+    make_generator(data_dir+'/train/', NUM_TRAIN, batch_size,image_size, IW =True),
+    make_generator(data_dir+'/val/', 10000, batch_size,image_size, IW =True)
+  )
 def load(batch_size, data_dir='/home/ishaan/data/imagenet64',image_size = 64, NUM_TRAIN = 7000):
-    return (
-        make_generator(data_dir, NUM_TRAIN, batch_size,image_size, pharse='train'),
-        make_generator(data_dir, 10000, batch_size,image_size, pharse='val')
-    )
-
+  return (
+    make_generator(data_dir, NUM_TRAIN, batch_size,image_size, pharse='train'),
+    make_generator(data_dir, 10000, batch_size,image_size, pharse='val')
+  )
+'''
 if __name__ == '__main__':
-    train_gen, valid_gen = load(64)
+  train_gen, valid_gen = load(64)
+  t0 = time.time()
+  for i, batch in enumerate(train_gen(), start=1):
+    print "{}\t{}".format(str(time.time() - t0), batch[0][0,0,0,0])
+    if i == 1000:
+      break
     t0 = time.time()
-    for i, batch in enumerate(train_gen(), start=1):
-        print "{}\t{}".format(str(time.time() - t0), batch[0][0,0,0,0])
-        if i == 1000:
-            break
-        t0 = time.time()
+'''