refactor codes and fix bug of c_dim

Author: carpedm20

README.md

@@ -33,22 +33,22 @@ First, download dataset with:
 
 To train a model with downloaded dataset:
 
-    $ python main.py --dataset mnist --input_height=28 --output_height=28 --c_dim=1 --is_train
-    $ python main.py --dataset celebA --input_height=108 --is_train --is_crop True
+    $ python main.py --dataset mnist --input_height=28 --output_height=28 --train
+    $ python main.py --dataset celebA --input_height=108 --train --crop
 
 To test with an existing model:
 
-    $ python main.py --dataset mnist --input_height=28 --output_height=28 --c_dim=1
-    $ python main.py --dataset celebA --input_height=108 --is_crop True
+    $ python main.py --dataset mnist --input_height=28 --output_height=28
+    $ python main.py --dataset celebA --input_height=108 --crop
 
 Or, you can use your own dataset (without central crop) by:
 
     $ mkdir data/DATASET_NAME
     ... add images to data/DATASET_NAME ...
-    $ python main.py --dataset DATASET_NAME --is_train
+    $ python main.py --dataset DATASET_NAME --train
     $ python main.py --dataset DATASET_NAME
     $ # example
-    $ python main.py --dataset=eyes --input_fname_pattern="*_cropped.png" --c_dim=1 --is_train
+    $ python main.py --dataset=eyes --input_fname_pattern="*_cropped.png" --train
 
 ## Results
 

main.py

@@ -21,8 +21,8 @@
 flags.DEFINE_string("input_fname_pattern", "*.jpg", "Glob pattern of filename of input images [*]")
 flags.DEFINE_string("checkpoint_dir", "checkpoint", "Directory name to save the checkpoints [checkpoint]")
 flags.DEFINE_string("sample_dir", "samples", "Directory name to save the image samples [samples]")
-flags.DEFINE_boolean("is_train", False, "True for training, False for testing [False]")
-flags.DEFINE_boolean("is_crop", False, "True for training, False for testing [False]")
+flags.DEFINE_boolean("train", False, "True for training, False for testing [False]")
+flags.DEFINE_boolean("crop", False, "True for training, False for testing [False]")
 flags.DEFINE_boolean("visualize", False, "True for visualizing, False for nothing [False]")
 FLAGS = flags.FLAGS
 
@@ -56,7 +56,7 @@ def main(_):
           y_dim=10,
           dataset_name=FLAGS.dataset,
           input_fname_pattern=FLAGS.input_fname_pattern,
-          is_crop=FLAGS.is_crop,
+          crop=FLAGS.crop,
           checkpoint_dir=FLAGS.checkpoint_dir,
           sample_dir=FLAGS.sample_dir)
     else:
@@ -70,15 +70,16 @@ def main(_):
           sample_num=FLAGS.batch_size,
           dataset_name=FLAGS.dataset,
           input_fname_pattern=FLAGS.input_fname_pattern,
-          is_crop=FLAGS.is_crop,
+          crop=FLAGS.crop,
           checkpoint_dir=FLAGS.checkpoint_dir,
           sample_dir=FLAGS.sample_dir)
 
     show_all_variables()
-    if FLAGS.is_train:
+
+    if FLAGS.train:
       dcgan.train(FLAGS)
     else:
-      if not dcgan.load(FLAGS.checkpoint_dir):
+      if not dcgan.load(FLAGS.checkpoint_dir)[0]:
         raise Exception("[!] Train a model first, then run test mode")
 
 

model.py

@@ -14,7 +14,7 @@ def conv_out_size_same(size, stride):
   return int(math.ceil(float(size) / float(stride)))
 
 class DCGAN(object):
-  def __init__(self, sess, input_height=108, input_width=108, is_crop=True,
+  def __init__(self, sess, input_height=108, input_width=108, crop=True,
          batch_size=64, sample_num = 64, output_height=64, output_width=64,
          y_dim=None, z_dim=100, gf_dim=64, df_dim=64,
          gfc_dim=1024, dfc_dim=1024, c_dim=3, dataset_name='default',
@@ -33,7 +33,7 @@ def __init__(self, sess, input_height=108, input_width=108, is_crop=True,
       c_dim: (optional) Dimension of image color. For grayscale input, set to 1. [3]
     """
     self.sess = sess
-    self.is_crop = is_crop
+    self.crop = crop
 
     self.batch_size = batch_size
     self.sample_num = sample_num
@@ -52,7 +52,6 @@ def __init__(self, sess, input_height=108, input_width=108, is_crop=True,
     self.gfc_dim = gfc_dim
     self.dfc_dim = dfc_dim
 
-
     # batch normalization : deals with poor initialization helps gradient flow
     self.d_bn1 = batch_norm(name='d_bn1')
     self.d_bn2 = batch_norm(name='d_bn2')
@@ -76,17 +75,17 @@ def __init__(self, sess, input_height=108, input_width=108, is_crop=True,
       self.c_dim = self.data_X[0].shape[-1]
     else:
       self.data = glob(os.path.join("./data", self.dataset_name, self.input_fname_pattern))
-      self.c_dim = self.data[0].shape[-1]
+      self.c_dim = imread(self.data[0]).shape[-1]
 
-    self.is_grayscale = (self.c_dim == 1)
+    self.grayscale = (self.c_dim == 1)
 
     self.build_model()
 
   def build_model(self):
     if self.y_dim:
       self.y= tf.placeholder(tf.float32, [self.batch_size, self.y_dim], name='y')
 
-    if self.is_crop:
+    if self.crop:
       image_dims = [self.output_height, self.output_width, self.c_dim]
     else:
       image_dims = [self.input_height, self.input_width, self.c_dim]
@@ -179,9 +178,9 @@ def train(self, config):
                     input_width=self.input_width,
                     resize_height=self.output_height,
                     resize_width=self.output_width,
-                    is_crop=self.is_crop,
-                    is_grayscale=self.is_grayscale) for sample_file in sample_files]
-      if (self.is_grayscale):
+                    crop=self.crop,
+                    grayscale=self.grayscale) for sample_file in sample_files]
+      if (self.grayscale):
         sample_inputs = np.array(sample).astype(np.float32)[:, :, :, None]
       else:
         sample_inputs = np.array(sample).astype(np.float32)
@@ -215,9 +214,9 @@ def train(self, config):
                         input_width=self.input_width,
                         resize_height=self.output_height,
                         resize_width=self.output_width,
-                        is_crop=self.is_crop,
-                        is_grayscale=self.is_grayscale) for batch_file in batch_files]
-          if (self.is_grayscale):
+                        crop=self.crop,
+                        grayscale=self.grayscale) for batch_file in batch_files]
+          if self.grayscale:
             batch_images = np.array(batch).astype(np.float32)[:, :, :, None]
           else:
             batch_images = np.array(batch).astype(np.float32)

utils.py

@@ -24,16 +24,16 @@ def show_all_variables():
 
 def get_image(image_path, input_height, input_width,
               resize_height=64, resize_width=64,
-              is_crop=True, is_grayscale=False):
-  image = imread(image_path, is_grayscale)
+              crop=True, grayscale=False):
+  image = imread(image_path, grayscale)
   return transform(image, input_height, input_width,
-                   resize_height, resize_width, is_crop)
+                   resize_height, resize_width, crop)
 
 def save_images(images, size, image_path):
   return imsave(inverse_transform(images), size, image_path)
 
-def imread(path, is_grayscale = False):
-  if (is_grayscale):
+def imread(path, grayscale = False):
+  if (grayscale):
     return scipy.misc.imread(path, flatten = True).astype(np.float)
   else:
     return scipy.misc.imread(path).astype(np.float)
@@ -77,8 +77,8 @@ def center_crop(x, crop_h, crop_w,
       x[j:j+crop_h, i:i+crop_w], [resize_h, resize_w])
 
 def transform(image, input_height, input_width, 
-              resize_height=64, resize_width=64, is_crop=True):
-  if is_crop:
+              resize_height=64, resize_width=64, crop=True):
+  if crop:
     cropped_image = center_crop(
       image, input_height, input_width, 
       resize_height, resize_width)