Added the doc string in functions

Author: hardikbansal

Diff for: download_datasets.sh

@@ -0,0 +1,14 @@
+FILE=$1
+
+if [[ $FILE != "ae_photos" && $FILE != "apple2orange" && $FILE != "summer2winter_yosemite" &&  $FILE != "horse2zebra" && $FILE != "monet2photo" && $FILE != "cezanne2photo" && $FILE != "ukiyoe2photo" && $FILE != "vangogh2photo" && $FILE != "maps" && $FILE != "cityscapes" && $FILE != "facades" && $FILE != "iphone2dslr_flower" && $FILE != "ae_photos" ]]; then
+    echo "Available datasets are: apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, maps, cityscapes, facades, iphone2dslr_flower, ae_photos"
+    exit 1
+fi
+
+URL=https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/$FILE.zip
+ZIP_FILE=./input/$FILE.zip
+TARGET_DIR=./input/$FILE/
+wget -N $URL -O $ZIP_FILE
+mkdir $TARGET_DIR
+unzip $ZIP_FILE -d ./input/
+rm $ZIP_FILE

Diff for: main.py

@@ -46,6 +46,13 @@ class CycleGAN():
 
     def input_setup(self):
 
+        ''' 
+        This function basically setup variables for taking image input.
+
+        filenames_A/filenames_B -> takes the list of all training images
+        self.image_A/self.image_B -> Input image with each values ranging from [-1,1]
+        '''
+
         filenames_A = tf.train.match_filenames_once("./input/horse2zebra/trainA/*.jpg")    
         self.queue_length_A = tf.size(filenames_A)
         filenames_B = tf.train.match_filenames_once("./input/horse2zebra/trainB/*.jpg")    
@@ -65,16 +72,21 @@ def input_setup(self):
 
     def input_read(self, sess):
 
+
+        '''
+        It reads the input into from the image folder.
+
+        self.fake_images_A/self.fake_images_B -> List of generated images used for calculation of loss function of Discriminator
+        self.A_input/self.B_input -> Stores all the training images in python list
+        '''
+
         # Loading images into the tensors
         coord = tf.train.Coordinator()
         threads = tf.train.start_queue_runners(coord=coord)
 
         num_files_A = sess.run(self.queue_length_A)
         num_files_B = sess.run(self.queue_length_B)
 
-        images_A = []
-        images_B = []
-
         self.fake_images_A = np.zeros((pool_size,1,img_height, img_width, img_layer))
         self.fake_images_B = np.zeros((pool_size,1,img_height, img_width, img_layer))
 
@@ -99,6 +111,14 @@ def input_read(self, sess):
 
     def model_setup(self):
 
+        ''' This function sets up the model to train
+
+        self.input_A/self.input_B -> Set of training images.
+        self.fake_A/self.fake_B -> Generated images by corresponding generator of input_A and input_B
+        self.lr -> Learning rate variable
+        self.cyc_A/ self.cyc_B -> Images generated after feeding self.fake_A/self.fake_B to corresponding generator. This is use to calcualte cyclic loss
+        '''
+
         self.input_A = tf.placeholder(tf.float32, [batch_size, img_width, img_height, img_layer], name="input_A")
         self.input_B = tf.placeholder(tf.float32, [batch_size, img_width, img_height, img_layer], name="input_B")
 
@@ -131,6 +151,13 @@ def model_setup(self):
 
     def loss_calc(self):
 
+        ''' In this function we are defining the variables for loss calcultions and traning model
+
+        d_loss_A/d_loss_B -> loss for discriminator A/B
+        g_loss_A/g_loss_B -> loss for generator A/B
+        *_trainer -> Variaous trainer for above loss functions
+        *_summ -> Summary variables for above loss functions'''
+
         cyc_loss = tf.reduce_mean(tf.abs(self.input_A-self.cyc_A)) + tf.reduce_mean(tf.abs(self.input_B-self.cyc_B))
 
         disc_loss_A = tf.reduce_mean(tf.squared_difference(self.fake_rec_A,1))
@@ -181,6 +208,9 @@ def save_training_images(self, sess, epoch):
             imsave("./output/imgs/inputB_"+ str(epoch) + "_" + str(i)+".jpg",((self.B_input[i][0]+1)*127.5).astype(np.uint8))
 
     def fake_image_pool(self, num_fakes, fake, fake_pool):
+        ''' This function saves the generated image to corresponding pool of images.
+        In starting. It keeps on feeling the pool till it is full and then randomly selects an
+        already stored image and replace it with new one.'''
 
         if(num_fakes < pool_size):
             fake_pool[num_fakes] = fake
@@ -199,6 +229,9 @@ def fake_image_pool(self, num_fakes, fake, fake_pool):
     def train(self):
 
 
+        ''' Training Function '''
+
+
         # Load Dataset from the dataset folder
         self.input_setup()  
 
@@ -283,6 +316,9 @@ def train(self):
 
     def test(self):
 
+
+        ''' Testing Function'''
+
         print("Testing the results")
 
         self.input_setup()