update

README.md

@@ -16,8 +16,31 @@ Link coming soon
 
 # Usage
 
+## I. Decensoring hentai
 
 
+
+## I. Prepare the training data
+
+Put the images for training the "data/images" directory and convert images to npy format.
+
+```
+$ cd data
+$ python to_npy.py
+```
+
+The dataset will not be released 
+
+## II. Train the GLCIC model
+
+```
+$ cd src
+$ python train.py
+```
+
+You can download the trained model file: [glcic_model.tar.gz](
+https://drive.google.com/open?id=1jvP2czv_gX8Q1l0tUPNWLV8HLacK6n_Q)
+
 # To do
 - Add a user interface
 - Incorporate GAN loss into training

src/test.py

@@ -6,14 +6,13 @@ import os
 import matplotlib.pyplot as plt
 import sys
 sys.path.append('..')
-from network import Network
+from model import Model
 
 IMAGE_SIZE = 128
 LOCAL_SIZE = 64
 HOLE_MIN = 24
 HOLE_MAX = 48
 BATCH_SIZE = 16
-PRETRAIN_EPOCH = 100
 
 test_npy = './lfw.npy'
 
@@ -25,13 +24,13 @@ def test():
     local_completion = tf.placeholder(tf.float32, [BATCH_SIZE, LOCAL_SIZE, LOCAL_SIZE, 3])
     is_training = tf.placeholder(tf.bool, [])
 
-    model = Network(x, mask, local_x, global_completion, local_completion, is_training, batch_size=BATCH_SIZE)
+    model = Model(x, mask, local_x, global_completion, local_completion, is_training, batch_size=BATCH_SIZE)
     sess = tf.Session()
     init_op = tf.global_variables_initializer()
     sess.run(init_op)
 
     saver = tf.train.Saver()
-    saver.restore(sess, '../backup/latest')
+    saver.restore(sess, '../saved_models/latest')
 
     x_test = np.load(test_npy)
     np.random.shuffle(x_test)

src/test/.gitignore

@@ -1,3 +0,0 @@
-output/*
-*.npy
-!.gitkeep

src/train.py

@@ -33,9 +33,9 @@ def train():
     init_op = tf.global_variables_initializer()
     sess.run(init_op)
 
-    if tf.train.get_checkpoint_state('./backup'):
+    if tf.train.get_checkpoint_state('./saved_model'):
         saver = tf.train.Saver()
-        saver.restore(sess, './backup/latest')
+        saver.restore(sess, './saved_model/latest')
 
     x_train, x_test = load.load()
     x_train = np.array([a / 127.5 - 1 for a in x_train])
@@ -69,9 +69,9 @@ def train():
 
 
             saver = tf.train.Saver()
-            saver.save(sess, './backup/latest', write_meta_graph=False)
+            saver.save(sess, './saved_model/latest', write_meta_graph=False)
             if sess.run(epoch) == PRETRAIN_EPOCH:
-                saver.save(sess, './backup/pretrained', write_meta_graph=False)
+                saver.save(sess, './saved_model/pretrained', write_meta_graph=False)
 
 
         # Discrimitation
@@ -109,7 +109,7 @@ def train():
             cv2.imwrite('./output/{}.jpg'.format("{0:06d}".format(sess.run(epoch))), cv2.cvtColor(sample, cv2.COLOR_RGB2BGR))
 
             saver = tf.train.Saver()
-            saver.save(sess, './backup/latest', write_meta_graph=False)
+            saver.save(sess, './saved_model/latest', write_meta_graph=False)
 
 
 def get_points():