Update network.py

network.py

@@ -2,60 +2,53 @@ import tensorflow as tf
 import numpy as np
 import tf_slim as slim
 
+# Enable TensorFlow 1.x compatibility mode
+tf.compat.v1.disable_eager_execution()
 
-
-def resblock(inputs, out_channel=32, name='resblock'):
-    
-    
-   with tf.compat.v1.variable_scope(name, reuse=reuse):
-       x = slim.convolution2d(inputs, out_channel, [3, 3], 
+def resblock(inputs, out_channel=32, name='resblock', reuse=False):
+    with tf.compat.v1.variable_scope(name, reuse=reuse):
+        x = slim.convolution2d(inputs, out_channel, [3, 3], 
                                activation_fn=None, scope='conv1')
-       x = tf.nn.leaky_relu(x)
-       x = slim.convolution2d(x, out_channel, [3, 3], 
+        x = tf.nn.leaky_relu(x)
+        x = slim.convolution2d(x, out_channel, [3, 3], 
                                activation_fn=None, scope='conv2')
-       return x + inputs
-
-
-
+        return x + inputs
 
 def unet_generator(inputs, channel=32, num_blocks=4, name='generator', reuse=False):
-    with tf.variable_scope(name, reuse=reuse):
-        
+    with tf.compat.v1.variable_scope(name, reuse=reuse):        
         x0 = slim.convolution2d(inputs, channel, [7, 7], activation_fn=None)
         x0 = tf.nn.leaky_relu(x0)
         
         x1 = slim.convolution2d(x0, channel, [3, 3], stride=2, activation_fn=None)
         x1 = tf.nn.leaky_relu(x1)
-        x1 = slim.convolution2d(x1, channel*2, [3, 3], activation_fn=None)
+        x1 = slim.convolution2d(x1, channel * 2, [3, 3], activation_fn=None)
         x1 = tf.nn.leaky_relu(x1)
         
-        x2 = slim.convolution2d(x1, channel*2, [3, 3], stride=2, activation_fn=None)
+        x2 = slim.convolution2d(x1, channel * 2, [3, 3], stride=2, activation_fn=None)
         x2 = tf.nn.leaky_relu(x2)
-        x2 = slim.convolution2d(x2, channel*4, [3, 3], activation_fn=None)
+        x2 = slim.convolution2d(x2, channel * 4, [3, 3], activation_fn=None)
         x2 = tf.nn.leaky_relu(x2)
         
         for idx in range(num_blocks):
-            x2 = resblock(x2, out_channel=channel*4, name='block_{}'.format(idx))
+            x2 = resblock(x2, out_channel=channel * 4, name=f'block_{idx}', reuse=reuse)
             
-        x2 = slim.convolution2d(x2, channel*2, [3, 3], activation_fn=None)
+        x2 = slim.convolution2d(x2, channel * 2, [3, 3], activation_fn=None)
         x2 = tf.nn.leaky_relu(x2)
         
         h1, w1 = tf.shape(x2)[1], tf.shape(x2)[2]
-        x3 = tf.image.resize_bilinear(x2, (h1*2, w1*2))
-        x3 = slim.convolution2d(x3+x1, channel*2, [3, 3], activation_fn=None)
+        x3 = tf.image.resize(x2, (h1 * 2, w1 * 2), method='bilinear')
+        x3 = slim.convolution2d(x3 + x1, channel * 2, [3, 3], activation_fn=None)
         x3 = tf.nn.leaky_relu(x3)
         x3 = slim.convolution2d(x3, channel, [3, 3], activation_fn=None)
         x3 = tf.nn.leaky_relu(x3)
 
         h2, w2 = tf.shape(x3)[1], tf.shape(x3)[2]
-        x4 = tf.image.resize_bilinear(x3, (h2*2, w2*2))
-        x4 = slim.convolution2d(x4+x0, channel, [3, 3], activation_fn=None)
+        x4 = tf.image.resize(x3, (h2 * 2, w2 * 2), method='bilinear')
+        x4 = slim.convolution2d(x4 + x0, channel, [3, 3], activation_fn=None)
         x4 = tf.nn.leaky_relu(x4)
         x4 = slim.convolution2d(x4, 3, [7, 7], activation_fn=None)
         
         return x4
 
 if __name__ == '__main__':
-    
-
     pass