update for TF1.3, TL1.8.0+

Author: zsdonghao

main.py

@@ -9,7 +9,7 @@
 
 import tensorflow as tf
 import tensorlayer as tl
-from model import *
+from model import SRGAN_g, SRGAN_d, Vgg19_simple_api
 from utils import *
 from config import config, log_config
 
@@ -65,7 +65,9 @@ def train():
     _, logits_fake = SRGAN_d(net_g.outputs, is_train=True, reuse=True)
 
     net_g.print_params(False)
+    net_g.print_layers()
     net_d.print_params(False)
+    net_d.print_layers()
 
     ## vgg inference. 0, 1, 2, 3 BILINEAR NEAREST BICUBIC AREA
     t_target_image_224 = tf.image.resize_images(

model.py

@@ -21,7 +21,7 @@ def SRGAN_g(t_image, is_train=False, reuse=False):
     b_init = None  # tf.constant_initializer(value=0.0)
     g_init = tf.random_normal_initializer(1., 0.02)
     with tf.variable_scope("SRGAN_g", reuse=reuse) as vs:
-        tl.layers.set_name_reuse(reuse)
+        # tl.layers.set_name_reuse(reuse) # remove for TL 1.8.0+
         n = InputLayer(t_image, name='in')
         n = Conv2d(n, 64, (3, 3), (1, 1), act=tf.nn.relu, padding='SAME', W_init=w_init, name='n64s1/c')
         temp = n
@@ -32,12 +32,12 @@ def SRGAN_g(t_image, is_train=False, reuse=False):
             nn = BatchNormLayer(nn, act=tf.nn.relu, is_train=is_train, gamma_init=g_init, name='n64s1/b1/%s' % i)
             nn = Conv2d(nn, 64, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, b_init=b_init, name='n64s1/c2/%s' % i)
             nn = BatchNormLayer(nn, is_train=is_train, gamma_init=g_init, name='n64s1/b2/%s' % i)
-            nn = ElementwiseLayer([n, nn], tf.add, 'b_residual_add/%s' % i)
+            nn = ElementwiseLayer([n, nn], tf.add, name='b_residual_add/%s' % i)
             n = nn
 
         n = Conv2d(n, 64, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, b_init=b_init, name='n64s1/c/m')
         n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n64s1/b/m')
-        n = ElementwiseLayer([n, temp], tf.add, 'add3')
+        n = ElementwiseLayer([n, temp], tf.add, name='add3')
         # B residual blacks end
 
         n = Conv2d(n, 256, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, name='n256s1/1')
@@ -63,7 +63,7 @@ def SRGAN_g2(t_image, is_train=False, reuse=False):
     g_init = tf.random_normal_initializer(1., 0.02)
     size = t_image.get_shape().as_list()
     with tf.variable_scope("SRGAN_g", reuse=reuse) as vs:
-        tl.layers.set_name_reuse(reuse)
+        # tl.layers.set_name_reuse(reuse) # remove for TL 1.8.0+
         n = InputLayer(t_image, name='in')
         n = Conv2d(n, 64, (3, 3), (1, 1), act=tf.nn.relu, padding='SAME', W_init=w_init, name='n64s1/c')
         temp = n
@@ -74,12 +74,12 @@ def SRGAN_g2(t_image, is_train=False, reuse=False):
             nn = BatchNormLayer(nn, act=tf.nn.relu, is_train=is_train, gamma_init=g_init, name='n64s1/b1/%s' % i)
             nn = Conv2d(nn, 64, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, b_init=b_init, name='n64s1/c2/%s' % i)
             nn = BatchNormLayer(nn, is_train=is_train, gamma_init=g_init, name='n64s1/b2/%s' % i)
-            nn = ElementwiseLayer([n, nn], tf.add, 'b_residual_add/%s' % i)
+            nn = ElementwiseLayer([n, nn], tf.add, name='b_residual_add/%s' % i)
             n = nn
 
         n = Conv2d(n, 64, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, b_init=b_init, name='n64s1/c/m')
         n = BatchNormLayer(n, is_train=is_train, gamma_init=g_init, name='n64s1/b/m')
-        n = ElementwiseLayer([n, temp], tf.add, 'add3')
+        n = ElementwiseLayer([n, temp], tf.add, name='add3')
         # B residual blacks end
 
         # n = Conv2d(n, 256, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init, name='n256s1/1')
@@ -110,7 +110,7 @@ def SRGAN_d2(t_image, is_train=False, reuse=False):
     g_init = tf.random_normal_initializer(1., 0.02)
     lrelu = lambda x: tl.act.lrelu(x, 0.2)
     with tf.variable_scope("SRGAN_d", reuse=reuse) as vs:
-        tl.layers.set_name_reuse(reuse)
+        # tl.layers.set_name_reuse(reuse) # remove for TL 1.8.0+
         n = InputLayer(t_image, name='in')
         n = Conv2d(n, 64, (3, 3), (1, 1), act=lrelu, padding='SAME', W_init=w_init, name='n64s1/c')