api compatible with tensorflow 1.0 and some other modifies

srez_input.py

@@ -12,7 +12,7 @@ def setup_inputs(sess, filenames, image_size=None, capacity_factor=3):
     filename_queue = tf.train.string_input_producer(filenames)
     key, value = reader.read(filename_queue)
     channels = 3
-    image = tf.image.decode_jpeg(value, channels=channels, name="dataset_image")
+    image = tf.image.decode_image(value, channels=channels, name="dataset_image")
     image.set_shape([None, None, channels])
 
     # Crop and other random augmentations

srez_main.py

@@ -22,13 +22,13 @@
 tf.app.flags.DEFINE_integer('checkpoint_period', 10000,
                             "Number of batches in between checkpoints")
 
-tf.app.flags.DEFINE_string('dataset', 'dataset',
+tf.app.flags.DEFINE_string('dataset', '/home/data/CelebA/Img/img_align_celeba_png.7z/img_align_celeba_png',
                            "Path to the dataset directory.")
 
 tf.app.flags.DEFINE_float('epsilon', 1e-8,
                           "Fuzz term to avoid numerical instability")
 
-tf.app.flags.DEFINE_string('run', 'demo',
+tf.app.flags.DEFINE_string('run', 'train',
                             "Which operation to run. [demo|train]")
 
 tf.app.flags.DEFINE_float('gene_l1_factor', .90,
@@ -61,7 +61,10 @@
 tf.app.flags.DEFINE_string('train_dir', 'train',
                            "Output folder where training logs are dumped.")
 
-tf.app.flags.DEFINE_integer('train_time', 20,
+tf.app.flags.DEFINE_string('test_dataset', 'test',
+                            "testing dataset. Warning:file will be overided")
+
+tf.app.flags.DEFINE_integer('train_time', 60,
                             "Time in minutes to train the model")
 
 def prepare_dirs(delete_train_dir=False):
@@ -100,7 +103,7 @@ def setup_tensorflow():
     random.seed(FLAGS.random_seed)
     np.random.seed(FLAGS.random_seed)
 
-    summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph)
+    summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)
 
     return sess, summary_writer
 
@@ -133,6 +136,36 @@ def _demo():
     # Execute demo
     srez_demo.demo1(sess)
 
+def _test():
+    # Load checkpoint
+    if not tf.gfile.IsDirectory(FLAGS.checkpoint_dir):
+        raise FileNotFoundError("Could not find folder `%s'" % (FLAGS.checkpoint_dir,))
+    if not tf.gile.IsDirectory(FLAGS.test_dataset):
+        raise FileNotFoundError("Could not find folder `%s'" % (FLAGS.test_dataset,))
+    # Setup global tensorflow state
+    sess = tf.Session()
+
+    # Prepare directories
+    filenames = prepare_dirs(delete_train_dir=False)
+
+    # Setup async input queues
+    features, labels = srez_input.setup_inputs(sess, filenames)
+
+    # Create and initialize model
+    [gene_minput, gene_moutput,
+     gene_output, gene_var_list,
+     disc_real_output, disc_fake_output, disc_var_list] = \
+            srez_model.create_model(sess, features, labels)
+
+    # Restore variables from checkpoint
+    saver = tf.train.Saver()
+    filename = 'checkpoint_new.txt'
+    filename = os.path.join(FLAGS.checkpoint_dir, filename)
+    saver.restore(sess, filename)
+
+
+
+
 class TrainData(object):
     def __init__(self, dictionary):
         self.__dict__.update(dictionary)
@@ -178,13 +211,17 @@ def _train():
     train_data = TrainData(locals())
     srez_train.train_model(train_data)
 
+
+
 def main(argv=None):
     # Training or showing off?
 
     if FLAGS.run == 'demo':
         _demo()
     elif FLAGS.run == 'train':
         _train()
+    elif FLAGS.run == 'test':
+        _test()
 
 if __name__ == '__main__':
   tf.app.run()

srez_model.py

@@ -326,7 +326,7 @@ def _discriminator_model(sess, features, disc_input):
     mapsize = 3
     layers  = [64, 128, 256, 512]
 
-    old_vars = tf.all_variables()
+    old_vars = tf.global_variables()
 
     model = Model('DIS', 2*disc_input - 1)
 
@@ -352,7 +352,7 @@ def _discriminator_model(sess, features, disc_input):
     model.add_conv2d(1, mapsize=1, stride=1, stddev_factor=stddev_factor)
     model.add_mean()
 
-    new_vars  = tf.all_variables()
+    new_vars  = tf.global_variables()
     disc_vars = list(set(new_vars) - set(old_vars))
 
     return model.get_output(), disc_vars
@@ -363,7 +363,7 @@ def _generator_model(sess, features, labels, channels):
     mapsize = 3
     res_units  = [256, 128, 96]
 
-    old_vars = tf.all_variables()
+    old_vars = tf.global_variables()
 
     # See Arxiv 1603.05027
     model = Model('GEN', features)
@@ -396,7 +396,7 @@ def _generator_model(sess, features, labels, channels):
     model.add_conv2d(channels, mapsize=1, stride=1, stddev_factor=1.)
     model.add_sigmoid()
 
-    new_vars  = tf.all_variables()
+    new_vars  = tf.global_variables()
     gene_vars = list(set(new_vars) - set(old_vars))
 
     return model.get_output(), gene_vars
@@ -449,7 +449,7 @@ def _downscale(images, K):
 
 def create_generator_loss(disc_output, gene_output, features):
     # I.e. did we fool the discriminator?
-    cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(disc_output, tf.ones_like(disc_output))
+    cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_output, labels=tf.ones_like(disc_output))
     gene_ce_loss  = tf.reduce_mean(cross_entropy, name='gene_ce_loss')
 
     # I.e. does the result look like the feature?
@@ -466,10 +466,10 @@ def create_generator_loss(disc_output, gene_output, features):
 
 def create_discriminator_loss(disc_real_output, disc_fake_output):
     # I.e. did we correctly identify the input as real or not?
-    cross_entropy_real = tf.nn.sigmoid_cross_entropy_with_logits(disc_real_output, tf.ones_like(disc_real_output))
+    cross_entropy_real = tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_real_output, labels=tf.ones_like(disc_real_output))
     disc_real_loss     = tf.reduce_mean(cross_entropy_real, name='disc_real_loss')
 
-    cross_entropy_fake = tf.nn.sigmoid_cross_entropy_with_logits(disc_fake_output, tf.zeros_like(disc_fake_output))
+    cross_entropy_fake = tf.nn.sigmoid_cross_entropy_with_logits(logits=disc_fake_output,labels=tf.zeros_like(disc_fake_output))
     disc_fake_loss     = tf.reduce_mean(cross_entropy_fake, name='disc_fake_loss')
 
     return disc_real_loss, disc_fake_loss

srez_train.py

@@ -19,10 +19,10 @@ def _summarize_progress(train_data, feature, label, gene_output, batch, suffix,
 
     clipped = tf.maximum(tf.minimum(gene_output, 1.0), 0.0)
 
-    image   = tf.concat(2, [nearest, bicubic, clipped, label])
+    image   = tf.concat([nearest, bicubic, clipped, label],2)
 
     image = image[0:max_samples,:,:,:]
-    image = tf.concat(0, [image[i,:,:,:] for i in range(max_samples)])
+    image = tf.concat([image[i,:,:,:] for i in range(max_samples)],0)
     image = td.sess.run(image)
 
     filename = 'batch%06d_%s.png' % (batch, suffix)
@@ -62,8 +62,8 @@ def _save_checkpoint(train_data, batch):
 def train_model(train_data):
     td = train_data
 
-    summaries = tf.merge_all_summaries()
-    td.sess.run(tf.initialize_all_variables())
+    summaries = tf.summary.merge_all()
+    td.sess.run(tf.global_variables_initializer())
 
     lrval       = FLAGS.learning_rate_start
     start_time  = time.time()