Fixes for compatibility with TF 1.0 and python 3.

video_prediction/lstm_ops.py

@@ -38,17 +38,11 @@ def init_state(inputs,
   if inputs is not None:
     # Handle both the dynamic shape as well as the inferred shape.
     inferred_batch_size = inputs.get_shape().with_rank_at_least(1)[0]
-    batch_size = tf.shape(inputs)[0]
     dtype = inputs.dtype
   else:
     inferred_batch_size = 0
-    batch_size = 0
-
   initial_state = state_initializer(
-      tf.stack([batch_size] + state_shape),
-      dtype=dtype)
-  initial_state.set_shape([inferred_batch_size] + state_shape)
-
+      [inferred_batch_size] + state_shape, dtype=dtype)
   return initial_state
 
 

video_prediction/prediction_train.py

@@ -103,21 +103,24 @@ class Model(object):
                actions=None,
                states=None,
                sequence_length=None,
-               reuse_scope=None):
+               reuse_scope=None,
+               prefix=None):
 
     if sequence_length is None:
       sequence_length = FLAGS.sequence_length
 
-    self.prefix = prefix = tf.placeholder(tf.string, [])
+    if prefix is None:
+        prefix = tf.placeholder(tf.string, [])
+    self.prefix = prefix
     self.iter_num = tf.placeholder(tf.float32, [])
     summaries = []
 
     # Split into timesteps.
-    actions = tf.split(axis=1, num_or_size_splits=actions.get_shape()[1], value=actions)
+    actions = tf.split(axis=1, num_or_size_splits=int(actions.get_shape()[1]), value=actions)
     actions = [tf.squeeze(act) for act in actions]
-    states = tf.split(axis=1, num_or_size_splits=states.get_shape()[1], value=states)
+    states = tf.split(axis=1, num_or_size_splits=int(states.get_shape()[1]), value=states)
     states = [tf.squeeze(st) for st in states]
-    images = tf.split(axis=1, num_or_size_splits=images.get_shape()[1], value=images)
+    images = tf.split(axis=1, num_or_size_splits=int(images.get_shape()[1]), value=images)
     images = [tf.squeeze(img) for img in images]
 
     if reuse_scope is None:
@@ -183,17 +186,18 @@ class Model(object):
 
 def main(unused_argv):
 
-  print 'Constructing models and inputs.'
+  print('Constructing models and inputs.')
   with tf.variable_scope('model', reuse=None) as training_scope:
     images, actions, states = build_tfrecord_input(training=True)
-    model = Model(images, actions, states, FLAGS.sequence_length)
+    model = Model(images, actions, states, FLAGS.sequence_length,
+                  prefix='train')
 
   with tf.variable_scope('val_model', reuse=None):
     val_images, val_actions, val_states = build_tfrecord_input(training=False)
     val_model = Model(val_images, val_actions, val_states,
-                      FLAGS.sequence_length, training_scope)
+                      FLAGS.sequence_length, training_scope, prefix='val')
 
-  print 'Constructing saver.'
+  print('Constructing saver.')
   # Make saver.
   saver = tf.train.Saver(
       tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES), max_to_keep=0)
@@ -214,8 +218,7 @@ def main(unused_argv):
   # Run training.
   for itr in range(FLAGS.num_iterations):
     # Generate new batch of data.
-    feed_dict = {model.prefix: 'train',
-                 model.iter_num: np.float32(itr),
+    feed_dict = {model.iter_num: np.float32(itr),
                  model.lr: FLAGS.learning_rate}
     cost, _, summary_str = sess.run([model.loss, model.train_op, model.summ_op],
                                     feed_dict)
@@ -226,7 +229,6 @@ def main(unused_argv):
     if (itr) % VAL_INTERVAL == 2:
       # Run through validation set.
       feed_dict = {val_model.lr: 0.0,
-                   val_model.prefix: 'val',
                    val_model.iter_num: np.float32(itr)}
       _, val_summary_str = sess.run([val_model.train_op, val_model.summ_op],
                                      feed_dict)