Unbreak non-default batch sizes.

[voxel-flow] / voxel_flow_train.py

diff --git a/voxel_flow_train.py b/voxel_flow_train.py
index dfe87b198e63a3286bb674361b7608b4509a74d6..4440e798410e9ec4b16f638146a5d4fcf8862be4 100755 (executable)
--- a/voxel_flow_train.py
+++ b/voxel_flow_train.py
@@ -68,18 +68,18 @@ def train(dataset_frame1, dataset_frame2, dataset_frame3):
 
     # Create summaries
     summaries = tf.get_collection(tf.GraphKeys.SUMMARIES)
-    summaries.append(tf.scalar_summary('total_loss', total_loss))
-    summaries.append(tf.scalar_summary('reproduction_loss', reproduction_loss))
-    # summaries.append(tf.scalar_summary('prior_loss', prior_loss))
-    summaries.append(tf.image_summary('Input Image', input_placeholder, 3))
-    summaries.append(tf.image_summary('Output Image', prediction, 3))
-    summaries.append(tf.image_summary('Target Image', target_placeholder, 3))
+    summaries.append(tf.summary.scalar('total_loss', total_loss))
+    summaries.append(tf.summary.scalar('reproduction_loss', reproduction_loss))
+    # summaries.append(tf.summary.scalar('prior_loss', prior_loss))
+    summaries.append(tf.summary.image('Input Image', input_placeholder, 3))
+    summaries.append(tf.summary.image('Output Image', prediction, 3))
+    summaries.append(tf.summary.image('Target Image', target_placeholder, 3))
 
     # Create a saver.
     saver = tf.train.Saver(tf.all_variables())
 
     # Build the summary operation from the last tower summaries.
-    summary_op = tf.merge_all_summaries()
+    summary_op = tf.summary.merge_all()
 
     # Build an initialization operation to run below.
     init = tf.initialize_all_variables()
@@ -87,7 +87,7 @@ def train(dataset_frame1, dataset_frame2, dataset_frame3):
     sess.run(init)
 
     # Summary Writter
-    summary_writer = tf.train.SummaryWriter(
+    summary_writer = tf.summary.FileWriter(
       FLAGS.train_dir,
       graph=sess.graph)
 
@@ -118,7 +118,7 @@ def train(dataset_frame1, dataset_frame2, dataset_frame3):
     # load_fn_frame3 = partial(dataset_frame3.process_func)
     # p_queue_frame3 = PrefetchQueue(load_fn_frame3, data_list_frame3, FLAGS.batch_size, shuffle=False, num_workers=num_workers)
 
-    for step in xrange(0, FLAGS.max_steps):
+    for step in range(0, FLAGS.max_steps):
       batch_idx = step % epoch_num
       
       batch_data_list_frame1 = data_list_frame1[int(batch_idx * FLAGS.batch_size) : int((batch_idx + 1) * FLAGS.batch_size)]