X-Git-Url: https://git.sesse.net/?a=blobdiff_plain;f=voxel_flow_train.py;h=6b2bcb440a7598a5cdda25ca76ab8a1e70b4b44b;hb=d55e5f71d6a84b7f39ab143bc5b56bb24d35cba1;hp=f6d3abb67a0f062e6750c0b3bd8471174be1b8f6;hpb=c74057bc77fb9bc4eb75668896adfb85ddb99251;p=voxel-flow

diff --git a/voxel_flow_train.py b/voxel_flow_train.py
index f6d3abb..6b2bcb4 100755
--- a/voxel_flow_train.py
+++ b/voxel_flow_train.py
@@ -71,7 +71,8 @@ def train(dataset_frame1, dataset_frame2, dataset_frame3):
     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('Input Image (before)', input_placeholder[:, :, :, 0:3], 3));
+    summaries.append(tf.summary.image('Input Image (after)', input_placeholder[:, :, :, 3:6], 3));
     summaries.append(tf.summary.image('Output Image', prediction, 3))
     summaries.append(tf.summary.image('Target Image', target_placeholder, 3))
 
@@ -118,7 +119,7 @@ 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)]
@@ -149,11 +150,13 @@ def train(dataset_frame1, dataset_frame2, dataset_frame3):
         shuffle(data_list_frame3)
         print('Epoch Number: %d' % int(step / epoch_num))
       
-      # Output Summary 
       if step % 10 == 0:
-        # summary_str = sess.run(summary_op, feed_dict = feed_dict)
-        # summary_writer.add_summary(summary_str, step)
-	      print("Loss at step %d: %f" % (step, loss_value))
+        print("Loss at step %d: %f" % (step, loss_value))
+
+      if step % 100 == 0:
+        # Output Summary 
+        summary_str = sess.run(summary_op, feed_dict = feed_dict)
+        summary_writer.add_summary(summary_str, step)
 
       if step % 500 == 0:
         # Run a batch of images