Update for TensorFlow 1.0 API.

diff --git a/utils/geo_layer_utils.py b/utils/geo_layer_utils.py
index 5e4bfaba11bdf394ccc3743f8e4515b2d8c46868..497017b1f52866b6b6140e2b5c9dc41a4eff875c 100755 (executable)
--- a/utils/geo_layer_utils.py
+++ b/utils/geo_layer_utils.py
@@ -74,7 +74,7 @@ def bilinear_interp(im, x, y, name):
     idx_d = base_y1 + x1
 
     # Use indices to look up pixels
     idx_d = base_y1 + x1
 
     # Use indices to look up pixels

-    im_flat = tf.reshape(im, tf.pack([-1, channels]))
+    im_flat = tf.reshape(im, tf.stack([-1, channels]))

     im_flat = tf.to_float(im_flat)
     pixel_a = tf.gather(im_flat, idx_a)
     pixel_b = tf.gather(im_flat, idx_b)
     im_flat = tf.to_float(im_flat)
     pixel_a = tf.gather(im_flat, idx_a)
     pixel_b = tf.gather(im_flat, idx_b)


@@ -91,7 +91,7 @@ def bilinear_interp(im, x, y, name):
     wd = tf.expand_dims((1.0 - (x1_f - x)) * (1.0 - (y1_f - y)), 1)
 
     output = tf.add_n([wa*pixel_a, wb*pixel_b, wc*pixel_c, wd*pixel_d]) 
     wd = tf.expand_dims((1.0 - (x1_f - x)) * (1.0 - (y1_f - y)), 1)
 
     output = tf.add_n([wa*pixel_a, wb*pixel_b, wc*pixel_c, wd*pixel_d]) 

-    output = tf.reshape(output, shape=tf.pack([num_batch, height, width, channels]))
+    output = tf.reshape(output, shape=tf.stack([num_batch, height, width, channels]))

     return output
 
 def meshgrid(height, width):
     return output
 
 def meshgrid(height, width):


@@ -99,14 +99,14 @@ def meshgrid(height, width):
   """
   with tf.variable_scope('meshgrid'):
     x_t = tf.matmul(
   """
   with tf.variable_scope('meshgrid'):
     x_t = tf.matmul(

-        tf.ones(shape=tf.pack([height,1])),
+        tf.ones(shape=tf.stack([height,1])),

         tf.transpose(
               tf.expand_dims(
                   tf.linspace(-1.0,1.0,width),1),[1,0]))
     y_t = tf.matmul(
         tf.expand_dims(
             tf.linspace(-1.0, 1.0, height), 1),
         tf.transpose(
               tf.expand_dims(
                   tf.linspace(-1.0,1.0,width),1),[1,0]))
     y_t = tf.matmul(
         tf.expand_dims(
             tf.linspace(-1.0, 1.0, height), 1),

-        tf.ones(shape=tf.pack([1, width])))
+        tf.ones(shape=tf.stack([1, width])))

     x_t_flat = tf.reshape(x_t, (1,-1))
     y_t_flat = tf.reshape(y_t, (1,-1))
     # grid_x = tf.reshape(x_t_flat, [1, height, width, 1])
     x_t_flat = tf.reshape(x_t, (1,-1))
     y_t_flat = tf.reshape(y_t, (1,-1))
     # grid_x = tf.reshape(x_t_flat, [1, height, width, 1])


@@ -121,7 +121,7 @@ def vae_gaussian_layer(network, is_train=True, scope='gaussian_layer'):
     z_mean, z_logvar = tf.split(3, 2, network)  # Split into mean and variance
     if is_train:
       eps = tf.random_normal(tf.shape(z_mean))
     z_mean, z_logvar = tf.split(3, 2, network)  # Split into mean and variance
     if is_train:
       eps = tf.random_normal(tf.shape(z_mean))

-      z = tf.add(z_mean, tf.mul(eps, tf.exp(tf.mul(0.5, z_logvar))))
+      z = tf.add(z_mean, tf.multiply(eps, tf.exp(tf.multiply(0.5, z_logvar))))

     else:
       z = z_mean
     return z, z_mean, z_logvar
     else:
       z = z_mean
     return z, z_mean, z_logvar

diff --git a/voxel_flow_model.py b/voxel_flow_model.py
index d419acf6a4493cd534c881c60fbee73bd2e57bff..b3f554d59f935ef327a8a1b86d936461988cb779 100755 (executable)
--- a/voxel_flow_model.py
+++ b/voxel_flow_model.py
@@ -85,6 +85,6 @@ class Voxel_flow_model(object):
 
     mask = 0.5 * (1.0 + mask)
     mask = tf.tile(mask, [1, 1, 1, 3])
 
     mask = 0.5 * (1.0 + mask)
     mask = tf.tile(mask, [1, 1, 1, 3])

-    net = tf.mul(mask, output_1) + tf.mul(1.0 - mask, output_2)
+    net = tf.multiply(mask, output_1) + tf.multiply(1.0 - mask, output_2)

 
     return net
 
     return net

diff --git a/voxel_flow_train.py b/voxel_flow_train.py
index dfe87b198e63a3286bb674361b7608b4509a74d6..f6d3abb67a0f062e6750c0b3bd8471174be1b8f6 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)
 
     # 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.
 
     # 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()
 
     # 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
     sess.run(init)
 
     # Summary Writter

-    summary_writer = tf.train.SummaryWriter(
+    summary_writer = tf.summary.FileWriter(

       FLAGS.train_dir,
       graph=sess.graph)
 
       FLAGS.train_dir,
       graph=sess.graph)