path

1 files changed, 7 insertions, 5 deletions

diff --git a/cli/app/commands/biggan/search_class.py b/cli/app/commands/biggan/search_class.py
index 9076e26..8895a67 100644
--- a/cli/app/commands/biggan/search_class.py
+++ b/cli/app/commands/biggan/search_class.py
@@ -43,8 +43,6 @@ def cli(ctx, opt_fp_in, opt_dims, opt_steps, opt_limit, opt_video, opt_tag):
   """
 
   sess = tf.compat.v1.Session()
-  sess.run(tf.compat.v1.global_variables_initializer())
-  sess.run(tf.compat.v1.tables_initializer())
 
   if os.path.isdir(opt_fp_in):
     paths = glob(os.path.join(opt_fp_in, '*.jpg')) + \
@@ -95,17 +93,17 @@ def find_nearest_vector(sess, opt_fp_in, opt_dims, out_images, out_labels, out_l
   input_z = tf.compat.v1.Variable(z_initial, dtype=np.float32, constraint=lambda t: tf.clip_by_value(t, -2, 2))
   input_y = tf.compat.v1.Variable(y_initial, dtype=np.float32, constraint=lambda t: tf.clip_by_value(t, 0, 1))
   # input_trunc = tf.compat.v1.constant(1.0)
-  input_trunc = tf.placeholder(tf.float32, shape=None)
+  input_trunc = tf.compat.v1.placeholder(tf.float32, shape=None)
   output = generator({
     'z': input_z,
     'y': input_y,
     'truncation': input_trunc,
   })
 
-  target = tf.placeholder(tf.float32, shape=(batch_size, img_size, img_size, num_channels))
+  target = tf.compat.v1.placeholder(tf.float32, shape=(batch_size, img_size, img_size, num_channels))
 
   # loss = tf.losses.compute_weighted_loss(tf.square(output - target), weights=mask)
-  loss = tf.losses.mean_squared_error(target, output)
+  loss = tf.compat.v1.losses.mean_squared_error(target, output)
 
   train_step_z = tf.train.AdamOptimizer(z_lr).minimize(loss, var_list=[input_z], name='AdamOpterZ')
   train_step_y = tf.train.AdamOptimizer(y_lr).minimize(loss, var_list=[input_y], name='AdamOpterY')
@@ -123,6 +121,10 @@ def find_nearest_vector(sess, opt_fp_in, opt_dims, out_images, out_labels, out_l
   phi_target = np.expand_dims(phi_target, 0)
   phi_target = np.repeat(phi_target, batch_size, axis=0)
 
+  # IMPORTANT: initialize variables before running the session
+  sess.run(tf.compat.v1.global_variables_initializer())
+  sess.run(tf.compat.v1.tables_initializer())
+
   # feed_dict = {input_z: z, input_y: y, input_trunc: truncation}
   phi_start = sess.run(output, {
     input_trunc: truncation,