Fix TF deprecation warning.

google · Aug 20, 2019 · 41f99a3 · 41f99a3
1 parent 1fb20de
commit 41f99a3
Showing 1 changed file with 11 additions and 11 deletions.
diff --git a/frame_level_models.py b/frame_level_models.py
@@ -146,7 +146,7 @@ def create_model(self,
     max_frames = model_input.get_shape().as_list()[1]
     feature_size = model_input.get_shape().as_list()[2]
     reshaped_input = tf.reshape(model_input, [-1, feature_size])
-    tf.summary.histogram("input_hist", reshaped_input)
+    tf.compat.v1.summary.histogram("input_hist", reshaped_input)
 
     if add_batch_norm:
       reshaped_input = slim.batch_norm(
@@ -156,11 +156,11 @@ def create_model(self,
           is_training=is_training,
           scope="input_bn")
 
-    cluster_weights = tf.get_variable(
+    cluster_weights = tf.compat.v1.get_variable(
         "cluster_weights", [feature_size, cluster_size],
         initializer=tf.random_normal_initializer(stddev=1 /
                                                  math.sqrt(feature_size)))
-    tf.summary.histogram("cluster_weights", cluster_weights)
+    tf.compat.v1.summary.histogram("cluster_weights", cluster_weights)
     activation = tf.matmul(reshaped_input, cluster_weights)
     if add_batch_norm:
       activation = slim.batch_norm(
@@ -170,23 +170,23 @@ def create_model(self,
           is_training=is_training,
           scope="cluster_bn")
     else:
-      cluster_biases = tf.get_variable(
+      cluster_biases = tf.compat.v1.get_variable(
           "cluster_biases", [cluster_size],
           initializer=tf.random_normal_initializer(stddev=1 /
                                                    math.sqrt(feature_size)))
-      tf.summary.histogram("cluster_biases", cluster_biases)
+      tf.compat.v1.summary.histogram("cluster_biases", cluster_biases)
       activation += cluster_biases
     activation = act_fn(activation)
-    tf.summary.histogram("cluster_output", activation)
+    tf.compat.v1.summary.histogram("cluster_output", activation)
 
     activation = tf.reshape(activation, [-1, max_frames, cluster_size])
     activation = utils.FramePooling(activation, FLAGS.dbof_pooling_method)
 
-    hidden1_weights = tf.get_variable(
+    hidden1_weights = tf.compat.v1.get_variable(
         "hidden1_weights", [cluster_size, hidden1_size],
         initializer=tf.random_normal_initializer(stddev=1 /
                                                  math.sqrt(cluster_size)))
-    tf.summary.histogram("hidden1_weights", hidden1_weights)
+    tf.compat.v1.summary.histogram("hidden1_weights", hidden1_weights)
     activation = tf.matmul(activation, hidden1_weights)
     if add_batch_norm:
       activation = slim.batch_norm(
@@ -196,13 +196,13 @@ def create_model(self,
           is_training=is_training,
           scope="hidden1_bn")
     else:
-      hidden1_biases = tf.get_variable(
+      hidden1_biases = tf.compat.v1.get_variable(
           "hidden1_biases", [hidden1_size],
           initializer=tf.random_normal_initializer(stddev=0.01))
-      tf.summary.histogram("hidden1_biases", hidden1_biases)
+      tf.compat.v1.summary.histogram("hidden1_biases", hidden1_biases)
       activation += hidden1_biases
     activation = act_fn(activation)
-    tf.summary.histogram("hidden1_output", activation)
+    tf.compat.v1.summary.histogram("hidden1_output", activation)
 
     aggregated_model = getattr(video_level_models,
                                FLAGS.video_level_classifier_model)