TF deprecation warning fix for eval.py.wq

eval.py

@@ -23,9 +23,7 @@
 import losses
 import readers
 import tensorflow as tf
-from tensorflow import app
 from tensorflow import flags
-from tensorflow import gfile
 from tensorflow.python.lib.io import file_io
 import utils
 import video_level_models
@@ -88,7 +86,7 @@ def get_input_evaluation_tensors(reader,
   """
   logging.info("Using batch size of %d for evaluation.", batch_size)
   with tf.name_scope("eval_input"):
-    files = gfile.Glob(data_pattern)
+    files = tf.io.gfile.glob(data_pattern)
     if not files:
       raise IOError("Unable to find the evaluation files.")
     logging.info("number of evaluation files: %d", len(files))
@@ -131,22 +129,22 @@ def build_graph(reader,
   model_input_raw = input_data_dict["video_matrix"]
   labels_batch = input_data_dict["labels"]
   num_frames = input_data_dict["num_frames"]
-  tf.summary.histogram("model_input_raw", model_input_raw)
+  tf.compat.v1.summary.histogram("model_input_raw", model_input_raw)
 
   feature_dim = len(model_input_raw.get_shape()) - 1
 
   # Normalize input features.
   model_input = tf.nn.l2_normalize(model_input_raw, feature_dim)
 
-  with tf.variable_scope("tower"):
+  with tf.compat.v1.variable_scope("tower"):
     result = model.create_model(
         model_input,
         num_frames=num_frames,
         vocab_size=reader.num_classes,
         labels=labels_batch,
         is_training=False)
     predictions = result["predictions"]
-    tf.summary.histogram("model_activations", predictions)
+    tf.compat.v1.summary.histogram("model_activations", predictions)
     if "loss" in result.keys():
       label_loss = result["loss"]
     else:
@@ -162,7 +160,7 @@ def build_graph(reader,
   tf.add_to_collection("labels", tf.cast(labels_batch, tf.float32))
   if FLAGS.segment_labels:
     tf.add_to_collection("label_weights", input_data_dict["label_weights"])
-  tf.add_to_collection("summary_op", tf.summary.merge_all())
+  tf.add_to_collection("summary_op", tf.compat.v1.summary.merge_all())
 
 
 def evaluation_loop(fetches, saver, summary_writer, evl_metrics,
@@ -276,7 +274,7 @@ def evaluation_loop(fetches, saver, summary_writer, evl_metrics,
 
 def evaluate():
   """Starts main evaluation loop."""
-  tf.set_random_seed(0)  # for reproducibility
+  tf.compat.v1.set_random_seed(0)  # for reproducibility
 
   # Write json of flags
   model_flags_path = os.path.join(FLAGS.train_dir, "model_flags.json")
@@ -327,8 +325,8 @@ def evaluate():
     if FLAGS.segment_labels:
       fetches["label_weights"] = tf.get_collection("label_weights")[0]
 
-    saver = tf.train.Saver(tf.global_variables())
-    summary_writer = tf.summary.FileWriter(
+    saver = tf.compat.v1.train.Saver(tf.global_variables())
+    summary_writer = tf.compat.v1.summary.FileWriter(
         os.path.join(FLAGS.train_dir, "eval"), graph=tf.get_default_graph())
 
     evl_metrics = eval_util.EvaluationMetrics(reader.num_classes, FLAGS.top_k,
@@ -343,10 +341,10 @@ def evaluate():
 
 
 def main(unused_argv):
-  logging.set_verbosity(tf.logging.INFO)
+  logging.set_verbosity(logging.INFO)
   logging.info("tensorflow version: %s", tf.__version__)
   evaluate()
 
 
 if __name__ == "__main__":
-  app.run()
+  tf.compat.v1.app.run()