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tutorials/image/cifar10_estimator/generate_cifar10_tfrecords.py

@@ -55,7 +55,7 @@ def _get_file_names():
 
 
 def read_pickle_from_file(filename):
-  with open(filename, 'r') as f:
+  with tf.gfile.Open(filename, 'r') as f:
     data_dict = cPickle.load(f)
   return data_dict
 
@@ -63,22 +63,20 @@ def read_pickle_from_file(filename):
 def convert_to_tfrecord(input_files, output_file):
   """Converts a file to tfrecords."""
   print('Generating %s' % output_file)
-  record_writer = tf.python_io.TFRecordWriter(output_file)
-
-  for input_file in input_files:
-    data_dict = read_pickle_from_file(input_file)
-    data = data_dict['data']
-    labels = data_dict['labels']
-
-    num_entries_in_batch = len(labels)
-    for i in range(num_entries_in_batch):
-      example = tf.train.Example(
-          features=tf.train.Features(feature={
-              'image': _bytes_feature(data[i].tobytes()),
-              'label': _int64_feature(labels[i])
-          }))
-      record_writer.write(example.SerializeToString())
-  record_writer.close()
+  with tf.python_io.TFRecordWriter(output_file) as record_writer:
+    for input_file in input_files:
+      data_dict = read_pickle_from_file(input_file)
+      data = data_dict['data']
+      labels = data_dict['labels']
+
+      num_entries_in_batch = len(labels)
+      for i in range(num_entries_in_batch):
+        example = tf.train.Example(
+            features=tf.train.Features(feature={
+                'image': _bytes_feature(data[i].tobytes()),
+                'label': _int64_feature(labels[i])
+            }))
+        record_writer.write(example.SerializeToString())
 
 
 def main(unused_argv):