Merge branch 'master' into move_to_keraslayers_fasterrcnn_fpn_keras_feature_extractor

research/attention_ocr/python/datasets/fsns_test.py

@@ -91,7 +91,7 @@ class FsnsTest(tf.test.TestCase):
     image_tf, label_tf = provider.get(['image', 'label'])
 
     with self.test_session() as sess:
-      sess.run(tf.global_variables_initializer())
+      sess.run(tf.compat.v1.global_variables_initializer())
       with slim.queues.QueueRunners(sess):
         image_np, label_np = sess.run([image_tf, label_tf])
 

research/attention_ocr/python/datasets/testdata/fsns/download_data.py

@@ -10,7 +10,8 @@ KEEP_NUM_RECORDS = 5
 print('Downloading %s ...' % URL)
 urllib.request.urlretrieve(URL, DST_ORIG)
 
-print('Writing %d records from %s to %s ...' % (KEEP_NUM_RECORDS, DST_ORIG, DST))
+print('Writing %d records from %s to %s ...' %
+      (KEEP_NUM_RECORDS, DST_ORIG, DST))
 with tf.io.TFRecordWriter(DST) as writer:
-    for raw_record in itertools.islice(tf.python_io.tf_record_iterator(DST_ORIG), KEEP_NUM_RECORDS):
+    for raw_record in itertools.islice(tf.compat.v1.python_io.tf_record_iterator(DST_ORIG), KEEP_NUM_RECORDS):
         writer.write(raw_record)

research/attention_ocr/python/demo_inference.py

@@ -49,7 +49,7 @@ def load_images(file_pattern, batch_size, dataset_name):
   for i in range(batch_size):
     path = file_pattern % i
     print("Reading %s" % path)
-    pil_image = PIL.Image.open(tf.gfile.GFile(path, 'rb'))
+    pil_image = PIL.Image.open(tf.io.gfile.GFile(path, 'rb'))
     images_actual_data[i, ...] = np.asarray(pil_image)
   return images_actual_data
 
@@ -58,12 +58,13 @@ def create_model(batch_size, dataset_name):
   width, height = get_dataset_image_size(dataset_name)
   dataset = common_flags.create_dataset(split_name=FLAGS.split_name)
   model = common_flags.create_model(
-    num_char_classes=dataset.num_char_classes,
-    seq_length=dataset.max_sequence_length,
-    num_views=dataset.num_of_views,
-    null_code=dataset.null_code,
-    charset=dataset.charset)
-  raw_images = tf.placeholder(tf.uint8, shape=[batch_size, height, width, 3])
+      num_char_classes=dataset.num_char_classes,
+      seq_length=dataset.max_sequence_length,
+      num_views=dataset.num_of_views,
+      null_code=dataset.null_code,
+      charset=dataset.charset)
+  raw_images = tf.compat.v1.placeholder(
+      tf.uint8, shape=[batch_size, height, width, 3])
   images = tf.map_fn(data_provider.preprocess_image, raw_images,
                      dtype=tf.float32)
   endpoints = model.create_base(images, labels_one_hot=None)
@@ -76,9 +77,9 @@ def run(checkpoint, batch_size, dataset_name, image_path_pattern):
   images_data = load_images(image_path_pattern, batch_size,
                             dataset_name)
   session_creator = monitored_session.ChiefSessionCreator(
-    checkpoint_filename_with_path=checkpoint)
+      checkpoint_filename_with_path=checkpoint)
   with monitored_session.MonitoredSession(
-      session_creator=session_creator) as sess:
+          session_creator=session_creator) as sess:
     predictions = sess.run(endpoints.predicted_text,
                            feed_dict={images_placeholder: images_data})
   return [pr_bytes.decode('utf-8') for pr_bytes in predictions.tolist()]
@@ -87,10 +88,10 @@ def run(checkpoint, batch_size, dataset_name, image_path_pattern):
 def main(_):
   print("Predicted strings:")
   predictions = run(FLAGS.checkpoint, FLAGS.batch_size, FLAGS.dataset_name,
-                  FLAGS.image_path_pattern)
+                    FLAGS.image_path_pattern)
   for line in predictions:
     print(line)
 
 
 if __name__ == '__main__':
-  tf.app.run()
+  tf.compat.v1.app.run()

research/attention_ocr/python/demo_inference_test.py

@@ -14,12 +14,13 @@ class DemoInferenceTest(tf.test.TestCase):
     super(DemoInferenceTest, self).setUp()
     for suffix in ['.meta', '.index', '.data-00000-of-00001']:
       filename = _CHECKPOINT + suffix
-      self.assertTrue(tf.gfile.Exists(filename),
+      self.assertTrue(tf.io.gfile.exists(filename),
                       msg='Missing checkpoint file %s. '
                           'Please download and extract it from %s' %
                           (filename, _CHECKPOINT_URL))
     self._batch_size = 32
-    tf.flags.FLAGS.dataset_dir = os.path.join(os.path.dirname(__file__), 'datasets/testdata/fsns')
+    tf.flags.FLAGS.dataset_dir = os.path.join(
+        os.path.dirname(__file__), 'datasets/testdata/fsns')
 
   def test_moving_variables_properly_loaded_from_a_checkpoint(self):
     batch_size = 32
@@ -30,15 +31,15 @@ class DemoInferenceTest(tf.test.TestCase):
     images_data = demo_inference.load_images(image_path_pattern, batch_size,
                                              dataset_name)
     tensor_name = 'AttentionOcr_v1/conv_tower_fn/INCE/InceptionV3/Conv2d_2a_3x3/BatchNorm/moving_mean'
-    moving_mean_tf = tf.get_default_graph().get_tensor_by_name(
-      tensor_name + ':0')
-    reader = tf.train.NewCheckpointReader(_CHECKPOINT)
+    moving_mean_tf = tf.compat.v1.get_default_graph().get_tensor_by_name(
+        tensor_name + ':0')
+    reader = tf.compat.v1.train.NewCheckpointReader(_CHECKPOINT)
     moving_mean_expected = reader.get_tensor(tensor_name)
 
     session_creator = monitored_session.ChiefSessionCreator(
-      checkpoint_filename_with_path=_CHECKPOINT)
+        checkpoint_filename_with_path=_CHECKPOINT)
     with monitored_session.MonitoredSession(
-        session_creator=session_creator) as sess:
+            session_creator=session_creator) as sess:
       moving_mean_np = sess.run(moving_mean_tf,
                                 feed_dict={images_placeholder: images_data})
 
@@ -50,38 +51,38 @@ class DemoInferenceTest(tf.test.TestCase):
                                      'fsns',
                                      image_path_pattern)
     self.assertEqual([
-      u'Boulevard de Lunel░░░░░░░░░░░░░░░░░░░',
-      'Rue de Provence░░░░░░░░░░░░░░░░░░░░░░',
-      'Rue de Port Maria░░░░░░░░░░░░░░░░░░░░',
-      'Avenue Charles Gounod░░░░░░░░░░░░░░░░',
-      'Rue de l‘Aurore░░░░░░░░░░░░░░░░░░░░░░',
-      'Rue de Beuzeville░░░░░░░░░░░░░░░░░░░░',
-      'Rue d‘Orbey░░░░░░░░░░░░░░░░░░░░░░░░░░',
-      'Rue Victor Schoulcher░░░░░░░░░░░░░░░░',
-      'Rue de la Gare░░░░░░░░░░░░░░░░░░░░░░░',
-      'Rue des Tulipes░░░░░░░░░░░░░░░░░░░░░░',
-      'Rue André Maginot░░░░░░░░░░░░░░░░░░░░',
-      'Route de Pringy░░░░░░░░░░░░░░░░░░░░░░',
-      'Rue des Landelles░░░░░░░░░░░░░░░░░░░░',
-      'Rue des Ilettes░░░░░░░░░░░░░░░░░░░░░░',
-      'Avenue de Maurin░░░░░░░░░░░░░░░░░░░░░',
-      'Rue Théresa░░░░░░░░░░░░░░░░░░░░░░░░░░',  # GT='Rue Thérésa'
-      'Route de la Balme░░░░░░░░░░░░░░░░░░░░',
-      'Rue Hélène Roederer░░░░░░░░░░░░░░░░░░',
-      'Rue Emile Bernard░░░░░░░░░░░░░░░░░░░░',
-      'Place de la Mairie░░░░░░░░░░░░░░░░░░░',
-      'Rue des Perrots░░░░░░░░░░░░░░░░░░░░░░',
-      'Rue de la Libération░░░░░░░░░░░░░░░░░',
-      'Impasse du Capcir░░░░░░░░░░░░░░░░░░░░',
-      'Avenue de la Grand Mare░░░░░░░░░░░░░░',
-      'Rue Pierre Brossolette░░░░░░░░░░░░░░░',
-      'Rue de Provence░░░░░░░░░░░░░░░░░░░░░░',
-      'Rue du Docteur Mourre░░░░░░░░░░░░░░░░',
-      'Rue d‘Ortheuil░░░░░░░░░░░░░░░░░░░░░░░',
-      'Rue des Sarments░░░░░░░░░░░░░░░░░░░░░',
-      'Rue du Centre░░░░░░░░░░░░░░░░░░░░░░░░',
-      'Impasse Pierre Mourgues░░░░░░░░░░░░░░',
-      'Rue Marcel Dassault░░░░░░░░░░░░░░░░░░'
+        u'Boulevard de Lunel░░░░░░░░░░░░░░░░░░░',
+        'Rue de Provence░░░░░░░░░░░░░░░░░░░░░░',
+        'Rue de Port Maria░░░░░░░░░░░░░░░░░░░░',
+        'Avenue Charles Gounod░░░░░░░░░░░░░░░░',
+        'Rue de l‘Aurore░░░░░░░░░░░░░░░░░░░░░░',
+        'Rue de Beuzeville░░░░░░░░░░░░░░░░░░░░',
+        'Rue d‘Orbey░░░░░░░░░░░░░░░░░░░░░░░░░░',
+        'Rue Victor Schoulcher░░░░░░░░░░░░░░░░',
+        'Rue de la Gare░░░░░░░░░░░░░░░░░░░░░░░',
+        'Rue des Tulipes░░░░░░░░░░░░░░░░░░░░░░',
+        'Rue André Maginot░░░░░░░░░░░░░░░░░░░░',
+        'Route de Pringy░░░░░░░░░░░░░░░░░░░░░░',
+        'Rue des Landelles░░░░░░░░░░░░░░░░░░░░',
+        'Rue des Ilettes░░░░░░░░░░░░░░░░░░░░░░',
+        'Avenue de Maurin░░░░░░░░░░░░░░░░░░░░░',
+        'Rue Théresa░░░░░░░░░░░░░░░░░░░░░░░░░░',  # GT='Rue Thérésa'
+        'Route de la Balme░░░░░░░░░░░░░░░░░░░░',
+        'Rue Hélène Roederer░░░░░░░░░░░░░░░░░░',
+        'Rue Emile Bernard░░░░░░░░░░░░░░░░░░░░',
+        'Place de la Mairie░░░░░░░░░░░░░░░░░░░',
+        'Rue des Perrots░░░░░░░░░░░░░░░░░░░░░░',
+        'Rue de la Libération░░░░░░░░░░░░░░░░░',
+        'Impasse du Capcir░░░░░░░░░░░░░░░░░░░░',
+        'Avenue de la Grand Mare░░░░░░░░░░░░░░',
+        'Rue Pierre Brossolette░░░░░░░░░░░░░░░',
+        'Rue de Provence░░░░░░░░░░░░░░░░░░░░░░',
+        'Rue du Docteur Mourre░░░░░░░░░░░░░░░░',
+        'Rue d‘Ortheuil░░░░░░░░░░░░░░░░░░░░░░░',
+        'Rue des Sarments░░░░░░░░░░░░░░░░░░░░░',
+        'Rue du Centre░░░░░░░░░░░░░░░░░░░░░░░░',
+        'Impasse Pierre Mourgues░░░░░░░░░░░░░░',
+        'Rue Marcel Dassault░░░░░░░░░░░░░░░░░░'
     ], predictions)
 
 

research/attention_ocr/python/eval.py

@@ -45,8 +45,8 @@ flags.DEFINE_integer('number_of_steps', None,
 
 
 def main(_):
-  if not tf.gfile.Exists(FLAGS.eval_log_dir):
-    tf.gfile.MakeDirs(FLAGS.eval_log_dir)
+  if not tf.io.gfile.exists(FLAGS.eval_log_dir):
+    tf.io.gfile.makedirs(FLAGS.eval_log_dir)
 
   dataset = common_flags.create_dataset(split_name=FLAGS.split_name)
   model = common_flags.create_model(dataset.num_char_classes,
@@ -62,7 +62,7 @@ def main(_):
   eval_ops = model.create_summaries(
       data, endpoints, dataset.charset, is_training=False)
   slim.get_or_create_global_step()
-  session_config = tf.ConfigProto(device_count={"GPU": 0})
+  session_config = tf.compat.v1.ConfigProto(device_count={"GPU": 0})
   slim.evaluation.evaluation_loop(
       master=FLAGS.master,
       checkpoint_dir=FLAGS.train_log_dir,

research/attention_ocr/python/inception_preprocessing.py

@@ -38,7 +38,7 @@ def apply_with_random_selector(x, func, num_cases):
     The result of func(x, sel), where func receives the value of the
     selector as a python integer, but sel is sampled dynamically.
   """
-  sel = tf.random_uniform([], maxval=num_cases, dtype=tf.int32)
+  sel = tf.random.uniform([], maxval=num_cases, dtype=tf.int32)
   # Pass the real x only to one of the func calls.
   return control_flow_ops.merge([
       func(control_flow_ops.switch(x, tf.equal(sel, case))[1], case)
@@ -64,7 +64,7 @@ def distort_color(image, color_ordering=0, fast_mode=True, scope=None):
   Raises:
     ValueError: if color_ordering not in [0, 3]
   """
-  with tf.name_scope(scope, 'distort_color', [image]):
+  with tf.compat.v1.name_scope(scope, 'distort_color', [image]):
     if fast_mode:
       if color_ordering == 0:
         image = tf.image.random_brightness(image, max_delta=32. / 255.)
@@ -131,7 +131,7 @@ def distorted_bounding_box_crop(image,
   Returns:
     A tuple, a 3-D Tensor cropped_image and the distorted bbox
   """
-  with tf.name_scope(scope, 'distorted_bounding_box_crop', [image, bbox]):
+  with tf.compat.v1.name_scope(scope, 'distorted_bounding_box_crop', [image, bbox]):
     # Each bounding box has shape [1, num_boxes, box coords] and
     # the coordinates are ordered [ymin, xmin, ymax, xmax].
 
@@ -143,7 +143,7 @@ def distorted_bounding_box_crop(image,
     # bounding box. If no box is supplied, then we assume the bounding box is
     # the entire image.
     sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box(
-        tf.shape(image),
+        image_size=tf.shape(input=image),
         bounding_boxes=bbox,
         min_object_covered=min_object_covered,
         aspect_ratio_range=aspect_ratio_range,
@@ -188,7 +188,7 @@ def preprocess_for_train(image,
   Returns:
     3-D float Tensor of distorted image used for training with range [-1, 1].
   """
-  with tf.name_scope(scope, 'distort_image', [image, height, width, bbox]):
+  with tf.compat.v1.name_scope(scope, 'distort_image', [image, height, width, bbox]):
     if bbox is None:
       bbox = tf.constant(
           [0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
@@ -198,7 +198,7 @@ def preprocess_for_train(image,
     # the coordinates are ordered [ymin, xmin, ymax, xmax].
     image_with_box = tf.image.draw_bounding_boxes(
         tf.expand_dims(image, 0), bbox)
-    tf.summary.image('image_with_bounding_boxes', image_with_box)
+    tf.compat.v1.summary.image('image_with_bounding_boxes', image_with_box)
 
     distorted_image, distorted_bbox = distorted_bounding_box_crop(image, bbox)
     # Restore the shape since the dynamic slice based upon the bbox_size loses
@@ -206,8 +206,8 @@ def preprocess_for_train(image,
     distorted_image.set_shape([None, None, 3])
     image_with_distorted_box = tf.image.draw_bounding_boxes(
         tf.expand_dims(image, 0), distorted_bbox)
-    tf.summary.image('images_with_distorted_bounding_box',
-                     image_with_distorted_box)
+    tf.compat.v1.summary.image('images_with_distorted_bounding_box',
+                               image_with_distorted_box)
 
     # This resizing operation may distort the images because the aspect
     # ratio is not respected. We select a resize method in a round robin
@@ -218,11 +218,11 @@ def preprocess_for_train(image,
     num_resize_cases = 1 if fast_mode else 4
     distorted_image = apply_with_random_selector(
         distorted_image,
-        lambda x, method: tf.image.resize_images(x, [height, width], method=method),
+        lambda x, method: tf.image.resize(x, [height, width], method=method),
         num_cases=num_resize_cases)
 
-    tf.summary.image('cropped_resized_image',
-                     tf.expand_dims(distorted_image, 0))
+    tf.compat.v1.summary.image('cropped_resized_image',
+                               tf.expand_dims(distorted_image, 0))
 
     # Randomly flip the image horizontally.
     distorted_image = tf.image.random_flip_left_right(distorted_image)
@@ -233,8 +233,8 @@ def preprocess_for_train(image,
         lambda x, ordering: distort_color(x, ordering, fast_mode),
         num_cases=4)
 
-    tf.summary.image('final_distorted_image',
-                     tf.expand_dims(distorted_image, 0))
+    tf.compat.v1.summary.image('final_distorted_image',
+                               tf.expand_dims(distorted_image, 0))
     distorted_image = tf.subtract(distorted_image, 0.5)
     distorted_image = tf.multiply(distorted_image, 2.0)
     return distorted_image
@@ -265,7 +265,7 @@ def preprocess_for_eval(image,
   Returns:
     3-D float Tensor of prepared image.
   """
-  with tf.name_scope(scope, 'eval_image', [image, height, width]):
+  with tf.compat.v1.name_scope(scope, 'eval_image', [image, height, width]):
     if image.dtype != tf.float32:
       image = tf.image.convert_image_dtype(image, dtype=tf.float32)
     # Crop the central region of the image with an area containing 87.5% of
@@ -276,8 +276,8 @@ def preprocess_for_eval(image,
     if height and width:
       # Resize the image to the specified height and width.
       image = tf.expand_dims(image, 0)
-      image = tf.image.resize_bilinear(
-          image, [height, width], align_corners=False)
+      image = tf.image.resize(
+          image, [height, width], method=tf.image.ResizeMethod.BILINEAR)
       image = tf.squeeze(image, [0])
     image = tf.subtract(image, 0.5)
     image = tf.multiply(image, 2.0)

research/attention_ocr/python/metrics.py

@@ -34,20 +34,21 @@ def char_accuracy(predictions, targets, rej_char, streaming=False):
     a update_ops for execution and value tensor whose value on evaluation
     returns the total character accuracy.
   """
-  with tf.variable_scope('CharAccuracy'):
+  with tf.compat.v1.variable_scope('CharAccuracy'):
     predictions.get_shape().assert_is_compatible_with(targets.get_shape())
 
-    targets = tf.to_int32(targets)
+    targets = tf.cast(targets, dtype=tf.int32)
     const_rej_char = tf.constant(rej_char, shape=targets.get_shape())
-    weights = tf.to_float(tf.not_equal(targets, const_rej_char))
-    correct_chars = tf.to_float(tf.equal(predictions, targets))
-    accuracy_per_example = tf.div(
-        tf.reduce_sum(tf.multiply(correct_chars, weights), 1),
-        tf.reduce_sum(weights, 1))
+    weights = tf.cast(tf.not_equal(targets, const_rej_char), dtype=tf.float32)
+    correct_chars = tf.cast(tf.equal(predictions, targets), dtype=tf.float32)
+    accuracy_per_example = tf.compat.v1.div(
+        tf.reduce_sum(input_tensor=tf.multiply(
+            correct_chars, weights), axis=1),
+        tf.reduce_sum(input_tensor=weights, axis=1))
     if streaming:
       return tf.contrib.metrics.streaming_mean(accuracy_per_example)
     else:
-      return tf.reduce_mean(accuracy_per_example)
+      return tf.reduce_mean(input_tensor=accuracy_per_example)
 
 
 def sequence_accuracy(predictions, targets, rej_char, streaming=False):
@@ -66,25 +67,26 @@ def sequence_accuracy(predictions, targets, rej_char, streaming=False):
     returns the total sequence accuracy.
   """
 
-  with tf.variable_scope('SequenceAccuracy'):
+  with tf.compat.v1.variable_scope('SequenceAccuracy'):
     predictions.get_shape().assert_is_compatible_with(targets.get_shape())
 
-    targets = tf.to_int32(targets)
+    targets = tf.cast(targets, dtype=tf.int32)
     const_rej_char = tf.constant(
         rej_char, shape=targets.get_shape(), dtype=tf.int32)
     include_mask = tf.not_equal(targets, const_rej_char)
-    include_predictions = tf.to_int32(
-        tf.where(include_mask, predictions,
-                 tf.zeros_like(predictions) + rej_char))
-    correct_chars = tf.to_float(tf.equal(include_predictions, targets))
+    include_predictions = tf.cast(
+        tf.compat.v1.where(include_mask, predictions,
+                           tf.zeros_like(predictions) + rej_char), dtype=tf.int32)
+    correct_chars = tf.cast(
+        tf.equal(include_predictions, targets), dtype=tf.float32)
     correct_chars_counts = tf.cast(
-        tf.reduce_sum(correct_chars, reduction_indices=[1]), dtype=tf.int32)
+        tf.reduce_sum(input_tensor=correct_chars, axis=[1]), dtype=tf.int32)
     target_length = targets.get_shape().dims[1].value
     target_chars_counts = tf.constant(
         target_length, shape=correct_chars_counts.get_shape())
-    accuracy_per_example = tf.to_float(
-        tf.equal(correct_chars_counts, target_chars_counts))
+    accuracy_per_example = tf.cast(
+        tf.equal(correct_chars_counts, target_chars_counts), dtype=tf.float32)
     if streaming:
       return tf.contrib.metrics.streaming_mean(accuracy_per_example)
     else:
-      return tf.reduce_mean(accuracy_per_example)
+      return tf.reduce_mean(input_tensor=accuracy_per_example)

research/attention_ocr/python/metrics_test.py

@@ -38,8 +38,8 @@ class AccuracyTest(tf.test.TestCase):
       A session object that should be used as a context manager.
     """
     with self.cached_session() as sess:
-      sess.run(tf.global_variables_initializer())
-      sess.run(tf.local_variables_initializer())
+      sess.run(tf.compat.v1.global_variables_initializer())
+      sess.run(tf.compat.v1.local_variables_initializer())
       yield sess
 
   def _fake_labels(self):
@@ -55,7 +55,7 @@ class AccuracyTest(tf.test.TestCase):
     return incorrect
 
   def test_sequence_accuracy_identical_samples(self):
-    labels_tf = tf.convert_to_tensor(self._fake_labels())
+    labels_tf = tf.convert_to_tensor(value=self._fake_labels())
 
     accuracy_tf = metrics.sequence_accuracy(labels_tf, labels_tf,
                                             self.rej_char)
@@ -66,9 +66,9 @@ class AccuracyTest(tf.test.TestCase):
 
   def test_sequence_accuracy_one_char_difference(self):
     ground_truth_np = self._fake_labels()
-    ground_truth_tf = tf.convert_to_tensor(ground_truth_np)
+    ground_truth_tf = tf.convert_to_tensor(value=ground_truth_np)
     prediction_tf = tf.convert_to_tensor(
-        self._incorrect_copy(ground_truth_np, bad_indexes=((0, 0))))
+        value=self._incorrect_copy(ground_truth_np, bad_indexes=((0, 0))))
 
     accuracy_tf = metrics.sequence_accuracy(prediction_tf, ground_truth_tf,
                                             self.rej_char)
@@ -80,9 +80,9 @@ class AccuracyTest(tf.test.TestCase):
 
   def test_char_accuracy_one_char_difference_with_padding(self):
     ground_truth_np = self._fake_labels()
-    ground_truth_tf = tf.convert_to_tensor(ground_truth_np)
+    ground_truth_tf = tf.convert_to_tensor(value=ground_truth_np)
     prediction_tf = tf.convert_to_tensor(
-        self._incorrect_copy(ground_truth_np, bad_indexes=((0, 0))))
+        value=self._incorrect_copy(ground_truth_np, bad_indexes=((0, 0))))
 
     accuracy_tf = metrics.char_accuracy(prediction_tf, ground_truth_tf,
                                         self.rej_char)

research/attention_ocr/python/model.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Functions to build the Attention OCR model.
 
 Usage example:
@@ -26,6 +25,7 @@ Usage example:
 import sys
 import collections
 import logging
+import numpy as np
 import tensorflow as tf
 from tensorflow.contrib import slim
 from tensorflow.contrib.slim.nets import inception
@@ -35,29 +35,28 @@ import sequence_layers
 import utils
 
 OutputEndpoints = collections.namedtuple('OutputEndpoints', [
-  'chars_logit', 'chars_log_prob', 'predicted_chars', 'predicted_scores',
-  'predicted_text'
+    'chars_logit', 'chars_log_prob', 'predicted_chars', 'predicted_scores',
+    'predicted_text', 'predicted_length', 'predicted_conf',
+    'normalized_seq_conf'
 ])
 
 # TODO(gorban): replace with tf.HParams when it is released.
-ModelParams = collections.namedtuple('ModelParams', [
-  'num_char_classes', 'seq_length', 'num_views', 'null_code'
-])
+ModelParams = collections.namedtuple(
+    'ModelParams', ['num_char_classes', 'seq_length', 'num_views', 'null_code'])
 
 ConvTowerParams = collections.namedtuple('ConvTowerParams', ['final_endpoint'])
 
 SequenceLogitsParams = collections.namedtuple('SequenceLogitsParams', [
-  'use_attention', 'use_autoregression', 'num_lstm_units', 'weight_decay',
-  'lstm_state_clip_value'
+    'use_attention', 'use_autoregression', 'num_lstm_units', 'weight_decay',
+    'lstm_state_clip_value'
 ])
 
-SequenceLossParams = collections.namedtuple('SequenceLossParams', [
-  'label_smoothing', 'ignore_nulls', 'average_across_timesteps'
-])
+SequenceLossParams = collections.namedtuple(
+    'SequenceLossParams',
+    ['label_smoothing', 'ignore_nulls', 'average_across_timesteps'])
 
-EncodeCoordinatesParams = collections.namedtuple('EncodeCoordinatesParams', [
-  'enabled'
-])
+EncodeCoordinatesParams = collections.namedtuple('EncodeCoordinatesParams',
+                                                 ['enabled'])
 
 
 def _dict_to_array(id_to_char, default_character):
@@ -85,16 +84,16 @@ class CharsetMapper(object):
     """
     mapping_strings = tf.constant(_dict_to_array(charset, default_character))
     self.table = tf.contrib.lookup.index_to_string_table_from_tensor(
-      mapping=mapping_strings, default_value=default_character)
+        mapping=mapping_strings, default_value=default_character)
 
   def get_text(self, ids):
     """Returns a string corresponding to a sequence of character ids.
 
         Args:
           ids: a tensor with shape [batch_size, max_sequence_length]
-        """
-    return tf.reduce_join(
-      self.table.lookup(tf.to_int64(ids)), reduction_indices=1)
+    """
+    return tf.strings.reduce_join(
+        inputs=self.table.lookup(tf.cast(ids, dtype=tf.int64)), axis=1)
 
 
 def get_softmax_loss_fn(label_smoothing):
@@ -111,16 +110,153 @@ def get_softmax_loss_fn(label_smoothing):
 
     def loss_fn(labels, logits):
       return (tf.nn.softmax_cross_entropy_with_logits(
-        logits=logits, labels=labels))
+          logits=logits, labels=tf.stop_gradient(labels)))
   else:
 
     def loss_fn(labels, logits):
       return tf.nn.sparse_softmax_cross_entropy_with_logits(
-        logits=logits, labels=labels)
+          logits=logits, labels=labels)
 
   return loss_fn
 
 
+def get_tensor_dimensions(tensor):
+  """Returns the shape components of a 4D tensor with variable batch size.
+
+  Args:
+    tensor : A 4D tensor, whose last 3 dimensions are known at graph
+      construction time.
+
+  Returns:
+    batch_size : The first dimension as a tensor object.
+    height : The second dimension as a scalar value.
+    width : The third dimension as a scalar value.
+    num_features : The forth dimension as a scalar value.
+
+  Raises:
+    ValueError: if input tensor does not have 4 dimensions.
+  """
+  if len(tensor.get_shape().dims) != 4:
+    raise ValueError(
+        'Incompatible shape: len(tensor.get_shape().dims) != 4 (%d != 4)' %
+        len(tensor.get_shape().dims))
+  batch_size = tf.shape(input=tensor)[0]
+  height = tensor.get_shape().dims[1].value
+  width = tensor.get_shape().dims[2].value
+  num_features = tensor.get_shape().dims[3].value
+  return batch_size, height, width, num_features
+
+
+def lookup_indexed_value(indices, row_vecs):
+  """Lookup values in each row of 'row_vecs' indexed by 'indices'.
+
+  For each sample in the batch, look up the element for the corresponding
+  index.
+
+  Args:
+    indices : A tensor of shape (batch, )
+    row_vecs : A tensor of shape [batch, depth]
+
+  Returns:
+    A tensor of shape (batch, ) formed by row_vecs[i, indices[i]].
+  """
+  gather_indices = tf.stack((tf.range(
+      tf.shape(input=row_vecs)[0], dtype=tf.int32), tf.cast(indices, tf.int32)),
+      axis=1)
+  return tf.gather_nd(row_vecs, gather_indices)
+
+
+@utils.ConvertAllInputsToTensors
+def max_char_logprob_cumsum(char_log_prob):
+  """Computes the cumulative sum of character logprob for all sequence lengths.
+
+  Args:
+    char_log_prob: A tensor of shape [batch x seq_length x num_char_classes]
+      with log probabilities of a character.
+
+  Returns:
+    A tensor of shape [batch x (seq_length+1)] where each element x[_, j] is
+    the sum of the max char logprob for all positions upto j.
+    Note this duplicates the final column and produces (seq_length+1) columns
+    so the same function can be used regardless whether use_length_predictions
+    is true or false.
+  """
+  max_char_log_prob = tf.reduce_max(input_tensor=char_log_prob, axis=2)
+  # For an input array [a, b, c]) tf.cumsum returns [a, a + b, a + b + c] if
+  # exclusive set to False (default).
+  return tf.cumsum(max_char_log_prob, axis=1, exclusive=False)
+
+
+def find_length_by_null(predicted_chars, null_code):
+  """Determine sequence length by finding null_code among predicted char IDs.
+
+  Given the char class ID for each position, compute the sequence length.
+  Note that this function computes this based on the number of null_code,
+  instead of the position of the first null_code.
+
+  Args:
+    predicted_chars: A tensor of [batch x seq_length] where each element stores
+      the char class ID with max probability;
+    null_code: an int32, character id for the NULL.
+
+  Returns:
+    A [batch, ] tensor which stores the sequence length for each sample.
+  """
+  return tf.reduce_sum(
+      input_tensor=tf.cast(tf.not_equal(null_code, predicted_chars), tf.int32), axis=1)
+
+
+def axis_pad(tensor, axis, before=0, after=0, constant_values=0.0):
+  """Pad a tensor with the specified values along a single axis.
+
+  Args:
+    tensor: a Tensor;
+    axis: the dimension to add pad along to;
+    before: number of values to add before the contents of tensor in the
+      selected dimension;
+    after: number of values to add after the contents of tensor in the selected
+      dimension;
+    constant_values: the scalar pad value to use. Must be same type as tensor.
+
+  Returns:
+    A Tensor. Has the same type as the input tensor, but with a changed shape
+    along the specified dimension.
+  """
+  if before == 0 and after == 0:
+    return tensor
+  ndims = tensor.shape.ndims
+  padding_size = np.zeros((ndims, 2), dtype='int32')
+  padding_size[axis] = before, after
+  return tf.pad(
+      tensor=tensor,
+      paddings=tf.constant(padding_size),
+      constant_values=constant_values)
+
+
+def null_based_length_prediction(chars_log_prob, null_code):
+  """Computes length and confidence of prediction based on positions of NULLs.
+
+  Args:
+    chars_log_prob: A tensor of shape [batch x seq_length x num_char_classes]
+      with log probabilities of a character;
+    null_code: an int32, character id for the NULL.
+
+  Returns:
+    A tuple (text_log_prob, predicted_length), where
+    text_log_prob - is a tensor of the same shape as length_log_prob.
+    Element #0 of the output corresponds to probability of the empty string,
+    element #seq_length - is the probability of length=seq_length.
+    predicted_length is a tensor with shape [batch].
+  """
+  predicted_chars = tf.cast(
+      tf.argmax(input=chars_log_prob, axis=2), dtype=tf.int32)
+  # We do right pad to support sequences with seq_length elements.
+  text_log_prob = max_char_logprob_cumsum(
+      axis_pad(chars_log_prob, axis=1, after=1))
+  predicted_length = find_length_by_null(predicted_chars, null_code)
+  return text_log_prob, predicted_length
+
+
 class Model(object):
   """Class to create the Attention OCR Model."""
 
@@ -137,24 +273,24 @@ class Model(object):
       num_char_classes: size of character set.
       seq_length: number of characters in a sequence.
       num_views: Number of views (conv towers) to use.
-      null_code: A character code corresponding to a character which
-        indicates end of a sequence.
-      mparams: a dictionary with hyper parameters for methods,  keys -
-        function names, values - corresponding namedtuples.
+      null_code: A character code corresponding to a character which indicates
+        end of a sequence.
+      mparams: a dictionary with hyper parameters for methods,  keys - function
+        names, values - corresponding namedtuples.
       charset: an optional dictionary with a mapping between character ids and
-        utf8 strings. If specified the OutputEndpoints.predicted_text will
-        utf8 encoded strings corresponding to the character ids returned by
+        utf8 strings. If specified the OutputEndpoints.predicted_text will utf8
+        encoded strings corresponding to the character ids returned by
         OutputEndpoints.predicted_chars (by default the predicted_text contains
-        an empty vector). 
+        an empty vector).
         NOTE: Make sure you call tf.tables_initializer().run() if the charset
-        specified.
+          specified.
     """
     super(Model, self).__init__()
     self._params = ModelParams(
-      num_char_classes=num_char_classes,
-      seq_length=seq_length,
-      num_views=num_views,
-      null_code=null_code)
+        num_char_classes=num_char_classes,
+        seq_length=seq_length,
+        num_views=num_views,
+        null_code=null_code)
     self._mparams = self.default_mparams()
     if mparams:
       self._mparams.update(mparams)
@@ -162,21 +298,22 @@ class Model(object):
 
   def default_mparams(self):
     return {
-      'conv_tower_fn':
-        ConvTowerParams(final_endpoint='Mixed_5d'),
-      'sequence_logit_fn':
-        SequenceLogitsParams(
-          use_attention=True,
-          use_autoregression=True,
-          num_lstm_units=256,
-          weight_decay=0.00004,
-          lstm_state_clip_value=10.0),
-      'sequence_loss_fn':
-        SequenceLossParams(
-          label_smoothing=0.1,
-          ignore_nulls=True,
-          average_across_timesteps=False),
-      'encode_coordinates_fn': EncodeCoordinatesParams(enabled=False)
+        'conv_tower_fn':
+            ConvTowerParams(final_endpoint='Mixed_5d'),
+        'sequence_logit_fn':
+            SequenceLogitsParams(
+                use_attention=True,
+                use_autoregression=True,
+                num_lstm_units=256,
+                weight_decay=0.00004,
+                lstm_state_clip_value=10.0),
+        'sequence_loss_fn':
+            SequenceLossParams(
+                label_smoothing=0.1,
+                ignore_nulls=True,
+                average_across_timesteps=False),
+        'encode_coordinates_fn':
+            EncodeCoordinatesParams(enabled=False)
     }
 
   def set_mparam(self, function, **kwargs):
@@ -198,14 +335,14 @@ class Model(object):
     """
     mparams = self._mparams['conv_tower_fn']
     logging.debug('Using final_endpoint=%s', mparams.final_endpoint)
-    with tf.variable_scope('conv_tower_fn/INCE'):
+    with tf.compat.v1.variable_scope('conv_tower_fn/INCE'):
       if reuse:
-        tf.get_variable_scope().reuse_variables()
+        tf.compat.v1.get_variable_scope().reuse_variables()
       with slim.arg_scope(inception.inception_v3_arg_scope()):
         with slim.arg_scope([slim.batch_norm, slim.dropout],
                             is_training=is_training):
           net, _ = inception.inception_v3_base(
-            images, final_endpoint=mparams.final_endpoint)
+              images, final_endpoint=mparams.final_endpoint)
       return net
 
   def _create_lstm_inputs(self, net):
@@ -222,10 +359,10 @@ class Model(object):
     """
     num_features = net.get_shape().dims[1].value
     if num_features < self._params.seq_length:
-      raise AssertionError('Incorrect dimension #1 of input tensor'
-                           ' %d should be bigger than %d (shape=%s)' %
-                           (num_features, self._params.seq_length,
-                            net.get_shape()))
+      raise AssertionError(
+          'Incorrect dimension #1 of input tensor'
+          ' %d should be bigger than %d (shape=%s)' %
+          (num_features, self._params.seq_length, net.get_shape()))
     elif num_features > self._params.seq_length:
       logging.warning('Ignoring some features: use %d of %d (shape=%s)',
                       self._params.seq_length, num_features, net.get_shape())
@@ -236,7 +373,7 @@ class Model(object):
   def sequence_logit_fn(self, net, labels_one_hot):
     mparams = self._mparams['sequence_logit_fn']
     # TODO(gorban): remove /alias suffixes from the scopes.
-    with tf.variable_scope('sequence_logit_fn/SQLR'):
+    with tf.compat.v1.variable_scope('sequence_logit_fn/SQLR'):
       layer_class = sequence_layers.get_layer_class(mparams.use_attention,
                                                     mparams.use_autoregression)
       layer = layer_class(net, labels_one_hot, self._params, mparams)
@@ -252,16 +389,16 @@ class Model(object):
       A tensor with the same size as any input tensors.
     """
     batch_size, height, width, num_features = [
-      d.value for d in nets_list[0].get_shape().dims
+        d.value for d in nets_list[0].get_shape().dims
     ]
     xy_flat_shape = (batch_size, 1, height * width, num_features)
     nets_for_merge = []
-    with tf.variable_scope('max_pool_views', values=nets_list):
+    with tf.compat.v1.variable_scope('max_pool_views', values=nets_list):
       for net in nets_list:
         nets_for_merge.append(tf.reshape(net, xy_flat_shape))
       merged_net = tf.concat(nets_for_merge, 1)
       net = slim.max_pool2d(
-        merged_net, kernel_size=[len(nets_list), 1], stride=1)
+          merged_net, kernel_size=[len(nets_list), 1], stride=1)
       net = tf.reshape(net, (batch_size, height, width, num_features))
     return net
 
@@ -277,18 +414,20 @@ class Model(object):
     Returns:
       A tensor of shape [batch_size, seq_length, features_size].
     """
-    with tf.variable_scope('pool_views_fn/STCK'):
+    with tf.compat.v1.variable_scope('pool_views_fn/STCK'):
       net = tf.concat(nets, 1)
-      batch_size = net.get_shape().dims[0].value
+      batch_size = tf.shape(input=net)[0]
+      image_size = net.get_shape().dims[1].value * \
+          net.get_shape().dims[2].value
       feature_size = net.get_shape().dims[3].value
-      return tf.reshape(net, [batch_size, -1, feature_size])
+      return tf.reshape(net, tf.stack([batch_size, image_size, feature_size]))
 
   def char_predictions(self, chars_logit):
     """Returns confidence scores (softmax values) for predicted characters.
 
     Args:
-      chars_logit: chars logits, a tensor with shape
-        [batch_size x seq_length x num_char_classes]
+      chars_logit: chars logits, a tensor with shape [batch_size x seq_length x
+        num_char_classes]
 
     Returns:
       A tuple (ids, log_prob, scores), where:
@@ -301,12 +440,17 @@ class Model(object):
           with shape [batch_size x seq_length].
     """
     log_prob = utils.logits_to_log_prob(chars_logit)
-    ids = tf.to_int32(tf.argmax(log_prob, axis=2), name='predicted_chars')
+    ids = tf.cast(tf.argmax(input=log_prob, axis=2),
+                  name='predicted_chars', dtype=tf.int32)
     mask = tf.cast(
-      slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool)
+        slim.one_hot_encoding(ids, self._params.num_char_classes), tf.bool)
     all_scores = tf.nn.softmax(chars_logit)
-    selected_scores = tf.boolean_mask(all_scores, mask, name='char_scores')
-    scores = tf.reshape(selected_scores, shape=(-1, self._params.seq_length))
+    selected_scores = tf.boolean_mask(
+        tensor=all_scores, mask=mask, name='char_scores')
+    scores = tf.reshape(
+        selected_scores,
+        shape=(-1, self._params.seq_length),
+        name='predicted_scores')
     return ids, log_prob, scores
 
   def encode_coordinates_fn(self, net):
@@ -323,12 +467,12 @@ class Model(object):
     """
     mparams = self._mparams['encode_coordinates_fn']
     if mparams.enabled:
-      batch_size, h, w, _ = net.shape.as_list()
+      batch_size, h, w, _ = get_tensor_dimensions(net)
       x, y = tf.meshgrid(tf.range(w), tf.range(h))
       w_loc = slim.one_hot_encoding(x, num_classes=w)
       h_loc = slim.one_hot_encoding(y, num_classes=h)
       loc = tf.concat([h_loc, w_loc], 2)
-      loc = tf.tile(tf.expand_dims(loc, 0), [batch_size, 1, 1, 1])
+      loc = tf.tile(tf.expand_dims(loc, 0), tf.stack([batch_size, 1, 1, 1]))
       return tf.concat([net, loc], 3)
     else:
       return net
@@ -341,7 +485,8 @@ class Model(object):
     """Creates a base part of the Model (no gradients, losses or summaries).
 
     Args:
-      images: A tensor of shape [batch_size, height, width, channels].
+      images: A tensor of shape [batch_size, height, width, channels] with pixel
+        values in the range [0.0, 1.0].
       labels_one_hot: Optional (can be None) one-hot encoding for ground truth
         labels. If provided the function will create a model for training.
       scope: Optional variable_scope.
@@ -353,14 +498,19 @@ class Model(object):
     """
     logging.debug('images: %s', images)
     is_training = labels_one_hot is not None
-    with tf.variable_scope(scope, reuse=reuse):
+
+    # Normalize image pixel values to have a symmetrical range around zero.
+    images = tf.subtract(images, 0.5)
+    images = tf.multiply(images, 2.5)
+
+    with tf.compat.v1.variable_scope(scope, reuse=reuse):
       views = tf.split(
-        value=images, num_or_size_splits=self._params.num_views, axis=2)
+          value=images, num_or_size_splits=self._params.num_views, axis=2)
       logging.debug('Views=%d single view: %s', len(views), views[0])
 
       nets = [
-        self.conv_tower_fn(v, is_training, reuse=(i != 0))
-        for i, v in enumerate(views)
+          self.conv_tower_fn(v, is_training, reuse=(i != 0))
+          for i, v in enumerate(views)
       ]
       logging.debug('Conv tower: %s', nets[0])
 
@@ -374,18 +524,34 @@ class Model(object):
       logging.debug('chars_logit: %s', chars_logit)
 
       predicted_chars, chars_log_prob, predicted_scores = (
-        self.char_predictions(chars_logit))
+          self.char_predictions(chars_logit))
       if self._charset:
         character_mapper = CharsetMapper(self._charset)
         predicted_text = character_mapper.get_text(predicted_chars)
       else:
         predicted_text = tf.constant([])
+
+      text_log_prob, predicted_length = null_based_length_prediction(
+          chars_log_prob, self._params.null_code)
+      predicted_conf = lookup_indexed_value(predicted_length, text_log_prob)
+      # Convert predicted confidence from sum of logs to geometric mean
+      normalized_seq_conf = tf.exp(
+          tf.divide(predicted_conf,
+                    tf.cast(predicted_length + 1, predicted_conf.dtype)),
+          name='normalized_seq_conf')
+      predicted_conf = tf.identity(predicted_conf, name='predicted_conf')
+      predicted_text = tf.identity(predicted_text, name='predicted_text')
+      predicted_length = tf.identity(predicted_length, name='predicted_length')
+
     return OutputEndpoints(
-      chars_logit=chars_logit,
-      chars_log_prob=chars_log_prob,
-      predicted_chars=predicted_chars,
-      predicted_scores=predicted_scores,
-      predicted_text=predicted_text)
+        chars_logit=chars_logit,
+        chars_log_prob=chars_log_prob,
+        predicted_chars=predicted_chars,
+        predicted_scores=predicted_scores,
+        predicted_length=predicted_length,
+        predicted_text=predicted_text,
+        predicted_conf=predicted_conf,
+        normalized_seq_conf=normalized_seq_conf)
 
   def create_loss(self, data, endpoints):
     """Creates all losses required to train the model.
@@ -404,7 +570,7 @@ class Model(object):
     # multiple losses including regularization losses.
     self.sequence_loss_fn(endpoints.chars_logit, data.labels)
     total_loss = slim.losses.get_total_loss()
-    tf.summary.scalar('TotalLoss', total_loss)
+    tf.compat.v1.summary.scalar('TotalLoss', total_loss)
     return total_loss
 
   def label_smoothing_regularization(self, chars_labels, weight=0.1):
@@ -413,15 +579,15 @@ class Model(object):
     Uses the same method as in https://arxiv.org/abs/1512.00567.
 
     Args:
-      chars_labels: ground truth ids of charactes,
-        shape=[batch_size, seq_length];
+      chars_labels: ground truth ids of charactes, shape=[batch_size,
+        seq_length];
       weight: label-smoothing regularization weight.
 
     Returns:
       A sensor with the same shape as the input.
     """
     one_hot_labels = tf.one_hot(
-      chars_labels, depth=self._params.num_char_classes, axis=-1)
+        chars_labels, depth=self._params.num_char_classes, axis=-1)
     pos_weight = 1.0 - weight
     neg_weight = weight / self._params.num_char_classes
     return one_hot_labels * pos_weight + neg_weight
@@ -433,20 +599,20 @@ class Model(object):
     also ignore all null chars after the first one.
 
     Args:
-      chars_logits: logits for predicted characters,
-        shape=[batch_size, seq_length, num_char_classes];
-      chars_labels: ground truth ids of characters,
-        shape=[batch_size, seq_length];
+      chars_logits: logits for predicted characters, shape=[batch_size,
+        seq_length, num_char_classes];
+      chars_labels: ground truth ids of characters, shape=[batch_size,
+        seq_length];
       mparams: method hyper parameters.
 
     Returns:
       A Tensor with shape [batch_size] - the log-perplexity for each sequence.
     """
     mparams = self._mparams['sequence_loss_fn']
-    with tf.variable_scope('sequence_loss_fn/SLF'):
+    with tf.compat.v1.variable_scope('sequence_loss_fn/SLF'):
       if mparams.label_smoothing > 0:
         smoothed_one_hot_labels = self.label_smoothing_regularization(
-          chars_labels, mparams.label_smoothing)
+            chars_labels, mparams.label_smoothing)
         labels_list = tf.unstack(smoothed_one_hot_labels, axis=1)
       else:
         # NOTE: in case of sparse softmax we are not using one-hot
@@ -459,21 +625,21 @@ class Model(object):
       else:
         # Suppose that reject character is the last in the charset.
         reject_char = tf.constant(
-          self._params.num_char_classes - 1,
-          shape=(batch_size, seq_length),
-          dtype=tf.int64)
+            self._params.num_char_classes - 1,
+            shape=(batch_size, seq_length),
+            dtype=tf.int64)
         known_char = tf.not_equal(chars_labels, reject_char)
-        weights = tf.to_float(known_char)
+        weights = tf.cast(known_char, dtype=tf.float32)
 
       logits_list = tf.unstack(chars_logits, axis=1)
       weights_list = tf.unstack(weights, axis=1)
       loss = tf.contrib.legacy_seq2seq.sequence_loss(
-        logits_list,
-        labels_list,
-        weights_list,
-        softmax_loss_function=get_softmax_loss_fn(mparams.label_smoothing),
-        average_across_timesteps=mparams.average_across_timesteps)
-      tf.losses.add_loss(loss)
+          logits_list,
+          labels_list,
+          weights_list,
+          softmax_loss_function=get_softmax_loss_fn(mparams.label_smoothing),
+          average_across_timesteps=mparams.average_across_timesteps)
+      tf.compat.v1.losses.add_loss(loss)
       return loss
 
   def create_summaries(self, data, endpoints, charset, is_training):
@@ -482,8 +648,8 @@ class Model(object):
     Args:
       data: InputEndpoints namedtuple.
       endpoints: OutputEndpoints namedtuple.
-      charset: A dictionary with mapping between character codes and
-        unicode characters. Use the one provided by a dataset.charset.
+      charset: A dictionary with mapping between character codes and unicode
+        characters. Use the one provided by a dataset.charset.
       is_training: If True will create summary prefixes for training job,
         otherwise - for evaluation.
 
@@ -503,13 +669,14 @@ class Model(object):
     # tf.summary.text(sname('text/pr'), pr_text)
     # gt_text = charset_mapper.get_text(data.labels[:max_outputs,:])
     # tf.summary.text(sname('text/gt'), gt_text)
-    tf.summary.image(sname('image'), data.images, max_outputs=max_outputs)
+    tf.compat.v1.summary.image(
+        sname('image'), data.images, max_outputs=max_outputs)
 
     if is_training:
-      tf.summary.image(
-        sname('image/orig'), data.images_orig, max_outputs=max_outputs)
-      for var in tf.trainable_variables():
-        tf.summary.histogram(var.op.name, var)
+      tf.compat.v1.summary.image(
+          sname('image/orig'), data.images_orig, max_outputs=max_outputs)
+      for var in tf.compat.v1.trainable_variables():
+        tf.compat.v1.summary.histogram(var.op.name, var)
       return None
 
     else:
@@ -520,32 +687,36 @@ class Model(object):
         names_to_values[name] = value_update_tuple[0]
         names_to_updates[name] = value_update_tuple[1]
 
-      use_metric('CharacterAccuracy',
-                 metrics.char_accuracy(
-                   endpoints.predicted_chars,
-                   data.labels,
-                   streaming=True,
-                   rej_char=self._params.null_code))
+      use_metric(
+          'CharacterAccuracy',
+          metrics.char_accuracy(
+              endpoints.predicted_chars,
+              data.labels,
+              streaming=True,
+              rej_char=self._params.null_code))
       # Sequence accuracy computed by cutting sequence at the first null char
-      use_metric('SequenceAccuracy',
-                 metrics.sequence_accuracy(
-                   endpoints.predicted_chars,
-                   data.labels,
-                   streaming=True,
-                   rej_char=self._params.null_code))
+      use_metric(
+          'SequenceAccuracy',
+          metrics.sequence_accuracy(
+              endpoints.predicted_chars,
+              data.labels,
+              streaming=True,
+              rej_char=self._params.null_code))
 
       for name, value in names_to_values.items():
         summary_name = 'eval/' + name
-        tf.summary.scalar(summary_name, tf.Print(value, [value], summary_name))
+        tf.compat.v1.summary.scalar(
+            summary_name, tf.compat.v1.Print(value, [value], summary_name))
       return list(names_to_updates.values())
 
-  def create_init_fn_to_restore(self, master_checkpoint,
+  def create_init_fn_to_restore(self,
+                                master_checkpoint,
                                 inception_checkpoint=None):
     """Creates an init operations to restore weights from various checkpoints.
 
     Args:
-      master_checkpoint: path to a checkpoint which contains all weights for
-        the whole model.
+      master_checkpoint: path to a checkpoint which contains all weights for the
+        whole model.
       inception_checkpoint: path to a checkpoint which contains weights for the
         inception part only.
 
@@ -556,8 +727,8 @@ class Model(object):
     all_feed_dict = {}
 
     def assign_from_checkpoint(variables, checkpoint):
-      logging.info('Request to re-store %d weights from %s',
-                   len(variables), checkpoint)
+      logging.info('Request to re-store %d weights from %s', len(variables),
+                   checkpoint)
       if not variables:
         logging.error('Can\'t find any variables to restore.')
         sys.exit(1)
@@ -565,15 +736,18 @@ class Model(object):
       all_assign_ops.append(assign_op)
       all_feed_dict.update(feed_dict)
 
-    logging.info('variables_to_restore:\n%s' % utils.variables_to_restore().keys())
-    logging.info('moving_average_variables:\n%s' % [v.op.name for v in tf.moving_average_variables()])
-    logging.info('trainable_variables:\n%s' % [v.op.name for v in tf.trainable_variables()])
+    logging.info('variables_to_restore:\n%s',
+                 utils.variables_to_restore().keys())
+    logging.info('moving_average_variables:\n%s',
+                 [v.op.name for v in tf.compat.v1.moving_average_variables()])
+    logging.info('trainable_variables:\n%s',
+                 [v.op.name for v in tf.compat.v1.trainable_variables()])
     if master_checkpoint:
       assign_from_checkpoint(utils.variables_to_restore(), master_checkpoint)
 
     if inception_checkpoint:
       variables = utils.variables_to_restore(
-        'AttentionOcr_v1/conv_tower_fn/INCE', strip_scope=True)
+          'AttentionOcr_v1/conv_tower_fn/INCE', strip_scope=True)
       assign_from_checkpoint(variables, inception_checkpoint)
 
     def init_assign_fn(sess):

research/attention_ocr/python/model_export.py

+# Lint as: python3
+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Converts existing checkpoint into a SavedModel.
+
+Usage example:
+python model_export.py \
+  --logtostderr --checkpoint=model.ckpt-399731 \
+  --export_dir=/tmp/attention_ocr_export
+"""
+import os
+
+import tensorflow as tf
+from tensorflow import app
+from tensorflow.contrib import slim
+from tensorflow.python.platform import flags
+
+import common_flags
+import model_export_lib
+
+FLAGS = flags.FLAGS
+common_flags.define()
+
+flags.DEFINE_string('export_dir', None, 'Directory to export model files to.')
+flags.DEFINE_integer(
+    'image_width', None,
+    'Image width used during training (or crop width if used)'
+    ' If not set, the dataset default is used instead.')
+flags.DEFINE_integer(
+    'image_height', None,
+    'Image height used during training(or crop height if used)'
+    ' If not set, the dataset default is used instead.')
+flags.DEFINE_string('work_dir', '/tmp',
+                    'A directory to store temporary files.')
+flags.DEFINE_integer('version_number', 1, 'Version number of the model')
+flags.DEFINE_bool(
+    'export_for_serving', True,
+    'Whether the exported model accepts serialized tf.Example '
+    'protos as input')
+
+
+def get_checkpoint_path():
+  """Returns a path to a checkpoint based on specified commandline flags.
+
+  In order to specify a full path to a checkpoint use --checkpoint flag.
+  Alternatively, if --train_log_dir was specified it will return a path to the
+  most recent checkpoint.
+
+  Raises:
+    ValueError: in case it can't find a checkpoint.
+
+  Returns:
+    A string.
+  """
+  if FLAGS.checkpoint:
+    return FLAGS.checkpoint
+  else:
+    model_save_path = tf.train.latest_checkpoint(FLAGS.train_log_dir)
+    if not model_save_path:
+      raise ValueError('Can\'t find a checkpoint in: %s' % FLAGS.train_log_dir)
+    return model_save_path
+
+
+def export_model(export_dir,
+                 export_for_serving,
+                 batch_size=None,
+                 crop_image_width=None,
+                 crop_image_height=None):
+  """Exports a model to the named directory.
+
+  Note that --datatset_name and --checkpoint are required and parsed by the
+  underlying module common_flags.
+
+  Args:
+    export_dir: The output dir where model is exported to.
+    export_for_serving: If True, expects a serialized image as input and attach
+      image normalization as part of exported graph.
+    batch_size: For non-serving export, the input batch_size needs to be
+      specified.
+    crop_image_width: Width of the input image. Uses the dataset default if
+      None.
+    crop_image_height: Height of the input image. Uses the dataset default if
+      None.
+
+  Returns:
+    Returns the model signature_def.
+  """
+  # Dataset object used only to get all parameters for the model.
+  dataset = common_flags.create_dataset(split_name='test')
+  model = common_flags.create_model(
+      dataset.num_char_classes,
+      dataset.max_sequence_length,
+      dataset.num_of_views,
+      dataset.null_code,
+      charset=dataset.charset)
+  dataset_image_height, dataset_image_width, image_depth = dataset.image_shape
+
+  # Add check for charmap file
+  if not os.path.exists(dataset.charset_file):
+    raise ValueError('No charset defined at {}: export will fail'.format(
+        dataset.charset))
+
+  # Default to dataset dimensions, otherwise use provided dimensions.
+  image_width = crop_image_width or dataset_image_width
+  image_height = crop_image_height or dataset_image_height
+
+  if export_for_serving:
+    images_orig = tf.compat.v1.placeholder(
+        tf.string, shape=[batch_size], name='tf_example')
+    images_orig_float = model_export_lib.generate_tfexample_image(
+        images_orig,
+        image_height,
+        image_width,
+        image_depth,
+        name='float_images')
+  else:
+    images_shape = (batch_size, image_height, image_width, image_depth)
+    images_orig = tf.compat.v1.placeholder(
+        tf.uint8, shape=images_shape, name='original_image')
+    images_orig_float = tf.image.convert_image_dtype(
+        images_orig, dtype=tf.float32, name='float_images')
+
+  endpoints = model.create_base(images_orig_float, labels_one_hot=None)
+
+  sess = tf.compat.v1.Session()
+  saver = tf.compat.v1.train.Saver(
+      slim.get_variables_to_restore(), sharded=True)
+  saver.restore(sess, get_checkpoint_path())
+  tf.compat.v1.logging.info('Model restored successfully.')
+
+  # Create model signature.
+  if export_for_serving:
+    input_tensors = {
+        tf.saved_model.CLASSIFY_INPUTS: images_orig
+    }
+  else:
+    input_tensors = {'images': images_orig}
+  signature_inputs = model_export_lib.build_tensor_info(input_tensors)
+  # NOTE: Tensors 'image_float' and 'chars_logit' are used by the inference
+  # or to compute saliency maps.
+  output_tensors = {
+      'images_float': images_orig_float,
+      'predictions': endpoints.predicted_chars,
+      'scores': endpoints.predicted_scores,
+      'chars_logit': endpoints.chars_logit,
+      'predicted_length': endpoints.predicted_length,
+      'predicted_text': endpoints.predicted_text,
+      'predicted_conf': endpoints.predicted_conf,
+      'normalized_seq_conf': endpoints.normalized_seq_conf
+  }
+  for i, t in enumerate(
+      model_export_lib.attention_ocr_attention_masks(
+          dataset.max_sequence_length)):
+    output_tensors['attention_mask_%d' % i] = t
+  signature_outputs = model_export_lib.build_tensor_info(output_tensors)
+  signature_def = tf.compat.v1.saved_model.signature_def_utils.build_signature_def(
+      signature_inputs, signature_outputs,
+      tf.saved_model.CLASSIFY_METHOD_NAME)
+  # Save model.
+  builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(export_dir)
+  builder.add_meta_graph_and_variables(
+      sess, [tf.saved_model.SERVING],
+      signature_def_map={
+          tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
+              signature_def
+      },
+      main_op=tf.compat.v1.tables_initializer(),
+      strip_default_attrs=True)
+  builder.save()
+  tf.compat.v1.logging.info('Model has been exported to %s' % export_dir)
+
+  return signature_def
+
+
+def main(unused_argv):
+  if os.path.exists(FLAGS.export_dir):
+    raise ValueError('export_dir already exists: exporting will fail')
+
+  export_model(FLAGS.export_dir, FLAGS.export_for_serving, FLAGS.batch_size,
+               FLAGS.image_width, FLAGS.image_height)
+
+
+if __name__ == '__main__':
+  flags.mark_flag_as_required('dataset_name')
+  flags.mark_flag_as_required('export_dir')
+  app.run(main)

research/attention_ocr/python/model_export_lib.py

+# Lint as: python3
+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utility functions for exporting Attention OCR model."""
+
+import tensorflow as tf
+
+
+# Function borrowed from research/object_detection/core/preprocessor.py
+def normalize_image(image, original_minval, original_maxval, target_minval,
+                    target_maxval):
+  """Normalizes pixel values in the image.
+
+  Moves the pixel values from the current [original_minval, original_maxval]
+  range to a the [target_minval, target_maxval] range.
+  Args:
+    image: rank 3 float32 tensor containing 1 image -> [height, width,
+      channels].
+    original_minval: current image minimum value.
+    original_maxval: current image maximum value.
+    target_minval: target image minimum value.
+    target_maxval: target image maximum value.
+
+  Returns:
+    image: image which is the same shape as input image.
+  """
+  with tf.compat.v1.name_scope('NormalizeImage', values=[image]):
+    original_minval = float(original_minval)
+    original_maxval = float(original_maxval)
+    target_minval = float(target_minval)
+    target_maxval = float(target_maxval)
+    image = tf.cast(image, dtype=tf.float32)
+    image = tf.subtract(image, original_minval)
+    image = tf.multiply(image, (target_maxval - target_minval) /
+                        (original_maxval - original_minval))
+    image = tf.add(image, target_minval)
+    return image
+
+
+def generate_tfexample_image(input_example_strings,
+                             image_height,
+                             image_width,
+                             image_channels,
+                             name=None):
+  """Parses a 1D tensor of serialized tf.Example protos and returns image batch.
+
+  Args:
+    input_example_strings: A 1-Dimensional tensor of size [batch_size] and type
+      tf.string containing a serialized Example proto per image.
+    image_height: First image dimension.
+    image_width: Second image dimension.
+    image_channels: Third image dimension.
+    name: optional tensor name.
+
+  Returns:
+    A tensor with shape [batch_size, height, width, channels] of type float32
+    with values in the range [0..1]
+  """
+  batch_size = tf.shape(input=input_example_strings)[0]
+  images_shape = tf.stack(
+      [batch_size, image_height, image_width, image_channels])
+  tf_example_image_key = 'image/encoded'
+  feature_configs = {
+      tf_example_image_key:
+          tf.io.FixedLenFeature(
+              image_height * image_width * image_channels, dtype=tf.float32)
+  }
+  feature_tensors = tf.io.parse_example(
+      serialized=input_example_strings, features=feature_configs)
+  float_images = tf.reshape(
+      normalize_image(
+          feature_tensors[tf_example_image_key],
+          original_minval=0.0,
+          original_maxval=255.0,
+          target_minval=0.0,
+          target_maxval=1.0),
+      images_shape,
+      name=name)
+  return float_images
+
+
+def attention_ocr_attention_masks(num_characters):
+  # TODO(gorban): use tensors directly after replacing LSTM unroll methods.
+  prefix = ('AttentionOcr_v1/'
+            'sequence_logit_fn/SQLR/LSTM/attention_decoder/Attention_0')
+  names = ['%s/Softmax:0' % (prefix)]
+  for i in range(1, num_characters):
+    names += ['%s_%d/Softmax:0' % (prefix, i)]
+  return [tf.compat.v1.get_default_graph().get_tensor_by_name(n) for n in names]
+
+
+def build_tensor_info(tensor_dict):
+  return {
+      k: tf.compat.v1.saved_model.utils.build_tensor_info(t)
+      for k, t in tensor_dict.items()
+  }

research/attention_ocr/python/model_export_test.py

+# Lint as: python3
+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for model_export."""
+import os
+
+import numpy as np
+from absl.testing import flagsaver
+import tensorflow as tf
+
+import common_flags
+import model_export
+
+_CHECKPOINT = 'model.ckpt-399731'
+_CHECKPOINT_URL = (
+    'http://download.tensorflow.org/models/attention_ocr_2017_08_09.tar.gz')
+
+
+def _clean_up():
+  tf.io.gfile.rmtree(tf.compat.v1.test.get_temp_dir())
+
+
+def _create_tf_example_string(image):
+  """Create a serialized tf.Example proto for feeding the model."""
+  example = tf.train.Example()
+  example.features.feature['image/encoded'].float_list.value.extend(
+      list(np.reshape(image, (-1))))
+  return example.SerializeToString()
+
+
+class AttentionOcrExportTest(tf.test.TestCase):
+  """Tests for model_export.export_model."""
+
+  def setUp(self):
+    for suffix in ['.meta', '.index', '.data-00000-of-00001']:
+      filename = _CHECKPOINT + suffix
+      self.assertTrue(
+          tf.io.gfile.exists(filename),
+          msg='Missing checkpoint file %s. '
+          'Please download and extract it from %s' %
+          (filename, _CHECKPOINT_URL))
+    tf.flags.FLAGS.dataset_name = 'fsns'
+    tf.flags.FLAGS.checkpoint = _CHECKPOINT
+    tf.flags.FLAGS.dataset_dir = os.path.join(
+        os.path.dirname(__file__), 'datasets/testdata/fsns')
+    tf.test.TestCase.setUp(self)
+    _clean_up()
+    self.export_dir = os.path.join(
+        tf.compat.v1.test.get_temp_dir(), 'exported_model')
+    self.minimal_output_signature = {
+        'predictions': 'AttentionOcr_v1/predicted_chars:0',
+        'scores': 'AttentionOcr_v1/predicted_scores:0',
+        'predicted_length': 'AttentionOcr_v1/predicted_length:0',
+        'predicted_text': 'AttentionOcr_v1/predicted_text:0',
+        'predicted_conf': 'AttentionOcr_v1/predicted_conf:0',
+        'normalized_seq_conf': 'AttentionOcr_v1/normalized_seq_conf:0'
+    }
+
+  def create_input_feed(self, graph_def, serving):
+    """Returns the input feed for the model.
+
+    Creates random images, according to the size specified by dataset_name,
+    format it in the correct way depending on whether the model was exported
+    for serving, and return the correctly keyed feed_dict for inference.
+
+    Args:
+      graph_def: Graph definition of the loaded model.
+      serving: Whether the model was exported for Serving.
+
+    Returns:
+      The feed_dict suitable for model inference.
+    """
+    # Creates a dataset based on FLAGS.dataset_name.
+    self.dataset = common_flags.create_dataset('test')
+    # Create some random images to test inference for any dataset.
+    self.images = {
+        'img1':
+            np.random.uniform(low=64, high=192,
+                              size=self.dataset.image_shape).astype('uint8'),
+        'img2':
+            np.random.uniform(low=32, high=224,
+                              size=self.dataset.image_shape).astype('uint8'),
+    }
+    signature_def = graph_def.signature_def[
+        tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
+    if serving:
+      input_name = signature_def.inputs[
+          tf.saved_model.CLASSIFY_INPUTS].name
+      # Model for serving takes input: inputs['inputs'] = 'tf_example:0'
+      feed_dict = {
+          input_name: [
+              _create_tf_example_string(self.images['img1']),
+              _create_tf_example_string(self.images['img2'])
+          ]
+      }
+    else:
+      input_name = signature_def.inputs['images'].name
+      # Model for direct use takes input: inputs['images'] = 'original_image:0'
+      feed_dict = {
+          input_name: np.stack([self.images['img1'], self.images['img2']])
+      }
+    return feed_dict
+
+  def verify_export_load_and_inference(self, export_for_serving=False):
+    """Verify exported model can be loaded and inference can run successfully.
+
+    This function will load the exported model in self.export_dir, then create
+    some fake images according to the specification of FLAGS.dataset_name.
+    It then feeds the input through the model, and verify the minimal set of
+    output signatures are present.
+    Note: Model and dataset creation in the underlying library depends on the
+          following commandline flags:
+            FLAGS.dataset_name
+    Args:
+      export_for_serving: True if the model was exported for Serving. This
+        affects how input is fed into the model.
+    """
+    tf.compat.v1.reset_default_graph()
+    sess = tf.compat.v1.Session()
+    graph_def = tf.compat.v1.saved_model.loader.load(
+        sess=sess,
+        tags=[tf.saved_model.SERVING],
+        export_dir=self.export_dir)
+    feed_dict = self.create_input_feed(graph_def, export_for_serving)
+    results = sess.run(self.minimal_output_signature, feed_dict=feed_dict)
+
+    out_shape = (2,)
+    self.assertEqual(np.shape(results['predicted_conf']), out_shape)
+    self.assertEqual(np.shape(results['predicted_text']), out_shape)
+    self.assertEqual(np.shape(results['predicted_length']), out_shape)
+    self.assertEqual(np.shape(results['normalized_seq_conf']), out_shape)
+    out_shape = (2, self.dataset.max_sequence_length)
+    self.assertEqual(np.shape(results['scores']), out_shape)
+    self.assertEqual(np.shape(results['predictions']), out_shape)
+
+  @flagsaver.flagsaver
+  def test_fsns_export_for_serving_and_load_inference(self):
+    model_export.export_model(self.export_dir, True)
+    self.verify_export_load_and_inference(True)
+
+  @flagsaver.flagsaver
+  def test_fsns_export_and_load_inference(self):
+    model_export.export_model(self.export_dir, False, batch_size=2)
+    self.verify_export_load_and_inference(False)
+
+
+if __name__ == '__main__':
+  tf.test.main()

research/attention_ocr/python/model_test.py

@@ -12,11 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Tests for the model."""
+import string
 
 import numpy as np
-import string
 import tensorflow as tf
 from tensorflow.contrib import slim
 
@@ -32,6 +31,7 @@ def create_fake_charset(num_char_classes):
 
 
 class ModelTest(tf.test.TestCase):
+
   def setUp(self):
     tf.test.TestCase.setUp(self)
 
@@ -51,18 +51,21 @@ class ModelTest(tf.test.TestCase):
     self.chars_logit_shape = (self.batch_size, self.seq_length,
                               self.num_char_classes)
     self.length_logit_shape = (self.batch_size, self.seq_length + 1)
+    # Placeholder knows image dimensions, but not batch size.
+    self.input_images = tf.compat.v1.placeholder(
+        tf.float32,
+        shape=(None, self.image_height, self.image_width, 3),
+        name='input_node')
 
     self.initialize_fakes()
 
   def initialize_fakes(self):
     self.images_shape = (self.batch_size, self.image_height, self.image_width,
                          3)
-    self.fake_images = tf.constant(
-        self.rng.randint(low=0, high=255,
-                         size=self.images_shape).astype('float32'),
-        name='input_node')
-    self.fake_conv_tower_np = self.rng.randn(
-        *self.conv_tower_shape).astype('float32')
+    self.fake_images = self.rng.randint(
+        low=0, high=255, size=self.images_shape).astype('float32')
+    self.fake_conv_tower_np = self.rng.randn(*self.conv_tower_shape).astype(
+        'float32')
     self.fake_conv_tower = tf.constant(self.fake_conv_tower_np)
     self.fake_logits = tf.constant(
         self.rng.randn(*self.chars_logit_shape).astype('float32'))
@@ -74,33 +77,44 @@ class ModelTest(tf.test.TestCase):
 
   def create_model(self, charset=None):
     return model.Model(
-        self.num_char_classes, self.seq_length, num_views=4, null_code=62,
+        self.num_char_classes,
+        self.seq_length,
+        num_views=4,
+        null_code=62,
         charset=charset)
 
   def test_char_related_shapes(self):
-    ocr_model = self.create_model()
+    charset = create_fake_charset(self.num_char_classes)
+    ocr_model = self.create_model(charset=charset)
     with self.test_session() as sess:
       endpoints_tf = ocr_model.create_base(
-          images=self.fake_images, labels_one_hot=None)
-
-      sess.run(tf.global_variables_initializer())
-      endpoints = sess.run(endpoints_tf)
-
-      self.assertEqual((self.batch_size, self.seq_length,
-                        self.num_char_classes), endpoints.chars_logit.shape)
-      self.assertEqual((self.batch_size, self.seq_length,
-                        self.num_char_classes), endpoints.chars_log_prob.shape)
+          images=self.input_images, labels_one_hot=None)
+      sess.run(tf.compat.v1.global_variables_initializer())
+      tf.compat.v1.tables_initializer().run()
+      endpoints = sess.run(
+          endpoints_tf, feed_dict={self.input_images: self.fake_images})
+
+      self.assertEqual(
+          (self.batch_size, self.seq_length, self.num_char_classes),
+          endpoints.chars_logit.shape)
+      self.assertEqual(
+          (self.batch_size, self.seq_length, self.num_char_classes),
+          endpoints.chars_log_prob.shape)
       self.assertEqual((self.batch_size, self.seq_length),
                        endpoints.predicted_chars.shape)
       self.assertEqual((self.batch_size, self.seq_length),
                        endpoints.predicted_scores.shape)
+      self.assertEqual((self.batch_size,), endpoints.predicted_text.shape)
+      self.assertEqual((self.batch_size,), endpoints.predicted_conf.shape)
+      self.assertEqual((self.batch_size,), endpoints.normalized_seq_conf.shape)
 
   def test_predicted_scores_are_within_range(self):
     ocr_model = self.create_model()
 
     _, _, scores = ocr_model.char_predictions(self.fake_logits)
     with self.test_session() as sess:
-      scores_np = sess.run(scores)
+      scores_np = sess.run(
+          scores, feed_dict={self.input_images: self.fake_images})
 
     values_in_range = (scores_np >= 0.0) & (scores_np <= 1.0)
     self.assertTrue(
@@ -111,10 +125,11 @@ class ModelTest(tf.test.TestCase):
   def test_conv_tower_shape(self):
     with self.test_session() as sess:
       ocr_model = self.create_model()
-      conv_tower = ocr_model.conv_tower_fn(self.fake_images)
+      conv_tower = ocr_model.conv_tower_fn(self.input_images)
 
-      sess.run(tf.global_variables_initializer())
-      conv_tower_np = sess.run(conv_tower)
+      sess.run(tf.compat.v1.global_variables_initializer())
+      conv_tower_np = sess.run(
+          conv_tower, feed_dict={self.input_images: self.fake_images})
 
       self.assertEqual(self.conv_tower_shape, conv_tower_np.shape)
 
@@ -124,11 +139,12 @@ class ModelTest(tf.test.TestCase):
     # updates, gradients and variances. It also depends on the type of used
     # optimizer.
     ocr_model = self.create_model()
-    ocr_model.create_base(images=self.fake_images, labels_one_hot=None)
+    ocr_model.create_base(images=self.input_images, labels_one_hot=None)
     with self.test_session() as sess:
-      tfprof_root = tf.profiler.profile(
+      tfprof_root = tf.compat.v1.profiler.profile(
           sess.graph,
-          options=tf.profiler.ProfileOptionBuilder.trainable_variables_parameter())
+          options=tf.compat.v1.profiler.ProfileOptionBuilder
+          .trainable_variables_parameter())
 
       model_size_bytes = 4 * tfprof_root.total_parameters
       self.assertLess(model_size_bytes, 1 * 2**30)
@@ -147,9 +163,9 @@ class ModelTest(tf.test.TestCase):
     summaries = ocr_model.create_summaries(
         data, endpoints, charset, is_training=False)
     with self.test_session() as sess:
-      sess.run(tf.global_variables_initializer())
-      sess.run(tf.local_variables_initializer())
-      tf.tables_initializer().run()
+      sess.run(tf.compat.v1.global_variables_initializer())
+      sess.run(tf.compat.v1.local_variables_initializer())
+      tf.compat.v1.tables_initializer().run()
       sess.run(summaries)  # just check it is runnable
 
   def test_sequence_loss_function_without_label_smoothing(self):
@@ -158,7 +174,7 @@ class ModelTest(tf.test.TestCase):
 
     loss = model.sequence_loss_fn(self.fake_logits, self.fake_labels)
     with self.test_session() as sess:
-      loss_np = sess.run(loss)
+      loss_np = sess.run(loss, feed_dict={self.input_images: self.fake_images})
 
     # This test checks that the loss function is 'runnable'.
     self.assertEqual(loss_np.shape, tuple())
@@ -172,19 +188,21 @@ class ModelTest(tf.test.TestCase):
     Returns:
       a list of tensors with encoded image coordinates in them.
     """
-    batch_size, h, w, _ = net.shape.as_list()
+    batch_size = tf.shape(input=net)[0]
+    _, h, w, _ = net.shape.as_list()
     h_loc = [
-      tf.tile(
-          tf.reshape(
-              tf.contrib.layers.one_hot_encoding(
-                  tf.constant([i]), num_classes=h), [h, 1]), [1, w])
-      for i in range(h)
+        tf.tile(
+            tf.reshape(
+                tf.contrib.layers.one_hot_encoding(
+                    tf.constant([i]), num_classes=h), [h, 1]), [1, w])
+        for i in range(h)
     ]
     h_loc = tf.concat([tf.expand_dims(t, 2) for t in h_loc], 2)
     w_loc = [
-      tf.tile(
-          tf.contrib.layers.one_hot_encoding(tf.constant([i]), num_classes=w),
-          [h, 1]) for i in range(w)
+        tf.tile(
+            tf.contrib.layers.one_hot_encoding(
+                tf.constant([i]), num_classes=w),
+            [h, 1]) for i in range(w)
     ]
     w_loc = tf.concat([tf.expand_dims(t, 2) for t in w_loc], 2)
     loc = tf.concat([h_loc, w_loc], 2)
@@ -197,11 +215,12 @@ class ModelTest(tf.test.TestCase):
     conv_w_coords_tf = model.encode_coordinates_fn(self.fake_conv_tower)
 
     with self.test_session() as sess:
-      conv_w_coords = sess.run(conv_w_coords_tf)
+      conv_w_coords = sess.run(
+          conv_w_coords_tf, feed_dict={self.input_images: self.fake_images})
 
     batch_size, height, width, feature_size = self.conv_tower_shape
-    self.assertEqual(conv_w_coords.shape, (batch_size, height, width,
-                                           feature_size + height + width))
+    self.assertEqual(conv_w_coords.shape,
+                     (batch_size, height, width, feature_size + height + width))
 
   def test_disabled_coordinate_encoding_returns_features_unchanged(self):
     model = self.create_model()
@@ -209,7 +228,8 @@ class ModelTest(tf.test.TestCase):
     conv_w_coords_tf = model.encode_coordinates_fn(self.fake_conv_tower)
 
     with self.test_session() as sess:
-      conv_w_coords = sess.run(conv_w_coords_tf)
+      conv_w_coords = sess.run(
+          conv_w_coords_tf, feed_dict={self.input_images: self.fake_images})
 
     self.assertAllEqual(conv_w_coords, self.fake_conv_tower_np)
 
@@ -221,7 +241,8 @@ class ModelTest(tf.test.TestCase):
     conv_w_coords_tf = model.encode_coordinates_fn(fake_conv_tower)
 
     with self.test_session() as sess:
-      conv_w_coords = sess.run(conv_w_coords_tf)
+      conv_w_coords = sess.run(
+          conv_w_coords_tf, feed_dict={self.input_images: self.fake_images})
 
     # Original features
     self.assertAllEqual(conv_w_coords[0, :, :, :4],
@@ -252,8 +273,8 @@ class ModelTest(tf.test.TestCase):
       endpoints_tf = ocr_model.create_base(
           images=self.fake_images, labels_one_hot=None)
 
-      sess.run(tf.global_variables_initializer())
-      tf.tables_initializer().run()
+      sess.run(tf.compat.v1.global_variables_initializer())
+      tf.compat.v1.tables_initializer().run()
       endpoints = sess.run(endpoints_tf)
 
       self.assertEqual(endpoints.predicted_text.shape, (self.batch_size,))
@@ -261,14 +282,15 @@ class ModelTest(tf.test.TestCase):
 
 
 class CharsetMapperTest(tf.test.TestCase):
+
   def test_text_corresponds_to_ids(self):
     charset = create_fake_charset(36)
-    ids = tf.constant(
-        [[17, 14, 21, 21, 24], [32, 24, 27, 21, 13]], dtype=tf.int64)
+    ids = tf.constant([[17, 14, 21, 21, 24], [32, 24, 27, 21, 13]],
+                      dtype=tf.int64)
     charset_mapper = model.CharsetMapper(charset)
 
     with self.test_session() as sess:
-      tf.tables_initializer().run()
+      tf.compat.v1.tables_initializer().run()
       text = sess.run(charset_mapper.get_text(ids))
 
     self.assertAllEqual(text, [b'hello', b'world'])

research/attention_ocr/python/sequence_layers.py

@@ -111,12 +111,12 @@ class SequenceLayerBase(object):
     self._mparams = method_params
     self._net = net
     self._labels_one_hot = labels_one_hot
-    self._batch_size = net.get_shape().dims[0].value
+    self._batch_size = tf.shape(input=net)[0]
 
     # Initialize parameters for char logits which will be computed on the fly
     # inside an LSTM decoder.
     self._char_logits = {}
-    regularizer = slim.l2_regularizer(self._mparams.weight_decay)
+    regularizer = tf.keras.regularizers.l2(0.5 * (self._mparams.weight_decay))
     self._softmax_w = slim.model_variable(
         'softmax_w',
         [self._mparams.num_lstm_units, self._params.num_char_classes],
@@ -124,7 +124,7 @@ class SequenceLayerBase(object):
         regularizer=regularizer)
     self._softmax_b = slim.model_variable(
         'softmax_b', [self._params.num_char_classes],
-        initializer=tf.zeros_initializer(),
+        initializer=tf.compat.v1.zeros_initializer(),
         regularizer=regularizer)
 
   @abc.abstractmethod
@@ -203,8 +203,8 @@ class SequenceLayerBase(object):
       A tensor with shape [batch_size, num_char_classes]
     """
     if char_index not in self._char_logits:
-      self._char_logits[char_index] = tf.nn.xw_plus_b(inputs, self._softmax_w,
-                                                      self._softmax_b)
+      self._char_logits[char_index] = tf.compat.v1.nn.xw_plus_b(inputs, self._softmax_w,
+                                                                self._softmax_b)
     return self._char_logits[char_index]
 
   def char_one_hot(self, logit):
@@ -216,7 +216,7 @@ class SequenceLayerBase(object):
     Returns:
       A tensor with shape [batch_size, num_char_classes]
     """
-    prediction = tf.argmax(logit, axis=1)
+    prediction = tf.argmax(input=logit, axis=1)
     return slim.one_hot_encoding(prediction, self._params.num_char_classes)
 
   def get_input(self, prev, i):
@@ -244,10 +244,10 @@ class SequenceLayerBase(object):
     Returns:
       A tensor with shape [batch_size, seq_length, num_char_classes].
     """
-    with tf.variable_scope('LSTM'):
+    with tf.compat.v1.variable_scope('LSTM'):
       first_label = self.get_input(prev=None, i=0)
       decoder_inputs = [first_label] + [None] * (self._params.seq_length - 1)
-      lstm_cell = tf.contrib.rnn.LSTMCell(
+      lstm_cell = tf.compat.v1.nn.rnn_cell.LSTMCell(
           self._mparams.num_lstm_units,
           use_peepholes=False,
           cell_clip=self._mparams.lstm_state_clip_value,
@@ -259,9 +259,9 @@ class SequenceLayerBase(object):
           loop_function=self.get_input,
           cell=lstm_cell)
 
-    with tf.variable_scope('logits'):
+    with tf.compat.v1.variable_scope('logits'):
       logits_list = [
-          tf.expand_dims(self.char_logit(logit, i), dim=1)
+          tf.expand_dims(self.char_logit(logit, i), axis=1)
           for i, logit in enumerate(lstm_outputs)
       ]
 
@@ -275,7 +275,7 @@ class NetSlice(SequenceLayerBase):
   def __init__(self, *args, **kwargs):
     super(NetSlice, self).__init__(*args, **kwargs)
     self._zero_label = tf.zeros(
-        [self._batch_size, self._params.num_char_classes])
+        tf.stack([self._batch_size, self._params.num_char_classes]))
 
   def get_image_feature(self, char_index):
     """Returns a subset of image features for a character.
@@ -352,7 +352,7 @@ class Attention(SequenceLayerBase):
   def __init__(self, *args, **kwargs):
     super(Attention, self).__init__(*args, **kwargs)
     self._zero_label = tf.zeros(
-        [self._batch_size, self._params.num_char_classes])
+        tf.stack([self._batch_size, self._params.num_char_classes]))
 
   def get_eval_input(self, prev, i):
     """See SequenceLayerBase.get_eval_input for details."""

research/attention_ocr/python/sequence_layers_test.py

@@ -29,13 +29,13 @@ import sequence_layers
 
 def fake_net(batch_size, num_features, feature_size):
   return tf.convert_to_tensor(
-      np.random.uniform(size=(batch_size, num_features, feature_size)),
+      value=np.random.uniform(size=(batch_size, num_features, feature_size)),
       dtype=tf.float32)
 
 
 def fake_labels(batch_size, seq_length, num_char_classes):
   labels_np = tf.convert_to_tensor(
-      np.random.randint(
+      value=np.random.randint(
           low=0, high=num_char_classes, size=(batch_size, seq_length)))
   return slim.one_hot_encoding(labels_np, num_classes=num_char_classes)
 

research/attention_ocr/python/train.py

@@ -96,16 +96,16 @@ def get_training_hparams():
 def create_optimizer(hparams):
   """Creates optimized based on the specified flags."""
   if hparams.optimizer == 'momentum':
-    optimizer = tf.train.MomentumOptimizer(
+    optimizer = tf.compat.v1.train.MomentumOptimizer(
         hparams.learning_rate, momentum=hparams.momentum)
   elif hparams.optimizer == 'adam':
-    optimizer = tf.train.AdamOptimizer(hparams.learning_rate)
+    optimizer = tf.compat.v1.train.AdamOptimizer(hparams.learning_rate)
   elif hparams.optimizer == 'adadelta':
-    optimizer = tf.train.AdadeltaOptimizer(hparams.learning_rate)
+    optimizer = tf.compat.v1.train.AdadeltaOptimizer(hparams.learning_rate)
   elif hparams.optimizer == 'adagrad':
-    optimizer = tf.train.AdagradOptimizer(hparams.learning_rate)
+    optimizer = tf.compat.v1.train.AdagradOptimizer(hparams.learning_rate)
   elif hparams.optimizer == 'rmsprop':
-    optimizer = tf.train.RMSPropOptimizer(
+    optimizer = tf.compat.v1.train.RMSPropOptimizer(
         hparams.learning_rate, momentum=hparams.momentum)
   return optimizer
 
@@ -154,14 +154,14 @@ def train(loss, init_fn, hparams):
 
 
 def prepare_training_dir():
-  if not tf.gfile.Exists(FLAGS.train_log_dir):
+  if not tf.io.gfile.exists(FLAGS.train_log_dir):
     logging.info('Create a new training directory %s', FLAGS.train_log_dir)
-    tf.gfile.MakeDirs(FLAGS.train_log_dir)
+    tf.io.gfile.makedirs(FLAGS.train_log_dir)
   else:
     if FLAGS.reset_train_dir:
       logging.info('Reset the training directory %s', FLAGS.train_log_dir)
-      tf.gfile.DeleteRecursively(FLAGS.train_log_dir)
-      tf.gfile.MakeDirs(FLAGS.train_log_dir)
+      tf.io.gfile.rmtree(FLAGS.train_log_dir)
+      tf.io.gfile.makedirs(FLAGS.train_log_dir)
     else:
       logging.info('Use already existing training directory %s',
                    FLAGS.train_log_dir)
@@ -169,7 +169,7 @@ def prepare_training_dir():
 
 def calculate_graph_metrics():
   param_stats = model_analyzer.print_model_analysis(
-      tf.get_default_graph(),
+      tf.compat.v1.get_default_graph(),
       tfprof_options=model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
   return param_stats.total_parameters
 
@@ -186,7 +186,7 @@ def main(_):
   # If ps_tasks is zero, the local device is used. When using multiple
   # (non-local) replicas, the ReplicaDeviceSetter distributes the variables
   # across the different devices.
-  device_setter = tf.train.replica_device_setter(
+  device_setter = tf.compat.v1.train.replica_device_setter(
       FLAGS.ps_tasks, merge_devices=True)
   with tf.device(device_setter):
     data = data_provider.get_data(

research/attention_ocr/python/utils.py

@@ -37,16 +37,16 @@ def logits_to_log_prob(logits):
     probabilities.
   """
 
-  with tf.variable_scope('log_probabilities'):
+  with tf.compat.v1.variable_scope('log_probabilities'):
     reduction_indices = len(logits.shape.as_list()) - 1
     max_logits = tf.reduce_max(
-        logits, reduction_indices=reduction_indices, keep_dims=True)
+        input_tensor=logits, axis=reduction_indices, keepdims=True)
     safe_logits = tf.subtract(logits, max_logits)
     sum_exp = tf.reduce_sum(
-        tf.exp(safe_logits),
-        reduction_indices=reduction_indices,
-        keep_dims=True)
-    log_probs = tf.subtract(safe_logits, tf.log(sum_exp))
+        input_tensor=tf.exp(safe_logits),
+        axis=reduction_indices,
+        keepdims=True)
+    log_probs = tf.subtract(safe_logits, tf.math.log(sum_exp))
   return log_probs
 
 
@@ -78,3 +78,20 @@ def variables_to_restore(scope=None, strip_scope=False):
     return variable_map
   else:
     return {v.op.name: v for v in slim.get_variables_to_restore()}
+
+
+def ConvertAllInputsToTensors(func):
+  """A decorator to convert all function's inputs into tensors.
+
+  Args:
+    func: a function to decorate.
+
+  Returns:
+    A decorated function.
+  """
+
+  def FuncWrapper(*args):
+    tensors = [tf.convert_to_tensor(value=a) for a in args]
+    return func(*tensors)
+
+  return FuncWrapper

research/autoencoder/AdditiveGaussianNoiseAutoencoderRunner.py

-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-import sklearn.preprocessing as prep
-import tensorflow as tf
-from tensorflow.examples.tutorials.mnist import input_data
-
-from autoencoder_models.DenoisingAutoencoder import AdditiveGaussianNoiseAutoencoder
-
-mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
-
-
-def standard_scale(X_train, X_test):
-    preprocessor = prep.StandardScaler().fit(X_train)
-    X_train = preprocessor.transform(X_train)
-    X_test = preprocessor.transform(X_test)
-    return X_train, X_test
-
-
-def get_random_block_from_data(data, batch_size):
-    start_index = np.random.randint(0, len(data) - batch_size)
-    return data[start_index:(start_index + batch_size)]
-
-
-X_train, X_test = standard_scale(mnist.train.images, mnist.test.images)
-
-n_samples = int(mnist.train.num_examples)
-training_epochs = 20
-batch_size = 128
-display_step = 1
-
-autoencoder = AdditiveGaussianNoiseAutoencoder(
-    n_input=784,
-    n_hidden=200,
-    transfer_function=tf.nn.softplus,
-    optimizer=tf.train.AdamOptimizer(learning_rate = 0.001),
-    scale=0.01)
-
-for epoch in range(training_epochs):
-    avg_cost = 0.
-    total_batch = int(n_samples / batch_size)
-    # Loop over all batches
-    for i in range(total_batch):
-        batch_xs = get_random_block_from_data(X_train, batch_size)
-
-        # Fit training using batch data
-        cost = autoencoder.partial_fit(batch_xs)
-        # Compute average loss
-        avg_cost += cost / n_samples * batch_size
-
-    # Display logs per epoch step
-    if epoch % display_step == 0:
-        print("Epoch:", '%d,' % (epoch + 1),
-              "Cost:", "{:.9f}".format(avg_cost))
-
-print("Total cost: " + str(autoencoder.calc_total_cost(X_test)))

research/autoencoder/AutoencoderRunner.py

-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-import sklearn.preprocessing as prep
-import tensorflow as tf
-from tensorflow.examples.tutorials.mnist import input_data
-
-from autoencoder_models.Autoencoder import Autoencoder
-
-mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
-
-
-def standard_scale(X_train, X_test):
-    preprocessor = prep.StandardScaler().fit(X_train)
-    X_train = preprocessor.transform(X_train)
-    X_test = preprocessor.transform(X_test)
-    return X_train, X_test
-
-
-def get_random_block_from_data(data, batch_size):
-    start_index = np.random.randint(0, len(data) - batch_size)
-    return data[start_index:(start_index + batch_size)]
-
-
-X_train, X_test = standard_scale(mnist.train.images, mnist.test.images)
-
-n_samples = int(mnist.train.num_examples)
-training_epochs = 20
-batch_size = 128
-display_step = 1
-
-autoencoder = Autoencoder(n_layers=[784, 200],
-                          transfer_function = tf.nn.softplus,
-                          optimizer = tf.train.AdamOptimizer(learning_rate = 0.001))
-
-for epoch in range(training_epochs):
-    avg_cost = 0.
-    total_batch = int(n_samples / batch_size)
-    # Loop over all batches
-    for i in range(total_batch):
-        batch_xs = get_random_block_from_data(X_train, batch_size)
-
-        # Fit training using batch data
-        cost = autoencoder.partial_fit(batch_xs)
-        # Compute average loss
-        avg_cost += cost / n_samples * batch_size
-
-    # Display logs per epoch step
-    if epoch % display_step == 0:
-        print("Epoch:", '%d,' % (epoch + 1),
-              "Cost:", "{:.9f}".format(avg_cost))
-
-print("Total cost: " + str(autoencoder.calc_total_cost(X_test)))

research/autoencoder/MaskingNoiseAutoencoderRunner.py

-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-import sklearn.preprocessing as prep
-import tensorflow as tf
-from tensorflow.examples.tutorials.mnist import input_data
-
-from autoencoder_models.DenoisingAutoencoder import MaskingNoiseAutoencoder
-
-mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
-
-
-def standard_scale(X_train, X_test):
-    preprocessor = prep.StandardScaler().fit(X_train)
-    X_train = preprocessor.transform(X_train)
-    X_test = preprocessor.transform(X_test)
-    return X_train, X_test
-
-
-def get_random_block_from_data(data, batch_size):
-    start_index = np.random.randint(0, len(data) - batch_size)
-    return data[start_index:(start_index + batch_size)]
-
-
-X_train, X_test = standard_scale(mnist.train.images, mnist.test.images)
-
-n_samples = int(mnist.train.num_examples)
-training_epochs = 100
-batch_size = 128
-display_step = 1
-
-autoencoder = MaskingNoiseAutoencoder(
-    n_input=784,
-    n_hidden=200,
-    transfer_function=tf.nn.softplus,
-    optimizer=tf.train.AdamOptimizer(learning_rate=0.001),
-    dropout_probability=0.95)
-
-for epoch in range(training_epochs):
-    avg_cost = 0.
-    total_batch = int(n_samples / batch_size)
-    for i in range(total_batch):
-        batch_xs = get_random_block_from_data(X_train, batch_size)
-
-        cost = autoencoder.partial_fit(batch_xs)
-
-        avg_cost += cost / n_samples * batch_size
-
-    if epoch % display_step == 0:
-        print("Epoch:", '%d,' % (epoch + 1),
-              "Cost:", "{:.9f}".format(avg_cost))
-
-print("Total cost: " + str(autoencoder.calc_total_cost(X_test)))