Merge pull request #2166 from alexgorban/master

Demo script to do inference on a trained Attention OCR model

attention_ocr/python/demo_inference.py

+"""A script to run inference on a set of image files.
+
+NOTE #1: The Attention OCR model was trained only using FSNS train dataset and 
+it will work only for images which look more or less similar to french street 
+names. In order to apply it to images from a different distribution you need 
+to retrain (or at least fine-tune) it using images from that distribution. 
+
+NOTE #2: This script exists for demo purposes only. It is highly recommended
+to use tools and mechanisms provided by the TensorFlow Serving system to run
+inference on TensorFlow models in production:
+https://www.tensorflow.org/serving/serving_basic
+
+Usage:
+python demo_inference.py --batch_size=32 \
+  --image_path_pattern=./datasets/data/fsns/temp/fsns_train_%02d.png
+"""
+import numpy as np
+import PIL.Image
+
+import tensorflow as tf
+from tensorflow.python.platform import flags
+
+import common_flags
+import datasets
+import model as attention_ocr
+
+FLAGS = flags.FLAGS
+common_flags.define()
+
+# e.g. ./datasets/data/fsns/temp/fsns_train_%02d.png
+flags.DEFINE_string('image_path_pattern', '',
+                    'A file pattern with a placeholder for the image index.')
+
+
+def get_dataset_image_size(dataset_name):
+  # Ideally this info should be exposed through the dataset interface itself.
+  # But currently it is not available by other means.
+  ds_module = getattr(datasets, dataset_name)
+  height, width, _ = ds_module.DEFAULT_CONFIG['image_shape']
+  return width, height
+
+
+def load_images(file_pattern, batch_size, dataset_name):
+  width, height = get_dataset_image_size(dataset_name)
+  images_actual_data = np.ndarray(shape=(batch_size, height, width, 3),
+                                  dtype='float32')
+  for i in range(batch_size):
+    path = file_pattern % i
+    print("Reading %s" % path)
+    pil_image = PIL.Image.open(tf.gfile.GFile(path))
+    images_actual_data[i, ...] = np.asarray(pil_image)
+  return images_actual_data
+
+
+def load_model(checkpoint, 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)
+  images_placeholder = tf.placeholder(tf.float32,
+                                      shape=[batch_size, height, width, 3])
+  endpoints = model.create_base(images_placeholder, labels_one_hot=None)
+  init_fn = model.create_init_fn_to_restore(checkpoint)
+  return images_placeholder, endpoints, init_fn
+
+
+def main(_):
+  images_placeholder, endpoints, init_fn = load_model(FLAGS.checkpoint,
+                                                      FLAGS.batch_size,
+                                                      FLAGS.dataset_name)
+  images_data = load_images(FLAGS.image_path_pattern, FLAGS.batch_size,
+                            FLAGS.dataset_name)
+  with tf.Session() as sess:
+    tf.tables_initializer().run()  # required by the CharsetMapper
+    init_fn(sess)
+    predictions = sess.run(endpoints.predicted_text,
+                           feed_dict={images_placeholder: images_data})
+  print("Predicted strings:")
+  for line in predictions:
+    print(line)
+
+
+if __name__ == '__main__':
+  tf.app.run()

attention_ocr/python/model.py

@@ -34,9 +34,9 @@ import metrics
 import sequence_layers
 import utils
 
-
 OutputEndpoints = collections.namedtuple('OutputEndpoints', [
-    'chars_logit', 'chars_log_prob', 'predicted_chars', 'predicted_scores'
+  'chars_logit', 'chars_log_prob', 'predicted_chars', 'predicted_scores',
+  'predicted_text'
 ])
 
 # TODO(gorban): replace with tf.HParams when it is released.
@@ -129,7 +129,8 @@ class Model(object):
       seq_length,
       num_views,
       null_code,
-               mparams=None):
+      mparams=None,
+      charset=None):
     """Initialized model parameters.
 
     Args:
@@ -140,6 +141,13 @@ class Model(object):
         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
+        OutputEndpoints.predicted_chars (by default the predicted_text contains
+        an empty vector). 
+        NOTE: Make sure you call tf.tables_initializer().run() if the charset
+        specified.
     """
     super(Model, self).__init__()
     self._params = ModelParams(
@@ -150,6 +158,7 @@ class Model(object):
     self._mparams = self.default_mparams()
     if mparams:
       self._mparams.update(mparams)
+    self._charset = charset
 
   def default_mparams(self):
     return {
@@ -364,12 +373,17 @@ class Model(object):
 
       predicted_chars, chars_log_prob, predicted_scores = (
         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([])
     return OutputEndpoints(
         chars_logit=chars_logit,
         chars_log_prob=chars_log_prob,
         predicted_chars=predicted_chars,
-        predicted_scores=predicted_scores)
+        predicted_scores=predicted_scores,
+        predicted_text=predicted_text)
 
   def create_loss(self, data, endpoints):
     """Creates all losses required to train the model.
@@ -523,7 +537,8 @@ class Model(object):
         tf.summary.scalar(summary_name, tf.Print(value, [value], summary_name))
       return names_to_updates.values()
 
-  def create_init_fn_to_restore(self, master_checkpoint, inception_checkpoint):
+  def create_init_fn_to_restore(self, master_checkpoint,
+      inception_checkpoint=None):
     """Creates an init operations to restore weights from various checkpoints.
 
     Args:

attention_ocr/python/model_test.py

@@ -73,9 +73,10 @@ class ModelTest(tf.test.TestCase):
             high=self.num_char_classes,
             size=(self.batch_size, self.seq_length)).astype('int64'))
 
-  def create_model(self):
+  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()
@@ -244,6 +245,21 @@ class ModelTest(tf.test.TestCase):
 
     self.assertAllEqual(conv_w_coords_tf, conv_w_coords_alt_tf)
 
+  def test_predicted_text_has_correct_shape_w_charset(self):
+    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())
+      tf.tables_initializer().run()
+      endpoints = sess.run(endpoints_tf)
+
+      self.assertEqual(endpoints.predicted_text.shape, (self.batch_size,))
+      self.assertEqual(len(endpoints.predicted_text[0]), self.seq_length)
+
 
 class CharsetMapperTest(tf.test.TestCase):
   def test_text_corresponds_to_ids(self):