From 5c5dc8d5641b7c915f681109921dfb2b3e082a9b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 16 Oct 2017 17:59:11 -0700
Subject: Adding an ItemHandler that does lookups.  This allows decoding of
 tf.Examples where IDs are not materialized (e.g. 'image/object/class/text'
 present but 'image/object/class/label' not).

PiperOrigin-RevId: 172406978
---
 .../slim/python/slim/data/tfexample_decoder.py     | 36 ++++++++++++++++++++++
 .../python/slim/data/tfexample_decoder_test.py     | 31 +++++++++++++++++++
 2 files changed, 67 insertions(+)

diff --git a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py
index 094568389c..7a56df9e97 100644
--- a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py
+++ b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py
@@ -207,6 +207,42 @@ class Tensor(ItemHandler):
     return tensor
 
 
+class LookupTensor(Tensor):
+  """An ItemHandler that returns a parsed Tensor, the result of a lookup."""
+
+  def __init__(self,
+               tensor_key,
+               table,
+               shape_keys=None,
+               shape=None,
+               default_value=''):
+    """Initializes the LookupTensor handler.
+
+    See Tensor.  Simply calls a vocabulary (most often, a label mapping) lookup.
+
+    Args:
+      tensor_key: the name of the `TFExample` feature to read the tensor from.
+      table: A tf.lookup table.
+      shape_keys: Optional name or list of names of the TF-Example feature in
+        which the tensor shape is stored. If a list, then each corresponds to
+        one dimension of the shape.
+      shape: Optional output shape of the `Tensor`. If provided, the `Tensor` is
+        reshaped accordingly.
+      default_value: The value used when the `tensor_key` is not found in a
+        particular `TFExample`.
+
+    Raises:
+      ValueError: if both `shape_keys` and `shape` are specified.
+    """
+    self._table = table
+    super(LookupTensor, self).__init__(tensor_key, shape_keys, shape,
+                                       default_value)
+
+  def tensors_to_item(self, keys_to_tensors):
+    unmapped_tensor = super(LookupTensor, self).tensors_to_item(keys_to_tensors)
+    return self._table.lookup(unmapped_tensor)
+
+
 class SparseTensor(ItemHandler):
   """An ItemHandler for SparseTensors."""
 
diff --git a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder_test.py b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder_test.py
index 60d1eba07f..9c5a14d006 100644
--- a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder_test.py
+++ b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder_test.py
@@ -28,6 +28,7 @@ from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import image_ops
+from tensorflow.python.ops import lookup_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import parsing_ops
 from tensorflow.python.platform import test
@@ -811,6 +812,36 @@ class TFExampleDecoderTest(test.TestCase):
       self.assertAllEqual(np.squeeze(output_image[0, :, :, :]), image)
       self.assertAllEqual(np.squeeze(output_image[1, :, :, :]), image)
 
+  def testDecodeExampleWithLookup(self):
+
+    example = example_pb2.Example(features=feature_pb2.Features(feature={
+        'image/object/class/text': self._BytesFeature(
+            np.array(['cat', 'dog', 'guinea pig'])),
+    }))
+    serialized_example = example.SerializeToString()
+    # 'dog' -> 0, 'guinea pig' -> 1, 'cat' -> 2
+    table = lookup_ops.index_table_from_tensor(
+        constant_op.constant(['dog', 'guinea pig', 'cat']))
+
+    with self.test_session() as sess:
+      sess.run(lookup_ops.tables_initializer())
+
+      serialized_example = array_ops.reshape(serialized_example, shape=[])
+
+      keys_to_features = {
+          'image/object/class/text': parsing_ops.VarLenFeature(dtypes.string),
+      }
+
+      items_to_handlers = {
+          'labels':
+              tfexample_decoder.LookupTensor('image/object/class/text', table),
+      }
+
+      decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
+                                                   items_to_handlers)
+      obtained_class_ids = decoder.decode(serialized_example)[0].eval()
+
+    self.assertAllClose([2, 0, 1], obtained_class_ids)
 
 if __name__ == '__main__':
   test.main()
-- 
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