Add support for variable major dimension in dense features in example parser c++ op.

Full python support (including more comprehensive documentation) coming soon.
Change: 146852707

1 files changed, 37 insertions, 11 deletions

diff --git a/tensorflow/python/ops/parsing_ops.py b/tensorflow/python/ops/parsing_ops.py
index 079837bce3..77c7cd397a 100644
--- a/tensorflow/python/ops/parsing_ops.py
+++ b/tensorflow/python/ops/parsing_ops.py
@@ -476,8 +476,13 @@ def _parse_example_raw(serialized,
       The keys of the dict must match the dense_keys of the feature.
     dense_shapes: A list of tuples with the same length as `dense_keys`.
       The shape of the data for each dense feature referenced by `dense_keys`.
-      Required for any input tensors identified by `dense_keys` whose shapes are
-      anything other than `[]` or `[1]`.
+      Required for any input tensors identified by `dense_keys`.  Must be
+      either fully defined, or may contain an unknown first dimension.
+      An unknown first dimension means the feature is treated as having
+      a variable number of blocks, and the output shape along this dimension
+      is considered unknown at graph build time.  Padding is applied for
+      minibatch elements smaller than the maximum number of blocks for the
+      given feature along this dimension.
     name: A name for this operation (optional).
 
   Returns:
@@ -516,21 +521,42 @@ def _parse_example_raw(serialized,
           "Dense and sparse keys must not intersect; intersection: %s" %
           set(dense_keys).intersection(set(sparse_keys)))
 
+    # Convert dense_shapes to TensorShape object.
+    dense_shapes = [tensor_shape.as_shape(shape) for shape in dense_shapes]
+
     dense_defaults_vec = []
     for i, key in enumerate(dense_keys):
       default_value = dense_defaults.get(key)
-      if default_value is None:
-        default_value = constant_op.constant([], dtype=dense_types[i])
-      elif not isinstance(default_value, ops.Tensor):
-        key_name = "key_" + re.sub("[^A-Za-z0-9_.\\-/]", "_", key)
-        default_value = ops.convert_to_tensor(
-            default_value, dtype=dense_types[i], name=key_name)
-        default_value = array_ops.reshape(default_value, dense_shapes[i])
+      dense_shape = dense_shapes[i]
+      if (dense_shape.ndims is not None and dense_shape.ndims > 0 and
+          dense_shape[0].value is None):
+        # Variable stride dense shape, the default value should be a
+        # scalar padding value
+        if default_value is None:
+          default_value = ops.convert_to_tensor(
+              "" if dense_types[i] == dtypes.string else 0,
+              dtype=dense_types[i])
+        else:
+          # Reshape to a scalar to ensure user gets an error if they
+          # provide a tensor that's not intended to be a padding value
+          # (0 or 2+ elements).
+          key_name = "padding_" + re.sub("[^A-Za-z0-9_.\\-/]", "_", key)
+          default_value = ops.convert_to_tensor(
+              default_value, dtype=dense_types[i], name=key_name)
+          default_value = array_ops.reshape(default_value, [])
+      else:
+        if default_value is None:
+          default_value = constant_op.constant([], dtype=dense_types[i])
+        elif not isinstance(default_value, ops.Tensor):
+          key_name = "key_" + re.sub("[^A-Za-z0-9_.\\-/]", "_", key)
+          default_value = ops.convert_to_tensor(
+              default_value, dtype=dense_types[i], name=key_name)
+          default_value = array_ops.reshape(default_value, dense_shape)
 
       dense_defaults_vec.append(default_value)
 
-    dense_shapes = [tensor_shape.as_shape(shape).as_proto()
-                    for shape in dense_shapes]
+    # Finally, convert dense_shapes to TensorShapeProto
+    dense_shapes = [shape.as_proto() for shape in dense_shapes]
 
     # pylint: disable=protected-access
     outputs = gen_parsing_ops._parse_example(