Adds typing information to the module util.shape.

PiperOrigin-RevId: 425878021

Author: G4G

tensorflow_graphics/util/shape.py

@@ -18,6 +18,7 @@
 from __future__ import print_function
 
 import itertools
+from typing import Any, List, Optional, Tuple, Union
 
 import numpy as np
 import six
@@ -26,7 +27,8 @@
 import tensorflow as tf
 
 
-def _broadcast_shape_helper(shape_x, shape_y):
+def _broadcast_shape_helper(shape_x: tf.TensorShape,
+                            shape_y: tf.TensorShape) -> Optional[List[Any]]:
   """Helper function for is_broadcast_compatible and broadcast_shape.
 
   Args:
@@ -74,7 +76,8 @@ def _broadcast_shape_helper(shape_x, shape_y):
   return return_dims
 
 
-def is_broadcast_compatible(shape_x, shape_y):
+def is_broadcast_compatible(shape_x: tf.TensorShape,
+                            shape_y: tf.TensorShape) -> bool:
   """Returns True if `shape_x` and `shape_y` are broadcast compatible.
 
   Args:
@@ -90,7 +93,8 @@ def is_broadcast_compatible(shape_x, shape_y):
   return _broadcast_shape_helper(shape_x, shape_y) is not None
 
 
-def get_broadcasted_shape(shape_x, shape_y):
+def get_broadcasted_shape(shape_x: tf.TensorShape,
+                          shape_y: tf.TensorShape) -> Optional[List[Any]]:
   """Returns the common shape for broadcast compatible shapes.
 
   Args:
@@ -135,14 +139,15 @@ def _get_dim(tensor, axis):
   return tf.compat.dimension_value(tensor.shape[axis])
 
 
-def check_static(tensor,
-                 has_rank=None,
-                 has_rank_greater_than=None,
-                 has_rank_less_than=None,
+def check_static(tensor: tf.Tensor,
+                 has_rank: Optional[int] = None,
+                 has_rank_greater_than: Optional[int] = None,
+                 has_rank_less_than: Optional[int] = None,
                  has_dim_equals=None,
                  has_dim_greater_than=None,
                  has_dim_less_than=None,
-                 tensor_name='tensor'):
+                 tensor_name: str = 'tensor') -> None:
+  # TODO(cengizo): Typing for has_dim_equals, has_dim_greater(less)_than.
   """Checks static shapes for rank and dimension constraints.
 
   This function can be used to check a tensor's shape for multiple rank and
@@ -276,11 +281,12 @@ def _raise_error(tensor_names, batch_shapes):
       'Not all batch dimensions are identical: {}'.format(formatted_list))
 
 
-def compare_batch_dimensions(tensors,
-                             last_axes,
-                             broadcast_compatible,
-                             initial_axes=0,
-                             tensor_names=None):
+def compare_batch_dimensions(
+    tensors: Union[List[tf.Tensor], Tuple[tf.Tensor]],
+    last_axes: Union[int, List[int], Tuple[int]],
+    broadcast_compatible: bool,
+    initial_axes: Union[int, List[int], Tuple[int]] = 0,
+    tensor_names: Optional[Union[List[str], Tuple[str]]] = None) -> None:
   """Compares batch dimensions for tensors with static shapes.
 
   Args:
@@ -347,7 +353,10 @@ def compare_batch_dimensions(tensors,
           ]))
 
 
-def compare_dimensions(tensors, axes, tensor_names=None):
+def compare_dimensions(
+    tensors: Union[List[tf.Tensor], Tuple[tf.Tensor]],
+    axes: Union[int, List[int], Tuple[int]],
+    tensor_names: Optional[Union[List[str], Tuple[str]]] = None) -> None:
   """Compares dimensions of tensors with static or dynamic shapes.
 
   Args:
@@ -376,15 +385,19 @@ def compare_dimensions(tensors, axes, tensor_names=None):
                          list(tensor_names), list(axes), list(dimensions)))
 
 
-def is_static(tensor_shape):
+def is_static(
+    tensor_shape: Union[List[Any], Tuple[Any], tf.TensorShape]) -> bool:
   """Checks if the given tensor shape is static."""
   if isinstance(tensor_shape, (list, tuple)):
     return None not in tensor_shape
   else:
     return None not in tensor_shape.as_list()
 
 
-def add_batch_dimensions(tensor, tensor_name, batch_shape, last_axis=None):
+def add_batch_dimensions(tensor: tf.Tensor,
+                         tensor_name: str,
+                         batch_shape: List[int],
+                         last_axis: Optional[int] = None) -> tf.Tensor:
   """Broadcasts tensor to match batch dimensions.
 
   It will either broadcast to all provided batch dimensions, therefore