Internal change.

PiperOrigin-RevId: 419892069

tensorflow_graphics/nn/loss/chamfer_distance.py

@@ -21,9 +21,12 @@
 
 from tensorflow_graphics.util import export_api
 from tensorflow_graphics.util import shape
+from tensorflow_graphics.util import type_alias
 
 
-def evaluate(point_set_a, point_set_b, name="chamfer_distance_evaluate"):
+def evaluate(point_set_a: type_alias.TensorLike,
+             point_set_b: type_alias.TensorLike,
+             name: str = "chamfer_distance_evaluate") -> tf.Tensor:
   """Computes the Chamfer distance for the given two point sets.
 
   Note:

tensorflow_graphics/nn/loss/hausdorff_distance.py

@@ -21,9 +21,12 @@
 
 from tensorflow_graphics.util import export_api
 from tensorflow_graphics.util import shape
+from tensorflow_graphics.util import type_alias
 
 
-def evaluate(point_set_a, point_set_b, name="hausdorff_distance_evaluate"):
+def evaluate(point_set_a: type_alias.TensorLike,
+             point_set_b: type_alias.TensorLike,
+             name: str = "hausdorff_distance_evaluate") -> tf.Tensor:
   """Computes the Hausdorff distance from point_set_a to point_set_b.
 
   Note:

tensorflow_graphics/nn/metric/fscore.py

@@ -17,20 +17,23 @@
 from __future__ import division
 from __future__ import print_function
 
+from typing import Any, Callable
+
 import tensorflow as tf
 
 from tensorflow_graphics.nn.metric import precision as precision_module
 from tensorflow_graphics.nn.metric import recall as recall_module
 from tensorflow_graphics.util import export_api
 from tensorflow_graphics.util import safe_ops
 from tensorflow_graphics.util import shape
+from tensorflow_graphics.util import type_alias
 
 
-def evaluate(ground_truth,
-             prediction,
-             precision_function=precision_module.evaluate,
-             recall_function=recall_module.evaluate,
-             name="fscore_evaluate"):
+def evaluate(ground_truth: type_alias.TensorLike,
+             prediction: type_alias.TensorLike,
+             precision_function: Callable[..., Any] = precision_module.evaluate,
+             recall_function: Callable[..., Any] = recall_module.evaluate,
+             name: str = "fscore_evaluate") -> tf.Tensor:
   """Computes the fscore metric for the given ground truth and predicted labels.
 
   The fscore is calculated as 2 * (precision * recall) / (precision + recall)

tensorflow_graphics/nn/metric/intersection_over_union.py

@@ -23,12 +23,13 @@
 from tensorflow_graphics.util import asserts
 from tensorflow_graphics.util import export_api
 from tensorflow_graphics.util import shape
+from tensorflow_graphics.util import type_alias
 
 
-def evaluate(ground_truth_labels,
-             predicted_labels,
-             grid_size=1,
-             name="intersection_over_union_evaluate"):
+def evaluate(ground_truth_labels: type_alias.TensorLike,
+             predicted_labels: type_alias.TensorLike,
+             grid_size: int = 1,
+             name: str = "intersection_over_union_evaluate") -> tf.Tensor:
   """Computes the Intersection-Over-Union metric for the given ground truth and predicted labels.
 
   Note:

tensorflow_graphics/nn/metric/precision.py

@@ -17,23 +17,26 @@
 from __future__ import division
 from __future__ import print_function
 
+from typing import Any, Callable, List, Optional, Union, Tuple
+
 import tensorflow as tf
 
 from tensorflow_graphics.util import export_api
 from tensorflow_graphics.util import safe_ops
 from tensorflow_graphics.util import shape
+from tensorflow_graphics.util import type_alias
 
 
 def _cast_to_int(prediction):
   return tf.cast(x=prediction, dtype=tf.int32)
 
 
-def evaluate(ground_truth,
-             prediction,
-             classes=None,
-             reduce_average=True,
-             prediction_to_category_function=_cast_to_int,
-             name="precision_evaluate"):
+def evaluate(ground_truth: type_alias.TensorLike,
+             prediction: type_alias.TensorLike,
+             classes: Optional[Union[int, List[int], Tuple[int]]] = None,
+             reduce_average: bool = True,
+             prediction_to_category_function: Callable[..., Any] = _cast_to_int,
+             name: str = "precision_evaluate") -> tf.Tensor:
   """Computes the precision metric for the given ground truth and predictions.
 
   Note:

tensorflow_graphics/nn/metric/recall.py

@@ -17,23 +17,26 @@
 from __future__ import division
 from __future__ import print_function
 
+from typing import Any, Callable, List, Optional, Tuple, Union
+
 import tensorflow as tf
 
 from tensorflow_graphics.util import export_api
 from tensorflow_graphics.util import safe_ops
 from tensorflow_graphics.util import shape
+from tensorflow_graphics.util import type_alias
 
 
 def _cast_to_int(prediction):
   return tf.cast(x=prediction, dtype=tf.int32)
 
 
-def evaluate(ground_truth,
-             prediction,
-             classes=None,
-             reduce_average=True,
-             prediction_to_category_function=_cast_to_int,
-             name="recall_evaluate"):
+def evaluate(ground_truth: type_alias.TensorLike,
+             prediction: type_alias.TensorLike,
+             classes: Optional[Union[int, List[int], Tuple[int]]] = None,
+             reduce_average: bool = True,
+             prediction_to_category_function: Callable[..., Any] = _cast_to_int,
+             name: str = "recall_evaluate") -> tf.Tensor:
   """Computes the recall metric for the given ground truth and predictions.
 
   Note:

tensorflow_graphics/notebooks/mesh_segmentation_dataio.py

@@ -24,12 +24,15 @@
 from __future__ import division
 from __future__ import print_function
 
+from typing import Any, Callable, Dict, List, Tuple
+
 import numpy as np
 import tensorflow as tf
 
 from tensorflow_graphics.geometry.convolution import utils as conv_utils
 from tensorflow_graphics.geometry.representation.mesh import utils as mesh_utils
 from tensorflow_graphics.util import shape
+from tensorflow_graphics.util import type_alias
 
 DEFAULT_IO_PARAMS = {
     'batch_size': 8,
@@ -42,7 +45,11 @@
 }
 
 
-def adjacency_from_edges(edges, weights, num_edges, num_vertices):
+def adjacency_from_edges(
+    edges: type_alias.TensorLike,
+    weights: type_alias.TensorLike,
+    num_edges: type_alias.TensorLike,
+    num_vertices: type_alias.TensorLike) -> tf.SparseTensor:
   """Returns a batched sparse 1-ring adj tensor from edge list tensor.
 
   Args:
@@ -103,7 +110,9 @@ def adjacency_from_edges(edges, weights, num_edges, num_vertices):
   return adjacency
 
 
-def get_weighted_edges(faces, self_edges=True):
+def get_weighted_edges(
+    faces: np.ndarray,
+    self_edges: bool = True) -> Tuple[np.ndarray, np.ndarray]:
   r"""Gets unique edges and degree weights from a triangular mesh.
 
   The shorthands used below are:
@@ -136,12 +145,12 @@ def get_weighted_edges(faces, self_edges=True):
   return edges, weights
 
 
-def _tfrecords_to_dataset(tfrecords,
-                          parallel_threads,
-                          shuffle,
-                          repeat,
-                          sloppy,
-                          max_readers=16):
+def _tfrecords_to_dataset(tfrecords: List[str],
+                          parallel_threads: int,
+                          shuffle: bool,
+                          repeat: bool,
+                          sloppy: bool,
+                          max_readers: int = 16) -> tf.data.TFRecordDataset:
   """Creates a TFRecordsDataset that iterates over filenames in parallel.
 
   Args:
@@ -244,7 +253,9 @@ def _parse_mesh_data(mesh_data, mean_center=True):
   return mesh_data
 
 
-def create_dataset_from_tfrecords(tfrecords, params):
+def create_dataset_from_tfrecords(
+    tfrecords: List[str],
+    params: Dict[str, Any]) -> tf.data.Dataset:
   """Creates a mesh dataset given a list of tf records filenames.
 
   Args:
@@ -309,7 +320,10 @@ def _set_default_if_none(param, param_dict, default_val):
       drop_remainder=is_training)
 
 
-def create_input_from_dataset(dataset_fn, files, io_params):
+def create_input_from_dataset(
+    dataset_fn: Callable[..., Any],
+    files: List[str],
+    io_params: Dict[str, Any]) -> Tuple[Dict[str, Any], tf.Tensor]:
   """Creates input function given dataset generator and input files.
 
   Args:

tensorflow_graphics/notebooks/mesh_viewer.py

@@ -18,6 +18,8 @@
 from __future__ import division
 from __future__ import print_function
 
+from typing import Any, Dict
+
 import numpy as np
 from tensorflow_graphics.notebooks import threejs_visualization
 
@@ -32,7 +34,7 @@
 class Viewer(object):
   """A ThreeJS based viewer class for viewing 3D meshes."""
 
-  def _mesh_from_data(self, data):
+  def _mesh_from_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
     """Creates a dictionary of ThreeJS mesh objects from numpy data."""
     if 'vertices' not in data or 'faces' not in data:
       raise ValueError('Mesh Data must contain vertices and faces')
@@ -54,7 +56,7 @@ def _mesh_from_data(self, data):
     mesh['material'] = material
     return mesh
 
-  def __init__(self, source_mesh_data):
+  def __init__(self, source_mesh_data: Dict[str, Any]):
     context = threejs_visualization.build_context()
     self.context = context
     light1 = context.THREE.PointLight.new_object(0x808080)