Internal change

PiperOrigin-RevId: 376298243

official/core/base_task.py

@@ -38,7 +38,10 @@ class Task(tf.Module, metaclass=abc.ABCMeta):
   # Special keys in train/validate step returned logs.
   loss = "loss"
 
-  def __init__(self, params, logging_dir: str = None, name: str = None):
+  def __init__(self,
+               params,
+               logging_dir: Optional[str] = None,
+               name: Optional[str] = None):
     """Task initialization.
 
     Args:

official/modeling/optimization/ema_optimizer.py

@@ -14,7 +14,7 @@
 
 """Exponential moving average optimizer."""
 
-from typing import Text, List
+from typing import List, Optional, Text
 
 import tensorflow as tf
 
@@ -106,7 +106,7 @@ class ExponentialMovingAverage(tf.keras.optimizers.Optimizer):
   def _create_slots(self, var_list):
     self._optimizer._create_slots(var_list=var_list)  # pylint: disable=protected-access
 
-  def apply_gradients(self, grads_and_vars, name: Text = None):
+  def apply_gradients(self, grads_and_vars, name: Optional[Text] = None):
     result = self._optimizer.apply_gradients(grads_and_vars, name)
     self.update_average(self.iterations)
     return result

official/modeling/optimization/optimizer_factory.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 """Optimizer factory class."""
-from typing import Callable, Union
+from typing import Callable, Optional, Union
 
 import gin
 import tensorflow as tf
@@ -134,8 +134,8 @@ class OptimizerFactory:
   def build_optimizer(
       self,
       lr: Union[tf.keras.optimizers.schedules.LearningRateSchedule, float],
-      postprocessor: Callable[[tf.keras.optimizers.Optimizer],
-                              tf.keras.optimizers.Optimizer] = None):
+      postprocessor: Optional[Callable[[tf.keras.optimizers.Optimizer],
+                                       tf.keras.optimizers.Optimizer]] = None):
     """Build optimizer.
 
     Builds optimizer from config. It takes learning rate as input, and builds

official/nlp/modeling/models/xlnet.py

@@ -15,7 +15,7 @@
 """XLNet models."""
 # pylint: disable=g-classes-have-attributes
 
-from typing import Any, Mapping, Union
+from typing import Any, Mapping, Optional, Union
 
 import tensorflow as tf
 
@@ -99,7 +99,7 @@ class XLNetPretrainer(tf.keras.Model):
       network: Union[tf.keras.layers.Layer, tf.keras.Model],
       mlm_activation=None,
       mlm_initializer='glorot_uniform',
-      name: str = None,
+      name: Optional[str] = None,
       **kwargs):
     super().__init__(name=name, **kwargs)
     self._config = {

official/vision/beta/modeling/heads/dense_prediction_heads.py

@@ -36,7 +36,7 @@ class RetinaNetHead(tf.keras.layers.Layer):
       num_anchors_per_location: int,
       num_convs: int = 4,
       num_filters: int = 256,
-      attribute_heads: List[Dict[str, Any]] = None,
+      attribute_heads: Optional[List[Dict[str, Any]]] = None,
       use_separable_conv: bool = False,
       activation: str = 'relu',
       use_sync_bn: bool = False,

official/vision/beta/modeling/layers/detection_generator.py

@@ -593,7 +593,7 @@ class MultilevelDetectionGenerator(tf.keras.layers.Layer):
                raw_scores: Mapping[str, tf.Tensor],
                anchor_boxes: tf.Tensor,
                image_shape: tf.Tensor,
-               raw_attributes: Mapping[str, tf.Tensor] = None):
+               raw_attributes: Optional[Mapping[str, tf.Tensor]] = None):
     """Generates final detections.
 
     Args:

official/vision/beta/projects/assemblenet/modeling/assemblenet.py

@@ -411,7 +411,7 @@ class _ApplyEdgeWeight(layers.Layer):
 
   def __init__(self,
                weights_shape,
-               index: int = None,
+               index: Optional[int] = None,
                use_5d_mode: bool = False,
                model_edge_weights: Optional[List[Any]] = None,
                **kwargs):
@@ -471,7 +471,7 @@ class _ApplyEdgeWeight(layers.Layer):
 
   def call(self,
            inputs: List[tf.Tensor],
-           training: bool = None) -> Mapping[Any, List[tf.Tensor]]:
+           training: Optional[bool] = None) -> Mapping[Any, List[tf.Tensor]]:
     use_5d_mode = self._use_5d_mode
     dtype = inputs[0].dtype
     assert len(inputs) > 1
@@ -517,7 +517,7 @@ class _ApplyEdgeWeight(layers.Layer):
 
 
 def multi_connection_fusion(inputs: List[tf.Tensor],
-                            index: int = None,
+                            index: Optional[int] = None,
                             use_5d_mode: bool = False,
                             model_edge_weights: Optional[List[Any]] = None):
   """Do weighted summation of multiple different sized tensors.
@@ -893,7 +893,8 @@ class AssembleNetModel(tf.keras.Model):
                num_classes,
                num_frames: int,
                model_structure: List[Any],
-               input_specs: Mapping[str, tf.keras.layers.InputSpec] = None,
+               input_specs: Optional[Mapping[str,
+                                             tf.keras.layers.InputSpec]] = None,
                max_pool_preditions: bool = False,
                **kwargs):
     if not input_specs:
@@ -1018,7 +1019,8 @@ def build_assemblenet_v1(
     input_specs: tf.keras.layers.InputSpec,
     backbone_config: hyperparams.Config,
     norm_activation_config: hyperparams.Config,
-    l2_regularizer: tf.keras.regularizers.Regularizer = None) -> tf.keras.Model:
+    l2_regularizer: Optional[tf.keras.regularizers.Regularizer] = None
+) -> tf.keras.Model:
   """Builds assemblenet backbone."""
   del l2_regularizer
 
@@ -1058,7 +1060,7 @@ def build_assemblenet_model(
     input_specs: tf.keras.layers.InputSpec,
     model_config: cfg.AssembleNetModel,
     num_classes: int,
-    l2_regularizer: tf.keras.regularizers.Regularizer = None):
+    l2_regularizer: Optional[tf.keras.regularizers.Regularizer] = None):
   """Builds assemblenet model."""
   input_specs_dict = {'image': input_specs}
   backbone = build_assemblenet_v1(input_specs, model_config.backbone,

official/vision/beta/projects/movinet/modeling/movinet_model.py

@@ -16,7 +16,7 @@
 
 Reference: https://arxiv.org/pdf/2103.11511.pdf
 """
-from typing import Mapping
+from typing import Mapping, Optional
 
 from absl import logging
 import tensorflow as tf
@@ -31,16 +31,17 @@ from official.vision.beta.projects.movinet.modeling import movinet_layers
 class MovinetClassifier(tf.keras.Model):
   """A video classification class builder."""
 
-  def __init__(self,
-               backbone: tf.keras.Model,
-               num_classes: int,
-               input_specs: Mapping[str, tf.keras.layers.InputSpec] = None,
-               dropout_rate: float = 0.0,
-               kernel_initializer: str = 'HeNormal',
-               kernel_regularizer: tf.keras.regularizers.Regularizer = None,
-               bias_regularizer: tf.keras.regularizers.Regularizer = None,
-               output_states: bool = False,
-               **kwargs):
+  def __init__(
+      self,
+      backbone: tf.keras.Model,
+      num_classes: int,
+      input_specs: Optional[Mapping[str, tf.keras.layers.InputSpec]] = None,
+      dropout_rate: float = 0.0,
+      kernel_initializer: str = 'HeNormal',
+      kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
+      bias_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
+      output_states: bool = False,
+      **kwargs):
     """Movinet initialization function.
 
     Args:
@@ -144,7 +145,7 @@ def build_movinet_model(
     input_specs: tf.keras.layers.InputSpec,
     model_config: cfg.MovinetModel,
     num_classes: int,
-    l2_regularizer: tf.keras.regularizers.Regularizer = None):
+    l2_regularizer: Optional[tf.keras.regularizers.Regularizer] = None):
   """Builds movinet model."""
   logging.info('Building movinet model with num classes: %s', num_classes)
   if l2_regularizer is not None:

official/vision/detection/executor/distributed_executor.py

@@ -322,21 +322,21 @@ class DistributedExecutor(object):
 
     return test_step
 
-  def train(self,
-            train_input_fn: Callable[[params_dict.ParamsDict], tf.data.Dataset],
-            eval_input_fn: Callable[[params_dict.ParamsDict],
-                                    tf.data.Dataset] = None,
-            model_dir: Text = None,
-            total_steps: int = 1,
-            iterations_per_loop: int = 1,
-            train_metric_fn: Callable[[], Any] = None,
-            eval_metric_fn: Callable[[], Any] = None,
-            summary_writer_fn: Callable[[Text, Text],
-                                        SummaryWriter] = SummaryWriter,
-            init_checkpoint: Callable[[tf.keras.Model], Any] = None,
-            custom_callbacks: List[tf.keras.callbacks.Callback] = None,
-            continuous_eval: bool = False,
-            save_config: bool = True):
+  def train(
+      self,
+      train_input_fn: Callable[[params_dict.ParamsDict], tf.data.Dataset],
+      eval_input_fn: Optional[Callable[[params_dict.ParamsDict],
+                                       tf.data.Dataset]] = None,
+      model_dir: Optional[Text] = None,
+      total_steps: int = 1,
+      iterations_per_loop: int = 1,
+      train_metric_fn: Optional[Callable[[], Any]] = None,
+      eval_metric_fn: Optional[Callable[[], Any]] = None,
+      summary_writer_fn: Callable[[Text, Text], SummaryWriter] = SummaryWriter,
+      init_checkpoint: Optional[Callable[[tf.keras.Model], Any]] = None,
+      custom_callbacks: Optional[List[tf.keras.callbacks.Callback]] = None,
+      continuous_eval: bool = False,
+      save_config: bool = True):
     """Runs distributed training.
 
     Args:
@@ -590,7 +590,7 @@ class DistributedExecutor(object):
       eval_input_fn: Callable[[params_dict.ParamsDict], tf.data.Dataset],
       eval_metric_fn: Callable[[], Any],
       total_steps: int = -1,
-      eval_timeout: int = None,
+      eval_timeout: Optional[int] = None,
       min_eval_interval: int = 180,
       summary_writer_fn: Callable[[Text, Text], SummaryWriter] = SummaryWriter):
     """Runs distributed evaluation on model folder.
@@ -646,7 +646,7 @@ class DistributedExecutor(object):
                           eval_input_fn: Callable[[params_dict.ParamsDict],
                                                   tf.data.Dataset],
                           eval_metric_fn: Callable[[], Any],
-                          summary_writer: SummaryWriter = None):
+                          summary_writer: Optional[SummaryWriter] = None):
     """Runs distributed evaluation on the one checkpoint.
 
     Args:

official/vision/image_classification/efficientnet/efficientnet_model.py

@@ -160,9 +160,9 @@ def conv2d_block(inputs: tf.Tensor,
                  strides: Any = (1, 1),
                  use_batch_norm: bool = True,
                  use_bias: bool = False,
-                 activation: Any = None,
+                 activation: Optional[Any] = None,
                  depthwise: bool = False,
-                 name: Text = None):
+                 name: Optional[Text] = None):
   """A conv2d followed by batch norm and an activation."""
   batch_norm = common_modules.get_batch_norm(config.batch_norm)
   bn_momentum = config.bn_momentum
@@ -212,7 +212,7 @@ def conv2d_block(inputs: tf.Tensor,
 def mb_conv_block(inputs: tf.Tensor,
                   block: BlockConfig,
                   config: ModelConfig,
-                  prefix: Text = None):
+                  prefix: Optional[Text] = None):
   """Mobile Inverted Residual Bottleneck.
 
   Args:
@@ -432,8 +432,8 @@ class EfficientNet(tf.keras.Model):
   """
 
   def __init__(self,
-               config: ModelConfig = None,
-               overrides: Dict[Text, Any] = None):
+               config: Optional[ModelConfig] = None,
+               overrides: Optional[Dict[Text, Any]] = None):
     """Create an EfficientNet model.
 
     Args:
@@ -463,9 +463,9 @@ class EfficientNet(tf.keras.Model):
   @classmethod
   def from_name(cls,
                 model_name: Text,
-                model_weights_path: Text = None,
+                model_weights_path: Optional[Text] = None,
                 weights_format: Text = 'saved_model',
-                overrides: Dict[Text, Any] = None):
+                overrides: Optional[Dict[Text, Any]] = None):
     """Construct an EfficientNet model from a predefined model name.
 
     E.g., `EfficientNet.from_name('efficientnet-b0')`.