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PiperOrigin-RevId: 702114784

official/projects/assemblenet/modeling/rep_flow_2d_layer.py

@@ -269,7 +269,7 @@ def build(self, input_shape: tf.TensorShape):
           name='rf/bottleneck2')
       self._batch_norm = build_batch_norm(init_zero=True)
 
-  def call(self, inputs: tf.Tensor, training: bool = None) -> tf.Tensor:
+  def call(self, inputs: tf.Tensor, training: bool = None) -> tf.Tensor:  # pytype: disable=annotation-type-mismatch
     """Perform representation flows.
 
     Args:

official/projects/centernet/modeling/centernet_model.py

@@ -41,7 +41,7 @@ def __init__(self,
     self._detection_generator = detection_generator
     self._head = head
 
-  def call(self,  # pytype: disable=signature-mismatch  # overriding-parameter-count-checks
+  def call(self,  # pytype: disable=annotation-type-mismatch,signature-mismatch
            inputs: tf.Tensor,
            training: bool = None,
            **kwargs) -> Mapping[str, tf.Tensor]:

official/projects/detr/modeling/detr.py

@@ -222,7 +222,7 @@ def _generate_image_mask(self, inputs: tf.Tensor,
         mask, target_shape, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
     return mask
 
-  def call(self, inputs: tf.Tensor, training: bool = None) -> List[Any]:  # pytype: disable=signature-mismatch  # overriding-parameter-count-checks
+  def call(self, inputs: tf.Tensor, training: bool = None) -> List[Any]:  # pytype: disable=annotation-type-mismatch,signature-mismatch
     batch_size = tf.shape(inputs)[0]
     features = self._backbone(inputs)[self._backbone_endpoint_name]
     shape = tf.shape(features)

official/projects/maskconver/modeling/maskconver_model.py

@@ -95,7 +95,7 @@ def __init__(
         self.embedding_size,
         embeddings_regularizer=l2_regularizer)
 
-  def call(self, inputs: tf.Tensor,
+  def call(self, inputs: tf.Tensor,  # pytype: disable=annotation-type-mismatch
            image_info: Optional[tf.Tensor] = None,
            box_indices: Optional[tf.Tensor] = None,
            classes: Optional[tf.Tensor] = None,

official/projects/maskconver/modeling/multiscale_maskconver_model.py

@@ -87,7 +87,7 @@ def process_heatmap(self, heatmap: tf.Tensor) -> tf.Tensor:
     valid_mask = tf.abs(scoremap - scoremap_max_pool) < 1e-6
     return scoremap * tf.cast(valid_mask, scoremap.dtype)
 
-  def call(self, inputs: tf.Tensor,
+  def call(self, inputs: tf.Tensor,  # pytype: disable=annotation-type-mismatch
            image_info: Optional[tf.Tensor] = None,
            box_indices: Optional[tf.Tensor] = None,
            classes: Optional[tf.Tensor] = None,

official/projects/maxvit/modeling/maxvit.py

@@ -763,7 +763,7 @@ def _add_absolute_position_encoding(self, inputs: tf.Tensor) -> tf.Tensor:
       output = ops.maybe_reshape_to_1d(output)
     return output
 
-  def call(
+  def call(  # pytype: disable=annotation-type-mismatch
       self, inputs: tf.Tensor, mask: Optional[Any] = None, training: bool = None
   ) -> Mapping[str, tf.Tensor]:
     logging.info(

official/projects/mosaic/modeling/mosaic_model.py

@@ -66,7 +66,7 @@ def __init__(self,
     self.head = head
     self.mask_scoring_head = mask_scoring_head
 
-  def call(self,  # pytype: disable=signature-mismatch  # overriding-parameter-count-checks
+  def call(self,  # pytype: disable=annotation-type-mismatch,signature-mismatch
            inputs: tf.Tensor,
            training: bool = None) -> Dict[str, tf.Tensor]:
     backbone_features = self.backbone(inputs)

official/projects/panoptic/modeling/panoptic_deeplab_model.py

@@ -60,7 +60,7 @@ def __init__(
     self.instance_head = instance_head
     self.post_processor = post_processor
 
-  def call(  # pytype: disable=signature-mismatch  # overriding-parameter-count-checks
+  def call(  # pytype: disable=annotation-type-mismatch,signature-mismatch
       self, inputs: tf.Tensor,
       image_info: tf.Tensor,
       training: bool = None):

official/projects/pix2seq/modeling/pix2seq_model.py

@@ -339,7 +339,7 @@ def _generate_image_mask(
     )
     return mask
 
-  def call(
+  def call(  # pytype: disable=annotation-type-mismatch
       self,
       inputs: tf.Tensor,
       targets: Optional[tf.Tensor] = None,
@@ -563,7 +563,7 @@ def encode_sources(
     # before passing to decoder.
     return tf.concat(encoded_sources, axis=1)
 
-  def call(self, inputs: dict[str, tf.Tensor], training: bool = None):
+  def call(self, inputs: dict[str, tf.Tensor], training: bool = None):  # pytype: disable=annotation-type-mismatch
     encoded = self.encode_sources(inputs["inputs"], inputs["pos_emb"], training)
 
     targets = inputs["tokens"]

official/projects/pointpillars/modeling/featurizers.py

@@ -109,7 +109,7 @@ def _get_batch_dims(self, batch_size: int) -> tf.Tensor:
     batch_dims = tf.expand_dims(batch_dims, axis=-1)
     return batch_dims
 
-  def _get_batch_size_and_dims(self,
+  def _get_batch_size_and_dims(self,  # pytype: disable=annotation-type-mismatch
                                training: bool = None) -> Tuple[int, tf.Tensor]:
     # We use training as a ternary indicator, None for test mode.
     # Test mode will be used for saving model and model inference.
@@ -125,7 +125,7 @@ def _get_batch_size_and_dims(self,
         batch_dims = self._eval_batch_dims
     return batch_size, batch_dims
 
-  def call(self,
+  def call(self,  # pytype: disable=annotation-type-mismatch
            pillars: tf.Tensor,
            indices: tf.Tensor,
            training: bool = None) -> tf.Tensor:

official/projects/pointpillars/modeling/models.py

@@ -114,7 +114,7 @@ def generate_outputs(
     })
     return outputs
 
-  def call(self,  # pytype: disable=signature-mismatch  # overriding-parameter-count-checks
+  def call(self,  # pytype: disable=annotation-type-mismatch,signature-mismatch
            pillars: tf.Tensor,
            indices: tf.Tensor,
            image_shape: Optional[tf.Tensor] = None,

official/projects/qat/vision/modeling/layers/nn_layers.py

@@ -921,7 +921,7 @@ def build(self, input_shape: Union[tf.TensorShape, List[tf.TensorShape]]):
 
     super().build(input_shape)
 
-  def call(self, inputs: tf.Tensor, training: bool = None):
+  def call(self, inputs: tf.Tensor, training: bool = None):  # pytype: disable=annotation-type-mismatch
     """Forward pass mask scoring head.
 
     Args:

official/projects/volumetric_models/modeling/nn_blocks_3d.py

@@ -131,7 +131,7 @@ def get_config(self):
     base_config = super(BasicBlock3DVolume, self).get_config()
     return dict(list(base_config.items()) + list(config.items()))
 
-  def call(self, inputs: tf.Tensor, training: bool = None) -> tf.Tensor:
+  def call(self, inputs: tf.Tensor, training: bool = None) -> tf.Tensor:  # pytype: disable=annotation-type-mismatch
     """Runs forward pass on the input tensor."""
     x = inputs
     for conv, norm in zip(self._convs, self._norms):

official/projects/yolo/modeling/yolo_model.py

@@ -53,7 +53,7 @@ def __init__(self,
     self._detection_generator = detection_generator
     self._fused = False
 
-  def call(self,
+  def call(self,  # pytype: disable=annotation-type-mismatch
            inputs: tf.Tensor,
            training: bool = None,
            mask: Any = None) -> Dict[str, tf.Tensor]:

official/projects/yolo/modeling/yolov7_model.py

@@ -50,7 +50,7 @@ def __init__(self, backbone, decoder, head, detection_generator, **kwargs):
     self._fused = False
     return
 
-  def call(self,
+  def call(self,  # pytype: disable=annotation-type-mismatch
            inputs: tf.Tensor,
            training: bool = None,
            mask: Any = None) -> Dict[str, tf.Tensor]: