Fix subset of linting rules

WORKSPACE

@@ -152,4 +152,3 @@ load(
 )
 
 nccl_configure(name = "local_config_nccl")
-

tensorflow_serving/apis/model_service_pb2.py

@@ -18,19 +18,25 @@
 # python -m grpc.tools.protoc --python_out=. --grpc_python_out=. -I. tensorflow_serving/apis/model_service.proto
 
 import sys
+
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pb2
 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
+
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
 
 
-from tensorflow_serving.apis import get_model_status_pb2 as tensorflow__serving_dot_apis_dot_get__model__status__pb2
-from tensorflow_serving.apis import model_management_pb2 as tensorflow__serving_dot_apis_dot_model__management__pb2
+from tensorflow_serving.apis import (
+    get_model_status_pb2 as tensorflow__serving_dot_apis_dot_get__model__status__pb2,
+)
+from tensorflow_serving.apis import (
+    model_management_pb2 as tensorflow__serving_dot_apis_dot_model__management__pb2,
+)
 
 DESCRIPTOR = _descriptor.FileDescriptor(
     name='tensorflow_serving/apis/model_service.proto',

tensorflow_serving/apis/model_service_pb2_grpc.py

@@ -19,14 +19,16 @@
 
 import grpc
 
-from tensorflow_serving.apis import get_model_status_pb2 as tensorflow__serving_dot_apis_dot_get__model__status__pb2
-from tensorflow_serving.apis import model_management_pb2 as tensorflow__serving_dot_apis_dot_model__management__pb2
+from tensorflow_serving.apis import (
+  get_model_status_pb2 as tensorflow__serving_dot_apis_dot_get__model__status__pb2,
+)
+from tensorflow_serving.apis import (
+  model_management_pb2 as tensorflow__serving_dot_apis_dot_model__management__pb2,
+)
 
 
-class ModelServiceStub(object):
-  """ModelService provides methods to query and update the state of the server,
-  e.g. which models/versions are being served.
-  """
+class ModelServiceStub:
+  """ModelService provides methods to query and update the state of the server, e.g. which models/versions are being served."""
 
   def __init__(self, channel):
     """Constructor.
@@ -50,25 +52,22 @@ def __init__(self, channel):
     )
 
 
-class ModelServiceServicer(object):
-  """ModelService provides methods to query and update the state of the server,
-  e.g. which models/versions are being served.
-  """
+class ModelServiceServicer:
+  """ModelService provides methods to query and update the state of the server e.g. which models/versions are being served."""
 
-  def GetModelStatus(self, request, context):
-    """Gets status of model. If the ModelSpec in the request does not specify
-    version, information about all versions of the model will be returned. If
-    the ModelSpec in the request does specify a version, the status of only
-    that version will be returned.
+  def GetModelStatus(self, request, context): # noqa: ARG002
+    """Gets status of model.
+
+    If the ModelSpec in the request does not specify version, information about all versions of the model will be returned. If the ModelSpec in the request does specify a version, the status of only that version will be returned.
     """
     context.set_code(grpc.StatusCode.UNIMPLEMENTED)
     context.set_details('Method not implemented!')
     raise NotImplementedError('Method not implemented!')
 
-  def HandleReloadConfigRequest(self, request, context):
-    """Reloads the set of served models. The new config supersedes the old one,
-    so if a model is omitted from the new config it will be unloaded and no
-    longer served.
+  def HandleReloadConfigRequest(self, request, context): # noqa: ARG002
+    """Reloads the set of served models.
+
+    The new config supersedes the old one, so if a model is omitted from the new config it will be unloaded and no longer served.
     """
     context.set_code(grpc.StatusCode.UNIMPLEMENTED)
     context.set_details('Method not implemented!')

tensorflow_serving/apis/prediction_service_pb2.py

@@ -22,23 +22,34 @@
 # source: tensorflow_serving/apis/prediction_service.proto
 
 import sys
+
 _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
 from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pb2
 from google.protobuf import message as _message
 from google.protobuf import reflection as _reflection
 from google.protobuf import symbol_database as _symbol_database
-from google.protobuf import descriptor_pb2
+
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
 
 
-from tensorflow_serving.apis import classification_pb2 as tensorflow__serving_dot_apis_dot_classification__pb2
-from tensorflow_serving.apis import get_model_metadata_pb2 as tensorflow__serving_dot_apis_dot_get__model__metadata__pb2
-from tensorflow_serving.apis import inference_pb2 as tensorflow__serving_dot_apis_dot_inference__pb2
-from tensorflow_serving.apis import predict_pb2 as tensorflow__serving_dot_apis_dot_predict__pb2
-from tensorflow_serving.apis import regression_pb2 as tensorflow__serving_dot_apis_dot_regression__pb2
-
+from tensorflow_serving.apis import (
+  classification_pb2 as tensorflow__serving_dot_apis_dot_classification__pb2,
+)
+from tensorflow_serving.apis import (
+  get_model_metadata_pb2 as tensorflow__serving_dot_apis_dot_get__model__metadata__pb2,
+)
+from tensorflow_serving.apis import (
+  inference_pb2 as tensorflow__serving_dot_apis_dot_inference__pb2,
+)
+from tensorflow_serving.apis import (
+  predict_pb2 as tensorflow__serving_dot_apis_dot_predict__pb2,
+)
+from tensorflow_serving.apis import (
+  regression_pb2 as tensorflow__serving_dot_apis_dot_regression__pb2,
+)
 
 DESCRIPTOR = _descriptor.FileDescriptor(
   name='tensorflow_serving/apis/prediction_service.proto',

tensorflow_serving/apis/prediction_service_pb2_grpc.py

@@ -18,17 +18,27 @@
 # python -m grpc.tools.protoc --python_out=. --grpc_python_out=. -I. tensorflow_serving/apis/prediction_service.proto
 import grpc
 
-from tensorflow_serving.apis import classification_pb2 as tensorflow__serving_dot_apis_dot_classification__pb2
-from tensorflow_serving.apis import get_model_metadata_pb2 as tensorflow__serving_dot_apis_dot_get__model__metadata__pb2
-from tensorflow_serving.apis import inference_pb2 as tensorflow__serving_dot_apis_dot_inference__pb2
-from tensorflow_serving.apis import predict_pb2 as tensorflow__serving_dot_apis_dot_predict__pb2
-from tensorflow_serving.apis import regression_pb2 as tensorflow__serving_dot_apis_dot_regression__pb2
+from tensorflow_serving.apis import (
+  classification_pb2 as tensorflow__serving_dot_apis_dot_classification__pb2,
+)
+from tensorflow_serving.apis import (
+  get_model_metadata_pb2 as tensorflow__serving_dot_apis_dot_get__model__metadata__pb2,
+)
+from tensorflow_serving.apis import (
+  inference_pb2 as tensorflow__serving_dot_apis_dot_inference__pb2,
+)
+from tensorflow_serving.apis import (
+  predict_pb2 as tensorflow__serving_dot_apis_dot_predict__pb2,
+)
+from tensorflow_serving.apis import (
+  regression_pb2 as tensorflow__serving_dot_apis_dot_regression__pb2,
+)
 
 
-class PredictionServiceStub(object):
-  """open source marker; do not remove
-  PredictionService provides access to machine-learned models loaded by
-  model_servers.
+class PredictionServiceStub:
+  """PredictionService provides access to machine-learned models loaded by model_servers.
+
+  open source marker; do not remove
   """
 
   def __init__(self, channel):
@@ -64,43 +74,38 @@ def __init__(self, channel):
         )
 
 
-class PredictionServiceServicer(object):
-  """open source marker; do not remove
-  PredictionService provides access to machine-learned models loaded by
-  model_servers.
+class PredictionServiceServicer:
+  """PredictionService provides access to machine-learned models loaded by model_servers.
+
+  open source marker; do not remove
   """
 
-  def Classify(self, request, context):
-    """Classify.
-    """
+  def Classify(self, request, context): # noqa: ARG002
+    """Classify."""
     context.set_code(grpc.StatusCode.UNIMPLEMENTED)
     context.set_details('Method not implemented!')
     raise NotImplementedError('Method not implemented!')
 
-  def Regress(self, request, context):
-    """Regress.
-    """
+  def Regress(self, request, context): # noqa: ARG002
+    """Regress."""
     context.set_code(grpc.StatusCode.UNIMPLEMENTED)
     context.set_details('Method not implemented!')
     raise NotImplementedError('Method not implemented!')
 
-  def Predict(self, request, context):
-    """Predict -- provides access to loaded TensorFlow model.
-    """
+  def Predict(self, request, context): # noqa: ARG002
+    """Predict -- provides access to loaded TensorFlow model."""
     context.set_code(grpc.StatusCode.UNIMPLEMENTED)
     context.set_details('Method not implemented!')
     raise NotImplementedError('Method not implemented!')
 
-  def MultiInference(self, request, context):
-    """MultiInference API for multi-headed models.
-    """
+  def MultiInference(self, request, context): # noqa: ARG002
+    """MultiInference API for multi-headed models."""
     context.set_code(grpc.StatusCode.UNIMPLEMENTED)
     context.set_details('Method not implemented!')
     raise NotImplementedError('Method not implemented!')
 
-  def GetModelMetadata(self, request, context):
-    """GetModelMetadata - provides access to metadata for loaded models.
-    """
+  def GetModelMetadata(self, request, context): # noqa: ARG002
+    """GetModelMetadata - provides access to metadata for loaded models."""
     context.set_code(grpc.StatusCode.UNIMPLEMENTED)
     context.set_details('Method not implemented!')
     raise NotImplementedError('Method not implemented!')

tensorflow_serving/batching/test_util/matrix_half_plus_two_saved_model.py

@@ -14,21 +14,23 @@
 # ==============================================================================
 
 import tensorflow.compat.v1 as tf
+
 FLAGS = tf.app.flags.FLAGS
 
 tf.app.flags.DEFINE_string("output_dir", "/tmp/matrix_half_plus_two/1",
                            "The directory where to write SavedModel files.")
 
 
 def _generate_saved_model_for_matrix_half_plus_two(export_dir):
-  """Creates SavedModel for half plus two model that accepts batches of
-       3*3 matrices.
-       The model divides all elements in each matrix by 2 and adds 2 to them.
-       So, for one input matrix [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
-       the result will be [[2.5, 3, 3.5], [4, 4.5, 5], [5.5, 6, 6.5]].
-    Args:
-      export_dir: The directory where to write SavedModel files.
-    """
+  """Creates SavedModel for half plus two model that accepts batches of 3*3 matrices.
+
+     The model divides all elements in each matrix by 2 and adds 2 to them.
+     So, for one input matrix [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
+     the result will be [[2.5, 3, 3.5], [4, 4.5, 5], [5.5, 6, 6.5]].
+
+  Args:
+    export_dir: The directory where to write SavedModel files.
+  """
   builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
   with tf.Session() as session:
     x = tf.placeholder(tf.float32, shape=[None, 3, 3], name="x")

tensorflow_serving/example/mnist_client.py

@@ -25,19 +25,16 @@
     mnist_client.py --num_tests=100 --server=localhost:9000
 """
 
-from __future__ import print_function
 
 import sys
 import threading
 
 import grpc
+import mnist_input_data
 import numpy
 import tensorflow as tf
 
-from tensorflow_serving.apis import predict_pb2
-from tensorflow_serving.apis import prediction_service_pb2_grpc
-import mnist_input_data
-
+from tensorflow_serving.apis import predict_pb2, prediction_service_pb2_grpc
 
 tf.compat.v1.app.flags.DEFINE_integer(
     'concurrency', 1, 'maximum number of concurrent inference requests')
@@ -48,7 +45,7 @@
 FLAGS = tf.compat.v1.app.flags.FLAGS
 
 
-class _ResultCounter(object):
+class _ResultCounter:
   """Counter for the prediction results."""
 
   def __init__(self, num_tests, concurrency):
@@ -92,6 +89,7 @@ def _create_rpc_callback(label, result_counter):
   Args:
     label: The correct label for the predicted example.
     result_counter: Counter for the prediction result.
+
   Returns:
     The callback function.
   """

tensorflow_serving/example/mnist_input_data.py

@@ -17,7 +17,6 @@
 
 """Functions for downloading and reading MNIST data."""
 
-from __future__ import print_function
 
 import gzip
 import os
@@ -42,7 +41,7 @@ def maybe_download(filename, work_directory):
   if not os.path.exists(filepath):
     filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
     statinfo = os.stat(filepath)
-    print('Successfully downloaded %s %d bytes.' % (filename, statinfo.st_size))
+    print(f'Successfully downloaded {filename} {statinfo.st_size} bytes.')
   return filepath
 
 
@@ -53,20 +52,19 @@ def _read32(bytestream):
 
 def extract_images(filename):
   """Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
-  print('Extracting %s' % filename)
+  print(f'Extracting {filename}')
   with gzip.open(filename) as bytestream:
     magic = _read32(bytestream)
     if magic != 2051:
       raise ValueError(
-          'Invalid magic number %d in MNIST image file: %s' %
-          (magic, filename))
+          f'Invalid magic number {magic} in MNIST image file: {filename}'
+      )
     num_images = _read32(bytestream)
     rows = _read32(bytestream)
     cols = _read32(bytestream)
     buf = bytestream.read(rows * cols * num_images)
     data = numpy.frombuffer(buf, dtype=numpy.uint8)
-    data = data.reshape(num_images, rows, cols, 1)
-    return data
+    return data.reshape(num_images, rows, cols, 1)
 
 
 def dense_to_one_hot(labels_dense, num_classes=10):
@@ -80,13 +78,13 @@ def dense_to_one_hot(labels_dense, num_classes=10):
 
 def extract_labels(filename, one_hot=False):
   """Extract the labels into a 1D uint8 numpy array [index]."""
-  print('Extracting %s' % filename)
+  print(f'Extracting {filename}')
   with gzip.open(filename) as bytestream:
     magic = _read32(bytestream)
     if magic != 2049:
       raise ValueError(
-          'Invalid magic number %d in MNIST label file: %s' %
-          (magic, filename))
+          f'Invalid magic number {magic} in MNIST label file: {filename}'
+      )
     num_items = _read32(bytestream)
     buf = bytestream.read(num_items)
     labels = numpy.frombuffer(buf, dtype=numpy.uint8)
@@ -95,19 +93,17 @@ def extract_labels(filename, one_hot=False):
     return labels
 
 
-class DataSet(object):
+class DataSet:
   """Class encompassing test, validation and training MNIST data set."""
 
   def __init__(self, images, labels, fake_data=False, one_hot=False):
     """Construct a DataSet. one_hot arg is used only if fake_data is true."""
-
     if fake_data:
       self._num_examples = 10000
       self.one_hot = one_hot
     else:
       assert images.shape[0] == labels.shape[0], (
-          'images.shape: %s labels.shape: %s' % (images.shape,
-                                                 labels.shape))
+          f'images.shape: {images.shape} labels.shape: {labels.shape}')
       self._num_examples = images.shape[0]
 
       # Convert shape from [num examples, rows, columns, depth]
@@ -157,7 +153,7 @@ def next_batch(self, batch_size, fake_data=False):
       self._epochs_completed += 1
       # Shuffle the data
       perm = numpy.arange(self._num_examples)
-      numpy.random.shuffle(perm)
+      numpy.random.default_rng().shuffle(perm)
       self._images = self._images[perm]
       self._labels = self._labels[perm]
       # Start next epoch
@@ -171,7 +167,7 @@ def next_batch(self, batch_size, fake_data=False):
 def read_data_sets(train_dir, fake_data=False, one_hot=False):
   """Return training, validation and testing data sets."""
 
-  class DataSets(object):
+  class DataSets:
     pass
 
   data_sets = DataSets()