google · uburuntu · Feb 22, 2021 · Feb 22, 2021 · Feb 22, 2021 · Feb 22, 2021
diff --git a/README.md b/README.md
@@ -41,7 +41,7 @@ trainer = single_trainer.SingleTrainer(
         label_index=0,
         logits_dimension=2,
         record_defaults=[0, 0, 0, 0],
-        filename="model_search/data/testdata/csv_random_data.csv")),
+        filename="model_search/data/testdata/csv_random_data.csv"),
     spec=constants.DEFAULT_DNN)
 
 trainer.try_models(
@@ -153,7 +153,8 @@ class Block(object, metaclass=abc.ABCMeta):
       output_tensors: A list of the output tensors.
     """
 
-  @abc.abstractproperty
+  @property
+  @abc.abstractmethod
   def is_input_order_important(self):
     """Is the order of the entries in the input tensor important.
 

diff --git a/model_search/architecture/architecture_utils.py b/model_search/architecture/architecture_utils.py
@@ -241,7 +241,6 @@ def fix_architecture_order(architecture, problem_type):
   if problem_type != phoenix_spec_pb2.PhoenixSpec.CNN:
     return architecture
 
-  output_architecture = []
   flattens = tuple(block for block in architecture
                    if "FLATTEN" in blocks.BlockType(block).name)
   if not flattens:

diff --git a/model_search/architecture/graph_architecture.py b/model_search/architecture/graph_architecture.py
@@ -102,7 +102,8 @@ class Combiner(metaclass=abc.ABCMeta):
   inputs, so we cannot make all Reducers output only one Tensor.
   """
 
-  @abc.abstractproperty
+  @property
+  @abc.abstractmethod
   def name(self):
     """Name of the Combiner."""
 

diff --git a/model_search/blocks.py b/model_search/blocks.py
@@ -61,7 +61,8 @@ def build(self, input_tensors, is_training, lengths=None):
       output_tensors: A list of the output tensors.
     """
 
-  @abc.abstractproperty
+  @property
+  @abc.abstractmethod
   def is_input_order_important(self):
     """Is the order of the entries in the input tensor important.
 

diff --git a/model_search/blocks_test.py b/model_search/blocks_test.py
@@ -474,7 +474,7 @@ class BlocksImportTest(parameterized.TestCase, tf.test.TestCase):
   def test_ability_to_import(self, block, input_rank):
     # Force graph mode
     with tf.compat.v1.Graph().as_default():
-      input_tensor = tf.zeros([32] * (input_rank))
+      input_tensor = tf.zeros([32] * input_rank)
 
       with arg_scope(architecture_utils.DATA_FORMAT_OPS, data_format='NHWC'):
         with tf.compat.v1.variable_scope('scope_a'):

diff --git a/model_search/controller_test.py b/model_search/controller_test.py
@@ -47,7 +47,7 @@ def _create_spec(replay_towers=0, ensemble_type="none"):
   return output
 
 
-_TRIALS = [trial_module.Trial({"id": id + 1}) for id in range(105)]
+_TRIALS = [trial_module.Trial({"id": id_ + 1}) for id_ in range(105)]
 
 
 def _create_trials(num_trials):

diff --git a/model_search/generators/base_tower_generator.py b/model_search/generators/base_tower_generator.py
@@ -59,7 +59,8 @@ def _wait_for_chief(self, model_dir):
       if not self._phoenix_spec.HasField("replay"):
         self._metadata.unblock_stopped_infeasible_trial(my_id)
 
-  @abc.abstractproperty
+  @property
+  @abc.abstractmethod
   def generator_name(self):
     """The name of the generator."""
 

diff --git a/model_search/metadata/metadata.py b/model_search/metadata/metadata.py
@@ -19,7 +19,8 @@
 class MetaData(object, metaclass=abc.ABCMeta):
   """Api for metadata storage."""
 
-  @abc.abstractproperty
+  @property
+  @abc.abstractmethod
   def name(self):
     """The name of the metadata storage handler."""
 

diff --git a/model_search/ops/svdf_conv.py b/model_search/ops/svdf_conv.py
@@ -104,6 +104,7 @@ def __init__(self,
     self.dropout = min(1., max(0., dropout))
     self.num_filters = self.rank * self.units
     self._dropout_mask = None
+    self.num_features = None
     self.feature_kernel = []
     self.time_kernel = []
     self.bias = []

diff --git a/model_search/oss_trainer_lib.py b/model_search/oss_trainer_lib.py
@@ -170,7 +170,7 @@ def loss_and_metric_and_predictions_fn(provider):
   if getattr(provider, "get_predictions_fn", None) is not None:
     predictions_fn = provider.get_predictions_fn()
 
-  return (loss_fn, metric_fn, predictions_fn)
+  return loss_fn, metric_fn, predictions_fn
 
 
 def make_run_config(model_dir=None, use_tpu=False):

diff --git a/model_search/phoenix.py b/model_search/phoenix.py
@@ -559,7 +559,7 @@ def _increment_global_step(self, train_op, train_steps, tower_name):
 
     increment_amount = 1
     tower_size = architecture_utils.get_architecture_size(tower_name)
-    if (self._phoenix_spec.use_parameter_scaled_training and tower_size):
+    if self._phoenix_spec.use_parameter_scaled_training and tower_size:
       train_step_per_block = max(
           int(train_steps // self._phoenix_spec.maximum_depth), 1)
       tower_train_steps = tower_size * train_step_per_block

diff --git a/model_search/search/categorical_harmonica.py b/model_search/search/categorical_harmonica.py
@@ -300,7 +300,6 @@ def get_suggestion(self, trials, hparams, my_trial_id=None, model_dir=None):
     # Expanding to polynomial features
     x = self._get_polynomial_expansion(feature_extender, x)
 
-    zeroed_coeff = None
     relevant_variables = []
     for _ in range(self._num_of_restarts):
       logging.info("Running linear regression..")

diff --git a/model_search/search/categorical_harmonica_test.py b/model_search/search/categorical_harmonica_test.py
@@ -304,10 +304,10 @@ def test_extract_relevant_variables_indices(self):
     output = algorithm._extract_relevant_variables_indices(
         feature_extender, np.array([1, 1, 1] + [0] * 7 + [1]))
     logging.info(output)
-    self.assertAllEqual(output, set([0, 1, 2, 3]))
+    self.assertAllEqual(output, {0, 1, 2, 3})
     output = algorithm._extract_relevant_variables_indices(
         feature_extender, np.array([1, 0, 0] + [0] * 7 + [1]))
-    self.assertAllEqual(output, set([2, 3]))
+    self.assertAllEqual(output, {2, 3})
     output = algorithm._extract_relevant_variables_indices(
         feature_extender, np.array([1, 0, 0] + [0] * 7 + [0]))
     self.assertEmpty(output)
@@ -329,7 +329,7 @@ def test_get_good_architecture_with_relevant_variables(self):
         expected_output,
         algorithm._get_good_architecture(
             feature_extender, 20, np.array([0, 0.5, 0.6, -0.2] + [0] * 33),
-            set([3, 3, 3, 3])))
+            {3, 3, 3, 3}))
 
 
 if __name__ == "__main__":

diff --git a/model_search/single_trainer.py b/model_search/single_trainer.py
@@ -35,6 +35,7 @@ def __init__(self, data, spec):
       self._spec = spec
 
     self._tuner_id = "tuner-1"
+    self._oracle = None
 
   def try_models(self, number_models, train_steps, eval_steps, root_dir,
                  batch_size, experiment_name, experiment_owner):