Fix lint with flake8

Signed-off-by: kta-intel <[email protected]>

openfl-workspace/tf_2dunet/src/tf_2dunet.py

@@ -5,12 +5,12 @@
 
 import tensorflow.compat.v1 as tf
 
-from openfl.federated import TensorFlowTaskRunner_v1
+from openfl.federated import TensorFlowTaskRunnerV1
 
 tf.disable_v2_behavior()
 
 
-class TensorFlow2DUNet(TensorFlowTaskRunner_v1):
+class TensorFlow2DUNet(TensorFlowTaskRunnerV1):
     """Initialize.
 
     Args:

openfl-workspace/tf_cnn_histology/src/dataloader.py

@@ -11,6 +11,7 @@
 
 logger = getLogger(__name__)
 
+
 class TensorFlowHistologyInMemory(TensorFlowDataLoader):
     """TensorFlow Data Loader for Colorectal Histology Dataset."""
 

openfl-workspace/tf_cnn_histology/src/taskrunner.py

@@ -59,7 +59,7 @@ def build_model(self,
         print(tf.config.threading.get_intra_op_parallelism_threads())
         print(tf.config.threading.get_inter_op_parallelism_threads())
 
-        ## Define Model using Functional API
+        # Define Model using Functional API
 
         inputs = tf.keras.layers.Input(shape=input_shape)
         conv = tf.keras.layers.Conv2D(
@@ -107,7 +107,6 @@ def build_model(self,
 
         return model
 
-
     def train_(self, batch_generator, metrics: list = None, **kwargs):
         """Train single epoch.
 

openfl-workspace/tf_cnn_mnist/src/taskrunner.py

@@ -46,22 +46,30 @@ def build_model(self,
             tensorflow.python.keras.engine.sequential.Sequential
 
         """
-        
+
         model = tf.keras.models.Sequential([
-            tf.keras.layers.Conv2D(16, kernel_size=(4, 4), strides=(2, 2), activation='relu', input_shape=input_shape),
-            tf.keras.layers.Conv2D(32, kernel_size=(4, 4), strides=(2, 2), activation='relu'),
+            tf.keras.layers.Conv2D(16, 
+                                   kernel_size=(4, 4), 
+                                   strides=(2, 2), 
+                                   activation='relu', 
+                                   input_shape=input_shape),
+            tf.keras.layers.Conv2D(32, 
+                                   kernel_size=(4, 4), 
+                                   strides=(2, 2), 
+                                   activation='relu'),
             tf.keras.layers.Flatten(),
-            tf.keras.layers.Dense(100, activation='relu'),
-            tf.keras.layers.Dense(num_classes, activation='softmax')
+            tf.keras.layers.Dense(100, 
+                                  activation='relu'),
+            tf.keras.layers.Dense(num_classes, 
+                                  activation='softmax')
         ])
 
         model.compile(loss=tf.keras.losses.categorical_crossentropy,
                       optimizer=tf.keras.optimizers.Adam(),
                       metrics=['accuracy'])
 
         return model
-
-
+
     def train_(self, batch_generator, metrics: list = None, **kwargs):
         """Train single epoch.
 

openfl/federated/__init__.py

@@ -9,9 +9,8 @@
 from .data import DataLoader  # NOQA
 
 if importlib.util.find_spec('tensorflow'):
-    from .task import TensorFlowTaskRunner, TensorFlowTaskRunner_v1, KerasTaskRunner, FederatedModel  # NOQA
+    from .task import TensorFlowTaskRunner, TensorFlowTaskRunnerV1, KerasTaskRunner, FederatedModel  # NOQA
     from .data import TensorFlowDataLoader, KerasDataLoader, FederatedDataSet  # NOQA
 if importlib.util.find_spec('torch'):
     from .task import PyTorchTaskRunner, FederatedModel  # NOQA
     from .data import PyTorchDataLoader, FederatedDataSet  # NOQA
-

openfl/federated/task/__init__.py

@@ -16,7 +16,7 @@
 from .runner import TaskRunner  # NOQA
 
 if importlib.util.find_spec('tensorflow'):
-    from .runner_tf import TensorFlowTaskRunner, TensorFlowTaskRunner_v1  # NOQA
+    from .runner_tf import TensorFlowTaskRunner, TensorFlowTaskRunnerV1  # NOQA
     from .runner_keras import KerasTaskRunner  # NOQA
     from .fl_model import FederatedModel  # NOQA
 if importlib.util.find_spec('torch'):

openfl/federated/task/runner_keras.py

@@ -82,8 +82,8 @@ def train_task(self, col_name, round_num, input_tensor_dict,
         for epoch in range(epochs):
             self.logger.info(f'Run {epoch} epoch of {round_num} round')
             results = self.train_(self.data_loader.get_train_loader(batch_size),
-                                           metrics=metrics,
-                                           **kwargs)
+                                  metrics=metrics,
+                                  **kwargs)
 
         # output metric tensors (scalar)
         origin = col_name

openfl/federated/task/runner_tf.py

@@ -9,6 +9,9 @@
 from openfl.utilities.split import split_tensor_dict_for_holdouts
 from .runner import TaskRunner
 
+import tensorflow.compat.v1
+from tqdm import tqdm
+
 
 class TensorFlowTaskRunner(TaskRunner):
     """The base model for Keras models in the federation."""
@@ -70,8 +73,8 @@ def train_task(self, col_name, round_num, input_tensor_dict,
         for epoch in range(epochs):
             self.logger.info(f'Run {epoch} epoch of {round_num} round')
             results = self.train_(self.data_loader.get_train_loader(batch_size),
-                                           metrics=metrics,
-                                           **kwargs)
+                                  metrics=metrics,
+                                  **kwargs)
 
         # output metric tensors (scalar)
         origin = col_name
@@ -260,7 +263,7 @@ def _get_weights_names(obj, with_opt_vars):
                 weight_names = [weight.name for weight in obj.weights]
             else:
                 weight_names = [weight.name for weight in obj.variables]
-                
+
         weight_names = [weight.name for weight in obj.weights]
         return weight_names
 
@@ -296,7 +299,6 @@ def _get_weights_dict(obj, suffix='', with_opt_vars=False):
             weight_names = [weight.name for weight in obj.weights]
             weight_values = obj.get_weights()
 
-
         for name, value in zip(weight_names, weight_values):
             weights_dict[name + suffix] = value
         return weights_dict
@@ -315,7 +317,7 @@ def _set_weights_dict(obj, weights_dict, with_opt_vars=False):
         Returns:
             None
         """
-                
+
         if with_opt_vars:
             # When acquiring optimizer weights, check optimizer version.
             # Current optimizer does not use 'weights' attributes
@@ -344,15 +346,15 @@ def get_tensor_dict(self, with_opt_vars, suffix=''):
         Returns:
             dict: The tensor dictionary.
         """
-        
+
         model_weights = self._get_weights_dict(self.model, suffix)
 
         if with_opt_vars:
-            
+
             opt_weights = self._get_weights_dict(self.model.optimizer, suffix, with_opt_vars)
 
             model_weights.update(opt_weights)
-            
+
             if len(opt_weights) == 0:
                 self.logger.debug(
                     "WARNING: We didn't find variables for the optimizer.")
@@ -384,11 +386,11 @@ def set_tensor_dict(self, tensor_dict, with_opt_vars):
             if 'legacy' in self.model.optimizer.__class__.__module__:
                 opt_weight_names = [
                     weight.name for weight in self.model.optimizer.weights
-                    ]
+                ]
             else:
                 opt_weight_names = [
                     weight.name for weight in self.model.optimizer.variables
-                    ]
+                ]
 
             opt_weights_dict = {
                 name: tensor_dict[name] for name in opt_weight_names
@@ -565,11 +567,7 @@ def initialize_tensorkeys_for_functions(self, with_opt_vars=False):
         ]
 
 
-import tensorflow.compat.v1
-from tqdm import tqdm
-
-
-class TensorFlowTaskRunner_v1(TaskRunner):
+class TensorFlowTaskRunnerV1(TaskRunner):
     """
     Base class for TensorFlow models in the Federated Learning solution.
 
@@ -999,4 +997,4 @@ def tf_set_tensor_dict(tensor_dict, session, variables,
     for k, v in tensor_dict.items():
         session.run(assign_ops[k], feed_dict={placeholders[k]: v})
 
-    return assign_ops, placeholders
+    return assign_ops, placeholders