update yapf

Author: quantumiracle

tensorlayer/cost.py

@@ -374,7 +374,7 @@ def iou_coe(output, target, threshold=0.5, axis=(1, 2, 3), smooth=1e-5):
 
 
 def sequence_loss_by_example(
-        logits, targets, weights, average_across_timesteps=True, softmax_loss_function=None, name=None
+    logits, targets, weights, average_across_timesteps=True, softmax_loss_function=None, name=None
 ):
     """Weighted cross-entropy loss for a sequence of logits (per example). see original tensorflow code :
     <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py#L1057>
@@ -782,7 +782,7 @@ def mn_i(weights, name='maxnorm_i_regularizer'):
 
 
 def huber_loss(
-        output, target, is_mean=True, delta=1.0, dynamichuber=False, reverse=False, axis=-1, epsilon=0.00001, name=None
+    output, target, is_mean=True, delta=1.0, dynamichuber=False, reverse=False, axis=-1, epsilon=0.00001, name=None
 ):
     """Huber Loss operation, see ``https://en.wikipedia.org/wiki/Huber_loss`` .
     Reverse Huber Loss operation, see  ''https://statweb.stanford.edu/~owen/reports/hhu.pdf''.

tensorlayer/db.py

@@ -48,7 +48,7 @@ class TensorHub(object):
 
     # @deprecated_alias(db_name='dbname', user_name='username', end_support_version=2.1)
     def __init__(
-            self, ip='localhost', port=27017, dbname='dbname', username='None', password='password', project_name=None
+        self, ip='localhost', port=27017, dbname='dbname', username='None', password='password', project_name=None
     ):
         self.ip = ip
         self.port = port
@@ -640,7 +640,7 @@ def run_top_task(self, task_name=None, sort=None, **kwargs):
         logging.info("[Database] Start Task: key: {} sort: {} push time: {}".format(task_name, sort, _datetime))
         _script = _script.decode('utf-8')
         with tf.Graph().as_default():  #  # as graph: # clear all TF graphs
-            exec (_script, globals())
+            exec(_script, globals())
 
         # set status to finished
         _ = self.db.Task.find_one_and_update({'_id': _id}, {'$set': {'status': 'finished'}})

tensorlayer/distributed.py

@@ -94,9 +94,9 @@ class Trainer(object):
     """
 
     def __init__(
-            self, training_dataset, build_training_func, optimizer, optimizer_args, batch_size=32, prefetch_size=None,
-            checkpoint_dir=None, scaling_learning_rate=True, log_step_size=1, validation_dataset=None,
-            build_validation_func=None, max_iteration=float('inf')
+        self, training_dataset, build_training_func, optimizer, optimizer_args, batch_size=32, prefetch_size=None,
+        checkpoint_dir=None, scaling_learning_rate=True, log_step_size=1, validation_dataset=None,
+        build_validation_func=None, max_iteration=float('inf')
     ):
         # Initialize Horovod.
         hvd.init()
@@ -395,9 +395,9 @@ def create_task_spec_def():
 
 @deprecated(date="2018-10-30", instructions="Using the TensorLayer distributed trainer.")
 def create_distributed_session(
-        task_spec=None, checkpoint_dir=None, scaffold=None, hooks=None, chief_only_hooks=None, save_checkpoint_secs=600,
-        save_summaries_steps=object(), save_summaries_secs=object(), config=None, stop_grace_period_secs=120,
-        log_step_count_steps=100
+    task_spec=None, checkpoint_dir=None, scaffold=None, hooks=None, chief_only_hooks=None, save_checkpoint_secs=600,
+    save_summaries_steps=object(), save_summaries_secs=object(), config=None, stop_grace_period_secs=120,
+    log_step_count_steps=100
 ):
     """Creates a distributed session.
 

tensorlayer/files/dataset_loaders/imdb_dataset.py

@@ -13,8 +13,8 @@
 
 
 def load_imdb_dataset(
-        path='data', nb_words=None, skip_top=0, maxlen=None, test_split=0.2, seed=113, start_char=1, oov_char=2,
-        index_from=3
+    path='data', nb_words=None, skip_top=0, maxlen=None, test_split=0.2, seed=113, start_char=1, oov_char=2,
+    index_from=3
 ):
     """Load IMDB dataset.
 

tensorlayer/files/utils.py

@@ -839,8 +839,8 @@ def load_matt_mahoney_text8_dataset(path='data'):
 
 
 def load_imdb_dataset(
-        path='data', nb_words=None, skip_top=0, maxlen=None, test_split=0.2, seed=113, start_char=1, oov_char=2,
-        index_from=3
+    path='data', nb_words=None, skip_top=0, maxlen=None, test_split=0.2, seed=113, start_char=1, oov_char=2,
+    index_from=3
 ):
     """Load IMDB dataset.
 

tensorlayer/layers/activation.py

@@ -54,11 +54,11 @@ class PRelu(Layer):
     """
 
     def __init__(
-            self,
-            channel_shared=False,
-            in_channels=None,
-            a_init=truncated_normal(mean=0.0, stddev=0.05),
-            name=None  # "prelu"
+        self,
+        channel_shared=False,
+        in_channels=None,
+        a_init=truncated_normal(mean=0.0, stddev=0.05),
+        name=None  # "prelu"
     ):
 
         super(PRelu, self).__init__(name)
@@ -141,11 +141,11 @@ class PRelu6(Layer):
     """
 
     def __init__(
-            self,
-            channel_shared=False,
-            in_channels=None,
-            a_init=truncated_normal(mean=0.0, stddev=0.05),
-            name=None  # "prelu6"
+        self,
+        channel_shared=False,
+        in_channels=None,
+        a_init=truncated_normal(mean=0.0, stddev=0.05),
+        name=None  # "prelu6"
     ):
 
         super(PRelu6, self).__init__(name)
@@ -229,11 +229,11 @@ class PTRelu6(Layer):
     """
 
     def __init__(
-            self,
-            channel_shared=False,
-            in_channels=None,
-            a_init=truncated_normal(mean=0.0, stddev=0.05),
-            name=None  # "ptrelu6"
+        self,
+        channel_shared=False,
+        in_channels=None,
+        a_init=truncated_normal(mean=0.0, stddev=0.05),
+        name=None  # "ptrelu6"
     ):
 
         super(PTRelu6, self).__init__(name)

tensorlayer/layers/convolution/binary_conv.py

@@ -61,19 +61,19 @@ class BinaryConv2d(Layer):
     """
 
     def __init__(
-            self,
-            n_filter=32,
-            filter_size=(3, 3),
-            strides=(1, 1),
-            act=None,
-            padding='SAME',
-            use_gemm=False,
-            data_format="channels_last",
-            dilation_rate=(1, 1),
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None  # 'binary_cnn2d',
+        self,
+        n_filter=32,
+        filter_size=(3, 3),
+        strides=(1, 1),
+        act=None,
+        padding='SAME',
+        use_gemm=False,
+        data_format="channels_last",
+        dilation_rate=(1, 1),
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None  # 'binary_cnn2d',
     ):
         super().__init__(name, act=act)
         self.n_filter = n_filter

tensorlayer/layers/convolution/deformable_conv.py

@@ -71,17 +71,17 @@ class DeformableConv2d(Layer):
 
     # @deprecated_alias(layer='prev_layer', end_support_version=1.9)  # TODO remove this line for the 1.9 release
     def __init__(
-            self,
-            offset_layer=None,
-            # shape=(3, 3, 1, 100),
-            n_filter=32,
-            filter_size=(3, 3),
-            act=None,
-            padding='SAME',
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None  # 'deformable_conv_2d',
+        self,
+        offset_layer=None,
+        # shape=(3, 3, 1, 100),
+        n_filter=32,
+        filter_size=(3, 3),
+        act=None,
+        padding='SAME',
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None  # 'deformable_conv_2d',
     ):
         super().__init__(name, act=act)
 

tensorlayer/layers/convolution/depthwise_conv.py

@@ -69,18 +69,18 @@ class DepthwiseConv2d(Layer):
 
     # https://zhuanlan.zhihu.com/p/31551004  https://github.com/xiaohu2015/DeepLearning_tutorials/blob/master/CNNs/MobileNet.py
     def __init__(
-            self,
-            filter_size=(3, 3),
-            strides=(1, 1),
-            act=None,
-            padding='SAME',
-            data_format='channels_last',
-            dilation_rate=(1, 1),
-            depth_multiplier=1,
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None  # 'depthwise_conv2d'
+        self,
+        filter_size=(3, 3),
+        strides=(1, 1),
+        act=None,
+        padding='SAME',
+        data_format='channels_last',
+        dilation_rate=(1, 1),
+        depth_multiplier=1,
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None  # 'depthwise_conv2d'
     ):
         super().__init__(name, act=act)
         self.filter_size = filter_size

tensorlayer/layers/convolution/dorefa_conv.py

@@ -65,21 +65,21 @@ class DorefaConv2d(Layer):
     """
 
     def __init__(
-            self,
-            bitW=1,
-            bitA=3,
-            n_filter=32,
-            filter_size=(3, 3),
-            strides=(1, 1),
-            act=None,
-            padding='SAME',
-            use_gemm=False,
-            data_format="channels_last",
-            dilation_rate=(1, 1),
-            W_init=tl.initializers.truncated_normal(stddev=0.02),
-            b_init=tl.initializers.constant(value=0.0),
-            in_channels=None,
-            name=None  # 'dorefa_cnn2d',
+        self,
+        bitW=1,
+        bitA=3,
+        n_filter=32,
+        filter_size=(3, 3),
+        strides=(1, 1),
+        act=None,
+        padding='SAME',
+        use_gemm=False,
+        data_format="channels_last",
+        dilation_rate=(1, 1),
+        W_init=tl.initializers.truncated_normal(stddev=0.02),
+        b_init=tl.initializers.constant(value=0.0),
+        in_channels=None,
+        name=None  # 'dorefa_cnn2d',
     ):
         super().__init__(name, act=act)
         self.bitW = bitW