fix lint (#542)

.flake8

@@ -1,4 +1,4 @@
 [flake8]
 max-line-length = 120
-ignore = E305,E402,E721,E741,F401,F403,F405,F821,F841,F999
+ignore = W605,E305,E402,E721,E741,F401,F403,F405,F821,F841,F999
 exclude = tensorboardX/proto

tensorboardX/embedding.py

@@ -5,6 +5,7 @@
 # see https://github.com/lanpa/tensorboardX/issues/516
 TB_MAX_SPRITE_SIZE = 8192
 
+
 def maybe_upload_file(local_path):
     '''Upload a file to remote cloud storage
     if the path starts with gs:// or s3://
@@ -32,7 +33,6 @@ def maybe_upload_file(local_path):
             blob.upload_from_filename(local_path)
 
 
-
 def make_tsv(metadata, save_path, metadata_header=None):
     if not metadata_header:
         metadata = [str(x) for x in metadata]

tensorboardX/pytorch_graph.py

@@ -14,6 +14,7 @@
 methods_IO = []
 backward_compat_mode = False
 
+
 class NodeBase(object):
     def __init__(self,
                  debugName=None,
@@ -74,7 +75,7 @@ def __init__(self, node_cpp, valid_methods):
 class NodePyIO(NodePy):
     def __init__(self, node_cpp, input_or_output=None, debugName=''):
         super(NodePyIO, self).__init__(node_cpp, methods_IO)
-        self.tensor_size = [] # tensor_size
+        self.tensor_size = []  # tensor_size
         # Kind attribute string is purely descriptive and will be shown
         # in detailed information for the node in TensorBoard's graph plugin.
         #
@@ -221,9 +222,9 @@ def find_time_for(node_name):
                         should_show_warning = True
 
                     node_stats.append(
-                            NodeExecStats(node_name=v.debugName,
-                                        all_start_micros=int(time.time() * 1e7),
-                                        all_end_rel_micros=total_time))
+                        NodeExecStats(node_name=v.debugName,
+                                      all_start_micros=int(time.time() * 1e7),
+                                      all_end_rel_micros=total_time))
 
             if v.tensor_size and len(v.tensor_size) > 0:  # assume data is float32, only parameter is counted
                 node_stats.append(
@@ -256,7 +257,7 @@ def parse(graph, args=None, profile_result=None):
     if not backward_compat_mode:
         try:
             inputnodes[0].debugName()
-        except:
+        except AttributeError:
             backward_compat_mode = True
 
     nodes_py = GraphPy()
@@ -267,23 +268,22 @@ def parse(graph, args=None, profile_result=None):
             continue
         nodes_py.append(NodePyIO(node, input_or_output='Input', debugName=node.debugName()))
 
-
     for node in graph.nodes():
         # These nodes refers to parameters such as kernel size, stride, etc.
         # The graph will be very tedious if we include all of them. So skip.
         # p.s. Those Constant will be composed by 'prim::listConstruct' and then
         # send to common OPs such as Maxpool, Conv, Linear.
         # We can let user pass verbosity value to dicide how detailed the graph is.
-        if node.kind()=='prim::Constant':
+        if node.kind() == 'prim::Constant':
             continue
 
         # By observation, prim::GetAttr are parameter related. ClassType is used to decorate its scope.
-        if node.kind()=='prim::GetAttr':
+        if node.kind() == 'prim::GetAttr':
             assert node.scopeName() == ''
 
             # Since `populate_namespace_from_OP_to_IO` is already available, we just ignore this.
             # TODO: When it comes to shared parameter, will it still work?
-            if " : ClassType" in  node.__repr__():
+            if " : ClassType" in node.__repr__():
                 continue
 
             nodes_py.append(NodePyIO(node, debugName=list(node.outputs())[0].debugName()))
@@ -295,6 +295,7 @@ def parse(graph, args=None, profile_result=None):
     nodes_py.populate_namespace_from_OP_to_IO()
     return nodes_py.to_proto()
 
+
 def recursive_to_cuda(x):
     """
     Recursively convert tensors in a tuple or list to GPU tensor.
@@ -306,6 +307,7 @@ def recursive_to_cuda(x):
     else:
         return [recursive_to_cuda(_x) for _x in x]
 
+
 def graph(model, args, verbose=False, use_cuda=False, **kwargs):
     """
     This method processes a PyTorch model and produces a `GraphDef` proto
@@ -339,7 +341,7 @@ def graph(model, args, verbose=False, use_cuda=False, **kwargs):
         try:
             if use_cuda:
                 model.cuda()
-                args = recursive_to_cuda(args) 
+                args = recursive_to_cuda(args)
             with torch.autograd.profiler.profile(record_shapes=True, use_cuda=use_cuda) as prof:
                 result = model(*args)
 

tensorboardX/record_writer.py

@@ -122,9 +122,7 @@ def __init__(self, path):
         self.path = path
         self.buffer = io.BytesIO()
 
-        from google.cloud import storage
         client = storage.Client()
-
         bucket_name, filepath = self.bucket_and_path()
         bucket = storage.Bucket(client, bucket_name)
         self.blob = storage.Blob(filepath, bucket)

tensorboardX/summary.py

@@ -83,7 +83,6 @@ def hparams(hparam_dict=None, metric_dict=None):
     # mt = MetricInfo(name=MetricName(tag='accuracy'), display_name='accuracy', description='', dataset_type=DatasetType.DATASET_VALIDATION)  # noqa E501
     # exp = Experiment(name='123', description='456', time_created_secs=100.0, hparam_infos=[hp], metric_infos=[mt], user='tw')  # noqa E501
 
-
     hps = []
 
     ssi = SessionStartInfo()
@@ -119,7 +118,6 @@ def hparams(hparam_dict=None, metric_dict=None):
                                                                  content=content.SerializeToString()))
     exp = Summary(value=[Summary.Value(tag=EXPERIMENT_TAG, metadata=smd)])
 
-
     sei = SessionEndInfo(status=Status.STATUS_SUCCESS)
     content = HParamsPluginData(session_end_info=sei, version=PLUGIN_DATA_VERSION)
     smd = SummaryMetadata(plugin_data=SummaryMetadata.PluginData(plugin_name=PLUGIN_NAME,

tensorboardX/writer.py

@@ -334,7 +334,7 @@ def _get_file_writer(self):
             self.all_writers = {self.file_writer.get_logdir(): self.file_writer}
         return self.file_writer
 
-    def add_hparams(self, hparam_dict=None, metric_dict=None, name=None, global_step = None):
+    def add_hparams(self, hparam_dict=None, metric_dict=None, name=None, global_step=None):
         """Add a set of hyperparameters to be compared in tensorboard.
 
         Args:
@@ -367,7 +367,7 @@ def add_hparams(self, hparam_dict=None, metric_dict=None, name=None, global_step
 
         if not name:
             name = str(time.time())
-            
+
         with SummaryWriter(logdir=os.path.join(self.file_writer.get_logdir(), name)) as w_hp:
             w_hp.file_writer.add_summary(exp)
             w_hp.file_writer.add_summary(ssi)