lint with flake8-bugbear

scripts/dump-model-params.py

@@ -90,7 +90,7 @@ if __name__ == '__main__':
                 op_name = e.args[0]
                 _import_external_ops(op_name)
             except tf.errors.NotFoundError as e:
-                _import_external_ops(e.message)
+                _import_external_ops(str(e))
             else:
                 break
 

tensorpack/callbacks/param.py

@@ -55,11 +55,11 @@ class HyperParam(object):
 class GraphVarParam(HyperParam):
     """ A variable in the graph (e.g. learning_rate) can be a hyperparam."""
 
-    def __init__(self, name, shape=[]):
+    def __init__(self, name, shape=()):
         """
         Args:
             name(str): name of the variable.
-            shape(list): shape of the variable.
+            shape(tuple): shape of the variable.
         """
         self.name = name
         self.shape = shape

tensorpack/callbacks/prof.py

@@ -244,7 +244,7 @@ class GPUMemoryTracker(Callback):
 
     _chief_only = False
 
-    def __init__(self, devices=[0]):
+    def __init__(self, devices=(0,)):
         """
         Args:
             devices([int] or [str]): list of GPU devices to track memory on.

tensorpack/callbacks/saver.py

@@ -80,7 +80,7 @@ class ModelSaver(Callback):
                 global_step=tf.train.get_global_step(),
                 write_meta_graph=False)
             logger.info("Model saved to %s." % tf.train.get_checkpoint_state(self.checkpoint_dir).model_checkpoint_path)
-        except (OSError, IOError, tf.errors.PermissionDeniedError,
+        except (IOError, tf.errors.PermissionDeniedError,
                 tf.errors.ResourceExhaustedError):   # disk error sometimes.. just ignore it
             logger.exception("Exception in ModelSaver!")
 

tensorpack/callbacks/steps.py

@@ -51,10 +51,10 @@ class ProgressBar(Callback):
 
     _chief_only = False
 
-    def __init__(self, names=[]):
+    def __init__(self, names=()):
         """
         Args:
-            names(list): list of string, the names of the tensors to monitor
+            names(tuple[str]): the names of the tensors to monitor
                 on the progress bar.
         """
         super(ProgressBar, self).__init__()

tensorpack/dataflow/dataset/caltech101.py

@@ -82,7 +82,7 @@ except ImportError:
 if __name__ == "__main__":
     ds = Caltech101Silhouettes("train")
     ds.reset_state()
-    for (img, label) in ds:
+    for _ in ds:
         from IPython import embed
 
         embed()

tensorpack/dataflow/dataset/ilsvrc.py

@@ -300,7 +300,7 @@ if __name__ == '__main__':
     ds = ILSVRC12('/home/wyx/data/fake_ilsvrc/', 'train', shuffle=False)
     ds.reset_state()
 
-    for k in ds:
+    for _ in ds:
         from IPython import embed
         embed()
         break

tensorpack/dataflow/dataset/mnist.py

@@ -132,7 +132,7 @@ class FashionMnist(Mnist):
 if __name__ == '__main__':
     ds = Mnist('train')
     ds.reset_state()
-    for (img, label) in ds:
+    for _ in ds:
         from IPython import embed
         embed()
         break

tensorpack/dataflow/format.py

@@ -145,7 +145,7 @@ class LMDBData(RNGDataFlow):
         with self._guard:
             if not self._shuffle:
                 c = self._txn.cursor()
-                while c.next():
+                while next(c):
                     k, v = c.item()
                     if k != b'__keys__':
                         yield [k, v]

tensorpack/dataflow/imgaug/base.py

@@ -50,7 +50,7 @@ def _default_repr(self):
             defaults[k] = argspec.kwonlydefaults[k]
 
     argstr = []
-    for idx, f in enumerate(fields):
+    for f in fields:
         assert hasattr(self, f), \
             "Attribute {} in {} not found! Default __repr__ only works if " \
             "the instance has attributes that match the constructor.".format(f, classname)

tensorpack/dataflow/imgaug/transform.py

@@ -375,7 +375,7 @@ if __name__ == '__main__':
     draw_points(orig_image, coords)
     print(coords)
 
-    for k in range(1):
+    for _ in range(1):
         coords = trans.apply_coords(coords)
         image = trans.apply_image(image)
     print(coords)

tensorpack/input_source/input_source.py

@@ -570,7 +570,7 @@ class StagingInput(FeedfreeInput):
 
         def _prefill(self, sess):
             logger.info("Pre-filling StagingArea ...")
-            for k in range(self.nr_stage):
+            for _ in range(self.nr_stage):
                 self.stage_op.run(session=sess)
             logger.info("{} element{} put into StagingArea on each tower.".format(
                 self.nr_stage, "s were" if self.nr_stage > 1 else " was"))

tensorpack/tfutils/collection.py

@@ -80,7 +80,7 @@ class CollectionGuard(object):
     original = None
 
     def __init__(self, name, check_diff,
-                 freeze_keys=[],
+                 freeze_keys=(),
                  diff_whitelist=None):
         """
         Args:

tensorpack/tfutils/export.py

@@ -89,7 +89,7 @@ class ModelExporter(object):
                 logger.info("Output graph written to {}.".format(filename))
 
     def export_serving(self, filename,
-                       tags=[tf.saved_model.SERVING if is_tfv2() else tf.saved_model.tag_constants.SERVING],
+                       tags=(tf.saved_model.SERVING if is_tfv2() else tf.saved_model.tag_constants.SERVING,),
                        signature_name='prediction_pipeline'):
         """
         Converts a checkpoint and graph to a servable for TensorFlow Serving.
@@ -97,7 +97,7 @@ class ModelExporter(object):
 
         Args:
             filename (str): path for export directory
-            tags (list): list of user specified tags
+            tags (tuple): tuple of user specified tags
             signature_name (str): name of signature for prediction
 
         Note:
@@ -140,7 +140,7 @@ class ModelExporter(object):
                 method_name=tfv1.saved_model.signature_constants.PREDICT_METHOD_NAME)
 
             builder.add_meta_graph_and_variables(
-                sess, tags,
+                sess, list(tags),
                 signature_def_map={signature_name: prediction_signature})
             builder.save()
             logger.info("SavedModel created at {}.".format(filename))

tensorpack/tfutils/optimizer.py

@@ -236,7 +236,7 @@ if __name__ == '__main__':
     sess = tf.Session()
     sess.run(tf.global_variables_initializer())
     with sess.as_default():
-        for k in range(20):
+        for _ in range(20):
             min_op.run()
             print(x.eval())
         print(tf.train.get_or_create_global_step().eval())

tensorpack/tfutils/sessinit.py

@@ -91,12 +91,12 @@ class SaverRestore(SessionInit):
     """
     Restore a tensorflow checkpoint saved by :class:`tf.train.Saver` or :class:`ModelSaver`.
     """
-    def __init__(self, model_path, prefix=None, ignore=[]):
+    def __init__(self, model_path, prefix=None, ignore=()):
         """
         Args:
             model_path (str): a model name (model-xxxx) or a ``checkpoint`` file.
             prefix (str): during restore, add a ``prefix/`` for every variable in this checkpoint.
-            ignore (list[str]): list of tensor names that should be ignored during loading, e.g. learning-rate
+            ignore (tuple[str]): tensor names that should be ignored during loading, e.g. learning-rate
         """
         if model_path.endswith('.npy') or model_path.endswith('.npz'):
             logger.warn("SaverRestore expect a TF checkpoint, but got a model path '{}'.".format(model_path) +

tensorpack/utils/utils.py

@@ -261,7 +261,7 @@ def find_library_full_path(name):
                     if 'lib' + name + '.so' in basename:
                         if os.path.isfile(sofile):
                             return os.path.realpath(sofile)
-        except (OSError, IOError):
+        except IOError:
             # can fail in certain environment (e.g. chroot)
             # if the pids are incorrectly mapped
             pass

tox.ini

 [flake8]
 max-line-length = 120
 # See https://pep8.readthedocs.io/en/latest/intro.html#error-codes
-ignore = E265,E741,E742,E743,W504,W605,C408
+ignore = E265,E741,E742,E743,W504,W605,C408,B007,B008
 exclude = .git,
           __init__.py,
           setup.py,