Fix some PEP8 warnings, 4

chainer_/models/dpn.py

@@ -526,7 +526,7 @@ def get_dpn(num_layers,
         inc = incs[i]
         channels[i][0] = bws[i] + 3 * inc
         for j in range(1, k_sec[i]):
-            channels[i][j] = channels[i][j-1] + inc
+            channels[i][j] = channels[i][j - 1] + inc
 
     net = DPN(
         channels=channels,

chainer_/models/shufflenetv2.py

@@ -361,7 +361,7 @@ def shufflenetv2_wd2(**kwargs):
     root : str, default '~/.chainer/models'
         Location for keeping the model parameters.
     """
-    return get_shufflenetv2(width_scale=(12.0/29.0), model_name="shufflenetv2_wd2", **kwargs)
+    return get_shufflenetv2(width_scale=(12.0 / 29.0), model_name="shufflenetv2_wd2", **kwargs)
 
 
 def shufflenetv2_w1(**kwargs):
@@ -395,7 +395,7 @@ def shufflenetv2_w2d3(**kwargs):
     root : str, default '~/.chainer/models'
         Location for keeping the model parameters.
     """
-    return get_shufflenetv2(width_scale=(44.0/29.0), model_name="shufflenetv2_w2d3", **kwargs)
+    return get_shufflenetv2(width_scale=(44.0 / 29.0), model_name="shufflenetv2_w2d3", **kwargs)
 
 
 def shufflenetv2_w2(**kwargs):
@@ -412,7 +412,7 @@ def shufflenetv2_w2(**kwargs):
     root : str, default '~/.chainer/models'
         Location for keeping the model parameters.
     """
-    return get_shufflenetv2(width_scale=(61.0/29.0), model_name="shufflenetv2_w2", **kwargs)
+    return get_shufflenetv2(width_scale=(61.0 / 29.0), model_name="shufflenetv2_w2", **kwargs)
 
 
 def _test():

common/env_stats.py

@@ -31,7 +31,6 @@ def get_pip_versions(package_list,
         except (subprocess.CalledProcessError, OSError):
             out_text = None
         module_versions[module] = out_text
-
     return module_versions
 
 
@@ -91,7 +90,7 @@ def get_pyenv_info(packages,
     # get versions from __version__ string
     modules_versions = get_package_versions(packages)
     pyenv_info.update(modules_versions)
-    
+
     # get versions from pip
     if type(pip_packages) == list and len(pip_packages) > 0 and pip_packages[0]:
         modules_versions_pip = get_pip_versions(pip_packages, python_version)
@@ -101,7 +100,7 @@ def get_pyenv_info(packages,
         # set python version
         try:
             pyenv_info["python"] = "{0}.{1}.{2}".format(*sys.version_info[0:3])
-        except:
+        except BaseException:
             pyenv_info["python"] = "unknown"
 
     if pwd:
@@ -117,10 +116,10 @@ def get_pyenv_info(packages,
                 command = ['cd {}; git log -n 1'.format(pyenv_info['pwd'])]
             out_bytes = subprocess.check_output(command, shell=True)
             out_text = out_bytes.decode('utf-8')
-        except:
+        except BaseException:
             out_text = 'unknown'
         pyenv_info["git"] = out_text.strip()
-    
+
     return pyenv_info
 
 

gluon/lr_scheduler.py

@@ -100,7 +100,7 @@ def update(self, i, epoch):
             elif self.warmup_mode == 'linear':
                 self.learning_rate = self.warmup_lr + (self.base_lr - self.warmup_lr) * T / self.warmup_N
             elif self.warmup_mode == 'poly':
-                self.learning_rate = self.warmup_lr + (self.base_lr - self.warmup_lr) * \
+                self.learning_rate = self.warmup_lr + (self.base_lr - self.warmup_lr) *\
                                      pow(T / self.warmup_N, self.power)
             elif self.warmup_mode == 'cosine':
                 self.learning_rate = self.warmup_lr + (self.base_lr - self.warmup_lr) *\

gluon/models/dpn.py

@@ -514,7 +514,7 @@ def get_dpn(num_layers,
         inc = incs[i]
         channels[i][0] = bws[i] + 3 * inc
         for j in range(1, k_sec[i]):
-            channels[i][j] = channels[i][j-1] + inc
+            channels[i][j] = channels[i][j - 1] + inc
 
     net = DPN(
         channels=channels,

gluon/models/mobilenet.py

@@ -1,6 +1,6 @@
 """
     MobileNet & FD-MobileNet, implemented in Gluon.
-    Original papers: 
+    Original papers:
     - 'MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,'
        https://arxiv.org/abs/1704.04861.
     - 'FD-MobileNet: Improved MobileNet with A Fast Downsampling Strategy,' https://arxiv.org/abs/1802.03750.

gluon/models/shufflenetv2.py

@@ -360,7 +360,7 @@ def shufflenetv2_wd2(**kwargs):
     root : str, default '~/.mxnet/models'
         Location for keeping the model parameters.
     """
-    return get_shufflenetv2(width_scale=(12.0/29.0), model_name="shufflenetv2_wd2", **kwargs)
+    return get_shufflenetv2(width_scale=(12.0 / 29.0), model_name="shufflenetv2_wd2", **kwargs)
 
 
 def shufflenetv2_w1(**kwargs):
@@ -394,7 +394,7 @@ def shufflenetv2_w2d3(**kwargs):
     root : str, default '~/.mxnet/models'
         Location for keeping the model parameters.
     """
-    return get_shufflenetv2(width_scale=(44.0/29.0), model_name="shufflenetv2_w2d3", **kwargs)
+    return get_shufflenetv2(width_scale=(44.0 / 29.0), model_name="shufflenetv2_w2d3", **kwargs)
 
 
 def shufflenetv2_w2(**kwargs):
@@ -411,7 +411,7 @@ def shufflenetv2_w2(**kwargs):
     root : str, default '~/.mxnet/models'
         Location for keeping the model parameters.
     """
-    return get_shufflenetv2(width_scale=(61.0/29.0), model_name="shufflenetv2_w2", **kwargs)
+    return get_shufflenetv2(width_scale=(61.0 / 29.0), model_name="shufflenetv2_w2", **kwargs)
 
 
 def _test():

gluon/utils.py

@@ -209,4 +209,4 @@ def validate(acc_top1,
         acc_top5.update(labels_list, outputs_list)
     _, top1 = acc_top1.get()
     _, top5 = acc_top5.get()
-    return 1-top1, 1-top5
+    return 1.0 - top1, 1.0 - top5

pytorch/models/dpn.py

@@ -369,9 +369,9 @@ def __init__(self,
         super(DPN, self).__init__()
 
         self.features = DualPathSequential(
-                return_two=False,
-                first_ordinals=1,
-                last_ordinals=0)
+            return_two=False,
+            first_ordinals=1,
+            last_ordinals=0)
         self.features.add_module("init_block", DPNInitBlock(
             in_channels=in_channels,
             out_channels=init_block_channels,
@@ -510,7 +510,7 @@ def get_dpn(num_layers,
         inc = incs[i]
         channels[i][0] = bws[i] + 3 * inc
         for j in range(1, k_sec[i]):
-            channels[i][j] = channels[i][j-1] + inc
+            channels[i][j] = channels[i][j - 1] + inc
 
     net = DPN(
         channels=channels,

pytorch/models/model_store.py

@@ -193,7 +193,7 @@ class requests_failed_to_import(object):
         dirname = os.path.dirname(os.path.abspath(os.path.expanduser(fname)))
         if not os.path.exists(dirname):
             os.makedirs(dirname)
-        while retries+1 > 0:
+        while retries + 1 > 0:
             # Disable pyling too broad Exception
             # pylint: disable=W0703
             try:

pytorch/models/shufflenetv2.py

@@ -356,7 +356,7 @@ def shufflenetv2_wd2(**kwargs):
     root : str, default '~/.torch/models'
         Location for keeping the model parameters.
     """
-    return get_shufflenetv2(width_scale=(12.0/29.0), model_name="shufflenetv2_wd2", **kwargs)
+    return get_shufflenetv2(width_scale=(12.0 / 29.0), model_name="shufflenetv2_wd2", **kwargs)
 
 
 def shufflenetv2_w1(**kwargs):
@@ -386,7 +386,7 @@ def shufflenetv2_w2d3(**kwargs):
     root : str, default '~/.torch/models'
         Location for keeping the model parameters.
     """
-    return get_shufflenetv2(width_scale=(44.0/29.0), model_name="shufflenetv2_w2d3", **kwargs)
+    return get_shufflenetv2(width_scale=(44.0 / 29.0), model_name="shufflenetv2_w2d3", **kwargs)
 
 
 def shufflenetv2_w2(**kwargs):
@@ -401,7 +401,7 @@ def shufflenetv2_w2(**kwargs):
     root : str, default '~/.torch/models'
         Location for keeping the model parameters.
     """
-    return get_shufflenetv2(width_scale=(61.0/29.0), model_name="shufflenetv2_w2", **kwargs)
+    return get_shufflenetv2(width_scale=(61.0 / 29.0), model_name="shufflenetv2_w2", **kwargs)
 
 
 def _test():

pytorch/models/squeezenet.py

@@ -187,9 +187,9 @@ def __init__(self,
 
         self.output = nn.Sequential()
         self.output.add_module('final_conv', nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=num_classes,
-                kernel_size=1))
+            in_channels=in_channels,
+            out_channels=num_classes,
+            kernel_size=1))
         self.output.add_module('final_activ', nn.ReLU(inplace=True))
         self.output.add_module('final_pool', nn.AvgPool2d(
             kernel_size=13,

train_pt.py

@@ -419,7 +419,7 @@ def main():
         batch_size=batch_size,
         num_workers=args.num_workers)
 
-    num_training_samples = 1281167
+    # num_training_samples = 1281167
     optimizer, lr_scheduler, start_epoch = prepare_trainer(
         net=net,
         optimizer_name=args.optimizer_name,