fix pylint warnings

example/yolov3_coco2017/train.py

@@ -41,23 +41,22 @@ from config import ConfigYOLOV3ResNet18
 def get_lr(learning_rate, start_step, global_step, decay_step, decay_rate, steps=False):
     """Set learning rate."""
     lr_each_step = []
-    lr = learning_rate
     for i in range(global_step):
         if steps:
-            lr_each_step.append(lr * (decay_rate ** (i // decay_step)))
+            lr_each_step.append(learning_rate * (decay_rate ** (i // decay_step)))
         else:
-            lr_each_step.append(lr * (decay_rate ** (i / decay_step)))
+            lr_each_step.append(learning_rate * (decay_rate ** (i / decay_step)))
     lr_each_step = np.array(lr_each_step).astype(np.float32)
     lr_each_step = lr_each_step[start_step:]
     return lr_each_step
 
 
-def init_net_param(net, init='ones'):
-    """Init the parameters in net."""
+def init_net_param(net, init_value='ones'):
+    """Init:wq the parameters in net."""
     params = net.trainable_params()
     for p in params:
         if isinstance(p.data, Tensor) and 'beta' not in p.name and 'gamma' not in p.name and 'bias' not in p.name:
-            p.set_parameter_data(initializer(init, p.data.shape(), p.data.dtype()))
+            p.set_parameter_data(initializer(init_value, p.data.shape(), p.data.dtype()))
 
 
 if __name__ == '__main__':

mindspore/_akg/utils/format_transform.py

@@ -15,9 +15,9 @@
 """format transform function"""
 import _akg
 
-def refine_reduce_axis(input, axis):
+def refine_reduce_axis(input_content, axis):
     """make reduce axis legal."""
-    shape = get_shape(input)
+    shape = get_shape(input_content)
     if axis is None:
         axis = [i for i in range(len(shape))]
     elif isinstance(axis, int):

mindspore/model_zoo/Bert_NEZHA/bert_for_pre_training.py

@@ -55,7 +55,7 @@ class ClipGradients(nn.Cell):
                   grads,
                   clip_type,
                   clip_value):
-        if clip_type != 0 and clip_type != 1:
+        if clip_type not in (0, 1):
             return grads
 
         new_grads = ()