fix pylint

model_zoo/official/cv/psenet/src/dataset.py

@@ -14,19 +14,20 @@
 # ============================================================================
 
 
+import math
 import os
 import random
-import math
-import cv2
-import pyclipper
-import numpy as np
-from PIL import Image
+
 import Polygon as plg
+import cv2
+import numpy as np
+import pyclipper
+from PIL import Image
+from src.config import config
+
 import mindspore.dataset.engine as de
 import mindspore.dataset.vision.py_transforms as py_transforms
 
-from src.config import config
-
 __all__ = ['train_dataset_creator', 'test_dataset_creator']
 
 def get_img(img_path):

model_zoo/official/cv/psenet/src/network_define.py

@@ -15,14 +15,16 @@
 
 
 import time
+
+import numpy as np
+
 import mindspore.nn as nn
-from mindspore.ops import functional as F
-from mindspore.ops import composite as C
 from mindspore import ParameterTuple
 from mindspore.common.tensor import Tensor
-from mindspore.train.callback import Callback
 from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
-import numpy as np
+from mindspore.ops import composite as C
+from mindspore.ops import functional as F
+from mindspore.train.callback import Callback
 
 __all__ = ['LossCallBack', 'WithLossCell', 'TrainOneStepCell']
 

tests/ut/python/ops/test_dynamic_shape.py

@@ -13,12 +13,12 @@
 # limitations under the License.
 # ============================================================================
 """ test dynamic shape """
-from mindspore import Tensor, context, nn, Parameter
-from mindspore.ops import operations as P
-from mindspore import dtype as mstype
-
 import numpy as np
 
+from mindspore import Tensor, context, nn, Parameter
+from mindspore import dtype as mstype
+from mindspore.ops import operations as P
+
 context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
 
 
@@ -32,6 +32,7 @@ def test_sparse_apply_proximal_ada_grad():
             self.lr = 0.01
             self.l1 = 0.0
             self.l2 = 0.0
+
         def construct(self, grad, indices):
             out = self.sparse_apply_proximal_adagrad(self.var, self.accum, self.lr, self.l1, self.l2, grad, indices)
             return out[0]