!1044 clean pylint warning in test dir

Merge pull request !1044 from jinyaohui/clean_pylint_test

tests/st/ops/ascend/test_add.py

@@ -20,18 +20,23 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 context.set_context(enable_task_sink=True)
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
         self.add = P.TensorAdd()
-        
+
     def construct(self, x, y):
         return self.add(x, y)
 
-x = np.ones([1,3,3,4]).astype(np.float32)
-y = np.ones([1,3,3,4]).astype(np.float32)
+
+x = np.ones([1, 3, 3, 4]).astype(np.float32)
+y = np.ones([1, 3, 3, 4]).astype(np.float32)
+
 
 def test_net():
     add = Net()

tests/st/ops/ascend/test_addn.py

@@ -20,15 +20,19 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
         self.add = P.AddN()
-        
+
     def construct(self, x, y):
         return self.add((x, y))
 
+
 def test_net():
     x = np.random.randn(1, 3, 3, 4).astype(np.float32)
     y = np.random.randn(1, 3, 3, 4).astype(np.float32)

tests/st/ops/ascend/test_aicpu_ops/test_expand_dims.py

@@ -18,97 +18,110 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
-  def __init__(self):
-    super(Net, self).__init__()
-    self.expand_dims = P.ExpandDims()
+    def __init__(self):
+        super(Net, self).__init__()
+        self.expand_dims = P.ExpandDims()
 
-  def construct(self, tensor, dim):
-    return self.expand_dims(tensor, dim)
+    def construct(self, tensor, dim):
+        return self.expand_dims(tensor, dim)
 
 
 def test_net_bool():
-  x = np.random.randn(1, 16, 1, 1).astype(np.bool)
-  net = Net()
-  output = net(Tensor(x), -1)
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.bool)
+    net = Net()
+    output = net(Tensor(x), -1)
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
 
 def test_net_int8():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int8)
-  net = Net()
-  output = net(Tensor(x), -1)
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
-  
+    x = np.random.randn(1, 16, 1, 1).astype(np.int8)
+    net = Net()
+    output = net(Tensor(x), -1)
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
+
 def test_net_uint8():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
-  net = Net()
-  output = net(Tensor(x), -1)
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
+    net = Net()
+    output = net(Tensor(x), -1)
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
 
 def test_net_int16():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int16)
-  net = Net()
-  output = net(Tensor(x), -1)
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.int16)
+    net = Net()
+    output = net(Tensor(x), -1)
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
 
 def test_net_uint16():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
-  net = Net()
-  output = net(Tensor(x), -1)
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
+    net = Net()
+    output = net(Tensor(x), -1)
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
 
 def test_net_int32():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int32)
-  net = Net()
-  output = net(Tensor(x), -1)
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.int32)
+    net = Net()
+    output = net(Tensor(x), -1)
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
 
 def test_net_uint32():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
-  net = Net()
-  output = net(Tensor(x), -1)
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
+    net = Net()
+    output = net(Tensor(x), -1)
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
 
 def test_net_int64():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int64)
-  net = Net()
-  output = net(Tensor(x), -1)
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.int64)
+    net = Net()
+    output = net(Tensor(x), -1)
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
 
 def test_net_uint64():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
-  net = Net()
-  output = net(Tensor(x), -1)
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
+    net = Net()
+    output = net(Tensor(x), -1)
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
 
 def test_net_float16():
-  x = np.random.randn(1, 16, 1, 1).astype(np.float16)
-  net = Net()
-  output = net(Tensor(x), -1)
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.float16)
+    net = Net()
+    output = net(Tensor(x), -1)
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
 
 def test_net_float32():
-  x = np.random.randn(1, 16, 1, 1).astype(np.float32)
-  net = Net()
-  output = net(Tensor(x), -1)
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.float32)
+    net = Net()
+    output = net(Tensor(x), -1)
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))
+
 
 def test_net_float64():
-  x = np.random.randn(1, 16, 1, 1).astype(np.float64)
-  net = Net()
-  output = net(Tensor(x), -1)
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.expand_dims(x, -1)))
-  
+    x = np.random.randn(1, 16, 1, 1).astype(np.float64)
+    net = Net()
+    output = net(Tensor(x), -1)
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.expand_dims(x, -1)))

tests/st/ops/ascend/test_aicpu_ops/test_flatten.py

@@ -17,83 +17,94 @@ from mindspore.ops import operations as P
 import mindspore.nn as nn
 import numpy as np
 import mindspore.context as context
+
 context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
-  def __init__(self):
-    super(Net, self).__init__()
-    self.flatten = P.Flatten()
+    def __init__(self):
+        super(Net, self).__init__()
+        self.flatten = P.Flatten()
 
-  def construct(self, tensor):
-    return self.flatten(tensor)
+    def construct(self, tensor):
+        return self.flatten(tensor)
 
 
 def test_net_int8():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int8)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.flatten()))
-  
+    x = np.random.randn(1, 16, 1, 1).astype(np.int8)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.flatten()))
+
+
 def test_net_uint8():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.flatten()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.flatten()))
+
 
 def test_net_int16():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int16)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.flatten()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.int16)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.flatten()))
+
 
 def test_net_uint16():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.flatten()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.flatten()))
+
 
 def test_net_int32():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int32)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.flatten()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.int32)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.flatten()))
+
 
 def test_net_uint32():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.flatten()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.flatten()))
+
 
 def test_net_int64():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int64)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.flatten()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.int64)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.flatten()))
+
 
 def test_net_uint64():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.flatten()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.flatten()))
+
 
 def test_net_float16():
-  x = np.random.randn(1, 16, 1, 1).astype(np.float16)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.flatten()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.float16)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.flatten()))
+
 
 def test_net_float32():
-  x = np.random.randn(1, 16, 1, 1).astype(np.float32)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.flatten()))
-  
+    x = np.random.randn(1, 16, 1, 1).astype(np.float32)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.flatten()))

tests/st/ops/ascend/test_aicpu_ops/test_is_finite.py

@@ -18,97 +18,110 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
-  def __init__(self):
-    super(Net, self).__init__()
-    self.isfinite = P.IsFinite()
+    def __init__(self):
+        super(Net, self).__init__()
+        self.isfinite = P.IsFinite()
 
-  def construct(self, tensor):
-    return self.isfinite(tensor)
+    def construct(self, tensor):
+        return self.isfinite(tensor)
 
 
 def test_net_bool():
-  x = np.random.randn(1, 16, 1, 1).astype(np.bool)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.isfinite(x)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.bool)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.isfinite(x)))
+
 
 def test_net_int8():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int8)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.isfinite(x)))
-  
+    x = np.random.randn(1, 16, 1, 1).astype(np.int8)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.isfinite(x)))
+
+
 def test_net_uint8():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.isfinite(x)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.isfinite(x)))
+
 
 def test_net_int16():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int16)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.isfinite(x)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.int16)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.isfinite(x)))
+
 
 def test_net_uint16():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.isfinite(x)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.isfinite(x)))
+
 
 def test_net_int32():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int32)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.isfinite(x)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.int32)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.isfinite(x)))
+
 
 def test_net_uint32():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.isfinite(x)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.isfinite(x)))
+
 
 def test_net_int64():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int64)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.isfinite(x)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.int64)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.isfinite(x)))
+
 
 def test_net_uint64():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.isfinite(x)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.isfinite(x)))
+
 
 def test_net_float16():
-  x = np.random.randn(1, 16, 1, 1).astype(np.float16)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.isfinite(x)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.float16)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.isfinite(x)))
+
 
 def test_net_float32():
-  x = np.random.randn(1, 16, 1, 1).astype(np.float32)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.isfinite(x)))
+    x = np.random.randn(1, 16, 1, 1).astype(np.float32)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.isfinite(x)))
+
 
 def test_net_float64():
-  x = np.random.randn(1, 16, 1, 1).astype(np.float64)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.isfinite(x)))
-  
+    x = np.random.randn(1, 16, 1, 1).astype(np.float64)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.isfinite(x)))

tests/st/ops/ascend/test_aicpu_ops/test_reshape.py

@@ -18,97 +18,110 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
-  def __init__(self):
-    super(Net, self).__init__()
-    self.reshape = P.Reshape()
+    def __init__(self):
+        super(Net, self).__init__()
+        self.reshape = P.Reshape()
 
-  def construct(self, tensor):
-    return self.reshape(tensor, (4,4))
+    def construct(self, tensor):
+        return self.reshape(tensor, (4, 4))
 
 
 def test_net_bool():
-  x = np.random.randn(1, 16, 1, 1).astype(np.bool)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+    x = np.random.randn(1, 16, 1, 1).astype(np.bool)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
+
 
 def test_net_int8():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int8)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
-  
+    x = np.random.randn(1, 16, 1, 1).astype(np.int8)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
+
+
 def test_net_uint8():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
+
 
 def test_net_int16():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int16)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+    x = np.random.randn(1, 16, 1, 1).astype(np.int16)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
+
 
 def test_net_uint16():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
+
 
 def test_net_int32():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int32)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+    x = np.random.randn(1, 16, 1, 1).astype(np.int32)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
+
 
 def test_net_uint32():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
+
 
 def test_net_int64():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int64)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+    x = np.random.randn(1, 16, 1, 1).astype(np.int64)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
+
 
 def test_net_uint64():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
+
 
 def test_net_float16():
-  x = np.random.randn(1, 16, 1, 1).astype(np.float16)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+    x = np.random.randn(1, 16, 1, 1).astype(np.float16)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
+
 
 def test_net_float32():
-  x = np.random.randn(1, 16, 1, 1).astype(np.float32)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
+    x = np.random.randn(1, 16, 1, 1).astype(np.float32)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))
+
 
 def test_net_float64():
-  x = np.random.randn(1, 16, 1, 1).astype(np.float64)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == np.reshape(x, (4,4))))
-  
+    x = np.random.randn(1, 16, 1, 1).astype(np.float64)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == np.reshape(x, (4, 4))))

tests/st/ops/ascend/test_aicpu_ops/test_squeeze.py

@@ -17,97 +17,110 @@ from mindspore.ops import operations as P
 import mindspore.nn as nn
 import numpy as np
 import mindspore.context as context
+
 context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
-  def __init__(self):
-    super(Net, self).__init__()
-    self.squeeze = P.Squeeze()
+    def __init__(self):
+        super(Net, self).__init__()
+        self.squeeze = P.Squeeze()
 
-  def construct(self, tensor):
-    return self.squeeze(tensor)
+    def construct(self, tensor):
+        return self.squeeze(tensor)
 
 
 def test_net_bool():
-  x = np.random.randn(1, 16, 1, 1).astype(np.bool)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.squeeze()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.bool)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.squeeze()))
+
 
 def test_net_int8():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int8)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.squeeze()))
-  
+    x = np.random.randn(1, 16, 1, 1).astype(np.int8)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.squeeze()))
+
+
 def test_net_uint8():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.squeeze()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.squeeze()))
+
 
 def test_net_int16():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int16)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.squeeze()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.int16)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.squeeze()))
+
 
 def test_net_uint16():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.squeeze()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint16)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.squeeze()))
+
 
 def test_net_int32():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int32)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.squeeze()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.int32)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.squeeze()))
+
 
 def test_net_uint32():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.squeeze()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint32)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.squeeze()))
+
 
 def test_net_int64():
-  x = np.random.randn(1, 16, 1, 1).astype(np.int64)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.squeeze()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.int64)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.squeeze()))
+
 
 def test_net_uint64():
-  x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.squeeze()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.uint64)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.squeeze()))
+
 
 def test_net_float16():
-  x = np.random.randn(1, 16, 1, 1).astype(np.float16)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.squeeze()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.float16)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.squeeze()))
+
 
 def test_net_float32():
-  x = np.random.randn(1, 16, 1, 1).astype(np.float32)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.squeeze()))
+    x = np.random.randn(1, 16, 1, 1).astype(np.float32)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.squeeze()))
+
 
 def test_net_float64():
-  x = np.random.randn(1, 16, 1, 1).astype(np.float64)
-  net = Net()
-  output = net(Tensor(x))
-  print(output.asnumpy())
-  assert(np.all(output.asnumpy() == x.squeeze()))
-  
+    x = np.random.randn(1, 16, 1, 1).astype(np.float64)
+    net = Net()
+    output = net(Tensor(x))
+    print(output.asnumpy())
+    assert (np.all(output.asnumpy() == x.squeeze()))

tests/st/ops/ascend/test_apply_momentum.py

@@ -20,24 +20,29 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
         self.apply_momentum = P.ApplyMomentum(gradient_scale=1024.0)
         self.variable = Parameter(initializer(
-                                'normal', [2, 3, 3, 4]), name='variable')
+            'normal', [2, 3, 3, 4]), name='variable')
         self.accumulation = Parameter(initializer(
-                                'normal', [2, 3, 3, 4]), name='accumulation')
+            'normal', [2, 3, 3, 4]), name='accumulation')
         self.learning_rate = Parameter(initializer(
-                                'normal', [1, ]), name='learning_rate')
+            'normal', [1, ]), name='learning_rate')
         self.gradient = Parameter(initializer(
-                                'normal', [2, 3, 3, 4]), name='gradient')
+            'normal', [2, 3, 3, 4]), name='gradient')
         self.momentum = Parameter(initializer(
-                                'normal', [1, ]), name='momentum')
+            'normal', [1, ]), name='momentum')
+
     def construct(self):
         return self.apply_momentum(self.variable, self.accumulation, self.learning_rate, self.gradient, self.momentum)
 
+
 def test_net():
     apply_momentum = Net()
     output = apply_momentum()

tests/st/ops/ascend/test_biasAddGrad.py

@@ -21,22 +21,25 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
-  def __init__(self):
-    super(Net, self).__init__()
-    self.bias_add_grad = G.BiasAddGrad()
-    #self.dout = Parameter(initializer(
-                #'normal', [2, 3, 3, 4]), name='dout')
+    def __init__(self):
+        super(Net, self).__init__()
+        self.bias_add_grad = G.BiasAddGrad()
+        # self.dout = Parameter(initializer(
+        # 'normal', [2, 3, 3, 4]), name='dout')
 
+    @ms_function
+    def construct(self, dout):
+        return self.bias_add_grad(dout)
 
-  @ms_function
-  def construct(self, dout):
-    return self.bias_add_grad(dout)
 
-dout = np.ones([2,3,4,4]).astype(np.float32)
+dout = np.ones([2, 3, 4, 4]).astype(np.float32)
 bias_add_grad = Net()
 output = bias_add_grad(Tensor(dout))
-expect_output = np.array([32.,32.,32.]).astype(np.float32)
-assert np.all(output.asnumpy()==expect_output), "bias_add_grad execute failed, please check current code commit"
+expect_output = np.array([32., 32., 32.]).astype(np.float32)
+assert np.all(output.asnumpy() == expect_output), "bias_add_grad execute failed, please check current code commit"
 print(output.asnumpy())

tests/st/ops/ascend/test_bias_add_grad.py

@@ -21,17 +21,20 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
         self.bias_add_grad = G.BiasAddGrad()
-    
 
     @ms_function
     def construct(self, dout):
         return self.bias_add_grad(dout)
 
+
 def test_net():
     dout = np.random.rand(1, 1001).astype(np.float32)
     bias_add_grad = Net()

tests/st/ops/ascend/test_conv.py

@@ -20,32 +20,33 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
         out_channel = 64
         kernel_size = 7
         self.conv = P.Conv2D(out_channel,
-                                 kernel_size,
-                                 mode=1,
-                                 pad_mode="valid",
-                                 pad=0,
-                                 stride=1,
-                                 dilation=1,
-                                 group=1)
+                             kernel_size,
+                             mode=1,
+                             pad_mode="valid",
+                             pad=0,
+                             stride=1,
+                             dilation=1,
+                             group=1)
         self.w = Parameter(initializer(
-                                'normal', [64, 3, 7, 7]), name='w')
-
+            'normal', [64, 3, 7, 7]), name='w')
 
     @ms_function
     def construct(self, x):
         return self.conv(x, self.w)
 
 
-
 def test_net():
-    x = np.random.randn(32,3,224,224).astype(np.float32)
+    x = np.random.randn(32, 3, 224, 224).astype(np.float32)
     conv = Net()
     output = conv(Tensor(x))
     print(output.asnumpy())

tests/st/ops/ascend/test_conv2dGradFilter.py

@@ -21,37 +21,40 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(device_target="Ascend")
 
+
 class Net(nn.Cell):
-  def __init__(self):
-    super(Net, self).__init__()
-    self.conv2d_grad = G.Conv2DBackpropFilter(4,1)
-    yt = Tensor(np.array([[[[1, 0, -1], [1, 0, -1], [1, 0, -1]]]]).astype(np.float32))
-    self.y = Parameter(yt, name='y')
-    self.get_shape = P.Shape()
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv2d_grad = G.Conv2DBackpropFilter(4, 1)
+        yt = Tensor(np.array([[[[1, 0, -1], [1, 0, -1], [1, 0, -1]]]]).astype(np.float32))
+        self.y = Parameter(yt, name='y')
+        self.get_shape = P.Shape()
+
+    @ms_function
+    def construct(self, x, out):
+        return self.conv2d_grad(out, x, self.get_shape(self.y))
 
-  @ms_function
-  def construct(self, x, out):
-    return self.conv2d_grad(out, x, self.get_shape(self.y))
 
 x = Tensor(np.array([[[
-        [3, 0, 1, 2, 7, 4],
-        [1, 5, 8, 9, 3, 1],
-        [2, 7, 2, 5, 1, 3],
-        [0, 1, 3, 1, 7, 8],
-        [4, 2, 1, 6, 2, 8],
-        [2, 4, 5, 2, 3, 9]]]]).astype(np.float32))
+    [3, 0, 1, 2, 7, 4],
+    [1, 5, 8, 9, 3, 1],
+    [2, 7, 2, 5, 1, 3],
+    [0, 1, 3, 1, 7, 8],
+    [4, 2, 1, 6, 2, 8],
+    [2, 4, 5, 2, 3, 9]]]]).astype(np.float32))
 
 out = Tensor(np.array([[[
-        [ -5,  -4,   0,   8],
-        [-10,  -2,   2,   3],
-        [  0,  -2,  -4,  -7],
-        [ -3,  -2,  -3, -16]]]]).astype(np.float32))
+    [-5, -4, 0, 8],
+    [-10, -2, 2, 3],
+    [0, -2, -4, -7],
+    [-3, -2, -3, -16]]]]).astype(np.float32))
 
 operator = Net()
 output = operator(x, out)
-expect_out = np.array([[[[ -60., -142., -265.],[-104., -211., -322.],[-102., -144., -248.]]]]).astype(np.float32)
+expect_out = np.array([[[[-60., -142., -265.], [-104., -211., -322.], [-102., -144., -248.]]]]).astype(np.float32)
 print(output.asnumpy())
 print(expect_out)
-assert np.all(output.asnumpy()==expect_out), "conv2d_grad execute failed, please check current code commit"
+assert np.all(output.asnumpy() == expect_out), "conv2d_grad execute failed, please check current code commit"

tests/st/ops/ascend/test_conv_grad.py

@@ -21,8 +21,10 @@ import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 from mindspore.ops.composite import GradOperation
+
 context.set_context(device_target="Ascend")
 
+
 class Grad(nn.Cell):
     def __init__(self, network):
         super(Grad, self).__init__()
@@ -33,26 +35,28 @@ class Grad(nn.Cell):
     def construct(self, input, output_grad):
         return self.grad(self.network)(input, output_grad)
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
         out_channel = 512
         kernel_size = 2048
         self.conv = P.Conv2D(out_channel,
-                                 (kernel_size, kernel_size),
-                                 mode=1,
-                                 pad_mode="same",
-                                 pad=3,
-                                 stride=2,
-                                 dilation=1,
-                                 group=1)
+                             (kernel_size, kernel_size),
+                             mode=1,
+                             pad_mode="same",
+                             pad=3,
+                             stride=2,
+                             dilation=1,
+                             group=1)
         self.w = Parameter(initializer(
-                                'normal', [512, 2048, 1, 1]), name='w')
+            'normal', [512, 2048, 1, 1]), name='w')
 
     @ms_function
     def construct(self, x):
         return self.conv(x, self.w)
 
+
 def test_net():
     x = np.ones([32, 2048, 7, 7]).astype(np.float32)
     sens = np.ones([32, 512, 7, 7]).astype(np.float32)

tests/st/ops/ascend/test_dense.py

@@ -20,7 +20,10 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -30,6 +33,7 @@ class Net(nn.Cell):
     def construct(self, x):
         return self.dense(x)
 
+
 def test_net():
     x = np.random.randn(32, 2048).astype(np.float32)
     net = Net()

tests/st/ops/ascend/test_dense_grad.py

@@ -21,8 +21,10 @@ import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 from mindspore.ops.composite import GradOperation
+
 context.set_context(device_target="Ascend")
 
+
 class Grad(nn.Cell):
     def __init__(self, network):
         super(Grad, self).__init__()
@@ -33,6 +35,7 @@ class Grad(nn.Cell):
     def construct(self, input, output_grad):
         return self.grad(self.network)(input, output_grad)
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -41,6 +44,7 @@ class Net(nn.Cell):
     def construct(self, x):
         return self.dense(x)
 
+
 def test_net():
     x = np.random.randn(32, 2048).astype(np.float32)
     sens = np.random.randn(32, 1001).astype(np.float32)

tests/st/ops/ascend/test_drop_out_gen_mask.py

@@ -17,6 +17,7 @@ from mindspore.ops import operations as P
 import mindspore.nn as nn
 import numpy as np
 import mindspore.context as context
+
 context.set_context(mode=context.GRAPH_MODE,
                     device_target="Ascend")
 

tests/st/ops/ascend/test_full_connection.py

@@ -21,6 +21,7 @@ import mindspore.context as context
 
 context.set_context(device_target="Ascend")
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()

tests/st/ops/ascend/test_fused_batchnorm.py

@@ -20,7 +20,10 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -35,7 +38,7 @@ class Net(nn.Cell):
 
 
 def test_net():
-    x = np.random.randn(1,64,112,112).astype(np.float32)
+    x = np.random.randn(1, 64, 112, 112).astype(np.float32)
     # mean = np.random.randn(1,16,1,1).astype(np.float32)
     # variance = np.random.randn(1,16,1,1).astype(np.float32)
     fusedBn = Net()
@@ -45,4 +48,3 @@ def test_net():
 
     print("***********output y*********")
     print(output.asnumpy())
-

tests/st/ops/ascend/test_fused_batchnorm_grad.py

@@ -21,8 +21,11 @@ import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 from mindspore.ops.composite import GradOperation
-#context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+# context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 context.set_context(device_target="Ascend")
+
+
 class Grad(nn.Cell):
     def __init__(self, network):
         super(Grad, self).__init__()
@@ -33,6 +36,7 @@ class Grad(nn.Cell):
     def construct(self, input, output_grad):
         return self.grad(self.network)(input, output_grad)
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -47,8 +51,8 @@ class Net(nn.Cell):
 
 
 def test_net():
-    x = np.random.randn(1,64,112,112).astype(np.float32)
-    sens = np.random.randn(1,64,112,112).astype(np.float32)
+    x = np.random.randn(1, 64, 112, 112).astype(np.float32)
+    sens = np.random.randn(1, 64, 112, 112).astype(np.float32)
     net = Grad(Net())
     output = net(Tensor(x), Tensor(sens))
     print("***********x*********")

tests/st/ops/ascend/test_image_gradients.py

@@ -20,6 +20,8 @@ from mindspore import Tensor
 from mindspore.common.api import ms_function
 
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -31,32 +33,32 @@ class Net(nn.Cell):
 
 
 def test_image_gradients():
-    image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32)
-    expected_dy = np.array([[[[2,2],[0,0]]]]).astype(np.int32)
-    expected_dx = np.array([[[[1,0],[1,0]]]]).astype(np.int32)
+    image = Tensor(np.array([[[[1, 2], [3, 4]]]]), dtype=mstype.int32)
+    expected_dy = np.array([[[[2, 2], [0, 0]]]]).astype(np.int32)
+    expected_dx = np.array([[[[1, 0], [1, 0]]]]).astype(np.int32)
     net = Net()
     dy, dx = net(image)
-    assert np.any(dx.asnumpy()-expected_dx) == False
-    assert np.any(dy.asnumpy()-expected_dy) == False
+    assert np.any(dx.asnumpy() - expected_dx) == False
+    assert np.any(dy.asnumpy() - expected_dy) == False
 
 
 def test_image_gradients_multi_channel_depth():
     # 4 x 2 x 2 x 2
     dtype = mstype.int32
-    image = Tensor(np.array([[[[1,2],[3,4]], [[5,6],[7,8]]],
-                             [[[3,5],[7,9]], [[11,13],[15,17]]],
-                             [[[5,10],[15,20]], [[25,30],[35,40]]],
-                             [[[10,20],[30,40]], [[50,60],[70,80]]]]), dtype=dtype)
-    expected_dy = Tensor(np.array([[[[2,2],[0,0]], [[2,2],[0,0]]],
-                                   [[[4,4],[0,0]], [[4,4],[0,0]]],
-                                   [[[10,10],[0,0]], [[10,10],[0,0]]],
-                                   [[[20,20],[0,0]], [[20,20],[0,0]]]]), dtype=dtype)
-    expected_dx = Tensor(np.array([[[[1,0],[1,0]], [[1,0],[1,0]]],
-                                   [[[2,0],[2,0]], [[2,0],[2,0]]],
-                                   [[[5,0],[5,0]], [[5,0],[5,0]]],
-                                   [[[10,0],[10,0]], [[10,0],[10,0]]]]), dtype=dtype)
+    image = Tensor(np.array([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]],
+                             [[[3, 5], [7, 9]], [[11, 13], [15, 17]]],
+                             [[[5, 10], [15, 20]], [[25, 30], [35, 40]]],
+                             [[[10, 20], [30, 40]], [[50, 60], [70, 80]]]]), dtype=dtype)
+    expected_dy = Tensor(np.array([[[[2, 2], [0, 0]], [[2, 2], [0, 0]]],
+                                   [[[4, 4], [0, 0]], [[4, 4], [0, 0]]],
+                                   [[[10, 10], [0, 0]], [[10, 10], [0, 0]]],
+                                   [[[20, 20], [0, 0]], [[20, 20], [0, 0]]]]), dtype=dtype)
+    expected_dx = Tensor(np.array([[[[1, 0], [1, 0]], [[1, 0], [1, 0]]],
+                                   [[[2, 0], [2, 0]], [[2, 0], [2, 0]]],
+                                   [[[5, 0], [5, 0]], [[5, 0], [5, 0]]],
+                                   [[[10, 0], [10, 0]], [[10, 0], [10, 0]]]]), dtype=dtype)
     net = Net()
     dy, dx = net(image)
 
-    assert np.any(dx.asnumpy()-expected_dx.asnumpy()) == False
-    assert np.any(dy.asnumpy()-expected_dy.asnumpy()) == False
+    assert np.any(dx.asnumpy() - expected_dx.asnumpy()) == False
+    assert np.any(dy.asnumpy() - expected_dy.asnumpy()) == False

tests/st/ops/ascend/test_matmul.py

@@ -20,7 +20,10 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -30,8 +33,10 @@ class Net(nn.Cell):
     def construct(self, x1, x2):
         return self.matmul(x1, x2)
 
-x1 = np.random.randn(1,3).astype(np.float32)
-x2 = np.random.randn(3,4).astype(np.float32)
+
+x1 = np.random.randn(1, 3).astype(np.float32)
+x2 = np.random.randn(3, 4).astype(np.float32)
+
 
 def test_net():
     matmul = Net()

tests/st/ops/ascend/test_maxpool.py

@@ -20,12 +20,13 @@ import numpy as np
 import mindspore.context as context
 
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
         self.maxpool = P.MaxPool(pad_mode="SAME", window=3, stride=2)
 
-
     @ms_function
     def construct(self, x):
         output = self.maxpool(x)
@@ -33,7 +34,7 @@ class Net(nn.Cell):
 
 
 def test_net():
-    x = np.random.randn(32,64,112,112).astype(np.float32)
+    x = np.random.randn(32, 64, 112, 112).astype(np.float32)
     maxpool = Net()
     output = maxpool(Tensor(x))
     print(output.asnumpy())

tests/st/ops/ascend/test_maxpool_grad.py

@@ -19,6 +19,7 @@ from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
 from mindspore.ops.composite import GradOperation
+
 context.set_context(device_target="Ascend")
 
 

tests/st/ops/ascend/test_maxpool_with_argmax_grad.py

@@ -21,8 +21,10 @@ import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 from mindspore.ops.composite import GradOperation
+
 context.set_context(device_target="Ascend")
 
+
 class Grad(nn.Cell):
     def __init__(self, network):
         super(Grad, self).__init__()
@@ -33,6 +35,7 @@ class Grad(nn.Cell):
     def construct(self, input, output_grad):
         return self.grad(self.network)(input, output_grad)
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -43,8 +46,9 @@ class Net(nn.Cell):
 
     @ms_function
     def construct(self, x):
-            output = self.maxpool(x)
-            return output[0]
+        output = self.maxpool(x)
+        return output[0]
+
 
 def test_net():
     x = np.random.randn(32, 64, 112, 112).astype(np.float32)

tests/st/ops/ascend/test_relu.py

@@ -20,7 +20,10 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -30,8 +33,9 @@ class Net(nn.Cell):
     def construct(self, x):
         return self.relu(x)
 
+
 def test_net():
-    x = np.random.randn(2,3,3,4).astype(np.float32)
+    x = np.random.randn(2, 3, 3, 4).astype(np.float32)
     relu = Net()
     output = relu(Tensor(x))
     print(x)

tests/st/ops/ascend/test_relu_grad.py

@@ -21,8 +21,10 @@ import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 from mindspore.ops.composite import GradOperation
+
 context.set_context(device_target="Ascend")
 
+
 class Grad(nn.Cell):
     def __init__(self, network):
         super(Grad, self).__init__()
@@ -33,6 +35,7 @@ class Grad(nn.Cell):
     def construct(self, input, output_grad):
         return self.grad(self.network)(input, output_grad)
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -41,9 +44,10 @@ class Net(nn.Cell):
     def construct(self, x):
         return self.relu(x)
 
+
 def test_net():
-    x = np.random.randn(2,3,3,4).astype(np.float32)
-    sens = np.random.randn(2,3,3,4).astype(np.float32)
+    x = np.random.randn(2, 3, 3, 4).astype(np.float32)
+    sens = np.random.randn(2, 3, 3, 4).astype(np.float32)
     net = Grad(Net())
     output = net(Tensor(x), Tensor(sens))
     print(len(output))

tests/st/ops/ascend/test_reshape.py

@@ -18,18 +18,22 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
-  def __init__(self):
-    super(Net, self).__init__()
-    self.reshape = P.Reshape()
-  @ms_function
-  def construct(self, tensor):
-    return self.reshape(tensor, (1,16))
+    def __init__(self):
+        super(Net, self).__init__()
+        self.reshape = P.Reshape()
+
+    @ms_function
+    def construct(self, tensor):
+        return self.reshape(tensor, (1, 16))
 
 
 def test_net():
-  x = np.random.randn(1, 16, 1, 1).astype(np.float16)
-  reshape = Net()
-  output = reshape(Tensor(x))
-  print(output.asnumpy())
+    x = np.random.randn(1, 16, 1, 1).astype(np.float16)
+    reshape = Net()
+    output = reshape(Tensor(x))
+    print(output.asnumpy())

tests/st/ops/ascend/test_simplemean.py

@@ -20,7 +20,10 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -29,7 +32,8 @@ class Net(nn.Cell):
     @ms_function
     def construct(self, x):
         return self.simplemean(x, (-2, -1))
-    
+
+
 def test_net():
     x = np.random.randn(32, 2048, 7, 7).astype(np.float32)
     simplemean = Net()

tests/st/ops/ascend/test_simplemean_grad.py

@@ -21,8 +21,10 @@ import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 from mindspore.ops.composite import GradOperation
+
 context.set_context(device_target="Ascend")
 
+
 class Grad(nn.Cell):
     def __init__(self, network):
         super(Grad, self).__init__()
@@ -33,6 +35,7 @@ class Grad(nn.Cell):
     def construct(self, input, output_grad):
         return self.grad(self.network)(input, output_grad)
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -41,9 +44,10 @@ class Net(nn.Cell):
     def construct(self, x):
         return self.simplemean(x, (-2, -1))
 
+
 def test_net():
-    x = np.random.randn(32,2048,7,7).astype(np.float32)
-    sens = np.random.randn(32,2048, 1, 1).astype(np.float32)
+    x = np.random.randn(32, 2048, 7, 7).astype(np.float32)
+    sens = np.random.randn(32, 2048, 1, 1).astype(np.float32)
     net = Grad(Net())
     output = net(Tensor(x), Tensor(sens))
     print(output.asnumpy())

tests/st/ops/ascend/test_sparseSoftmaxCrossEntropyWithLogits.py

@@ -18,6 +18,7 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
 
 
@@ -30,9 +31,10 @@ class Net(nn.Cell):
     def construct(self, features, labels):
         return self.SparseSoftmaxCrossEntropyWithLogits(features, labels)
 
+
 def np_sparse_softmax_cross_entropy_with_logits(labels_shape, logits_shape, logits_dtype):
     num_class = logits_shape[1]
-    labels =  np.random.randint(low=0, high=num_class - 1, size=labels_shape).astype(np.int32)
+    labels = np.random.randint(low=0, high=num_class - 1, size=labels_shape).astype(np.int32)
     logits = np.random.rand(*logits_shape).astype(logits_dtype)
     features = logits
     features_reshape = np.reshape(features, [-1, num_class])
@@ -48,7 +50,7 @@ def np_sparse_softmax_cross_entropy_with_logits(labels_shape, logits_shape, logi
     loss = -np.sum(labels_mat * np.log(probs + 1.0e-20), axis=1)
     bp_res = np.reshape(bp, features.shape)
     loss_res = np.reshape(loss, labels.shape)
-    loss_res = np.sum(loss_res, axis=0)/loss_res.shape[0]
+    loss_res = np.sum(loss_res, axis=0) / loss_res.shape[0]
     return labels, logits, loss_res, bp_res
 
 
@@ -65,4 +67,6 @@ def test_net():
     print(loss_me.asnumpy().flatten())
     print("-------------------------")
     print(expect)
+
+
 test_net()

tests/st/ops/ascend/test_sparse_softmax_cross_entropy_with_logits_grad.py

@@ -21,6 +21,7 @@ import mindspore.context as context
 
 context.set_context(device_target="Ascend")
 
+
 class Net(nn.Cell):
     def __init__(self, is_grad=False):
         super(Net, self).__init__()

tests/st/ops/ascend/test_tbe_ops/test_AssignAdd.py

@@ -20,11 +20,13 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 
 class Net(nn.Cell):
     """Net definition"""
+
     def __init__(self):
         super(Net, self).__init__()
         self.AssignAdd = P.AssignAdd()
@@ -39,8 +41,8 @@ class Net(nn.Cell):
 def test_net():
     """test AssignAdd"""
     net = Net()
-    x = Tensor(np.ones([1]).astype(np.float32)*100)
+    x = Tensor(np.ones([1]).astype(np.float32) * 100)
 
     print("MyPrintResult dataX:", x)
     result = net(x)
-    print("MyPrintResult data::", result.asnumpy())
+    print("MyPrintResult data::", result.asnumpy())

tests/st/ops/ascend/test_tbe_ops/test_AssignSub.py

@@ -20,11 +20,13 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 
 class Net(nn.Cell):
     """Net definition"""
+
     def __init__(self):
         super(Net, self).__init__()
         self.AssignSub = P.AssignSub()
@@ -39,8 +41,8 @@ class Net(nn.Cell):
 def test_net():
     """test AssignSub"""
     net = Net()
-    x = Tensor(np.ones([1]).astype(np.int32)*100)
+    x = Tensor(np.ones([1]).astype(np.int32) * 100)
 
     print("MyPrintResult dataX:", x)
     result = net(x)
-    print("MyPrintResult data::", result.asnumpy())
+    print("MyPrintResult data::", result.asnumpy())

tests/st/ops/ascend/test_tbe_ops/test_ReduceMean.py

@@ -20,7 +20,10 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self, keep_dims, axis):
         super(Net, self).__init__()
@@ -31,8 +34,10 @@ class Net(nn.Cell):
     def construct(self, inputs):
         return self.reduce_mean(inputs, self.axis)
 
+
 x1 = np.random.randn(64).astype(np.float32)
 
+
 def test_net():
     keepdims = False
     axis = -1

tests/st/ops/ascend/test_tbe_ops/test_add.py

@@ -21,6 +21,7 @@ import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -29,8 +30,9 @@ class Net(nn.Cell):
     def construct(self, x, y):
         return self.add(x, y)
 
-x = np.random.randn(1,3,3,4).astype(np.float32)
-y = np.random.randn(1,3,3,4).astype(np.float32)
+
+x = np.random.randn(1, 3, 3, 4).astype(np.float32)
+y = np.random.randn(1, 3, 3, 4).astype(np.float32)
 
 
 def test_net():

tests/st/ops/ascend/test_tbe_ops/test_addn.py

@@ -20,15 +20,19 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
         self.add = P.AddN()
-        
+
     def construct(self, x, y):
         return self.add((x, y))
 
+
 def test_net():
     x = np.random.randn(1, 3, 3, 4).astype(np.float32)
     y = np.random.randn(1, 3, 3, 4).astype(np.float32)

tests/st/ops/ascend/test_tbe_ops/test_apply_adam.py

@@ -19,6 +19,7 @@ from mindspore.nn import Dense, SoftmaxCrossEntropyWithLogits
 from mindspore.nn import TrainOneStepCell, WithLossCell
 
 import mindspore.context as context
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", impl_type="tbe")
 context.set_context(enable_task_sink=True)
 
@@ -44,16 +45,16 @@ class Adam:
         label = Tensor(label_np_onehot)
 
         ms_dense = Dense(in_channels=self.input_channels,
-                        out_channels=self.output_channels,
-                        weight_init=weight_np,
-                        bias_init=bias, has_bias=True)
+                         out_channels=self.output_channels,
+                         weight_init=weight_np,
+                         bias_init=bias, has_bias=True)
         criterion = SoftmaxCrossEntropyWithLogits()
         optimizer = nn.Adam(ms_dense.trainable_params(),
-                         learning_rate=1e-3,
-                         beta1=0.9, beta2=0.999, eps=self.epsilon,
-                         use_locking=False,
-                         use_nesterov=False, weight_decay=0.0,
-                         loss_scale=1.0)
+                            learning_rate=1e-3,
+                            beta1=0.9, beta2=0.999, eps=self.epsilon,
+                            use_locking=False,
+                            use_nesterov=False, weight_decay=0.0,
+                            loss_scale=1.0)
 
         net_with_criterion = WithLossCell(ms_dense, criterion)
         train_network = TrainOneStepCell(net_with_criterion, optimizer)
@@ -68,5 +69,5 @@ class Adam:
 
 
 def test_adam():
-    fact = Adam(batch_num=8, input_channels=20, output_channels=5, epoch=5, lr=0.1, weight_decay=0.0, epsilon= 1e-8)
+    fact = Adam(batch_num=8, input_channels=20, output_channels=5, epoch=5, lr=0.1, weight_decay=0.0, epsilon=1e-8)
     fact.train_mindspore_impl()

tests/st/ops/ascend/test_tbe_ops/test_apply_momentum.py

@@ -21,23 +21,26 @@ import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
         self.apply_momentum = P.ApplyMomentum(gradient_scale=1024.0)
         self.variable = Parameter(initializer(
-                                'normal', [2, 3, 3, 4]), name='variable')
+            'normal', [2, 3, 3, 4]), name='variable')
         self.accumulation = Parameter(initializer(
-                                'normal', [2, 3, 3, 4]), name='accumulation')
+            'normal', [2, 3, 3, 4]), name='accumulation')
         self.learning_rate = Parameter(initializer(
-                                'normal', [1, ]), name='learning_rate')
+            'normal', [1, ]), name='learning_rate')
         self.gradient = Parameter(initializer(
-                                'normal', [2, 3, 3, 4]), name='gradient')
+            'normal', [2, 3, 3, 4]), name='gradient')
         self.momentum = Parameter(initializer(
-                                'normal', [1, ]), name='momentum')
+            'normal', [1, ]), name='momentum')
+
     def construct(self):
         return self.apply_momentum(self.variable, self.accumulation, self.learning_rate, self.gradient, self.momentum)
 
+
 def test_net():
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
     apply_momentum = Net()

tests/st/ops/ascend/test_tbe_ops/test_batchmatmul.py

@@ -19,8 +19,10 @@ from mindspore.nn import Cell
 from mindspore.train.model import Model
 import pytest
 from mindspore import context
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
+
 class Net(Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -30,17 +32,20 @@ class Net(Cell):
         x = self.batchmatmul(inputa, inputb)
         return x
 
+
 def tf_me_batchmatmul(inputa, inputb):
     net = Net()
     net.set_train()
     model = Model(net)
     out_me = model.predict(Tensor(inputa), Tensor(inputb))
 
+
 def test_batchmatmul_normal_shape1():
     inputa = np.random.randn(128, 16, 128).astype(np.float32)
     inputb = np.random.randn(128, 128, 64).astype(np.float32)
     tf_me_batchmatmul(Tensor(inputa), Tensor(inputb))
 
+
 def test_batchmatmul_normal_shape2():
     inputa = np.random.randn(1, 16, 128, 128).astype(np.float32)
     inputb = np.random.randn(1, 16, 128, 64).astype(np.float32)

tests/st/ops/ascend/test_tbe_ops/test_batchnorm.py

@@ -21,6 +21,7 @@ import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -35,7 +36,7 @@ class Net(nn.Cell):
 
 
 def test_net():
-    x = np.random.randn(1,64,112,112).astype(np.float32)
+    x = np.random.randn(1, 64, 112, 112).astype(np.float32)
     # mean = np.random.randn(1,16,1,1).astype(np.float32)
     # variance = np.random.randn(1,16,1,1).astype(np.float32)
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
@@ -55,4 +56,3 @@ def test_net():
 
     print("***********output y*********")
     print(output.asnumpy())
-

tests/st/ops/ascend/test_tbe_ops/test_batchnorm_grad.py

@@ -21,8 +21,11 @@ import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 from mindspore.ops.composite import GradOperation
-#context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+# context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 context.set_context(device_target="Ascend")
+
+
 class Grad(nn.Cell):
     def __init__(self, network):
         super(Grad, self).__init__()
@@ -48,7 +51,7 @@ class Net(nn.Cell):
 
 
 def test_net():
-    x = np.random.randn(1,64,112,112).astype(np.float32)
-    sens = np.random.randn(1,64,112,112).astype(np.float32)
+    x = np.random.randn(1, 64, 112, 112).astype(np.float32)
+    sens = np.random.randn(1, 64, 112, 112).astype(np.float32)
     net = Grad(Net())
     output = net(Tensor(x), Tensor(sens))

tests/st/ops/ascend/test_tbe_ops/test_bias_add.py

@@ -20,11 +20,13 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 
 class Net(nn.Cell):
     """Net definition"""
+
     def __init__(self,
                  output_channels,
                  bias_init='zeros',
@@ -51,4 +53,3 @@ def test_compile():
     # enable it when staging function is ready
     output = net(input_data)
     print(output.asnumpy())
-

tests/st/ops/ascend/test_tbe_ops/test_bias_add_grad.py

@@ -21,7 +21,10 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -31,6 +34,7 @@ class Net(nn.Cell):
     def construct(self, dout):
         return self.bias_add_grad(dout)
 
+
 def test_net():
     dout = np.random.rand(1, 1001).astype(np.float32)
     bias_add_grad = Net()

tests/st/ops/ascend/test_tbe_ops/test_concat.py

@@ -20,11 +20,12 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 
 class Net(nn.Cell):
-    def __init__( self):
+    def __init__(self):
         super(Net, self).__init__()
 
         self.cat = P.Concat(axis=1)
@@ -46,4 +47,4 @@ def test_net():
     print(np.arange(2 * 2).reshape(2, 2))
     print(np.arange(2 * 3).reshape(2, 3))
     print(output)
-    assert(output.asnumpy() == expect).all()
+    assert (output.asnumpy() == expect).all()

tests/st/ops/ascend/test_tbe_ops/test_conv.py

@@ -21,31 +21,30 @@ import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
         out_channel = 64
         kernel_size = 7
         self.conv = P.Conv2D(out_channel,
-                                 kernel_size,
-                                 mode=1,
-                                 pad_mode="valid",
-                                 pad=0,
-                                 stride=1,
-                                 dilation=1,
-                                 group=1)
+                             kernel_size,
+                             mode=1,
+                             pad_mode="valid",
+                             pad=0,
+                             stride=1,
+                             dilation=1,
+                             group=1)
         self.w = Parameter(initializer(
-                                'normal', [64, 3, 7, 7]), name='w')
-
+            'normal', [64, 3, 7, 7]), name='w')
 
     @ms_function
     def construct(self, x):
         return self.conv(x, self.w)
 
 
-
 def test_net():
-    x = np.random.randn(32,3,224,224).astype(np.float32)
+    x = np.random.randn(32, 3, 224, 224).astype(np.float32)
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
     conv = Net()
     output = conv(Tensor(x))

tests/st/ops/ascend/test_tbe_ops/test_conv2d_backprop_filter.py

@@ -21,6 +21,7 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(device_target='Ascend')
 
 
@@ -37,19 +38,21 @@ class Net(nn.Cell):
                                                   stride=1,
                                                   dilation=1,
                                                   group=1)
-        self.w = Parameter(initializer(Tensor(np.array([[[[1, 0, -1], [1, 0, -1], [1, 0, -1]]]]).astype(np.float32)), [1, 1, 3, 3]), name='w')
+        self.w = Parameter(
+            initializer(Tensor(np.array([[[[1, 0, -1], [1, 0, -1], [1, 0, -1]]]]).astype(np.float32)), [1, 1, 3, 3]),
+            name='w')
         self.x = Parameter(initializer(Tensor(np.array([[[
             [3, 0, 1, 2, 7, 4],
             [1, 5, 8, 9, 3, 1],
             [2, 7, 2, 5, 1, 3],
             [0, 1, 3, 1, 7, 8],
             [4, 2, 1, 6, 2, 8],
-            [2, 4, 5, 2, 3, 9]]]]).astype(np.float32)), [1,1,6,6]), name='x')
+            [2, 4, 5, 2, 3, 9]]]]).astype(np.float32)), [1, 1, 6, 6]), name='x')
         self.out = Parameter(initializer(Tensor(np.array([[[
-            [ -5, -4,  0,  8],
-            [-10, -2,  2,  3],
-            [  0, -2, -4, -7],
-            [ -3, -2, -3, -16]]]]).astype(np.float32)),[1,1,4,4]), name='y')
+            [-5, -4, 0, 8],
+            [-10, -2, 2, 3],
+            [0, -2, -4, -7],
+            [-3, -2, -3, -16]]]]).astype(np.float32)), [1, 1, 4, 4]), name='y')
         self.get_shape = P.Shape()
 
     @ms_function
@@ -67,7 +70,7 @@ def test_conv2d_backprop_filter():
        [-104, -211, -322]
        [-102, -144, -248]]]]
     """
-    expect = np.array([[[[ -60, -142, -265],
+    expect = np.array([[[[-60, -142, -265],
                          [-104, -211, -322],
                          [-102, -144, -248]]]]).astype(np.float32)
     print(output)

tests/st/ops/ascend/test_tbe_ops/test_conv2d_backprop_input.py

@@ -20,6 +20,7 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(device_target="Ascend")
 
 
@@ -36,19 +37,21 @@ class Net(nn.Cell):
                                                 stride=1,
                                                 dilation=1,
                                                 group=1)
-        self.w = Parameter(initializer(Tensor(np.array([[[[1, 0, -1], [1, 0, -1], [1, 0, -1]]]]).astype(np.float32)), [1, 1, 3, 3]), name='w')
+        self.w = Parameter(
+            initializer(Tensor(np.array([[[[1, 0, -1], [1, 0, -1], [1, 0, -1]]]]).astype(np.float32)), [1, 1, 3, 3]),
+            name='w')
         self.x = Parameter(initializer(Tensor(np.array([[[
             [3, 0, 1, 2, 7, 4],
             [1, 5, 8, 9, 3, 1],
             [2, 7, 2, 5, 1, 3],
             [0, 1, 3, 1, 7, 8],
             [4, 2, 1, 6, 2, 8],
-            [2, 4, 5, 2, 3, 9]]]]).astype(np.float32)), [1,1,6,6]), name='x')
+            [2, 4, 5, 2, 3, 9]]]]).astype(np.float32)), [1, 1, 6, 6]), name='x')
         self.out = Parameter(initializer(Tensor(np.array([[[
-            [ -5, -4,  0,  8],
-            [-10, -2,  2,  3],
-            [  0, -2, -4, -7],
-            [ -3, -2, -3, -16]]]]).astype(np.float32)),[1,1,4,4]), name='y')
+            [-5, -4, 0, 8],
+            [-10, -2, 2, 3],
+            [0, -2, -4, -7],
+            [-3, -2, -3, -16]]]]).astype(np.float32)), [1, 1, 4, 4]), name='y')
         self.get_shape = P.Shape()
 
     @ms_function
@@ -69,11 +72,11 @@ def test_conv2d_backprop_input():
        [ -3,  -4,  -4, -19,   7,  23]
        [ -3,  -2,   0, -14,   3,  16]]]]
     """
-    expect = np.array([[[[ -5,  -4,   5,  12,   0,  -8],
-                         [-15,  -6,  17,  17,  -2, -11],
-                         [-15,  -8,  13,  12,   2,  -4],
-                         [-13,  -6,   8, -14,   5,  20],
-                         [ -3,  -4,  -4, -19,   7,  23],
-                         [ -3,  -2,   0, -14,   3,  16]]]]).astype(np.float32)
+    expect = np.array([[[[-5, -4, 5, 12, 0, -8],
+                         [-15, -6, 17, 17, -2, -11],
+                         [-15, -8, 13, 12, 2, -4],
+                         [-13, -6, 8, -14, 5, 20],
+                         [-3, -4, -4, -19, 7, 23],
+                         [-3, -2, 0, -14, 3, 16]]]]).astype(np.float32)
     print(output)
     assert (output.asnumpy() == expect).all()

tests/st/ops/ascend/test_tbe_ops/test_dropout_do_mask.py

@@ -20,9 +20,11 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 from mindspore import log as logger
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -33,7 +35,7 @@ class Net(nn.Cell):
 
 
 def test_net():
-    x = np.random.randn(2,5,8).astype(np.float32)
+    x = np.random.randn(2, 5, 8).astype(np.float32)
     mask = np.random.randn(16).astype(np.uint8)
     keep_prob = 1
 
@@ -48,4 +50,3 @@ def test_net():
 
     logger.info("***********output y*********")
     logger.info(output.asnumpy())
-

tests/st/ops/ascend/test_tbe_ops/test_gelu.py

@@ -21,6 +21,7 @@ import math
 import pytest
 from mindspore import context
 from mindspore import log as logger
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 
@@ -52,6 +53,7 @@ def test_gelu_input_dim_0():
     with pytest.raises(ValueError):
         gelu_forward_cmp(input_shape)
 
+
 def test_gelu_input_dim_10240_1024():
     input_shape = [10240, 1024]
     gelu_forward_cmp(input_shape)
@@ -96,6 +98,7 @@ def test_gelu_input_dim_128_4096():
     input_shape = [128, 4096]
     gelu_forward_cmp(input_shape)
 
+
 @pytest.mark.lower_bs
 def test_gelu_input_dim_160_1024():
     input_shape = [160, 1024]

tests/st/ops/ascend/test_tbe_ops/test_gelu_grad_sens.py

@@ -25,6 +25,7 @@ from mindspore import log as logger
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
+
 class Grad(Cell):
     def __init__(self, network):
         super(Grad, self).__init__()
@@ -55,6 +56,7 @@ def gelu_backward_cmp(input_shape):
     logger.info("---------me--------")
     logger.info(output_grad_me)
 
+
 # ----------    LARGE INPUT  ---------------
 
 class MEGeluLargeIn(Cell):
@@ -67,6 +69,7 @@ class MEGeluLargeIn(Cell):
         x = self.matmul(x1, x2)
         return self.gelu(x)
 
+
 class GradLargeIn(Cell):
     def __init__(self, network):
         super(GradLargeIn, self).__init__()
@@ -86,5 +89,5 @@ def gelu_backward_me_large_in_impl(x1, x2, output_grad):
 
 
 def test_grad_gelu_input_10240_1024():
-    input_shape = [10240,1024]
+    input_shape = [10240, 1024]
     gelu_backward_cmp(input_shape)

tests/st/ops/ascend/test_tbe_ops/test_greater.py

@@ -20,8 +20,10 @@ from mindspore.common.tensor import Tensor
 from mindspore.train.model import Model
 from mindspore import log as logger
 from mindspore import context
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
+
 class Greater(Cell):
     def __init__(self):
         super(Greater, self).__init__()
@@ -30,6 +32,7 @@ class Greater(Cell):
     def construct(self, inputa, inputb):
         return self.greater(inputa, inputb)
 
+
 def me_greater(inputa, inputb):
     net = Greater()
     net.set_train()
@@ -42,10 +45,11 @@ def me_greater(inputa, inputb):
     logger.info(inputb)
     return out.asnumpy()
 
+
 @pytest.mark.ssd_tbe
 def test_greater_2d_scalar0():
     a = np.random.randint(-5, 5, [8, 32]).astype(np.int32)
     b = np.random.randint(-5, 5, [8, 32]).astype(np.int32)
     out_me = me_greater(Tensor(a), Tensor(b))
     logger.info("Check me result:")
-    logger.info(out_me)
+    logger.info(out_me)

tests/st/ops/ascend/test_tbe_ops/test_layernorm.py

@@ -20,8 +20,10 @@ from mindspore.train.model import Model
 from mindspore import log as logger
 import pytest
 from mindspore import context
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
+
 class Net(Cell):
     def __init__(self, input_shape, begin_norm_axis, begin_params_axis, gamma, beta):
         super(Net, self).__init__()
@@ -31,6 +33,7 @@ class Net(Cell):
         x = self.layernorm(input)
         return x
 
+
 def pt_me_layernorm(input_data, normalized_shape, gamma, beta, axis):
     net = Net(normalized_shape, begin_norm_axis=axis,
               begin_params_axis=axis,
@@ -42,6 +45,7 @@ def pt_me_layernorm(input_data, normalized_shape, gamma, beta, axis):
     logger.info("Check me result:")
     logger.info(out_me.asnumpy())
 
+
 @pytest.mark.lower_bs
 def test_normal_layernorm_1_128_1024_axis_2():
     """
@@ -52,4 +56,4 @@ def test_normal_layernorm_1_128_1024_axis_2():
     gamma.fill(1.1)
     beta = np.random.randn(1024).astype(np.float32)
     beta.fill(0.1)
-    pt_me_layernorm(input_data, (1024, ), gamma, beta, 2)
+    pt_me_layernorm(input_data, (1024,), gamma, beta, 2)

tests/st/ops/ascend/test_tbe_ops/test_layernorm_grad.py

@@ -19,18 +19,21 @@ from mindspore.nn import Cell
 from mindspore.ops.composite import GradOperation
 from mindspore import log as logger
 from mindspore import context
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
+
 class Grad(Cell):
     def __init__(self, network):
         super(Grad, self).__init__()
         self.grad = GradOperation(name="get_all", get_all=True, sens_param=True)
         self.network = network
 
-    def construct(self, input, output_grad,):
+    def construct(self, input, output_grad, ):
         gout = self.grad(self.network)(input, output_grad)
         return gout
 
+
 class Net(Cell):
     def __init__(self, input_shape, begin_norm_axis, begin_params_axis, gamma, beta):
         super(Net, self).__init__()
@@ -40,6 +43,7 @@ class Net(Cell):
         x = self.layernorm(input)
         return x
 
+
 def py_me_layernorm_grad(input_data, normalized_shape, gamma, beta, axis, gradients):
     input_me = Tensor(input_data)
     net_me = Grad(Net(normalized_shape, begin_norm_axis=axis,
@@ -52,6 +56,7 @@ def py_me_layernorm_grad(input_data, normalized_shape, gamma, beta, axis, gradie
     logger.info("Check me result:")
     logger.info(out_grad.asnumpy())
 
+
 def test_normal_layernorm_grad_normalize_2d():
     """
     1 input[1, 128, 1024],normalized_shape=[1024],element_affine=False

tests/st/ops/ascend/test_tbe_ops/test_less.py

@@ -18,7 +18,10 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -28,8 +31,10 @@ class Net(nn.Cell):
     def construct(self, x1, x2):
         return self.less(x1, x2)
 
-x1 = np.random.randn(3,4).astype(np.float16)
-x2 = np.random.randn(3,4).astype(np.float16)
+
+x1 = np.random.randn(3, 4).astype(np.float16)
+x2 = np.random.randn(3, 4).astype(np.float16)
+
 
 def test_net():
     less = Net()
@@ -37,4 +42,3 @@ def test_net():
     print(x1)
     print(x2)
     print(output.asnumpy())
-

tests/st/ops/ascend/test_tbe_ops/test_less_equal.py

@@ -18,7 +18,10 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -28,8 +31,10 @@ class Net(nn.Cell):
     def construct(self, x1, x2):
         return self.less_equal(x1, x2)
 
-x1 = np.random.randn(3,4).astype(np.float16)
-x2 = np.random.randn(3,4).astype(np.float16)
+
+x1 = np.random.randn(3, 4).astype(np.float16)
+x2 = np.random.randn(3, 4).astype(np.float16)
+
 
 def test_net():
     less_equal = Net()
@@ -37,4 +42,3 @@ def test_net():
     print(x1)
     print(x2)
     print(output.asnumpy())
-

tests/st/ops/ascend/test_tbe_ops/test_logical_and.py

@@ -18,7 +18,10 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -28,12 +31,14 @@ class Net(nn.Cell):
     def construct(self, x1, x2):
         return self.logical_and(x1, x2)
 
+
 x1 = [True, True, False, False, True, True, False, False]
 x2 = [True, False, False, True, True, False, False, True]
+
+
 def test_net():
     logical_and = Net()
     output = logical_and(Tensor(x1), Tensor(x2))
     print(x1)
     print(x2)
     print(output.asnumpy())
-

tests/st/ops/ascend/test_tbe_ops/test_logical_not.py

@@ -18,7 +18,10 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -28,11 +31,12 @@ class Net(nn.Cell):
     def construct(self, x1):
         return self.logical_not(x1)
 
+
 x1 = [True, True, False, False, True, True, False, False]
 
+
 def test_net():
     logical_not = Net()
     output = logical_not(Tensor(x1))
     print(x1)
     print(output.asnumpy())
-

tests/st/ops/ascend/test_tbe_ops/test_logical_or.py

@@ -18,7 +18,10 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -28,12 +31,14 @@ class Net(nn.Cell):
     def construct(self, x1, x2):
         return self.logical_or(x1, x2)
 
+
 x1 = [True, True, False, False, True, True, False, False]
 x2 = [True, False, False, True, True, False, False, True]
+
+
 def test_net():
     logical_or = Net()
     output = logical_or(Tensor(x1), Tensor(x2))
     print(x1)
     print(x2)
     print(output.asnumpy())
-

tests/st/ops/ascend/test_tbe_ops/test_matmul.py

@@ -21,6 +21,7 @@ import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -30,8 +31,10 @@ class Net(nn.Cell):
     def construct(self, x1, x2):
         return self.matmul(x1, x2)
 
-x1 = np.random.randn(1,3).astype(np.float32)
-x2 = np.random.randn(3,4).astype(np.float32)
+
+x1 = np.random.randn(1, 3).astype(np.float32)
+x2 = np.random.randn(3, 4).astype(np.float32)
+
 
 def test_net():
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")

tests/st/ops/ascend/test_tbe_ops/test_matmul_failed.py

@@ -20,7 +20,10 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -30,8 +33,10 @@ class Net(nn.Cell):
     def construct(self, x1, x2):
         return self.matmul(x1, x2)
 
-x1 = np.random.randn(10,1).astype(np.float32)
-x2 = np.random.randn(100,1).astype(np.float32)
+
+x1 = np.random.randn(10, 1).astype(np.float32)
+x2 = np.random.randn(100, 1).astype(np.float32)
+
 
 def test_net():
     matmul = Net()

tests/st/ops/ascend/test_tbe_ops/test_maximum.py

@@ -22,14 +22,16 @@ from mindspore.ops import operations as P
 
 context.set_context(device_target="Ascend")
 
+
 class Max(nn.Cell):
-    def __init__(self,dtype):
+    def __init__(self, dtype):
         super(Max, self).__init__()
         self.max = P.Maximum()
 
     def construct(self, inputa, inputb):
         return self.max(inputa, inputb)
 
+
 def me_max(inputa, inputb, dtype=ms.float32):
     context.set_context(mode=context.GRAPH_MODE)
     net = Max(dtype)
@@ -44,14 +46,16 @@ def me_max(inputa, inputb, dtype=ms.float32):
     print(out)
     return out.asnumpy()
 
-def cmp_max(a,b):
+
+def cmp_max(a, b):
     out = np.maximum(a, b)
     out_ms = me_max(a, b)
     print("-------ms------")
     print("numpy out :{}".format(out))
     print("ms out :{}".format(out_ms))
 
+
 def test_maximum_2_2():
     a = np.random.randn(2, 2).astype(np.float32)
     b = np.random.randn(2, 2).astype(np.float32)
-    cmp_max(a,b)
+    cmp_max(a, b)

tests/st/ops/ascend/test_tbe_ops/test_maximum_grad.py

@@ -22,6 +22,7 @@ from mindspore.ops import operations as P
 context.set_context(device_target="Ascend")
 grad = C.GradOperation('get_all', get_all=True, sens_param=True)
 
+
 class MaxNetMe(Cell):
     def __init__(self):
         super(MaxNetMe, self).__init__()
@@ -31,6 +32,7 @@ class MaxNetMe(Cell):
         x = self.max(inputA, inputB)
         return x
 
+
 class GradWrap(Cell):
     def __init__(self, network):
         super(GradWrap, self).__init__()
@@ -40,6 +42,7 @@ class GradWrap(Cell):
         gout = grad(self.network)(inputA, inputB, sens)
         return gout
 
+
 def gen_data(inputA_np, inputB_np, grad=None):
     inputA_me = inputA_np
     if isinstance(inputA_np, np.ndarray) == True:
@@ -61,7 +64,8 @@ def gen_data(inputA_np, inputB_np, grad=None):
     print(output[0].asnumpy())
     print(output[1].asnumpy())
 
+
 def test_net():
     inputA_np = np.random.randn(1, 3, 2, 2).astype(np.float32)
     inputB_np = np.random.randn(1, 3, 2, 2).astype(np.float32)
-    gen_data(inputA_np, inputB_np)
+    gen_data(inputA_np, inputB_np)

tests/st/ops/ascend/test_tbe_ops/test_maxpool.py

@@ -19,12 +19,12 @@ from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
         self.maxpool = P.MaxPool(padding="SAME", ksize=3, strides=2)
 
-
     @ms_function
     def construct(self, x):
         output = self.maxpool(x)
@@ -32,7 +32,7 @@ class Net(nn.Cell):
 
 
 def test_net():
-    x = np.random.randn(32,64,112,112).astype(np.float16)
+    x = np.random.randn(32, 64, 112, 112).astype(np.float16)
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
     maxpool = Net()
     output = maxpool(Tensor(x))

tests/st/ops/ascend/test_tbe_ops/test_maxpool_grad.py

@@ -19,6 +19,7 @@ from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
 from mindspore.ops.composite import GradOperation
+
 context.set_context(device_target="Ascend")
 
 

tests/st/ops/ascend/test_tbe_ops/test_minimum.py

@@ -22,7 +22,10 @@ from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 import mindspore as ms
 from mindspore.train.model import Model
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
 class Min(nn.Cell):
     def __init__(self, dtype):
         super(Min, self).__init__()
@@ -46,7 +49,8 @@ def me_min(inputa, inputb, dtype=ms.float32):
     print(out)
     return out.asnumpy()
 
-def cmp_min(a,b):
+
+def cmp_min(a, b):
     print(a)
     print(b)
 
@@ -55,8 +59,8 @@ def cmp_min(a,b):
     out_me = me_min(a, b)
     print(out_me)
 
+
 def test_minimum_2_2():
     a = np.random.randn(2, 2, 1, 1).astype(np.float32)
     b = np.random.randn(2, 2, 1, 1).astype(np.float32)
-    cmp_min(a,b)
-
+    cmp_min(a, b)

tests/st/ops/ascend/test_tbe_ops/test_minimum_grad.py

@@ -22,6 +22,8 @@ from mindspore.ops.operations import Minimum
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 grad = C.GradOperation('get_all', get_all=True, sens_param=True)
+
+
 class MinNetMe(Cell):
     def __init__(self):
         super(MinNetMe, self).__init__()
@@ -41,6 +43,7 @@ class GradWrap(Cell):
         gout = grad(self.network)(inputA, inputB, sens)
         return gout
 
+
 def gen_data(inputA_np, inputB_np, grad=None):
     inputA_me = inputA_np
     if isinstance(inputA_np, np.ndarray) == True:
@@ -51,7 +54,7 @@ def gen_data(inputA_np, inputB_np, grad=None):
         inputB_me = Tensor(inputB_np)
 
     if grad is None:
-       grad = np.random.randn(1, 3, 2, 2).astype(np.float32)
+        grad = np.random.randn(1, 3, 2, 2).astype(np.float32)
 
     print(inputA_np)
     print(inputB_np)

tests/st/ops/ascend/test_tbe_ops/test_mul.py

@@ -18,7 +18,10 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -28,8 +31,10 @@ class Net(nn.Cell):
     def construct(self, x1, x2):
         return self.mul(x1, x2)
 
-x1 = np.random.randn(3,4).astype(np.float32)
-x2 = np.random.randn(3,4).astype(np.float32)
+
+x1 = np.random.randn(3, 4).astype(np.float32)
+x2 = np.random.randn(3, 4).astype(np.float32)
+
 
 def test_net():
     mul = Net()

tests/st/ops/ascend/test_tbe_ops/test_npu_alloc_float_status.py

@@ -18,7 +18,10 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -28,8 +31,8 @@ class Net(nn.Cell):
     def construct(self):
         return self.npu_alloc_float_status()
 
+
 def test_net():
     npu_alloc_float_status = Net()
     output = npu_alloc_float_status()
     print(output.asnumpy())
-

tests/st/ops/ascend/test_tbe_ops/test_npu_clear_float_status.py

@@ -18,7 +18,10 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -28,11 +31,12 @@ class Net(nn.Cell):
     def construct(self, x1):
         return self.npu_clear_float_status(x1)
 
+
 x1 = np.random.randn(8).astype(np.float32)
 
+
 def test_net():
     npu_clear_float_status = Net()
     output = npu_clear_float_status(Tensor(x1))
     print(x1)
     print(output.asnumpy())
-

tests/st/ops/ascend/test_tbe_ops/test_npu_get_float_status.py

@@ -18,7 +18,10 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -28,11 +31,12 @@ class Net(nn.Cell):
     def construct(self, x1):
         return self.npu_get_float_status(x1)
 
+
 x1 = np.random.randn(8).astype(np.float32)
 
+
 def test_net():
     npu_get_float_status = Net()
     output = npu_get_float_status(Tensor(x1))
     print(x1)
     print(output.asnumpy())
-

tests/st/ops/ascend/test_tbe_ops/test_pad.py

@@ -18,21 +18,24 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
 
 
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
-        self.pad = P.Pad(paddings=((3,2), (2,3)))
+        self.pad = P.Pad(paddings=((3, 2), (2, 3)))
 
     @ms_function
     def construct(self, x):
         x = self.pad(x)
         return x
 
+
 x = np.random.random(size=(2, 2)).astype(np.float32)
 
+
 def test_net():
     pad = Net()
     output = pad(Tensor(x))

tests/st/ops/ascend/test_tbe_ops/test_pow.py

@@ -23,8 +23,10 @@ from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 import mindspore as ms
 from mindspore.train.model import Model
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
+
 class PowMe(Cell):
     def __init__(self):
         super(PowMe, self).__init__()
@@ -33,6 +35,7 @@ class PowMe(Cell):
     def construct(self, input, exp):
         return self.pow(input, exp)
 
+
 def pow_forward_me_impl(input, exp):
     n = PowMe()
     n.set_train()
@@ -40,6 +43,7 @@ def pow_forward_me_impl(input, exp):
     out = m.predict(input, exp)
     return out.asnumpy()
 
+
 def pow_forward_cmp(input_shape, exp_shape):
     if len(input_shape) == 0:
         input_np = np.absolute(np.random.randn())
@@ -54,14 +58,14 @@ def pow_forward_cmp(input_shape, exp_shape):
         exp_np = np.absolute(np.random.randn(*exp_shape).astype(np.float32))
     exp_tf = exp_np
     exp_me = Tensor(exp_np, dtype=ms.float32)
-	
+
     out_me = pow_forward_me_impl(input_me, exp_me)
     print(input_me)
     print(exp_me)
     print(out_me)
-	
+
+
 def test_pow_input_scalar_exp_scalar():
     input_shape = []
     exp_shape = []
     pow_forward_cmp(input_shape, exp_shape)
-

tests/st/ops/ascend/test_tbe_ops/test_realdiv.py

@@ -18,7 +18,10 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -28,8 +31,10 @@ class Net(nn.Cell):
     def construct(self, x1, x2):
         return self.realdiv(x1, x2)
 
-x1 = np.random.randn(3,4).astype(np.float32)
-x2 = np.random.randn(3,4).astype(np.float32)
+
+x1 = np.random.randn(3, 4).astype(np.float32)
+x2 = np.random.randn(3, 4).astype(np.float32)
+
 
 def test_net():
     realdiv = Net()

tests/st/ops/ascend/test_tbe_ops/test_reciprocal.py

@@ -18,7 +18,10 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -28,11 +31,12 @@ class Net(nn.Cell):
     def construct(self, x1):
         return self.reciprocal(x1)
 
+
 x1 = np.random.randn(3, 4).astype(np.float32)
 
+
 def test_net():
     reciprocal = Net()
     output = reciprocal(Tensor(x1))
     print(x1)
     print(output.asnumpy())
-

tests/st/ops/ascend/test_tbe_ops/test_relu.py

@@ -20,7 +20,10 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -30,8 +33,9 @@ class Net(nn.Cell):
     def construct(self, x):
         return self.relu(x)
 
+
 def test_net():
-    x = np.random.randn(2,3,3,4).astype(np.float32)
+    x = np.random.randn(2, 3, 3, 4).astype(np.float32)
     relu = Net()
     output = relu(Tensor(x))
     print(x)

tests/st/ops/ascend/test_tbe_ops/test_relu_grad.py

@@ -21,8 +21,10 @@ import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 from mindspore.ops.composite import GradOperation
+
 context.set_context(device_target="Ascend")
 
+
 class Grad(nn.Cell):
     def __init__(self, network):
         super(Grad, self).__init__()
@@ -33,6 +35,7 @@ class Grad(nn.Cell):
     def construct(self, input, output_grad):
         return self.grad(self.network)(input, output_grad)
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -41,9 +44,10 @@ class Net(nn.Cell):
     def construct(self, x):
         return self.relu(x)
 
+
 def test_net():
-    x = np.random.randn(2,3,3,4).astype(np.float32)
-    sens = np.random.randn(2,3,3,4).astype(np.float32)
+    x = np.random.randn(2, 3, 3, 4).astype(np.float32)
+    sens = np.random.randn(2, 3, 3, 4).astype(np.float32)
     net = Grad(Net())
     output = net(Tensor(x), Tensor(sens))
     print(len(output))

tests/st/ops/ascend/test_tbe_ops/test_relu_v2_grad.py

@@ -21,8 +21,10 @@ import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 from mindspore.ops.composite import GradOperation
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
+
 class Grad(nn.Cell):
     def __init__(self, network):
         super(Grad, self).__init__()
@@ -33,6 +35,7 @@ class Grad(nn.Cell):
     def construct(self, input):
         return self.grad(self.network)(input)
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -41,8 +44,9 @@ class Net(nn.Cell):
     def construct(self, x):
         return self.relu_v2(x)
 
+
 def test_net():
-    x = Tensor(np.ones((2,3,3,4)).astype(np.float32))
+    x = Tensor(np.ones((2, 3, 3, 4)).astype(np.float32))
     relu_net = Net()
     relu_output = relu_net(x)
     net = Grad(Net())

tests/st/ops/ascend/test_tbe_ops/test_resize_nearest_neighbor.py

@@ -18,8 +18,10 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -29,6 +31,7 @@ class Net(nn.Cell):
     def construct(self, x):
         return self.upsample(x)
 
+
 def test_net():
     x = np.random.random(size=(32, 3, 32, 32)).astype(np.float32)
     upsample = Net()

tests/st/ops/ascend/test_tbe_ops/test_resize_nearest_neighbor_grad.py

@@ -19,6 +19,7 @@ from mindspore.ops.composite import GradOperation
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
 
 
@@ -49,4 +50,4 @@ def test_net():
     grad = Grad(Net())
     output = grad(Tensor(image), Tensor(grads))
     print("=================output====================")
-    print(output)
+    print(output)

tests/st/ops/ascend/test_tbe_ops/test_scatter_nd.py

@@ -20,6 +20,7 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 
@@ -29,12 +30,13 @@ class Net(nn.Cell):
         self.scatternd = P.ScatterNd()
 
     def construct(self, indices, update):
-        return self.scatternd(indices, update, (3,3))
+        return self.scatternd(indices, update, (3, 3))
 
 
 indices = np.array([[0, 1], [1, 1]]).astype(np.int32)
 update = np.array([3.2, 1.1]).astype(np.float32)
 
+
 def test_net():
     scatternd = Net()
     print(indices)

tests/st/ops/ascend/test_tbe_ops/test_select.py

@@ -23,7 +23,10 @@ from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 import mindspore as ms
 from mindspore.train.model import Model
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
 class Select(Cell):
     def __init__(self, dtype):
         super(Select, self).__init__()
@@ -32,6 +35,7 @@ class Select(Cell):
     def construct(self, cond, inputa, inputb):
         return self.select(cond, inputa, inputb)
 
+
 def me_select(cond, inputa, inputb, dtype=ms.float32):
     net = Select(dtype)
     net.set_train()
@@ -45,9 +49,10 @@ def me_select(cond, inputa, inputb, dtype=ms.float32):
 
     out = model.predict(Tensor(cond), inputa, inputb)
     return out.asnumpy()
-	
-def cmp_select(input_cond,inputa,inputb):
-    cond =  input_cond > 0.5
+
+
+def cmp_select(input_cond, inputa, inputb):
+    cond = input_cond > 0.5
     out_me = me_select(cond, inputa, inputb)
     print(input_cond)
     print(cond)
@@ -55,9 +60,9 @@ def cmp_select(input_cond,inputa,inputb):
     print(inputb)
     print(out_me)
 
+
 def test_select_2_2():
     input_cond = np.random.rand(2, 2)
-    inputa = np.random.randn(2,2).astype(np.float32)
-    inputb = np.random.randn(2,2).astype(np.float32)
-    cmp_select(input_cond,inputa,inputb)
-
+    inputa = np.random.randn(2, 2).astype(np.float32)
+    inputb = np.random.randn(2, 2).astype(np.float32)
+    cmp_select(input_cond, inputa, inputb)

tests/st/ops/ascend/test_tbe_ops/test_sigmoid.py

@@ -18,8 +18,10 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -29,6 +31,7 @@ class Net(nn.Cell):
     def construct(self, x):
         return self.sigmoid(x)
 
+
 def test_net():
     x = np.random.random(size=(2, 3)).astype(np.float32)
     sigmoid = Net()

tests/st/ops/ascend/test_tbe_ops/test_sigmoid_cross_entropy_with_logits.py

@@ -21,6 +21,7 @@ import mindspore.context as context
 
 context.set_context(device_target="Ascend")
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()

tests/st/ops/ascend/test_tbe_ops/test_sigmoid_cross_entropy_with_logits_grad.py

@@ -22,6 +22,7 @@ import mindspore.context as context
 
 context.set_context(device_target="Ascend")
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()

tests/st/ops/ascend/test_tbe_ops/test_sigmoid_grad.py

@@ -19,6 +19,7 @@ from mindspore.ops.composite import GradOperation
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
 
 
@@ -42,6 +43,7 @@ class Grad(nn.Cell):
     def construct(self, x, y):
         return self.grad(self.network)(x, y)
 
+
 def test_net():
     x = np.random.random(size=(2, 3, 4, 5, 6)).astype(np.float32)
     y = np.random.random(size=(2, 3, 4, 5, 6)).astype(np.float32)
@@ -49,4 +51,3 @@ def test_net():
     output = net(Tensor(x), Tensor(y))
     print("=================output====================")
     print(output.asnumpy())
-

tests/st/ops/ascend/test_tbe_ops/test_slice.py

@@ -20,26 +20,28 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 
 class Slice(nn.Cell):
-    def __init__( self):
+    def __init__(self):
         super(Slice, self).__init__()
 
         self.cat = P.Slice()
         self.x1 = Parameter(initializer(
-            Tensor(np.array([[[1, -1, 1], [2, -2, 2]], [[3, -3, 3], [4, -4, 4]], [[5, -5, 5], [6, -6, 6]]]).astype(np.float32)), [3,2,3]), name='x1')
+            Tensor(np.array([[[1, -1, 1], [2, -2, 2]], [[3, -3, 3], [4, -4, 4]], [[5, -5, 5], [6, -6, 6]]]).astype(
+                np.float32)), [3, 2, 3]), name='x1')
 
     @ms_function
     def construct(self):
-        return self.cat(self.x1, (0,1, 0), (2, 1, 3))
+        return self.cat(self.x1, (0, 1, 0), (2, 1, 3))
 
 
 def test_slice():
     cat = Slice()
     output = cat()
-    expect = [[[2., -2.,  2.]],
-              [[4., -4.,  4.]]]
+    expect = [[[2., -2., 2.]],
+              [[4., -4., 4.]]]
     print(output)
-    assert (output.asnumpy() == expect).all()
+    assert (output.asnumpy() == expect).all()

tests/st/ops/ascend/test_tbe_ops/test_smooth_l1_loss.py

@@ -18,6 +18,7 @@ import mindspore.nn as nn
 import mindspore.context as context
 from mindspore import Tensor
 from mindspore.ops import operations as P
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 

tests/st/ops/ascend/test_tbe_ops/test_smooth_l1_loss_grad.py

@@ -31,6 +31,7 @@ class Net(nn.Cell):
     def construct(self, pred, gt):
         return self.SmoothL1Loss(pred, gt)
 
+
 class Grad(nn.Cell):
     def __init__(self, network):
         super(Grad, self).__init__()

tests/st/ops/ascend/test_tbe_ops/test_softmax.py

@@ -20,17 +20,22 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
         self.Softmax = P.Softmax()
-        
+
     def construct(self, x):
         return self.Softmax(x)
 
+
 x = np.array([[5, 1]]).astype(np.float32)
 
+
 def test_net():
     softmax = Net()
     output = softmax(Tensor(x))

tests/st/ops/ascend/test_tbe_ops/test_softmax_cross_entropy_with_logits.py

@@ -18,6 +18,7 @@ import mindspore.nn as nn
 from mindspore.common.api import ms_function
 import numpy as np
 import mindspore.context as context
+
 context.set_context(device_target="Ascend")
 
 
@@ -36,4 +37,4 @@ def test_net():
     labels = np.random.randn(32, 1001).astype(np.float16)
     SoftmaxCrossEntropyWithLogits = Net()
     output = SoftmaxCrossEntropyWithLogits(Tensor(features), Tensor(labels))
-    #print(output.asnumpy())
+    # print(output.asnumpy())

tests/st/ops/ascend/test_tbe_ops/test_split.py

@@ -20,7 +20,10 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -29,7 +32,8 @@ class Net(nn.Cell):
     def construct(self, x):
         return self.split(x)
 
-x = np.random.randn(2,4).astype(np.float32)
+
+x = np.random.randn(2, 4).astype(np.float32)
 
 
 def test_net():

tests/st/ops/ascend/test_tbe_ops/test_sqrt.py

@@ -20,17 +20,22 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
         self.sqrt = P.Sqrt()
-        
+
     def construct(self, x):
         return self.sqrt(x)
 
+
 x = np.array([1.0, 4.0, 9.0]).astype(np.float32)
 
+
 def test_net():
     sqrt = Net()
     output = sqrt(Tensor(x))

tests/st/ops/ascend/test_tbe_ops/test_square.py

@@ -20,17 +20,22 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
         self.square = P.Square()
-        
+
     def construct(self, x):
         return self.square(x)
 
+
 x = np.array([1.0, 4.0, 9.0]).astype(np.float32)
 
+
 def test_net():
     square = Net()
     output = square(Tensor(x))

tests/st/ops/ascend/test_tbe_ops/test_stridedslice.py

@@ -19,7 +19,10 @@ from mindspore.nn import Cell
 from mindspore.train.model import Model
 import pytest
 import mindspore.context as context
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
 class Net(Cell):
     def __init__(self, begin, end, stride):
         super(Net, self).__init__()
@@ -32,6 +35,7 @@ class Net(Cell):
         x = self.stridedslice(input, self.begin, self.end, self.stride)
         return x
 
+
 def me_stridedslice(input1, begin, end, stride):
     input_me = Tensor(input1)
     net = Net(begin, end, stride)
@@ -40,17 +44,19 @@ def me_stridedslice(input1, begin, end, stride):
     output = model.predict(input_me)
     print(output.asnumpy())
 
+
 def test_stridedslice_input_2d():
     input = np.random.randn(5, 5).astype(np.int32)
-    begin = (0,0)
-    end = (2,2)
-    stride = (1,1)
+    begin = (0, 0)
+    end = (2, 2)
+    stride = (1, 1)
 
     me_stridedslice(input, begin, end, stride)
 
+
 def test_stridedslice_input_3d():
     input = np.random.randn(5, 5, 5).astype(np.float32)
-    begin = (0,0,0)
-    end = (3,3,3)
-    stride = (1,1,1)
+    begin = (0, 0, 0)
+    end = (3, 3, 3)
+    stride = (1, 1, 1)
     me_stridedslice(input, begin, end, stride)

tests/st/ops/ascend/test_tbe_ops/test_stridedslice_grad.py

@@ -19,8 +19,10 @@ from mindspore.nn import Cell
 from mindspore.ops.composite import GradOperation
 from mindspore import context
 import pytest
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
+
 class Grad(Cell):
     def __init__(self, network):
         super(Grad, self).__init__()
@@ -31,6 +33,7 @@ class Grad(Cell):
         gout = self.grad(self.network)(input, output_grad)
         return gout
 
+
 class Net(Cell):
     def __init__(self, begin, end, stride):
         super(Net, self).__init__()
@@ -43,6 +46,7 @@ class Net(Cell):
         x = self.stridedslice(input, self.begin, self.end, self.stride)
         return x
 
+
 def me_stridedslice(input, begin, end, stride, gradients):
     input_me = Tensor(input)
     out_grad_me = Tensor(gradients)
@@ -51,6 +55,7 @@ def me_stridedslice(input, begin, end, stride, gradients):
     out_grad = net_me(input_me, out_grad_me)
     print(out_grad.asnumpy())
 
+
 def test_grad_stridedslice_1d():
     input = np.random.randn(2).astype(np.float32)
     begin = (0,)

tests/st/ops/ascend/test_tbe_ops/test_sub.py

@@ -20,17 +20,21 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
         self.sub = P.Sub()
-        
+
     def construct(self, x, y):
         return self.sub(x, y)
 
-x = np.random.randn(1,3,3,4).astype(np.float32)
-y = np.random.randn(1,3,3,4).astype(np.float32)
+
+x = np.random.randn(1, 3, 3, 4).astype(np.float32)
+y = np.random.randn(1, 3, 3, 4).astype(np.float32)
 
 
 def test_net():

tests/st/ops/ascend/test_tbe_ops/test_tanh.py

@@ -21,6 +21,7 @@ from mindspore.ops import operations as P
 
 context.set_context(device_target="Ascend")
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -29,9 +30,12 @@ class Net(nn.Cell):
     def construct(self, x):
         return self.tanh(x)
 
+
 input_shape = [1]
 input_np = np.random.randn(*input_shape).astype(np.float32)
 input_me = Tensor(input_np)
+
+
 def test_net():
     context.set_context(mode=context.GRAPH_MODE)
     tanh = Net()
@@ -40,4 +44,4 @@ def test_net():
     out = m.predict(input_me)
     print("out_me.dtype={}".format(out.dtype))
     print("out_me.asnumpy={}".format(out.asnumpy()))
-    return out.asnumpy()
+    return out.asnumpy()

tests/st/ops/ascend/test_tbe_ops/test_tanh_grad.py

@@ -22,6 +22,7 @@ from mindspore.ops.operations import _grad_ops as G
 
 context.set_context(device_target="Ascend")
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -30,9 +31,12 @@ class Net(nn.Cell):
     def construct(self, y, dy):
         return self.tanh_grad(y, dy)
 
+
 input_shape = [1]
 input_np = np.random.randn(*input_shape).astype(np.float32)
 input_me = Tensor(input_np)
+
+
 def test_net():
     context.set_context(mode=context.GRAPH_MODE)
     tanh_grad = Net()
@@ -41,4 +45,4 @@ def test_net():
     out = m.predict(input_me, input_me)
     print("out_me.dtype={}".format(out.dtype))
     print("out_me.asnumpy={}".format(out.asnumpy()))
-    return out.asnumpy()
+    return out.asnumpy()

tests/st/ops/ascend/test_tbe_ops/test_tile.py

@@ -20,6 +20,7 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 

tests/st/ops/ascend/test_tbe_ops/test_topk.py

@@ -20,7 +20,10 @@ import numpy as np
 import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
+
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self, k):
         super(Net, self).__init__()
@@ -32,7 +35,7 @@ class Net(nn.Cell):
 
 
 def test_net():
-    x = np.random.randn(4,4).astype(np.float16)
+    x = np.random.randn(4, 4).astype(np.float16)
     k = 2
     TopK = Net(k)
     output = TopK(Tensor(x))
@@ -41,4 +44,3 @@ def test_net():
 
     print("***********output y*********")
     print(output[0].asnumpy())
-