!1044 clean pylint warning in test dir

Merge pull request !1044 from jinyaohui/clean_pylint_test

tests/st/ops/ascend/test_add.py

@@ -20,8 +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")
 context.set_context(enable_task_sink=True)
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -30,9 +33,11 @@ class Net(nn.Cell):
     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)
 
+
 def test_net():
     add = Net()
     output = add(Tensor(x), Tensor(y))

tests/st/ops/ascend/test_addn.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__()
@@ -29,6 +32,7 @@ class Net(nn.Cell):
     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

@@ -18,7 +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(mode=context.PYNATIVE_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -35,6 +38,7 @@ def test_net_bool():
     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()
@@ -42,6 +46,7 @@ def test_net_int8():
     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()
@@ -49,6 +54,7 @@ def test_net_uint8():
     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()
@@ -56,6 +62,7 @@ def test_net_int16():
     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()
@@ -63,6 +70,7 @@ def test_net_uint16():
     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()
@@ -70,6 +78,7 @@ def test_net_int32():
     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()
@@ -77,6 +86,7 @@ def test_net_uint32():
     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()
@@ -84,6 +94,7 @@ def test_net_int64():
     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()
@@ -91,6 +102,7 @@ def test_net_uint64():
     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()
@@ -98,6 +110,7 @@ def test_net_float16():
     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()
@@ -105,10 +118,10 @@ def test_net_float32():
     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)))
-  

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

@@ -17,7 +17,10 @@ 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__()
@@ -34,6 +37,7 @@ def test_net_int8():
     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()
@@ -41,6 +45,7 @@ def test_net_uint8():
     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()
@@ -48,6 +53,7 @@ def test_net_int16():
     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()
@@ -55,6 +61,7 @@ def test_net_uint16():
     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()
@@ -62,6 +69,7 @@ def test_net_int32():
     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()
@@ -69,6 +77,7 @@ def test_net_uint32():
     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()
@@ -76,6 +85,7 @@ def test_net_int64():
     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()
@@ -83,6 +93,7 @@ def test_net_uint64():
     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()
@@ -90,10 +101,10 @@ def test_net_float16():
     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()))
-  

tests/st/ops/ascend/test_aicpu_ops/test_is_finite.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(mode=context.PYNATIVE_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -35,6 +38,7 @@ def test_net_bool():
     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()
@@ -42,6 +46,7 @@ def test_net_int8():
     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()
@@ -49,6 +54,7 @@ def test_net_uint8():
     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()
@@ -56,6 +62,7 @@ def test_net_int16():
     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()
@@ -63,6 +70,7 @@ def test_net_uint16():
     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()
@@ -70,6 +78,7 @@ def test_net_int32():
     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()
@@ -77,6 +86,7 @@ def test_net_uint32():
     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()
@@ -84,6 +94,7 @@ def test_net_int64():
     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()
@@ -91,6 +102,7 @@ def test_net_uint64():
     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()
@@ -98,6 +110,7 @@ def test_net_float16():
     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()
@@ -105,10 +118,10 @@ def test_net_float32():
     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)))
-  

tests/st/ops/ascend/test_aicpu_ops/test_reshape.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(mode=context.PYNATIVE_MODE, device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -35,6 +38,7 @@ def test_net_bool():
     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()
@@ -42,6 +46,7 @@ def test_net_int8():
     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()
@@ -49,6 +54,7 @@ def test_net_uint8():
     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()
@@ -56,6 +62,7 @@ def test_net_int16():
     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()
@@ -63,6 +70,7 @@ def test_net_uint16():
     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()
@@ -70,6 +78,7 @@ def test_net_int32():
     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()
@@ -77,6 +86,7 @@ def test_net_uint32():
     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()
@@ -84,6 +94,7 @@ def test_net_int64():
     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()
@@ -91,6 +102,7 @@ def test_net_uint64():
     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()
@@ -98,6 +110,7 @@ def test_net_float16():
     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()
@@ -105,10 +118,10 @@ def test_net_float32():
     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))))
-  

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

@@ -17,7 +17,10 @@ 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__()
@@ -34,6 +37,7 @@ def test_net_bool():
     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()
@@ -41,6 +45,7 @@ def test_net_int8():
     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()
@@ -48,6 +53,7 @@ def test_net_uint8():
     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()
@@ -55,6 +61,7 @@ def test_net_int16():
     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()
@@ -62,6 +69,7 @@ def test_net_uint16():
     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()
@@ -69,6 +77,7 @@ def test_net_int32():
     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()
@@ -76,6 +85,7 @@ def test_net_uint32():
     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()
@@ -83,6 +93,7 @@ def test_net_int64():
     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()
@@ -90,6 +101,7 @@ def test_net_uint64():
     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()
@@ -97,6 +109,7 @@ def test_net_float16():
     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()
@@ -104,10 +117,10 @@ def test_net_float32():
     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()))
-  

tests/st/ops/ascend/test_apply_momentum.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__()
@@ -35,9 +38,11 @@ class Net(nn.Cell):
             'normal', [2, 3, 3, 4]), name='gradient')
         self.momentum = Parameter(initializer(
             '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

@@ -21,7 +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(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -29,11 +32,11 @@ class Net(nn.Cell):
         # self.dout = Parameter(initializer(
         # 'normal', [2, 3, 3, 4]), name='dout')
 
-
     @ms_function
     def construct(self, dout):
         return self.bias_add_grad(dout)
 
+
 dout = np.ones([2, 3, 4, 4]).astype(np.float32)
 bias_add_grad = Net()
 output = bias_add_grad(Tensor(dout))

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

@@ -20,7 +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__()
@@ -37,13 +40,11 @@ class Net(nn.Cell):
         self.w = Parameter(initializer(
             '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)
     conv = Net()

tests/st/ops/ascend/test_conv2dGradFilter.py

@@ -21,8 +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(device_target="Ascend")
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -35,6 +37,7 @@ class Net(nn.Cell):
     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],

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__()
@@ -33,6 +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__()
@@ -53,6 +56,7 @@ class Net(nn.Cell):
     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__()
@@ -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(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__()

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__()

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__()
@@ -30,9 +33,11 @@ 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)
 
+
 def test_net():
     matmul = Net()
     output = matmul(Tensor(x1), Tensor(x2))

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)

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__()
@@ -46,6 +49,7 @@ class Net(nn.Cell):
         output = self.maxpool(x)
         return output[0]
 
+
 def test_net():
     x = np.random.randn(32, 64, 112, 112).astype(np.float32)
     sens = np.random.randn(32, 64, 56, 56).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__()
@@ -30,6 +33,7 @@ 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)
     relu = Net()