Fix pylint warning.

tests/ut/cpp/python_input/gtest_input/pipeline/parse/parse_abnormal.py

@@ -32,7 +32,7 @@ def rec2():
     return rec1()
 
 
-def test_keep_roots_recursion(x, y):
+def test_keep_roots_recursion():
     return rec1() + nonrec()
 
 

tests/ut/cpp/python_input/gtest_input/pipeline/parse/parse_compile.py

@@ -48,8 +48,6 @@ loss = nn.MSELoss()
 
 
 def test_build():
-    input_data = Tensor(np.random.randint(0, 255, [1, 3, 224, 224]))
-    input_label = Tensor(np.random.randint(0, 10, [1, 10]))
     net = Net()
     opt = Momentum(net.get_parameters(), learning_rate=0.1, momentum=0.9)
-    model = Model(net, loss_fn=loss, optimizer=opt, metrics=None)
+    Model(net, loss_fn=loss, optimizer=opt, metrics=None)

tests/ut/cpp/python_input/gtest_input/pipeline/parse/parse_primitive.py

@@ -35,16 +35,18 @@ log.setLevel(level=logging.ERROR)
 relu_test = Primitive('relu_test')
 
 
-def test_ops_f1(x, y):
-    foo = relu_test(x)
-    return foo
+def test_ops_f1(x):
+    test = relu_test(x)
+    return test
 
 
 # use method2: create instance outside function use an operator with parameters
 class Conv_test(Primitive):
     @prim_attr_register
     def __init__(self, stride=0, pad=1):
-        print('in conv_test init', self.stride)
+        self.stride = stride
+        self.pad = pad
+        print('in conv_test init', self.stride, self.pad)
 
     def __call__(self, x=0, y=1, z=2):
         pass
@@ -65,7 +67,7 @@ class ResNet(nn.Cell):
         self.weight = Parameter(tensor, name="weight")
         self.conv = Conv_test(3, 5)
 
-    def construct(self, x, y, train="train"):
+    def construct(self, x, y):
         return x + y * self.weight + self.conv(x)
 
     def get_params(self):
@@ -78,7 +80,7 @@ class SimpleNet(nn.Cell):
         self.weight = Parameter(tensor, name="weight")
         self.network = network
 
-    def construct(self, x, y, train="train"):
+    def construct(self, x, y):
         return self.network(x) + self.weight * y
 
     def get_params(self):
@@ -106,7 +108,7 @@ class SimpleNet_1(nn.Cell):
         super(SimpleNet_1, self).__init__()
         self.conv = Conv_test(2, 3)
 
-    def construct(self, x, y, train="train"):
+    def construct(self, x, y):
         return self.conv(x, y)
 
     def get_params(self):

tests/ut/cpp/python_input/gtest_input/pipeline/parse/parser_integrate.py

@@ -15,9 +15,8 @@
 """
 file: parser_integrate.py
 """
-import mindspore._c_expression as me
 import numpy as np
-
+import mindspore._c_expression as me
 import mindspore.nn as nn
 from mindspore.common import dtype
 from mindspore.common.api import ms_function, _executor
@@ -110,9 +109,9 @@ def test_tensor_add():
     Y.set_dtype(dtype.float32)
     X = me.tensor(np.ones([2, 3]))
     Y = me.tensor(np.ones([2, 3]))
-    sum = add(X, Y)
+    tensor_add = add(X, Y)
     print("test tensor add")
-    return sum
+    return tensor_add
 
 
 def loss_func(x, y):
@@ -129,7 +128,7 @@ def test_resetnet50_build():
     X.set_dtype(dtype.float32)
     Y.set_dtype(dtype.float32)
     network = resnet50()
-    model = Model(network=network, loss_fn=loss_func, optimizer=optimizer)
+    Model(network=network, loss_fn=loss_func, optimizer=optimizer)
 
 
 class Net(nn.Cell):
@@ -146,20 +145,20 @@ class TestNet(nn.Cell):
         super(TestNet, self).__init__()
         self.param = Parameter(Tensor([1, 3, 16, 50]), "param")
 
-    def construct(self, input):
-        self.param = self.param + input
+    def construct(self, inputs):
+        self.param = self.param + inputs
         return self.param
 
 
 def test_compile_conv2d():
     net = Net()
-    input = Tensor(np.ones([1, 3, 16, 50]).astype(np.float32))
-    _executor.compile(net, input)
+    inputs = Tensor(np.ones([1, 3, 16, 50]).astype(np.float32))
+    _executor.compile(net, inputs)
 
 
 def test_none(x, y):
     def func(x, y):
-        if y == None:
+        if y is None:
             return x
         return x + y
 

tests/ut/cpp/python_input/gtest_input/pipeline/parse/parser_test.py

@@ -176,20 +176,19 @@ def test_funcdef(x, y):
     def mymax(a, b):
         if a > b:
             return a
-        else:
-            return b
+        return b
 
     t = mymax(x, y)
     return t
 
 
-def test_tuple_fn(x, y):
+def test_tuple_fn(y):
     l = (1, 2, 3, 5, 7)
     l = l + l[y]
     return l
 
 
-def test_list_fn(x, y):
+def test_list_fn(y):
     l = [1, 2, 3, 5, 7]
     l = l + l[y]
     return l
@@ -265,7 +264,7 @@ def test_simple_closure(a, b):
     return f() * g()
 
 
-def test_assign_tuple(x, y):
+def test_assign_tuple():
     a = 1
     b = 2
     t = a, b