提交 c6d8a4dc 编写于 作者: S seatea

Fix pylint warning.

上级 831ceba6
...@@ -32,7 +32,7 @@ def rec2(): ...@@ -32,7 +32,7 @@ def rec2():
return rec1() return rec1()
def test_keep_roots_recursion(x, y): def test_keep_roots_recursion():
return rec1() + nonrec() return rec1() + nonrec()
......
...@@ -48,8 +48,6 @@ loss = nn.MSELoss() ...@@ -48,8 +48,6 @@ loss = nn.MSELoss()
def test_build(): 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() net = Net()
opt = Momentum(net.get_parameters(), learning_rate=0.1, momentum=0.9) 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)
...@@ -35,16 +35,18 @@ log.setLevel(level=logging.ERROR) ...@@ -35,16 +35,18 @@ log.setLevel(level=logging.ERROR)
relu_test = Primitive('relu_test') relu_test = Primitive('relu_test')
def test_ops_f1(x, y): def test_ops_f1(x):
foo = relu_test(x) test = relu_test(x)
return foo return test
# use method2: create instance outside function use an operator with parameters # use method2: create instance outside function use an operator with parameters
class Conv_test(Primitive): class Conv_test(Primitive):
@prim_attr_register @prim_attr_register
def __init__(self, stride=0, pad=1): 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): def __call__(self, x=0, y=1, z=2):
pass pass
...@@ -65,7 +67,7 @@ class ResNet(nn.Cell): ...@@ -65,7 +67,7 @@ class ResNet(nn.Cell):
self.weight = Parameter(tensor, name="weight") self.weight = Parameter(tensor, name="weight")
self.conv = Conv_test(3, 5) 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) return x + y * self.weight + self.conv(x)
def get_params(self): def get_params(self):
...@@ -78,7 +80,7 @@ class SimpleNet(nn.Cell): ...@@ -78,7 +80,7 @@ class SimpleNet(nn.Cell):
self.weight = Parameter(tensor, name="weight") self.weight = Parameter(tensor, name="weight")
self.network = network self.network = network
def construct(self, x, y, train="train"): def construct(self, x, y):
return self.network(x) + self.weight * y return self.network(x) + self.weight * y
def get_params(self): def get_params(self):
...@@ -106,7 +108,7 @@ class SimpleNet_1(nn.Cell): ...@@ -106,7 +108,7 @@ class SimpleNet_1(nn.Cell):
super(SimpleNet_1, self).__init__() super(SimpleNet_1, self).__init__()
self.conv = Conv_test(2, 3) self.conv = Conv_test(2, 3)
def construct(self, x, y, train="train"): def construct(self, x, y):
return self.conv(x, y) return self.conv(x, y)
def get_params(self): def get_params(self):
......
...@@ -15,9 +15,8 @@ ...@@ -15,9 +15,8 @@
""" """
file: parser_integrate.py file: parser_integrate.py
""" """
import mindspore._c_expression as me
import numpy as np import numpy as np
import mindspore._c_expression as me
import mindspore.nn as nn import mindspore.nn as nn
from mindspore.common import dtype from mindspore.common import dtype
from mindspore.common.api import ms_function, _executor from mindspore.common.api import ms_function, _executor
...@@ -110,9 +109,9 @@ def test_tensor_add(): ...@@ -110,9 +109,9 @@ def test_tensor_add():
Y.set_dtype(dtype.float32) Y.set_dtype(dtype.float32)
X = me.tensor(np.ones([2, 3])) X = me.tensor(np.ones([2, 3]))
Y = 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") print("test tensor add")
return sum return tensor_add
def loss_func(x, y): def loss_func(x, y):
...@@ -129,7 +128,7 @@ def test_resetnet50_build(): ...@@ -129,7 +128,7 @@ def test_resetnet50_build():
X.set_dtype(dtype.float32) X.set_dtype(dtype.float32)
Y.set_dtype(dtype.float32) Y.set_dtype(dtype.float32)
network = resnet50() 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): class Net(nn.Cell):
...@@ -146,20 +145,20 @@ class TestNet(nn.Cell): ...@@ -146,20 +145,20 @@ class TestNet(nn.Cell):
super(TestNet, self).__init__() super(TestNet, self).__init__()
self.param = Parameter(Tensor([1, 3, 16, 50]), "param") self.param = Parameter(Tensor([1, 3, 16, 50]), "param")
def construct(self, input): def construct(self, inputs):
self.param = self.param + input self.param = self.param + inputs
return self.param return self.param
def test_compile_conv2d(): def test_compile_conv2d():
net = Net() net = Net()
input = Tensor(np.ones([1, 3, 16, 50]).astype(np.float32)) inputs = Tensor(np.ones([1, 3, 16, 50]).astype(np.float32))
_executor.compile(net, input) _executor.compile(net, inputs)
def test_none(x, y): def test_none(x, y):
def func(x, y): def func(x, y):
if y == None: if y is None:
return x return x
return x + y return x + y
......
...@@ -176,20 +176,19 @@ def test_funcdef(x, y): ...@@ -176,20 +176,19 @@ def test_funcdef(x, y):
def mymax(a, b): def mymax(a, b):
if a > b: if a > b:
return a return a
else:
return b return b
t = mymax(x, y) t = mymax(x, y)
return t return t
def test_tuple_fn(x, y): def test_tuple_fn(y):
l = (1, 2, 3, 5, 7) l = (1, 2, 3, 5, 7)
l = l + l[y] l = l + l[y]
return l return l
def test_list_fn(x, y): def test_list_fn(y):
l = [1, 2, 3, 5, 7] l = [1, 2, 3, 5, 7]
l = l + l[y] l = l + l[y]
return l return l
...@@ -265,7 +264,7 @@ def test_simple_closure(a, b): ...@@ -265,7 +264,7 @@ def test_simple_closure(a, b):
return f() * g() return f() * g()
def test_assign_tuple(x, y): def test_assign_tuple():
a = 1 a = 1
b = 2 b = 2
t = a, b t = a, b
......
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