clean pylint

mindspore/dataset/text/utils.py

@@ -16,8 +16,8 @@ Some basic function for nlp
 """
 from enum import IntEnum
 
-import mindspore._c_dataengine as cde
 import numpy as np
+import mindspore._c_dataengine as cde
 
 from .validators import check_from_file, check_from_list, check_from_dict
 

mindspore/nn/layer/lstm.py

@@ -13,15 +13,16 @@
 # limitations under the License.
 # ============================================================================
 """lstm"""
-from mindspore.ops import operations as P
-from mindspore.nn.cell import Cell
-from mindspore.common.parameter import Parameter, ParameterTuple
-from mindspore.common.initializer import initializer
-from mindspore._checkparam import Validator as validator
-from mindspore import context
+import numpy as np
+
 import mindspore.nn as nn
+from mindspore import context
+from mindspore._checkparam import Validator as validator
+from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter, ParameterTuple
 from mindspore.common.tensor import Tensor
-import numpy as np
+from mindspore.nn.cell import Cell
+from mindspore.ops import operations as P
 
 __all__ = ['LSTM', 'LSTMCell']
 

tests/mindspore_test_framework/utils/check_gradient.py

@@ -18,8 +18,8 @@
 
 from typing import Callable, List, Any
 
-import mindspore._c_expression as _c_expression
 import numpy as np
+import mindspore._c_expression as _c_expression
 
 from mindspore import ParameterTuple
 from mindspore import Tensor

tests/perf_test/bert/test_bert_train.py

@@ -22,9 +22,9 @@ import os
 import mindspore.common.dtype as mstype
 import mindspore.context as context
 from mindspore import Tensor
-from model_zoo.bert.src import BertConfig, BertNetworkWithLoss, BertTrainOneStepCell, BertTrainOneStepWithLossScaleCell
 from mindspore.nn.optim import AdamWeightDecayDynamicLR
 from mindspore.train.loss_scale_manager import DynamicLossScaleManager
+from model_zoo.bert.src import BertConfig, BertNetworkWithLoss, BertTrainOneStepCell, BertTrainOneStepWithLossScaleCell
 from ...dataset_mock import MindData
 from ...ops_common import nn, np, batch_tuple_tensor, build_construct_graph
 

tests/st/nccl/test_nccl_all_reduce_op.py

@@ -91,10 +91,10 @@ class Net2(nn.Cell):
         self.all_reduce3 = P.AllReduce(self.op2, group=NCCL_WORLD_COMM_GROUP)
 
     def construct(self):
-        x = self.all_reduce1(self.x1)
-        y = self.all_reduce2(x)
+        x_ = self.all_reduce1(self.x1)
+        y = self.all_reduce2(x_)
         z = self.all_reduce3(y)
-        return (x, y, z)
+        return (x_, y, z)
 
 
 def test_AllReduce2():

tests/st/networks/models/bert/bert_tdt_lossscale.py

@@ -17,8 +17,11 @@
 
 import os
 import time
-import pytest
+
 import numpy as np
+import pytest
+from src.bert_for_pre_training import BertNetworkWithLoss, BertTrainOneStepWithLossScaleCell
+from src.bert_model import BertConfig
 
 import mindspore.common.dtype as mstype
 import mindspore.dataset.engine.datasets as de
@@ -26,8 +29,6 @@ import mindspore.dataset.transforms.c_transforms as C
 from mindspore import context
 from mindspore import log as logger
 from mindspore.common.tensor import Tensor
-from src.bert_model import BertConfig
-from src.bert_for_pre_training import BertNetworkWithLoss, BertTrainOneStepWithLossScaleCell
 from mindspore.nn.optim import Lamb
 from mindspore.train.callback import Callback
 from mindspore.train.loss_scale_manager import DynamicLossScaleManager

tests/st/networks/test_gpu_lenet.py

@@ -13,8 +13,9 @@
 # limitations under the License.
 # ============================================================================
 
-import numpy as np
 import os
+
+import numpy as np
 import pytest
 
 import mindspore.context as context

tests/st/networks/test_gpu_resnet.py

@@ -397,5 +397,5 @@ def test_trainTensor_amp(num_classes=10, epoch=18, batch_size=16):
         loss = train_network(data, label)
         losses.append(loss)
     assert (losses[-1][0].asnumpy() < 1)
-    assert (losses[-1][1].asnumpy() == False)
+    assert not losses[-1][1].asnumpy()
     assert (losses[-1][2].asnumpy() > 1)

tests/st/networks/test_network_main.py

@@ -19,9 +19,8 @@ Usage:
     python test_network_main.py --net lenet --target Ascend
 """
 import argparse
+
 import numpy as np
-import os
-import time
 from models.alexnet import AlexNet
 from models.lenet import LeNet
 from models.resnetv1_5 import resnet50

tests/ut/python/nn/test_clip_by_norm.py

@@ -35,10 +35,10 @@ def test_clip_by_norm_const():
             super(Network, self).__init__()
             self.norm_value = Tensor(np.array([1]).astype(np.float32))
             self.clip = nn.ClipByNorm()
-        
+
         def construct(self, x):
             return self.clip(x, self.norm_value)
 
     net = Network()
     x = Tensor(np.array([[-2, 0, 0], [0, 3, 4]]).astype(np.float32))
-    output = net(x)
+    net(x)

tests/ut/python/ops/test_ops.py

@@ -14,6 +14,7 @@
 # ============================================================================
 """ test ops """
 import functools
+
 import numpy as np
 
 import mindspore.nn as nn
@@ -898,7 +899,7 @@ test_case_nn_ops = [
         'skip': ['backward']}),
     ('BasicLSTMCell', {
         'block': P.BasicLSTMCell(keep_prob=1.0, forget_bias=1.0, state_is_tuple=True, activation='tanh'),
-        'desc_inputs': [[128, 128], [128, 128], [128, 128], [512, 256, 1, 1],[512, 1, 1, 1]],
+        'desc_inputs': [[128, 128], [128, 128], [128, 128], [512, 256, 1, 1], [512, 1, 1, 1]],
         'desc_bprop': [[128, 128], [128, 128], [128, 128], [128, 128], [128, 128], [128, 128], [128, 128]],
         'skip': []}),
     ('TopK', {
@@ -1324,7 +1325,7 @@ test_case_array_ops = [
         'desc_inputs': [(Tensor(np.array([1], np.float32)),
                          Tensor(np.array([1], np.float32)),
                          Tensor(np.array([1], np.float32)))],
-        'desc_bprop': [[3, ]]}),
+        'desc_bprop': [[3,]]}),
     ('Pack_0', {
         'block': NetForPackInput(P.Pack()),
         'desc_inputs': [[2, 2], [2, 2], [2, 2]],
@@ -1486,8 +1487,7 @@ test_case = functools.reduce(lambda x, y: x + y, test_case_lists)
 
 test_exec_case = test_case
 
-test_backward_exec_case = filter(lambda x: 'skip' not in x[1] or
-                                           'backward' not in x[1]['skip'], test_case)
+test_backward_exec_case = filter(lambda x: 'skip' not in x[1] or 'backward' not in x[1]['skip'], test_case)
 
 
 @non_graph_engine

tests/ut/python/optimizer/test_debug_location.py

@@ -144,7 +144,7 @@ def test_op_forward_infererror():
     input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
     input_me = Tensor(input_np)
     net = Net3()
-    with pytest.raises(TypeError) as e:
+    with pytest.raises(TypeError):
         net(input_me)
 
 
@@ -162,7 +162,7 @@ def test_sequential_resolve_error():
     input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
     input_me = Tensor(input_np)
     net = SequenceNet()
-    with pytest.raises(RuntimeError) as e:
+    with pytest.raises(RuntimeError):
         net(input_me)
 
 

tests/ut/python/pynative_mode/nn/test_dropout.py

@@ -19,7 +19,6 @@ import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore import context
 from mindspore import dtype as mstype
-from mindspore.common.api import _executor
 
 context.set_context(device_target="Ascend")
 

tests/ut/python/pynative_mode/test_graph_return_const_param.py

@@ -44,7 +44,7 @@ class ChooseInitParameterWithInput(nn.Cell):
         self.x = Parameter(Tensor(np.ones(2), dtype=mstype.int32), name='x')
 
     @ms_function
-    def construct(self, input):
+    def construct(self, input_data):
         return self.x
 
 
@@ -57,7 +57,7 @@ def test_choose_init_param():
 
 def test_choose_param_with_input():
     choose = ChooseInitParameterWithInput()
-    input = Tensor(np.zeros(2), dtype=mstype.int32)
+    input_data = Tensor(np.zeros(2), dtype=mstype.int32)
     expect = Tensor(np.ones(2), dtype=mstype.int32)
-    out = choose(input)
+    out = choose(input_data)
     assert np.allclose(expect.asnumpy(), out.asnumpy())

tests/ut/python/pynative_mode/test_hook.py

@@ -1,10 +1,11 @@
 import numpy as np
+
 import mindspore.nn as nn
 import mindspore.ops.operations as P
-from mindspore.ops import composite as C
 from mindspore import context, Tensor, ParameterTuple
 from mindspore.common.initializer import TruncatedNormal
 from mindspore.nn import WithLossCell, Momentum
+from mindspore.ops import composite as C
 
 context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
 
@@ -45,7 +46,6 @@ class LeNet5(nn.Cell):
     Lenet network
     Args:
         num_class (int): Num classes. Default: 10.
-        
     Returns:
         Tensor, output tensor
 

tests/ut/python/pynative_mode/test_insert_grad_of.py

@@ -21,7 +21,6 @@ from mindspore import Tensor
 from mindspore import context
 from mindspore.common.api import ms_function
 from mindspore.ops import composite as C
-from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 from ....mindspore_test_framework.utils.bprop_util import bprop
 from ....mindspore_test_framework.utils.debug_util import PrintShapeTypeCell, PrintGradShapeTypeCell

tests/ut/python/pynative_mode/test_parse_method.py

@@ -19,9 +19,10 @@
 @Desc  : test parse the object's method
 """
 import logging
+from dataclasses import dataclass
+
 import numpy as np
 import pytest
-from dataclasses import dataclass
 
 import mindspore.nn as nn
 from mindspore import context

tests/ut/python/pynative_mode/test_training.py

@@ -19,9 +19,7 @@ import mindspore.nn as nn
 from mindspore import context
 from mindspore.common.tensor import Tensor
 from mindspore.nn import WithGradCell, WithLossCell
-from mindspore.nn.optim import Momentum
 from mindspore.ops import operations as P
-from mindspore.train.model import Model
 from ..ut_filter import non_graph_engine
 
 

tests/ut/python/train/quant/test_quant.py

@@ -13,14 +13,9 @@
 # limitations under the License.
 # ============================================================================
 """ tests for quant """
-import numpy as np
-from mobilenetv2_combined import MobileNetV2
-
 import mindspore.context as context
-from mindspore import Tensor
 from mindspore import nn
 from mindspore.nn.layer import combined
-from mindspore.train.quant import quant as qat
 
 context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
 

tests/ut/python/utils/test_callback.py

@@ -74,7 +74,7 @@ class LossNet(nn.Cell):
         return out
 
 
-def test_Model_Checkpoint_prefix_invalid():
+def test_model_checkpoint_prefix_invalid():
     """Test ModelCheckpoint prefix invalid."""
     with pytest.raises(ValueError):
         ModelCheckpoint(123)
@@ -338,9 +338,9 @@ def test_step_end_save_graph():
     ckpoint_cb.begin(run_context)
     # import pdb;pdb.set_trace()
     ckpoint_cb.step_end(run_context)
-    assert os.path.exists('./test_files/test-graph.meta') == True
+    assert os.path.exists('./test_files/test-graph.meta')
     if os.path.exists('./test_files/test-graph.meta'):
         os.chmod('./test_files/test-graph.meta', stat.S_IWRITE)
         os.remove('./test_files/test-graph.meta')
     ckpoint_cb.step_end(run_context)
-    assert os.path.exists('./test_files/test-graph.meta') == False
+    assert not os.path.exists('./test_files/test-graph.meta')

tests/ut/python/utils/test_serialize.py

@@ -71,16 +71,16 @@ def setup_module():
 def test_save_graph():
     """ test_exec_save_graph """
 
-    class Net(nn.Cell):
+    class Net1(nn.Cell):
         def __init__(self):
-            super(Net, self).__init__()
+            super(Net1, self).__init__()
             self.add = P.TensorAdd()
 
         def construct(self, x, y):
             z = self.add(x, y)
             return z
 
-    net = Net()
+    net = Net1()
     net.set_train()
     out_me_list = []
     x = Tensor(np.random.rand(2, 1, 2, 3).astype(np.float32))