clean pylint

mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_left_impl.py

@@ -244,8 +244,8 @@ def check_supported(input_x1, input_x2, bias=None, output_y={}, trans_a=False, t
 
     return True
 
-
-# pylint: disable=locally-disabled,too-many-arguments, too-many-locals, too-many-statements
+# pylint: disable=locally-disabled,too-many-arguments, too-many-locals, too-many-statements,
+# pylint: disable=inconsistent-return-statements
 # @util.check_input_type(dict, dict, (dict, NoneType), dict, bool, bool, str)
 @op_info_register(matmul_cube_dense_left_op_info)
 def CusMatMulCubeDenseLeft(input_x1, input_x2, bias=None, output_y={}, trans_a=False, trans_b=False,

mindspore/ops/_op_impl/_custom_op/matmul_cube_dense_right_impl.py

@@ -40,6 +40,7 @@ matmul_cube_dense_right_op_info = TBERegOp("CusMatMulCubeDenseRight") \
     .get_op_info()
 
 
+# pylint: disable=inconsistent-return-statements
 @op_info_register(matmul_cube_dense_right_op_info)
 def CusMatMulCubeDenseRight(input_x1, input_x2, input_x3, bias=None, output_y={}, trans_a=False, trans_b=False,
                             kernel_name="matmulcube"):

model_zoo/Transformer/src/transformer_for_train.py

@@ -31,6 +31,8 @@ from .transformer_model import TransformerModel
 GRADIENT_CLIP_TYPE = 1
 GRADIENT_CLIP_VALUE = 5.0
 
+
+# pylint: disable=consider-using-in
 class ClipGradients(nn.Cell):
     """
     Clip gradients.
@@ -48,6 +50,7 @@ class ClipGradients(nn.Cell):
         self.clip_by_norm = nn.ClipByNorm()
         self.cast = P.Cast()
         self.dtype = P.DType()
+
     def construct(self,
                   grads,
                   clip_type,
@@ -217,10 +220,12 @@ class TransformerTrainOneStepCell(nn.Cell):
 grad_scale = C.MultitypeFuncGraph("grad_scale")
 reciprocal = P.Reciprocal()
 
+
 @grad_scale.register("Tensor", "Tensor")
 def tensor_grad_scale(scale, grad):
     return grad * F.cast(reciprocal(scale), F.dtype(grad))
 
+
 class TransformerTrainOneStepWithLossScaleCell(nn.Cell):
     """
     Encapsulation class of Transformer network training.

model_zoo/bert/src/bert_for_pre_training.py

@@ -34,6 +34,9 @@ GRADIENT_CLIP_VALUE = 1.0
 
 _nn_clip_by_norm = nn.ClipByNorm()
 clip_grad = C.MultitypeFuncGraph("clip_grad")
+
+
+# pylint: disable=consider-using-in
 @clip_grad.register("Number", "Number", "Tensor")
 def _clip_grad(clip_type, clip_value, grad):
     """
@@ -57,6 +60,7 @@ def _clip_grad(clip_type, clip_value, grad):
         new_grad = _nn_clip_by_norm(grad, F.cast(F.tuple_to_array((clip_value,)), dt))
     return new_grad
 
+
 class GetMaskedLMOutput(nn.Cell):
     """
     Get masked lm output.
@@ -377,6 +381,7 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
             self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32),
                                         name="loss_scale")
         self.add_flags(has_effect=True)
+
     def construct(self,
                   input_ids,
                   input_mask,

tests/mindspore_test_framework/apps/test_bert_parts.py

@@ -15,14 +15,15 @@
 
 """Test bert submodules."""
 
-import numpy as np
 import os
 
-from mindspore import Tensor
-from mindspore import nn, context
+import numpy as np
 from mindspore.model_zoo.Bert_NEZHA import EmbeddingLookup, GetMaskedLMOutput, \
     BertConfig, BertPreTraining, BertNetworkWithLoss
 from mindspore.model_zoo.Bert_NEZHA.bert_model import BertModel
+
+from mindspore import Tensor
+from mindspore import nn, context
 from ..mindspore_test import mindspore_test
 from ..pipeline.forward.compile_forward import pipeline_for_compile_forward_anf_graph_for_case_by_case_config, \
     pipeline_for_compile_forward_ge_graph_for_case_by_case_config

tests/mindspore_test_framework/components/executor/check_exceptions.py

@@ -15,9 +15,10 @@
 
 """Component that Check if the function raises the expected Exception."""
 
-import pytest
 import sys
 
+import pytest
+
 from ...components.icomponent import IExectorComponent
 from ...utils import keyword
 

tests/mindspore_test_framework/utils/check_gradient.py

@@ -16,9 +16,10 @@
 """Implementation of Numerical gradients checking."""
 # pylint: disable=missing-docstring
 
+from typing import Callable, List, Any
+
 import mindspore._c_expression as _c_expression
 import numpy as np
-from typing import Callable, List, Any
 
 from mindspore import ParameterTuple
 from mindspore import Tensor

tests/mindspore_test_framework/utils/dataset_util.py

@@ -15,9 +15,10 @@
 
 """Dataset utils."""
 
-import numpy as np
 import random
 
+import numpy as np
+
 from mindspore import Tensor
 
 

tests/mindspore_test_framework/utils/debug_util.py

@@ -24,8 +24,7 @@ from mindspore.ops import operations as P
 from mindspore.ops._grad.grad_base import bprop_getters
 from mindspore.ops.primitive import prim_attr_register, PrimitiveWithInfer
 
-logging.basicConfig(level=logging.DEBUG, format=
-'[%(levelname)s] %(asctime)s %(pathname)s:%(lineno)d %(message)s')
+logging.basicConfig(level=logging.DEBUG, format='[%(levelname)s] %(asctime)s %(pathname)s:%(lineno)d %(message)s')
 logger = logging.getLogger(__name__)
 
 

tests/mindspore_test_framework/utils/other_util.py

@@ -14,9 +14,8 @@
 # ============================================================================
 
 """Other utils."""
-
-import mindspore._c_expression as _c_expression
 import numpy as np
+import mindspore._c_expression as _c_expression
 
 from mindspore.common.tensor import Tensor
 

tests/st/networks/models/bert/src/bert_for_pre_training.py

@@ -34,6 +34,9 @@ GRADIENT_CLIP_VALUE = 1.0
 
 _nn_clip_by_norm = nn.ClipByNorm()
 clip_grad = C.MultitypeFuncGraph("clip_grad")
+
+
+# pylint: disable=consider-using-in
 @clip_grad.register("Number", "Number", "Tensor")
 def _clip_grad(clip_type, clip_value, grad):
     """
@@ -57,6 +60,7 @@ def _clip_grad(clip_type, clip_value, grad):
         new_grad = _nn_clip_by_norm(grad, F.cast(F.tuple_to_array((clip_value,)), dt))
     return new_grad
 
+
 class GetMaskedLMOutput(nn.Cell):
     """
     Get masked lm output.
@@ -377,6 +381,7 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
             self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32),
                                         name="loss_scale")
         self.add_flags(has_effect=True)
+
     def construct(self,
                   input_ids,
                   input_mask,

tests/st/ops/ascend/test_autocast.py

@@ -23,10 +23,12 @@ from mindspore.ops import functional as F, composite as C
 import mindspore.context as context
 import pytest
 
+
 class TensorIntAutoCast(nn.Cell):
     def __init__(self, ):
         super(TensorIntAutoCast, self).__init__()
         self.i = 2
+
     def construct(self, t):
         z = F.tensor_mul(t, self.i)
         return z
@@ -36,6 +38,7 @@ class TensorFPAutoCast(nn.Cell):
     def __init__(self, ):
         super(TensorFPAutoCast, self).__init__()
         self.f = 1.2
+
     def construct(self, t):
         z = F.tensor_mul(t, self.f)
         return z
@@ -45,13 +48,16 @@ class TensorBoolAutoCast(nn.Cell):
     def __init__(self, ):
         super(TensorBoolAutoCast, self).__init__()
         self.f = True
+
     def construct(self, t):
         z = F.tensor_mul(t, self.f)
         return z
 
+
 class TensorAutoCast(nn.Cell):
     def __init__(self, ):
         super(TensorAutoCast, self).__init__()
+
     def construct(self, t1, t2):
         z = F.tensor_mul(t1, t2)
         return z
@@ -210,7 +216,6 @@ def test_tensor_auto_cast():
     with pytest.raises(TypeError):
         net(t_uint64, t_fp64)
 
-
     with pytest.raises(TypeError):
         tfp(t_uint16)
     with pytest.raises(TypeError):

tests/st/ops/ascend/test_ops_infer.py

@@ -21,6 +21,7 @@ import mindspore.common.dtype as mstype
 from mindspore import Tensor
 from mindspore.ops import operations as P
 from mindspore import context
+
 context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
 
 
@@ -29,6 +30,7 @@ def test_cast_op_attr():
         def __init__(self):
             super(CastNet, self).__init__()
             self.cast = P.Cast()
+
         def construct(self, x, t):
             return self.cast(x, t)
 
@@ -37,6 +39,7 @@ def test_cast_op_attr():
             super(CastTypeTest, self).__init__()
             self.net = net
             self.cast = P.Cast()
+
         def construct(self, x, y, z):
             cast_op = self.cast
             t1 = cast_op(x, mstype.float32)
@@ -46,6 +49,7 @@ def test_cast_op_attr():
             t4 = cast_net(y, mstype.int32)
             t5 = cast_net(z, mstype.float16)
             return (t1, t2, t3, t4, t5)
+
     net = CastTypeTest(CastNet())
     t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.int32))
     t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))

tests/st/ops/cpu/test_transpose_op.py

@@ -142,4 +142,6 @@ def test_transpose():
     assert (output[1].asnumpy() == expect1).all()
     assert (output[2].asnumpy() == expect2).all()
     assert (output[3].asnumpy() == expect3).all()
+
+
 test_transpose()

tests/ut/cpp/python_input/gtest_input/optimizer/opt_test.py

@@ -1043,6 +1043,7 @@ def test_print_tuple_wrapper(tag):
     return fns[tag]
 
 
+# pylint: disable=unnecessary-semicolon
 def test_constant_duplicate_mul(tag):
     fns = FnDict()
     Mul = Primitive('Mul');

tests/ut/python/dtype/test_dictionary.py

@@ -152,7 +152,7 @@ def test_dict_set_item():
 
     x = Tensor(np.ones([2, 2, 3], np.float32))
     net = DictSetNet()
-    out = net(x)
+    _ = net(x)
 
 
 # if the dictionary item does not exist, create a new one
@@ -168,4 +168,4 @@ def test_dict_set_item_create_new():
             return my_dict
     x = Tensor(np.ones([2, 2, 3], np.float32))
     net = DictSetNet()
-    out = net(x)
+    _ = net(x)

tests/ut/python/dtype/test_hypermap.py

@@ -81,31 +81,3 @@ def test_hypermap_func_const():
 
     net = NetMap()
     assert net() == (8, 12, 16)
-
-
-"""
-def test_hypermap_func_variable():
-    class NetMap(Cell):
-        def __init__(self):
-            super(NetMap, self).__init__()
-
-        def double(self, x):
-            return 2 * x
-
-        def triple(self, x):
-            return 3 * x
-
-        def square(self, x):
-            return x * x
-
-        def construct(self, x):
-            _list = [self.double, self.triple, self.square]
-            return map(lambda f: f(x), _list)
-
-    x = Tensor(np.ones([3, 2, 3], np.float32))
-    net = NetMap()
-
-    with pytest.raises(RuntimeError) as ex:
-        net(x)
-    assert "HyperMap don't support Closure with free variable yet" in str(ex.value)
-"""

tests/ut/python/exec/test_AssignAdd.py

@@ -99,4 +99,4 @@ def test_assignadd_scalar_cast():
     net = AssignAddNet()
     x = Tensor(np.ones([1]).astype(np.int64) * 102)
     # _executor.compile(net, 1)
-    result = net(x)
+    _ = net(x)

tests/ut/python/ir/test_tensor.py

@@ -429,9 +429,9 @@ def test_tensor_dtype_np_int64():
 
 def test_tensor_dtype_fp32_to_bool():
     with pytest.raises(RuntimeError):
-        input = np.random.randn(2, 3, 4, 5).astype(np.float32)
-        input = ms.Tensor(input)
-        input_me = ms.Tensor(input, dtype=ms.bool_)
+        input_ = np.random.randn(2, 3, 4, 5).astype(np.float32)
+        input_ = ms.Tensor(input_)
+        _ = ms.Tensor(input_, dtype=ms.bool_)
 
 
 def test_tensor_operation():

tests/ut/python/keep_order/test_keep_order.py

@@ -41,10 +41,10 @@ class Func(nn.Cell):
 
     def construct(self, x, y):
         init = self.alloc_status()
-        sum = add(x, y)
+        sum_ = add(x, y)
         product = mul1(x, y)
         flag = self.get_status(init)
-        out = add2(sum, product)
+        out = add2(sum_, product)
         clear = self.clear_status(flag)
         out = F.depend(out, clear)
         return out
@@ -88,7 +88,7 @@ def test_sens():
     sens = Tensor(np.ones([3, 3]).astype(np.float32))
     net = Net()
     net.add_flags(has_effect=True)
-    out = net(x, y, sens)
+    _ = net(x, y, sens)
 
 
 class Net_hyper(nn.Cell):
@@ -119,7 +119,7 @@ def test_hyper_add():
     sens = Tensor(np.ones([3, 3]).astype(np.float32))
     net = Net_hyper()
     net.add_flags(has_effect=True)
-    out = net(x, y, sens)
+    _ = net(x, y, sens)
 
 
 def test_keep_order_io_effect_exception_return_dtype():

tests/ut/python/model/test_mix_precision.py

@@ -148,9 +148,6 @@ def test_cast():
     _executor.compile(net, x)
 
 
-"""test grad of PReLU, which cause AddN(generated by grad) fail"""
-
-
 class IRBlockZ(nn.Cell):
     def __init__(self, inplanes, planes):
         super(IRBlockZ, self).__init__()

tests/ut/python/nn/test_pooling.py

@@ -46,6 +46,7 @@ class MaxNet(nn.Cell):
                  kernel_size,
                  stride=None,
                  padding=0):
+        _ = padding
         super(MaxNet, self).__init__()
         self.maxpool = nn.MaxPool2d(kernel_size,
                                     stride)
@@ -73,5 +74,5 @@ class Avg1dNet(nn.Cell):
 
 def test_avg1d():
     net = Avg1dNet(6, 1)
-    input = Tensor(np.random.randint(0, 255, [1, 3, 6]).astype(np.float32))
-    _executor.compile(net, input)
+    input_ = Tensor(np.random.randint(0, 255, [1, 3, 6]).astype(np.float32))
+    _executor.compile(net, input_)

tests/ut/python/nn/test_psnr.py

@@ -52,19 +52,19 @@ def test_compile_psnr_grayscale():
 def test_psnr_max_val_negative():
     max_val = -1
     with pytest.raises(ValueError):
-        net = PSNRNet(max_val)
+        _ = PSNRNet(max_val)
 
 
 def test_psnr_max_val_bool():
     max_val = True
     with pytest.raises(TypeError):
-        net = PSNRNet(max_val)
+        _ = PSNRNet(max_val)
 
 
 def test_psnr_max_val_zero():
     max_val = 0
     with pytest.raises(ValueError):
-        net = PSNRNet(max_val)
+        _ = PSNRNet(max_val)
 
 
 def test_psnr_different_shape():

tests/ut/python/nn/test_ssim.py

@@ -51,59 +51,59 @@ def test_compile_grayscale():
 def test_ssim_max_val_negative():
     max_val = -1
     with pytest.raises(ValueError):
-        net = SSIMNet(max_val)
+        _ = SSIMNet(max_val)
 
 
 def test_ssim_max_val_bool():
     max_val = True
     with pytest.raises(TypeError):
-        net = SSIMNet(max_val)
+        _ = SSIMNet(max_val)
 
 
 def test_ssim_max_val_zero():
     max_val = 0
     with pytest.raises(ValueError):
-        net = SSIMNet(max_val)
+        _ = SSIMNet(max_val)
 
 
 def test_ssim_filter_size_float():
     with pytest.raises(TypeError):
-        net = SSIMNet(filter_size=1.1)
+        _ = SSIMNet(filter_size=1.1)
 
 
 def test_ssim_filter_size_zero():
     with pytest.raises(ValueError):
-        net = SSIMNet(filter_size=0)
+        _ = SSIMNet(filter_size=0)
 
 
 def test_ssim_filter_sigma_zero():
     with pytest.raises(ValueError):
-        net = SSIMNet(filter_sigma=0.0)
+        _ = SSIMNet(filter_sigma=0.0)
 
 
 def test_ssim_filter_sigma_negative():
     with pytest.raises(ValueError):
-        net = SSIMNet(filter_sigma=-0.1)
+        _ = SSIMNet(filter_sigma=-0.1)
 
 
 def test_ssim_k1_k2_wrong_value():
     with pytest.raises(ValueError):
-        net = SSIMNet(k1=1.1)
+        _ = SSIMNet(k1=1.1)
     with pytest.raises(ValueError):
-        net = SSIMNet(k1=1.0)
+        _ = SSIMNet(k1=1.0)
     with pytest.raises(ValueError):
-        net = SSIMNet(k1=0.0)
+        _ = SSIMNet(k1=0.0)
     with pytest.raises(ValueError):
-        net = SSIMNet(k1=-1.0)
+        _ = SSIMNet(k1=-1.0)
 
     with pytest.raises(ValueError):
-        net = SSIMNet(k2=1.1)
+        _ = SSIMNet(k2=1.1)
     with pytest.raises(ValueError):
-        net = SSIMNet(k2=1.0)
+        _ = SSIMNet(k2=1.0)
     with pytest.raises(ValueError):
-        net = SSIMNet(k2=0.0)
+        _ = SSIMNet(k2=0.0)
     with pytest.raises(ValueError):
-        net = SSIMNet(k2=-1.0)
+        _ = SSIMNet(k2=-1.0)
 
 
 def test_ssim_different_shape():

tests/ut/python/onnx/test_onnx.py

@@ -64,13 +64,13 @@ class BatchNormTester(nn.Cell):
 
 def test_batchnorm_train_onnx_export():
     "test onnx export interface does not modify trainable flag of a network"
-    input = Tensor(np.ones([1, 3, 32, 32]).astype(np.float32) * 0.01)
+    input_ = Tensor(np.ones([1, 3, 32, 32]).astype(np.float32) * 0.01)
     net = BatchNormTester(3)
     net.set_train()
     if not net.training:
         raise ValueError('netowrk is not in training mode')
     onnx_file = 'batch_norm.onnx'
-    export(net, input, file_name=onnx_file, file_format='ONNX')
+    export(net, input_, file_name=onnx_file, file_format='ONNX')
 
     if not net.training:
         raise ValueError('netowrk is not in training mode')
@@ -172,6 +172,7 @@ net_cfgs = [
 
 
 def get_id(cfg):
+    _ = cfg
     return list(map(lambda x: x[0], net_cfgs))