!1042 clean pylint warning in test_framwork dir

Merge pull request !1042 from jinyaohui/clean_pylint

tests/perf_test/bert/test_bert_train.py

@@ -18,13 +18,13 @@
 # pylint: disable=missing-docstring, arguments-differ, W0612
 
 import os
+
 import mindspore.common.dtype as mstype
 import mindspore.context as context
 from mindspore import Tensor
-from mindspore.nn.optim import AdamWeightDecayDynamicLR
 from mindspore.model_zoo.Bert_NEZHA import BertConfig, BertNetworkWithLoss, BertTrainOneStepCell, \
     BertTrainOneStepWithLossScaleCell
-from mindspore.nn.wrap.loss_scale import FixedLossScaleUpdateCell
+from mindspore.nn.optim import AdamWeightDecayDynamicLR
 from mindspore.train.loss_scale_manager import DynamicLossScaleManager
 from ...dataset_mock import MindData
 from ...ops_common import nn, np, batch_tuple_tensor, build_construct_graph

tests/perf_test/mindrecord/imagenet/imagenet_to_mindrecord.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 # ============================================================================
 """use ImageNetToMR tool generate mindrecord"""
-import os
 from mindspore.mindrecord import ImageNetToMR
 
 IMAGENET_MAP_FILE = "../../../ut/data/mindrecord/testImageNetDataWhole/labels_map.txt"
@@ -21,6 +20,7 @@ IMAGENET_IMAGE_DIR = "../../../ut/data/mindrecord/testImageNetDataWhole/images"
 MINDRECORD_FILE = "./imagenet.mindrecord"
 PARTITION_NUMBER = 16
 
+
 def imagenet_to_mindrecord():
     imagenet_transformer = ImageNetToMR(IMAGENET_MAP_FILE,
                                         IMAGENET_IMAGE_DIR,
@@ -28,5 +28,6 @@ def imagenet_to_mindrecord():
                                         PARTITION_NUMBER)
     imagenet_transformer.transform()
 
+
 if __name__ == '__main__':
     imagenet_to_mindrecord()

tests/perf_test/mindrecord/imagenet/imagenet_to_tfrecord.py

@@ -15,6 +15,7 @@
 """generate tfrecord"""
 import collections
 import os
+
 import tensorflow as tf
 
 IMAGENET_MAP_FILE = "../../../ut/data/mindrecord/testImageNetDataWhole/labels_map.txt"
@@ -22,6 +23,7 @@ IMAGENET_IMAGE_DIR = "../../../ut/data/mindrecord/testImageNetDataWhole/images"
 TFRECORD_FILE = "./imagenet.tfrecord"
 PARTITION_NUMBER = 16
 
+
 def get_imagenet_filename_label_pic(map_file, image_dir):
     """
     Get data from imagenet.
@@ -69,18 +71,22 @@ def get_imagenet_filename_label_pic(map_file, image_dir):
                 continue
             yield str(file_name), int(label), image_bytes
 
+
 def create_int_feature(values):
     feature = tf.train.Feature(int64_list=tf.train.Int64List(value=[values]))
     return feature
 
+
 def create_string_feature(values):
     feature = tf.train.Feature(bytes_list=tf.train.BytesList(value=[bytes(values, encoding='utf-8')]))
     return feature
 
+
 def create_bytes_feature(values):
     feature = tf.train.Feature(bytes_list=tf.train.BytesList(value=[values]))
     return feature
 
+
 def imagenet_to_tfrecord():
     writers = []
     for i in range(PARTITION_NUMBER):
@@ -109,5 +115,6 @@ def imagenet_to_tfrecord():
 
     print("Write {} total examples".format(total_written))
 
+
 if __name__ == '__main__':
     imagenet_to_tfrecord()

tests/perf_test/mindrecord/imagenet/perf_read_imagenet.py

@@ -14,17 +14,20 @@
 # ============================================================================
 """test dataset performance about mindspore.MindDataset, mindspore.TFRecordDataset, tf.data.TFRecordDataset"""
 import time
-import mindspore.dataset as ds
-from mindspore.mindrecord import FileReader
 
 import tensorflow as tf
 
+import mindspore.dataset as ds
+from mindspore.mindrecord import FileReader
+
 print_step = 5000
 
+
 def print_log(count):
     if count % print_step == 0:
         print("Read {} rows ...".format(count))
 
+
 def use_filereader(mindrecord):
     start = time.time()
     columns_list = ["data", "label"]
@@ -38,6 +41,7 @@ def use_filereader(mindrecord):
     end = time.time()
     print("Read by FileReader - total rows: {}, cost time: {}s".format(num_iter, end - start))
 
+
 def use_minddataset(mindrecord):
     start = time.time()
     columns_list = ["data", "label"]
@@ -51,6 +55,7 @@ def use_minddataset(mindrecord):
     end = time.time()
     print("Read by MindDataset - total rows: {}, cost time: {}s".format(num_iter, end - start))
 
+
 def use_tfrecorddataset(tfrecord):
     start = time.time()
     columns_list = ["data", "label"]
@@ -66,8 +71,10 @@ def use_tfrecorddataset(tfrecord):
     end = time.time()
     print("Read by TFRecordDataset - total rows: {}, cost time: {}s".format(num_iter, end - start))
 
+
 def use_tensorflow_tfrecorddataset(tfrecord):
     start = time.time()
+
     def _parse_record(example_photo):
         features = {
             'file_name': tf.io.FixedLenFeature([], tf.string),
@@ -87,6 +94,7 @@ def use_tensorflow_tfrecorddataset(tfrecord):
     end = time.time()
     print("Read by TensorFlow TFRecordDataset - total rows: {}, cost time: {}s".format(num_iter, end - start))
 
+
 if __name__ == '__main__':
     # use MindDataset
     mindrecord = './imagenet.mindrecord00'

tests/perf_test/test_lenet.py

@@ -18,15 +18,14 @@
 import numpy as np
 
 import mindspore.nn as nn
-from mindspore.common.api import _executor
+import mindspore.ops.composite as C
 from mindspore import Tensor
-from mindspore.model_zoo.lenet import LeNet
 from mindspore import context
-import mindspore.ops.composite as C
+from mindspore.common.api import _executor
+from mindspore.model_zoo.lenet import LeNet
 
 context.set_context(mode=context.GRAPH_MODE)
 
-
 batch_size = 1
 channel = 1
 height = 32
@@ -36,6 +35,7 @@ num_class = 10
 
 class LeNetGrad(nn.Cell):
     """Backward of LeNet"""
+
     def __init__(self, network):
         super(LeNetGrad, self).__init__()
         self.grad_op = C.grad_all_with_sens

tests/perf_test/test_resnet_infer.py

@@ -17,10 +17,11 @@
 
 import numpy as np
 
-from mindspore.common.api import _executor
 from mindspore import Tensor
+from mindspore.common.api import _executor
 from .resnet_example import resnet50
 
+
 def test_compile():
     net = resnet50()
     inp = Tensor(np.ones([1, 3, 224, 224]).astype(np.float32))

tests/perf_test/test_resnet_pynative.py

@@ -20,9 +20,9 @@
 import numpy as np
 
 from mindspore import Tensor
-from ..train_step_wrap import train_step_without_opt
 from .resnet_example import resnet50
-from ..vm_impl import *
+from ..train_step_wrap import train_step_without_opt
+
 
 def test_resnet50_pynative():
     net = train_step_without_opt(resnet50())

tests/perf_test/test_resnet_train.py

@@ -17,13 +17,15 @@
 
 import numpy as np
 
-from mindspore.common.api import _executor
 import mindspore.context as context
 from mindspore import Tensor
-from ..train_step_wrap import train_step_with_loss_warp
+from mindspore.common.api import _executor
 from .resnet_example import resnet50
+from ..train_step_wrap import train_step_with_loss_warp
+
 context.set_context(mode=context.GRAPH_MODE)
 
+
 def test_train_step():
     net = train_step_with_loss_warp(resnet50())
     net.set_train()

tests/train_step_wrap.py

@@ -16,15 +16,15 @@
 train step wrap
 """
 import mindspore.nn as nn
-from mindspore.ops import functional as F
+from mindspore import ParameterTuple
 from mindspore.ops import composite as C
-from mindspore.ops import operations as P
-from mindspore import Parameter, ParameterTuple
+
 
 class TrainStepWrap(nn.Cell):
     """
     TrainStepWrap definition
     """
+
     def __init__(self, network):
         super(TrainStepWrap, self).__init__()
         self.network = network
@@ -39,10 +39,12 @@ class TrainStepWrap(nn.Cell):
         grads = self.grad(self.network, weights)(x, label)
         return self.optimizer(grads)
 
+
 class NetWithLossClass(nn.Cell):
     """
     NetWithLossClass definition
     """
+
     def __init__(self, network):
         super(NetWithLossClass, self).__init__(auto_prefix=False)
         self.loss = nn.SoftmaxCrossEntropyWithLogits()
@@ -61,6 +63,7 @@ class TrainStepWrap2(nn.Cell):
     """
     TrainStepWrap2 definition
     """
+
     def __init__(self, network, sens):
         super(TrainStepWrap2, self).__init__()
         self.network = network
@@ -76,13 +79,16 @@ class TrainStepWrap2(nn.Cell):
         grads = self.grad(self.network, weights)(x, self.sens)
         return self.optimizer(grads)
 
+
 def train_step_with_sens(network, sens):
     return TrainStepWrap2(network, sens)
 
+
 class TrainStepWrapWithoutOpt(nn.Cell):
     """
     TrainStepWrapWithoutOpt definition
     """
+
     def __init__(self, network):
         super(TrainStepWrapWithoutOpt, self).__init__()
         self.network = network
@@ -93,5 +99,6 @@ class TrainStepWrapWithoutOpt(nn.Cell):
         grads = self.grad(self.network, self.weights)(x, label)
         return grads
 
+
 def train_step_without_opt(network):
     return TrainStepWrapWithoutOpt(NetWithLossClass(network))

tests/ut/python/dtype/test_dictionary.py

@@ -28,6 +28,7 @@ context.set_context(mode=context.GRAPH_MODE)
 def Xtest_arg_dict():
     class DictNet(Cell):
         """DictNet definition"""
+
         def __init__(self):
             super(DictNet, self).__init__()
             self.max = P.Maximum()
@@ -48,6 +49,7 @@ def Xtest_arg_dict():
 def test_const_dict():
     class DictNet(Cell):
         """DictNet1 definition"""
+
         def __init__(self):
             super(DictNet, self).__init__()
             self.max = P.Maximum()
@@ -58,6 +60,7 @@ def test_const_dict():
             a = self.max(self.dictionary["x"], self.dictionary["y"])
             b = self.min(self.dictionary["x"], self.dictionary["y"])
             return a + b
+
     net = DictNet()
     net()
 
@@ -65,6 +68,7 @@ def test_const_dict():
 def test_dict_set_or_get_item():
     class DictNet(Cell):
         """DictNet1 definition"""
+
         def __init__(self):
             super(DictNet, self).__init__()
             self.dict_ = {"x": 1, "y": 2}
@@ -91,6 +95,7 @@ def test_dict_set_or_get_item():
 def test_dict_set_or_get_item_2():
     class DictNet(Cell):
         """DictNet1 definition"""
+
         def __init__(self):
             super(DictNet, self).__init__()
 
@@ -117,6 +122,7 @@ def test_dict_set_or_get_item_2():
 def test_dict_set_or_get_item_3():
     class DictNet(Cell):
         """DictNet1 definition"""
+
         def __init__(self):
             super(DictNet, self).__init__()
             self.dict_ = {"x": Tensor(np.ones([2, 2, 3], np.float32)), "y": 1}
@@ -130,5 +136,3 @@ def test_dict_set_or_get_item_3():
 
     net = DictNet()
     assert net() == Tensor(np.ones([4, 2, 3], np.float32))
-
-

tests/ut/python/dtype/test_hypermap.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 # ============================================================================
 import numpy as np
-import pytest
 
 from mindspore import Tensor, context
 from mindspore.nn import Cell

tests/ut/python/exec/__init__.py

@@ -15,6 +15,7 @@
 """setup for pytest"""
 import mindspore.context as context
 
+
 # pylint: disable=unused-argument
 def setup_module(module):
     context.set_context(mode=context.GRAPH_MODE)

tests/ut/python/exec/resnet_example.py

@@ -16,6 +16,7 @@
 resnet50 example
 """
 import numpy as np
+
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.ops import operations as P

tests/ut/python/exec/test_AssignAdd.py

@@ -16,19 +16,21 @@
 test assign add
 """
 import numpy as np
+
+import mindspore as ms
+import mindspore.context as context
 import mindspore.nn as nn
-from mindspore.ops import operations as P
-from mindspore.common.initializer import initializer
 from mindspore import Tensor, Parameter
-import mindspore as ms
+from mindspore.common.initializer import initializer
+from mindspore.ops import operations as P
 from ..ut_filter import non_graph_engine
-from mindspore.common.api import _executor
-import mindspore.context as context
-import pytest
+
 context.set_context(mode=context.GRAPH_MODE)
 
+
 class Net(nn.Cell):
     """Net definition"""
+
     def __init__(self):
         super(Net, self).__init__()
         self.AssignAdd = P.AssignAdd()
@@ -39,18 +41,19 @@ class Net(nn.Cell):
         out = self.AssignAdd(self.inputdata, x)
         return out
 
+
 @non_graph_engine
 def test_AssignAdd_1():
     """test AssignAdd 1"""
     import mindspore.context as context
     context.set_context(mode=context.GRAPH_MODE)
     net = Net()
-    x = Tensor(np.ones([1]).astype(np.int64)*100)
+    x = Tensor(np.ones([1]).astype(np.int64) * 100)
 
     print("MyPrintResult dataX:", x)
     result = net(x)
     print("MyPrintResult data::", result)
-    expect = np.ones([1]).astype(np.int64)*101
+    expect = np.ones([1]).astype(np.int64) * 101
     diff = result.asnumpy() - expect
 
     print("MyPrintExpect:", expect)
@@ -58,18 +61,19 @@ def test_AssignAdd_1():
     error = np.ones(shape=[1]) * 1.0e-3
     assert np.all(diff < error)
 
+
 @non_graph_engine
 def test_AssignAdd_2():
     """test AssignAdd 2"""
     import mindspore.context as context
     context.set_context(mode=context.GRAPH_MODE)
     net = Net()
-    x = Tensor(np.ones([1]).astype(np.int64)*102)
+    x = Tensor(np.ones([1]).astype(np.int64) * 102)
 
     print("MyPrintResult dataX:", x)
     result = net(x)
     print("MyPrintResult data::", result.asnumpy())
-    expect = np.ones([1]).astype(np.int64)*103
+    expect = np.ones([1]).astype(np.int64) * 103
     diff = result.asnumpy() - expect
 
     print("MyPrintExpect:", expect)
@@ -77,8 +81,10 @@ def test_AssignAdd_2():
     error = np.ones(shape=[1]) * 1.0e-3
     assert np.all(diff < error)
 
+
 class AssignAddNet(nn.Cell):
     """Net definition"""
+
     def __init__(self):
         super(AssignAddNet, self).__init__()
         self.AssignAdd = P.AssignAdd()
@@ -89,9 +95,10 @@ class AssignAddNet(nn.Cell):
         z1 = self.AssignAdd(self.inputdata, self.one)
         return z1
 
+
 @non_graph_engine
 def test_assignadd_scalar_cast():
     net = AssignAddNet()
-    x = Tensor(np.ones([1]).astype(np.int64)*102)
-    #_executor.compile(net, 1)
+    x = Tensor(np.ones([1]).astype(np.int64) * 102)
+    # _executor.compile(net, 1)
     result = net(x)

tests/ut/python/exec/test_activation.py

@@ -14,6 +14,7 @@
 # ============================================================================
 """ test Activations """
 import numpy as np
+
 import mindspore.nn as nn
 from mindspore import Tensor
 from ..ut_filter import non_graph_engine

tests/ut/python/exec/test_assign_sub.py

@@ -16,15 +16,17 @@
 test assign sub
 """
 import numpy as np
+
+import mindspore.context as context
 import mindspore.nn as nn
 import mindspore.ops.operations as P
 from mindspore import Tensor
-import mindspore.context as context
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
 
 context.set_context(mode=context.GRAPH_MODE)
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()

tests/ut/python/exec/test_batchnorm.py

@@ -14,6 +14,7 @@
 # ============================================================================
 """ut for batchnorm layer"""
 import numpy as np
+
 import mindspore.nn as nn
 from mindspore import Tensor
 from ..ut_filter import non_graph_engine

tests/ut/python/exec/test_bias_add.py

@@ -14,14 +14,17 @@
 # ============================================================================
 """ test BiasAdd """
 import numpy as np
+
 import mindspore.nn as nn
-from mindspore.ops import operations as P
-from mindspore.common.initializer import initializer
 from mindspore import Tensor, Parameter
+from mindspore.common.initializer import initializer
+from mindspore.ops import operations as P
 from ..ut_filter import non_graph_engine
 
+
 class Net(nn.Cell):
     """Net definition"""
+
     def __init__(self,
                  output_channels,
                  bias_init='zeros',

tests/ut/python/exec/test_conv.py

@@ -14,6 +14,7 @@
 # ============================================================================
 """test conv"""
 import numpy as np
+
 import mindspore.nn as nn
 from mindspore import Tensor
 from ..ut_filter import non_graph_engine
@@ -25,6 +26,7 @@ out_channels = 64
 
 class Net(nn.Cell):
     """Net definition"""
+
     def __init__(self,
                  cin,
                  cout,
@@ -70,6 +72,7 @@ def test_compile2():
     output = net(input_data)
     print(output.asnumpy())
 
+
 @non_graph_engine
 def test_compile3():
     net = Net(3, 1, (3, 3), weight_init='ONES')

tests/ut/python/exec/test_dense.py

@@ -14,12 +14,15 @@
 # ============================================================================
 """ test Dense """
 import numpy as np
+
 import mindspore.nn as nn
 from mindspore import Tensor
 from ..ut_filter import non_graph_engine
 
+
 class Net(nn.Cell):
     """Net definition"""
+
     def __init__(self,
                  input_channels,
                  output_channels,

tests/ut/python/exec/test_eval.py

@@ -14,11 +14,12 @@
 # ============================================================================
 """test eval"""
 import numpy as np
+
 import mindspore as ms
 import mindspore.nn as nn
-from mindspore.common.api import _executor
 from mindspore import Tensor
 from mindspore import context
+from mindspore.common.api import _executor
 from ..ut_filter import non_graph_engine
 
 

tests/ut/python/exec/test_flatten.py

@@ -16,8 +16,8 @@
 import numpy as np
 
 import mindspore.nn as nn
-from mindspore.ops import operations as P
 from mindspore import Tensor
+from mindspore.ops import operations as P
 from ..ut_filter import non_graph_engine
 
 

tests/ut/python/exec/test_pooling.py

@@ -15,12 +15,12 @@
 """
 test pooling api
 """
-import numpy as np
 import mindspore.nn as nn
-from mindspore import Tensor
+
 
 class MaxNet(nn.Cell):
     """MaxNet definition"""
+
     def __init__(self,
                  kernel_size,
                  stride=None):

tests/ut/python/exec/test_softmax.py

@@ -16,9 +16,11 @@
 test softmax api
 """
 import numpy as np
+
 import mindspore.nn as nn
 from mindspore import Tensor
 
+
 class Net(nn.Cell):
     def __init__(self, dim):
         super(Net, self).__init__()

tests/ut/python/exec/test_tensor_add.py

@@ -14,10 +14,12 @@
 # ============================================================================
 """ test TensorAdd """
 import numpy as np
+
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.ops import operations as P
 
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()

tests/ut/python/exec/test_train.py

@@ -14,29 +14,33 @@
 # ============================================================================
 """ test model train """
 import numpy as np
+
 import mindspore.nn as nn
-from mindspore.ops import operations as P
-from mindspore.common.initializer import initializer
 from mindspore import Tensor, Parameter, Model
+from mindspore.common.initializer import initializer
 from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
 from mindspore.nn.optim import Momentum
+from mindspore.ops import operations as P
+
 
 # fn is a funcation use i as input
 def lr_gen(fn, epoch_size):
     for i in range(epoch_size):
         yield fn(i)
 
+
 def me_train_tensor(net, input_np, label_np, epoch_size=2):
     """me_train_tensor"""
     loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
-    opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr_gen(lambda i: 0.1, epoch_size), 0.9, 0.01, 1024)
+    opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr_gen(lambda i: 0.1, epoch_size), 0.9,
+                   0.01, 1024)
     Model(net, loss, opt)
     _network = nn.WithLossCell(net, loss)
     _train_net = nn.TrainOneStepCell(_network, opt)
     _train_net.set_train()
     label_np = np.argmax(label_np, axis=-1).astype(np.int32)
     for epoch in range(0, epoch_size):
-        print(f"epoch %d"%(epoch))
+        print(f"epoch %d" % (epoch))
         _train_net(Tensor(input_np), Tensor(label_np))
 
 
@@ -52,6 +56,7 @@ def test_bias_add(test_with_simu):
 
     class Net(nn.Cell):
         """Net definition"""
+
         def __init__(self,
                      output_channels,
                      bias_init='zeros',
@@ -87,6 +92,7 @@ def test_conv(test_with_simu):
 
     class Net(nn.Cell):
         "Net definition"""
+
         def __init__(self,
                      cin,
                      cout,
@@ -116,6 +122,7 @@ def test_net():
 
     class Net(nn.Cell):
         """Net definition"""
+
         def __init__(self):
             super(Net, self).__init__()
             Tensor(np.ones([64, 3, 7, 7]).astype(np.float32) * 0.01)
@@ -141,6 +148,7 @@ def test_net():
     label_np = np.ones([32, 12]).astype(np.int32)
     me_train_tensor(net, input_np, label_np)
 
+
 def test_bn():
     """test_bn"""
     import mindspore.context as context
@@ -151,6 +159,7 @@ def test_bn():
 
     class Net(nn.Cell):
         """Net definition"""
+
         def __init__(self, cin, cout):
             super(Net, self).__init__()
             self.bn = nn.BatchNorm2d(cin)

tests/ut/python/exec/test_train_with_lars.py

@@ -23,6 +23,7 @@ from mindspore.ops import composite as C
 from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 
+
 def get_reordered_parameters(parameters):
     """get_reordered_parameters"""
     # put the bias parameter to the end
@@ -36,12 +37,15 @@ def get_reordered_parameters(parameters):
     reordered_params = tuple(non_bias_param + bias_param)
     return len(non_bias_param), len(reordered_params), reordered_params
 
+
 def get_net_trainable_reordered_params(net):
     params = net.trainable_params()
     return get_reordered_parameters(params)
 
+
 class TrainOneStepWithLarsCell(nn.Cell):
     """TrainOneStepWithLarsCell definition"""
+
     def __init__(self, network, optimizer, sens=1.0):
         super(TrainOneStepWithLarsCell, self).__init__(auto_prefix=False)
         self.network = network
@@ -66,11 +70,13 @@ class TrainOneStepWithLarsCell(nn.Cell):
         new_grads = lars_grads + bias_grads
         return F.depend(loss, self.optimizer(new_grads))
 
+
 # fn is a funcation use i as input
 def lr_gen(fn, epoch_size):
     for i in range(epoch_size):
         yield fn(i)
 
+
 def me_train_tensor(net, input_np, label_np, epoch_size=2):
     """me_train_tensor"""
     loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)

tests/ut/python/ir/test_dtype.py

@@ -14,12 +14,14 @@
 # ============================================================================
 """test_dtype"""
 from dataclasses import dataclass
+
 import numpy as np
 import pytest
 
 import mindspore as ms
 from mindspore.common import dtype
 
+
 def test_dtype_to_nptype():
     """test_dtype2nptype"""
     assert ms.dtype_to_nptype(ms.bool_) == np.bool_
@@ -59,6 +61,7 @@ def test_dtype_to_pytype():
 @dataclass
 class Foo:
     x: int
+
     def inf(self):
         return self.x
 

tests/ut/python/ir/test_tensor.py

@@ -25,25 +25,27 @@ import mindspore as ms
 import mindspore.common.api as me
 import mindspore.nn as nn
 from mindspore import Tensor
-from mindspore.common.parameter import Parameter
 from mindspore.common.initializer import initializer
+from mindspore.common.parameter import Parameter
 from ..ut_filter import non_graph_engine
 
-
 ndarr = np.ones((2, 3))
 
+
 def test_tensor_flatten():
     with pytest.raises(AttributeError):
-        lst = [1, 2, 3, 4,]
+        lst = [1, 2, 3, 4, ]
         tensor_list = ms.Tensor(lst, ms.float32)
         tensor_list = tensor_list.Flatten()
         print(tensor_list)
 
+
 def test_tensor_list():
     lst = [[1.0, 2.0, 1.0], [1.0, 10.0, 9.0]]
     tensor_list = ms.Tensor(lst, ms.float32)
     print(tensor_list)
 
+
 def test_tensor():
     """test_tensor"""
     t1 = ms.Tensor(ndarr)
@@ -63,6 +65,7 @@ def test_tensor():
     assert isinstance(t4, ms.Tensor)
     assert t4.dtype() == ms.int64
 
+
 def test_tensor_type_float16():
     t_float16 = ms.Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float16))
     assert isinstance(t_float16, ms.Tensor)
@@ -107,6 +110,7 @@ def test_tensor_type_float64_user_define():
     assert t_float64.shape() == (2, 3)
     assert t_float64.dtype() == ms.float64
 
+
 def test_tensor_type_bool():
     # init a tensor with bool type
     ts_bool_array = ms.Tensor(np.zeros([2, 3], np.bool), ms.bool_)
@@ -122,6 +126,7 @@ def test_tensor_type_bool():
     assert t_bool_array.shape() == (2, 3)
     assert t_bool_array.dtype() == ms.bool_
 
+
 def test_tensor_type_int8():
     t_int8_array = ms.Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int8))
     assert isinstance(t_int8_array, ms.Tensor)
@@ -154,6 +159,7 @@ def test_tensor_type_int64():
     assert t_int64.shape() == (2, 3)
     assert t_int64.dtype() == ms.int64
 
+
 def test_tensor_type_uint8():
     t_uint8_array = ms.Tensor(np.array([[1, 2, 3], [4, 5, 6]], dtype=np.uint8))
     assert isinstance(t_uint8_array, ms.Tensor)
@@ -181,6 +187,7 @@ def test_tensor_type_uint64():
     assert t_uint64.shape() == (2, 3)
     assert t_uint64.dtype() == ms.uint64
 
+
 def test_set_type():
     t = ms.Tensor(ndarr)
     t.set_dtype(ms.float32)
@@ -202,15 +209,17 @@ def test_sub():
     z = x - y
     assert isinstance(z, ms.Tensor)
 
+
 @non_graph_engine
 def test_div():
-    x = ms.Tensor(np.array([[2,6,10],[12, 4, 8]]).astype(np.float32))
-    y = ms.Tensor(np.array([[2,2,5],[6, 1, 2]]).astype(np.float32))
+    x = ms.Tensor(np.array([[2, 6, 10], [12, 4, 8]]).astype(np.float32))
+    y = ms.Tensor(np.array([[2, 2, 5], [6, 1, 2]]).astype(np.float32))
     z = x / y
     z2 = x / 2
     assert isinstance(z, ms.Tensor)
     assert isinstance(z2, ms.Tensor)
 
+
 @non_graph_engine
 def test_parameter():
     x = Parameter(initializer(1, [1], ms.float32), name="beta1_power")
@@ -220,6 +229,7 @@ def test_parameter():
 
 class Net(nn.Cell):
     """Net definition"""
+
     def __init__(self, dim):
         super(Net, self).__init__()
         self.dim = dim
@@ -266,6 +276,7 @@ def test_tensor_contiguous():
     assert True, rt_f.flags['C_CONTIGUOUS']
     print("rt_f flags = ", rt_f.flags)
 
+
 def test_tensor_contiguous2():
     input_data = np.random.randn(32, 112, 112, 3).astype(np.float32)
     input_me = input_data.transpose(0, 3, 1, 2)
@@ -274,36 +285,43 @@ def test_tensor_contiguous2():
     out_f = tensor_f_float32.asnumpy()
     print("out_f flags = ", out_f.flags)
 
+
 def test_tensor_input_string():
     with pytest.raises(TypeError):
         input_data = 'ccc'
         ms.Tensor(input_data)
 
+
 def test_tensor_input_tuple_string():
     with pytest.raises(TypeError):
         input_data = (2, 3, '4', 5)
         ms.Tensor(input_data)
 
+
 def test_tensor_input_list_string():
     with pytest.raises(TypeError):
         input_data = [[2, 3, '4', 5], [1, 2, 3, 4]]
         ms.Tensor(input_data)
 
+
 def test_tensor_input_none():
     with pytest.raises(TypeError):
         input_data = None
         ms.Tensor(input_data, np.int64)
 
+
 # pylint: disable=no-value-for-parameter
 def test_tensor_input_empty():
     with pytest.raises(TypeError):
         ms.Tensor()
 
+
 def test_tensor_input_ndarray_str():
     with pytest.raises(TypeError):
         inp = np.array(["88", 2, 4])
         ms.Tensor(inp)
 
+
 def test_tensor_input_ndarray_bool():
     inp = np.array([True, 2, 4])
     ms.Tensor(inp)
@@ -311,86 +329,103 @@ def test_tensor_input_ndarray_bool():
     inp = np.array([False, 2, 4])
     ms.Tensor(inp)
 
+
 def test_tensor_input_ndarray_complex():
     with pytest.raises(TypeError):
         inp = np.array([20j, 2, 4])
         ms.Tensor(inp)
 
+
 def test_tensor_input_ndarray_none():
     with pytest.raises(TypeError):
         inp = np.array([None, 2, 4])
         ms.Tensor(inp)
 
+
 def test_tensor_input_ndarray_dict():
     with pytest.raises(TypeError):
         inp = {'a': 6, 'b': 7}
         ms.Tensor(inp)
 
+
 def test_tensor_input_np_nan():
     with pytest.raises(TypeError):
         input_data = (1, 2, 3, np.nan)
         ms.Tensor(input_data, np.int64)
 
+
 def test_tensor_input_tuple_inf():
     with pytest.raises(TypeError):
         input_data = (1, 2, 3, float("inf"))
         ms.Tensor(input_data, np.int64)
 
+
 def test_tensor_input_dict():
     with pytest.raises(TypeError):
         input_data = {'a': 6, 'b': 7}
         ms.Tensor(input_data, np.int64)
 
+
 def test_tensor_input_complex():
     with pytest.raises(TypeError):
         input_data = (1, 2j, 3)
         ms.Tensor(input_data, np.int64)
 
+
 def test_tensor_dtype_np_float():
     with pytest.raises(TypeError):
         input_data = np.random.randn(32, 112, 112, 3).astype(np.float)
         ms.Tensor(input_data, np.float)
 
+
 def test_tensor_dtype_np_float16():
     with pytest.raises(TypeError):
         input_data = np.random.randn(32, 112, 112, 3).astype(np.float16)
         ms.Tensor(input_data, np.float16)
 
+
 def test_tensor_dtype_np_float32():
     with pytest.raises(TypeError):
         input_data = np.random.randn(32, 112, 112, 3).astype(np.float32)
         ms.Tensor(input_data, np.float32)
 
+
 def test_tensor_dtype_np_float64():
     with pytest.raises(TypeError):
         input_data = np.random.randn(32, 112, 112, 3).astype(np.float64)
         ms.Tensor(input_data, np.float64)
 
+
 def test_tensor_dtype_np_int():
     with pytest.raises(TypeError):
         input_data = np.random.randn(32, 112, 112, 3).astype(np.int)
         ms.Tensor(input_data, np.int)
 
+
 def test_tensor_dtype_np_int8():
     with pytest.raises(TypeError):
         input_data = np.random.randn(32, 112, 112, 3).astype(np.int8)
         ms.Tensor(input_data, np.int8)
 
+
 def test_tensor_dtype_np_int16():
     with pytest.raises(TypeError):
         input_data = np.random.randn(32, 112, 112, 3).astype(np.int16)
         ms.Tensor(input_data, np.int16)
 
+
 def test_tensor_dtype_np_int32():
     with pytest.raises(TypeError):
         input_data = np.random.randn(32, 112, 112, 3).astype(np.int32)
         ms.Tensor(input_data, np.int32)
 
+
 def test_tensor_dtype_np_int64():
     with pytest.raises(TypeError):
         input_data = np.random.randn(32, 112, 112, 3).astype(np.int64)
         ms.Tensor(input_data, np.int64)
 
+
 def test_tensor_dtype_fp32_to_bool():
     with pytest.raises(RuntimeError):
         input = np.random.randn(2, 3, 4, 5).astype(np.float32)
@@ -399,7 +434,7 @@ def test_tensor_dtype_fp32_to_bool():
 
 
 def test_tensor_operation():
-    x = Tensor(np.ones((3,3)) * 4)
+    x = Tensor(np.ones((3, 3)) * 4)
     res = x + 1
     assert np.all(res.asnumpy() == np.ones((3, 3)) * 5)
     res = 1 + x

tests/ut/python/ir/test_tensor_py.py

@@ -14,10 +14,11 @@
 # ============================================================================
 """test tensor py"""
 import numpy as np
+
+import mindspore as ms
+from mindspore.common.api import _executor
 from mindspore.nn import Cell
 from mindspore.ops import operations as P
-from mindspore.common.api import _executor
-import mindspore as ms
 from ..ut_filter import non_graph_engine
 
 
@@ -93,6 +94,7 @@ def test_float():
 
 def test_tensor_method_sub():
     """test_tensor_method_sub"""
+
     class Net(Cell):
         def __init__(self):
             super(Net, self).__init__()
@@ -111,6 +113,7 @@ def test_tensor_method_sub():
 
 def test_tensor_method_mul():
     """test_tensor_method_mul"""
+
     class Net(Cell):
         def __init__(self):
             super(Net, self).__init__()
@@ -129,6 +132,7 @@ def test_tensor_method_mul():
 
 def test_tensor_method_div():
     """test_tensor_method_div"""
+
     class Net(Cell):
         def __init__(self):
             super(Net, self).__init__()

tests/ut/python/keep_order/test_keep_order.py

@@ -13,24 +13,25 @@
 # limitations under the License.
 # ============================================================================
 import numpy as np
-from mindspore.common.api import ms_function
-from mindspore.common.tensor import Tensor
-from mindspore.ops import operations as P
-import mindspore.ops.functional as F
+
 import mindspore.context as context
-from mindspore.ops import composite as C
-from mindspore.ops.composite import core
-from mindspore.common import dtype as mstype
 import mindspore.nn as nn
+import mindspore.ops.functional as F
+from mindspore.common import dtype as mstype
+from mindspore.common.tensor import Tensor
+from mindspore.ops import composite as C
+from mindspore.ops import operations as P
 
 context.set_context(mode=context.GRAPH_MODE)
 add1 = P.TensorAdd()
 mul1 = P.MatMul()
 add2 = P.TensorAdd()
 
+
 def add(x, y):
     return add1(x, y)
 
+
 class Func(nn.Cell):
     def __init__(self):
         super(Func, self).__init__()
@@ -48,7 +49,10 @@ class Func(nn.Cell):
         out = F.depend(out, clear)
         return out
 
+
 grad_s = C.GradOperation('grad_with_sens', get_all=True, sens_param=True)
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -69,6 +73,7 @@ class Net(nn.Cell):
         out = F.depend(out, clear)
         return out
 
+
 def test_add():
     x = Tensor(np.ones([3, 3]).astype(np.float32))
     y = Tensor(np.ones([3, 3]).astype(np.float32))
@@ -76,6 +81,7 @@ def test_add():
     func.add_flags(has_effect=True)
     func(x, y)
 
+
 def test_sens():
     x = Tensor(np.ones([3, 3]).astype(np.float32))
     y = Tensor(np.ones([3, 3]).astype(np.float32))
@@ -84,6 +90,7 @@ def test_sens():
     net.add_flags(has_effect=True)
     out = net(x, y, sens)
 
+
 class Net_hyper(nn.Cell):
     def __init__(self):
         super(Net_hyper, self).__init__()
@@ -105,6 +112,7 @@ class Net_hyper(nn.Cell):
         out = F.depend(out, clear)
         return out
 
+
 def test_hyper_add():
     x = Tensor(np.ones([3, 3]).astype(np.float32))
     y = Tensor(np.ones([3, 3]).astype(np.float32))
@@ -113,10 +121,11 @@ def test_hyper_add():
     net.add_flags(has_effect=True)
     out = net(x, y, sens)
 
+
 def test_keep_order_io_effect_exception_return_dtype():
     class Net(nn.Cell):
         def __init__(self):
-            super().__init__()	
+            super().__init__()
             self.alloc_status = P.NPUAllocFloatStatus()
             self.get_status = P.NPUGetFloatStatus()
             self.clear_status = P.NPUClearFloatStatus()
@@ -126,16 +135,16 @@ def test_keep_order_io_effect_exception_return_dtype():
             self.neg = P.Neg()
             self.add_flags(has_effect=True)
 
-        def construct(self, x):  
+        def construct(self, x):
             init = self.alloc_status()
             self.clear_status(init)
-            res = self.sub(x, self.neg(x))             
+            res = self.sub(x, self.neg(x))
             self.get_status(init)
             dtype = self.dtype(res)
             return dtype
 
-    value = 655 
+    value = 655
     data = np.full((8, 5, 3, 1), value, dtype=np.float16)
     x = Tensor(data, dtype=mstype.float16)
     net = Net()
-    data = net(x)
+    data = net(x)

tests/ut/python/metrics/test_accuracy.py

@@ -14,10 +14,13 @@
 # ============================================================================
 """test accuracy"""
 import math
+
 import numpy as np
 import pytest
-from mindspore.nn.metrics import Accuracy
+
 from mindspore import Tensor
+from mindspore.nn.metrics import Accuracy
+
 
 def test_classification_accuracy():
     """test_classification_accuracy"""
@@ -29,8 +32,9 @@ def test_classification_accuracy():
     metric.update(x, y)
     accuracy = metric.eval()
     accuracy2 = metric(x, y2)
-    assert math.isclose(accuracy, 2/3)
-    assert math.isclose(accuracy2, 2/3)
+    assert math.isclose(accuracy, 2 / 3)
+    assert math.isclose(accuracy2, 2 / 3)
+
 
 def test_multilabel_accuracy():
     x = Tensor(np.array([[0, 1, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]]))
@@ -39,7 +43,8 @@ def test_multilabel_accuracy():
     metric.clear()
     metric.update(x, y)
     accuracy = metric.eval()
-    assert accuracy == 1/3
+    assert accuracy == 1 / 3
+
 
 def test_shape_accuracy():
     x = Tensor(np.array([[0, 1, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]]))
@@ -49,6 +54,7 @@ def test_shape_accuracy():
     with pytest.raises(ValueError):
         metric.update(x, y)
 
+
 def test_shape_accuracy2():
     x = Tensor(np.array([[0, 1, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]]))
     y = Tensor(np.array([0, 1, 1, 1]))
@@ -57,6 +63,7 @@ def test_shape_accuracy2():
     with pytest.raises(ValueError):
         metric.update(x, y)
 
+
 def test_shape_accuracy3():
     x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
     y = Tensor(np.array([[1, 0, 1], [1, 1, 1]]))
@@ -65,6 +72,7 @@ def test_shape_accuracy3():
     with pytest.raises(ValueError):
         metric.update(x, y)
 
+
 def test_shape_accuracy4():
     x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
     y = Tensor(np.array(1))
@@ -73,6 +81,7 @@ def test_shape_accuracy4():
     with pytest.raises(ValueError):
         metric.update(x, y)
 
+
 def test_type_accuracy():
     with pytest.raises(TypeError):
         Accuracy('test')

tests/ut/python/metrics/test_error.py

@@ -14,10 +14,12 @@
 # ============================================================================
 """test error"""
 import math
+
 import numpy as np
 import pytest
-from mindspore.nn.metrics import MAE, MSE
+
 from mindspore import Tensor
+from mindspore.nn.metrics import MAE, MSE
 
 
 def test_MAE():
@@ -27,7 +29,7 @@ def test_MAE():
     error.clear()
     error.update(x, y)
     result = error.eval()
-    assert math.isclose(result, 0.15/4)
+    assert math.isclose(result, 0.15 / 4)
 
 
 def test_input_MAE():
@@ -52,7 +54,7 @@ def test_MSE():
     error.clear()
     error.update(x, y)
     result = error.eval()
-    assert math.isclose(result, 0.0125/4)
+    assert math.isclose(result, 0.0125 / 4)
 
 
 def test_input_MSE():

tests/ut/python/metrics/test_fbeta.py

@@ -13,11 +13,11 @@
 # limitations under the License.
 # ============================================================================
 # """test_fbeta"""
-import math
 import numpy as np
 import pytest
-from mindspore.nn.metrics import get_metric_fn, Fbeta
+
 from mindspore import Tensor
+from mindspore.nn.metrics import get_metric_fn, Fbeta
 
 
 def test_classification_fbeta():
@@ -32,9 +32,9 @@ def test_classification_fbeta():
     fbeta_mean = metric.eval(True)
     fbeta2 = metric(x, y2)
 
-    assert np.allclose(fbeta, np.array([2/3, 2/3]))
-    assert np.allclose(fbeta2, np.array([2/3, 2/3]))
-    assert np.allclose(fbeta_mean, 2/3)
+    assert np.allclose(fbeta, np.array([2 / 3, 2 / 3]))
+    assert np.allclose(fbeta2, np.array([2 / 3, 2 / 3]))
+    assert np.allclose(fbeta_mean, 2 / 3)
 
 
 def test_fbeta_update1():
@@ -46,6 +46,7 @@ def test_fbeta_update1():
     with pytest.raises(ValueError):
         metric.update(x, y)
 
+
 def test_fbeta_update2():
     x1 = Tensor(np.array([[0.2, 0.5, 0.7], [0.3, 0.1, 0.2], [0.9, 0.6, 0.5]]))
     y1 = Tensor(np.array([1, 0, 2]))

tests/ut/python/metrics/test_loss.py

@@ -15,8 +15,9 @@
 """test loss"""
 import numpy as np
 import pytest
-from mindspore.nn.metrics import Loss
+
 from mindspore import Tensor
+from mindspore.nn.metrics import Loss
 
 
 def test_loss_inputs_error():

tests/ut/python/metrics/test_metric_factory.py

@@ -14,9 +14,11 @@
 # ============================================================================
 """test_metric_factory"""
 import math
+
 import numpy as np
-from mindspore.nn.metrics import get_metric_fn
+
 from mindspore import Tensor
+from mindspore.nn.metrics import get_metric_fn
 
 
 def test_classification_accuracy():
@@ -26,7 +28,7 @@ def test_classification_accuracy():
     metric.clear()
     metric.update(x, y)
     accuracy = metric.eval()
-    assert math.isclose(accuracy, 2/3)
+    assert math.isclose(accuracy, 2 / 3)
 
 
 def test_classification_accuracy_by_alias():
@@ -36,7 +38,7 @@ def test_classification_accuracy_by_alias():
     metric.clear()
     metric.update(x, y)
     accuracy = metric.eval()
-    assert math.isclose(accuracy, 2/3)
+    assert math.isclose(accuracy, 2 / 3)
 
 
 def test_classification_precision():

tests/ut/python/metrics/test_precision.py

@@ -14,10 +14,12 @@
 # ============================================================================
 """test_precision"""
 import math
+
 import numpy as np
 import pytest
-from mindspore.nn.metrics import Precision
+
 from mindspore import Tensor
+from mindspore.nn.metrics import Precision
 
 
 def test_classification_precision():
@@ -43,7 +45,7 @@ def test_multilabel_precision():
     metric.update(x, y)
     precision = metric.eval()
 
-    assert np.equal(precision, np.array([1, 2/3, 1])).all()
+    assert np.equal(precision, np.array([1, 2 / 3, 1])).all()
 
 
 def test_average_precision():
@@ -54,7 +56,7 @@ def test_average_precision():
     metric.update(x, y)
     precision = metric.eval(True)
 
-    assert math.isclose(precision, (1 + 2/3 + 1) / 3)
+    assert math.isclose(precision, (1 + 2 / 3 + 1) / 3)
 
 
 def test_num_precision():

tests/ut/python/metrics/test_recall.py

@@ -14,10 +14,12 @@
 # ============================================================================
 """test recall"""
 import math
+
 import numpy as np
 import pytest
-from mindspore.nn.metrics import Recall
+
 from mindspore import Tensor
+from mindspore.nn.metrics import Recall
 
 
 def test_classification_recall():
@@ -43,7 +45,7 @@ def test_multilabel_recall():
     metric.update(x, y)
     recall = metric.eval()
 
-    assert np.equal(recall, np.array([2/3, 2/3, 1])).all()
+    assert np.equal(recall, np.array([2 / 3, 2 / 3, 1])).all()
 
 
 def test_average_recall():
@@ -54,7 +56,7 @@ def test_average_recall():
     metric.update(x, y)
     recall = metric.eval(True)
 
-    assert math.isclose(recall, (2/3 + 2/3 + 1) / 3)
+    assert math.isclose(recall, (2 / 3 + 2 / 3 + 1) / 3)
 
 
 def test_num_recall():

tests/ut/python/metrics/test_topk.py

@@ -14,10 +14,12 @@
 # ============================================================================
 """test topk"""
 import math
+
 import numpy as np
 import pytest
-from mindspore.nn.metrics import TopKCategoricalAccuracy, Top1CategoricalAccuracy, Top5CategoricalAccuracy
+
 from mindspore import Tensor
+from mindspore.nn.metrics import TopKCategoricalAccuracy, Top1CategoricalAccuracy, Top5CategoricalAccuracy
 
 
 def test_type_topk():
@@ -54,8 +56,8 @@ def test_topk():
     topk.update(x, y)
     result = topk.eval()
     result2 = topk(x, y2)
-    assert math.isclose(result, 2/3)
-    assert math.isclose(result2, 2/3)
+    assert math.isclose(result, 2 / 3)
+    assert math.isclose(result2, 2 / 3)
 
 
 def test_zero_topk():
@@ -79,8 +81,8 @@ def test_top1():
     topk.update(x, y)
     result = topk.eval()
     result2 = topk(x, y2)
-    assert math.isclose(result, 1/3)
-    assert math.isclose(result2, 1/3)
+    assert math.isclose(result, 1 / 3)
+    assert math.isclose(result2, 1 / 3)
 
 
 def test_top5():
@@ -97,5 +99,5 @@ def test_top5():
     topk.update(x, y)
     result = topk.eval()
     result2 = topk(x, y2)
-    assert math.isclose(result, 2/3)
-    assert math.isclose(result2, 2/3)
+    assert math.isclose(result, 2 / 3)
+    assert math.isclose(result2, 2 / 3)

tests/ut/python/model/__init__.py

@@ -15,6 +15,7 @@
 """setup for pytest"""
 import mindspore.context as context
 
+
 # pylint: disable=unused-argument
 def setup_module(module):
     context.set_context(mode=context.GRAPH_MODE)

tests/ut/python/model/res18_example.py

@@ -17,10 +17,10 @@ resnet50 example
 """
 import numpy as np
 
-from mindspore.common.api import _executor
+import mindspore.nn as nn  # pylint: disable=C0414
 from mindspore import Tensor
+from mindspore.common.api import _executor
 from mindspore.ops.operations import TensorAdd
-import mindspore.nn as nn # pylint: disable=C0414
 from ...train_step_wrap import train_step_with_loss_warp
 
 

tests/ut/python/model/test_bert.py

@@ -15,9 +15,8 @@
 """ test bert cell """
 import numpy as np
 import pytest
-from mindspore import Model
-from mindspore.nn.optim import AdamWeightDecay
-from mindspore.model_zoo.Bert_NEZHA import BertConfig, BertModel, BertNetworkWithLoss, BertTrainOneStepCell
+
+from mindspore.model_zoo.Bert_NEZHA import BertConfig, BertModel
 from ....dataset_mock import MindData
 
 

tests/ut/python/model/test_bert_cell.py

@@ -14,26 +14,30 @@
 # ============================================================================
 """ test bert of graph compile """
 import functools
+
 import numpy as np
+
 import mindspore.common.dtype as mstype
+import mindspore.nn as nn
+import mindspore.ops.composite as C
+from mindspore.common.initializer import TruncatedNormal
+from mindspore.common.parameter import ParameterTuple
 from mindspore.common.tensor import Tensor
+from mindspore.model_zoo.Bert_NEZHA import BertPretrainingLoss, GetNextSentenceOutput
+from mindspore.model_zoo.Bert_NEZHA.bert_for_pre_training import ClipGradients
 from mindspore.model_zoo.Bert_NEZHA.bert_model import BertConfig, \
     EmbeddingLookup, EmbeddingPostprocessor, BertOutput, RelaPosMatrixGenerator, \
     RelaPosEmbeddingsGenerator, SaturateCast, BertAttention, BertSelfAttention, \
     BertEncoderCell, BertTransformer, CreateAttentionMaskFromInputMask, BertModel
 from mindspore.nn.layer.basic import Norm
-from mindspore.model_zoo.Bert_NEZHA import BertPretrainingLoss, GetNextSentenceOutput
-import mindspore.nn as nn
-from mindspore.common.initializer import TruncatedNormal
-from mindspore.common.parameter import ParameterTuple
 from mindspore.nn.optim import AdamWeightDecay, AdamWeightDecayDynamicLR
-from mindspore.model_zoo.Bert_NEZHA.bert_for_pre_training import ClipGradients
-import mindspore.ops.composite as C
-from mindspore.ops import functional as F
-from ....ops_common import convert
 from ....mindspore_test_framework.mindspore_test import mindspore_test
-from ....mindspore_test_framework.pipeline.forward.compile_forward import pipeline_for_compile_forward_ge_graph_for_case_by_case_config
-from ....mindspore_test_framework.pipeline.gradient.compile_gradient import pipeline_for_compile_grad_ge_graph_for_case_by_case_config
+from ....mindspore_test_framework.pipeline.forward.compile_forward import \
+    pipeline_for_compile_forward_ge_graph_for_case_by_case_config
+from ....mindspore_test_framework.pipeline.gradient.compile_gradient import \
+    pipeline_for_compile_grad_ge_graph_for_case_by_case_config
+from ....ops_common import convert
+
 
 def bert_trans():
     """bert_trans"""
@@ -53,10 +57,12 @@ def bert_trans():
     net.set_train()
     return net
 
+
 def set_train(net):
     net.set_train()
     return net
 
+
 class NetForAdam(nn.Cell):
     def __init__(self):
         super(NetForAdam, self).__init__()
@@ -66,8 +72,10 @@ class NetForAdam(nn.Cell):
         x = self.dense(x)
         return x
 
+
 class TrainStepWrapForAdam(nn.Cell):
     """TrainStepWrapForAdam definition"""
+
     def __init__(self, network):
         super(TrainStepWrapForAdam, self).__init__()
         self.network = network
@@ -81,8 +89,10 @@ class TrainStepWrapForAdam(nn.Cell):
         grads = self.clip_gradients(grads, 1, 1.0)
         return self.optimizer(grads)
 
+
 class TrainStepWrapForAdamDynamicLr(nn.Cell):
     """TrainStepWrapForAdamDynamicLr definition"""
+
     def __init__(self, network):
         super(TrainStepWrapForAdamDynamicLr, self).__init__()
         self.network = network
@@ -95,16 +105,19 @@ class TrainStepWrapForAdamDynamicLr(nn.Cell):
         grads = C.grad_by_list_with_sens(self.network, weights)(x, self.sens)
         return self.optimizer(grads)
 
+
 class TempC2Wrap(nn.Cell):
-    def __init__(self, op, c1=None, c2=None,):
+    def __init__(self, op, c1=None, c2=None, ):
         super(TempC2Wrap, self).__init__()
         self.op = op
         self.c1 = c1
         self.c2 = c2
+
     def construct(self, x1):
         x = self.op(x1, self.c1, self.c2)
         return x
 
+
 test_case_cell_ops = [
     ('Norm_keepdims', {
         'block': Norm(keep_dims=True),
@@ -373,7 +386,7 @@ test_case_cell_ops = [
         'block': set_train(nn.Dense(in_channels=768,
                                     out_channels=3072,
                                     activation='gelu',
-                                    weight_init=TruncatedNormal(0.02),)),
+                                    weight_init=TruncatedNormal(0.02), )),
         'desc_inputs': [[3, 768]],
         'desc_bprop': [[3, 3072]]}),
     ('GetNextSentenceOutput', {
@@ -396,26 +409,28 @@ test_case_cell_ops = [
         'block': TempC2Wrap(ClipGradients(), 1, 1.0),
         'desc_inputs': [tuple(convert(shp) for shp in [[1], [1], [1]])],
         'skip': ['backward', 'exec']}),
-    ]
+]
 
-test_case = functools.reduce(lambda x, y: x+y, [test_case_cell_ops])
+test_case = functools.reduce(lambda x, y: x + y, [test_case_cell_ops])
 # use -k to select certain testcast
 # pytest  tests/python/ops/test_ops.py::test_backward -k LayerNorm
 
 
 test_exec_case = filter(lambda x: 'skip' not in x[1] or
-                        'exec' not in x[1]['skip'], test_case)
+                                  'exec' 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'] and 'backward_exec'
-                                 not in x[1]['skip'], test_case)
+                                           'backward' not in x[1]['skip'] and 'backward_exec'
+                                           not in x[1]['skip'], test_case)
 test_check_gradient_case = filter(lambda x: 'skip' not in x[1] or
-                                  'backward' not in x[1]['skip'] and 'backward_exec'
-                                  not in x[1]['skip'], test_case)
+                                            'backward' not in x[1]['skip'] and 'backward_exec'
+                                            not in x[1]['skip'], test_case)
+
 
 @mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config)
 def test_exec():
     return test_exec_case
 
+
 @mindspore_test(pipeline_for_compile_grad_ge_graph_for_case_by_case_config)
 def test_backward_exec():
     return test_backward_exec_case

tests/ut/python/model/test_lenet.py

@@ -15,16 +15,19 @@
 """test lenet"""
 import numpy as np
 
+import mindspore.context as context
 import mindspore.nn as nn
-from mindspore.common.api import _executor
 from mindspore import Tensor
+from mindspore.common.api import _executor
 from mindspore.ops import operations as P
-import mindspore.context as context
 from ....train_step_wrap import train_step_with_loss_warp, train_step_with_sens
 
 context.set_context(mode=context.GRAPH_MODE)
+
+
 class LeNet5(nn.Cell):
     """LeNet5 definition"""
+
     def __init__(self):
         super(LeNet5, self).__init__()
         self.conv1 = nn.Conv2d(1, 6, 5, pad_mode='valid')

tests/ut/python/model/test_lenet_core_after_exception.py

@@ -14,8 +14,8 @@
 # ============================================================================
 """test_lenet_core_after_exception"""
 import numpy as np
-
 import pytest
+
 import mindspore.nn as nn
 from mindspore.common.api import _executor
 from mindspore.common.tensor import Tensor
@@ -25,6 +25,7 @@ from ....train_step_wrap import train_step_with_loss_warp
 
 class LeNet5(nn.Cell):
     """LeNet5 definition"""
+
     def __init__(self):
         super(LeNet5, self).__init__()
         self.conv1 = nn.Conv2d(3, 6, 5, pad_mode="valid")

tests/ut/python/model/test_mix_precision.py

@@ -15,23 +15,24 @@
 """test_mix_precision"""
 import numpy as np
 
-import mindspore.nn as nn
 import mindspore.common.dtype as mstype
+import mindspore.nn as nn
+from mindspore import Tensor, context
+from mindspore.common import ParameterTuple
 from mindspore.common.api import _executor
 from mindspore.common.parameter import Parameter
-from mindspore.common import ParameterTuple
-from mindspore import Tensor, context
-from mindspore.ops import operations as P
-from mindspore.ops import composite as C
-from mindspore.nn import TrainOneStepCell, WithLossCell
 from mindspore.nn import Momentum
-from ....train_step_wrap import train_step_with_loss_warp
-from tests.ops_common import convert
+from mindspore.nn import TrainOneStepCell, WithLossCell
+from mindspore.ops import composite as C
+from mindspore.ops import operations as P
 from mindspore.train.parallel_utils import ParallelMode
+from tests.ops_common import convert
+from ....train_step_wrap import train_step_with_loss_warp
 
 
 class LeNet5(nn.Cell):
     """LeNet5"""
+
     def __init__(self):
         super(LeNet5, self).__init__()
         self.conv1 = nn.Conv2d(1, 6, 5, pad_mode='valid')
@@ -148,10 +149,13 @@ def test_cast():
 
 
 """test grad of PReLU, which cause AddN(generated by grad) fail"""
+
+
 class IRBlockZ(nn.Cell):
     def __init__(self, inplanes, planes):
         super(IRBlockZ, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=1, pad_mode="same", group=1, has_bias=False, dilation=1)
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=1, pad_mode="same", group=1, has_bias=False,
+                               dilation=1)
         self.act_layer = nn.PReLU(planes)
 
     def construct(self, x):

tests/ut/python/model/test_utils.py

@@ -14,6 +14,7 @@
 # ============================================================================
 """test_dataset_utils"""
 import pytest
+
 import mindspore as ms
 from mindspore.train._utils import _construct_tensor_list
 

tests/ut/python/nn/__init__.py

@@ -15,6 +15,7 @@
 """setup for pytest"""
 import mindspore.context as context
 
+
 # pylint: disable=unused-argument
 def setup_module(module):
     context.set_context(mode=context.GRAPH_MODE)

tests/ut/python/nn/optim/test_adam.py

@@ -15,16 +15,18 @@
 """ test adam """
 import numpy as np
 import pytest
+
 import mindspore.nn as nn
-from mindspore.common.api import _executor
 from mindspore import Tensor, Parameter
+from mindspore.common.api import _executor
 from mindspore.nn import TrainOneStepCell, WithLossCell
-from mindspore.ops import operations as P
 from mindspore.nn.optim import AdamWeightDecay, AdamWeightDecayDynamicLR
+from mindspore.ops import operations as P
 
 
 class Net(nn.Cell):
     """ Net definition """
+
     def __init__(self):
         super(Net, self).__init__()
         self.weight = Parameter(Tensor(np.ones([64, 10]).astype(np.float32)), name="weight")

tests/ut/python/nn/optim/test_ftrl.py

@@ -15,6 +15,7 @@
 """ test FTRL """
 
 import numpy as np
+
 import mindspore.nn as nn
 from mindspore import Tensor, Parameter
 from mindspore.common.api import _executor
@@ -47,4 +48,3 @@ def test_ftrl():
     net_with_loss = WithLossCell(net, loss)
     train_network = TrainOneStepCell(net_with_loss, optimizer)
     _executor.compile(train_network, inputs, label)
-

tests/ut/python/nn/optim/test_lamb.py

@@ -14,16 +14,18 @@
 # ============================================================================
 """ test lamb """
 import numpy as np
+
 import mindspore.nn as nn
-from mindspore.common.api import _executor
 from mindspore import Tensor, Parameter
+from mindspore.common.api import _executor
 from mindspore.nn import TrainOneStepCell, WithLossCell
-from mindspore.ops import operations as P
 from mindspore.nn.optim import Lamb
+from mindspore.ops import operations as P
 
 
 class Net(nn.Cell):
     """ Net definition """
+
     def __init__(self):
         super(Net, self).__init__()
         self.weight = Parameter(Tensor(np.ones([64, 10]).astype(np.float32)), name="weight")
@@ -38,6 +40,7 @@ class Net(nn.Cell):
 
 class NetWithoutWeight(nn.Cell):
     """ NetWithoutWeight definition """
+
     def __init__(self):
         super(NetWithoutWeight, self).__init__()
         self.matmul = P.MatMul()

tests/ut/python/nn/optim/test_lars.py

@@ -12,15 +12,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+from collections import Counter
+
 import numpy as np
+
 import mindspore.nn as nn
 from mindspore import Tensor, Parameter
+from mindspore.common import dtype as mstype
 from mindspore.common.api import _executor
 from mindspore.nn import TrainOneStepCell, WithLossCell
 from mindspore.nn.optim import LARS, Momentum
 from mindspore.ops import operations as P
-from mindspore.common import dtype as mstype 
-from collections import Counter
 
 
 def multisteplr(total_steps, milestone, base_lr=0.9, gamma=0.1, dtype=mstype.float32):
@@ -56,7 +58,7 @@ def test_lars_multi_step_lr():
     lr = multisteplr(10, [2, 6])
     SGD = Momentum(net.trainable_params(), lr, 0.9)
     optimizer = LARS(SGD, epsilon=1e-08, hyperpara=0.02, decay_filter=lambda x: 'bn' not in x.name,
-                       lars_filter=lambda x: 'bn' not in x.name)
+                     lars_filter=lambda x: 'bn' not in x.name)
 
     net_with_loss = WithLossCell(net, loss)
     train_network = TrainOneStepCell(net_with_loss, optimizer)
@@ -77,4 +79,4 @@ def test_lars_float_lr():
 
     net_with_loss = WithLossCell(net, loss)
     train_network = TrainOneStepCell(net_with_loss, optimizer)
-    _executor.compile(train_network, inputs, label)
+    _executor.compile(train_network, inputs, label)

tests/ut/python/nn/optim/test_lr_schedule.py

@@ -14,19 +14,17 @@
 # ============================================================================
 """ test_lr_schedule """
 import numpy as np
-from mindspore.nn import Cell
-from mindspore.ops.operations import BiasAdd, MatMul
+
 from mindspore import Parameter, ParameterTuple, Tensor
-from mindspore.nn import WithLossCell
-from mindspore.nn.optim import Momentum
-from mindspore.nn import SoftmaxCrossEntropyWithLogits
-from mindspore.ops.composite import grad_by_list
-from mindspore.ops import functional as F
+from mindspore.nn import Cell
 from mindspore.nn.optim import Optimizer
+from mindspore.ops.composite import grad_by_list
+from mindspore.ops.operations import BiasAdd, MatMul
 
 
 class Net(Cell):
     """ Net definition """
+
     def __init__(self):
         super(Net, self).__init__()
         self.weight = Parameter(Tensor(np.ones([64, 10])), name="weight")
@@ -41,6 +39,7 @@ class Net(Cell):
 
 class _TrainOneStepCell(Cell):
     """ _TrainOneStepCell definition """
+
     def __init__(self, network, optimizer):
         """
         Append an optimizer to the training network after that the construct
@@ -67,4 +66,3 @@ class _TrainOneStepCell(Cell):
         if self.lr_schedule:
             self.schedule.update_lr(*args)
         return self.optimizer(grads)
-

tests/ut/python/nn/optim/test_momentum.py

@@ -14,16 +14,18 @@
 # ============================================================================
 """ test momentum """
 import numpy as np
+
 import mindspore.nn as nn
-from mindspore.common.api import _executor
 from mindspore import Tensor, Parameter
+from mindspore.common.api import _executor
 from mindspore.nn import TrainOneStepCell, WithLossCell
-from mindspore.ops import operations as P
 from mindspore.nn.optim import Momentum
+from mindspore.ops import operations as P
 
 
 class Net(nn.Cell):
     """ Net definition """
+
     def __init__(self):
         super(Net, self).__init__()
         self.weight = Parameter(Tensor(np.ones([64, 10]).astype(np.float32)), name="weight")

tests/ut/python/nn/optim/test_optimizer.py

@@ -15,9 +15,10 @@
 """ test optimizer """
 import numpy as np
 import pytest
+
 from mindspore import Tensor
-from mindspore.nn.optim import Optimizer, SGD, Adam, AdamWeightDecay, AdamWeightDecayDynamicLR
 from mindspore.common.parameter import Parameter
+from mindspore.nn.optim import Optimizer, SGD, Adam, AdamWeightDecay, AdamWeightDecayDynamicLR
 
 
 class IterableObjc:
@@ -30,6 +31,7 @@ class IterableObjc:
 
 params = IterableObjc()
 
+
 class TestOptimizer():
     def test_init(self):
         Optimizer(0.5, params)
@@ -44,6 +46,7 @@ class TestOptimizer():
 
 class TestAdam():
     """ TestAdam definition """
+
     def test_init(self):
         Adam(params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False,
              use_nesterov=False, weight_decay=0.0, loss_scale=1.0)
@@ -58,6 +61,7 @@ class TestAdam():
 
 class TestSGD():
     """ TestSGD definition """
+
     def test_init(self):
         with pytest.raises(ValueError):
             SGD(params, learning_rate=0.1, momentum=-0.1, dampening=0, weight_decay=0, nesterov=False)
@@ -68,6 +72,7 @@ class TestSGD():
 
 class TestNullParam():
     """ TestNullParam definition """
+
     def test_optim_init(self):
         with pytest.raises(ValueError):
             Optimizer(0.1, None)
@@ -84,8 +89,10 @@ class TestNullParam():
         with pytest.raises(ValueError):
             SGD(None)
 
+
 class TestUnsupportParam():
     """ TestUnsupportParam definition """
+
     def test_optim_init(self):
         with pytest.raises(ValueError):
             Optimizer(0.1, (1, 2, 3))

tests/ut/python/nn/optim/test_rmsprop.py

@@ -15,16 +15,18 @@
 """ test adam """
 import numpy as np
 import pytest
+
 import mindspore.nn as nn
-from mindspore.common.api import _executor
 from mindspore import Tensor, Parameter
+from mindspore.common.api import _executor
 from mindspore.nn import TrainOneStepCell, WithLossCell
-from mindspore.ops import operations as P
 from mindspore.nn.optim import RMSProp
+from mindspore.ops import operations as P
 
 
 class Net(nn.Cell):
     """ Net definition """
+
     def __init__(self):
         super(Net, self).__init__()
         self.weight = Parameter(Tensor(np.ones([64, 10]).astype(np.float32)), name="weight")
@@ -59,4 +61,3 @@ def test_rmsprop_e():
 
     with pytest.raises(TypeError):
         RMSProp(net.get_parameters(), momentum=1, learning_rate=0.1)
-

tests/ut/python/nn/test_activation.py

@@ -14,11 +14,13 @@
 # ============================================================================
 """ test Activations """
 import numpy as np
+
 import mindspore.nn as nn
-from mindspore.common.api import _executor
 from mindspore import Tensor
+from mindspore.common.api import _executor
 from ..ut_filter import non_graph_engine
 
+
 class SoftmaxNet(nn.Cell):
     def __init__(self, dim):
         super(SoftmaxNet, self).__init__()

tests/ut/python/nn/test_batchnorm.py

@@ -17,8 +17,8 @@ import numpy as np
 import pytest
 
 import mindspore.nn as nn
-from mindspore.common.api import _executor
 from mindspore import Tensor, Parameter
+from mindspore.common.api import _executor
 
 
 def test_bn_pars_valid1():
@@ -62,11 +62,12 @@ class GroupNet(nn.Cell):
     def __init__(self):
         super(GroupNet, self).__init__()
         self.group_bn = nn.GroupNorm()
+
     def construct(self, x):
         return self.group_bn(x)
 
 
 def test_compile_groupnorm():
     net = nn.GroupNorm(16, 64)
-    input_data = Tensor(np.random.rand(1,64,256,256).astype(np.float32))
+    input_data = Tensor(np.random.rand(1, 64, 256, 256).astype(np.float32))
     _executor.compile(net, input_data)

tests/ut/python/nn/test_cell.py

@@ -15,11 +15,10 @@
 """ test cell """
 import numpy as np
 import pytest
-import mindspore.context as context
+
 import mindspore.nn as nn
 from mindspore import Tensor, Parameter
 from mindspore.common.api import _executor
-from ..ut_filter import non_graph_engine
 
 
 class ModA(nn.Cell):
@@ -90,7 +89,7 @@ class ConvNet(nn.Cell):
         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
         self.flatten = nn.Flatten()
         self.fc = nn.Dense(
-            int(ConvNet.image_h*ConvNet.image_w*ConvNet.output_ch/(4*4)),
+            int(ConvNet.image_h * ConvNet.image_w * ConvNet.output_ch / (4 * 4)),
             num_classes)
 
     def construct(self, x):

tests/ut/python/nn/test_cell_wrapper.py

@@ -12,14 +12,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-import pytest
 import numpy as np
+import pytest
+
 import mindspore.nn as nn
 from mindspore import Tensor, Parameter
+from mindspore.common import dtype as mstype
 from mindspore.common.api import _executor
 from mindspore.nn import TrainOneStepCell, WithLossCell, ParameterUpdate
 from mindspore.nn.optim import Momentum
-from mindspore.common import dtype as mstype
 from mindspore.ops import operations as P
 
 

tests/ut/python/nn/test_checkparameter.py

@@ -14,10 +14,10 @@
 # ============================================================================
 """ test checkparameter """
 import pytest
+
 from mindspore._checkparam import check_int, check_int_positive, \
     check_input_format, check_bool, twice
 
-
 kernel_size = 5
 kernel_size1 = twice(kernel_size)
 assert kernel_size1 == (5, 5)

tests/ut/python/nn/test_clip_by_norm.py

@@ -14,11 +14,12 @@
 # ============================================================================
 """ test clip_by_norm """
 import numpy as np
-import pytest
+
 import mindspore.nn as nn
 from mindspore import Tensor
 from ..ut_filter import non_graph_engine
 
+
 @non_graph_engine
 def test_clip_by_norm():
     clip_by_norm = nn.ClipByNorm()

tests/ut/python/nn/test_container.py

@@ -14,12 +14,13 @@
 # ============================================================================
 """ test container """
 from collections import OrderedDict
+
 import numpy as np
 import pytest
+
 import mindspore.nn as nn
 from mindspore import Tensor
 
-
 weight = Tensor(np.ones([2, 2]))
 conv2 = nn.Conv2d(3, 64, (3, 3), stride=2, padding=0)
 
@@ -31,6 +32,7 @@ avg_pool = nn.AvgPool2d(kernel_size, stride)
 
 class TestSequentialCell():
     """ TestSequentialCell """
+
     def test_SequentialCell_init(self):
         m = nn.SequentialCell()
         assert type(m).__name__ == 'SequentialCell'
@@ -86,6 +88,7 @@ class TestSequentialCell():
 
 class TestCellList():
     """ TestCellList """
+
     def test_init1(self):
         cell_list = nn.CellList([conv2, avg_pool])
         assert len(cell_list) == 2
@@ -118,7 +121,6 @@ class TestCellList():
             cell = item
         assert type(cell).__name__ == 'AvgPool2d'
 
-
     def test_add(self):
         cell_list = nn.CellList([conv2, avg_pool])
         cell_list += [conv2]

tests/ut/python/nn/test_dense.py

@@ -15,10 +15,11 @@
 """ test nn.Dense """
 import numpy as np
 import pytest
-import mindspore.nn as nn
-from mindspore.common.api import _executor
+
 import mindspore.context as context
+import mindspore.nn as nn
 from mindspore import Tensor
+from mindspore.common.api import _executor
 from ..ut_filter import non_graph_engine
 
 
@@ -68,6 +69,7 @@ def test_dense_channels_error():
 
 class Net(nn.Cell):
     """ Net definition """
+
     def __init__(self,
                  input_channels,
                  output_channels,

tests/ut/python/nn/test_dropout.py

@@ -15,12 +15,14 @@
 """ Test Dropout """
 import numpy as np
 import pytest
+
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore import context
 
 context.set_context(device_target="Ascend")
 
+
 def test_check_dropout_3():
     Tensor(np.ones([20, 16, 50]).astype(np.int32))
     with pytest.raises(ValueError):

tests/ut/python/nn/test_dynamic_lr.py

@@ -14,7 +14,7 @@
 # ============================================================================
 """ Test Dynamic Learning Rate """
 import pytest
-import mindspore
+
 from mindspore.nn import dynamic_lr as dr
 
 milestone = [10, 20, 30]
@@ -29,8 +29,9 @@ min_lr = 0.01
 max_lr = 0.1
 power = 0.5
 
+
 class TestInputs:
-    def test_milestone1(self):        
+    def test_milestone1(self):
         milestone1 = 1
         with pytest.raises(TypeError):
             dr.piecewise_constant_lr(milestone1, learning_rates)
@@ -58,7 +59,7 @@ class TestInputs:
         lr = True
         with pytest.raises(TypeError):
             dr.exponential_decay_lr(lr, decay_rate, total_step, step_per_epoch, decay_epoch)
-        
+
         with pytest.raises(TypeError):
             dr.polynomial_decay_lr(lr, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
 
@@ -71,7 +72,7 @@ class TestInputs:
             dr.polynomial_decay_lr(lr, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
 
     def test_end_learning_rate_type(self):
-        lr = True        
+        lr = True
         with pytest.raises(TypeError):
             dr.polynomial_decay_lr(learning_rate, lr, total_step, step_per_epoch, decay_epoch, power)
 
@@ -127,7 +128,7 @@ class TestInputs:
         step_per_epoch1 = -1
         with pytest.raises(ValueError):
             dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch1, decay_epoch)
-        
+
         with pytest.raises(ValueError):
             dr.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch1, decay_epoch)
 
@@ -226,9 +227,10 @@ def test_cosine_decay():
     lr = dr.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch)
     assert len(lr) == total_step
 
+
 def test_polynomial_decay():
     lr1 = dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
     assert len(lr1) == total_step
     lr2 = dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power,
-                                True)
+                                 True)
     assert len(lr2) == total_step

tests/ut/python/nn/test_embedding.py

@@ -14,11 +14,10 @@
 # ============================================================================
 """ test_embedding """
 import numpy as np
-import pytest
 
-from mindspore.model_zoo.Bert_NEZHA import EmbeddingLookup, EmbeddingPostprocessor
 from mindspore import Tensor
 from mindspore import dtype as mstype
+from mindspore.model_zoo.Bert_NEZHA import EmbeddingLookup, EmbeddingPostprocessor
 from ..ut_filter import non_graph_engine
 
 
@@ -39,6 +38,7 @@ def test_check_embedding_lookup_2():
                         use_one_hot_embeddings=True)
     m(Tensor(np.ones([128]), mstype.int32))
 
+
 @non_graph_engine
 def test_check_embedding_lookup_3():
     m = EmbeddingLookup(vocab_size=32000,
@@ -48,11 +48,12 @@ def test_check_embedding_lookup_3():
                         initializer_range=0.01)
     m(Tensor(np.ones([128]), mstype.int32))
 
+
 @non_graph_engine
 def test_embedding_post_1():
     m = EmbeddingPostprocessor(embedding_size=768,
-                                embedding_shape=[1, 128, 768],
-                                use_token_type=True)
+                               embedding_shape=[1, 128, 768],
+                               use_token_type=True)
     m(Tensor(np.ones([128]), mstype.int32), Tensor(np.ones([1, 128, 768]), mstype.float32))
 
 

tests/ut/python/nn/test_flatten.py

@@ -16,9 +16,10 @@
 test flatten api
 """
 import numpy as np
+
 import mindspore.nn as nn
-from mindspore.common.api import _executor
 from mindspore import Tensor
+from mindspore.common.api import _executor
 
 
 class Net(nn.Cell):

tests/ut/python/nn/test_image_gradients.py

@@ -15,14 +15,17 @@
 """ test image gradients """
 import numpy as np
 import pytest
-import mindspore.nn as nn
-import mindspore.context as context
+
 import mindspore.common.dtype as mstype
+import mindspore.context as context
+import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common.api import _executor
 from mindspore.common.api import ms_function
 
 context.set_context(device_target="Ascend")
+
+
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
@@ -32,9 +35,10 @@ class Net(nn.Cell):
     def construct(self, x):
         return self.image_gradients(x)
 
+
 def test_compile():
     # input shape 1 x 1 x 2 x 2
-    image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32)
+    image = Tensor(np.array([[[[1, 2], [3, 4]]]]), dtype=mstype.int32)
     net = Net()
     _executor.compile(net, image)
 
@@ -42,16 +46,17 @@ def test_compile():
 def test_compile_multi_channel():
     # input shape 4 x 2 x 2 x 2
     dtype = mstype.int32
-    image = Tensor(np.array([[[[1,2],[3,4]], [[5,6],[7,8]]],
-                             [[[3,5],[7,9]], [[11,13],[15,17]]],
-                             [[[5,10],[15,20]], [[25,30],[35,40]]],
-                             [[[10,20],[30,40]], [[50,60],[70,80]]]]), dtype=dtype)
+    image = Tensor(np.array([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]],
+                             [[[3, 5], [7, 9]], [[11, 13], [15, 17]]],
+                             [[[5, 10], [15, 20]], [[25, 30], [35, 40]]],
+                             [[[10, 20], [30, 40]], [[50, 60], [70, 80]]]]), dtype=dtype)
     net = Net()
     _executor.compile(net, image)
 
+
 def test_invalid_5d_input():
     dtype = mstype.float32
     image = Tensor(np.random.random([4, 1, 16, 16, 1]), dtype=dtype)
     net = Net()
     with pytest.raises(ValueError):
-        _executor.compile(net, image)
+        _executor.compile(net, image)

tests/ut/python/nn/test_loss.py

@@ -14,12 +14,12 @@
 # ============================================================================
 """ test loss """
 import numpy as np
-import pytest
+
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common.api import _executor
 from ..ut_filter import non_graph_engine
-import mindspore
+
 
 def test_L1Loss():
     loss = nn.L1Loss()
@@ -60,5 +60,5 @@ def test_SoftmaxCrossEntropyExpand():
     loss = nn.SoftmaxCrossEntropyExpand()
 
     logits = Tensor(np.random.randint(0, 9, [100, 10]).astype(np.float32))
-    labels = Tensor(np.random.randint(0, 9, [10,]).astype(np.float32))
+    labels = Tensor(np.random.randint(0, 9, [10, ]).astype(np.float32))
     _executor.compile(loss, logits, labels)

tests/ut/python/nn/test_lstm.py

@@ -14,6 +14,7 @@
 # ============================================================================
 """ test lstm """
 import pytest
+
 import mindspore.context as context
 from mindspore import nn
 from ..ut_filter import run_on_gpu
@@ -22,6 +23,7 @@ from ....ops_common import convert
 
 class LstmTestNet(nn.Cell):
     """ LstmTestNet definition """
+
     def __init__(self, input_size, hidden_size, num_layers, has_bias, batch_first, bidirectional):
         super(LstmTestNet, self).__init__()
         self.lstm = nn.LSTM(input_size=input_size,
@@ -32,7 +34,6 @@ class LstmTestNet(nn.Cell):
                             bidirectional=bidirectional,
                             dropout=0.0)
 
-
     def construct(self, inp, h0, c0):
         return self.lstm(inp, (h0, c0))
 
@@ -86,6 +87,7 @@ def test_compile(args):
     out = net(*inputs)
     print(f"out: {out}")
 
+
 @run_on_gpu
 @pytest.mark.parametrize('args', test_case_cell_ops, ids=lambda x: x[0])
 def test_execute(args):

tests/ut/python/nn/test_nn_embedding.py

@@ -14,10 +14,11 @@
 # ============================================================================
 """ test nn embedding """
 import numpy as np
+
 from mindspore import Tensor
 from mindspore.common import dtype
-from mindspore.nn import Embedding
 from mindspore.common.api import _executor
+from mindspore.nn import Embedding
 from ..ut_filter import non_graph_engine
 
 
@@ -41,6 +42,7 @@ def test_check_embedding_3():
     input_data = Tensor(np.ones([8, 128]), dtype.int32)
     _executor.compile(net, input_data)
 
+
 @non_graph_engine
 def test_print_embedding():
     net = Embedding(20000, 768, False)

tests/ut/python/nn/test_nn_pad.py

@@ -13,13 +13,12 @@
 # limitations under the License.
 # ============================================================================
 """ test nn pad """
-from mindspore import Tensor
-from mindspore.ops import operations as P
+import numpy as np
+
 import mindspore.nn as nn
-from mindspore.ops.composite import GradOperation
+from mindspore import Tensor
 from mindspore.common.api import ms_function
-import numpy as np
-import mindspore.context as context
+from mindspore.ops.composite import GradOperation
 
 
 class Net(nn.Cell):

tests/ut/python/nn/test_norm.py

@@ -14,6 +14,7 @@
 # ============================================================================
 """ test norm """
 import numpy as np
+
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common.api import _executor

tests/ut/python/nn/test_parameter.py

@@ -15,10 +15,11 @@
 """ test parameter """
 import numpy as np
 import pytest
+
 from mindspore import Tensor, Parameter, ParameterTuple
+from mindspore._checkparam import _check_str_by_regular
 from mindspore.common import dtype as mstype
 from mindspore.common.initializer import initializer
-from mindspore._checkparam import _check_str_by_regular
 
 
 def test_parameter_init():
@@ -30,7 +31,7 @@ def test_parameter_init():
 def test_parameter_tuple_illegal():
     p1 = Parameter(initializer(0, [1], mstype.int32), name="global_step1")
     p2 = Parameter(initializer(0, [1], mstype.int32), name="global_step2")
-    plist = [p1,p2]
+    plist = [p1, p2]
     plist2 = [p1, "str"]
     ptuple = (p1, p2)
     ptuple_str = ("2", "1")
@@ -100,21 +101,21 @@ def test_parameter_init_illegal():
     with pytest.raises(TypeError):
         Parameter(tensor, name=data_str, requires_grad=data_tuple)
 
-    Parameter(tensor, name=data_str, requires_grad=data_bool,layerwise_parallel=data_bool)
+    Parameter(tensor, name=data_str, requires_grad=data_bool, layerwise_parallel=data_bool)
     with pytest.raises(TypeError):
-        Parameter(tensor, name=data_str, requires_grad=data_bool,layerwise_parallel=dat)
+        Parameter(tensor, name=data_str, requires_grad=data_bool, layerwise_parallel=dat)
     with pytest.raises(TypeError):
-        Parameter(tensor, name=data_str, requires_grad=data_bool,layerwise_parallel=tensor)
+        Parameter(tensor, name=data_str, requires_grad=data_bool, layerwise_parallel=tensor)
     with pytest.raises(TypeError):
-        Parameter(tensor, name=data_str, requires_grad=data_bool,layerwise_parallel=data_none)
+        Parameter(tensor, name=data_str, requires_grad=data_bool, layerwise_parallel=data_none)
     with pytest.raises(TypeError):
-        Parameter(tensor, name=data_str, requires_grad=data_bool,layerwise_parallel=data_str)
+        Parameter(tensor, name=data_str, requires_grad=data_bool, layerwise_parallel=data_str)
     with pytest.raises(TypeError):
-        Parameter(tensor, name=data_str, requires_grad=data_bool,layerwise_parallel=data_int)
+        Parameter(tensor, name=data_str, requires_grad=data_bool, layerwise_parallel=data_int)
     with pytest.raises(TypeError):
-        Parameter(tensor, name=data_str, requires_grad=data_bool,layerwise_parallel=data_list)
+        Parameter(tensor, name=data_str, requires_grad=data_bool, layerwise_parallel=data_list)
     with pytest.raises(TypeError):
-        Parameter(tensor, name=data_str, requires_grad=data_bool,layerwise_parallel=data_tuple)
+        Parameter(tensor, name=data_str, requires_grad=data_bool, layerwise_parallel=data_tuple)
 
 
 def test_check_str_by_regular():

tests/ut/python/nn/test_pooling.py

@@ -16,9 +16,10 @@
 test pooling api
 """
 import numpy as np
+
 import mindspore.nn as nn
-from mindspore.common.api import _executor
 from mindspore import Tensor
+from mindspore.common.api import _executor
 
 
 class AvgNet(nn.Cell):
@@ -40,6 +41,7 @@ def test_compile_avg():
 
 class MaxNet(nn.Cell):
     """ MaxNet definition """
+
     def __init__(self,
                  kernel_size,
                  stride=None,
@@ -68,7 +70,8 @@ class Avg1dNet(nn.Cell):
     def construct(self, x):
         return self.avg1d(x)
 
+
 def test_avg1d():
     net = Avg1dNet(6, 1)
     input = Tensor(np.random.randint(0, 255, [1, 3, 6]).astype(np.float32))
-    _executor.compile(net, input)
+    _executor.compile(net, input)

tests/ut/python/nn/test_psnr.py

@@ -17,11 +17,11 @@ test psnr
 """
 import numpy as np
 import pytest
+
 import mindspore.nn as nn
+from mindspore import Tensor
 from mindspore.common import dtype as mstype
 from mindspore.common.api import _executor
-from mindspore import Tensor
-
 
 
 class PSNRNet(nn.Cell):
@@ -40,6 +40,7 @@ def test_compile_psnr():
     img2 = Tensor(np.random.random((8, 3, 16, 16)))
     _executor.compile(net, img1, img2)
 
+
 def test_compile_psnr_grayscale():
     max_val = 255
     net = PSNRNet(max_val)
@@ -47,21 +48,25 @@ def test_compile_psnr_grayscale():
     img2 = Tensor(np.random.randint(0, 256, (8, 1, 16, 16), np.uint8))
     _executor.compile(net, img1, img2)
 
+
 def test_psnr_max_val_negative():
     max_val = -1
     with pytest.raises(ValueError):
         net = PSNRNet(max_val)
 
+
 def test_psnr_max_val_bool():
     max_val = True
     with pytest.raises(TypeError):
         net = PSNRNet(max_val)
 
+
 def test_psnr_max_val_zero():
     max_val = 0
     with pytest.raises(ValueError):
         net = PSNRNet(max_val)
 
+
 def test_psnr_different_shape():
     shape_1 = (8, 3, 16, 16)
     shape_2 = (8, 3, 8, 8)
@@ -71,6 +76,7 @@ def test_psnr_different_shape():
     with pytest.raises(ValueError):
         _executor.compile(net, img1, img2)
 
+
 def test_psnr_different_dtype():
     dtype_1 = mstype.float32
     dtype_2 = mstype.float16
@@ -80,6 +86,7 @@ def test_psnr_different_dtype():
     with pytest.raises(TypeError):
         _executor.compile(net, img1, img2)
 
+
 def test_psnr_invalid_5d_input():
     shape_1 = (8, 3, 16, 16)
     shape_2 = (8, 3, 8, 8)

tests/ut/python/nn/test_ssim.py

@@ -17,10 +17,11 @@ test ssim
 """
 import numpy as np
 import pytest
-import mindspore.nn as nn
+
 import mindspore.common.dtype as mstype
-from mindspore.common.api import _executor
+import mindspore.nn as nn
 from mindspore import Tensor
+from mindspore.common.api import _executor
 
 
 class SSIMNet(nn.Cell):
@@ -38,44 +39,53 @@ def test_compile():
     img2 = Tensor(np.random.random((8, 3, 16, 16)))
     _executor.compile(net, img1, img2)
 
+
 def test_compile_grayscale():
     max_val = 255
-    net = SSIMNet(max_val = max_val)
+    net = SSIMNet(max_val=max_val)
     img1 = Tensor(np.random.randint(0, 256, (8, 1, 16, 16), np.uint8))
     img2 = Tensor(np.random.randint(0, 256, (8, 1, 16, 16), np.uint8))
     _executor.compile(net, img1, img2)
 
+
 def test_ssim_max_val_negative():
     max_val = -1
     with pytest.raises(ValueError):
         net = SSIMNet(max_val)
 
+
 def test_ssim_max_val_bool():
     max_val = True
     with pytest.raises(TypeError):
         net = SSIMNet(max_val)
 
+
 def test_ssim_max_val_zero():
     max_val = 0
     with pytest.raises(ValueError):
         net = SSIMNet(max_val)
 
+
 def test_ssim_filter_size_float():
     with pytest.raises(TypeError):
         net = SSIMNet(filter_size=1.1)
 
+
 def test_ssim_filter_size_zero():
     with pytest.raises(ValueError):
         net = SSIMNet(filter_size=0)
 
+
 def test_ssim_filter_sigma_zero():
     with pytest.raises(ValueError):
         net = SSIMNet(filter_sigma=0.0)
 
+
 def test_ssim_filter_sigma_negative():
     with pytest.raises(ValueError):
         net = SSIMNet(filter_sigma=-0.1)
 
+
 def test_ssim_k1_k2_wrong_value():
     with pytest.raises(ValueError):
         net = SSIMNet(k1=1.1)
@@ -95,6 +105,7 @@ def test_ssim_k1_k2_wrong_value():
     with pytest.raises(ValueError):
         net = SSIMNet(k2=-1.0)
 
+
 def test_ssim_different_shape():
     shape_1 = (8, 3, 16, 16)
     shape_2 = (8, 3, 8, 8)
@@ -104,6 +115,7 @@ def test_ssim_different_shape():
     with pytest.raises(ValueError):
         _executor.compile(net, img1, img2)
 
+
 def test_ssim_different_dtype():
     dtype_1 = mstype.float32
     dtype_2 = mstype.float16
@@ -113,6 +125,7 @@ def test_ssim_different_dtype():
     with pytest.raises(TypeError):
         _executor.compile(net, img1, img2)
 
+
 def test_ssim_invalid_5d_input():
     shape_1 = (8, 3, 16, 16)
     shape_2 = (8, 3, 8, 8)

tests/ut/python/nn/test_structure_output.py

@@ -17,9 +17,9 @@ test_structure_output
 """
 import numpy as np
 
+import mindspore.ops.operations as P
 from mindspore import Tensor, context
 from mindspore.nn import Cell
-import mindspore.ops.operations as P
 from mindspore.ops.functional import depend
 
 context.set_context(mode=context.GRAPH_MODE)

tests/ut/python/optimizer/__init__.py

@@ -13,4 +13,3 @@
 # limitations under the License.
 # ============================================================================
 """ init vm impl """
-from ....vm_impl import vm

tests/ut/python/optimizer/test_debug_location.py

@@ -13,27 +13,26 @@
 # limitations under the License.
 # ============================================================================
 import numpy as np
-import mindspore.nn as nn
 import pytest
-from mindspore import context
+
+import mindspore.nn as nn
 from mindspore import Tensor, Parameter
+from mindspore import context
+from mindspore.common import dtype as mstype
+from mindspore.nn.optim import Momentum
 from mindspore.nn.wrap.cell_wrapper import WithLossCell
-from mindspore.train.loss_scale_manager import FixedLossScaleManager, DynamicLossScaleManager
 from mindspore.nn.wrap.loss_scale import TrainOneStepWithLossScaleCell
-from mindspore.ops import operations as P
-from mindspore.nn.optim import Momentum
 from mindspore.ops import functional as F
-from mindspore.common import dtype as mstype
-from mindspore.train import Model
-from ....dataset_mock import MindData
-from mindspore.nn.optim import Lamb
-from mindspore.ops._utils import _get_broadcast_shape
-from mindspore.ops.primitive import Primitive, PrimitiveWithInfer, prim_attr_register
+from mindspore.ops import operations as P
 from mindspore.ops._grad.grad_base import bprop_getters
 from mindspore.ops._grad.grad_math_ops import binop_grad_common
+from mindspore.ops._utils import _get_broadcast_shape
+from mindspore.ops.primitive import PrimitiveWithInfer, prim_attr_register
+from mindspore.train.loss_scale_manager import DynamicLossScaleManager
 
 context.set_context(mode=context.GRAPH_MODE)
 
+
 class MockNeg(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self):
@@ -47,6 +46,7 @@ class MockNeg(PrimitiveWithInfer):
         raise TypeError("InferError")
         return input_x
 
+
 class MockSub(PrimitiveWithInfer):
     @prim_attr_register
     def __init__(self):
@@ -59,6 +59,7 @@ class MockSub(PrimitiveWithInfer):
     def infer_dtype(self, x_dtype, y_dtype):
         return x_dtype
 
+
 @bprop_getters.register(MockSub)
 def get_bprop_mock_sub(self):
     """Grad definition for `MockSub` operation."""
@@ -66,8 +67,10 @@ def get_bprop_mock_sub(self):
 
     def bprop(x, y, out, dout):
         return binop_grad_common(x, y, dout, neg_func(dout))
+
     return bprop
 
+
 class Net(nn.Cell):
     def __init__(self, in_features, out_features):
         super(Net, self).__init__()
@@ -80,6 +83,7 @@ class Net(nn.Cell):
         output = self.add(self.matmul(input, self.weight), self.bias)
         return output
 
+
 class NetFP16(nn.Cell):
     def __init__(self, in_features, out_features):
         super(NetFP16, self).__init__()
@@ -90,16 +94,19 @@ class NetFP16(nn.Cell):
         self.cast = P.Cast()
 
     def construct(self, input):
-        output = self.cast(self.add(self.matmul(self.cast(input, mstype.float16), self.cast(self.weight, mstype.float16)),
-                          self.cast(self.bias, mstype.float16)), mstype.float32)
+        output = self.cast(
+            self.add(self.matmul(self.cast(input, mstype.float16), self.cast(self.weight, mstype.float16)),
+                     self.cast(self.bias, mstype.float16)), mstype.float32)
         return output
 
+
 def get_axis(x):
     shape = F.shape(x)
     length = F.tuple_len(shape)
     perm = F.make_range(0, length)
     return perm
 
+
 class MSELoss(nn.Cell):
     def __init__(self):
         super(MSELoss, self).__init__()
@@ -107,17 +114,21 @@ class MSELoss(nn.Cell):
         self.square = P.Square()
         self.reduce_mean = P.ReduceMean()
         self.sub = MockSub()
+
     def construct(self, data, label):
         diff = self.sub(data, label)
         return self.reduce_mean(self.square(diff), get_axis(diff))
 
+
 class NegCell(nn.Cell):
     def __init__(self):
         super(NegCell, self).__init__()
         self.neg = MockNeg()
+
     def construct(self, x):
         return self.neg(x)
 
+
 class Net3(nn.Cell):
     def __init__(self):
         super().__init__()
@@ -146,6 +157,7 @@ class SequenceNet(nn.Cell):
         x = self.seq(x) + bbb
         return x
 
+
 def test_sequential_resolve_error():
     input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
     input_me = Tensor(input_np)
@@ -153,6 +165,7 @@ def test_sequential_resolve_error():
     with pytest.raises(RuntimeError) as e:
         net(input_me)
 
+
 def test_compile_grad_error():
     inputs = Tensor(np.ones([16, 16]).astype(np.float32))
     label = Tensor(np.zeros([16, 16]).astype(np.float32))
@@ -164,9 +177,8 @@ def test_compile_grad_error():
     net_with_loss = WithLossCell(net, loss)
     scale_manager = DynamicLossScaleManager()
     update_cell = scale_manager.get_update_cell()
-    train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell = update_cell)
+    train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=update_cell)
     train_network.set_train()
     with pytest.raises(TypeError) as e:
         train_network(inputs, label)
-        print (e)
-
+        print(e)

tests/ut/python/optimizer/test_optimize_with_loss_scale.py

@@ -13,19 +13,20 @@
 # limitations under the License.
 # ============================================================================
 import numpy as np
+
 import mindspore.nn as nn
-from mindspore import context
 from mindspore import Tensor, Parameter
+from mindspore import context
+from mindspore.common import dtype as mstype
+from mindspore.nn.optim import Lamb
+from mindspore.nn.optim import Momentum, Adam
 from mindspore.nn.wrap.cell_wrapper import WithLossCell
 from mindspore.nn.wrap.loss_scale import TrainOneStepWithLossScaleCell
-from mindspore.train.loss_scale_manager import FixedLossScaleManager, DynamicLossScaleManager
-from mindspore.ops import operations as P
-from mindspore.nn.optim import Momentum, Adam
 from mindspore.ops import functional as F
-from mindspore.common import dtype as mstype
+from mindspore.ops import operations as P
 from mindspore.train import Model
+from mindspore.train.loss_scale_manager import FixedLossScaleManager, DynamicLossScaleManager
 from ....dataset_mock import MindData
-from mindspore.nn.optim import Lamb
 
 context.set_context(mode=context.GRAPH_MODE)
 
@@ -36,6 +37,7 @@ class MindDataSet(MindData):
                                           np_types=dataset_types,
                                           output_shapes=dataset_shapes,
                                           input_indexs=(0, 1))
+
     def __next__(self):
         if self._size < self._iter_num:
             raise StopIteration
@@ -45,6 +47,7 @@ class MindDataSet(MindData):
             next.append(Tensor(np.ones(shape).astype(type)))
         return tuple(next)
 
+
 class Net(nn.Cell):
     def __init__(self, in_features, out_features):
         super(Net, self).__init__()
@@ -57,6 +60,7 @@ class Net(nn.Cell):
         output = self.add(self.matmul(input, self.weight), self.bias)
         return output
 
+
 class NetFP16(nn.Cell):
     def __init__(self, in_features, out_features):
         super(NetFP16, self).__init__()
@@ -67,10 +71,12 @@ class NetFP16(nn.Cell):
         self.cast = P.Cast()
 
     def construct(self, input):
-        output = self.cast(self.add(self.matmul(self.cast(input, mstype.float16), self.cast(self.weight, mstype.float16)),
-                          self.cast(self.bias, mstype.float16)), mstype.float32)
+        output = self.cast(
+            self.add(self.matmul(self.cast(input, mstype.float16), self.cast(self.weight, mstype.float16)),
+                     self.cast(self.bias, mstype.float16)), mstype.float32)
         return output
 
+
 def get_axis(x):
     shape_op = P.Shape()
     shape = shape_op(x)
@@ -78,6 +84,7 @@ def get_axis(x):
     perm = F.make_range(0, length)
     return perm
 
+
 class MSELoss(nn.Cell):
     def __init__(self):
         super(MSELoss, self).__init__()
@@ -89,6 +96,7 @@ class MSELoss(nn.Cell):
         diff = data - label
         return self.reduce_mean(self.square(diff), get_axis(diff))
 
+
 def test_momentum_compile():
     inputs = Tensor(np.ones([15, 1]).astype(np.float32))
     label = Tensor(np.zeros([15, 1]).astype(np.float32))
@@ -104,6 +112,7 @@ def test_momentum_compile():
     output = train_network(inputs, label, scaling_sens)
     print("the result is ", output)
 
+
 def test_compile_fp16_not_overflow():
     inputs = Tensor(np.ones([16, 16]).astype(np.float32))
     label = Tensor(np.zeros([16, 16]).astype(np.float32))
@@ -119,6 +128,7 @@ def test_compile_fp16_not_overflow():
     output = train_network(inputs, label, scaling_sens)
     print("the result is ", output)
 
+
 def test_compile_fp16_lr_overflow():
     inputs = Tensor(np.ones([16, 16]).astype(np.float32))
     label = Tensor(np.zeros([16, 16]).astype(np.float32))
@@ -134,6 +144,7 @@ def test_compile_fp16_lr_overflow():
     output = train_network(inputs, label, scaling_sens)
     print("the result is ", output)
 
+
 def test_compile_fp16_overflow():
     inputs = Tensor(np.ones([16, 16]).astype(np.float32))
     label = Tensor(np.zeros([16, 16]).astype(np.float32))
@@ -148,6 +159,7 @@ def test_compile_fp16_overflow():
     output = train_network(inputs, label, scaling_sens)
     print("the result is ", output)
 
+
 def test_compile_fp16_lr_overflow_with_lossscale_update():
     inputs = Tensor(np.ones([16, 16]).astype(np.float32))
     label = Tensor(np.zeros([16, 16]).astype(np.float32))
@@ -165,6 +177,7 @@ def test_compile_fp16_lr_overflow_with_lossscale_update():
     output = train_network(inputs, label, scaling_sens)
     print("the result is ", output)
 
+
 def test_compile_f16_model_train():
     dataset_types = (np.float32, np.float32)
     dataset_shapes = ((16, 16), (16, 16))
@@ -205,11 +218,12 @@ def test_compile_fp16_lr_overflow_fixed_feed():
     net_with_loss = WithLossCell(net, loss)
     scale_manager = FixedLossScaleManager()
     update_cell = scale_manager.get_update_cell()
-    train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell = update_cell)
+    train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=update_cell)
     train_network.set_train()
     output = train_network(inputs, label, scaling_sens)
     print("the result is ", output)
 
+
 def test_compile_fp16_lr_overflow_dynamic_feed():
     inputs = Tensor(np.ones([16, 16]).astype(np.float32))
     label = Tensor(np.zeros([16, 16]).astype(np.float32))
@@ -222,11 +236,12 @@ def test_compile_fp16_lr_overflow_dynamic_feed():
     net_with_loss = WithLossCell(net, loss)
     scale_manager = DynamicLossScaleManager()
     update_cell = scale_manager.get_update_cell()
-    train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell = update_cell)
+    train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=update_cell)
     train_network.set_train()
     output = train_network(inputs, label, scaling_sens)
     print("the result is ", output)
 
+
 def test_compile_fp16_lr_overflow_fixed_graph():
     inputs = Tensor(np.ones([16, 16]).astype(np.float32))
     label = Tensor(np.zeros([16, 16]).astype(np.float32))
@@ -238,11 +253,12 @@ def test_compile_fp16_lr_overflow_fixed_graph():
     net_with_loss = WithLossCell(net, loss)
     scale_manager = FixedLossScaleManager(drop_overflow_update=True)
     update_cell = scale_manager.get_update_cell()
-    train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell = update_cell)
+    train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=update_cell)
     train_network.set_train()
     output = train_network(inputs, label)
     print("the result is ", output)
 
+
 def test_compile_fp16_lr_overflow_dynamic_graph():
     inputs = Tensor(np.ones([16, 16]).astype(np.float32))
     label = Tensor(np.zeros([16, 16]).astype(np.float32))
@@ -254,11 +270,12 @@ def test_compile_fp16_lr_overflow_dynamic_graph():
     net_with_loss = WithLossCell(net, loss)
     scale_manager = DynamicLossScaleManager()
     update_cell = scale_manager.get_update_cell()
-    train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell = update_cell)
+    train_network = TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=update_cell)
     train_network.set_train()
     output = train_network(inputs, label)
     print("the result is ", output)
 
+
 def test_adam_compile():
     inputs = Tensor(np.ones([15, 1]).astype(np.float32))
     label = Tensor(np.zeros([15, 1]).astype(np.float32))

tests/ut/python/optimizer/test_optimize_with_parameter_groups.py

@@ -14,20 +14,22 @@
 # ============================================================================
 import numpy as np
 import pytest
+
 import mindspore.common.dtype as mstype
 import mindspore.nn as nn
-from mindspore.nn.optim import Momentum, SGD, RMSProp, Adam
 from mindspore import context
 from mindspore.common.api import _executor
 from mindspore.common.tensor import Tensor
-from mindspore.ops import operations as P
 from mindspore.nn import TrainOneStepCell, WithLossCell
+from mindspore.nn.optim import Momentum, SGD, RMSProp, Adam
+from mindspore.ops import operations as P
 
 context.set_context(mode=context.GRAPH_MODE)
 
 
 class LeNet5(nn.Cell):
     """ LeNet5 definition """
+
     def __init__(self):
         super(LeNet5, self).__init__()
         self.conv1 = nn.Conv2d(1, 6, 5, pad_mode='valid')

tests/ut/python/parameter_feature/test_parameter.py

@@ -13,16 +13,17 @@
 # limitations under the License.
 # ============================================================================
 import numpy as np
+
 import mindspore.context as context
+import mindspore.ops.composite as C
 from mindspore import Tensor, Parameter
 from mindspore.nn import Cell
 from mindspore.ops import operations as P
-import mindspore.ops.composite as C
 
 context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
 
-def test_parser_three_default_mixed_args_subnet():
 
+def test_parser_three_default_mixed_args_subnet():
     class SubNetDefaultMixedArgs(Cell):
         def __init__(self):
             super().__init__()
@@ -55,7 +56,7 @@ def test_net_vararg_kwonlyarg_kwarg():
             super(FirstNet, self).__init__()
             self.net = SecondNet()
 
-        def construct(self, x=1, z=2+2+4, y=3):
+        def construct(self, x=1, z=2 + 2 + 4, y=3):
             c = self.net(22, 33, x, y, z, 2, 3, 4, 5, key1=10, key2=20, key3=30, key4=40)
             return c
 
@@ -74,13 +75,14 @@ def test_net_vararg_kwonlyarg_kwarg():
     net = FirstNet()
     net()
 
+
 def test_net_vararg_normal_input():
     class FirstNet(Cell):
         def __init__(self):
             super(FirstNet, self).__init__()
             self.net = SecondNet()
 
-        def construct(self, x=1, z=2+2+4, y=3):
+        def construct(self, x=1, z=2 + 2 + 4, y=3):
             c = self.net(22, 33, x, y, z, 2, 3, 4, 5, key1=10, key2=20, key3=30, key4=40)
             return c
 
@@ -95,10 +97,12 @@ def test_net_vararg_normal_input():
             d = var[0] * var[1] * var[2] * var[3]
             e = key1 - key2 - kwargs["key3"] + kwargs["key4"]
             return a + b + c + d + e
+
     x = Tensor(np.ones((2, 3, 4), np.int32))
     net = FirstNet()
     net(x, x, x)
 
+
 def test_prim_vararg_kwonlyarg():
     class FirstNet(Cell):
         def __init__(self):
@@ -201,9 +205,11 @@ def test_net_variable_and_weights():
     z = Tensor(np.ones((4,), np.float32))
     net(x, y, z)
 
+
 def test_net_vargs_expand():
     class InputBackward(Cell):
         """ InputBackward definition """
+
         def __init__(self, network, c1=None, c2=None):
             super(InputBackward, self).__init__()
             self.network = network
@@ -214,9 +220,11 @@ def test_net_vargs_expand():
 
         def construct(self, *inputs):
             return self.grad(self.network)(*inputs)
+
     class AddNet(Cell):
         def __init__(self):
             super(AddNet, self).__init__()
+
         def construct(self, x, y):
             return x + y
 
@@ -227,7 +235,7 @@ def test_net_vargs_expand():
 
     net.set_train()
     net(x, y, sens)
-    
+
 
 def test_mixed_precision_const_parameter():
     class NetLoss(Cell):
@@ -237,6 +245,7 @@ def test_mixed_precision_const_parameter():
             self.up_sample1 = P.ResizeBilinear((14, 14))
             self.up_sample2 = P.ResizeBilinear((28, 28))
             self.up_sample3 = P.ResizeBilinear((36, 36))
+
         def construct(self, x, y, z, *args):
             ret = 0
             if args[0] == self.shape(z)[2]:
@@ -250,20 +259,23 @@ def test_mixed_precision_const_parameter():
                 ret = x * y
             ret = ret * z
             return ret
+
     class NetMain(Cell):
         def __init__(self, loss_fn):
             super(NetMain, self).__init__()
             self.loss_fn = loss_fn
             self.shape = P.Shape()
+
         def construct(self, x, y, z):
             size_x = self.shape(x)[2]
             size_y = self.shape(y)[2]
             ret = self.loss_fn(x, y, z, size_x, size_y)
             return ret
+
     loss_fn = NetLoss()
     net = NetMain(loss_fn)
     net.add_flags_recursive(fp32=True)
     x = Tensor(np.ones((1, 3, 28, 28), np.float32))
     y = Tensor(np.ones((1, 3, 14, 14), np.float32))
     z = Tensor(np.ones((1, 3, 28, 28), np.float32))
-    out = net(x, y, z)
+    out = net(x, y, z)

tests/ut/python/parameter_feature/test_var_grad.py

@@ -13,14 +13,14 @@
 # limitations under the License.
 # ============================================================================
 import numpy as np
-from mindspore import context
+
+import mindspore.ops.composite as C
 from mindspore import Tensor, Parameter
+from mindspore import context
+from mindspore.common import dtype as mstype
+from mindspore.common.parameter import ParameterTuple
 from mindspore.nn import Cell
 from mindspore.ops import operations as P
-import mindspore.ops.composite as C
-from mindspore.common.api import _executor
-from mindspore.common.parameter import ParameterTuple
-from mindspore.common import dtype as mstype
 
 context.set_context(mode=context.GRAPH_MODE)
 
@@ -34,6 +34,7 @@ def test_net_vargs_expand():
 
         def construct(self, x, y):
             return x + y
+
     x = Tensor(np.random.normal(0, 1, [3, 4, 5]).astype(np.float32))
     y = Tensor(np.random.normal(0, 1, [3, 4, 5]).astype(np.float32))
     sens = Tensor(np.random.normal(0, 1, [3, 4, 5]).astype(np.float32))
@@ -51,7 +52,7 @@ class VarNet(Cell):
         self.net = net
 
     def construct(self, *args):
-        return self.net(*args)*self.w + self.b
+        return self.net(*args) * self.w + self.b
 
 
 class SecondNet(Cell):
@@ -95,6 +96,7 @@ class Bprop(Cell):
 
 def test_all_var_args_grad_with_sens():
     """"test grad_by_list_with_sens with all var args input"""
+
     class GradNet(Cell):
         def __init__(self, net):
             super(GradNet, self).__init__()
@@ -103,6 +105,7 @@ def test_all_var_args_grad_with_sens():
 
         def construct(self, *inputs):
             return C.grad_by_list_with_sens(self.net, self.weights)(*inputs)
+
     x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
     y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
     sens = Tensor(1.0, dtype=mstype.float32)
@@ -120,6 +123,7 @@ def test_grad_list_var_args():
 
         def construct(self, *inputs):
             return C.grad_by_list(self.net, self.weights)(*inputs)
+
     x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
     y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
     net = VarNet(SecondNet())
@@ -136,6 +140,7 @@ def test_grad_all_var_args():
 
         def construct(self, *inputs):
             return C.grad_all(self.net)(*inputs)
+
     x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
     y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
     net = VarNet(SecondNet())
@@ -152,6 +157,7 @@ def test_grad_all_var_args_with_sens():
 
         def construct(self, *inputs):
             return C.grad_all_with_sens(self.net)(*inputs)
+
     x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
     y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
     sens = Tensor(1.0, dtype=mstype.float32)
@@ -169,6 +175,7 @@ def test_grad_var_args_with_sens():
 
         def construct(self, *inputs):
             return C.grad_with_sens(self.net)(*inputs)
+
     x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
     y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
     sens = Tensor(1.0, dtype=mstype.float32)
@@ -206,6 +213,7 @@ def test_var_args_grad():
 
         def construct(self, x, y, sens):
             return C.grad_by_list_with_sens(self.net, self.weights)(x, y, sens)
+
     x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
     y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
     sens = Tensor(1.0, dtype=mstype.float32)
@@ -216,13 +224,14 @@ def test_var_args_grad():
 
 def test_var_args_positional():
     """"test grad_all with var args in inner graph"""
+
     class VarNet(Cell):
         def __init__(self, net):
             super(VarNet, self).__init__()
             self.net = net
 
         def construct(self, x, y):
-            return self.net(x, y)*x
+            return self.net(x, y) * x
 
     class SecondNet(Cell):
         def __init__(self):
@@ -239,6 +248,7 @@ def test_var_args_positional():
 
         def construct(self, x, y):
             return C.grad_all(self.net)(x, y)
+
     x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
     y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
     net = VarNet(SecondNet())
@@ -258,6 +268,7 @@ def test_grad_within_if_else():
 
         def construct(self, *inputs):
             return self.grad(*inputs)
+
     x = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
     y = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
     sens = Tensor(1.0, dtype=mstype.float32)
@@ -309,6 +320,7 @@ def test_grad_for_concat():
 
         def grad_cmp(self):
             input_grad_mindspore = self.grad_mindspore_impl()
+
     fact = ConcatFactory(input_shape=(
         (2, 184320, 1), (2, 46080, 1), (2, 11520, 1), (2, 2880, 1), (2, 720, 1)), axis=1)
     fact.grad_cmp()

tests/ut/python/test_log.py

@@ -15,12 +15,13 @@
 """
 log test
 """
+import logging
 import os
-import sys
-import time
 import re
 import shutil
-import logging
+import sys
+import time
+
 
 def test_log_stdout():
     # Clean up environment variables
@@ -50,8 +51,8 @@ def test_log_setlevel():
     _rm_env_config()
     os.environ['GLOG_v'] = '0'
     from mindspore import log as logger
-    #logger_instance = logger._get_logger()
-    #del logger_instance
+    # logger_instance = logger._get_logger()
+    # del logger_instance
     loglevel = logger.get_level()
     log_str = 'print debug informations'
     logger.debug("5 test log message debug:%s", log_str)
@@ -87,7 +88,7 @@ def test_log_file():
               "\[.*:.*[0-9]\] test log message warning"
     match_obj = re.match(pattern, result)
 
-    #Clear test file
+    # Clear test file
     if os.path.exists(file_path):
         shutil.rmtree(file_path)
 
@@ -100,7 +101,7 @@ def test_log_backup_count():
     """
     test backup count
     """
-    #logger.reset_log_config(level=logging.INFO, console=False,
+    # logger.reset_log_config(level=logging.INFO, console=False,
     #                        filepath=file_path, maxBytes=1000, backupCount=10)
     _rm_env_config()
     file_path = '/tmp/log/mindspore_test'
@@ -141,12 +142,12 @@ def test_log_verify_envconfig():
     # level is not a number
     _rm_env_config()
     os.environ['GLOG_v'] = 'test'
-    verify_dict_0  = logger._get_env_config()
+    verify_dict_0 = logger._get_env_config()
 
     # level is not in range
     _rm_env_config()
     os.environ['GLOG_v'] = '100'
-    verify_dict_1  = logger._get_env_config()
+    verify_dict_1 = logger._get_env_config()
 
     # console is not a number
     _rm_env_config()
@@ -236,7 +237,6 @@ def test_log_repeated_print():
     logger._global_logger = None
 
 
-
 def test_log_getconfig():
     _rm_env_config()
     os.environ['GLOG_v'] = '3'
@@ -307,7 +307,7 @@ def test_log_ms_import():
     targetdict = {'GLOG_v': '2', 'GLOG_logtostderr': '1'}
     level = ms.get_level()
     assert configdict == targetdict and level == '2'
-	
+
 
 def _rm_env_config():
     envlist = ['GLOG_v', 'GLOG_logtostderr', 'GLOG_log_dir', 'logger_maxBytes', 'logger_backupCount']

tests/ut/python/train/summary/__init__.py

@@ -15,6 +15,7 @@
 """setup for pytest"""
 import mindspore.context as context
 
+
 # pylint: disable=unused-argument
 def setup_module(module):
     context.set_context(mode=context.GRAPH_MODE)

tests/ut/python/train/summary/test_graph_summary.py

@@ -13,15 +13,16 @@
 # limitations under the License.
 # ============================================================================
 """ test_graph_summary """
-import os
 import logging
+import os
+
 import numpy as np
-import pytest
+
 import mindspore.nn as nn
-from mindspore.nn.optim import Momentum
 from mindspore import Model, context
-from mindspore.train.summary.summary_record import SummaryRecord
+from mindspore.nn.optim import Momentum
 from mindspore.train.callback import SummaryStep
+from mindspore.train.summary.summary_record import SummaryRecord
 from .....dataset_mock import MindData
 
 CUR_DIR = os.getcwd()

tests/ut/python/train/summary/test_histogram_summary.py

@@ -21,8 +21,8 @@ import tempfile
 import numpy as np
 
 from mindspore.common.tensor import Tensor
-from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data
 from mindspore.train.summary._summary_adapter import _calc_histogram_bins
+from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data
 from .summary_reader import SummaryReader
 
 CUR_DIR = os.getcwd()

tests/ut/python/train/summary/test_image_summary.py

@@ -18,16 +18,18 @@
 @Date  : 2019-07-4
 @Desc  : test summary function
 """
-import os
 import logging
+import os
+
 import numpy as np
+
 import mindspore.nn as nn
-from mindspore.train.summary.summary_record import SummaryRecord, \
-    _cache_summary_tensor_data
+from mindspore import Model, context
 from mindspore import Tensor
 from mindspore.nn.optim import Momentum
-from mindspore import Model, context
 from mindspore.train.callback import SummaryStep
+from mindspore.train.summary.summary_record import SummaryRecord, \
+    _cache_summary_tensor_data
 from .....dataset_mock import MindData
 
 CUR_DIR = os.getcwd()

tests/ut/python/train/summary/test_summary.py

@@ -18,16 +18,18 @@
 @Date  : 2019-07-4
 @Desc  : test summary function
 """
-import os
 import logging
+import os
 import random
+
 import numpy as np
 import pytest
-from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data
-from mindspore.train.callback import SummaryStep
-from mindspore.common.tensor import Tensor
+
 import mindspore.nn as nn
+from mindspore.common.tensor import Tensor
 from mindspore.ops import operations as P
+from mindspore.train.callback import SummaryStep
+from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data
 
 CUR_DIR = os.getcwd()
 SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/"
@@ -129,7 +131,8 @@ def test_scalar_summary_sample_with_shape_1():
 # Test: test with ge
 class SummaryDemo(nn.Cell):
     """ SummaryDemo definition """
-    def __init__(self,):
+
+    def __init__(self, ):
         super(SummaryDemo, self).__init__()
         self.s = P.ScalarSummary()
         self.histogram_summary = P.HistogramSummary()
@@ -218,9 +221,9 @@ def test_validate():
     with pytest.raises(ValueError):
         sr.record(2.0)
     with pytest.raises(ValueError):
-        sr.record((1,3))
+        sr.record((1, 3))
     with pytest.raises(ValueError):
-        sr.record([2,3])
+        sr.record([2, 3])
     with pytest.raises(ValueError):
         sr.record("str")
     with pytest.raises(ValueError):
@@ -235,8 +238,8 @@ def test_validate():
     with pytest.raises(ValueError):
         SummaryStep(sr, "str")
     with pytest.raises(ValueError):
-        SummaryStep(sr, (1,2))
+        SummaryStep(sr, (1, 2))
     with pytest.raises(ValueError):
-        SummaryStep(sr, [3,4])
+        SummaryStep(sr, [3, 4])
     with pytest.raises(ValueError):
         SummaryStep(sr, sr)

tests/ut/python/train/summary/test_summary_abnormal_input.py

@@ -18,11 +18,13 @@
 @Date  : 2019-08-5
 @Desc  : test summary function of abnormal input
 """
-import os
 import logging
+import os
+
 import numpy as np
-from mindspore.train.summary.summary_record import SummaryRecord
+
 from mindspore.common.tensor import Tensor
+from mindspore.train.summary.summary_record import SummaryRecord
 
 CUR_DIR = os.getcwd()
 SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/"
@@ -65,6 +67,7 @@ def test_summaryrecord_input_null_string():
         assert False
     log.debug("finished test_summaryrecord_input_null_string")
 
+
 def test_summaryrecord_input_None():
     log.debug("begin test_summaryrecord_input_None")
     # step 0: create the thread
@@ -76,6 +79,7 @@ def test_summaryrecord_input_None():
         assert False
     log.debug("finished test_summaryrecord_input_None")
 
+
 def test_summaryrecord_input_relative_dir_1():
     log.debug("begin test_summaryrecord_input_relative_dir_1")
     # step 0: create the thread

tests/ut/python/train/summary/test_summary_ops_params_valid_check.py

@@ -18,16 +18,17 @@
 @Date  : 2019-08-5
 @Desc  : test summary function of ops params valid check
 """
-import os
 import logging
+import os
 import random
+
 import numpy as np
 import pytest
-from mindspore.train.summary.summary_record import SummaryRecord
-from mindspore.common.tensor import Tensor
+
 import mindspore.nn as nn
+from mindspore.common.tensor import Tensor
 from mindspore.ops import operations as P
-
+from mindspore.train.summary.summary_record import SummaryRecord
 
 CUR_DIR = os.getcwd()
 SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/"
@@ -38,6 +39,7 @@ log.setLevel(level=logging.ERROR)
 
 class SummaryDemoTag(nn.Cell):
     """ SummaryDemoTag definition """
+
     def __init__(self, tag1, tag2, tag3):
         super(SummaryDemoTag, self).__init__()
         self.s = P.ScalarSummary()
@@ -58,6 +60,7 @@ class SummaryDemoTag(nn.Cell):
 
 class SummaryDemoTagForSet(nn.Cell):
     """ SummaryDemoTagForSet definition """
+
     def __init__(self, tag_tuple):
         super(SummaryDemoTagForSet, self).__init__()
         self.s = P.ScalarSummary()
@@ -75,6 +78,7 @@ class SummaryDemoTagForSet(nn.Cell):
 
 class SummaryDemoValue(nn.Cell):
     """ SummaryDemoValue definition """
+
     def __init__(self, value):
         super(SummaryDemoValue, self).__init__()
         self.s = P.ScalarSummary()
@@ -88,8 +92,10 @@ class SummaryDemoValue(nn.Cell):
         self.s("y", self.v)
         return z
 
+
 class SummaryDemoValueForSet(nn.Cell):
     """ SummaryDemoValueForSet definition """
+
     def __init__(self, value, tag_tuple):
         super(SummaryDemoValueForSet, self).__init__()
         self.s = P.ScalarSummary()
@@ -106,11 +112,12 @@ class SummaryDemoValueForSet(nn.Cell):
 
 class HistogramSummaryNet(nn.Cell):
     "HistogramSummaryNet definition"
+
     def __init__(self, value):
         self.histogram_summary = P.HistogramSummary()
         self.add = P.TensorAdd()
         self.value = value
-    
+
     def construct(self, tensors1, tensor2):
         self.histogram_summary("value", self.value)
         return self.add(tensors1, tensor2)
@@ -246,7 +253,7 @@ def test_histogram_summary_use_valid_value():
     """Test histogram summary with valid value"""
     log.debug("Begin test_histogram_summary_use_valid_value")
     try:
-        net = HistogramSummaryNet(Tensor(np.array([1,2,3])))
+        net = HistogramSummaryNet(Tensor(np.array([1, 2, 3])))
         run_case(net)
     except:
         assert True

tests/ut/python/train/summary/test_tensor_summary.py

@@ -18,13 +18,15 @@
 @Date  : 2019-07-4
 @Desc  : test summary function
 """
-import os
 import logging
+import os
+
 import numpy as np
-from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data
-from mindspore.common.tensor import Tensor
+
 import mindspore.nn as nn
+from mindspore.common.tensor import Tensor
 from mindspore.ops import operations as P
+from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data
 
 CUR_DIR = os.getcwd()
 SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/"
@@ -93,7 +95,6 @@ def test_tensor_summary_sample():
     log.debug("finished test_tensor_summary_sample")
 
 
-
 def get_test_data_check(step):
     """ get_test_data_check """
     test_data_list = []
@@ -111,7 +112,8 @@ def get_test_data_check(step):
 # Test: test with ge
 class SummaryDemo(nn.Cell):
     """ SummaryDemo definition """
-    def __init__(self,):
+
+    def __init__(self, ):
         super(SummaryDemo, self).__init__()
         self.s = P.TensorSummary()
         self.add = P.TensorAdd()
@@ -123,6 +125,7 @@ class SummaryDemo(nn.Cell):
         self.s("y1", y)
         return z
 
+
 def test_tensor_summary_with_ge():
     """ test_tensor_summary_with_ge """
     log.debug("begin test_tensor_summary_with_ge")
@@ -140,7 +143,7 @@ def test_tensor_summary_with_ge():
     steps = 100
     for i in range(1, steps):
         x = Tensor(np.array([[i], [i]]).astype(np.float32))
-        y = Tensor(np.array([[i+1], [i+1]]).astype(np.float32))
+        y = Tensor(np.array([[i + 1], [i + 1]]).astype(np.float32))
         net(x, y)
         test_writer.record(i)
 

tests/ut/python/train/test_amp.py

@@ -15,12 +15,11 @@
 """ auto mixed precision """
 import numpy as np
 import pytest
+
+import mindspore.context as context
+from mindspore import Tensor
 from mindspore import amp
 from mindspore import nn
-from mindspore import Tensor
-from mindspore.common import dtype as mstype
-import mindspore.context as context
-from mindspore.model_zoo.resnet import resnet50
 from mindspore.train import Model
 from ....dataset_mock import MindData
 
@@ -96,6 +95,7 @@ class MindDataSet(MindData):
                                           np_types=dataset_types,
                                           output_shapes=dataset_shapes,
                                           input_indexs=(0, 1))
+
     def __next__(self):
         if self._size < self._iter_num:
             raise StopIteration
@@ -122,6 +122,7 @@ def test_compile_model_train_O0():
         # not actual run, the metrics step will fail, check if compile ok.
         model.eval(dataset)
 
+
 def test_compile_model_train_O2():
     dataset_types = (np.float32, np.float32)
     dataset_shapes = ((16, 16), (16, 16))

tests/ut/python/train/test_run_config.py

@@ -14,6 +14,7 @@
 # ============================================================================
 """ test_run_config """
 import pytest
+
 from mindspore.train.callback import CheckpointConfig
 
 

tests/ut/python/train/test_training.py

@@ -14,12 +14,14 @@
 # ============================================================================
 """ test_training """
 import logging
+
 import numpy as np
 import pytest
+
 import mindspore.nn as nn
+from mindspore import Model, context
 from mindspore import Tensor
 from mindspore.nn.optim import Momentum
-from mindspore import Model, context
 from mindspore.train.callback import SummaryStep
 from ..ut_filter import non_graph_engine
 from ....dataset_mock import MindData

tests/ut/python/transform/test_transform.py

@@ -19,12 +19,12 @@
 @Desc  : test mindspore compile method
 """
 import logging
+
 import numpy as np
+
 import mindspore.nn as nn
-from mindspore import Tensor, Parameter, Model
+from mindspore import Tensor, Parameter
 from mindspore.ops import operations as P
-from ..ut_filter import non_graph_engine
-
 
 log = logging.getLogger("test")
 log.setLevel(level=logging.ERROR)
@@ -104,6 +104,7 @@ class ResidualBlock(nn.Cell):
 
 class ResNet(nn.Cell):
     """ ResNet definition """
+
     def __init__(self, tensor):
         super(ResNet, self).__init__()
         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
@@ -118,6 +119,7 @@ class ResNet(nn.Cell):
 
 class LeNet(nn.Cell):
     """ LeNet definition """
+
     def __init__(self):
         super(LeNet, self).__init__()
         self.relu = nn.ReLU()
@@ -165,4 +167,3 @@ class Net(nn.Cell):
 
     def construct(self, input_x):
         return self.softmax(input_x)
-

tests/ut/python/utils/test_callback.py

@@ -15,19 +15,19 @@
 """test callback function."""
 import os
 import stat
+
 import numpy as np
 import pytest
 
-import mindspore.nn as nn
 import mindspore.common.dtype as mstype
-from mindspore import context
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
 from mindspore.common.tensor import Tensor
-from mindspore.nn.optim import Momentum
 from mindspore.nn import TrainOneStepCell, WithLossCell
+from mindspore.nn.optim import Momentum
 from mindspore.train.callback import ModelCheckpoint, _check_file_name_prefix, RunContext, _checkpoint_cb_for_save_op, \
     LossMonitor, _InternalCallbackParam, _chg_ckpt_file_name_if_same_exist, \
     _build_callbacks, CheckpointConfig, _set_cur_net
-from mindspore.common.api import ms_function
 
 
 class Net(nn.Cell):

tests/ut/python/utils/test_checkparam.py

@@ -15,6 +15,7 @@
 """ test_checkparam """
 import numpy as np
 import pytest
+
 import mindspore
 import mindspore.nn as nn
 from mindspore import Model, context
@@ -23,6 +24,7 @@ from mindspore.common.tensor import Tensor
 
 class LeNet5(nn.Cell):
     """ LeNet5 definition """
+
     def __init__(self):
         super(LeNet5, self).__init__()
         self.conv1 = nn.Conv2d(3, 6, 5, pad_mode="valid")