optim pylint

tests/st/auto_parallel/test_expand_loss.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 # ============================================================================
 import os
-import pytest
 
 
 def test_expand_loss():

tests/st/auto_parallel/test_resnet50_expand_loss.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 # ============================================================================
 import os
-import pytest
 
 
 def test_expand_loss():

tests/st/model_zoo_tests/wide_and_deep/train_and_test_multinpu_ci_data_parallel.py

@@ -14,7 +14,6 @@
 # ============================================================================
 """train_multinpu."""
 
-
 import os
 import sys
 import numpy as np
@@ -35,7 +34,6 @@ context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirr
 init()
 
 
-
 def get_WideDeep_net(config):
     WideDeep_net = WideDeepModel(config)
     loss_net = NetWithLossClass(WideDeep_net, config)
@@ -48,6 +46,7 @@ class ModelBuilder():
     """
     ModelBuilder
     """
+
     def __init__(self):
         pass
 
@@ -101,14 +100,13 @@ def test_train_eval():
     print("=====" * 5 + "model.eval() initialized: {}".format(out))
     model.train(epochs, ds_train,
                 callbacks=[TimeMonitor(ds_train.get_dataset_size()), eval_callback, callback, ckpoint_cb])
-    expect_out0 = [0.792634,0.799862,0.803324]
-    expect_out6 = [0.796580,0.803908,0.807262]
+    expect_out0 = [0.792634, 0.799862, 0.803324]
+    expect_out6 = [0.796580, 0.803908, 0.807262]
     if get_rank() == 0:
         assert np.allclose(eval_callback.eval_values, expect_out0)
     if get_rank() == 6:
         assert np.allclose(eval_callback.eval_values, expect_out6)
 
 
-
 if __name__ == "__main__":
     test_train_eval()

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

@@ -16,8 +16,10 @@
 """train bert network without lossscale"""
 
 import os
-import pytest
+
 import numpy as np
+from src.bert_for_pre_training import BertNetworkWithLoss, BertTrainOneStepWithLossScaleCell
+from src.bert_model import BertConfig
 
 import mindspore.common.dtype as mstype
 import mindspore.dataset.engine.datasets as de
@@ -25,14 +27,11 @@ import mindspore.dataset.transforms.c_transforms as C
 from mindspore import context
 from mindspore import log as logger
 from mindspore.common.tensor import Tensor
+from mindspore.nn import learning_rate_schedule as lr_schedules
 from mindspore.nn.optim import Lamb
 from mindspore.train.callback import Callback
 from mindspore.train.loss_scale_manager import DynamicLossScaleManager
 from mindspore.train.model import Model
-from mindspore.nn import learning_rate_schedule as lr_schedules
-from src.bert_for_pre_training import BertNetworkWithLoss, BertTrainOneStepWithLossScaleCell
-from src.bert_model import BertConfig
-
 
 DATA_DIR = ["/home/workspace/mindspore_dataset/bert/example/examples.tfrecord"]
 SCHEMA_DIR = "/home/workspace/mindspore_dataset/bert/example/datasetSchema.json"

tests/st/networks/test_gpu_alexnet.py

@@ -23,10 +23,8 @@ import pytest
 import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
-from mindspore.common.initializer import initializer
 from mindspore.nn import TrainOneStepCell, WithLossCell
 from mindspore.nn.optim import Momentum
-from mindspore.ops import operations as P
 
 context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
 

tests/st/networks/test_gpu_lstm.py

@@ -21,7 +21,6 @@ import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common.initializer import initializer
 from mindspore.common.parameter import Parameter
-from mindspore.nn import Dense
 from mindspore.nn import TrainOneStepCell, WithLossCell
 from mindspore.nn.optim import Momentum
 from mindspore.ops import operations as P

tests/st/ops/ascend/test_aicpu_ops/test_poisson.py

@@ -18,7 +18,6 @@ import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.ops import operations as P
-from mindspore.common import dtype as mstype
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 

tests/st/ops/cpu/test_slice_grad_op.py

@@ -54,6 +54,7 @@ def test_slice_grad():
     print("output:\n", output)
     assert (output.asnumpy() == expect).all()
 
+
 class SliceGrad2(nn.Cell):
     def __init__(self):
         super(SliceGrad2, self).__init__()
@@ -62,6 +63,7 @@ class SliceGrad2(nn.Cell):
     def construct(self, dy, x):
         return self.slicegrad(dy, x, (0, 1, 0), (2, 2, 2))
 
+
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
@@ -71,10 +73,11 @@ def test_slice_grad2():
     grad = SliceGrad2()
     output = grad(dy, x)
     print("output:\n", output)
-    expect = [[[0., 0.], [2., 3.], [4.,  5.]],
+    expect = [[[0., 0.], [2., 3.], [4., 5.]],
               [[0., 0.], [8., 9.], [10., 11.]]]
     assert (output.asnumpy() == expect).all()
 
+
 if __name__ == '__main__':
     test_slice_grad()
     test_slice_grad2()

tests/st/ops/cpu/test_slice_op.py

@@ -21,10 +21,10 @@ import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
 from mindspore.ops import operations as P
-from mindspore.ops.operations import _grad_ops as G
 
 context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
 
+
 class Slice(nn.Cell):
     def __init__(self):
         super(Slice, self).__init__()
@@ -33,6 +33,7 @@ class Slice(nn.Cell):
     def construct(self, x):
         return self.slice(x, (0, 1, 0), (2, 1, 3))
 
+
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
@@ -47,6 +48,7 @@ def test_slice():
     print("output:\n", output)
     assert (output.asnumpy() == expect).all()
 
+
 class Slice2(nn.Cell):
     def __init__(self):
         super(Slice2, self).__init__()
@@ -55,12 +57,13 @@ class Slice2(nn.Cell):
     def construct(self, x):
         return self.slice(x, (1, 0, 0), (1, 2, 3))
 
+
 @pytest.mark.level0
 @pytest.mark.platform_x86_cpu
 @pytest.mark.env_onecard
 def test_slice2():
     x = Tensor(np.arange(3 * 2 * 3).reshape(3, 2, 3), mstype.float32)
-    expect = [[[6., 7.,  8.],
+    expect = [[[6., 7., 8.],
                [9., 10., 11.]]]
 
     slice_op = Slice2()
@@ -68,6 +71,7 @@ def test_slice2():
     print("output:\n", output)
     assert (output.asnumpy() == expect).all()
 
+
 if __name__ == '__main__':
     test_slice()
     test_slice2()

tests/st/ops/custom_ops_tbe/cus_add3.py

@@ -14,9 +14,11 @@
 # ============================================================================
 from mindspore.ops import prim_attr_register, PrimitiveWithInfer
 
+
 # sum = input1 + input2 + const_bias
 class CusAdd3(PrimitiveWithInfer):
     """Custom add3 definition"""
+
     @prim_attr_register
     def __init__(self, const_bias=0.0):
         self.init_prim_io_names(inputs=['input1', 'input2'], outputs=['sum3'])

tests/st/summary/test_cpu_summary.py

@@ -24,8 +24,8 @@ import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.ops import operations as P
-from tests.summary_utils import SummaryReader
 from mindspore.train.summary.summary_record import SummaryRecord
+from tests.summary_utils import SummaryReader
 
 context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
 

tests/ut/python/dataset/test_minddataset_padded.py

@@ -16,17 +16,15 @@
 This is the test module for mindrecord
 """
 import collections
-import json
-import numpy as np
 import os
-import pytest
 import re
 import string
 
+import numpy as np
+import pytest
+
 import mindspore.dataset as ds
-import mindspore.dataset.transforms.vision.c_transforms as vision
 from mindspore import log as logger
-from mindspore.dataset.transforms.vision import Inter
 from mindspore.mindrecord import FileWriter
 
 FILES_NUM = 4
@@ -52,9 +50,9 @@ def add_and_remove_cv_file():
         writer = FileWriter(CV_FILE_NAME, FILES_NUM)
         data = get_data(CV_DIR_NAME)
         cv_schema_json = {"id": {"type": "int32"},
-                        "file_name": {"type": "string"},
-                        "label": {"type": "int32"},
-                        "data": {"type": "bytes"}}
+                          "file_name": {"type": "string"},
+                          "label": {"type": "int32"},
+                          "data": {"type": "bytes"}}
         writer.add_schema(cv_schema_json, "img_schema")
         writer.add_index(["file_name", "label"])
         writer.write_raw_data(data)
@@ -85,14 +83,14 @@ def add_and_remove_nlp_file():
         writer = FileWriter(NLP_FILE_NAME, FILES_NUM)
         data = [x for x in get_nlp_data(NLP_FILE_POS, NLP_FILE_VOCAB, 10)]
         nlp_schema_json = {"id": {"type": "string"}, "label": {"type": "int32"},
-                        "rating": {"type": "float32"},
-                        "input_ids": {"type": "int64",
-                                        "shape": [-1]},
-                        "input_mask": {"type": "int64",
-                                        "shape": [1, -1]},
-                        "segment_ids": {"type": "int64",
-                                        "shape": [2, -1]}
-                        }
+                           "rating": {"type": "float32"},
+                           "input_ids": {"type": "int64",
+                                         "shape": [-1]},
+                           "input_mask": {"type": "int64",
+                                          "shape": [1, -1]},
+                           "segment_ids": {"type": "int64",
+                                           "shape": [2, -1]}
+                           }
         writer.set_header_size(1 << 14)
         writer.set_page_size(1 << 15)
         writer.add_schema(nlp_schema_json, "nlp_schema")
@@ -110,6 +108,7 @@ def add_and_remove_nlp_file():
             os.remove("{}".format(x))
             os.remove("{}.db".format(x))
 
+
 def test_cv_minddataset_reader_basic_padded_samples(add_and_remove_cv_file):
     """tutorial for cv minderdataset."""
     columns_list = ["label", "file_name", "data"]
@@ -130,7 +129,7 @@ def test_cv_minddataset_reader_basic_padded_samples(add_and_remove_cv_file):
         if item['label'] == -1:
             num_padded_iter += 1
             assert item['file_name'] == bytes(padded_sample['file_name'],
-                    encoding='utf8')
+                                              encoding='utf8')
             assert item['label'] == padded_sample['label']
             assert (item['data'] == np.array(list(padded_sample['data']))).all()
         num_iter += 1
@@ -177,6 +176,7 @@ def test_cv_minddataset_partition_padded_samples(add_and_remove_cv_file):
     partitions(5, 5, 3)
     partitions(9, 8, 2)
 
+
 def test_cv_minddataset_partition_padded_samples_multi_epoch(add_and_remove_cv_file):
     """tutorial for cv minddataset."""
     columns_list = ["data", "file_name", "label"]
@@ -248,6 +248,7 @@ def test_cv_minddataset_partition_padded_samples_multi_epoch(add_and_remove_cv_f
     partitions(5, 5, 3)
     partitions(9, 8, 2)
 
+
 def test_cv_minddataset_partition_padded_samples_no_dividsible(add_and_remove_cv_file):
     """tutorial for cv minddataset."""
     columns_list = ["data", "file_name", "label"]
@@ -273,6 +274,7 @@ def test_cv_minddataset_partition_padded_samples_no_dividsible(add_and_remove_cv
     with pytest.raises(RuntimeError):
         partitions(4, 1)
 
+
 def test_cv_minddataset_partition_padded_samples_dataset_size_no_divisible(add_and_remove_cv_file):
     columns_list = ["data", "file_name", "label"]
 
@@ -291,8 +293,10 @@ def test_cv_minddataset_partition_padded_samples_dataset_size_no_divisible(add_a
                                       num_padded=num_padded)
             with pytest.raises(RuntimeError):
                 data_set.get_dataset_size() == 3
+
     partitions(4, 1)
 
+
 def test_cv_minddataset_partition_padded_samples_no_equal_column_list(add_and_remove_cv_file):
     columns_list = ["data", "file_name", "label"]
 
@@ -314,9 +318,11 @@ def test_cv_minddataset_partition_padded_samples_no_equal_column_list(add_and_re
             logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
             logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
             logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+
     with pytest.raises(Exception, match="padded_sample cannot match columns_list."):
         partitions(4, 2)
 
+
 def test_cv_minddataset_partition_padded_samples_no_column_list(add_and_remove_cv_file):
     data = get_data(CV_DIR_NAME)
     padded_sample = data[0]
@@ -336,9 +342,11 @@ def test_cv_minddataset_partition_padded_samples_no_column_list(add_and_remove_c
             logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
             logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
             logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+
     with pytest.raises(Exception, match="padded_sample is specified and requires columns_list as well."):
         partitions(4, 2)
 
+
 def test_cv_minddataset_partition_padded_samples_no_num_padded(add_and_remove_cv_file):
     columns_list = ["data", "file_name", "label"]
     data = get_data(CV_DIR_NAME)
@@ -357,9 +365,11 @@ def test_cv_minddataset_partition_padded_samples_no_num_padded(add_and_remove_cv
             logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
             logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
             logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+
     with pytest.raises(Exception, match="padded_sample is specified and requires num_padded as well."):
         partitions(4, 2)
 
+
 def test_cv_minddataset_partition_padded_samples_no_padded_samples(add_and_remove_cv_file):
     columns_list = ["data", "file_name", "label"]
     data = get_data(CV_DIR_NAME)
@@ -378,18 +388,18 @@ def test_cv_minddataset_partition_padded_samples_no_padded_samples(add_and_remov
             logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
             logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
             logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+
     with pytest.raises(Exception, match="num_padded is specified but padded_sample is not."):
         partitions(4, 2)
 
 
-
 def test_nlp_minddataset_reader_basic_padded_samples(add_and_remove_nlp_file):
     columns_list = ["input_ids", "id", "rating"]
 
     data = [x for x in get_nlp_data(NLP_FILE_POS, NLP_FILE_VOCAB, 10)]
     padded_sample = data[0]
     padded_sample['id'] = "-1"
-    padded_sample['input_ids'] = np.array([-1,-1,-1,-1], dtype=np.int64)
+    padded_sample['input_ids'] = np.array([-1, -1, -1, -1], dtype=np.int64)
     padded_sample['rating'] = 1.0
     num_readers = 4
 
@@ -406,7 +416,9 @@ def test_nlp_minddataset_reader_basic_padded_samples(add_and_remove_nlp_file):
             for item in data_set.create_dict_iterator():
                 logger.info("-------------- item[id]: {} ------------------------".format(item["id"]))
                 logger.info("-------------- item[rating]: {} --------------------".format(item["rating"]))
-                logger.info("-------------- item[input_ids]: {}, shape: {} -----------------".format(item["input_ids"], item["input_ids"].shape))
+                logger.info("-------------- item[input_ids]: {}, shape: {} -----------------".format(
+                    item["input_ids"],
+                    item["input_ids"].shape))
                 if item['id'] == bytes('-1', encoding='utf-8'):
                     num_padded_iter += 1
                     assert item['id'] == bytes(padded_sample['id'], encoding='utf-8')
@@ -420,13 +432,14 @@ def test_nlp_minddataset_reader_basic_padded_samples(add_and_remove_nlp_file):
     partitions(5, 5, 3)
     partitions(9, 8, 2)
 
+
 def test_nlp_minddataset_reader_basic_padded_samples_multi_epoch(add_and_remove_nlp_file):
     columns_list = ["input_ids", "id", "rating"]
 
     data = [x for x in get_nlp_data(NLP_FILE_POS, NLP_FILE_VOCAB, 10)]
     padded_sample = data[0]
     padded_sample['id'] = "-1"
-    padded_sample['input_ids'] = np.array([-1,-1,-1,-1], dtype=np.int64)
+    padded_sample['input_ids'] = np.array([-1, -1, -1, -1], dtype=np.int64)
     padded_sample['rating'] = 1.0
     num_readers = 4
     repeat_size = 3
@@ -451,7 +464,9 @@ def test_nlp_minddataset_reader_basic_padded_samples_multi_epoch(add_and_remove_
             for item in data_set.create_dict_iterator():
                 logger.info("-------------- item[id]: {} ------------------------".format(item["id"]))
                 logger.info("-------------- item[rating]: {} --------------------".format(item["rating"]))
-                logger.info("-------------- item[input_ids]: {}, shape: {} -----------------".format(item["input_ids"], item["input_ids"].shape))
+                logger.info("-------------- item[input_ids]: {}, shape: {} -----------------".format(
+                    item["input_ids"],
+                    item["input_ids"].shape))
                 if item['id'] == bytes('-1', encoding='utf-8'):
                     num_padded_iter += 1
                     assert item['id'] == bytes(padded_sample['id'], encoding='utf-8')
@@ -488,7 +503,7 @@ def test_nlp_minddataset_reader_basic_padded_samples_check_whole_reshuffle_resul
 
     padded_sample = {}
     padded_sample['id'] = "-1"
-    padded_sample['input_ids'] = np.array([-1,-1,-1,-1], dtype=np.int64)
+    padded_sample['input_ids'] = np.array([-1, -1, -1, -1], dtype=np.int64)
     padded_sample['rating'] = 1.0
     num_readers = 4
     repeat_size = 3
@@ -512,14 +527,15 @@ def test_nlp_minddataset_reader_basic_padded_samples_check_whole_reshuffle_resul
                 logger.info("-------------- item[id]: {} ------------------------".format(item["id"]))
                 logger.info("-------------- item[rating]: {} --------------------".format(item["rating"]))
                 logger.info("-------------- item[input_ids]: {}, shape: {} -----------------"
-                    .format(item["input_ids"], item["input_ids"].shape))
+                            .format(item["input_ids"], item["input_ids"].shape))
                 if item['id'] == bytes('-1', encoding='utf-8'):
                     num_padded_iter += 1
                     assert item['id'] == bytes(padded_sample['id'], encoding='utf-8')
                     assert (item['input_ids'] == padded_sample['input_ids']).all()
                     assert (item['rating'] == padded_sample['rating']).all()
                 # save epoch result
-                epoch_result[partition_id][int(inner_num_iter / dataset_size)][inner_num_iter % dataset_size] = item["id"]
+                epoch_result[partition_id][int(inner_num_iter / dataset_size)][inner_num_iter % dataset_size] = item[
+                    "id"]
                 num_iter += 1
                 inner_num_iter += 1
             assert epoch_result[partition_id][0] not in (epoch_result[partition_id][1], epoch_result[partition_id][2])
@@ -651,6 +667,7 @@ def inputs(vectors, maxlen=50):
     segment = [0] * maxlen
     return input_, mask, segment
 
+
 if __name__ == '__main__':
     test_cv_minddataset_reader_basic_padded_samples(add_and_remove_cv_file)
     test_cv_minddataset_partition_padded_samples(add_and_remove_cv_file)

tests/ut/python/dataset/test_sampler.py

@@ -216,6 +216,7 @@ def test_sampler_chain():
     assert test_config(5, 3) == [3]
     assert test_config(5, 4) == [4]
 
+
 def test_add_sampler_invalid_input():
     manifest_file = "../data/dataset/testManifestData/test5trainimgs.json"
     _ = {(172876, 0): 0, (54214, 0): 1, (54214, 1): 2, (173673, 0): 3, (64631, 1): 4}
@@ -231,7 +232,7 @@ def test_add_sampler_invalid_input():
 
     sampler = ds.SequentialSampler()
     with pytest.raises(ValueError) as info:
-        data2 = ds.ManifestDataset(manifest_file, sampler=sampler, num_samples=20)        
+        data2 = ds.ManifestDataset(manifest_file, sampler=sampler, num_samples=20)
     assert "Conflicting arguments during sampler assignments" in str(info.value)
 
 

tests/ut/python/dataset/test_serdes_dataset.py

@@ -19,7 +19,10 @@ import filecmp
 import glob
 import json
 import os
+
 import numpy as np
+from test_minddataset_sampler import add_and_remove_cv_file, get_data, CV_DIR_NAME, CV_FILE_NAME
+from util import config_get_set_num_parallel_workers
 
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.c_transforms as c
@@ -27,8 +30,6 @@ import mindspore.dataset.transforms.vision.c_transforms as vision
 from mindspore import log as logger
 from mindspore.dataset.transforms.vision import Inter
 
-from test_minddataset_sampler import add_and_remove_cv_file, get_data, CV_DIR_NAME, CV_FILE_NAME
-from util import config_get_set_num_parallel_workers
 
 def test_imagefolder(remove_json_files=True):
     """

tests/ut/python/ops/test_control_ops.py

@@ -29,7 +29,6 @@ from mindspore.common import ms_function
 
 context.set_context(mode=context.GRAPH_MODE)
 
-
 grad_by_list = C.GradOperation(get_by_list=True)
 grad_all = C.GradOperation(get_all=True)
 grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True)
@@ -123,6 +122,7 @@ def test_if_none():
     net = Net(z)
     assert np.all(net(x, y).asnumpy() == y.asnumpy())
 
+
 def test_if_str_is_not_none_right():
     class Net(nn.Cell):
         def __init__(self, z: str):
@@ -455,8 +455,10 @@ def test_parser_switch_layer_switch_in_bprop():
             super(OneInputBprop, self).__init__()
             self.op = P.ReLU()
             self.funcs = funcs
+
         def construct(self, i, x):
-            return  self.op(x)
+            return self.op(x)
+
         def bprop(self, i, x, out, dout):
             return i, self.funcs[i](x, dout)
 
@@ -475,6 +477,7 @@ def test_parser_switch_layer_switch_in_bprop():
 
         def construct(self, x, y):
             return self.op(x, y)
+
     func1 = Add()
     func2 = Mul()
     funcs = (func1, func2)
@@ -572,6 +575,7 @@ def test_switch_layer_env_eliminate():
             weights = self.weights
             grad = self.grad_op(self.net, weights)(x, index)
             return grad
+
     net = Net()
     net2 = NetGrad(net)
     x = Tensor(np.ones((3, 1, 12, 12)), ms.float32)
@@ -601,6 +605,7 @@ def test_switch_layer_single_layer():
             weights = self.weights
             grad = self.grad_op(self.net, weights)(x, index)
             return grad
+
     net = Net()
     net2 = NetGrad(net)
     x = Tensor(np.ones((3, 1, 12, 12)), ms.float32)
@@ -638,6 +643,7 @@ def test_if_nested_compile():
                 else:
                     res = self.squre(self.value)
             return res
+
     x = Tensor(1.0, dtype=ms.float32)
     y = Tensor(2.0, dtype=ms.float32)
     net = Net()
@@ -660,6 +666,7 @@ def test_if_inside_for():
                 else:
                     res = res - y
             return res
+
     c1 = Tensor(1, dtype=ms.int32)
     c2 = Tensor(1, dtype=ms.int32)
     net = Net()
@@ -671,6 +678,7 @@ def test_while_in_while():
     c2 = Tensor(2, dtype=ms.int32)
     c3 = Tensor(3, dtype=ms.int32)
     c4 = Tensor(4, dtype=ms.int32)
+
     @ms_function
     def while_in_while(x, y, z, u):
         out = c4
@@ -683,6 +691,7 @@ def test_while_in_while():
 
         out = out + 3
         return out
+
     while_in_while(c1, c2, c3, c4)
 
 
@@ -692,6 +701,7 @@ def test_tensor_cond():
             super(Net, self).__init__()
             self.t = Tensor(np.array(0, np.bool))
             self.t1 = Tensor(np.array([True], np.bool))
+
         def construct(self, x, y):
             t = 0
             if self.t:
@@ -703,18 +713,19 @@ def test_tensor_cond():
             else:
                 t = t + x * y
             return t
-            
-            
+
     x = Tensor(np.ones([6, 8, 10], np.int32))
     y = Tensor(np.ones([6, 8, 10], np.int32))
     net = Net()
     out = net(x, y)
 
+
 def test_tensor_cond_exception():
     class Net(nn.Cell):
         def __init__(self):
             super(Net, self).__init__()
             self.t = Tensor(np.array([True, False], np.bool))
+
         def construct(self, x, y):
             t = 0
             if self.t:
@@ -722,19 +733,20 @@ def test_tensor_cond_exception():
             else:
                 t = t - x / y
             return t
-            
-            
+
     x = Tensor(np.ones([6, 8, 10], np.int32))
     y = Tensor(np.ones([6, 8, 10], np.int32))
     net = Net()
     with pytest.raises(ValueError):
         out = net(x, y)
 
+
 def test_while_scalar():
     class Net(nn.Cell):
         def __init__(self):
             super(Net, self).__init__()
             self.x = 10
+
         def construct(self, x, y):
             i = 0
             t = 0
@@ -742,17 +754,20 @@ def test_while_scalar():
                 t = t + x + y
                 i = i + 1
             return t
+
     net = Net()
     x = Tensor(np.ones([6, 8, 10], np.int32))
     y = Tensor(np.ones([6, 8, 10], np.int32))
     out = net(x, y)
 
+
 def test_while_tensor():
     class Net(nn.Cell):
         def __init__(self):
             super(Net, self).__init__()
             self.t = Tensor(np.ones([6, 8, 10], np.int32))
             self.count = Tensor(np.array([10], np.int32))
+
         def construct(self, x, y):
             i = 0
             t = self.t
@@ -760,6 +775,7 @@ def test_while_tensor():
                 t = t + x + y
                 i = i + 1
             return t
+
     net = Net()
     x = Tensor(np.ones([6, 8, 10], np.int32))
     y = Tensor(np.ones([6, 8, 10], np.int32))
@@ -770,7 +786,7 @@ def test_large_for_loop():
     class Net(nn.Cell):
         def __init__(self):
             super(Net, self).__init__()
-            self.flatten = P.ReLU() #nn.Flatten()
+            self.flatten = P.ReLU()  # nn.Flatten()
 
         def construct(self, x):
             for elem in range(1, 1900):
@@ -791,7 +807,7 @@ def test_large_for_loop_with_continue_break():
     class Net(nn.Cell):
         def __init__(self):
             super(Net, self).__init__()
-            self.flatten = P.ReLU() #nn.Flatten()
+            self.flatten = P.ReLU()  # nn.Flatten()
 
         def construct(self, x):
             idx = 0
@@ -854,7 +870,7 @@ def test_tensor_all_construct_lack_branch():
             if input1.all():
                 return self.logicaland(input1, input2)
             while input1.any():
-                return  self.logicalor(input1, input2)
+                return self.logicalor(input1, input2)
             # NOTICE: here missing return statement, default return None
 
     input_np_1 = np.random.choice([True], size=(2, 3, 4, 5))
@@ -891,28 +907,29 @@ def test_parser_switch_layer_func_primitive():
 def test_recursive_call():
     class Net(nn.Cell):
         """ Net definition """
-    
+
         def __init__(self):
             super(Net, self).__init__()
             self.fc = nn.Dense(10, 10)  # padding=0
-            #self.net2 = Net2()
-    
+            # self.net2 = Net2()
+
         def construct(self, x):
             net2 = Net2()
             x = net2(x)
             out = self.fc(x)
             return out
-    
+
     class Net2(nn.Cell):
         def __init__(self):
             super(Net2, self).__init__()
             self.net = Net()
             self.fc = nn.Dense(10, 10)
+
         def construct(self, x):
             x = self.net(x)
             out = self.fc(x)
             return out
-    
+
     context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
     old_max_call_depth = context.get_context('max_call_depth')
     context.set_context(max_call_depth=80)
@@ -949,7 +966,6 @@ def test_switch_layer_shape_join_failed():
 
     funcs = (func1, func2)
 
-
     net = AddFuncNet(funcs, func3)
 
     inp = Tensor(np.random.randn(2, 3, 4, 5).astype(np.float32))
@@ -980,7 +996,6 @@ def test_switch_layer_dtype_join_failed():
             x = self.op(x)
             return x
 
-
     func1 = nn.ReLU()
     func2 = Cast(mstype.int32)
     funcs = (func1, func2)

tests/ut/python/ops/test_math_ops.py

@@ -14,8 +14,8 @@
 # ============================================================================
 """ test math ops """
 import functools
+
 import numpy as np
-import pytest
 
 import mindspore as ms
 import mindspore.context as context
@@ -31,6 +31,7 @@ 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.forward.verify_exception \
     import pipeline_for_verify_exception_for_case_by_case_config
+
 context.set_context(mode=context.GRAPH_MODE)
 
 # pylint: disable=W0613

tests/ut/python/ops/test_ops.py

@@ -35,7 +35,6 @@ 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
 
-
 grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True)
 
 
@@ -266,6 +265,7 @@ class ScatterNdSub(nn.Cell):
         out = self.scatter_nd_sub(self.ref, indices, updates)
         return out
 
+
 class ScatterNdAdd(nn.Cell):
     """ScatterNdAdd net definition"""
 
@@ -311,7 +311,7 @@ class ScatterDiv(nn.Cell):
     def __init__(self, ref_shape, dtype=np.float32, use_locking=False):
         super(ScatterDiv, self).__init__()
         self.scatter_div = P.ScatterDiv(use_locking)
-        self.ref = Parameter(Tensor(np.ones(ref_shape, dtype)*10), name="ref")
+        self.ref = Parameter(Tensor(np.ones(ref_shape, dtype) * 10), name="ref")
 
     def construct(self, indices, updates):
         out = self.scatter_div(self.ref, indices, updates)
@@ -633,7 +633,7 @@ class CTCGreedyDecoderNet(nn.Cell):
         self.assert_op = P.Assert(300)
 
     def construct(self, inputs, sequence_length):
-        out = self.ctc_greedy_decoder(inputs,sequence_length)
+        out = self.ctc_greedy_decoder(inputs, sequence_length)
         self.assert_op(True, (out[0], out[1], out[2], out[3]))
         return out[2]
 
@@ -711,12 +711,13 @@ class BasicLSTMCellNet(nn.Cell):
     def construct(self, x, h, c, w, b):
         return self.lstm(x, h, c, w, b)
 
+
 class EditDistance(nn.Cell):
     def __init__(self, hypothesis_shape, truth_shape, normalize=True):
         super(EditDistance, self).__init__()
         self.edit_distance = P.EditDistance(normalize)
         self.hypothesis_shape = hypothesis_shape
-        self.truth_shape =truth_shape
+        self.truth_shape = truth_shape
 
     def construct(self, hypothesis_indices, hypothesis_values, truth_indices, truth_values):
         return self.edit_distance(hypothesis_indices, hypothesis_values, self.hypothesis_shape,

tests/ut/python/train/test_amp.py

@@ -20,13 +20,11 @@ import mindspore.context as context
 from mindspore import Tensor
 from mindspore import amp
 from mindspore import nn
-from mindspore.train import Model
+from mindspore.communication.management import init
 from mindspore.context import ParallelMode
-from mindspore.common import dtype as mstype
+from mindspore.train import Model
 from ....dataset_mock import MindData
-from mindspore.parallel._auto_parallel_context import auto_parallel_context
-from mindspore.communication.management import init
-from tests.ut.python.model.resnet import resnet50
+
 
 def setup_module(module):
     _ = module
@@ -144,6 +142,7 @@ def test_compile_model_train_O2():
         # not actual run, the metrics step will fail, check if compile ok.
         model.eval(dataset)
 
+
 def test_compile_model_train_O2_parallel():
     dataset_types = (np.float32, np.float32)
     dataset_shapes = ((16, 16), (16, 16))

tests/ut/python/utils/test_initializer.py

@@ -141,6 +141,7 @@ def test_init_abnormal():
     with py.raises(TypeError):
         init.initializer([''], [5, 4], ms.float32)
 
+
 def test_initializer_reinit():
     weights = init.initializer("XavierUniform", shape=(10, 1, 10, 10), dtype=ms.float16)
     assert weights.dtype == ms.float16
@@ -152,7 +153,8 @@ def test_initializer_reinit():
     weights = init.initializer(weights, (10, 1))
     assert weights.dtype == ms.float16
     assert weights.shape == (10, 1)
-    
+
+
 def test_init_xavier_uniform():
     """ test_init_xavier_uniform """
     gain = 1.2