10:00 26/5 clean pylint

tests/perf_test/mindrecord/imagenet/perf_read_imagenet.py

@@ -34,7 +34,7 @@ def use_filereader(mindrecord):
                         num_consumer=4,
                         columns=columns_list)
     num_iter = 0
-    for index, item in enumerate(reader.get_next()):
+    for _, _ in enumerate(reader.get_next()):
         num_iter += 1
         print_log(num_iter)
     end = time.time()
@@ -48,7 +48,7 @@ def use_minddataset(mindrecord):
                               columns_list=columns_list,
                               num_parallel_workers=4)
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for _ in data_set.create_dict_iterator():
         num_iter += 1
         print_log(num_iter)
     end = time.time()
@@ -64,7 +64,7 @@ def use_tfrecorddataset(tfrecord):
                                   shuffle=ds.Shuffle.GLOBAL)
     data_set = data_set.shuffle(10000)
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for _ in data_set.create_dict_iterator():
         num_iter += 1
         print_log(num_iter)
     end = time.time()
@@ -87,7 +87,7 @@ def use_tensorflow_tfrecorddataset(tfrecord):
                                        num_parallel_reads=4)
     data_set = data_set.map(_parse_record, num_parallel_calls=4)
     num_iter = 0
-    for item in data_set.__iter__():
+    for _ in data_set.__iter__():
         num_iter += 1
         print_log(num_iter)
     end = time.time()
@@ -96,18 +96,18 @@ def use_tensorflow_tfrecorddataset(tfrecord):
 
 if __name__ == '__main__':
     # use MindDataset
-    mindrecord = './imagenet.mindrecord00'
-    use_minddataset(mindrecord)
+    mindrecord_test = './imagenet.mindrecord00'
+    use_minddataset(mindrecord_test)
 
     # use TFRecordDataset
-    tfrecord = ['imagenet.tfrecord00', 'imagenet.tfrecord01', 'imagenet.tfrecord02', 'imagenet.tfrecord03',
-                'imagenet.tfrecord04', 'imagenet.tfrecord05', 'imagenet.tfrecord06', 'imagenet.tfrecord07',
-                'imagenet.tfrecord08', 'imagenet.tfrecord09', 'imagenet.tfrecord10', 'imagenet.tfrecord11',
-                'imagenet.tfrecord12', 'imagenet.tfrecord13', 'imagenet.tfrecord14', 'imagenet.tfrecord15']
-    use_tfrecorddataset(tfrecord)
+    tfrecord_test = ['imagenet.tfrecord00', 'imagenet.tfrecord01', 'imagenet.tfrecord02', 'imagenet.tfrecord03',
+                     'imagenet.tfrecord04', 'imagenet.tfrecord05', 'imagenet.tfrecord06', 'imagenet.tfrecord07',
+                     'imagenet.tfrecord08', 'imagenet.tfrecord09', 'imagenet.tfrecord10', 'imagenet.tfrecord11',
+                     'imagenet.tfrecord12', 'imagenet.tfrecord13', 'imagenet.tfrecord14', 'imagenet.tfrecord15']
+    use_tfrecorddataset(tfrecord_test)
 
     # use TensorFlow TFRecordDataset
-    use_tensorflow_tfrecorddataset(tfrecord)
+    use_tensorflow_tfrecorddataset(tfrecord_test)
 
     # use FileReader
     # use_filereader(mindrecord)

tests/ut/data/dataset/testPyfuncMap/pyfuncmap.py

@@ -29,7 +29,7 @@ def test_case_0():
     # apply dataset operations
     ds1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
 
-    ds1 = ds1.map(input_column_names=col, output_column_names="out", operation=(lambda x: x + x))
+    ds1 = ds1.map(input_columns=col, output_columns="out", operations=(lambda x: x + x))
 
     print("************** Output Tensor *****************")
     for data in ds1.create_dict_iterator():  # each data is a dictionary
@@ -49,7 +49,7 @@ def test_case_1():
     # apply dataset operations
     ds1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
 
-    ds1 = ds1.map(input_column_names=col, output_column_names=["out0", "out1"], operation=(lambda x: (x, x + x)))
+    ds1 = ds1.map(input_columns=col, output_columns=["out0", "out1"], operations=(lambda x: (x, x + x)))
 
     print("************** Output Tensor *****************")
     for data in ds1.create_dict_iterator():  # each data is a dictionary
@@ -72,7 +72,7 @@ def test_case_2():
     # apply dataset operations
     ds1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
 
-    ds1 = ds1.map(input_column_names=col, output_column_names="out", operation=(lambda x, y: x + y))
+    ds1 = ds1.map(input_columns=col, output_columns="out", operations=(lambda x, y: x + y))
 
     print("************** Output Tensor *****************")
     for data in ds1.create_dict_iterator():  # each data is a dictionary
@@ -93,8 +93,8 @@ def test_case_3():
     # apply dataset operations
     ds1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
 
-    ds1 = ds1.map(input_column_names=col, output_column_names=["out0", "out1", "out2"],
-                  operation=(lambda x, y: (x, x + y, x + x + y)))
+    ds1 = ds1.map(input_columns=col, output_columns=["out0", "out1", "out2"],
+                  operations=(lambda x, y: (x, x + y, x + x + y)))
 
     print("************** Output Tensor *****************")
     for data in ds1.create_dict_iterator():  # each data is a dictionary
@@ -119,8 +119,8 @@ def test_case_4():
     # apply dataset operations
     ds1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
 
-    ds1 = ds1.map(input_column_names=col, output_column_names=["out0", "out1", "out2"], num_parallel_workers=4,
-                  operation=(lambda x, y: (x, x + y, x + x + y)))
+    ds1 = ds1.map(input_columns=col, output_columns=["out0", "out1", "out2"], num_parallel_workers=4,
+                  operations=(lambda x, y: (x, x + y, x + x + y)))
 
     print("************** Output Tensor *****************")
     for data in ds1.create_dict_iterator():  # each data is a dictionary

tests/ut/python/dataset/prep_data.py

@@ -22,11 +22,11 @@ def create_data_cache_dir():
     cwd = os.getcwd()
     target_directory = os.path.join(cwd, "data_cache")
     try:
-        if not (os.path.exists(target_directory)):
+        if not os.path.exists(target_directory):
             os.mkdir(target_directory)
     except OSError:
         print("Creation of the directory %s failed" % target_directory)
-    return target_directory;
+    return target_directory
 
 
 def download_and_uncompress(files, source_url, target_directory, is_tar=False):
@@ -53,13 +53,13 @@ def download_and_uncompress(files, source_url, target_directory, is_tar=False):
 
 
 def download_mnist(target_directory=None):
-    if target_directory == None:
+    if target_directory is None:
         target_directory = create_data_cache_dir()
 
         ##create mnst directory
         target_directory = os.path.join(target_directory, "mnist")
         try:
-            if not (os.path.exists(target_directory)):
+            if not os.path.exists(target_directory):
                 os.mkdir(target_directory)
         except OSError:
             print("Creation of the directory %s failed" % target_directory)
@@ -78,14 +78,14 @@ CIFAR_URL = "https://www.cs.toronto.edu/~kriz/"
 
 
 def download_cifar(target_directory, files, directory_from_tar):
-    if target_directory == None:
+    if target_directory is None:
         target_directory = create_data_cache_dir()
 
     download_and_uncompress([files], CIFAR_URL, target_directory, is_tar=True)
 
     ##if target dir was specify move data from directory created by tar
     ##and put data into target dir
-    if target_directory != None:
+    if target_directory is not None:
         tar_dir_full_path = os.path.join(target_directory, directory_from_tar)
         all_files = os.path.join(tar_dir_full_path, "*")
         cmd = "mv " + all_files + " " + target_directory

tests/ut/python/dataset/test_Tensor.py

@@ -12,10 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-import mindspore._c_dataengine as cde
-
 import numpy as np
 
+import mindspore._c_dataengine as cde
+
 
 def test_shape():
     x = [2, 3]

tests/ut/python/dataset/test_apply.py

@@ -221,7 +221,7 @@ def test_apply_exception_case():
     try:
         data2 = data1.apply(dataset_fn)
         data3 = data1.apply(dataset_fn)
-        for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
+        for _, _ in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
             pass
         assert False
     except ValueError:

tests/ut/python/dataset/test_cifarop.py

@@ -35,10 +35,10 @@ def test_case_dataset_cifar10():
     data1 = ds.Cifar10Dataset(DATA_DIR_10, 100)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator():
         # in this example, each dictionary has keys "image" and "label"
         num_iter += 1
-    assert (num_iter == 100)
+    assert num_iter == 100
 
 
 def test_case_dataset_cifar100():
@@ -50,10 +50,10 @@ def test_case_dataset_cifar100():
     data1 = ds.Cifar100Dataset(DATA_DIR_100, 100)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator():
         # in this example, each dictionary has keys "image" and "label"
         num_iter += 1
-    assert (num_iter == 100)
+    assert num_iter == 100
 
 
 if __name__ == '__main__':

tests/ut/python/dataset/test_config.py

@@ -15,10 +15,10 @@
 """
 Testing configuration manager
 """
+import os
 import filecmp
 import glob
 import numpy as np
-import os
 
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.vision.c_transforms as vision
@@ -89,7 +89,7 @@ def test_pipeline():
     ds.serialize(data2, "testpipeline2.json")
 
     # check that the generated output is different
-    assert (filecmp.cmp('testpipeline.json', 'testpipeline2.json'))
+    assert filecmp.cmp('testpipeline.json', 'testpipeline2.json')
 
     # this test passes currently because our num_parallel_workers don't get updated.
 

tests/ut/python/dataset/test_datasets_celeba.py

@@ -33,9 +33,9 @@ def test_celeba_dataset_label():
         logger.info("----------attr--------")
         logger.info(item["attr"])
         for index in range(len(expect_labels[count])):
-            assert (item["attr"][index] == expect_labels[count][index])
+            assert item["attr"][index] == expect_labels[count][index]
         count = count + 1
-    assert (count == 2)
+    assert count == 2
 
 
 def test_celeba_dataset_op():
@@ -54,7 +54,7 @@ def test_celeba_dataset_op():
         logger.info("----------image--------")
         logger.info(item["image"])
         count = count + 1
-    assert (count == 4)
+    assert count == 4
 
 
 def test_celeba_dataset_ext():
@@ -69,9 +69,9 @@ def test_celeba_dataset_ext():
         logger.info("----------attr--------")
         logger.info(item["attr"])
         for index in range(len(expect_labels[count])):
-            assert (item["attr"][index] == expect_labels[count][index])
+            assert item["attr"][index] == expect_labels[count][index]
         count = count + 1
-    assert (count == 1)
+    assert count == 1
 
 
 def test_celeba_dataset_distribute():
@@ -83,7 +83,7 @@ def test_celeba_dataset_distribute():
         logger.info("----------attr--------")
         logger.info(item["attr"])
         count = count + 1
-    assert (count == 1)
+    assert count == 1
 
 
 if __name__ == '__main__':

tests/ut/python/dataset/test_datasets_imagefolder.py

@@ -35,7 +35,7 @@ def test_imagefolder_basic():
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 44)
+    assert num_iter == 44
 
 
 def test_imagefolder_numsamples():
@@ -55,7 +55,7 @@ def test_imagefolder_numsamples():
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 10)
+    assert num_iter == 10
 
 
 def test_imagefolder_numshards():
@@ -75,7 +75,7 @@ def test_imagefolder_numshards():
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 11)
+    assert num_iter == 11
 
 
 def test_imagefolder_shardid():
@@ -95,7 +95,7 @@ def test_imagefolder_shardid():
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 11)
+    assert num_iter == 11
 
 
 def test_imagefolder_noshuffle():
@@ -115,7 +115,7 @@ def test_imagefolder_noshuffle():
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 44)
+    assert num_iter == 44
 
 
 def test_imagefolder_extrashuffle():
@@ -136,7 +136,7 @@ def test_imagefolder_extrashuffle():
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 88)
+    assert num_iter == 88
 
 
 def test_imagefolder_classindex():
@@ -157,11 +157,11 @@ def test_imagefolder_classindex():
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
-        assert (item["label"] == golden[num_iter])
+        assert item["label"] == golden[num_iter]
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 22)
+    assert num_iter == 22
 
 
 def test_imagefolder_negative_classindex():
@@ -182,11 +182,11 @@ def test_imagefolder_negative_classindex():
         # in this example, each dictionary has keys "image" and "label"
         logger.info("image is {}".format(item["image"]))
         logger.info("label is {}".format(item["label"]))
-        assert (item["label"] == golden[num_iter])
+        assert item["label"] == golden[num_iter]
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 22)
+    assert num_iter == 22
 
 
 def test_imagefolder_extensions():
@@ -207,7 +207,7 @@ def test_imagefolder_extensions():
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 44)
+    assert num_iter == 44
 
 
 def test_imagefolder_decode():
@@ -228,7 +228,7 @@ def test_imagefolder_decode():
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 44)
+    assert num_iter == 44
 
 
 def test_sequential_sampler():
@@ -255,7 +255,7 @@ def test_sequential_sampler():
         num_iter += 1
 
     logger.info("Result: {}".format(result))
-    assert (result == golden)
+    assert result == golden
 
 
 def test_random_sampler():
@@ -276,7 +276,7 @@ def test_random_sampler():
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 44)
+    assert num_iter == 44
 
 
 def test_distributed_sampler():
@@ -297,7 +297,7 @@ def test_distributed_sampler():
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 5)
+    assert num_iter == 5
 
 
 def test_pk_sampler():
@@ -318,7 +318,7 @@ def test_pk_sampler():
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 12)
+    assert num_iter == 12
 
 
 def test_subset_random_sampler():
@@ -340,7 +340,7 @@ def test_subset_random_sampler():
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 12)
+    assert num_iter == 12
 
 
 def test_weighted_random_sampler():
@@ -362,7 +362,7 @@ def test_weighted_random_sampler():
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 11)
+    assert num_iter == 11
 
 
 def test_imagefolder_rename():
@@ -382,7 +382,7 @@ def test_imagefolder_rename():
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 10)
+    assert num_iter == 10
 
     data1 = data1.rename(input_columns=["image"], output_columns="image2")
 
@@ -394,7 +394,7 @@ def test_imagefolder_rename():
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 10)
+    assert num_iter == 10
 
 
 def test_imagefolder_zip():
@@ -419,7 +419,7 @@ def test_imagefolder_zip():
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))
-    assert (num_iter == 10)
+    assert num_iter == 10
 
 
 if __name__ == '__main__':

tests/ut/python/dataset/test_datasets_imagenet.py

@@ -12,8 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-import pytest
-
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.c_transforms as data_trans
 import mindspore.dataset.transforms.vision.c_transforms as vision

tests/ut/python/dataset/test_datasets_imagenet_distribution.py

@@ -12,8 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-import pytest
-
 import mindspore.dataset as ds
 from mindspore import log as logger
 
@@ -30,7 +28,7 @@ def test_tf_file_normal():
     data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
     data1 = data1.repeat(1)
     num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
+    for _ in data1.create_dict_iterator():  # each data is a dictionary
         num_iter += 1
 
     logger.info("Number of data in data1: {}".format(num_iter))

tests/ut/python/dataset/test_datasets_manifestop.py

@@ -16,7 +16,6 @@ import numpy as np
 
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.c_transforms as data_trans
-import mindspore.dataset.transforms.vision.c_transforms as vision
 from mindspore import log as logger
 
 DATA_FILE = "../data/dataset/testManifestData/test.manifest"
@@ -34,9 +33,9 @@ def test_manifest_dataset_train():
             cat_count = cat_count + 1
         elif item["label"].size == 1 and item["label"] == 1:
             dog_count = dog_count + 1
-    assert (cat_count == 2)
-    assert (dog_count == 1)
-    assert (count == 4)
+    assert cat_count == 2
+    assert dog_count == 1
+    assert count == 4
 
 
 def test_manifest_dataset_eval():
@@ -46,36 +45,36 @@ def test_manifest_dataset_eval():
         logger.info("item[image] is {}".format(item["image"]))
         count = count + 1
         if item["label"] != 0 and item["label"] != 1:
-            assert (0)
-    assert (count == 2)
+            assert 0
+    assert count == 2
 
 
 def test_manifest_dataset_class_index():
     class_indexing = {"dog": 11}
     data = ds.ManifestDataset(DATA_FILE, decode=True, class_indexing=class_indexing)
     out_class_indexing = data.get_class_indexing()
-    assert (out_class_indexing == {"dog": 11})
+    assert out_class_indexing == {"dog": 11}
     count = 0
     for item in data.create_dict_iterator():
         logger.info("item[image] is {}".format(item["image"]))
         count = count + 1
         if item["label"] != 11:
-            assert (0)
-    assert (count == 1)
+            assert 0
+    assert count == 1
 
 
 def test_manifest_dataset_get_class_index():
     data = ds.ManifestDataset(DATA_FILE, decode=True)
     class_indexing = data.get_class_indexing()
-    assert (class_indexing == {'cat': 0, 'dog': 1, 'flower': 2})
+    assert class_indexing == {'cat': 0, 'dog': 1, 'flower': 2}
     data = data.shuffle(4)
     class_indexing = data.get_class_indexing()
-    assert (class_indexing == {'cat': 0, 'dog': 1, 'flower': 2})
+    assert class_indexing == {'cat': 0, 'dog': 1, 'flower': 2}
     count = 0
     for item in data.create_dict_iterator():
         logger.info("item[image] is {}".format(item["image"]))
         count = count + 1
-    assert (count == 4)
+    assert count == 4
 
 
 def test_manifest_dataset_multi_label():
@@ -83,10 +82,10 @@ def test_manifest_dataset_multi_label():
     count = 0
     expect_label = [1, 0, 0, [0, 2]]
     for item in data.create_dict_iterator():
-        assert (item["label"].tolist() == expect_label[count])
+        assert item["label"].tolist() == expect_label[count]
         logger.info("item[image] is {}".format(item["image"]))
         count = count + 1
-    assert (count == 4)
+    assert count == 4
 
 
 def multi_label_hot(x):
@@ -109,7 +108,7 @@ def test_manifest_dataset_multi_label_onehot():
     data = data.batch(2)
     count = 0
     for item in data.create_dict_iterator():
-        assert (item["label"].tolist() == expect_label[count])
+        assert item["label"].tolist() == expect_label[count]
         logger.info("item[image] is {}".format(item["image"]))
         count = count + 1
 

tests/ut/python/dataset/test_datasets_sharding.py

@@ -27,7 +27,7 @@ def test_imagefolder_shardings(print_res=False):
         res = []
         for item in data1.create_dict_iterator():  # each data is a dictionary
             res.append(item["label"].item())
-        if (print_res):
+        if print_res:
             logger.info("labels of dataset: {}".format(res))
         return res
 
@@ -39,12 +39,12 @@ def test_imagefolder_shardings(print_res=False):
     assert (sharding_config(2, 0, 55, False, dict()) == [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3])  # 22 rows
     assert (sharding_config(2, 1, 55, False, dict()) == [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3])  # 22 rows
     # total 22 in dataset rows because of class indexing which takes only 2 folders
-    assert (len(sharding_config(4, 0, None, True, {"class1": 111, "class2": 999})) == 6)
-    assert (len(sharding_config(4, 2, 3, True, {"class1": 111, "class2": 999})) == 3)
+    assert len(sharding_config(4, 0, None, True, {"class1": 111, "class2": 999})) == 6
+    assert len(sharding_config(4, 2, 3, True, {"class1": 111, "class2": 999})) == 3
     # test with repeat
     assert (sharding_config(4, 0, 12, False, dict(), 3) == [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3] * 3)
     assert (sharding_config(4, 0, 5, False, dict(), 5) == [0, 0, 0, 1, 1] * 5)
-    assert (len(sharding_config(5, 1, None, True, {"class1": 111, "class2": 999}, 4)) == 20)
+    assert len(sharding_config(5, 1, None, True, {"class1": 111, "class2": 999}, 4)) == 20
 
 
 def test_tfrecord_shardings1(print_res=False):
@@ -176,8 +176,8 @@ def test_voc_shardings(print_res=False):
     # then takes the first 2 bc num_samples = 2
     assert (sharding_config(3, 2, 2, False, 4) == [2268, 607] * 4)
     # test that each epoch, each shard_worker returns a different sample
-    assert (len(sharding_config(2, 0, None, True, 1)) == 5)
-    assert (len(set(sharding_config(11, 0, None, True, 10))) > 1)
+    assert len(sharding_config(2, 0, None, True, 1)) == 5
+    assert len(set(sharding_config(11, 0, None, True, 10))) > 1
 
 
 def test_cifar10_shardings(print_res=False):
@@ -196,8 +196,8 @@ def test_cifar10_shardings(print_res=False):
 
     # 60000 rows in total. CIFAR reads everything in memory which would make each test case very slow
     # therefore, only 2 test cases for now.
-    assert (sharding_config(10000, 9999, 7, False, 1) == [9])
-    assert (sharding_config(10000, 0, 4, False, 3) == [0, 0, 0])
+    assert sharding_config(10000, 9999, 7, False, 1) == [9]
+    assert sharding_config(10000, 0, 4, False, 3) == [0, 0, 0]
 
 
 def test_cifar100_shardings(print_res=False):

tests/ut/python/dataset/test_datasets_textfileop.py

@@ -27,7 +27,7 @@ def test_textline_dataset_one_file():
     for i in data.create_dict_iterator():
         logger.info("{}".format(i["text"]))
         count += 1
-    assert (count == 3)
+    assert count == 3
 
 
 def test_textline_dataset_all_file():
@@ -36,7 +36,7 @@ def test_textline_dataset_all_file():
     for i in data.create_dict_iterator():
         logger.info("{}".format(i["text"]))
         count += 1
-    assert (count == 5)
+    assert count == 5
 
 
 def test_textline_dataset_totext():
@@ -46,8 +46,8 @@ def test_textline_dataset_totext():
     line = ["This is a text file.", "Another file.",
             "Be happy every day.", "End of file.", "Good luck to everyone."]
     for i in data.create_dict_iterator():
-        str = i["text"].item().decode("utf8")
-        assert (str == line[count])
+        strs = i["text"].item().decode("utf8")
+        assert strs == line[count]
         count += 1
     assert (count == 5)
     # Restore configuration num_parallel_workers
@@ -57,17 +57,17 @@ def test_textline_dataset_totext():
 def test_textline_dataset_num_samples():
     data = ds.TextFileDataset(DATA_FILE, num_samples=2)
     count = 0
-    for i in data.create_dict_iterator():
+    for _ in data.create_dict_iterator():
         count += 1
-    assert (count == 2)
+    assert count == 2
 
 
 def test_textline_dataset_distribution():
     data = ds.TextFileDataset(DATA_ALL_FILE, num_shards=2, shard_id=1)
     count = 0
-    for i in data.create_dict_iterator():
+    for _ in data.create_dict_iterator():
         count += 1
-    assert (count == 3)
+    assert count == 3
 
 
 def test_textline_dataset_repeat():
@@ -78,16 +78,16 @@ def test_textline_dataset_repeat():
             "This is a text file.", "Be happy every day.", "Good luck to everyone.",
             "This is a text file.", "Be happy every day.", "Good luck to everyone."]
     for i in data.create_dict_iterator():
-        str = i["text"].item().decode("utf8")
-        assert (str == line[count])
+        strs = i["text"].item().decode("utf8")
+        assert strs == line[count]
         count += 1
-    assert (count == 9)
+    assert count == 9
 
 
 def test_textline_dataset_get_datasetsize():
     data = ds.TextFileDataset(DATA_FILE)
     size = data.get_dataset_size()
-    assert (size == 3)
+    assert size == 3
 
 
 if __name__ == "__main__":

tests/ut/python/dataset/test_decode.py

@@ -15,9 +15,8 @@
 """
 Testing Decode op in DE
 """
-import cv2
 import numpy as np
-from util import diff_mse
+import cv2
 
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.vision.c_transforms as vision

tests/ut/python/dataset/test_filterop.py

@@ -88,7 +88,7 @@ def test_filter_by_generator_with_repeat():
         ret_data.append(item["data"])
     assert num_iter == 44
     for i in range(4):
-        for ii in range(len(expected_rs)):
+        for ii, _ in enumerate(expected_rs):
             index = i * len(expected_rs) + ii
             assert ret_data[index] == expected_rs[ii]
 
@@ -106,7 +106,7 @@ def test_filter_by_generator_with_repeat_after():
         ret_data.append(item["data"])
     assert num_iter == 44
     for i in range(4):
-        for ii in range(len(expected_rs)):
+        for ii, _ in enumerate(expected_rs):
             index = i * len(expected_rs) + ii
             assert ret_data[index] == expected_rs[ii]
 
@@ -167,7 +167,7 @@ def test_filter_by_generator_with_shuffle():
     dataset_s = dataset.shuffle(4)
     dataset_f = dataset_s.filter(predicate=filter_func_shuffle, num_parallel_workers=4)
     num_iter = 0
-    for item in dataset_f.create_dict_iterator():
+    for _ in dataset_f.create_dict_iterator():
         num_iter += 1
     assert num_iter == 21
 
@@ -184,7 +184,7 @@ def test_filter_by_generator_with_shuffle_after():
     dataset_f = dataset.filter(predicate=filter_func_shuffle_after, num_parallel_workers=4)
     dataset_s = dataset_f.shuffle(4)
     num_iter = 0
-    for item in dataset_s.create_dict_iterator():
+    for _ in dataset_s.create_dict_iterator():
         num_iter += 1
     assert num_iter == 21
 
@@ -258,8 +258,7 @@ def filter_func_map(col1, col2):
 def filter_func_map_part(col1):
     if col1 < 3:
         return True
-    else:
-        return False
+    return False
 
 
 def filter_func_map_all(col1, col2):
@@ -276,7 +275,7 @@ def func_map(data_col1, data_col2):
 
 
 def func_map_part(data_col1):
-    return (data_col1)
+    return data_col1
 
 
 # test with  map
@@ -473,7 +472,6 @@ def test_filte_case_dataset_cifar10():
     ds.config.load('../data/dataset/declient_filter.cfg')
     dataset_c = ds.Cifar10Dataset(dataset_dir=DATA_DIR_10, num_samples=100000, shuffle=False)
     dataset_f1 = dataset_c.filter(input_columns=["image", "label"], predicate=filter_func_cifar, num_parallel_workers=1)
-    num_iter = 0
     for item in dataset_f1.create_dict_iterator():
         # in this example, each dictionary has keys "image" and "label"
         assert item["label"] % 3 == 0

tests/ut/python/dataset/test_generator.py

@@ -184,7 +184,7 @@ def test_case_6():
     de_types = [mstype.int8, mstype.int16, mstype.int32, mstype.int64, mstype.uint8, mstype.uint16, mstype.uint32,
                 mstype.uint64, mstype.float32, mstype.float64]
 
-    for i in range(len(np_types)):
+    for i, _ in enumerate(np_types):
         type_tester_with_type_check(np_types[i], de_types[i])
 
 
@@ -219,7 +219,7 @@ def test_case_7():
     de_types = [mstype.int8, mstype.int16, mstype.int32, mstype.int64, mstype.uint8, mstype.uint16, mstype.uint32,
                 mstype.uint64, mstype.float32, mstype.float64]
 
-    for i in range(len(np_types)):
+    for i, _ in enumerate(np_types):
         type_tester_with_type_check_2c(np_types[i], [None, de_types[i]])
 
 
@@ -526,7 +526,7 @@ def test_sequential_sampler():
 def test_random_sampler():
     source = [(np.array([x]),) for x in range(64)]
     ds1 = ds.GeneratorDataset(source, ["data"], shuffle=True)
-    for data in ds1.create_dict_iterator():  # each data is a dictionary
+    for _ in ds1.create_dict_iterator():  # each data is a dictionary
         pass
 
 
@@ -611,7 +611,7 @@ def test_schema():
     de_types = [mstype.int8, mstype.int16, mstype.int32, mstype.int64, mstype.uint8, mstype.uint16, mstype.uint32,
                 mstype.uint64, mstype.float32, mstype.float64]
 
-    for i in range(len(np_types)):
+    for i, _ in enumerate(np_types):
         type_tester_with_type_check_2c_schema(np_types[i], [de_types[i], de_types[i]])
 
 
@@ -630,8 +630,7 @@ def manual_test_keyborad_interrupt():
             return 1024
 
     ds1 = ds.GeneratorDataset(MyDS(), ["data"], num_parallel_workers=4).repeat(2)
-    i = 0
-    for data in ds1.create_dict_iterator():  # each data is a dictionary
+    for _ in ds1.create_dict_iterator():  # each data is a dictionary
         pass
 
 

tests/ut/python/dataset/test_iterator.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-import copy
 import numpy as np
 import pytest
 

tests/ut/python/dataset/test_minddataset.py

@@ -320,7 +320,7 @@ def test_cv_minddataset_issue_888(add_and_remove_cv_file):
     data = data.shuffle(2)
     data = data.repeat(9)
     num_iter = 0
-    for item in data.create_dict_iterator():
+    for _ in data.create_dict_iterator():
         num_iter += 1
     assert num_iter == 18
 
@@ -572,7 +572,7 @@ def test_cv_minddataset_reader_basic_tutorial_5_epoch(add_and_remove_cv_file):
     num_readers = 4
     data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers)
     assert data_set.get_dataset_size() == 10
-    for epoch in range(5):
+    for _ in range(5):
         num_iter = 0
         for data in data_set:
             logger.info("data is {}".format(data))
@@ -603,7 +603,7 @@ def test_cv_minddataset_reader_basic_tutorial_5_epoch_with_batch(add_and_remove_
 
     data_set = data_set.batch(2)
     assert data_set.get_dataset_size() == 5
-    for epoch in range(5):
+    for _ in range(5):
         num_iter = 0
         for data in data_set:
             logger.info("data is {}".format(data))

tests/ut/python/dataset/test_minddataset_exception.py

@@ -91,7 +91,7 @@ def test_invalid_mindrecord():
     with pytest.raises(Exception, match="MindRecordOp init failed"):
         data_set = ds.MindDataset('dummy.mindrecord', columns_list, num_readers)
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for _ in data_set.create_dict_iterator():
             num_iter += 1
         assert num_iter == 0
     os.remove('dummy.mindrecord')
@@ -105,7 +105,7 @@ def test_minddataset_lack_db():
     with pytest.raises(Exception, match="MindRecordOp init failed"):
         data_set = ds.MindDataset(CV_FILE_NAME, columns_list, num_readers)
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for _ in data_set.create_dict_iterator():
             num_iter += 1
         assert num_iter == 0
     os.remove(CV_FILE_NAME)
@@ -119,7 +119,7 @@ def test_cv_minddataset_pk_sample_error_class_column():
     with pytest.raises(Exception, match="MindRecordOp launch failed"):
         data_set = ds.MindDataset(CV_FILE_NAME, columns_list, num_readers, sampler=sampler)
         num_iter = 0
-        for item in data_set.create_dict_iterator():
+        for _ in data_set.create_dict_iterator():
             num_iter += 1
     os.remove(CV_FILE_NAME)
     os.remove("{}.db".format(CV_FILE_NAME))

tests/ut/python/dataset/test_minddataset_multi_images_and_ndarray.py

@@ -15,8 +15,8 @@
 """
 This is the test module for mindrecord
 """
-import numpy as np
 import os
+import numpy as np
 
 import mindspore.dataset as ds
 from mindspore import log as logger

tests/ut/python/dataset/test_minddataset_sampler.py

@@ -15,16 +15,10 @@
 """
 This is the test module for mindrecord
 """
-import collections
-import json
-import numpy as np
 import os
 import pytest
-import re
-import string
 
 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.dataset.text import to_str

tests/ut/python/dataset/test_mixup_label_smoothing.py

@@ -49,7 +49,7 @@ def test_one_hot_op():
         label = data["label"]
         logger.info("label is {}".format(label))
         logger.info("golden_label is {}".format(golden_label))
-        assert (label.all() == golden_label.all())
+        assert label.all() == golden_label.all()
         logger.info("====test one hot op ok====")
 
 

tests/ut/python/dataset/test_normalizeOp.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 # ==============================================================================
 
-import matplotlib.pyplot as plt
 import numpy as np
 
 import mindspore.dataset as ds
@@ -50,6 +49,7 @@ def get_normalized(image_id):
         if num_iter == image_id:
             return normalize_np(image)
         num_iter += 1
+    return None
 
 
 def test_normalize_op():

tests/ut/python/dataset/test_onehot_op.py

@@ -19,7 +19,6 @@ import numpy as np
 
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.c_transforms as data_trans
-import mindspore.dataset.transforms.vision.c_transforms as vision
 from mindspore import log as logger
 
 DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]

tests/ut/python/dataset/test_pad.py

@@ -15,7 +15,6 @@
 """
 Testing Pad op in DE
 """
-import matplotlib.pyplot as plt
 import numpy as np
 from util import diff_mse
 
@@ -118,7 +117,7 @@ def test_pad_grayscale():
     for shape1, shape2 in zip(dataset_shape_1, dataset_shape_2):
         # validate that the first two dimensions are the same
         # we have a little inconsistency here because the third dimension is 1 after py_vision.Grayscale
-        assert (shape1[0:1] == shape2[0:1])
+        assert shape1[0:1] == shape2[0:1]
 
 
 if __name__ == "__main__":

tests/ut/python/dataset/test_pad_batch.py

@@ -13,8 +13,8 @@
 # limitations under the License.
 # ==============================================================================
 
-import numpy as np
 import time
+import numpy as np
 
 import mindspore.dataset as ds
 
@@ -117,8 +117,7 @@ def batch_padding_performance_3d():
     data1 = data1.batch(batch_size=24, drop_remainder=True, pad_info=pad_info)
     start_time = time.time()
     num_batches = 0
-    ret = []
-    for data in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator():
         num_batches += 1
     res = "total number of batch:" + str(num_batches) + " time elapsed:" + str(time.time() - start_time)
     # print(res)
@@ -134,7 +133,7 @@ def batch_padding_performance_1d():
     data1 = data1.batch(batch_size=24, drop_remainder=True, pad_info=pad_info)
     start_time = time.time()
     num_batches = 0
-    for data in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator():
         num_batches += 1
     res = "total number of batch:" + str(num_batches) + " time elapsed:" + str(time.time() - start_time)
     # print(res)
@@ -150,7 +149,7 @@ def batch_pyfunc_padding_3d():
     data1 = data1.batch(batch_size=24, drop_remainder=True)
     start_time = time.time()
     num_batches = 0
-    for data in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator():
         num_batches += 1
     res = "total number of batch:" + str(num_batches) + " time elapsed:" + str(time.time() - start_time)
     # print(res)
@@ -165,7 +164,7 @@ def batch_pyfunc_padding_1d():
     data1 = data1.batch(batch_size=24, drop_remainder=True)
     start_time = time.time()
     num_batches = 0
-    for data in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator():
         num_batches += 1
     res = "total number of batch:" + str(num_batches) + " time elapsed:" + str(time.time() - start_time)
     # print(res)
@@ -197,7 +196,7 @@ def test_pad_via_map():
     res_from_map = pad_map_config()
     res_from_batch = pad_batch_config()
     assert len(res_from_batch) == len(res_from_batch)
-    for i in range(len(res_from_map)):
+    for i, _ in enumerate(res_from_map):
         assert np.array_equal(res_from_map[i], res_from_batch[i])
 
 

tests/ut/python/dataset/test_random_crop_and_resize.py

@@ -15,8 +15,9 @@
 """
 Testing RandomCropAndResize op in DE
 """
-import cv2
 import numpy as np
+import cv2
+
 import mindspore.dataset.transforms.vision.c_transforms as c_vision
 import mindspore.dataset.transforms.vision.py_transforms as py_vision
 import mindspore.dataset.transforms.vision.utils as mode

tests/ut/python/dataset/test_random_crop_decode_resize.py

@@ -15,9 +15,9 @@
 """
 Testing RandomCropDecodeResize op in DE
 """
-import cv2
 import matplotlib.pyplot as plt
 import numpy as np
+import cv2
 
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.vision.c_transforms as vision

tests/ut/python/dataset/test_random_dataset.py

@@ -12,8 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-from pathlib import Path
-
 import mindspore.common.dtype as mstype
 import mindspore.dataset as ds
 from mindspore import log as logger
@@ -39,7 +37,7 @@ def test_randomdataset_basic1():
         num_iter += 1
 
     logger.info("Number of data in ds1: ", num_iter)
-    assert (num_iter == 200)
+    assert num_iter == 200
 
 
 # Another simple test
@@ -65,7 +63,7 @@ def test_randomdataset_basic2():
         num_iter += 1
 
     logger.info("Number of data in ds1: ", num_iter)
-    assert (num_iter == 40)
+    assert num_iter == 40
 
 
 if __name__ == '__main__':

tests/ut/python/dataset/test_random_rotation.py

@@ -15,9 +15,9 @@
 """
 Testing RandomRotation op in DE
 """
-import cv2
 import matplotlib.pyplot as plt
 import numpy as np
+import cv2
 
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.vision.c_transforms as c_vision

tests/ut/python/dataset/test_rename.py

@@ -34,7 +34,7 @@ def test_rename():
 
     num_iter = 0
 
-    for i, item in enumerate(data.create_dict_iterator()):
+    for _, item in enumerate(data.create_dict_iterator()):
         logger.info("item[mask] is {}".format(item["masks"]))
         np.testing.assert_equal(item["masks"], item["input_ids"])
         logger.info("item[seg_ids] is {}".format(item["seg_ids"]))

tests/ut/python/dataset/test_rgb_hsv.py

@@ -159,7 +159,7 @@ def test_rgb_hsv_pipeline():
         ori_img = data1["image"]
         cvt_img = data2["image"]
         assert_allclose(ori_img.flatten(), cvt_img.flatten(), rtol=1e-5, atol=0)
-        assert (ori_img.shape == cvt_img.shape)
+        assert ori_img.shape == cvt_img.shape
 
 
 if __name__ == "__main__":

tests/ut/python/dataset/test_serdes_dataset.py

@@ -57,7 +57,7 @@ def test_imagefolder(remove_json_files=True):
     # data1 should still work after saving.
     ds.serialize(data1, "imagenet_dataset_pipeline.json")
     ds1_dict = ds.serialize(data1)
-    assert (validate_jsonfile("imagenet_dataset_pipeline.json") is True)
+    assert validate_jsonfile("imagenet_dataset_pipeline.json") is True
 
     # Print the serialized pipeline to stdout
     ds.show(data1)
@@ -68,8 +68,8 @@ def test_imagefolder(remove_json_files=True):
     # Serialize the pipeline we just deserialized.
     # The content of the json file should be the same to the previous serialize.
     ds.serialize(data2, "imagenet_dataset_pipeline_1.json")
-    assert (validate_jsonfile("imagenet_dataset_pipeline_1.json") is True)
-    assert (filecmp.cmp('imagenet_dataset_pipeline.json', 'imagenet_dataset_pipeline_1.json'))
+    assert validate_jsonfile("imagenet_dataset_pipeline_1.json") is True
+    assert filecmp.cmp('imagenet_dataset_pipeline.json', 'imagenet_dataset_pipeline_1.json')
 
     # Deserialize the latest json file again
     data3 = ds.deserialize(json_filepath="imagenet_dataset_pipeline_1.json")
@@ -78,16 +78,16 @@ def test_imagefolder(remove_json_files=True):
     # Iterate and compare the data in the original pipeline (data1) against the deserialized pipeline (data2)
     for item1, item2, item3, item4 in zip(data1.create_dict_iterator(), data2.create_dict_iterator(),
                                           data3.create_dict_iterator(), data4.create_dict_iterator()):
-        assert (np.array_equal(item1['image'], item2['image']))
-        assert (np.array_equal(item1['image'], item3['image']))
-        assert (np.array_equal(item1['label'], item2['label']))
-        assert (np.array_equal(item1['label'], item3['label']))
-        assert (np.array_equal(item3['image'], item4['image']))
-        assert (np.array_equal(item3['label'], item4['label']))
+        assert np.array_equal(item1['image'], item2['image'])
+        assert np.array_equal(item1['image'], item3['image'])
+        assert np.array_equal(item1['label'], item2['label'])
+        assert np.array_equal(item1['label'], item3['label'])
+        assert np.array_equal(item3['image'], item4['image'])
+        assert np.array_equal(item3['label'], item4['label'])
         num_samples += 1
 
     logger.info("Number of data in data1: {}".format(num_samples))
-    assert (num_samples == 6)
+    assert num_samples == 6
 
     # Remove the generated json file
     if remove_json_files:
@@ -106,26 +106,26 @@ def test_mnist_dataset(remove_json_files=True):
     data1 = data1.batch(batch_size=10, drop_remainder=True)
 
     ds.serialize(data1, "mnist_dataset_pipeline.json")
-    assert (validate_jsonfile("mnist_dataset_pipeline.json") is True)
+    assert validate_jsonfile("mnist_dataset_pipeline.json") is True
 
     data2 = ds.deserialize(json_filepath="mnist_dataset_pipeline.json")
     ds.serialize(data2, "mnist_dataset_pipeline_1.json")
-    assert (validate_jsonfile("mnist_dataset_pipeline_1.json") is True)
-    assert (filecmp.cmp('mnist_dataset_pipeline.json', 'mnist_dataset_pipeline_1.json'))
+    assert validate_jsonfile("mnist_dataset_pipeline_1.json") is True
+    assert filecmp.cmp('mnist_dataset_pipeline.json', 'mnist_dataset_pipeline_1.json')
 
     data3 = ds.deserialize(json_filepath="mnist_dataset_pipeline_1.json")
 
     num = 0
     for data1, data2, data3 in zip(data1.create_dict_iterator(), data2.create_dict_iterator(),
                                    data3.create_dict_iterator()):
-        assert (np.array_equal(data1['image'], data2['image']))
-        assert (np.array_equal(data1['image'], data3['image']))
-        assert (np.array_equal(data1['label'], data2['label']))
-        assert (np.array_equal(data1['label'], data3['label']))
+        assert np.array_equal(data1['image'], data2['image'])
+        assert np.array_equal(data1['image'], data3['image'])
+        assert np.array_equal(data1['label'], data2['label'])
+        assert np.array_equal(data1['label'], data3['label'])
         num += 1
 
     logger.info("mnist total num samples is {}".format(str(num)))
-    assert (num == 10)
+    assert num == 10
 
     if remove_json_files:
         delete_json_files()
@@ -146,13 +146,13 @@ def test_zip_dataset(remove_json_files=True):
                                          "column_1d", "column_2d", "column_3d", "column_binary"])
     data3 = ds.zip((data1, data2))
     ds.serialize(data3, "zip_dataset_pipeline.json")
-    assert (validate_jsonfile("zip_dataset_pipeline.json") is True)
-    assert (validate_jsonfile("zip_dataset_pipeline_typo.json") is False)
+    assert validate_jsonfile("zip_dataset_pipeline.json") is True
+    assert validate_jsonfile("zip_dataset_pipeline_typo.json") is False
 
     data4 = ds.deserialize(json_filepath="zip_dataset_pipeline.json")
     ds.serialize(data4, "zip_dataset_pipeline_1.json")
-    assert (validate_jsonfile("zip_dataset_pipeline_1.json") is True)
-    assert (filecmp.cmp('zip_dataset_pipeline.json', 'zip_dataset_pipeline_1.json'))
+    assert validate_jsonfile("zip_dataset_pipeline_1.json") is True
+    assert filecmp.cmp('zip_dataset_pipeline.json', 'zip_dataset_pipeline_1.json')
 
     rows = 0
     for d0, d3, d4 in zip(ds0, data3, data4):
@@ -165,7 +165,7 @@ def test_zip_dataset(remove_json_files=True):
             assert np.array_equal(t1, d4[offset + num_cols])
             offset += 1
         rows += 1
-    assert (rows == 12)
+    assert rows == 12
 
     if remove_json_files:
         delete_json_files()
@@ -197,7 +197,7 @@ def test_random_crop():
 
     for item1, item1_1, item2 in zip(data1.create_dict_iterator(), data1_1.create_dict_iterator(),
                                      data2.create_dict_iterator()):
-        assert (np.array_equal(item1['image'], item1_1['image']))
+        assert np.array_equal(item1['image'], item1_1['image'])
         image2 = item2["image"]
 
 
@@ -250,7 +250,7 @@ def test_minddataset(add_and_remove_cv_file):
     data = get_data(CV_DIR_NAME)
     assert data_set.get_dataset_size() == 5
     num_iter = 0
-    for item in data_set.create_dict_iterator():
+    for _ in data_set.create_dict_iterator():
         num_iter += 1
     assert num_iter == 5
 

tests/ut/python/dataset/test_shuffle.py

@@ -120,7 +120,7 @@ def test_shuffle_05():
 
 def test_shuffle_06():
     """
-    Test shuffle: with set seed, both datasets 
+    Test shuffle: with set seed, both datasets
     """
     logger.info("test_shuffle_06")
     # define parameters

tests/ut/python/dataset/test_skip.py

@@ -16,7 +16,6 @@ import numpy as np
 
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.vision.c_transforms as vision
-from mindspore import log as logger
 
 DATA_DIR_TF2 = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
 SCHEMA_DIR_TF2 = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
@@ -36,7 +35,7 @@ def test_tf_skip():
     data1 = data1.skip(2)
 
     num_iter = 0
-    for item in data1.create_dict_iterator():
+    for _ in data1.create_dict_iterator():
         num_iter += 1
     assert num_iter == 1
 

tests/ut/python/dataset/test_sync_wait.py

@@ -14,7 +14,6 @@
 # ==============================================================================
 
 import numpy as np
-import time
 
 import mindspore.dataset as ds
 from mindspore import log as logger
@@ -22,7 +21,7 @@ from mindspore import log as logger
 
 def gen():
     for i in range(100):
-        yield np.array(i),
+        yield (np.array(i),)
 
 
 class Augment:
@@ -38,7 +37,7 @@ class Augment:
 
 def test_simple_sync_wait():
     """
-    Test simple sync wait: test sync in dataset pipeline  
+    Test simple sync wait: test sync in dataset pipeline
     """
     logger.info("test_simple_sync_wait")
     batch_size = 4
@@ -51,7 +50,7 @@ def test_simple_sync_wait():
 
     count = 0
     for data in dataset.create_dict_iterator():
-        assert (data["input"][0] == count)
+        assert data["input"][0] == count
         count += batch_size
         data = {"loss": count}
         dataset.sync_update(condition_name="policy", data=data)
@@ -59,7 +58,7 @@ def test_simple_sync_wait():
 
 def test_simple_shuffle_sync():
     """
-    Test simple shuffle sync: test shuffle before sync  
+    Test simple shuffle sync: test shuffle before sync
     """
     logger.info("test_simple_shuffle_sync")
     shuffle_size = 4
@@ -83,7 +82,7 @@ def test_simple_shuffle_sync():
 
 def test_two_sync():
     """
-    Test two sync: dataset pipeline with with two sync_operators  
+    Test two sync: dataset pipeline with with two sync_operators
     """
     logger.info("test_two_sync")
     batch_size = 6
@@ -111,7 +110,7 @@ def test_two_sync():
 
 def test_sync_epoch():
     """
-    Test sync wait with epochs: test sync with epochs in dataset pipeline  
+    Test sync wait with epochs: test sync with epochs in dataset pipeline
     """
     logger.info("test_sync_epoch")
     batch_size = 30
@@ -122,11 +121,11 @@ def test_sync_epoch():
     dataset = dataset.map(input_columns=["input"], operations=[aug.preprocess])
     dataset = dataset.batch(batch_size, drop_remainder=True)
 
-    for epochs in range(3):
+    for _ in range(3):
         aug.update({"loss": 0})
         count = 0
         for data in dataset.create_dict_iterator():
-            assert (data["input"][0] == count)
+            assert data["input"][0] == count
             count += batch_size
             data = {"loss": count}
             dataset.sync_update(condition_name="policy", data=data)
@@ -134,7 +133,7 @@ def test_sync_epoch():
 
 def test_multiple_iterators():
     """
-    Test sync wait with multiple iterators: will start multiple 
+    Test sync wait with multiple iterators: will start multiple
     """
     logger.info("test_sync_epoch")
     batch_size = 30
@@ -153,7 +152,7 @@ def test_multiple_iterators():
     dataset2 = dataset2.batch(batch_size, drop_remainder=True)
 
     for item1, item2 in zip(dataset.create_dict_iterator(), dataset2.create_dict_iterator()):
-        assert (item1["input"][0] == item2["input"][0])
+        assert item1["input"][0] == item2["input"][0]
         data1 = {"loss": item1["input"][0]}
         data2 = {"loss": item2["input"][0]}
         dataset.sync_update(condition_name="policy", data=data1)
@@ -162,7 +161,7 @@ def test_multiple_iterators():
 
 def test_sync_exception_01():
     """
-    Test sync: with shuffle in sync mode 
+    Test sync: with shuffle in sync mode
     """
     logger.info("test_sync_exception_01")
     shuffle_size = 4
@@ -183,7 +182,7 @@ def test_sync_exception_01():
 
 def test_sync_exception_02():
     """
-    Test sync: with duplicated condition name  
+    Test sync: with duplicated condition name
     """
     logger.info("test_sync_exception_02")
     batch_size = 6

tests/ut/python/dataset/test_take.py

@@ -21,13 +21,13 @@ from mindspore import log as logger
 # In generator dataset: Number of rows is 3, its value is 0, 1, 2
 def generator():
     for i in range(3):
-        yield np.array([i]),
+        yield (np.array([i]),)
 
 
 # In generator dataset: Number of rows is 10, its value is 0, 1, 2 ... 10
 def generator_10():
     for i in range(10):
-        yield np.array([i]),
+        yield (np.array([i]),)
 
 
 def filter_func_ge(data):
@@ -47,8 +47,8 @@ def test_take_01():
     data1 = data1.repeat(2)
 
     # Here i refers to index, d refers to data element 
-    for i, d in enumerate(data1):
-        assert 0 == d[0][0]
+    for _, d in enumerate(data1):
+        assert d[0][0] == 0
 
     assert sum([1 for _ in data1]) == 2
 
@@ -97,7 +97,7 @@ def test_take_04():
     data1 = data1.take(4)
     data1 = data1.repeat(2)
 
-    # Here i refers to index, d refers to data element 
+    # Here i refers to index, d refers to data element
     for i, d in enumerate(data1):
         assert i % 3 == d[0][0]
 
@@ -113,7 +113,7 @@ def test_take_05():
 
     data1 = data1.take(2)
 
-    # Here i refers to index, d refers to data element 
+    # Here i refers to index, d refers to data element
     for i, d in enumerate(data1):
         assert i == d[0][0]
 
@@ -130,7 +130,7 @@ def test_take_06():
     data1 = data1.repeat(2)
     data1 = data1.take(4)
 
-    # Here i refers to index, d refers to data element 
+    # Here i refers to index, d refers to data element
     for i, d in enumerate(data1):
         assert i % 3 == d[0][0]
 
@@ -171,7 +171,7 @@ def test_take_09():
     data1 = data1.repeat(2)
     data1 = data1.take(-1)
 
-    # Here i refers to index, d refers to data element 
+    # Here i refers to index, d refers to data element
     for i, d in enumerate(data1):
         assert i % 3 == d[0][0]
 
@@ -188,7 +188,7 @@ def test_take_10():
     data1 = data1.take(-1)
     data1 = data1.repeat(2)
 
-    # Here i refers to index, d refers to data element 
+    # Here i refers to index, d refers to data element
     for i, d in enumerate(data1):
         assert i % 3 == d[0][0]
 
@@ -206,7 +206,7 @@ def test_take_11():
     data1 = data1.repeat(2)
     data1 = data1.take(-1)
 
-    # Here i refers to index, d refers to data element 
+    # Here i refers to index, d refers to data element
     for i, d in enumerate(data1):
         assert 2 * (i % 2) == d[0][0]
 
@@ -224,9 +224,9 @@ def test_take_12():
     data1 = data1.batch(2)
     data1 = data1.repeat(2)
 
-    # Here i refers to index, d refers to data element 
-    for i, d in enumerate(data1):
-        assert 0 == d[0][0]
+    # Here i refers to index, d refers to data element
+    for _, d in enumerate(data1):
+        assert d[0][0] == 0
 
     assert sum([1 for _ in data1]) == 2
 
@@ -243,9 +243,9 @@ def test_take_13():
     data1 = data1.batch(2)
     data1 = data1.repeat(2)
 
-    # Here i refers to index, d refers to data element 
-    for i, d in enumerate(data1):
-        assert 2 == d[0][0]
+    # Here i refers to index, d refers to data element
+    for _, d in enumerate(data1):
+        assert d[0][0] == 2
 
     assert sum([1 for _ in data1]) == 2
 
@@ -262,9 +262,9 @@ def test_take_14():
     data1 = data1.skip(1)
     data1 = data1.repeat(2)
 
-    # Here i refers to index, d refers to data element 
-    for i, d in enumerate(data1):
-        assert 2 == d[0][0]
+    # Here i refers to index, d refers to data element
+    for _, d in enumerate(data1):
+        assert d[0][0] == 2
 
     assert sum([1 for _ in data1]) == 2
 
@@ -279,7 +279,7 @@ def test_take_15():
     data1 = data1.take(6)
     data1 = data1.skip(2)
 
-    # Here i refers to index, d refers to data element 
+    # Here i refers to index, d refers to data element
     for i, d in enumerate(data1):
         assert (i + 2) == d[0][0]
 
@@ -296,7 +296,7 @@ def test_take_16():
     data1 = data1.skip(3)
     data1 = data1.take(5)
 
-    # Here i refers to index, d refers to data element 
+    # Here i refers to index, d refers to data element
     for i, d in enumerate(data1):
         assert (i + 3) == d[0][0]
 
@@ -313,7 +313,7 @@ def test_take_17():
     data1 = data1.take(8)
     data1 = data1.filter(predicate=filter_func_ge, num_parallel_workers=4)
 
-    # Here i refers to index, d refers to data element 
+    # Here i refers to index, d refers to data element
     for i, d in enumerate(data1):
         assert i == d[0][0]
 
@@ -334,9 +334,9 @@ def test_take_18():
     data1 = data1.batch(2)
     data1 = data1.repeat(2)
 
-    # Here i refers to index, d refers to data element 
-    for i, d in enumerate(data1):
-        assert 2 == d[0][0]
+    # Here i refers to index, d refers to data element
+    for _, d in enumerate(data1):
+        assert d[0][0] == 2
 
     assert sum([1 for _ in data1]) == 2
 

tests/ut/python/dataset/test_tfreader_op.py

@@ -33,7 +33,7 @@ def test_case_tf_shape():
     for data in ds1.create_dict_iterator():
         logger.info(data)
     output_shape = ds1.output_shapes()
-    assert (len(output_shape[-1]) == 1)
+    assert len(output_shape[-1]) == 1
 
 
 def test_case_tf_read_all_dataset():
@@ -41,7 +41,7 @@ def test_case_tf_read_all_dataset():
     ds1 = ds.TFRecordDataset(FILES, schema_file)
     assert ds1.get_dataset_size() == 12
     count = 0
-    for data in ds1.create_tuple_iterator():
+    for _ in ds1.create_tuple_iterator():
         count += 1
     assert count == 12
 
@@ -51,7 +51,7 @@ def test_case_num_samples():
     ds1 = ds.TFRecordDataset(FILES, schema_file, num_samples=8)
     assert ds1.get_dataset_size() == 8
     count = 0
-    for data in ds1.create_dict_iterator():
+    for _ in ds1.create_dict_iterator():
         count += 1
     assert count == 8
 
@@ -61,7 +61,7 @@ def test_case_num_samples2():
     ds1 = ds.TFRecordDataset(FILES, schema_file)
     assert ds1.get_dataset_size() == 7
     count = 0
-    for data in ds1.create_dict_iterator():
+    for _ in ds1.create_dict_iterator():
         count += 1
     assert count == 7
 
@@ -70,7 +70,7 @@ def test_case_tf_shape_2():
     ds1 = ds.TFRecordDataset(FILES, SCHEMA_FILE)
     ds1 = ds1.batch(2)
     output_shape = ds1.output_shapes()
-    assert (len(output_shape[-1]) == 2)
+    assert len(output_shape[-1]) == 2
 
 
 def test_case_tf_file():
@@ -175,10 +175,10 @@ def test_tf_record_shard():
     assert len(worker1_res) == 48
     assert len(worker1_res) == len(worker2_res)
     # check criteria 1
-    for i in range(len(worker1_res)):
-        assert (worker1_res[i] != worker2_res[i])
+    for i, _ in enumerate(worker1_res):
+        assert worker1_res[i] != worker2_res[i]
     # check criteria 2
-    assert (set(worker2_res) == set(worker1_res))
+    assert set(worker2_res) == set(worker1_res)
 
 
 def test_tf_shard_equal_rows():
@@ -197,16 +197,16 @@ def test_tf_shard_equal_rows():
     worker2_res = get_res(3, 1, 2)
     worker3_res = get_res(3, 2, 2)
     # check criteria 1
-    for i in range(len(worker1_res)):
-        assert (worker1_res[i] != worker2_res[i])
-        assert (worker2_res[i] != worker3_res[i])
+    for i, _ in enumerate(worker1_res):
+        assert worker1_res[i] != worker2_res[i]
+        assert worker2_res[i] != worker3_res[i]
     # Confirm each worker gets same number of rows
     assert len(worker1_res) == 28
     assert len(worker1_res) == len(worker2_res)
     assert len(worker2_res) == len(worker3_res)
 
     worker4_res = get_res(1, 0, 1)
-    assert (len(worker4_res) == 40)
+    assert len(worker4_res) == 40
 
 
 def test_case_tf_file_no_schema_columns_list():

tests/ut/python/dataset/test_var_batch_map.py

@@ -59,7 +59,7 @@ def test_batch_corner_cases():
 # to a pyfunc which makes a deep copy of the row
 def test_variable_size_batch():
     def check_res(arr1, arr2):
-        for ind in range(len(arr1)):
+        for ind, _ in enumerate(arr1):
             if not np.array_equal(arr1[ind], np.array(arr2[ind])):
                 return False
         return len(arr1) == len(arr2)
@@ -143,7 +143,7 @@ def test_variable_size_batch():
 
 def test_basic_batch_map():
     def check_res(arr1, arr2):
-        for ind in range(len(arr1)):
+        for ind, _ in enumerate(arr1):
             if not np.array_equal(arr1[ind], np.array(arr2[ind])):
                 return False
         return len(arr1) == len(arr2)
@@ -176,7 +176,7 @@ def test_basic_batch_map():
 
 def test_batch_multi_col_map():
     def check_res(arr1, arr2):
-        for ind in range(len(arr1)):
+        for ind, _ in enumerate(arr1):
             if not np.array_equal(arr1[ind], np.array(arr2[ind])):
                 return False
         return len(arr1) == len(arr2)
@@ -224,7 +224,7 @@ def test_batch_multi_col_map():
 
 def test_var_batch_multi_col_map():
     def check_res(arr1, arr2):
-        for ind in range(len(arr1)):
+        for ind, _ in enumerate(arr1):
             if not np.array_equal(arr1[ind], np.array(arr2[ind])):
                 return False
         return len(arr1) == len(arr2)
@@ -269,7 +269,7 @@ def test_var_batch_var_resize():
         return ([np.copy(c[0:s, 0:s, :]) for c in col],)
 
     def add_one(batchInfo):
-        return (batchInfo.get_batch_num() + 1)
+        return batchInfo.get_batch_num() + 1
 
     data1 = ds.ImageFolderDatasetV2("../data/dataset/testPK/data/", num_parallel_workers=4, decode=True)
     data1 = data1.batch(batch_size=add_one, drop_remainder=True, input_columns=["image"], per_batch_map=np_psedo_resize)
@@ -303,7 +303,7 @@ def test_exception():
 
     data2 = ds.GeneratorDataset((lambda: gen(100)), ["num"]).batch(4, input_columns=["num"], per_batch_map=bad_map_func)
     try:
-        for item in data2.create_dict_iterator():
+        for _ in data2.create_dict_iterator():
             pass
         assert False
     except RuntimeError:

tests/ut/python/mindrecord/test_mindrecord_base.py

@@ -13,9 +13,9 @@
 # limitations under the License.
 # ============================================================================
 """test mindrecord base"""
-import numpy as np
 import os
 import uuid
+import numpy as np
 from utils import get_data, get_nlp_data
 
 from mindspore import log as logger

tests/ut/python/mindrecord/test_mindrecord_multi_images.py

@@ -12,8 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """test write multiple images"""
-import numpy as np
 import os
+import numpy as np
 from utils import get_two_bytes_data, get_multi_bytes_data
 
 from mindspore import log as logger

tests/ut/python/mindrecord/test_mnist_to_mr.py

@@ -14,9 +14,9 @@
 """test mnist to mindrecord tool"""
 import gzip
 import os
+import numpy as np
 
 import cv2
-import numpy as np
 import pytest
 
 from mindspore import log as logger