!1713 [Dataset] Clean pylint.

tests/ut/python/dataset/test_datasets_voc.py

@@ -124,12 +124,12 @@ def test_case_2():
     num_iter = 0
     for _ in dataset1.create_dict_iterator():
         num_iter += 1
-    assert (num_iter == 5)
+    assert num_iter == 5
 
     num_iter = 0
     for _ in dataset2.create_dict_iterator():
         num_iter += 1
-    assert (num_iter == 5)
+    assert num_iter == 5
 
 
 def test_voc_exception():

tests/ut/python/dataset/test_five_crop.py

@@ -14,9 +14,8 @@
 """
 Testing FiveCrop in DE
 """
-import matplotlib.pyplot as plt
-import numpy as np
 import pytest
+import numpy as np
 
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.vision.py_transforms as vision

tests/ut/python/dataset/test_sampler.py

@@ -170,6 +170,7 @@ def test_subset_sampler():
     map_ = {(172876, 0): 0, (54214, 0): 1, (54214, 1): 2, (173673, 0): 3, (64631, 1): 4}
 
     def test_config(num_samples, start_index, subset_size):
+        _ = num_samples
         sampler = ds.SubsetSampler(start_index, subset_size)
         d = ds.ManifestDataset(manifest_file, sampler=sampler)
 

tests/ut/python/dataset/test_sync_wait.py

@@ -204,7 +204,6 @@ def test_sync_exception_03():
     Test sync: with wrong batch size
     """
     logger.info("test_sync_exception_03")
-    batch_size = 6
 
     dataset = ds.GeneratorDataset(gen, column_names=["input"])
 
@@ -223,7 +222,6 @@ def test_sync_exception_04():
     Test sync: with negative batch size in update
     """
     logger.info("test_sync_exception_04")
-    batch_size = 6
 
     dataset = ds.GeneratorDataset(gen, column_names=["input"])
 
@@ -233,7 +231,7 @@ def test_sync_exception_04():
     dataset = dataset.map(input_columns=["input"], operations=[aug.preprocess])
     count = 0
     try:
-        for item in dataset.create_dict_iterator():
+        for _ in dataset.create_dict_iterator():
             count += 1
             data = {"loss": count}
             # dataset.disable_sync()
@@ -246,7 +244,6 @@ def test_sync_exception_05():
     Test sync: with wrong batch size in update
     """
     logger.info("test_sync_exception_05")
-    batch_size = 6
 
     dataset = ds.GeneratorDataset(gen, column_names=["input"])
     count = 0
@@ -255,7 +252,7 @@ def test_sync_exception_05():
     dataset = dataset.sync_wait(condition_name="every batch", callback=aug.update)
     dataset = dataset.map(input_columns=["input"], operations=[aug.preprocess])
     try:
-        for item in dataset.create_dict_iterator():
+        for _ in dataset.create_dict_iterator():
             dataset.disable_sync()
             count += 1
             data = {"loss": count}

tests/ut/python/dataset/test_ten_crop.py

@@ -17,9 +17,9 @@ Testing TenCrop in DE
 import pytest
 import numpy as np
 
+from util import visualize, save_and_check_md5
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.vision.py_transforms as vision
-from util import visualize, save_and_check_md5
 from mindspore import log as logger
 
 GENERATE_GOLDEN = False
@@ -123,7 +123,7 @@ def test_ten_crop_list_size_error_msg():
     logger.info("test_ten_crop_list_size_error_msg")
 
     with pytest.raises(TypeError) as info:
-        transforms = [
+        _ = [
             vision.Decode(),
             vision.TenCrop([200, 200, 200]),
             lambda images: np.stack([vision.ToTensor()(image) for image in images])  # 4D stack of 10 images
@@ -139,7 +139,7 @@ def test_ten_crop_invalid_size_error_msg():
     logger.info("test_ten_crop_invalid_size_error_msg")
 
     with pytest.raises(ValueError) as info:
-        transforms = [
+        _ = [
             vision.Decode(),
             vision.TenCrop(0),
             lambda images: np.stack([vision.ToTensor()(image) for image in images])  # 4D stack of 10 images
@@ -148,7 +148,7 @@ def test_ten_crop_invalid_size_error_msg():
     assert error_msg == str(info.value)
 
     with pytest.raises(ValueError) as info:
-        transforms = [
+        _ = [
             vision.Decode(),
             vision.TenCrop(-10),
             lambda images: np.stack([vision.ToTensor()(image) for image in images])  # 4D stack of 10 images