Cleanup dataset UT: Remove unneeded data files and tests

tests/ut/cpp/dataset/rename_op_test.cc

@@ -51,7 +51,7 @@ TEST_F(MindDataTestRenameOp, TestRenameOpDefault) {
   auto my_tree = std::make_shared<ExecutionTree>();
   // Creating TFReaderOp
 
-  std::string dataset_path = datasets_root_path_ + "/test_tf_file_3_images_1/train-0000-of-0001.data";
+  std::string dataset_path = datasets_root_path_ + "/test_tf_file_3_images/train-0000-of-0001.data";
   std::shared_ptr<TFReaderOp> my_tfreader_op;
   rc = TFReaderOp::Builder()
       .SetDatasetFilesList({dataset_path})

tests/ut/cpp/dataset/zip_op_test.cc

@@ -58,7 +58,7 @@ TEST_F(MindDataTestZipOp, MindDataTestZipOpDefault) {
   auto my_tree = std::make_shared<ExecutionTree>();
   // Creating TFReaderOp
 
-  std::string dataset_path = datasets_root_path_ + "/test_tf_file_3_images_1/train-0000-of-0001.data";
+  std::string dataset_path = datasets_root_path_ + "/test_tf_file_3_images/train-0000-of-0001.data";
   std::string dataset_path2 = datasets_root_path_ + "/testBatchDataset/test.data";
   std::shared_ptr<TFReaderOp> my_tfreader_op;
   rc = TFReaderOp::Builder()
@@ -142,7 +142,7 @@ TEST_F(MindDataTestZipOp, MindDataTestZipOpRepeat) {
   MS_LOG(INFO) << "UT test TestZipRepeat.";
   auto my_tree = std::make_shared<ExecutionTree>();
 
-  std::string dataset_path = datasets_root_path_ + "/test_tf_file_3_images_1/train-0000-of-0001.data";
+  std::string dataset_path = datasets_root_path_ + "/test_tf_file_3_images/train-0000-of-0001.data";
   std::string dataset_path2 = datasets_root_path_ + "/testBatchDataset/test.data";
   std::shared_ptr<TFReaderOp> my_tfreader_op;
   rc = TFReaderOp::Builder()

tests/ut/data/dataset/test_tf_file_3_images_1/datasetSchema.json

-{
-  "datasetType": "TF",
-  "numRows": 3,
-  "columns": {
-    "label": {
-      "type": "int64",
-      "rank": 1,
-      "t_impl": "flex"
-    }
-  }
-}

tests/ut/data/dataset/test_tf_file_3_images_2/datasetSchema.json

-{
-  "datasetType": "TF",
-  "numRows": 3,
-  "columns": {
-    "image": {
-      "type": "uint8",
-      "rank": 1,
-      "t_impl": "cvmat"
-    }
-  }
-}

tests/ut/python/dataset/test_datasets_imagenet.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-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"]
-SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
-
-
-def test_case_repeat():
-    """
-    a simple repeat operation.
-    """
-    logger.info("Test Simple Repeat")
-    # define parameters
-    repeat_count = 2
-
-    # apply dataset operations
-    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
-    data1 = data1.repeat(repeat_count)
-
-    num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
-        # in this example, each dictionary has keys "image" and "label"
-        logger.info("image is: {}".format(item["image"]))
-        logger.info("label is: {}".format(item["label"]))
-        num_iter += 1
-
-    logger.info("Number of data in data1: {}".format(num_iter))
-
-
-def test_case_shuffle():
-    """
-        a simple shuffle operation.
-    """
-    logger.info("Test Simple Shuffle")
-    # define parameters
-    buffer_size = 8
-    seed = 10
-
-    # apply dataset operations
-    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
-    ds.config.set_seed(seed)
-    data1 = data1.shuffle(buffer_size=buffer_size)
-
-    for item in data1.create_dict_iterator():
-        logger.info("image is: {}".format(item["image"]))
-        logger.info("label is: {}".format(item["label"]))
-
-
-def test_case_0():
-    """
-    Test Repeat then Shuffle
-    """
-    logger.info("Test Repeat then Shuffle")
-    # define parameters
-    repeat_count = 2
-    buffer_size = 7
-    seed = 9
-
-    # apply dataset operations
-    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
-    data1 = data1.repeat(repeat_count)
-    ds.config.set_seed(seed)
-    data1 = data1.shuffle(buffer_size=buffer_size)
-
-    num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
-        # in this example, each dictionary has keys "image" and "label"
-        logger.info("image is: {}".format(item["image"]))
-        logger.info("label is: {}".format(item["label"]))
-        num_iter += 1
-
-    logger.info("Number of data in data1: {}".format(num_iter))
-
-
-def test_case_0_reverse():
-    """
-    Test Shuffle then Repeat
-    """
-    logger.info("Test Shuffle then Repeat")
-    # define parameters
-    repeat_count = 2
-    buffer_size = 10
-    seed = 9
-
-    # apply dataset operations
-    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
-    ds.config.set_seed(seed)
-    data1 = data1.shuffle(buffer_size=buffer_size)
-    data1 = data1.repeat(repeat_count)
-
-    num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
-        # in this example, each dictionary has keys "image" and "label"
-        logger.info("image is: {}".format(item["image"]))
-        logger.info("label is: {}".format(item["label"]))
-        num_iter += 1
-
-    logger.info("Number of data in data1: {}".format(num_iter))
-
-
-def test_case_3():
-    """
-    Test Map
-    """
-    logger.info("Test Map Rescale and Resize, then Shuffle")
-    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
-    # define data augmentation parameters
-    rescale = 1.0 / 255.0
-    shift = 0.0
-    resize_height, resize_width = 224, 224
-
-    # define map operations
-    decode_op = vision.Decode()
-    rescale_op = vision.Rescale(rescale, shift)
-    # resize_op = vision.Resize(resize_height, resize_width,
-    #                            InterpolationMode.DE_INTER_LINEAR)  # Bilinear mode
-    resize_op = vision.Resize((resize_height, resize_width))
-
-    # apply map operations on images
-    data1 = data1.map(input_columns=["image"], operations=decode_op)
-    data1 = data1.map(input_columns=["image"], operations=rescale_op)
-    data1 = data1.map(input_columns=["image"], operations=resize_op)
-
-    # # apply ont-hot encoding on labels
-    num_classes = 4
-    one_hot_encode = data_trans.OneHot(num_classes)  # num_classes is input argument
-    data1 = data1.map(input_columns=["label"], operations=one_hot_encode)
-    #
-    # # apply Datasets
-    buffer_size = 100
-    seed = 10
-    batch_size = 2
-    ds.config.set_seed(seed)
-    data1 = data1.shuffle(buffer_size=buffer_size)  # 10000 as in imageNet train script
-    data1 = data1.batch(batch_size, drop_remainder=True)
-
-    num_iter = 0
-    for item in data1.create_dict_iterator():  # each data is a dictionary
-        # in this example, each dictionary has keys "image" and "label"
-        logger.info("image is: {}".format(item["image"]))
-        logger.info("label is: {}".format(item["label"]))
-        num_iter += 1
-
-    logger.info("Number of data in data1: {}".format(num_iter))
-
-
-if __name__ == '__main__':
-    logger.info('===========now test Repeat============')
-    # logger.info('Simple Repeat')
-    test_case_repeat()
-    logger.info('\n')
-
-    logger.info('===========now test Shuffle===========')
-    # logger.info('Simple Shuffle')
-    test_case_shuffle()
-    logger.info('\n')
-
-    # Note: cannot work with different shapes, hence not for image
-    # logger.info('===========now test Batch=============')
-    # # logger.info('Simple Batch')
-    # test_case_batch()
-    # logger.info('\n')
-
-    logger.info('===========now test case 0============')
-    # logger.info('Repeat then Shuffle')
-    test_case_0()
-    logger.info('\n')
-
-    logger.info('===========now test case 0 reverse============')
-    # # logger.info('Shuffle then  Repeat')
-    test_case_0_reverse()
-    logger.info('\n')
-
-    # logger.info('===========now test case 1============')
-    # # logger.info('Repeat with Batch')
-    # test_case_1()
-    # logger.info('\n')
-
-    # logger.info('===========now test case 2============')
-    # # logger.info('Batch with Shuffle')
-    # test_case_2()
-    # logger.info('\n')
-
-    # for image augmentation only
-    logger.info('===========now test case 3============')
-    logger.info('Map then Shuffle')
-    test_case_3()
-    logger.info('\n')

tests/ut/python/dataset/test_datasets_imagenet_distribution.py

-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-import mindspore.dataset as ds
-from mindspore import log as logger
-
-DATA_DIR = ["../data/dataset/test_tf_file_3_images2/train-0000-of-0001.data",
-            "../data/dataset/test_tf_file_3_images2/train-0000-of-0002.data",
-            "../data/dataset/test_tf_file_3_images2/train-0000-of-0003.data",
-            "../data/dataset/test_tf_file_3_images2/train-0000-of-0004.data"]
-
-SCHEMA_DIR = "../data/dataset/test_tf_file_3_images2/datasetSchema.json"
-
-
-def test_tf_file_normal():
-    # apply dataset operations
-    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
-    data1 = data1.repeat(1)
-    num_iter = 0
-    for _ in data1.create_dict_iterator():  # each data is a dictionary
-        num_iter += 1
-
-    logger.info("Number of data in data1: {}".format(num_iter))
-    assert num_iter == 12
-
-
-if __name__ == '__main__':
-    logger.info('=======test normal=======')
-    test_tf_file_normal()

tests/ut/python/dataset/test_onehot_op.py

@@ -13,12 +13,13 @@
 # limitations under the License.
 # ==============================================================================
 """
-Testing the one_hot op in DE
+Testing the OneHot Op
 """
 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 c_vision
 from mindspore import log as logger
 from util import diff_mse
 
@@ -37,15 +38,15 @@ def one_hot(index, depth):
 
 def test_one_hot():
     """
-    Test one_hot
+    Test OneHot Tensor Operator
     """
-    logger.info("Test one_hot")
+    logger.info("test_one_hot")
 
     depth = 10
 
     # First dataset
     data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
-    one_hot_op = data_trans.OneHot(depth)
+    one_hot_op = data_trans.OneHot(num_classes=depth)
     data1 = data1.map(input_columns=["label"], operations=one_hot_op, columns_order=["label"])
 
     # Second dataset
@@ -58,8 +59,54 @@ def test_one_hot():
         label2 = one_hot(item2["label"][0], depth)
         mse = diff_mse(label1, label2)
         logger.info("DE one_hot: {}, Numpy one_hot: {}, diff: {}".format(label1, label2, mse))
+        assert mse == 0
         num_iter += 1
+    assert num_iter == 3
+
+def test_one_hot_post_aug():
+    """
+    Test One Hot Encoding after Multiple Data Augmentation Operators
+    """
+    logger.info("test_one_hot_post_aug")
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
+
+    # Define data augmentation parameters
+    rescale = 1.0 / 255.0
+    shift = 0.0
+    resize_height, resize_width = 224, 224
+
+    # Define map operations
+    decode_op = c_vision.Decode()
+    rescale_op = c_vision.Rescale(rescale, shift)
+    resize_op = c_vision.Resize((resize_height, resize_width))
+
+    # Apply map operations on images
+    data1 = data1.map(input_columns=["image"], operations=decode_op)
+    data1 = data1.map(input_columns=["image"], operations=rescale_op)
+    data1 = data1.map(input_columns=["image"], operations=resize_op)
+
+    # Apply one-hot encoding on labels
+    depth = 4
+    one_hot_encode = data_trans.OneHot(depth)
+    data1 = data1.map(input_columns=["label"], operations=one_hot_encode)
+
+    # Apply datasets ops
+    buffer_size = 100
+    seed = 10
+    batch_size = 2
+    ds.config.set_seed(seed)
+    data1 = data1.shuffle(buffer_size=buffer_size)
+    data1 = data1.batch(batch_size, drop_remainder=True)
+
+    num_iter = 0
+    for item in data1.create_dict_iterator():
+        logger.info("image is: {}".format(item["image"]))
+        logger.info("label is: {}".format(item["label"]))
+        num_iter += 1
+
+    assert num_iter == 1
 
 
 if __name__ == "__main__":
     test_one_hot()
+    test_one_hot_post_aug()

tests/ut/python/dataset/test_repeat.py

@@ -12,25 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
+"""
+Test Repeat Op
+"""
 import numpy as np
-from util import save_and_check
 
 import mindspore.dataset as ds
 import mindspore.dataset.transforms.vision.c_transforms as vision
 from mindspore import log as logger
+from util import save_and_check_dict
 
 DATA_DIR_TF = ["../data/dataset/testTFTestAllTypes/test.data"]
 SCHEMA_DIR_TF = "../data/dataset/testTFTestAllTypes/datasetSchema.json"
-COLUMNS_TF = ["col_1d", "col_2d", "col_3d", "col_binary", "col_float",
-              "col_sint16", "col_sint32", "col_sint64"]
-GENERATE_GOLDEN = False
-
-IMG_DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
-IMG_SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
 
 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"
 
+GENERATE_GOLDEN = False
+
 
 def test_tf_repeat_01():
     """
@@ -39,14 +38,13 @@ def test_tf_repeat_01():
     logger.info("Test Simple Repeat")
     # define parameters
     repeat_count = 2
-    parameters = {"params": {'repeat_count': repeat_count}}
 
     # apply dataset operations
     data1 = ds.TFRecordDataset(DATA_DIR_TF, SCHEMA_DIR_TF, shuffle=False)
     data1 = data1.repeat(repeat_count)
 
     filename = "repeat_result.npz"
-    save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN)
 
 
 def test_tf_repeat_02():
@@ -99,14 +97,13 @@ def test_tf_repeat_04():
     logger.info("Test Simple Repeat Column List")
     # define parameters
     repeat_count = 2
-    parameters = {"params": {'repeat_count': repeat_count}}
     columns_list = ["col_sint64", "col_sint32"]
     # apply dataset operations
     data1 = ds.TFRecordDataset(DATA_DIR_TF, SCHEMA_DIR_TF, columns_list=columns_list, shuffle=False)
     data1 = data1.repeat(repeat_count)
 
     filename = "repeat_list_result.npz"
-    save_and_check(data1, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    save_and_check_dict(data1, filename, generate_golden=GENERATE_GOLDEN)
 
 
 def generator():
@@ -115,6 +112,7 @@ def generator():
 
 
 def test_nested_repeat1():
+    logger.info("test_nested_repeat1")
     data = ds.GeneratorDataset(generator, ["data"])
     data = data.repeat(2)
     data = data.repeat(3)
@@ -126,6 +124,7 @@ def test_nested_repeat1():
 
 
 def test_nested_repeat2():
+    logger.info("test_nested_repeat2")
     data = ds.GeneratorDataset(generator, ["data"])
     data = data.repeat(1)
     data = data.repeat(1)
@@ -137,6 +136,7 @@ def test_nested_repeat2():
 
 
 def test_nested_repeat3():
+    logger.info("test_nested_repeat3")
     data = ds.GeneratorDataset(generator, ["data"])
     data = data.repeat(1)
     data = data.repeat(2)
@@ -148,6 +148,7 @@ def test_nested_repeat3():
 
 
 def test_nested_repeat4():
+    logger.info("test_nested_repeat4")
     data = ds.GeneratorDataset(generator, ["data"])
     data = data.repeat(2)
     data = data.repeat(1)
@@ -159,6 +160,7 @@ def test_nested_repeat4():
 
 
 def test_nested_repeat5():
+    logger.info("test_nested_repeat5")
     data = ds.GeneratorDataset(generator, ["data"])
     data = data.batch(3)
     data = data.repeat(2)
@@ -171,6 +173,7 @@ def test_nested_repeat5():
 
 
 def test_nested_repeat6():
+    logger.info("test_nested_repeat6")
     data = ds.GeneratorDataset(generator, ["data"])
     data = data.repeat(2)
     data = data.batch(3)
@@ -183,6 +186,7 @@ def test_nested_repeat6():
 
 
 def test_nested_repeat7():
+    logger.info("test_nested_repeat7")
     data = ds.GeneratorDataset(generator, ["data"])
     data = data.repeat(2)
     data = data.repeat(3)
@@ -195,6 +199,7 @@ def test_nested_repeat7():
 
 
 def test_nested_repeat8():
+    logger.info("test_nested_repeat8")
     data = ds.GeneratorDataset(generator, ["data"])
     data = data.batch(2, drop_remainder=False)
     data = data.repeat(2)
@@ -210,6 +215,7 @@ def test_nested_repeat8():
 
 
 def test_nested_repeat9():
+    logger.info("test_nested_repeat9")
     data = ds.GeneratorDataset(generator, ["data"])
     data = data.repeat()
     data = data.repeat(3)
@@ -221,6 +227,7 @@ def test_nested_repeat9():
 
 
 def test_nested_repeat10():
+    logger.info("test_nested_repeat10")
     data = ds.GeneratorDataset(generator, ["data"])
     data = data.repeat(3)
     data = data.repeat()
@@ -232,6 +239,7 @@ def test_nested_repeat10():
 
 
 def test_nested_repeat11():
+    logger.info("test_nested_repeat11")
     data = ds.GeneratorDataset(generator, ["data"])
     data = data.repeat(2)
     data = data.repeat(3)

tests/ut/python/dataset/test_tfreader_op.py

@@ -12,21 +12,30 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
+"""
+Test TFRecordDataset Ops
+"""
 import numpy as np
 import pytest
-from util import save_and_check
 
 import mindspore.common.dtype as mstype
 import mindspore.dataset as ds
 from mindspore import log as logger
+from util import save_and_check_dict
 
 FILES = ["../data/dataset/testTFTestAllTypes/test.data"]
 DATASET_ROOT = "../data/dataset/testTFTestAllTypes/"
 SCHEMA_FILE = "../data/dataset/testTFTestAllTypes/datasetSchema.json"
+DATA_FILES2 = ["../data/dataset/test_tf_file_3_images2/train-0000-of-0001.data",
+               "../data/dataset/test_tf_file_3_images2/train-0000-of-0002.data",
+               "../data/dataset/test_tf_file_3_images2/train-0000-of-0003.data",
+               "../data/dataset/test_tf_file_3_images2/train-0000-of-0004.data"]
+SCHEMA_FILE2 = "../data/dataset/test_tf_file_3_images2/datasetSchema.json"
 GENERATE_GOLDEN = False
 
 
-def test_case_tf_shape():
+def test_tfrecord_shape():
+    logger.info("test_tfrecord_shape")
     schema_file = "../data/dataset/testTFTestAllTypes/datasetSchemaRank0.json"
     ds1 = ds.TFRecordDataset(FILES, schema_file)
     ds1 = ds1.batch(2)
@@ -36,7 +45,8 @@ def test_case_tf_shape():
     assert len(output_shape[-1]) == 1
 
 
-def test_case_tf_read_all_dataset():
+def test_tfrecord_read_all_dataset():
+    logger.info("test_tfrecord_read_all_dataset")
     schema_file = "../data/dataset/testTFTestAllTypes/datasetSchemaNoRow.json"
     ds1 = ds.TFRecordDataset(FILES, schema_file)
     assert ds1.get_dataset_size() == 12
@@ -46,7 +56,8 @@ def test_case_tf_read_all_dataset():
     assert count == 12
 
 
-def test_case_num_samples():
+def test_tfrecord_num_samples():
+    logger.info("test_tfrecord_num_samples")
     schema_file = "../data/dataset/testTFTestAllTypes/datasetSchema7Rows.json"
     ds1 = ds.TFRecordDataset(FILES, schema_file, num_samples=8)
     assert ds1.get_dataset_size() == 8
@@ -56,7 +67,8 @@ def test_case_num_samples():
     assert count == 8
 
 
-def test_case_num_samples2():
+def test_tfrecord_num_samples2():
+    logger.info("test_tfrecord_num_samples2")
     schema_file = "../data/dataset/testTFTestAllTypes/datasetSchema7Rows.json"
     ds1 = ds.TFRecordDataset(FILES, schema_file)
     assert ds1.get_dataset_size() == 7
@@ -66,42 +78,41 @@ def test_case_num_samples2():
     assert count == 7
 
 
-def test_case_tf_shape_2():
+def test_tfrecord_shape2():
+    logger.info("test_tfrecord_shape2")
     ds1 = ds.TFRecordDataset(FILES, SCHEMA_FILE)
     ds1 = ds1.batch(2)
     output_shape = ds1.output_shapes()
     assert len(output_shape[-1]) == 2
 
 
-def test_case_tf_file():
-    logger.info("reading data from: {}".format(FILES[0]))
-    parameters = {"params": {}}
+def test_tfrecord_files_basic():
+    logger.info("test_tfrecord_files_basic")
 
     data = ds.TFRecordDataset(FILES, SCHEMA_FILE, shuffle=ds.Shuffle.FILES)
-    filename = "tfreader_result.npz"
-    save_and_check(data, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    filename = "tfrecord_files_basic.npz"
+    save_and_check_dict(data, filename, generate_golden=GENERATE_GOLDEN)
 
 
-def test_case_tf_file_no_schema():
-    logger.info("reading data from: {}".format(FILES[0]))
-    parameters = {"params": {}}
+def test_tfrecord_no_schema():
+    logger.info("test_tfrecord_no_schema")
 
     data = ds.TFRecordDataset(FILES, shuffle=ds.Shuffle.FILES)
-    filename = "tf_file_no_schema.npz"
-    save_and_check(data, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    filename = "tfrecord_no_schema.npz"
+    save_and_check_dict(data, filename, generate_golden=GENERATE_GOLDEN)
 
 
-def test_case_tf_file_pad():
-    logger.info("reading data from: {}".format(FILES[0]))
-    parameters = {"params": {}}
+def test_tfrecord_pad():
+    logger.info("test_tfrecord_pad")
 
     schema_file = "../data/dataset/testTFTestAllTypes/datasetSchemaPadBytes10.json"
     data = ds.TFRecordDataset(FILES, schema_file, shuffle=ds.Shuffle.FILES)
-    filename = "tf_file_padBytes10.npz"
-    save_and_check(data, parameters, filename, generate_golden=GENERATE_GOLDEN)
+    filename = "tfrecord_pad_bytes10.npz"
+    save_and_check_dict(data, filename, generate_golden=GENERATE_GOLDEN)
 
 
-def test_tf_files():
+def test_tfrecord_read_files():
+    logger.info("test_tfrecord_read_files")
     pattern = DATASET_ROOT + "/test.data"
     data = ds.TFRecordDataset(pattern, SCHEMA_FILE, shuffle=ds.Shuffle.FILES)
     assert sum([1 for _ in data]) == 12
@@ -123,7 +134,19 @@ def test_tf_files():
     assert sum([1 for _ in data]) == 24
 
 
-def test_tf_record_schema():
+def test_tfrecord_multi_files():
+    logger.info("test_tfrecord_multi_files")
+    data1 = ds.TFRecordDataset(DATA_FILES2, SCHEMA_FILE2, shuffle=False)
+    data1 = data1.repeat(1)
+    num_iter = 0
+    for _ in data1.create_dict_iterator():
+        num_iter += 1
+
+    assert num_iter == 12
+
+
+def test_tfrecord_schema():
+    logger.info("test_tfrecord_schema")
     schema = ds.Schema()
     schema.add_column('col_1d', de_type=mstype.int64, shape=[2])
     schema.add_column('col_2d', de_type=mstype.int64, shape=[2, 2])
@@ -142,7 +165,8 @@ def test_tf_record_schema():
             assert np.array_equal(t1, t2)
 
 
-def test_tf_record_shuffle():
+def test_tfrecord_shuffle():
+    logger.info("test_tfrecord_shuffle")
     ds.config.set_seed(1)
     data1 = ds.TFRecordDataset(FILES, schema=SCHEMA_FILE, shuffle=ds.Shuffle.GLOBAL)
     data2 = ds.TFRecordDataset(FILES, schema=SCHEMA_FILE, shuffle=ds.Shuffle.FILES)
@@ -153,7 +177,8 @@ def test_tf_record_shuffle():
             assert np.array_equal(t1, t2)
 
 
-def test_tf_record_shard():
+def test_tfrecord_shard():
+    logger.info("test_tfrecord_shard")
     tf_files = ["../data/dataset/tf_file_dataset/test1.data", "../data/dataset/tf_file_dataset/test2.data",
                 "../data/dataset/tf_file_dataset/test3.data", "../data/dataset/tf_file_dataset/test4.data"]
 
@@ -181,7 +206,8 @@ def test_tf_record_shard():
     assert set(worker2_res) == set(worker1_res)
 
 
-def test_tf_shard_equal_rows():
+def test_tfrecord_shard_equal_rows():
+    logger.info("test_tfrecord_shard_equal_rows")
     tf_files = ["../data/dataset/tf_file_dataset/test1.data", "../data/dataset/tf_file_dataset/test2.data",
                 "../data/dataset/tf_file_dataset/test3.data", "../data/dataset/tf_file_dataset/test4.data"]
 
@@ -209,7 +235,8 @@ def test_tf_shard_equal_rows():
     assert len(worker4_res) == 40
 
 
-def test_case_tf_file_no_schema_columns_list():
+def test_tfrecord_no_schema_columns_list():
+    logger.info("test_tfrecord_no_schema_columns_list")
     data = ds.TFRecordDataset(FILES, shuffle=False, columns_list=["col_sint16"])
     row = data.create_dict_iterator().get_next()
     assert row["col_sint16"] == [-32768]
@@ -219,7 +246,8 @@ def test_case_tf_file_no_schema_columns_list():
     assert "col_sint32" in str(info.value)
 
 
-def test_tf_record_schema_columns_list():
+def test_tfrecord_schema_columns_list():
+    logger.info("test_tfrecord_schema_columns_list")
     schema = ds.Schema()
     schema.add_column('col_1d', de_type=mstype.int64, shape=[2])
     schema.add_column('col_2d', de_type=mstype.int64, shape=[2, 2])
@@ -238,7 +266,8 @@ def test_tf_record_schema_columns_list():
     assert "col_sint32" in str(info.value)
 
 
-def test_case_invalid_files():
+def test_tfrecord_invalid_files():
+    logger.info("test_tfrecord_invalid_files")
     valid_file = "../data/dataset/testTFTestAllTypes/test.data"
     invalid_file = "../data/dataset/testTFTestAllTypes/invalidFile.txt"
     files = [invalid_file, valid_file, SCHEMA_FILE]
@@ -266,19 +295,20 @@ def test_case_invalid_files():
 
 
 if __name__ == '__main__':
-    test_case_tf_shape()
-    test_case_tf_read_all_dataset()
-    test_case_num_samples()
-    test_case_num_samples2()
-    test_case_tf_shape_2()
-    test_case_tf_file()
-    test_case_tf_file_no_schema()
-    test_case_tf_file_pad()
-    test_tf_files()
-    test_tf_record_schema()
-    test_tf_record_shuffle()
-    test_tf_record_shard()
-    test_tf_shard_equal_rows()
-    test_case_tf_file_no_schema_columns_list()
-    test_tf_record_schema_columns_list()
-    test_case_invalid_files()
+    test_tfrecord_shape()
+    test_tfrecord_read_all_dataset()
+    test_tfrecord_num_samples()
+    test_tfrecord_num_samples2()
+    test_tfrecord_shape2()
+    test_tfrecord_files_basic()
+    test_tfrecord_no_schema()
+    test_tfrecord_pad()
+    test_tfrecord_read_files()
+    test_tfrecord_multi_files()
+    test_tfrecord_schema()
+    test_tfrecord_shuffle()
+    test_tfrecord_shard()
+    test_tfrecord_shard_equal_rows()
+    test_tfrecord_no_schema_columns_list()
+    test_tfrecord_schema_columns_list()
+    test_tfrecord_invalid_files()