diff --git a/mindspore/explainer/_image_classification_runner.py b/mindspore/explainer/_image_classification_runner.py
index ba398ff65d..d3a173e34d 100644
--- a/mindspore/explainer/_image_classification_runner.py
+++ b/mindspore/explainer/_image_classification_runner.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -199,7 +199,7 @@ class ImageClassificationRunner:
         """
         self._verify_data_n_settings(check_all=True)
 
-        with SummaryRecord(self._summary_dir) as summary:
+        with SummaryRecord(self._summary_dir, raise_exception=True) as summary:
             print("Start running and writing......")
             begin = time()
 
diff --git a/mindspore/train/callback/_summary_collector.py b/mindspore/train/callback/_summary_collector.py
index 93c79bef73..84f35398f2 100644
--- a/mindspore/train/callback/_summary_collector.py
+++ b/mindspore/train/callback/_summary_collector.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -207,7 +207,9 @@ class SummaryCollector(Callback):
         self._dataset_sink_mode = True
 
     def __enter__(self):
-        self._record = SummaryRecord(log_dir=self._summary_dir, max_file_size=self._max_file_size)
+        self._record = SummaryRecord(log_dir=self._summary_dir,
+                                     max_file_size=self._max_file_size,
+                                     raise_exception=False)
         self._first_step, self._dataset_sink_mode = True, True
         return self
 
@@ -319,7 +321,14 @@ class SummaryCollector(Callback):
                              f'expect the follow keys: {list(self._DEFAULT_SPECIFIED_DATA.keys())}')
 
         if 'histogram_regular' in specified_data:
-            check_value_type('histogram_regular', specified_data.get('histogram_regular'), (str, type(None)))
+            regular = specified_data.get('histogram_regular')
+            check_value_type('histogram_regular', regular, (str, type(None)))
+            if isinstance(regular, str):
+                try:
+                    re.match(regular, '')
+                except re.error as exc:
+                    raise ValueError(f'For `collect_specified_data`, the value of `histogram_regular` '
+                                     f'is not a valid regular expression. Detail: {str(exc)}.')
 
         bool_items = set(self._DEFAULT_SPECIFIED_DATA) - {'histogram_regular'}
         for item in bool_items:
diff --git a/mindspore/train/summary/_writer_pool.py b/mindspore/train/summary/_writer_pool.py
index a583b17083..4fbfa8bcfa 100644
--- a/mindspore/train/summary/_writer_pool.py
+++ b/mindspore/train/summary/_writer_pool.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -57,14 +57,17 @@ class WriterPool(ctx.Process):
     Args:
         base_dir (str): The base directory to hold all the files.
         max_file_size (Optional[int]): The maximum size of each file that can be written to disk in bytes.
+        raise_exception (bool, optional): Sets whether to throw an exception when an RuntimeError exception occurs
+            in recording data. Default: False, this means that error logs are printed and no exception is thrown.
         filedict (dict): The mapping from plugin to filename.
     """
 
-    def __init__(self, base_dir, max_file_size, **filedict) -> None:
+    def __init__(self, base_dir, max_file_size, raise_exception=False, **filedict) -> None:
         super().__init__()
         self._base_dir, self._filedict = base_dir, filedict
         self._queue, self._writers_ = ctx.Queue(ctx.cpu_count() * 2), None
         self._max_file_size = max_file_size
+        self._raise_exception = raise_exception
         self.start()
 
     def run(self):
@@ -119,8 +122,14 @@ class WriterPool(ctx.Process):
         for writer in self._writers[:]:
             try:
                 writer.write(plugin, data)
-            except RuntimeError as e:
-                logger.warning(e.args[0])
+            except RuntimeError as exc:
+                logger.error(str(exc))
+                self._writers.remove(writer)
+                writer.close()
+                if self._raise_exception:
+                    raise
+            except RuntimeWarning as exc:
+                logger.warning(str(exc))
                 self._writers.remove(writer)
                 writer.close()
 
diff --git a/mindspore/train/summary/summary_record.py b/mindspore/train/summary/summary_record.py
index f7db1d857f..4c0e199723 100644
--- a/mindspore/train/summary/summary_record.py
+++ b/mindspore/train/summary/summary_record.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -20,6 +20,7 @@ import threading
 from collections import defaultdict
 
 from mindspore import log as logger
+from mindspore.nn import Cell
 
 from ..._c_expression import Tensor
 from ..._checkparam import Validator
@@ -29,7 +30,7 @@ from ._explain_adapter import check_explain_proto
 from ._writer_pool import WriterPool
 
 # for the moment, this lock is for caution's sake,
-# there are actually no any concurrencies happening.
+# there are actually no any concurrences happening.
 _summary_lock = threading.Lock()
 # cache the summary data
 _summary_tensor_cache = {}
@@ -56,10 +57,6 @@ def _get_summary_tensor_data():
         return data
 
 
-def _dictlist():
-    return defaultdict(list)
-
-
 class SummaryRecord:
     """
     SummaryRecord is used to record the summary data and lineage data.
@@ -80,12 +77,13 @@ class SummaryRecord:
         file_prefix (str): The prefix of file. Default: "events".
         file_suffix (str): The suffix of file. Default: "_MS".
         network (Cell): Obtain a pipeline through network for saving graph summary. Default: None.
-        max_file_size (Optional[int]): The maximum size of each file that can be written to disk (in bytes). \
+        max_file_size (int, optional): The maximum size of each file that can be written to disk (in bytes). \
             Unlimited by default. For example, to write not larger than 4GB, specify `max_file_size=4 * 1024**3`.
+        raise_exception (bool, optional): Sets whether to throw an exception when an RuntimeError exception occurs
+            in recording data. Default: False, this means that error logs are printed and no exception is thrown.
 
     Raises:
-        TypeError: If the type of `max_file_size` is not int, or the type of `file_prefix` or `file_suffix` is not str.
-        RuntimeError: If the log_dir is not a normalized absolute path name.
+        TypeError: If the parameter type is incorrect.
 
     Examples:
         >>> # use in with statement to auto close
@@ -100,10 +98,11 @@ class SummaryRecord:
         ...     summary_record.close()
     """
 
-    def __init__(self, log_dir, file_prefix="events", file_suffix="_MS", network=None, max_file_size=None):
+    def __init__(self, log_dir, file_prefix="events", file_suffix="_MS",
+                 network=None, max_file_size=None, raise_exception=False):
 
         self._closed, self._event_writer = False, None
-        self._mode, self._data_pool = 'train', _dictlist()
+        self._mode, self._data_pool = 'train', defaultdict(list)
 
         Validator.check_str_by_regular(file_prefix)
         Validator.check_str_by_regular(file_suffix)
@@ -120,6 +119,8 @@ class SummaryRecord:
             logger.warning("The 'max_file_size' should be greater than 0.")
             max_file_size = None
 
+        Validator.check_value_type(arg_name='raise_exception', arg_value=raise_exception, valid_types=bool)
+
         self.prefix = file_prefix
         self.suffix = file_suffix
         self.network = network
@@ -127,16 +128,15 @@ class SummaryRecord:
 
         # create the summary writer file
         self.event_file_name = get_event_file_name(self.prefix, self.suffix)
-        try:
-            self.full_file_name = os.path.join(self.log_path, self.event_file_name)
-        except Exception as ex:
-            raise RuntimeError(ex)
+        self.full_file_name = os.path.join(self.log_path, self.event_file_name)
 
+        filename_dict = dict(summary=self.full_file_name,
+                             lineage=get_event_file_name(self.prefix, '_lineage'),
+                             explainer=get_event_file_name(self.prefix, '_explain'))
         self._event_writer = WriterPool(log_dir,
                                         max_file_size,
-                                        summary=self.full_file_name,
-                                        lineage=get_event_file_name(self.prefix, '_lineage'),
-                                        explainer=get_event_file_name(self.prefix, '_explain'))
+                                        raise_exception,
+                                        **filename_dict)
         _get_summary_tensor_data()
         atexit.register(self.close)
 
@@ -195,8 +195,8 @@ class SummaryRecord:
                 - The data type of value should be a 'Explain' object when the plugin is 'explainer',
                   see mindspore/ccsrc/summary.proto.
         Raises:
-            ValueError: When the name is not valid.
-            TypeError: When the value is not a Tensor.
+            ValueError: If the parameter value is invalid.
+            TypeError: If the parameter type is error.
 
         Examples:
             >>> with SummaryRecord(log_dir="./summary_dir", file_prefix="xxx_", file_suffix="_yyy") as summary_record:
@@ -238,6 +238,10 @@ class SummaryRecord:
         Returns:
             bool, whether the record process is successful or not.
 
+        Raises:
+            TypeError: If the parameter type is error.
+            RuntimeError: If the disk space is insufficient.
+
         Examples:
             >>> with SummaryRecord(log_dir="./summary_dir", file_prefix="xxx_", file_suffix="_yyy") as summary_record:
             ...     summary_record.record(step=2)
@@ -245,11 +249,12 @@ class SummaryRecord:
             True
         """
         logger.debug("SummaryRecord step is %r.", step)
+        Validator.check_value_type(arg_name='step', arg_value=step, valid_types=int)
+        Validator.check_value_type(arg_name='train_network', arg_value=train_network, valid_types=[Cell, type(None)])
+
         if self._closed:
             logger.error("The record writer is closed.")
             return False
-        if not isinstance(step, int) or isinstance(step, bool):
-            raise ValueError("`step` should be int")
         # Set the current summary of train step
         if self.network is not None and not self.has_graph:
             graph_proto = self.network.get_func_graph_proto()
@@ -294,7 +299,7 @@ class SummaryRecord:
                     value['step'] = step
             return self._data_pool
         finally:
-            self._data_pool = _dictlist()
+            self._data_pool = defaultdict(list)
 
     @property
     def log_dir(self):
diff --git a/mindspore/train/summary/writer.py b/mindspore/train/summary/writer.py
index 1a8e424473..b74ac7587b 100644
--- a/mindspore/train/summary/writer.py
+++ b/mindspore/train/summary/writer.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -60,8 +60,8 @@ class BaseWriter:
             self._max_file_size -= required_length
             self.writer.Write(data)
         else:
-            raise RuntimeError(f"'max_file_size' reached: There are {self._max_file_size} bytes remaining, "
-                               f"but the '{self._filepath}' requires to write {required_length} bytes.")
+            raise RuntimeWarning(f"'max_file_size' reached: There are {self._max_file_size} bytes remaining, "
+                                 f"but the '{self._filepath}' requires to write {required_length} bytes.")
 
     def flush(self):
         """Flush the writer."""
diff --git a/tests/ut/python/train/summary/test_image_summary.py b/tests/ut/python/train/summary/test_image_summary.py
index addeaec212..6801f3f1b4 100644
--- a/tests/ut/python/train/summary/test_image_summary.py
+++ b/tests/ut/python/train/summary/test_image_summary.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -12,12 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-"""
-@File  : test_image_summary.py
-@Author:
-@Date  : 2019-07-4
-@Desc  : test summary function
-"""
+"""test_image_summary"""
 import logging
 import os
 import numpy as np
@@ -70,23 +65,14 @@ def get_test_data(step):
 # Test: call method on parse graph code
 def test_image_summary_sample():
     """ test_image_summary_sample """
-    log.debug("begin test_image_summary_sample")
-    # step 0: create the thread
     with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_IMAGE") as test_writer:
-        # step 1: create the test data for summary
 
-        # step 2: create the Event
         for i in range(1, 5):
             test_data = get_test_data(i)
             _cache_summary_tensor_data(test_data)
             test_writer.record(i)
             test_writer.flush()
 
-        # step 3: send the event to mq
-
-        # step 4: accept the event and write the file
-        log.debug("finished test_image_summary_sample")
-
 
 class Net(nn.Cell):
     """ Net definition """
@@ -175,23 +161,11 @@ class ImageSummaryCallback(Callback):
 def test_image_summary_train():
     """ test_image_summary_train """
     dataset = get_dataset()
-
-    log.debug("begin test_image_summary_sample")
-    # step 0: create the thread
     with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_IMAGE") as test_writer:
-        # step 1: create the test data for summary
-
-        # step 2: create the Event
-
         model = get_model()
         callback = ImageSummaryCallback(test_writer)
         model.train(2, dataset, callbacks=[callback])
 
-        # step 3: send the event to mq
-
-        # step 4: accept the event and write the file
-        log.debug("finished test_image_summary_sample")
-
 
 def test_image_summary_data():
     """ test_image_summary_data """
@@ -207,13 +181,6 @@ def test_image_summary_data():
         test_data_list.append(dct)
         i += 1
 
-    log.debug("begin test_image_summary_sample")
-    # step 0: create the thread
     with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_IMAGE") as test_writer:
-        # step 1: create the test data for summary
-
-        # step 2: create the Event
         _cache_summary_tensor_data(test_data_list)
         test_writer.record(1)
-
-        log.debug("finished test_image_summary_sample")
diff --git a/tests/ut/python/train/summary/test_summary.py b/tests/ut/python/train/summary/test_summary.py
index b069d89954..cf60780413 100644
--- a/tests/ut/python/train/summary/test_summary.py
+++ b/tests/ut/python/train/summary/test_summary.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -12,17 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-"""
-@File  : test_summary.py
-@Author:
-@Date  : 2019-07-4
-@Desc  : test summary function
-"""
-import logging
+"""Test summary."""
 import os
 import random
+
 import numpy as np
-import pytest
+
 
 import mindspore.nn as nn
 from mindspore.common.tensor import Tensor
@@ -32,9 +27,6 @@ from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary
 CUR_DIR = os.getcwd()
 SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/"
 
-log = logging.getLogger("test")
-log.setLevel(level=logging.ERROR)
-
 
 def get_test_data(step):
     """ get_test_data """
@@ -58,26 +50,14 @@ def get_test_data(step):
     return test_data_list
 
 
-# Test 1: summary sample of scalar
 def test_scalar_summary_sample():
     """ test_scalar_summary_sample """
-    log.debug("begin test_scalar_summary_sample")
-    # step 0: create the thread
     with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR") as test_writer:
-        # step 1: create the test data for summary
-
-        # step 2: create the Event
-        for i in range(1, 500):
+        for i in range(1, 5):
             test_data = get_test_data(i)
             _cache_summary_tensor_data(test_data)
             test_writer.record(i)
 
-        # step 3: send the event to mq
-
-        # step 4: accept the event and write the file
-
-        log.debug("finished test_scalar_summary_sample")
-
 
 def get_test_data_shape_1(step):
     """ get_test_data_shape_1 """
@@ -104,23 +84,12 @@ def get_test_data_shape_1(step):
 # Test: shape = (1,)
 def test_scalar_summary_sample_with_shape_1():
     """ test_scalar_summary_sample_with_shape_1 """
-    log.debug("begin test_scalar_summary_sample_with_shape_1")
-    # step 0: create the thread
     with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR") as test_writer:
-        # step 1: create the test data for summary
-
-        # step 2: create the Event
         for i in range(1, 100):
             test_data = get_test_data_shape_1(i)
             _cache_summary_tensor_data(test_data)
             test_writer.record(i)
 
-        # step 3: send the event to mq
-
-        # step 4: accept the event and write the file
-
-        log.debug("finished test_scalar_summary_sample")
-
 
 # Test: test with ge
 class SummaryDemo(nn.Cell):
@@ -143,13 +112,7 @@ class SummaryDemo(nn.Cell):
 
 def test_scalar_summary_with_ge():
     """ test_scalar_summary_with_ge """
-    log.debug("begin test_scalar_summary_with_ge")
-
-    # step 0: create the thread
     with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR") as test_writer:
-        # step 1: create the network for summary
-        x = Tensor(np.array([1.1]).astype(np.float32))
-        y = Tensor(np.array([1.2]).astype(np.float32))
         net = SummaryDemo()
         net.set_train()
 
@@ -161,45 +124,17 @@ def test_scalar_summary_with_ge():
             net(x, y)
             test_writer.record(i)
 
-        log.debug("finished test_scalar_summary_with_ge")
-
 
 # test the problem of two consecutive use cases going wrong
 def test_scalar_summary_with_ge_2():
     """ test_scalar_summary_with_ge_2 """
-    log.debug("begin test_scalar_summary_with_ge_2")
-
-    # step 0: create the thread
     with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR") as test_writer:
-        # step 1: create the network for summary
-        x = Tensor(np.array([1.1]).astype(np.float32))
-        y = Tensor(np.array([1.2]).astype(np.float32))
         net = SummaryDemo()
         net.set_train()
 
-        # step 2: create the Event
         steps = 100
         for i in range(1, steps):
             x = Tensor(np.array([1.1]).astype(np.float32))
             y = Tensor(np.array([1.2]).astype(np.float32))
             net(x, y)
             test_writer.record(i)
-
-        log.debug("finished test_scalar_summary_with_ge_2")
-
-
-def test_validate():
-    with SummaryRecord(SUMMARY_DIR) as sr:
-        sr.record(1)
-        with pytest.raises(ValueError):
-            sr.record(False)
-        with pytest.raises(ValueError):
-            sr.record(2.0)
-        with pytest.raises(ValueError):
-            sr.record((1, 3))
-        with pytest.raises(ValueError):
-            sr.record([2, 3])
-        with pytest.raises(ValueError):
-            sr.record("str")
-        with pytest.raises(ValueError):
-            sr.record(sr)
diff --git a/tests/ut/python/train/summary/test_summary_abnormal_input.py b/tests/ut/python/train/summary/test_summary_abnormal_input.py
deleted file mode 100644
index 388952feca..0000000000
--- a/tests/ut/python/train/summary/test_summary_abnormal_input.py
+++ /dev/null
@@ -1,133 +0,0 @@
-# Copyright 2020 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.
-# ============================================================================
-"""
-@File  : test_summary_abnormal_input.py
-@Author:
-@Date  : 2019-08-5
-@Desc  : test summary function of abnormal input
-"""
-import logging
-import os
-import numpy as np
-
-from mindspore.common.tensor import Tensor
-from mindspore.train.summary.summary_record import SummaryRecord
-
-CUR_DIR = os.getcwd()
-SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/"
-
-log = logging.getLogger("test")
-log.setLevel(level=logging.ERROR)
-
-
-def get_test_data(step):
-    """ get_test_data """
-    test_data_list = []
-    tag1 = "x1[:Scalar]"
-    tag2 = "x2[:Scalar]"
-    np1 = np.array(step + 1).astype(np.float32)
-    np2 = np.array(step + 2).astype(np.float32)
-
-    dict1 = {}
-    dict1["name"] = tag1
-    dict1["data"] = Tensor(np1)
-
-    dict2 = {}
-    dict2["name"] = tag2
-    dict2["data"] = Tensor(np2)
-
-    test_data_list.append(dict1)
-    test_data_list.append(dict2)
-
-    return test_data_list
-
-
-# Test: call method on parse graph code
-def test_summaryrecord_input_null_string():
-    log.debug("begin test_summaryrecord_input_null_string")
-    # step 0: create the thread
-    try:
-        with SummaryRecord(""):
-            pass
-    except:
-        assert True
-    else:
-        assert False
-    log.debug("finished test_summaryrecord_input_null_string")
-
-
-def test_summaryrecord_input_None():
-    log.debug("begin test_summaryrecord_input_None")
-    # step 0: create the thread
-    try:
-        with SummaryRecord(None):
-            pass
-    except:
-        assert True
-    else:
-        assert False
-    log.debug("finished test_summaryrecord_input_None")
-
-
-def test_summaryrecord_input_relative_dir_1():
-    log.debug("begin test_summaryrecord_input_relative_dir_1")
-    # step 0: create the thread
-    try:
-        with SummaryRecord("./test_temp_summary_event_file/"):
-            pass
-    except:
-        assert False
-    else:
-        assert True
-    log.debug("finished test_summaryrecord_input_relative_dir_1")
-
-
-def test_summaryrecord_input_relative_dir_2():
-    log.debug("begin test_summaryrecord_input_relative_dir_2")
-    # step 0: create the thread
-    try:
-        with SummaryRecord("../summary/"):
-            pass
-    except:
-        assert False
-    else:
-        assert True
-    log.debug("finished test_summaryrecord_input_relative_dir_2")
-
-
-def test_summaryrecord_input_invalid_type_dir():
-    log.debug("begin test_summaryrecord_input_invalid_type_dir")
-    # step 0: create the thread
-    try:
-        with SummaryRecord(32):
-            pass
-    except:
-        assert True
-    else:
-        assert False
-    log.debug("finished test_summaryrecord_input_invalid_type_dir")
-
-
-def test_mulit_layer_directory():
-    log.debug("begin test_mulit_layer_directory")
-    # step 0: create the thread
-    try:
-        with SummaryRecord("./test_temp_summary_event_file/test/t1/"):
-            pass
-    except:
-        assert False
-    else:
-        assert True
-    log.debug("finished test_mulit_layer_directory")
diff --git a/tests/ut/python/train/summary/test_summary_collector.py b/tests/ut/python/train/summary/test_summary_collector.py
index 3349cf8287..36bde6f88f 100644
--- a/tests/ut/python/train/summary/test_summary_collector.py
+++ b/tests/ut/python/train/summary/test_summary_collector.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -187,6 +187,14 @@ class TestSummaryCollector:
 
         assert expected_msg == str(exc.value)
 
+    def test_params_with_histogram_regular_value_error(self):
+        """Test histogram regular."""
+        summary_dir = tempfile.mkdtemp(dir=self.base_summary_dir)
+        with pytest.raises(ValueError) as exc:
+            SummaryCollector(summary_dir, collect_specified_data={'histogram_regular': '*'})
+
+        assert 'For `collect_specified_data`, the value of `histogram_regular`' in str(exc.value)
+
     def test_params_with_collect_specified_data_unexpected_key(self):
         """Test the collect_specified_data parameter with unexpected key."""
         summary_dir = tempfile.mkdtemp(dir=self.base_summary_dir)
@@ -260,7 +268,7 @@ class TestSummaryCollector:
         cb_params.train_dataset_element = image_data
         with SummaryCollector((tempfile.mkdtemp(dir=self.base_summary_dir))) as summary_collector:
             summary_collector._collect_input_data(cb_params)
-            # Note Here need to asssert the result and expected data
+            # Note Here need to assert the result and expected data
 
     @mock.patch.object(SummaryRecord, 'add_value')
     def test_collect_dataset_graph_success(self, mock_add_value):
@@ -296,7 +304,6 @@ class TestSummaryCollector:
 
         assert summary_collector._is_parse_loss_success
 
-
     def test_get_optimizer_from_cb_params_success(self):
         """Test get optimizer success from cb params."""
         cb_params = _InternalCallbackParam()
diff --git a/tests/ut/python/train/summary/test_summary_record.py b/tests/ut/python/train/summary/test_summary_record.py
new file mode 100644
index 0000000000..4f317174b8
--- /dev/null
+++ b/tests/ut/python/train/summary/test_summary_record.py
@@ -0,0 +1,80 @@
+# Copyright 2021 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.
+# ============================================================================
+"""test_summary_abnormal_input"""
+import os
+import shutil
+import tempfile
+
+import numpy as np
+import pytest
+
+from mindspore.common.tensor import Tensor
+from mindspore.train.summary.summary_record import SummaryRecord
+
+
+def get_test_data(step):
+    """ get_test_data """
+    test_data_list = []
+    tag1 = "x1[:Scalar]"
+    tag2 = "x2[:Scalar]"
+    np1 = np.array(step + 1).astype(np.float32)
+    np2 = np.array(step + 2).astype(np.float32)
+
+    dict1 = {}
+    dict1["name"] = tag1
+    dict1["data"] = Tensor(np1)
+
+    dict2 = {}
+    dict2["name"] = tag2
+    dict2["data"] = Tensor(np2)
+
+    test_data_list.append(dict1)
+    test_data_list.append(dict2)
+
+    return test_data_list
+
+
+class TestSummaryRecord:
+    """Test SummaryRecord"""
+    def setup_class(self):
+        """Run before test this class."""
+        self.base_summary_dir = tempfile.mkdtemp(suffix='summary')
+
+    def teardown_class(self):
+        """Run after test this class."""
+        if os.path.exists(self.base_summary_dir):
+            shutil.rmtree(self.base_summary_dir)
+
+    @pytest.mark.parametrize("log_dir", ["", None, 32])
+    def test_log_dir_with_type_error(self, log_dir):
+        with pytest.raises(TypeError):
+            with SummaryRecord(log_dir):
+                pass
+
+    @pytest.mark.parametrize("raise_exception", ["", None, 32])
+    def test_raise_exception_with_type_error(self, raise_exception):
+        summary_dir = tempfile.mkdtemp(dir=self.base_summary_dir)
+        with pytest.raises(TypeError) as exc:
+            with SummaryRecord(log_dir=summary_dir, raise_exception=raise_exception):
+                pass
+
+        assert "raise_exception" in str(exc.value)
+
+    @pytest.mark.parametrize("step", [False, 2.0, (1, 3), [2, 3], "str"])
+    def test_step_of_record_with_type_error(self, step):
+        summary_dir = tempfile.mkdtemp(dir=self.base_summary_dir)
+        with pytest.raises(TypeError):
+            with SummaryRecord(summary_dir) as sr:
+                sr.record(step)