fix sometimes deserialized protobuf data cannot be pickled to be sent to another process.

1. delete original message in HistogramContainer
2. Wrap image content in ImageContainer

mindinsight/datavisual/data_transform/histogram_container.py

@@ -26,8 +26,7 @@ class HistogramContainer:
     """
 
     def __init__(self, histogram_message: Summary.Histogram):
-        self._msg = histogram_message
-        original_buckets = [Bucket(bucket.left, bucket.width, bucket.count) for bucket in self._msg.buckets]
+        original_buckets = [Bucket(bucket.left, bucket.width, bucket.count) for bucket in histogram_message.buckets]
         # Ensure buckets are sorted from min to max.
         original_buckets.sort(key=lambda bucket: bucket.left)
         self._count = sum(bucket.count for bucket in original_buckets)

mindinsight/datavisual/data_transform/image_container.py

@@ -0,0 +1,30 @@
+# 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.
+# ============================================================================
+"""Image container."""
+from mindinsight.datavisual.proto_files.mindinsight_summary_pb2 import Summary
+
+
+class ImageContainer:
+    """
+    Container for image to allow pickling.
+
+    Args:
+        image_message (Summary.Image): Image proto buffer message.
+    """
+    def __init__(self, image_message: Summary.Image):
+        self.height = image_message.height
+        self.width = image_message.width
+        self.colorspace = image_message.colorspace
+        self.encoded_image = image_message.encoded_image

mindinsight/datavisual/data_transform/ms_data_loader.py

@@ -39,6 +39,7 @@ from mindinsight.datavisual.data_transform.events_data import TensorEvent
 from mindinsight.datavisual.data_transform.graph import MSGraph
 from mindinsight.datavisual.data_transform.histogram import Histogram
 from mindinsight.datavisual.data_transform.histogram_container import HistogramContainer
+from mindinsight.datavisual.data_transform.image_container import ImageContainer
 from mindinsight.datavisual.data_transform.tensor_container import TensorContainer
 from mindinsight.datavisual.proto_files import mindinsight_anf_ir_pb2 as anf_ir_pb2
 from mindinsight.datavisual.proto_files import mindinsight_summary_pb2 as summary_pb2
@@ -462,7 +463,7 @@ class _SummaryParser(_Parser):
                     tensor_event_value = getattr(value, plugin)
                     logger.debug("Processing plugin value: %s.", plugin_name_enum)
 
-                    if plugin == 'histogram':
+                    if plugin == PluginNameEnum.HISTOGRAM.value:
                         tensor_event_value = HistogramContainer(tensor_event_value)
                         # Drop steps if original_buckets_count exceeds HistogramContainer.MAX_ORIGINAL_BUCKETS_COUNT
                         # to avoid time-consuming re-sample process.
@@ -470,8 +471,10 @@ class _SummaryParser(_Parser):
                             logger.info('original_buckets_count exceeds '
                                         'HistogramContainer.MAX_ORIGINAL_BUCKETS_COUNT')
                             continue
-                    elif plugin == 'tensor':
+                    elif plugin == PluginNameEnum.TENSOR.value:
                         tensor_event_value = TensorContainer(tensor_event_value)
+                    elif plugin == PluginNameEnum.IMAGE.value:
+                        tensor_event_value = ImageContainer(tensor_event_value)
 
                     tensor_event = TensorEvent(wall_time=event.wall_time,
                                                step=event.step,