add uncertainty, 1-ch saliency and separated datafiles

bugfix and add pillow to requirements.txt

modify summary format

bugfix

use sample_id in summary

fix CI problem

url encode '/' as well

fix ut

fix ut

fix ut

fix uncertainty enable checking

fix review comment

enhance exception raising

enhance comment

mindinsight/backend/explainer/explainer_api.py

@@ -31,6 +31,7 @@ from mindinsight.datavisual.data_transform.summary_watcher import SummaryWatcher
 from mindinsight.datavisual.utils.tools import get_train_id
 from mindinsight.explainer.manager.explain_manager import ExplainManager
 from mindinsight.explainer.encapsulator.explain_job_encap import ExplainJobEncap
+from mindinsight.explainer.encapsulator.datafile_encap import DatafileEncap
 from mindinsight.explainer.encapsulator.saliency_encap import SaliencyEncap
 from mindinsight.explainer.encapsulator.evaluation_encap import EvaluationEncap
 
@@ -54,12 +55,14 @@ class ExplainManagerHolder:
         cls.static_instance.start_load_data()
 
 
-def _image_url_formatter(train_id, image_id, image_type):
+def _image_url_formatter(train_id, image_path, image_type):
     """Returns image url."""
-    train_id = urllib.parse.quote(str(train_id))
-    image_id = urllib.parse.quote(str(image_id))
-    image_type = urllib.parse.quote(str(image_type))
-    return f"{URL_PREFIX}/explainer/image?train_id={train_id}&image_id={image_id}&type={image_type}"
+    data = {
+        "train_id": train_id,
+        "path": image_path,
+        "type": image_type
+    }
+    return f"{URL_PREFIX}/explainer/image?{urllib.parse.urlencode(data)}"
 
 
 def _read_post_request(post_request):
@@ -129,10 +132,10 @@ def query_saliency():
     sorted_name = data.get("sorted_name", "")
     sorted_type = data.get("sorted_type", "descending")
 
-    if sorted_name not in ("", "confidence"):
-        raise ParamValueError("sorted_name")
+    if sorted_name not in ("", "confidence", "uncertainty"):
+        raise ParamValueError(f"sorted_name: {sorted_name}, valid options: '' 'confidence' 'uncertainty'")
     if sorted_type not in ("ascending", "descending"):
-        raise ParamValueError("sorted_type")
+        raise ParamValueError(f"sorted_type: {sorted_type}, valid options: 'confidence' 'uncertainty'")
 
     encapsulator = SaliencyEncap(
         _image_url_formatter,
@@ -170,19 +173,19 @@ def query_image():
     train_id = get_train_id(request)
     if train_id is None:
         raise ParamMissError("train_id")
-    image_id = request.args.get("image_id")
-    if image_id is None:
-        raise ParamMissError("image_id")
+    image_path = request.args.get("path")
+    if image_path is None:
+        raise ParamMissError("path")
     image_type = request.args.get("type")
     if image_type is None:
         raise ParamMissError("type")
     if image_type not in ("original", "overlay"):
-        raise ParamValueError(f"type:{image_type}")
+        raise ParamValueError(f"type:{image_type}, valid options: 'original' 'overlay'")
 
-    encapsulator = ExplainJobEncap(ExplainManagerHolder.get_instance())
-    image = encapsulator.query_image_binary(train_id, image_id, image_type)
+    encapsulator = DatafileEncap(ExplainManagerHolder.get_instance())
+    image = encapsulator.query_image_binary(train_id, image_path, image_type)
     if image is None:
-        raise ImageNotExistError(f"image_id:{image_id}")
+        raise ImageNotExistError(f"{image_path}")
 
     return image
 

mindinsight/datavisual/proto_files/mindinsight_summary.proto

@@ -108,35 +108,40 @@ message Summary {
 
 message Explain {
     message Inference{
-		repeated float ground_truth_prob = 1;
-		repeated int32 predicted_label = 2;
-		repeated float predicted_prob = 3;
+        repeated float ground_truth_prob = 1;
+        repeated int32 predicted_label = 2;
+        repeated float predicted_prob = 3;
+        repeated float ground_truth_prob_sd = 4;
+        repeated float ground_truth_prob_itl95_low = 5;
+        repeated float ground_truth_prob_itl95_hi = 6;
+        repeated float predicted_prob_sd = 7;
+        repeated float predicted_prob_itl95_low = 8;
+        repeated float predicted_prob_itl95_hi = 9;
     }
 
     message Explanation{
-		optional string explain_method = 1;
-		optional int32 label = 2;
-		optional bytes heatmap = 3;
-	}
+        optional string explain_method = 1;
+        optional int32 label = 2;
+        optional string heatmap_path = 3;
+    }
 
     message Benchmark{
-		optional string benchmark_method = 1;
-		optional string explain_method = 2;
-		optional float total_score = 3;
-		repeated float label_score = 4;
-	}
+        optional string benchmark_method = 1;
+        optional string explain_method = 2;
+        optional float total_score = 3;
+        repeated float label_score = 4;
+    }
 
     message Metadata{
-		repeated string label = 1;
-		repeated string explain_method = 2;
-		repeated string benchmark_method = 3;
-	}
+        repeated string label = 1;
+        repeated string explain_method = 2;
+        repeated string benchmark_method = 3;
+    }
 
-    optional string image_id = 1;  // The Metadata and image id must have one fill in
-    optional bytes image_data = 2;
+    optional int32 sample_id = 1;  // The Metadata and sample id must have one fill in
+    optional string image_path = 2;
     repeated int32 ground_truth_label = 3;
 
-
     optional Inference inference = 4;
     repeated Explanation explanation = 5;
     repeated Benchmark benchmark = 6;

mindinsight/datavisual/proto_files/mindinsight_summary_pb2.py

@@ -19,7 +19,7 @@ DESCRIPTOR = _descriptor.FileDescriptor(
   package='mindinsight',
   syntax='proto2',
   serialized_options=b'\370\001\001',
-  serialized_pb=b'\n\x19mindinsight_summary.proto\x12\x0bmindinsight\x1a\x18mindinsight_anf_ir.proto\"\xc3\x01\n\x05\x45vent\x12\x11\n\twall_time\x18\x01 \x02(\x01\x12\x0c\n\x04step\x18\x02 \x01(\x03\x12\x11\n\x07version\x18\x03 \x01(\tH\x00\x12,\n\tgraph_def\x18\x04 \x01(\x0b\x32\x17.mindinsight.GraphProtoH\x00\x12\'\n\x07summary\x18\x05 \x01(\x0b\x32\x14.mindinsight.SummaryH\x00\x12\'\n\x07\x65xplain\x18\x06 \x01(\x0b\x32\x14.mindinsight.ExplainH\x00\x42\x06\n\x04what\"\xc0\x04\n\x07Summary\x12)\n\x05value\x18\x01 \x03(\x0b\x32\x1a.mindinsight.Summary.Value\x1aQ\n\x05Image\x12\x0e\n\x06height\x18\x01 \x02(\x05\x12\r\n\x05width\x18\x02 \x02(\x05\x12\x12\n\ncolorspace\x18\x03 \x02(\x05\x12\x15\n\rencoded_image\x18\x04 \x02(\x0c\x1a\xf0\x01\n\tHistogram\x12\x36\n\x07\x62uckets\x18\x01 \x03(\x0b\x32%.mindinsight.Summary.Histogram.bucket\x12\x11\n\tnan_count\x18\x02 \x01(\x03\x12\x15\n\rpos_inf_count\x18\x03 \x01(\x03\x12\x15\n\rneg_inf_count\x18\x04 \x01(\x03\x12\x0b\n\x03max\x18\x05 \x01(\x01\x12\x0b\n\x03min\x18\x06 \x01(\x01\x12\x0b\n\x03sum\x18\x07 \x01(\x01\x12\r\n\x05\x63ount\x18\x08 \x01(\x03\x1a\x34\n\x06\x62ucket\x12\x0c\n\x04left\x18\x01 \x02(\x01\x12\r\n\x05width\x18\x02 \x02(\x01\x12\r\n\x05\x63ount\x18\x03 \x02(\x03\x1a\xc3\x01\n\x05Value\x12\x0b\n\x03tag\x18\x01 \x02(\t\x12\x16\n\x0cscalar_value\x18\x03 \x01(\x02H\x00\x12+\n\x05image\x18\x04 \x01(\x0b\x32\x1a.mindinsight.Summary.ImageH\x00\x12*\n\x06tensor\x18\x08 \x01(\x0b\x32\x18.mindinsight.TensorProtoH\x00\x12\x33\n\thistogram\x18\t \x01(\x0b\x32\x1e.mindinsight.Summary.HistogramH\x00\x42\x07\n\x05value\"\xff\x04\n\x07\x45xplain\x12\x10\n\x08image_id\x18\x01 \x01(\t\x12\x12\n\nimage_data\x18\x02 \x01(\x0c\x12\x1a\n\x12ground_truth_label\x18\x03 \x03(\x05\x12\x31\n\tinference\x18\x04 \x01(\x0b\x32\x1e.mindinsight.Explain.Inference\x12\x35\n\x0b\x65xplanation\x18\x05 \x03(\x0b\x32 .mindinsight.Explain.Explanation\x12\x31\n\tbenchmark\x18\x06 \x03(\x0b\x32\x1e.mindinsight.Explain.Benchmark\x12/\n\x08metadata\x18\x07 \x01(\x0b\x32\x1d.mindinsight.Explain.Metadata\x12\x0e\n\x06status\x18\x08 \x01(\t\x1aW\n\tInference\x12\x19\n\x11ground_truth_prob\x18\x01 \x03(\x02\x12\x17\n\x0fpredicted_label\x18\x02 \x03(\x05\x12\x16\n\x0epredicted_prob\x18\x03 \x03(\x02\x1a\x45\n\x0b\x45xplanation\x12\x16\n\x0e\x65xplain_method\x18\x01 \x01(\t\x12\r\n\x05label\x18\x02 \x01(\x05\x12\x0f\n\x07heatmap\x18\x03 \x01(\x0c\x1ag\n\tBenchmark\x12\x18\n\x10\x62\x65nchmark_method\x18\x01 \x01(\t\x12\x16\n\x0e\x65xplain_method\x18\x02 \x01(\t\x12\x13\n\x0btotal_score\x18\x03 \x01(\x02\x12\x13\n\x0blabel_score\x18\x04 \x03(\x02\x1aK\n\x08Metadata\x12\r\n\x05label\x18\x01 \x03(\t\x12\x16\n\x0e\x65xplain_method\x18\x02 \x03(\t\x12\x18\n\x10\x62\x65nchmark_method\x18\x03 \x03(\tB\x03\xf8\x01\x01'
+  serialized_pb=b'\n\x19mindinsight_summary.proto\x12\x0bmindinsight\x1a\x18mindinsight_anf_ir.proto\"\xc3\x01\n\x05\x45vent\x12\x11\n\twall_time\x18\x01 \x02(\x01\x12\x0c\n\x04step\x18\x02 \x01(\x03\x12\x11\n\x07version\x18\x03 \x01(\tH\x00\x12,\n\tgraph_def\x18\x04 \x01(\x0b\x32\x17.mindinsight.GraphProtoH\x00\x12\'\n\x07summary\x18\x05 \x01(\x0b\x32\x14.mindinsight.SummaryH\x00\x12\'\n\x07\x65xplain\x18\x06 \x01(\x0b\x32\x14.mindinsight.ExplainH\x00\x42\x06\n\x04what\"\xc0\x04\n\x07Summary\x12)\n\x05value\x18\x01 \x03(\x0b\x32\x1a.mindinsight.Summary.Value\x1aQ\n\x05Image\x12\x0e\n\x06height\x18\x01 \x02(\x05\x12\r\n\x05width\x18\x02 \x02(\x05\x12\x12\n\ncolorspace\x18\x03 \x02(\x05\x12\x15\n\rencoded_image\x18\x04 \x02(\x0c\x1a\xf0\x01\n\tHistogram\x12\x36\n\x07\x62uckets\x18\x01 \x03(\x0b\x32%.mindinsight.Summary.Histogram.bucket\x12\x11\n\tnan_count\x18\x02 \x01(\x03\x12\x15\n\rpos_inf_count\x18\x03 \x01(\x03\x12\x15\n\rneg_inf_count\x18\x04 \x01(\x03\x12\x0b\n\x03max\x18\x05 \x01(\x01\x12\x0b\n\x03min\x18\x06 \x01(\x01\x12\x0b\n\x03sum\x18\x07 \x01(\x01\x12\r\n\x05\x63ount\x18\x08 \x01(\x03\x1a\x34\n\x06\x62ucket\x12\x0c\n\x04left\x18\x01 \x02(\x01\x12\r\n\x05width\x18\x02 \x02(\x01\x12\r\n\x05\x63ount\x18\x03 \x02(\x03\x1a\xc3\x01\n\x05Value\x12\x0b\n\x03tag\x18\x01 \x02(\t\x12\x16\n\x0cscalar_value\x18\x03 \x01(\x02H\x00\x12+\n\x05image\x18\x04 \x01(\x0b\x32\x1a.mindinsight.Summary.ImageH\x00\x12*\n\x06tensor\x18\x08 \x01(\x0b\x32\x18.mindinsight.TensorProtoH\x00\x12\x33\n\thistogram\x18\t \x01(\x0b\x32\x1e.mindinsight.Summary.HistogramH\x00\x42\x07\n\x05value\"\xcb\x06\n\x07\x45xplain\x12\x11\n\tsample_id\x18\x01 \x01(\x05\x12\x12\n\nimage_path\x18\x02 \x01(\t\x12\x1a\n\x12ground_truth_label\x18\x03 \x03(\x05\x12\x31\n\tinference\x18\x04 \x01(\x0b\x32\x1e.mindinsight.Explain.Inference\x12\x35\n\x0b\x65xplanation\x18\x05 \x03(\x0b\x32 .mindinsight.Explain.Explanation\x12\x31\n\tbenchmark\x18\x06 \x03(\x0b\x32\x1e.mindinsight.Explain.Benchmark\x12/\n\x08metadata\x18\x07 \x01(\x0b\x32\x1d.mindinsight.Explain.Metadata\x12\x0e\n\x06status\x18\x08 \x01(\t\x1a\x9c\x02\n\tInference\x12\x19\n\x11ground_truth_prob\x18\x01 \x03(\x02\x12\x17\n\x0fpredicted_label\x18\x02 \x03(\x05\x12\x16\n\x0epredicted_prob\x18\x03 \x03(\x02\x12\x1c\n\x14ground_truth_prob_sd\x18\x04 \x03(\x02\x12#\n\x1bground_truth_prob_itl95_low\x18\x05 \x03(\x02\x12\"\n\x1aground_truth_prob_itl95_hi\x18\x06 \x03(\x02\x12\x19\n\x11predicted_prob_sd\x18\x07 \x03(\x02\x12 \n\x18predicted_prob_itl95_low\x18\x08 \x03(\x02\x12\x1f\n\x17predicted_prob_itl95_hi\x18\t \x03(\x02\x1aJ\n\x0b\x45xplanation\x12\x16\n\x0e\x65xplain_method\x18\x01 \x01(\t\x12\r\n\x05label\x18\x02 \x01(\x05\x12\x14\n\x0cheatmap_path\x18\x03 \x01(\t\x1ag\n\tBenchmark\x12\x18\n\x10\x62\x65nchmark_method\x18\x01 \x01(\t\x12\x16\n\x0e\x65xplain_method\x18\x02 \x01(\t\x12\x13\n\x0btotal_score\x18\x03 \x01(\x02\x12\x13\n\x0blabel_score\x18\x04 \x03(\x02\x1aK\n\x08Metadata\x12\r\n\x05label\x18\x01 \x03(\t\x12\x16\n\x0e\x65xplain_method\x18\x02 \x03(\t\x12\x18\n\x10\x62\x65nchmark_method\x18\x03 \x03(\tB\x03\xf8\x01\x01'
   ,
   dependencies=[mindinsight__anf__ir__pb2.DESCRIPTOR,])
 
@@ -389,6 +389,48 @@ _EXPLAIN_INFERENCE = _descriptor.Descriptor(
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
       serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='ground_truth_prob_sd', full_name='mindinsight.Explain.Inference.ground_truth_prob_sd', index=3,
+      number=4, type=2, cpp_type=6, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='ground_truth_prob_itl95_low', full_name='mindinsight.Explain.Inference.ground_truth_prob_itl95_low', index=4,
+      number=5, type=2, cpp_type=6, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='ground_truth_prob_itl95_hi', full_name='mindinsight.Explain.Inference.ground_truth_prob_itl95_hi', index=5,
+      number=6, type=2, cpp_type=6, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='predicted_prob_sd', full_name='mindinsight.Explain.Inference.predicted_prob_sd', index=6,
+      number=7, type=2, cpp_type=6, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='predicted_prob_itl95_low', full_name='mindinsight.Explain.Inference.predicted_prob_itl95_low', index=7,
+      number=8, type=2, cpp_type=6, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
+    _descriptor.FieldDescriptor(
+      name='predicted_prob_itl95_hi', full_name='mindinsight.Explain.Inference.predicted_prob_itl95_hi', index=8,
+      number=9, type=2, cpp_type=6, label=3,
+      has_default_value=False, default_value=[],
+      message_type=None, enum_type=None, containing_type=None,
+      is_extension=False, extension_scope=None,
+      serialized_options=None, file=DESCRIPTOR),
   ],
   extensions=[
   ],
@@ -401,8 +443,8 @@ _EXPLAIN_INFERENCE = _descriptor.Descriptor(
   extension_ranges=[],
   oneofs=[
   ],
-  serialized_start=1145,
-  serialized_end=1232,
+  serialized_start=1147,
+  serialized_end=1431,
 )
 
 _EXPLAIN_EXPLANATION = _descriptor.Descriptor(
@@ -427,9 +469,9 @@ _EXPLAIN_EXPLANATION = _descriptor.Descriptor(
       is_extension=False, extension_scope=None,
       serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
-      name='heatmap', full_name='mindinsight.Explain.Explanation.heatmap', index=2,
-      number=3, type=12, cpp_type=9, label=1,
-      has_default_value=False, default_value=b"",
+      name='heatmap_path', full_name='mindinsight.Explain.Explanation.heatmap_path', index=2,
+      number=3, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=b"".decode('utf-8'),
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
       serialized_options=None, file=DESCRIPTOR),
@@ -445,8 +487,8 @@ _EXPLAIN_EXPLANATION = _descriptor.Descriptor(
   extension_ranges=[],
   oneofs=[
   ],
-  serialized_start=1234,
-  serialized_end=1303,
+  serialized_start=1433,
+  serialized_end=1507,
 )
 
 _EXPLAIN_BENCHMARK = _descriptor.Descriptor(
@@ -496,8 +538,8 @@ _EXPLAIN_BENCHMARK = _descriptor.Descriptor(
   extension_ranges=[],
   oneofs=[
   ],
-  serialized_start=1305,
-  serialized_end=1408,
+  serialized_start=1509,
+  serialized_end=1612,
 )
 
 _EXPLAIN_METADATA = _descriptor.Descriptor(
@@ -540,8 +582,8 @@ _EXPLAIN_METADATA = _descriptor.Descriptor(
   extension_ranges=[],
   oneofs=[
   ],
-  serialized_start=1410,
-  serialized_end=1485,
+  serialized_start=1614,
+  serialized_end=1689,
 )
 
 _EXPLAIN = _descriptor.Descriptor(
@@ -552,16 +594,16 @@ _EXPLAIN = _descriptor.Descriptor(
   containing_type=None,
   fields=[
     _descriptor.FieldDescriptor(
-      name='image_id', full_name='mindinsight.Explain.image_id', index=0,
-      number=1, type=9, cpp_type=9, label=1,
-      has_default_value=False, default_value=b"".decode('utf-8'),
+      name='sample_id', full_name='mindinsight.Explain.sample_id', index=0,
+      number=1, type=5, cpp_type=1, label=1,
+      has_default_value=False, default_value=0,
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
       serialized_options=None, file=DESCRIPTOR),
     _descriptor.FieldDescriptor(
-      name='image_data', full_name='mindinsight.Explain.image_data', index=1,
-      number=2, type=12, cpp_type=9, label=1,
-      has_default_value=False, default_value=b"",
+      name='image_path', full_name='mindinsight.Explain.image_path', index=1,
+      number=2, type=9, cpp_type=9, label=1,
+      has_default_value=False, default_value=b"".decode('utf-8'),
       message_type=None, enum_type=None, containing_type=None,
       is_extension=False, extension_scope=None,
       serialized_options=None, file=DESCRIPTOR),
@@ -620,7 +662,7 @@ _EXPLAIN = _descriptor.Descriptor(
   oneofs=[
   ],
   serialized_start=846,
-  serialized_end=1485,
+  serialized_end=1689,
 )
 
 _EVENT.fields_by_name['graph_def'].message_type = mindinsight__anf__ir__pb2._GRAPHPROTO

mindinsight/explainer/common/enums.py

@@ -34,10 +34,9 @@ class DataManagerStatus(BaseEnum):
 class PluginNameEnum(BaseEnum):
     """Plugin Name Enum."""
     EXPLAIN = 'explain'
-    IMAGE_ID = 'image_id'
+    SAMPLE_ID = 'sample_id'
     BENCHMARK = 'benchmark'
     METADATA = 'metadata'
-    IMAGE_DATA = 'image_data'
     GROUND_TRUTH_LABEL = 'ground_truth_label'
     INFERENCE = 'inference'
     EXPLANATION = 'explanation'

mindinsight/explainer/encapsulator/datafile_encap.py

@@ -0,0 +1,121 @@
+# 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.
+# ============================================================================
+"""Datafile encapsulator."""
+
+import os
+import io
+
+from PIL import Image
+from PIL import UnidentifiedImageError
+import numpy as np
+
+from mindinsight.utils.exceptions import UnknownError
+from mindinsight.utils.exceptions import FileSystemPermissionError
+from mindinsight.datavisual.common.exceptions import ImageNotExistError
+from mindinsight.explainer.encapsulator.explain_data_encap import ExplainDataEncap
+
+# Max uint8 value. for converting RGB pixels to [0,1] intensity.
+_UINT8_MAX = 255
+
+# Color of low saliency.
+_SALIENCY_CMAP_LOW = (55, 25, 86, 255)
+
+# Color of high saliency.
+_SALIENCY_CMAP_HI = (255, 255, 0, 255)
+
+# Channel modes.
+_SINGLE_CHANNEL_MODE = "L"
+_RGBA_MODE = "RGBA"
+_RGB_MODE = "RGB"
+
+_PNG_FORMAT = "PNG"
+
+
+def _clean_train_id_b4_join(train_id):
+    """Clean train_id before joining to a path."""
+    if train_id.startswith("./") or train_id.startswith(".\\"):
+        return train_id[2:]
+    return train_id
+
+
+class DatafileEncap(ExplainDataEncap):
+    """Datafile encapsulator."""
+
+    def query_image_binary(self, train_id, image_path, image_type):
+        """
+        Query image binary content.
+
+        Args:
+            train_id (str): Job ID.
+            image_path (str): Image path relative to explain job's summary directory.
+            image_type (str): Image type, 'original' or 'overlay'.
+
+        Returns:
+            bytes, image binary.
+        """
+
+        abs_image_path = os.path.join(self.job_manager.summary_base_dir,
+                                      _clean_train_id_b4_join(train_id),
+                                      image_path)
+
+        if self._is_forbidden(abs_image_path):
+            raise FileSystemPermissionError("Forbidden.")
+
+        try:
+
+            if image_type != "overlay":
+                # no need to convert
+                with open(abs_image_path, "rb") as fp:
+                    return fp.read()
+
+            image = Image.open(abs_image_path)
+
+            if image.mode == _RGBA_MODE:
+                # It is RGBA already, do not convert.
+                with open(abs_image_path, "rb") as fp:
+                    return fp.read()
+
+        except FileNotFoundError:
+            raise ImageNotExistError(image_path)
+        except PermissionError:
+            raise FileSystemPermissionError(image_path)
+        except UnidentifiedImageError:
+            raise UnknownError(f"Invalid image file: {image_path}")
+
+        if image.mode == _SINGLE_CHANNEL_MODE:
+            saliency = np.asarray(image)/_UINT8_MAX
+        elif image.mode == _RGB_MODE:
+            saliency = np.asarray(image)
+            saliency = saliency[:, :, 0]/_UINT8_MAX
+        else:
+            raise UnknownError(f"Invalid overlay image mode:{image.mode}.")
+
+        rgba = np.empty((saliency.shape[0], saliency.shape[1], 4))
+        for c in range(3):
+            rgba[:, :, c] = saliency
+        rgba = rgba * _SALIENCY_CMAP_HI + (1-rgba) * _SALIENCY_CMAP_LOW
+        rgba[:, :, 3] = saliency * _UINT8_MAX
+
+        overlay = Image.fromarray(np.uint8(rgba), mode=_RGBA_MODE)
+        buffer = io.BytesIO()
+        overlay.save(buffer, format=_PNG_FORMAT)
+
+        return buffer.getvalue()
+
+    def _is_forbidden(self, path):
+        """Check if the path is outside summary base dir."""
+        base_dir = os.path.realpath(self.job_manager.summary_base_dir)
+        path = os.path.realpath(path)
+        return not path.startswith(base_dir)

mindinsight/explainer/encapsulator/evaluation_encap.py

@@ -17,6 +17,7 @@
 import copy
 
 from mindinsight.explainer.encapsulator.explain_data_encap import ExplainDataEncap
+from mindinsight.datavisual.common.exceptions import TrainJobNotExistError
 
 
 class EvaluationEncap(ExplainDataEncap):
@@ -26,5 +27,5 @@ class EvaluationEncap(ExplainDataEncap):
         """Query evaluation scores."""
         job = self.job_manager.get_job(train_id)
         if job is None:
-            return None
+            raise TrainJobNotExistError(train_id)
         return copy.deepcopy(job.explainer_scores)

mindinsight/explainer/encapsulator/explain_job_encap.py

@@ -17,8 +17,8 @@
 import copy
 from datetime import datetime
 
-from mindinsight.utils.exceptions import ParamValueError
 from mindinsight.explainer.encapsulator.explain_data_encap import ExplainDataEncap
+from mindinsight.datavisual.common.exceptions import TrainJobNotExistError
 
 
 class ExplainJobEncap(ExplainDataEncap):
@@ -34,7 +34,7 @@ class ExplainJobEncap(ExplainDataEncap):
             offset (int): Page offset.
             limit (int): Max. no. of items to be returned.
         Returns:
-            Tuple[int, List[Dict]], total no. of jobs and job list.
+            tuple[int, list[Dict]], total no. of jobs and job list.
         """
         total, dir_infos = self.job_manager.get_job_list(offset=offset, limit=limit)
         job_infos = [self._dir_2_info(dir_info) for dir_info in dir_infos]
@@ -47,36 +47,13 @@ class ExplainJobEncap(ExplainDataEncap):
         Args:
             train_id (str): Job ID.
         Returns:
-            Dict, the metadata.
+            dict, the metadata.
         """
         job = self.job_manager.get_job(train_id)
         if job is None:
-            return None
+            raise TrainJobNotExistError(train_id)
         return self._job_2_meta(job)
 
-    def query_image_binary(self, train_id, image_id, image_type):
-        """
-        Query image binary content.
-        Args:
-            train_id (str): Job ID.
-            image_id (str): Image ID.
-            image_type (str): Image type, 'original' or 'overlay'.
-        Returns:
-            bytes, image binary.
-        """
-        job = self.job_manager.get_job(train_id)
-
-        if job is None:
-            return None
-        if image_type == "original":
-            binary = job.retrieve_image(image_id)
-        elif image_type == "overlay":
-            binary = job.retrieve_overlay(image_id)
-        else:
-            raise ParamValueError(f"image_type:{image_type}")
-
-        return binary
-
     @classmethod
     def _dir_2_info(cls, dir_info):
         """Convert ExplainJob object to jsonable info object."""
@@ -111,5 +88,5 @@ class ExplainJobEncap(ExplainDataEncap):
         saliency_info["explainers"] = list(job.explainers)
         saliency_info["metrics"] = list(job.metrics)
         info["saliency"] = saliency_info
-        info["uncertainty"] = {"enabled": False}
+        info["uncertainty"] = {"enabled": job.uncertainty_enabled}
         return info

mindinsight/explainer/encapsulator/saliency_encap.py

@@ -16,16 +16,46 @@
 
 import copy
 
+from mindinsight.utils.exceptions import ParamValueError
 from mindinsight.explainer.encapsulator.explain_data_encap import ExplainDataEncap
 
 
-def _sort_key_confidence(sample):
+def _sort_key_min_confidence(sample):
+    """Samples sort key by the min. confidence."""
+    min_confidence = float("+inf")
+    for inference in sample["inferences"]:
+        if inference["confidence"] < min_confidence:
+            min_confidence = inference["confidence"]
+    return min_confidence
+
+
+def _sort_key_max_confidence(sample):
     """Samples sort key by the max. confidence."""
-    max_confid = None
+    max_confidence = float("-inf")
+    for inference in sample["inferences"]:
+        if inference["confidence"] > max_confidence:
+            max_confidence = inference["confidence"]
+    return max_confidence
+
+
+def _sort_key_min_confidence_sd(sample):
+    """Samples sort key by the min. confidence_sd."""
+    min_confidence_sd = float("+inf")
     for inference in sample["inferences"]:
-        if max_confid is None or inference["confidence"] > max_confid:
-            max_confid = inference["confidence"]
-    return max_confid
+        confidence_sd = inference.get("confidence_sd", float("+inf"))
+        if confidence_sd < min_confidence_sd:
+            min_confidence_sd = confidence_sd
+    return min_confidence_sd
+
+
+def _sort_key_max_confidence_sd(sample):
+    """Samples sort key by the max. confidence_sd."""
+    max_confidence_sd = float("-inf")
+    for inference in sample["inferences"]:
+        confidence_sd = inference.get("confidence_sd", float("-inf"))
+        if confidence_sd > max_confidence_sd:
+            max_confidence_sd = confidence_sd
+    return max_confidence_sd
 
 
 class SaliencyEncap(ExplainDataEncap):
@@ -47,15 +77,15 @@ class SaliencyEncap(ExplainDataEncap):
         Query saliency maps.
         Args:
             train_id (str): Job ID.
-            labels (List[str]): Label filter.
-            explainers (List[str]): Explainers of saliency maps to be shown.
+            labels (list[str]): Label filter.
+            explainers (list[str]): Explainers of saliency maps to be shown.
             limit (int): Max. no. of items to be returned.
             offset (int): Page offset.
             sorted_name (str): Field to be sorted.
             sorted_type (str): Sorting order, 'ascending' or 'descending'.
 
         Returns:
-            Tuple[int, List[dict]], total no. of samples after filtering and
+            tuple[int, list[dict]], total no. of samples after filtering and
                 list of sample result.
         """
         job = self.job_manager.get_job(train_id)
@@ -77,7 +107,19 @@ class SaliencyEncap(ExplainDataEncap):
 
         reverse = sorted_type == "descending"
         if sorted_name == "confidence":
-            samples.sort(key=_sort_key_confidence, reverse=reverse)
+            if reverse:
+                samples.sort(key=_sort_key_max_confidence, reverse=reverse)
+            else:
+                samples.sort(key=_sort_key_min_confidence, reverse=reverse)
+        elif sorted_name == "uncertainty":
+            if not job.uncertainty_enabled:
+                raise ParamValueError("Uncertainty is not enabled, sorted_name cannot be 'uncertainty'")
+            if reverse:
+                samples.sort(key=_sort_key_max_confidence_sd, reverse=reverse)
+            else:
+                samples.sort(key=_sort_key_min_confidence_sd, reverse=reverse)
+        elif sorted_name != "":
+            raise ParamValueError("sorted_name")
 
         sample_infos = []
         obj_offset = offset*limit
@@ -97,26 +139,23 @@ class SaliencyEncap(ExplainDataEncap):
         Args:
             sample (dict): Sample info.
             job (ExplainJob): Explain job.
-            explainers (List[str]): Explainer names.
+            explainers (list[str]): Explainer names.
         Returns:
-            Dict, the edited sample info.
+            dict, the edited sample info.
         """
-        sample["image"] = self._get_image_url(job.train_id, sample["id"], "original")
+        sample["image"] = self._get_image_url(job.train_id, sample['image'], "original")
         for inference in sample["inferences"]:
-
             new_list = []
             for saliency_map in inference["saliency_maps"]:
                 if explainers and saliency_map["explainer"] not in explainers:
                     continue
-                saliency_map["overlay"] = self._get_image_url(job.train_id,
-                                                              saliency_map["overlay"],
-                                                              "overlay")
+                saliency_map["overlay"] = self._get_image_url(job.train_id, saliency_map['overlay'], "overlay")
                 new_list.append(saliency_map)
             inference["saliency_maps"] = new_list
         return sample
 
-    def _get_image_url(self, train_id, image_id, image_type):
+    def _get_image_url(self, train_id, image_path, image_type):
         """Returns image's url."""
         if self._image_url_formatter is None:
-            return image_id
-        return self._image_url_formatter(train_id, image_id, image_type)
+            return image_path
+        return self._image_url_formatter(train_id, image_path, image_type)

mindinsight/explainer/manager/event_parse.py

@@ -21,7 +21,7 @@ from mindinsight.explainer.common.log import logger
 from mindinsight.utils.exceptions import UnknownError
 
 _IMAGE_DATA_TAGS = {
-    'image_data': PluginNameEnum.IMAGE_DATA.value,
+    'sample_id': PluginNameEnum.SAMPLE_ID.value,
     'ground_truth_label': PluginNameEnum.GROUND_TRUTH_LABEL.value,
     'inference': PluginNameEnum.INFERENCE.value,
     'explanation': PluginNameEnum.EXPLANATION.value
@@ -68,7 +68,7 @@ class EventParser:
 
     def parse_sample(self, sample: namedtuple) -> Optional[namedtuple]:
         """Parse the sample event."""
-        sample_id = sample.image_id
+        sample_id = sample.sample_id
 
         if sample_id not in self._sample_pool:
             self._sample_pool[sample_id] = sample
@@ -100,12 +100,12 @@ class EventParser:
         Check whether the image_container is ready for frontend display.
 
         Args:
-            image_container (nametuple): container consists of sample data
+            image_container (namedtuple): container consists of sample data
 
         Return:
             bool: whether the image_container if ready for display
         """
-        required_attrs = ['image_id', 'image_data', 'ground_truth_label', 'inference']
+        required_attrs = ['image_path', 'ground_truth_label', 'inference']
         for attr in required_attrs:
             if not EventParser.is_attr_ready(image_container, attr):
                 return False
@@ -117,7 +117,7 @@ class EventParser:
         Check whether the given attribute is ready in image_container.
 
         Args:
-            image_container (nametuple): container consist of sample data
+            image_container (namedtuple): container consist of sample data
             attr (str): attribute to check
 
         Returns:
@@ -141,8 +141,17 @@ class EventParser:
     def _parse_inference(self, event, sample_id):
         """Parse the inference event."""
         self._sample_pool[sample_id].inference.ground_truth_prob.extend(event.inference.ground_truth_prob)
+        self._sample_pool[sample_id].inference.ground_truth_prob_sd.extend(event.inference.ground_truth_prob_sd)
+        self._sample_pool[sample_id].inference.ground_truth_prob_itl95_low.\
+            extend(event.inference.ground_truth_prob_itl95_low)
+        self._sample_pool[sample_id].inference.ground_truth_prob_itl95_hi.\
+            extend(event.inference.ground_truth_prob_itl95_hi)
+
         self._sample_pool[sample_id].inference.predicted_label.extend(event.inference.predicted_label)
         self._sample_pool[sample_id].inference.predicted_prob.extend(event.inference.predicted_prob)
+        self._sample_pool[sample_id].inference.predicted_prob_sd.extend(event.inference.predicted_prob_sd)
+        self._sample_pool[sample_id].inference.predicted_prob_itl95_low.extend(event.inference.predicted_prob_itl95_low)
+        self._sample_pool[sample_id].inference.predicted_prob_itl95_hi.extend(event.inference.predicted_prob_itl95_hi)
 
     def _parse_explanation(self, event, sample_id):
         """Parse the explanation event."""
@@ -151,7 +160,7 @@ class EventParser:
                 new_explanation = self._sample_pool[sample_id].explanation.add()
                 new_explanation.explain_method = explanation_item.explain_method
                 new_explanation.label = explanation_item.label
-                new_explanation.heatmap = explanation_item.heatmap
+                new_explanation.heatmap_path = explanation_item.heatmap_path
 
     def _parse_sample_info(self, event, sample_id, tag):
         """Parse the event containing image info."""

mindinsight/explainer/manager/explain_job.py

@@ -45,6 +45,7 @@ class ExplainJob:
         self._event_parser = EventParser(self)
         self._latest_update_time = latest_update_time
         self._create_time = create_time
+        self._uncertainty_enabled = False
         self._labels = []
         self._metrics = []
         self._explainers = []
@@ -52,8 +53,6 @@ class ExplainJob:
         self._labels_info = {}
         self._explainer_score_dict = defaultdict(list)
         self._label_score_dict = defaultdict(dict)
-        self._overlay_dict = {}
-        self._image_dict = {}
 
     @property
     def all_classes(self):
@@ -147,6 +146,10 @@ class ExplainJob:
         """
         return None
 
+    @property
+    def uncertainty_enabled(self):
+        return self._uncertainty_enabled
+
     @property
     def create_time(self):
         """
@@ -220,37 +223,44 @@ class ExplainJob:
             self._labels_info[label_id] = {'label': label,
                                            'sample_ids': set()}
 
-    def _explanation_to_dict(self, explanation, sample_id):
+    def _explanation_to_dict(self, explanation):
         """Transfer the explanation from event to dict storage."""
-        explainer_name = explanation.explain_method
-        explain_label = explanation.label
-        saliency = explanation.heatmap
-        saliency_id = '{}_{}_{}'.format(
-            sample_id, explain_label, explainer_name)
         explain_info = {
-            'explainer': explainer_name,
-            'overlay': saliency_id,
+            'explainer': explanation.explain_method,
+            'overlay': explanation.heatmap_path,
         }
-        self._overlay_dict[saliency_id] = saliency
         return explain_info
 
     def _image_container_to_dict(self, sample_data):
         """Transfer the image container to dict storage."""
-        sample_id = sample_data.image_id
+        has_uncertainty = False
+        sample_id = sample_data.sample_id
 
         sample_info = {
             'id': sample_id,
-            'name': sample_id,
+            'image': sample_data.image_path,
+            'name': str(sample_id),
             'labels': [self._labels_info[x]['label']
                        for x in sample_data.ground_truth_label],
             'inferences': []}
-        self._image_dict[sample_id] = sample_data.image_data
 
         ground_truth_labels = list(sample_data.ground_truth_label)
         ground_truth_probs = list(sample_data.inference.ground_truth_prob)
         predicted_labels = list(sample_data.inference.predicted_label)
         predicted_probs = list(sample_data.inference.predicted_prob)
 
+        if sample_data.inference.predicted_prob_sd or sample_data.inference.ground_truth_prob_sd:
+            ground_truth_prob_sds = list(sample_data.inference.ground_truth_prob_sd)
+            ground_truth_prob_lows = list(sample_data.inference.ground_truth_prob_itl95_low)
+            ground_truth_prob_his = list(sample_data.inference.ground_truth_prob_itl95_hi)
+            predicted_prob_sds = list(sample_data.inference.predicted_prob_sd)
+            predicted_prob_lows = list(sample_data.inference.predicted_prob_itl95_low)
+            predicted_prob_his = list(sample_data.inference.predicted_prob_itl95_hi)
+            has_uncertainty = True
+        else:
+            ground_truth_prob_sds = ground_truth_prob_lows = ground_truth_prob_his = None
+            predicted_prob_sds = predicted_prob_lows = predicted_prob_his = None
+
         inference_info = {}
         for label, prob in zip(
                 ground_truth_labels + predicted_labels,
@@ -260,41 +270,31 @@ class ExplainJob:
                 'confidence': round(prob, _NUM_DIGIT),
                 'saliency_maps': []}
 
+        if ground_truth_prob_sds or predicted_prob_sds:
+            for label, sd, low, hi in zip(
+                    ground_truth_labels + predicted_labels,
+                    ground_truth_prob_sds + predicted_prob_sds,
+                    ground_truth_prob_lows + predicted_prob_lows,
+                    ground_truth_prob_his + predicted_prob_his):
+                inference_info[label]['confidence_sd'] = sd
+                inference_info[label]['confidence_itl95'] = [low, hi]
+
         if EventParser.is_attr_ready(sample_data, 'explanation'):
             for explanation in sample_data.explanation:
-                explanation_dict = self._explanation_to_dict(
-                    explanation, sample_id)
+                explanation_dict = self._explanation_to_dict(explanation)
                 inference_info[explanation.label]['saliency_maps'].append(explanation_dict)
 
         sample_info['inferences'] = list(inference_info.values())
-        return sample_info
+        return sample_info, has_uncertainty
 
     def _import_sample(self, sample):
         """Add sample object of given sample id."""
         for label_id in sample.ground_truth_label:
-            self._labels_info[label_id]['sample_ids'].add(sample.image_id)
+            self._labels_info[label_id]['sample_ids'].add(sample.sample_id)
 
-        sample_info = self._image_container_to_dict(sample)
+        sample_info, has_uncertainty = self._image_container_to_dict(sample)
         self._samples_info.update({sample_info['id']: sample_info})
-
-    def retrieve_image(self, image_id: str):
-        """
-        Retrieve image data from the job given image_id.
-
-        Return:
-            string, image data in base64 byte
-
-        """
-        return self._image_dict.get(image_id, None)
-
-    def retrieve_overlay(self, overlay_id: str):
-        """
-        Retrieve sample map from the job given overlay_id.
-
-        Return:
-            string, saliency_map data in base64 byte
-        """
-        return self._overlay_dict.get(overlay_id, None)
+        self._uncertainty_enabled |= has_uncertainty
 
     def get_all_samples(self):
         """
@@ -321,7 +321,7 @@ class ExplainJob:
     def _import_data_from_event(self, event):
         """Parse and import data from the event data."""
         tags = {
-            'image_id': PluginNameEnum.IMAGE_ID,
+            'sample_id': PluginNameEnum.SAMPLE_ID,
             'benchmark': PluginNameEnum.BENCHMARK,
             'metadata': PluginNameEnum.METADATA
         }
@@ -332,7 +332,7 @@ class ExplainJob:
             if tag not in event:
                 continue
 
-            if tag == PluginNameEnum.IMAGE_ID.value:
+            if tag == PluginNameEnum.SAMPLE_ID.value:
                 sample_event = event[tag]
                 sample_data = self._event_parser.parse_sample(sample_event)
                 if sample_data is not None:
@@ -385,8 +385,6 @@ class ExplainJob:
         self._labels_info.clear()
         self._explainer_score_dict.clear()
         self._label_score_dict.clear()
-        self._overlay_dict.clear()
-        self._image_dict.clear()
         self._event_parser.clear()
 
     def _update_benchmark(self, explainer_score_dict, labels_score_dict):

mindinsight/explainer/manager/explain_parser.py

@@ -47,8 +47,8 @@ class ImageDataContainer:
     """
 
     def __init__(self, explain_message: Explain):
-        self.image_id = explain_message.image_id
-        self.image_data = explain_message.image_data
+        self.sample_id = explain_message.sample_id
+        self.image_path = explain_message.image_path
         self.ground_truth_label = explain_message.ground_truth_label
         self.inference = explain_message.inference
         self.explanation = explain_message.explanation
@@ -153,7 +153,7 @@ class _ExplainParser(_SummaryParser):
         logger.debug("Deserialize event string completed.")
 
         fields = {
-            'image_id': PluginNameEnum.IMAGE_ID,
+            'sample_id': PluginNameEnum.SAMPLE_ID,
             'benchmark': PluginNameEnum.BENCHMARK,
             'metadata': PluginNameEnum.METADATA
         }
@@ -170,7 +170,7 @@ class _ExplainParser(_SummaryParser):
                 continue
 
             tensor_value = None
-            if field == PluginNameEnum.IMAGE_ID.value:
+            if field == PluginNameEnum.SAMPLE_ID.value:
                 tensor_value = _ExplainParser._add_image_data(tensor_event_value)
             elif field == PluginNameEnum.BENCHMARK.value:
                 tensor_value = _ExplainParser._add_benchmark(tensor_event_value)
@@ -184,7 +184,7 @@ class _ExplainParser(_SummaryParser):
     @staticmethod
     def _add_image_data(tensor_event_value):
         """
-        Parse image data based on image_id in Explain message
+        Parse image data based on sample_id in Explain message
 
         Args:
             tensor_event_value: the object of Explain message

requirements.txt

@@ -8,6 +8,7 @@ Jinja2>=2.10.1
 MarkupSafe>=1.1.1
 marshmallow>=2.19.2
 numpy>=1.17.0
+pillow>=6.2.0
 protobuf>=3.8.0
 psutil>=5.6.1
 pyyaml>=5.3.1

tests/ut/backend/explainer/test_explainer_api.py

@@ -20,6 +20,7 @@ from unittest.mock import patch
 from mindinsight.explainer.encapsulator.explain_job_encap import ExplainJobEncap
 from mindinsight.explainer.encapsulator.saliency_encap import SaliencyEncap
 from mindinsight.explainer.encapsulator.evaluation_encap import EvaluationEncap
+from mindinsight.explainer.encapsulator.datafile_encap import DatafileEncap
 from .conftest import EXPLAINER_ROUTES
 
 
@@ -182,13 +183,13 @@ class TestExplainerApi:
         expect_result = {"explainer_scores": explainer_scores}
         assert response.get_json() == expect_result
 
-    @patch.object(ExplainJobEncap, "query_image_binary")
+    @patch.object(DatafileEncap, "query_image_binary")
     def test_query_image(self, mock_query_image_binary, client):
         """Test query a image's binary content."""
 
         mock_query_image_binary.return_value = b'123'
 
-        response = client.get(f"{EXPLAINER_ROUTES['image']}?train_id=.%2Fmock_job_1&image_id=1&type=original")
+        response = client.get(f"{EXPLAINER_ROUTES['image']}?train_id=.%2Fmock_job_1&path=1&type=original")
 
         assert response.status_code == 200
         assert response.data == b'123'

tests/ut/explainer/encapsulator/mock_explain_manager.py

@@ -30,6 +30,7 @@ class MockExplainJob:
         self.min_confidence = 0.5
         self.explainers = ["Gradient"]
         self.metrics = ["Localization"]
+        self.uncertainty_enabled = False
         self.all_classes = [
             {
                 "id": 0,
@@ -77,6 +78,7 @@ class MockExplainJob:
         sample = {
             "id": "123",
             "name": "123",
+            "image": "123",
             "labels": ["car"],
             "inferences": [
                 {

tests/ut/explainer/encapsulator/test_explain_job_encap.py

@@ -41,13 +41,3 @@ class TestExplainJobEncap:
         job = self.encapsulator.query_meta("./mock_job_1")
         assert job is not None
         assert job["train_id"] == "./mock_job_1"
-
-    def test_query_image_binary(self):
-        """Test query images' binary content."""
-        image = self.encapsulator.query_image_binary("./mock_job_1", "1", "original")
-        assert image is not None
-        assert image == b'123'
-
-        image = self.encapsulator.query_image_binary("./mock_job_1", "4", "overlay")
-        assert image is not None
-        assert image == b'456'

tests/ut/explainer/encapsulator/test_saliency_encap.py

@@ -18,9 +18,9 @@ from mindinsight.explainer.encapsulator.saliency_encap import SaliencyEncap
 from .mock_explain_manager import MockExplainManager
 
 
-def _image_url_formatter(_, image_id, image_type):
+def _image_url_formatter(_, image_path, image_type):
     """Return image url."""
-    return f"{image_type}-{image_id}"
+    return f"{image_type}-{image_path}"
 
 
 class TestEvaluationEncap: